IT'S REALLY EXCITING TO SEE
PEOPLE HERE IN PERSON AND
ONLINE.
I WOULD LIKE TO WELCOME EVERYONE
TO THE BRAIN NEUROETHICS
WORKSHOP ON ETHICS OF SHARING
INDIVIDUAL LEVEL HUMAN BRAIN
DATA COLLECTED IN BIOMEDICAL
RESEARCH.
I WANT TO ESPECIALLY EXTEND A
WARM WELCOME TO OUR CO-CHAIRS
DR. CHRISTINE GRADY, AS WELL AS
THE MEMO WERS OF THE WORKING
GROUP, AND OUR INVITED SPEAKERS
AND PANELISTS JOINS US TODAY AND
TOMORROW.
I ALSO WOULD LIKE TO WELCOME OUR
IN PERSON AND VIRTUAL ATTENDEES
JOINING US FOR THIS HYBRID
WORKSHOP.
I'M A SCIENCE COMMITTEE
SPECIALIST FOR THE WORKING
GROUP.
AS MOST OF YOU MAY KNOW, THE
NEUROETHICS WORKING GROUP IS NOT
A FORMAL ADVISORY GROUP.
IT IS A WORKING GROUP OF THE
ADVISORY COUNCILS OF THE NIH
INSTITUTES AND CENTERS THAT
PARTICIPATE IN THE BRAIN
INITIATIVE.
AND WAS ESTABLISHED BECAUSE OF
THE NEED FOR INPUT ON
NEUROETHICS CONSIDERATIONS
RELATED TO RESEARCH SUPPORTED
THROUGH THE BRAIN INITIATIVE.
DELIBERATES ON ETHICAL QUESTIONS
THAT COME UP IN THE CONTEXT OF
BRAIN RESEARCH AND THOSE
DISCUSSIONS HAVE INFORMED AND
SERVED AS THE FOUNDATION FOR
THIS WORKSHOP TODAY.
SO A FEW HOUSEKEEPING THINGS
BEFORE WE GET STARTED.
AS A REMINDER, THIS WORKSHOP IS
BEING VIDEOCAST LIVE IN THE
PUBLIC DOMAIN AND THE RECORDING
WILL BE ARCHIVED FOR LATER
VIEWING ON THE NIH AND BRAIN
WEBSITE.
DURING PRESENTATIONS, WE ASK
THAT THE VIRTUAL ATTENDEES
PLEASE KEEP YOUR LINE MUTED AND
YOUR VIDEO OFF WHEN NOT
SPEAKING.
THIS IS HELPFUL FOR THE
VIDEOCAST.
DURING DISCUSSION, SO TO ASK
QUESTIONS, THOSE WHO ARE HERE AT
ROCKLEDGE, WE HAVE TWO ROAMING
MICS IN THE BACK OF THE ROOM AND
YOU'RE WELCOME TO USE THOSE TO
ASK QUESTIONS.
VIRTUAL ATTENDEES ARE ENCOURAGED
TO ANSWER QUESTIONS IN THE CHAT
OR USE YOUR "RAISE HAND" OPTION.
WE'LL BE MONITORING THAT CHAT.
AND WE'LL CALL ON YOU TO UNMUTE
YOURSELF AND DO OUR BEST TO
ANSWER AS MANY QUESTIONS AS WE
CAN FROM BOTH IN PERSON AND OUR
VIRTUAL ATTENDEES.
IF ANY ATTENDEES ARE HAVING
TECHNICAL PROBLEMS, PLEASE
CONTACT DERRICK SMITH WHO IS IN
THE BACK WHO WILL BE ABLE TO
ASSIST YOU AND WE WILL ALSO
INCLUDE HIS INFORMATION IN THE
CHAT.
FOR THOSE HERE IN PERSON, WHEN
WE GET TO LUNCH, JUST TO LET YOU
KNOW, THE ROCK CAFE IS YOUR
IN-PERSON OPTION IF YOU
DEPARTMENT BRING YOURSELF A
LUNCH.
IT IS A 5 MINUTE WALK ACROSS THE
STREET.
THERE'S A MENU OUTSIDE AND
YOU'RE ALSO ABLE TO CALL AHEAD
FOR A PICKUP, OR YOU CAN GO
THERE AND GRAB YOUR LUNCH DURING
OUR LUNCH BREAK.
I KNOW EVERYONE'S REALLY BUSY,
AND WE REALLY APPRECIATE YOUR
TIME AND YOUR INPUT TODAY.
NONE OF THIS COULD HAPPEN
WITHOUT A REALLY FABULOUS TEAM
THAT'S HELPED TO PREPARE THIS
WORKSHOP.
I ESPECIALLY WOULD LIKE TO THANK
DR. HENDRIKS, OUR LOGISTICS AND
TECH TEAM AND ROSA ROSALIE AND
ASSOCIATES, AND LAST BUT NOT
LEAST, OUR DEPUTY DIRECTOR FOR
BRAIN AND DR. JOHN NGAI, BRAIN
DIRECTOR, FOR THEIR SUPPORT OF
NEUROETHICS.
NOW I'LL TURN IT OVER TO JOHN.
[APPLAUSE]
>>THANK YOU, NINA.
I'D LIKE TO JOIN NINA IN
THANKING OUR NEUROETHICS WORKING
GROUP CO-CHAIRS, DR. CHRISTINE
GRADY AND DR. FARAHANI AND THE
REST OF THE WORKING GROUP FOR
PUTTING TOGETHER A TERRIFIC
PROGRAM HERE, IT'S GOING TO BE
AN EXCITING TWO DAYS.
THANK YOU ALL FOR BEING HERE IN
CYBERSPACE OR IN PERSON.
IT'S REALLY GREAT TO HAVE YOUR
PARTICIPATION AND I THINK WE'RE
GETTING THE HANG OF THESE HYBRID
MEETINGS, SO IF YOU HAVE
QUESTIONS EITHER HERE OR
VIRTUALLY, PLEASE ASK THEM,
WE'LL MAKE SURE THAT WE ANSWER
AS MANY OF THEM AS WE CAN.
SO WE'RE HERE TO HAVE AN IN
DEPTH DISCUSSION ABOUT THE
ETHICS INVOLVED IN SHARING HUMAN
BRAIN DATA THAT'S COLLECTED BY
MEDICAL RESEARCH AND BRAIN
INITIATIVE AS MANY OF YOU KNOW
HAS REALLY KIND OF BEEN PUSHING
THE LIMITS OF IT.
WE REALLY APPRECIATE THAT
SHARING DATA CAN ACCELERATE THE
SCIENTIFIC VALUE BUT THERE'S
ALSO AN INHERENT TENSION IN
DOING SO AND BALANCING THAT
VALUE WITH THE POTENTIAL RISKS
OF SHARING DATA AND PROTECTING
PATIENT PRIVACY AMONG OTHER
CONSIDERATIONS, AND AS WE START
PUSHING THE LIMITS OF THE KIND
OF DATA WE CAN ACTUALLY OBTAIN,
ANALYZE AND IMPUTE FROM, WE
REALLY -- I THINK WE'RE STARTING
TO CREATE SITUATIONS WHERE WE
REALLY DO NEED TO GET FURTHER
AHEAD OF THE GAME THAN WE ARE
RIGHT NOW.
NOW THE CONSIDERATIONS AROUND
DATA HAVE BEEN PART OF BRAIN
FROM THE VERY, VERY BEGINNING.
IT WAS NOTED VERY INTENTIONALLY
IN THE BRAIN 2025 STRATEGIC PLAN
THAT WAS ISSUED BACK IN 2014 AND
UPDATED AND ENHANCED THE BRAIN
2.0 REPORT.
SO ONE OF THE CO-AUTHORS IS
HERE, WE REALLY APPRECIATE THOSE
INSIGHTS, JIM, AND ALSO YOUR
BEING HERE FOR THIS PARTICULAR
WORKSHOP.
SO BRAIN 2.0 CALLED FOR
DEVELOPING GUIDELINES T FOR HOWO
RESPONSIBLY THINK ABOUT SHARING
DATA AS WELL AS DATA ACCESS,
INCLUDING PARTICIPANT DATA AND
AGAIN, WITH THE VERY STRONG
IMPERATIVE OF PROTECTING
PARTICIPANT PRIVACY.
NOW, IN LINE WITH OUR INITIAL
STRATEGIC PLAN, BRAIN HAS
ACTUALLY TRIED TO BE AHEAD OF
THE GAME IN TERMS OF DATA
SHARING.
BACK IN 2019, WE ISSUED OUR OWN
DATA SHARING GUIDELINES,
ACTUALLY REQUIREMENTS, THAT WERE
AHEAD OF THE NIH'S MUCH TOUTED
SHARING PLANS BUT THEY ALSO
DOVETAILED WITH THE NIH-WIDE
EFFORTS AND WE HOPE THAT WE'RE
AHEAD OF THE GAME AND AS I LIKE
TO SAY, NOT JUST CLOSER TO THE
ICEBERG, BUT I THINK THINKING
ABOUT THESE THINGS PROACTIVELY
IS REALLY THE ONLY WAY FOR US TO
GET IT DONE.
SO AGAIN, WE'RE LOOKING AT THE
TRADEOFFS BETWEEN THE VALUE OF
SHARING THESE CRITICAL DATASETS,
BUT ALSO AT THE SAME TIME,
RECOGNIZING THAT I IT IS WITH
INTENTION OF PATIENT PRIVACY AND
ACTUALLY DOING GOOD FOR THE
FOLKS WE'RE ACTUALLY HERE TO
HELP SO I'LL TURN THE FLOOR OVER
TO DR. HENDRICKS AND SHE'LL HELP
US SET THE STAGE FOR TODAY'S
DISCUSSION, SO PLEASE TAKE IT
AWAY.
[APPLAUSE]
>>THANK YOU, JOHN.
IT'S A PLEASURE TO GET US
STARTED TODAY.
SO I'M WAITING FOR ONE SECOND
FOR MY SLIDES.
OKAY.
SO IT MY GREAT PLEASURE TO GET
US STARTED AND INTRODUCE THIS
WORKSHOP AND WHAT WE PLAN TO DO.
SO I'LL GIVE A QUICK
PRESENTATION, FIRST PRESENTING
THE RELEVANT BACKGROUND TO THIS
WORKSHOP, OUR WAY OF THINKING
GOING INTO THE WORKSHOP, AND
THEN WE'LL DISCUSS THE AIMS OF
THE WORKSHOP AND SOME OF THE
ASSUMPTIONS AND THEMES THAT WE
HAVE GOING IN.
SO LET ME START BY EMPHASIZING
THE VALUE OF DATA SHARING.
SO AS SOME OF YOU MAY HAVE
NOTED, THIS IS NOT THE MAIN
FOCUS OF THIS WORKSHOP.
BUT THAT IS BECAUSE, IN FACT, IT
HAS BEEN THE MAIN FOCUS OF
SEVERAL WORKSHOPS THAT WE'VE
RECENTLY SEEN.
ORGANIZED BY OTHERS AMONG THE
NIH, NATIONAL ACADEMIES AND
OTHERS.
ALONG WITH OTHER EFFORTS.
SO TO BRIEFLY SUMMARIZE, DATA
SHARING CAN BE JUSTIFIED ON
SCIENTIFIC, ECONOMIC AND ETHICAL
GROUNDS.
IT CAN BENEFIT INVESTIGATORS, IT
CAN BENEFIT RESEARCH
PARTICIPANTS, BUT THE PRIMARY
GOAL OF DATA SHARING IS TO
ACCELERATE SCIENTIFIC VALUE,
HOPEFULLY BENEFITING PATIENTS
AND SOCIETY.
SO THE WAY THIS IS ACHIEVED IS
BECAUSE DATA SHARING ALLOWS FOR
FURTHER ANALYSIS OF THE DATA,
FOR EXAMPLE, CONDUCTING A META
ANALYSES, IT INCREASES
TRANSPARENCY AND ACCOUNTABILITY,
IT INCREASES COLLABORATION AND
INTERDISCIPLINARY RESEARCH, AND
IT MAXIMIZES THE VALUE GAINED
OUT OF THE STUDIES THAT WE DO.
FOR EXAMPLE, ENHANCING THE VALUE
WE GET OUT OF PARTICIPANTS'
CONTRIBUTIONS.
ALL IN ALL, THROUGH THESE
MECHANISMS, THIS MAY ALSO
IMPROVE THE TRUST IN SCIENCE.
SO IN THIS WORKSHOP, WE'LL TAKE
FOR GRANTED THAT DATA SHARING
CAN HAVE IMPORTANT VALUE.
I WILL SAY TWO COMMENTS HERE ON
THIS SLIDE, WHICH IS, ONE OF THE
THINGS I DO THINK IS WORTH
CONSIDERING WHEN THINKING ABOUT
THE BENEFITS OF DATA SHARING IS
WHETHER, WHEN WE TALK ABOUT
BENEFITS FOR SOCIETY AND FUTURE
PATIENTS, WHO THESE PATIENTS
ARE, WHO THESE COMMUNITIES ARE.
ARE THEY THE SAME COMMUNITIES AS
OUARE CONTRIBUTING TO THE DATA,R
ARE THEY DIFFERENT COMMUNITIES?
SO WE'LL TALK ABOUT THAT.
ANOTHER ELEMENT I WANTED TO
HIGHLIGHT IS THAT AT THIS
MOMENT, WE HAVE LIMITED DATA,
EMPIRICAL DATA TO QUANTIFY THE
DIFFERENTIAL VALUE GAINED BY
SHARING DIFFERENT TYPES OF DATA,
OR SHARING DATA IN DIFFERENT
TYPES OF WAYS.
I'M NOT QUESTIONING THE VALUE OF
DATA SHARING -- INFORMATION
PACKET ABOUT PROTECTING
PARTICIPANTS' PRIVACY, AND THEY
SHARED SOME POINTS TO CONSIDER
ABOUT INFORMED CONSENT LANGUAGE.
SO THIS IS NIH-WADE.
AS WAS MENTIONED, BRAIN HAS ITS
OWN DATA SHARING POLICY, WHICH
REQUIRES DATA SHARING FOR A
NUMBER OF TYPES OF GRANTS.
OKAY.
SO THAT'S AS FAR AS THE BENEFITS
OF DATA SHARING.
NOW HOW ABOUT THE POTENTIAL
RISKS OF DATA SHARING?
WHEN IT COMES TO THE POTENTIAL
RISKS, PEOPLE TEND TO TALK ABOUT
THREE DIFFERENT TYPES OF
COMMUNITIES TO WHICH THESE RISKS
OCCUR.
INDIVIDUALS WHO CONTRIBUTE TO
THAT DATA, THE COMMUNITIES OF
WHICH THEY'RE PART, AND THE
PROFESSIONALS WHO ARE INVOLVED
IN CONDUCTING THE SCIENCE.
SO LET START WITH THE FIRST.
SO THE BIOETHICS LITERATURE HAS
DISTINGUISHED DIFFERENT TYPES OF
POTENTIAL INDIVIDUAL HARMS THAT
MAY ACCRUE FROM SHARING
INDIVIDUAL LEVEL DATA.
FOR EXAMPLE, IT MIGHT IMPACT THE
PRIVACY OF RESEARCH
PARTICIPANTS, AND IT MAY PUT
THEM AT POTENTIAL RISK OF
DISCRIMINATION.
THESE ARE JUST SOME EXAMPLES.
I'M NOT GOING INTO DEC DEPTH HEE
BECAUSE WE'LL HAVE A FULL PANEL
ON THIS TOPIC BUT I WANT TO
HIGHLIGHT A FEW THINGS THAT I
THINK ARE IMPORTANT TO THINK
ABOUT IN THE UPCOMING PANELS.
FIRST OF WHICH IS THAT MOST OF
THESE POTENTIAL HARMS INVOLVE
IDENTIFICATION OF AN INDIVIDUAL,
AND THEN SOMEBODY DOING
SOMETHING WITH THIS DATA,
INTENTIONAL OR NOT, THAT MAY
HARM THE INDIVIDUAL.
FOR EXAMPLE, IT MIGHT BE
IDENTIFYING AN INDIVIDUAL AND
THEN EMPLOYING OR DOING
SOMETHING WITH THIS DATA, FOR
EXAMPLE, MAKING HIRING DECISIONS
BASED ON THIS DATA.
THERE ARE A COUPLE OF EXAMPLES
FROM THIS BUT MOST INDIVIDUAL
RISKS ARE TIED TO IDENTIFICATION
OF INDIVIDUALS.
OKAY.
SECOND, I THINK WHEN THINKING
ABOUT THESE POTENTIAL RISKS, I
THINK IT'S GENERALLY USEFUL TO
THINK ABOUT THEM IN TERMS OF THE
SOLVABILITY OF THE POTENTIAL
RISKS AND THE MAGNITUDE OF THESE
POTENTIAL RISKS.
AND IN OUR WORKSHOP, I HOPE TO
ENCOURAGE EVERYBODY TO
DISTINGUISH AS FAR AS WE CAN THE
RISKS THAT ARE AT THIS POINT
PRIMARILY THEORETICAL, THE RISKS
FOR WHICH WE HAVE PROOF OF
PRINCIPLE, AND CASES WHERE WE
HAVE EXTENDED EVIDENCE
ACKNOWLEDGING THAT JUST LIKE IN
TERMS OF THE VALUE OF DATA
SHARING, THE EVIDENCE BASE MIGHT
BE SMALLER THAN MIGHT BE IDEAL
TO MAKE OUR ANALYSES.
SO NOW LET'S LOOK AT THE
POTENTIAL COMMUNITY HARMS.
SO IN GENERAL, THIS LOOKS
SOMETHING LIKE THIS.
AN INDIVIDUAL OR A GROUP COULD
LEVERAGE DATA TO BENEFIT THEIR
PRIVATE AGENDA.
FOR EXAMPLE, THEY MIGHT USE THE
DATA TO CONFIRM A PRE-EXISTING
HYPOTHESIS ABOUT THE INNATE
VALUE OF A CERTAIN GROUP OF
INDIVIDUALS, WHICH MIGHT HAVE
IMPLICATIONS IN TERMS OF THE
HEALTH AND NON-HEALTH-RELATED
INTERESTS OF THIS GROUP.
SO AGAIN HERE, I THINK IT'S
HELPFUL TO THINK ABOUT THE
MAGNITUDE AND THE LIKELIHOOD OF
THESE HARMS AND WE'LL GET BACK
TO THESE.
OKAY.
SO THE HARMS IN TERMS -- THE
POTENTIAL HARMS FOR THE
PROFESSIONALS INVOLVED IN THE
RESEARCH.
SO I THINK IT'S PROBABLY FAIR TO
SAY THAT SPONSORS, RESEARCH,
INSTITUTIONS AND RESEARCH TEAMS
MAY HAVE LEGITIMATE INTERESTS
THAT COULD BE UNDERMINED BY DATA
SHARING DEPENDING ON THE MATTER
AT WHICH IT OCCURS.
FOR EXAMPLE, THESE COULD RELATE
TO -- THIS COULD BASICALLY BE
DERIVATIVES OF THE TYPES OF
HARMS WE JUST SAW, FOR EXAMPLE,
REPERCUSSIONS FROM DATA MISUSE
THAT GET BACK TO THOSE WHO
CONDUCTED THE SCIENCE.
IT COULD ALSO BE CONSIDERATIONS
AROUND INTELLECTUAL PROPERTY OR
THINGS LIKE CAREER ADVANCEMENT.
THIS WILL NOT BE THE MAIN FOCUS
OF THE RISKS THAT WE'RE GOING TO
FOCUS ON DURING THIS WORKSHOP.
OKAY.
SO THERE ARE A NUMBER OF
DIFFERENT TYPES OF SAFEGUARDS
AND PROTECTIONS THAT WE CAN
APPLY WHEN WE SHARE DATA.
THESE RANGE FROM
DEIDENTIFICATION, DATA SECURITY,
INFORMED CONSENT, SECURITY
ACCESS, REGULATIONS AND OTHERS.
I'M NOT GOING TO GO INTO DETAIL
HERE.
BUT WHAT I WANT TO POINT OUT IS
THAT IN SOME CASES, SOME OF
THESE SAFEGUARDS TO MINIMIZE
RISKS CAN ACTUALLY HAVE
IMPLICATIONS FOR THE VALUE OF
THE DATA WHEN WE SHARE IT.
SO I THINK THIS IS AN EXAMPLE ON
THE SLIDE WHICH WE SEE IMAGES
FROM MRI, AND BASICALLY THE
THOUGHT IS THAT MRI CONTAIN
IMAGES OF FACES.
SO THERE IS A REIDENTIFICATION
RISKS IF FACIAL FEATURES ARE
RECONSTRUCTED FROM MRI, AND THEN
LINKED TO PUBLICLY ACCESSIBLE
PHOTOS.
SO WHAT PEOPLE STARTED TO DO TO
DE-IDENTIFY THIS DATA IS TO BLUR
SOME OF THE FACES OR REMOVE
THESE DATA ALL TOGETHER, BUT THE
POINT I WANT TO MAKE HERE IS
THAT THAT BASICALLY CUTS AWAY
SOME OF THE DATA THAT WE'RE
SHARING, AND, THEREBY, REDUCES
THE AMOUNT OF DATA WE'RE SHARING
AND THE VALUE OF THE DATA THAT
WE'RE SHARING.
SO I THINK THIS IS A COMMON
EXAMPLE ACROSS THESE STRATEGIES,
IS THAT SOME OF THESE STRATEGIES
MAY REDUCE THE RISKS FOR
INDIVIDUALS AND COMMUNITIES, BUT
ALSO REDUCES SCIENTIFIC VALUE
WHEN WE SHARE THE DATA.
THIS IS WHAT BRINGS US TO PART
OF OUR ETHICAL DILEMMAS HERE,
WHICH IS THAT THERE ARE IN SOME
CASES, AT LEAST, TRADEOFFS
BETWEEN PROTECTIONS OF
INDIVIDUALS AND COMMUNITIES WHEN
WE SHARE THE DATA AND THE
SCIENTIFIC VALUE WHEN WE SHARE
DATA.
AND WHEN THESE ARE RELEVANT,
MANY PEOPLE ARGUE FO FOR APPLYIG
THESE SAFEGUARDS PROPORTIONALLY
TO THE RISK WHEN SHARING DATA.
WHICH MEANS THAT WE NEED TO
UNDERSTAND WHAT THESE RISKS ARE.
SO THAT LOOKS SOMETHING LIKE
THIS.
I THINK THIS IS THE GENERAL
TRADEOFF WHEN WE TALK ABOUT DATA
SHARING.
IT'S LIKE THE VALUE OF DATA
SHARING ON THE ONE HAND COMPARED
TO THE POTENTIAL RISKS AND COSTS
OF DATA SHARING, AND AS FAR AS
THE PRO FE PROTECTIONS WE MIGHT,
REDUCE THE VALUE OF DATA
SHARING, WE DON'T WANT TO
OVERAPPLY THEM FOR LOW RISK DATA
BUT WE WANT TO MAKE SURE THEY'RE
IN PLACE FOR THE HIGHER RISK
DATA.
SO THAT'S PART OF THE CHALLENGE.
OKAY.
I WANT TO QUICKLY PRESENT SOME
OF THE RESULTS OF A SURVEY WE
DID AMONG NEUROINVESTIGATORS.
WE ACTUALLY DID THE SURVEY IN
PART IN PREPARATION FOR THIS
WORKSHOP.
SO IT'S A SECOND RESEARCH
QUESTION OF THE SURVEY THAT'S
MOST RELEVANT FOR THIS
DISCUSSION, WHICH IS HOW
COMMONLY DOES NEUROSCIENCE DATA
HAVE FEATURES THAT HAVE BEEN
DESCRIBED IN THE LITERATURE AS
SENSITIVE, OR HOW RISK EE Y IS E
DATA?
SO WE COLLECTED DATA FROM
INVESTIGATORS IN SPRING OF 2021.
WE HAD ABOUT 400 P.I.s WHO
RESPONDED TO THE SURVEY.
WE REACHED THE SAMPLE SIZE
ALTHOUGH THE RESPONSE RATE WAS
LOWER THAN PERHAPS YOU WOULD
FIND IDEAL.
SO ALL THESE INVESTIGATORS WERE
INVESTIGATORS, ACTIVE GRANTS
INVOLVING HUMAN SUBJECTS AND
THEY WERE FUNDED BY BRAIN, NINDS
AND NIMH, ABOUT 11% OF THESE
INVESTIGATORS WERE FUNDED BY
BRAIN WHICH REPRESENTS A SMALLER
GROUP OF INVESTIGATORS TO START
WITH.
AND IN AS FAR AS THESE BRAIN
INVESTIGATORS DIFFERED FROM THE
OTHER GROUPS, I WILL HIGHLIGHT
THAT IN THE UPCOMING SLIDES.
SO FIRST LET'S LOOK AT WHAT DATA
WE ARE COLLECTING OR OUR
SCIENTISTS ARE COLLECTING.
SO THE LARGE MAJORITY, I THINK
IT WAS ABOUT 90% OF
INVESTIGATORS, COLLECT MULTIPLE
DIFFERENT MODALITIES AND TYPES
OF DATA.
FOR EXAMPLE, MEDICAL DATA,
BEHAVIORAL DATA AND NEURAL DATA.
THESE WERE THE MOST COMMONLY
TYPES OF DATA THAT WERE
COLLECTED ACROSS THE SAMPLE.
WE FOUND THAT OUR BRAIN P.I.s
MORE OFTEN COLLECTED NEURAL DATA
AND LESS OFTEN COLLECTED MEDICAL
DATA AND GENETICS DATA.
HERE ARE SOME OF THE
TECHNOLOGIES THEY'RE USING.
SO THE MOST ARE FMRI, STRUCTURAL
BRAIN IMAGING, FOLLOWED BY EEG.
DIFFERENT FROM WHAT'S IN THIS
GRAPH.
THE BRAIN INVESTIGATORS ALSO
COMMONLY USED -- MORE COMMONLY
USED INTRACRANIAL
ELECTROPHYSIOLOGY, DBS, MEG AND.
BCI, AND YOU'LL SEE THAT
REPRESENTED IN THE FIRST PANEL
THAT I'M LOOKING FORWARD TO
WITNESSING.
SO I THINK THE WAY IN WHICH THE
TYPES OF MODALITIES THAT ARE
BEING USED ARE RELEVANT HERE IN
THE FACT THAT PEOPLE ARE
COLLECTING MULTIPLE TYPES OF
DATA IS RELEVANT IS BECAUSE THE
THOUGHT IS GENERALLY THAT THE
MORE TYPES OF DATA YOU'RE
COLLECTING, THE HIGHER THE
POTENTIAL RISK OF REIDENTIFYING
INDIVIDUALS, BUT ALSO THE MORE
INFERENCES YOU CAN DRAW ABOUT
THESE INDIVIDUALS, WHICH HAS
BOTH SCIENTIFIC VALUE BUT ALSO
POTENTIAL RISKS.
SO NOW TO SOME OF THE
POTENTIALLY SENSITIVE NEURAL
FEATURES THAT'S BEING COLLECTED.
COMMON TYPES OF SENSITIVE
FEATURES INCLUDE COLLECTING
NEURAL CORRELATES OF BEHAVIOR,
EMOTIONS OR DECISION-MAKING, AND
PREDICTIVE DATA.
SO BRAIN WAS A LITTLE DIFFERENT
FROM THE OVERALL SAMPLE IN A
OVER 80% OF BRAIN P.I.s
REPORTED THAT THEY HAD SMALL END
STUDIES AS PART OF THEIR GRANT,
SO THESE WERE MORE COMM COMMON N
IS ON THE SLIDE.
THEY ALSO MORE COMMONLY 30%
RECORDING DATA OUTSIDE OF THE
CLINIC AND RECORDING OVER 10
HOURS OF RECORDING PER
PARTICIPANT.
MOST STUDIES INVOLVE ADULTS WHO
CAN CONSENT FOR THEMSELVES, A
MINORITY INVOLVE RESEARCH
PARTICIPANTS PART OF MORE
VULNERABLE GROUPS ALTHOUGH I
WILL SAY BRAIN STUDIES LESS
COMMONLY INCLUDED MINORS THAN
THE OVERALL SAMPLE HERE.
NOW IN TERMS OF THE VIEWS ON THE
POTENTIAL RISKS AND THE BRAIN
INVESTIGATORS DIDN'T DIFFER FROM
THE REST OF THE SAMPLE HERE SO
I'LL BE REPORTING ON THE ENTIRE
SET.
OKAY.
SO WE ASKED OUR INVESTIGATORS,
AFTER YOU DE-IDENTIFY YOUR DATA,
HOW LIKELY DO YOU THINK IT IS
THAT WITHIN THE NEXT 10 YEARS,
SOMEBODY WILL BE ABLE TO
REIDENTIFY YOUR DATA?
29% SAID OH, THAT'S DEFINITELY
NOT POSSIBLE.
THAT'S THE ANSWER I WOULD PREFER
IF I WERE A RESEARCH
PARTICIPANT.
SO I THINK THAT CONFIRMS AT
LEAST SOME OF THE UNCERTAINTY
THAT WE HAVE ABOUT POTENTIAL
REIDENTIFICATION RISKS.
WE ALSO ASKED THEM WHAT DO YOU
THINK THE LIKELIHOOD IS OF
SOMEBODY MISUSING YOUR DATA WITH
CONSEQUENCES FOR INDIVIDUALS OR
FOR COMMUNITIES?
AND THE LARGE MAJORITY SAID OH,
THAT'S PRETTY UNLIKELY, WHICH IS
GOOD.
THEN WE ASKED, IMAGINE IF IT
WERE TO OCCUR, HOW CONCERNED ARE
YOU ABOUT THE POTENTIAL HARMS OF
THIS OCCURRING?
AND THEN OVER MORE THAN HALF
SAID THEY'RE AT LEAST SLIGHTLY
CONCERNED ABOUT THE POTENTIAL
HARMS THAT COULD OWE CREW FROM M
THESE UNLIKELY EVENTS OF MISUSED
DATA.
WE ALSO ASKED INVESTIGATORS WHO
DO YOU THINK WOULD BE INTERESTED
IN SEEING YOUR DATA?
AND THESE ARE THEIR RESPONSES
AND I JUST WANT TO POINT OUT,
SOME OF THE GROUPS THAT WE KNOW
BIOETHICISTS OR RESEARCH
PARTICIPANTS HAVE RAISED
CONCERNS ABOUT IN TERMS OF HOW
THEY MIGHT HANDLE THE DATA.
THIS INCLUDES GROUPS LIKE
INSURANCE, EMPLOYERS, MAYBE
FOREIGN GOVERNMENTS OR LAW
ENFORCEMENT.
AND I'M NOT SAYING THESE USES
ARE NECESSARILY ALL BAD.
IT'S JUST THAT THERE'S SOME
INDIVIDUALS WHO HAVE RAISED
CONCERN ABOUT THEM.
AND WE FOUND THAT SOMEWHERE
BETWEEN 10 AND 25% OF P.I.s
THOUGHT, YEAH, I COULD SEE THESE
GROUPS BEING INTERESTED IN MY
DATA.
SO WE LOOKED AT ASSOCIATIONS
BETWEEN WHAT TYPES OF DATA
INVESTIGATORS WERE COLLECTING
AND THEIR CONCERNS ABOUT THE
POTENTIAL MISUSE OF DATA.
PERHAPS UNSURPRISINGLY, WE FOUND
THAT THOSE WHO THINK THEIR DATA
IS MORE READILY REIDENTIFIABLE
WERE MORE -- THOUGHT THE
LIKELIHOOD OF INDIVIDUAL AND
GROUP HARMS WAS MORE LIKELY, AND
THE MAGNITUDE WAS HIGHER TOO.
THEY WERE ALSO MORE LIKELY TO
THINK THAT NON-BIOMEDICAL
STAKEHOLDERS WOULD BE INTERESTED
IN THEIR DATA, LIKE INSURANCE,
EMPLOYERS AND SO ON.
INDIVIDUALS WHO ARE COLLECTING
DATA FROM VULNERABLE GROUPS WERE
MORE LIKELY TO THINK EDUCATIONAL
SYSTEMS WOULD BE INTERESTED IN
THEIR DATA, REPRESENTING MINORS
WHO ARE PART OF THIS GROUP.
THEY ALSO THOUGHT THAT GRAWP
HARMS WOGROUPHARMS WOULD BE MORD
WERE MORE CONCERNED ABOUT THE
LIKELIHOOD OF INDIVIDUAL AND
GROUP HARMS.
INDIVIDUALS WHO COLLECTED MORE
DATA TYPES WERE MORE LIKELY TO
THINK THAT NON-BIOMEDICAL
STAKEHOLDERS WOULD BE INTERESTED
IN THEIR DATA AND THOSE WHO
COLLECTED NEURAL DATA THOUGHT
GROUP HARMS WERE MORE LIKELY AND
WERE MORE CONCERNED ABOUT THE
POTENTIAL HARMS THAT COULD COME
FROM THEM.
SO WE ASKED THE INVESTIGATORS IF
THEY COULD IMAGINE ANY TYPES OF
DATA IN THEIR FIELD THAT THEY
THOUGHT SHOULD BE EXEMPT FROM
SHARING DATA BECAUSE OF THE
POTENTIAL RISKS TO PARTICIPANTS
OF SHARING THE DATA.
AND ABOUT 50% COULD COME UP WITH
A TYPE OF DATA FOR WHICH THEY
THOUGHT THIS APPLIED, AND THESE
ARE SOME OF THE ANSWERS THAT
THEY GAVE IN TERMS OF THE TYPES
OF DATA.
SO IF THIRST RESPONDENT, THE
FIRST P.I. BASICALLY JUST REFERS
TO THE THREE TYPES OF HARMS THAT
WE JUST DISCUSSED IN THE
INTRODUCTION: HARMS TO
INDIVIDUALS, TO THE RESEARCHERS
OR TO COMMUNITIES.
THE SECOND RESPONDENT HIGHLIGHTS
SOME TYPES OF DATA THEY THINK
MIGHT BE PARTICULAR LISTENSTIVE.
LIKE HIV OR OTHER DISEASE STATUS
THAT MAY LEAD TO DISCRIMINATION,
LIKE ABORTION HISTORY, HISTORY
OF ABUSE, AND SO ON.
THERE WERE ALSO SEVERAL
INVESTIGATORS WHO REFERRED TO
THE RISK OF REIDENTIFICATION,
SOMETIMES COMBINED WITH HAVING
HIGH RISK DATA.
FOR EXAMPLE, THIS INVESTIGATOR
WHO SAID A SMALL CATEGORY OF
VERY SENSITIVE ITEMS XZ A AND EN
THEN, ONLY IN SMALL DATASETS OR
WHERE REGULAR SAFEGUARDS WERE
NOT EMPLOYED TO DE-IDENTIFY DATA
AND THEN REFER TO THE IMPORTANCE
OF CONSENT.
THE FINAL QUESTION I WANT TO
PRESENT HERE IS WE ASKED THEM IF
THERE WAS A NEED FOR FURTHER
GUIDANCE OR BEST PRACTICES AND
THE ONES HIGHLIGHTED IN GREEN
ARE THE ONES MOST RELEVANT TO
OUR DISCUSSION TODAY.
SO I THINK MANY INVESTIGATORS
SAW THE POTENTIAL BENEFIT OF
HAVING MORE BEST PRACTICES FOR
INFORMED CONSENT, WHO CAN HAVE
ACCESS TO DATA, WHAT DATA SHOULD
NOT BE SHARED, AND APPROPRIATE
USES OF DATA.
OKAY.
SO TO SUMMARIZE WHAT WE LEARNED
IN THIS SURVEY, NEUROSCIENCE
DATA CAN HAVE FEATURES THAT HAVE
SOMETIMES BEEN DESCRIBED AS
SENSITIVE, HOPEFULLY WE'LL
UNPACK THAT MORE DURING THIS
WORKSHOP.
INVESTIGATORS THINK INDIVIDUAL
AND GROUP HARMS ARE UNLIKELY BUT
MIGHT BE HARMFUL IF THEY OCCUR,
AND THEY IDENTIFIED A NEED FOR
BEST PRACTICES AND SAFEGUARDS
WHEN SHARING DATA.
OKAY.
NOW WITH THAT, LET'S TURN TO
THIS WORKSHOP.
SO THE FIRST PART OF OUR
WORKSHOP AIM IS TO EXPLORE
MEANINGFUL WAYS TO CATEGORIZE
HUMAN BRAIN DATA BY THE
POTENTIAL RISKS OF DATA SHARING.
SO LET ME UNPACK THIS A LITTLE
BIT.
SO NIH AS WELL AS BRAIN WANT TO
RESPONSIBLY SHARE DATA, AND IN
ORDER TO DO THAT, IT'S IMPORTANT
FOR US TO UNDERSTAND WHAT RISKS
ARE ASSOCIATED WITH SHARING
DIFFERENT TYPES OF DATA.
DOES BRAIN DATA HAVE EQUAL RISKS
WHEN IT'S BEING SHARED OR DOES
THE RUSK DIF RISKS DIFFER.
IF THE RISKS ARE DIFFERENT, WHAT
TYPES OF DATA HAVE MORE OR LESS
RISKS DURING THE COURSE OF THESE
TWO DAYS?
WE HOPE TO IDENTIFY FEATURES OF
FUTURES IN TERMS OF DATA
COLLECTION AND SHARING PRACTICES
THAT MAY INFLUENCE HOW MANY
RISKS A DATASET MIGHT HAVE WHEN
YOU SHARE IT.
AS AN EXAMPLE, SMALL N STUDIES
MIGHT HAVE MORE RISK OF
REIDENTIFICATION, SMALL N
MEANING SMALL NUMBER OF
PARTICIPANTS.
AND DATASETS WITH MULTIPLE
MODALITIES MIGHT COME AT
INCREASED RISK OF
DEIDENTIFICATION AND MIGHT ALLOW
INDIVIDUALS TO DRAW MORE INS
FRENSES ABOUT INDIVIDUALS.
-- MORE
INFERENCES ABOUT INDIVIDUALS.
SO A SINGLE DATASET MIGHT HAVE
MULTIPLE OF THESE FEATURES THAT
TOGETHER INFLUENCE THE AMOUNT OF
RISK WHEN YOU SHARE IT.
THIS IS JUST ONE EXAMPLE, I'M
NOT SAYING THIS IS WHERE WE'RE
GOING, IT'S JUST ONE EXAMPLE
WHERE THE Y AXIS IS ABOUT THE
INFERENCES THAT WE CAN DRAW FROM
DATA, AND THE OTHER AXIS IS
ABOUT HOW IDENTIFIABLE THE DATA
IS, WITH THE RED QUADRANT BEING
THE HIGHEST RISK.
SO FOR EXAMPLE, IF WE HAVE ONE
DATA MODALITY, LET'S SAY,
HISTOLOGY DATA AND 100
PARTICIPANTS, IT MIGHT END UP
SOMEWHERE HERE.
THEN IF WE ONLY HAVE
3 PARTICIPANTS, IT BECOMES MORE
IDENTIFIABLE, AND IF WE HAVE
MULTIPLE MODALITIES, IT MIGHT
MOVE TO MORE INFERENCES BEING
DRAWN FROM THIS DATA.
AND I'M NOT SAYING WE'LL END UP
WITH A DWR A GRAPH THAT LOOKS LE
THIS.
IN FACT, PART OF THE GOAL OF
THIS WORKSHOP IS TO IDENTIFY
FIRST WHAT ARE ALL THESE
DIFFERENCES IN DATA IN TERMS OF
THE RISKS WHEN WE SHARE IT.
SECOND, TO IDENTIFY THE FACTORS
IN THE DATA OR IN THE DATA
SHARING PRACTICES THAT INFLUENCE
THESE RISKS, AND THIRD, HOW TO
MEANINGFULLY CATEGORIZE THESE
DATA.
MAYBE IT'S A SPECTRUM, MAYBE
IT'S DISTINCT BUCKETS.
I LOOK FORWARD TO HEARING
EVERYBODY'S PRESENTATIONS DURING
THE NEXT TWO DAYS AND TRYING TO
FIGURE THIS OUT.
SO ONCE WE FIGURED THAT PART
OUT, I THINK THE NEXT STEP IS TO
CONSIDER WHETHER THAT MEANS WE
SHOULD TREAT DATA DIFFERENTLY
WHEN WE SHARE IT.
SO I RECOGNIZE THAT WHAT I'VE
SAID IS A LITTLE AMBITIOUS, SO
WE HOPE TO DO A LOT OF THIS WORK
DURING THIS TWO-DAY WORKSHOP,
BUT OUR EXPECTATION IS THAT SOME
OF THE WORK MAY NEED TO BE DONE
AFTER THE WORKSHOP AND WE'LL
PROBABLY HAVE A PANEL MAYBE TO,
IN OUR AUGUST NWP MEETING TO TRY
TO MAKE THIS INTO MORE APPLIED
STEPS THAT WE CAN RECOMMEND TO
THE FIELD.
SO I JUST WANT TO REPEAT SOME OF
THE ASSUMPTIONS THAT I THINK MAY
HAVE BEEN CLEAR ALREADY.
THE FIRST ASSUMPTION IS THAT
DATA SHARING CAN PROVIDE
IMPORTANT SCIENTIFIC VALUE.
NIH IS COMMITTED TO RESPONSIBLE
DATA SHARING.
AND THE BRAIN INITIATIVE IS AT
THE FOREFRONT OF SEVERAL AREAS
OF CUTTING-EDGE SCIENCE, AS WELL
AS PROMOTING DATA SHARING.
WHICH GIVES US A GREAT
OPPORTUNITY IN THIS WORKSHOP.
OKAY.
SO HERE ARE SOME OF THE THEMES I
THINK WE'LL SEE REOCCURRING
THROUGHOUT THIS WORKSHOP.
FIRST, WHAT IS BRAIN DATA.
FOR EXAMPLE, IS IT ONLY DIRECT
INFERENCES FROM THE BRAIN,
SHOULD CERTAIN TYPES OF
BEHAVIORAL DATA LIKE PUPILLARY
RESPONSES OR PERHAPS LIKE THE
SPEED OF MOVEMENT AS FAR AS IT
MIGHT REFLECT POTENTIAL
PARKINSON'S DISEASE, SHOULD
THINGS LIKE THAT COUNT TOO, AND
IF SO, WHERE DO THEY FALL ON THE
SPECTRUM?
OTHER ETHICALLY SALIENT
DIFFERENCES REALLY BETWEEN SOME
TYPES OF BRAIN DATA AND OTHER
TYPES OF BIOMEDICAL DATA AND IF
SO, WHAT MAKES THEM DIFFERENT.
AND WE'LL GET BACK TO THIS AGAIN
AND AGAIN, HOW DO WE BALANCE THE
BENEFITS OF DATA SHARING WITH
THE POTENTIAL RISKS AND HOW DO
WE APPROPRIATELY APPLY
SAFEGUARDS?
SO THE BENEFITS OF DATA SHARING
ARE NOT THE MAIN FOCUS OF THIS
WORKSHOP, BUT THEY COME INTO
PLAY BECAUSE ALL OF THIS IS
ABOUT BALANCING.
WITH THAT, I AM VERY MUCH
LOOKING FORWARD TO THE UP COMING
FIVE PANELS AND WE'LL HAVE SMALL
BREAKOUT GROUPS TOMORROW
AFTERNOON.
THE FIRST PANELS WILL BE SCIENCE
PANELS.
THE FIRST ONE WILL BE MORE
TECHNOLOGY-BASED.
WE'LL HEAR FROM DIFFERENT TYPES
OF INVESTIGATORS ABOUT THE TYPES
OF DATA THAT NEUROTECHNOLOGIES
ARE COLLECTING, AND WHAT WE CAN
INFER FROM THESE DATA.
WE'LL THEN HAVE A PANEL THAT'S
STILL SCIENCE BUT MORE ACROSS
TECHNOLOGIES.
WHAT ADDED INFERENCES CAN WE
DRAW WHEN WE COMBINE DATA, WHEN
WE HAVE INVASIVE DEVICES, WHEN
WE CAN DECODE SEMANTIC
REPRESENTATIONS.
THEN WE HAVE TWO ETHICS PANELS,
THE FIRST TODAY ABOUT POTENTIAL
COMMUNITY RISKS, THE SECOND IS
THE FIRST PANEL FOR TOMORROW,
ABOUT THE RISKS TO INDIVIDUALS.
AND THEN WE'LL CLOSE WITH PECK
SPECULAPERSPECTIVES OF RESEARCH
PARTICIPANTS, WHICH IS THE PANEL
I'M LOOKING FORWARD TO MOST.
OKAY.
SO WITH THAT, I WOULD LIKE TO
PASS ALONG THE MIC TO DR. NINA
HSU AND SAMEER SHETH TO
INTRODUCE THE FIRST PANEL.
THANK YOU FOR JOINING US.
[APPLAUSE]
>>ALL RIGHT.
SO WE HAVE THE PLEASURE OF
MODERATING OUR FIRST PANEL ON
THE DATA THAT IS COLLECTED AND
STORED BY NEUROTECHNOLOGIES AND
THE INFERENCES THAT CAN BE MADE
FROM THESE DATA.
WE'LL HEAR FROM FIVE RESEARCHERS
WHO ARE SPECIALISTS OR CONDUCT
RESEARCH IN MANY OF THE
TECHNOLOGIES THAT SASKIA NOTED
IN OUR SURVEY FREQUENTLY USED IN
NEUROSCIENCE RESEARCH.
OUR FIRST SPEAKER -- OH, AND I
SHOULD MENTION TO YOU, AFTER
EACH SPEAKER GOES, WE'LL HAVE A
FEW MINUTES FOR SPEAKER-SPECIFIC
QUESTIONS.
WE'D LIKE TO MOSTLY FOCUS THESE
ON CLARIFYING OR TECHNICAL
QUESTIONS FOR THE SPEAKER.
AFTER ALL THE SPEAKERS HAVE
GONE, WE'LL HAVE PLENTY OF TIME
FOR PANEL DISCUSSION AND WITH
OUR NEWG MEMBERS, SPEAKERS AND
ATTENDEES.
SO WITH THAT, I'LL INTRODUCE OUR
FIRST SPEAKER, DR. SUSIE HUANG,
RADIOLOGIST IN THE DIVISION OF
NEURORADIOLOGY, DIRECTOR OF
CONNECTOMICS AND TRANSLATIONAL
IMAGING IN THE DEPARTMENT OF
RADIOLOGY AT MASSACHUSETTS
GENERAL HOSPITAL, AND AN
ASSOCIATE PROFESSOR OF RADIOLOGY
AT HARVARD MEDICAL SCHOOL.
SHE RECEIVED A PH.D. IN PHYSICAL
CHEMISTRY FROM THE UNIVERSITY OF
CALIFORNIA-LOS ANGELES AND AN
M.D. FROM HARVARD MEDICAL
SCHOOL.
HER CURRENT RESEARCH FOCUSES ON
THE DEVELOPMENT AND TRANSLATION
OF ADVANCED DIFFUSION MRI
METHODS FOR PROBING TISSUE
MICROSTRUCTURE IN THE CENTRAL
NERVOUS SYSTEM.
DR. HUANG.
>>THANKS SO MUCH, NINA.
AND THANK YOU TO THE ORGANIZERS
FOR THIS INVITATION TO ENGAGE IN
THIS REALLY INTERESTING TOPIC.
SO ASSIGNEE NA MENTION I'M ALSON
INITIATIVE NEXT GENERATION HUMAN
IMAGING TECHNOLOGY GRANT TO
DEVELOP THE NEXT GENERATION
CONNECTOME SCANNER FOR LOOKING
AT HUMAN BRAIN STRUCTURE AND
FUNCTION ACROSS SCALES.
WHAT DOES FUNCTIONAL MRI
ACTUALLY MEASURE?
IT'S IMPORTANT TO UNDERSTAND
THIS, BECAUSE IT ACTUALLY GETS
DOWN TO FUNDAMENTALLY THE KINDS
OF INFERENCES AND INFORMATION
WE'RE ABLE TO EXTRACT FROM THE
DATA.
SO FUNCTIONAL MRI ESSENTIALLY
MEASURES CHANGES IN BLOOD
OXYGENATION LEVEL THAT ARE
RELATED TO BUT ARE NOT A DIRECT
MEASURE OF NEURONAL ACTIVITY,
AND IT'S REALLY MEDIATED THROUGH
A FUNDAMENTAL PHYSIOLOGICAL
NEUROVASCULAR COUPLING.
SO THE IDEA IS THAT WE USE AN
ENDOGENOUS CONTRAST AGENT WHICH
IS HEMOGLOBIN IN BLOOD.
ITS DENSITY CAN BE MEASURED
THROUGH THE MRI SIGNAL THROUGH
WHAT'S KNOWN AS THE BLOOD
OXYGENATION LEVEL DEPENDENT
RESPONSE.
SO FOLLOWING AN ACTIVATION BY A
STIMULUS, THERE'S AN INCREASE IN
DEMAND FOR NUTRIENTS WITHIN THE
BRAIN AND CEREBRAL BLOOD FLOW
AND CEREBRAL BLOOD VOLUME WILL
OVERCOMPENSATE FOR THAT OXYGEN
DEMAND, LEADING TO CHANGES IN
THE LEVEL OF DEOXYHEMOGLOBIN IN
THE BLOOD AND THAT ULTIMATELY
LEADS TO THIS DETECTABLE BOLD
SIGNAL, WHICH IS WHAT WE MEASURE
IN F MRI.
AND SO TO DEPICT THIS IN KIND OF
MORE GRANULAR DETAIL, THIS IS
THE CANONICAL HEMODYNAMIC
THIS IS A
SCHEMATIC OF THE TIME COURSE OF
THE BOLD SIGNAL, AND IT
BASICALLY GIVES YOU AN IDEA OF
THE TIME COURSES THAT WE'RE
LOOKING AT IN FMRI.
SO ARE YOU SEEING THAT ON THE
SCREEN?
OKAY, PERFECT.
THANK YOU.
SO IN THIS SCHEMATIC OF THE
HEMODYNAMIC RESPONSE FUNCTION,
THE GREEN VERTICAL LINE
REPRESENTS A STIMULUS ONSET, AND
REALLY THE IMPULSE OF NEURAL
ACTIVITY THAT OCCURS AT THAT
TIME.
AND THE RED CURVE WHICH IS SHOWN
UNDERNEATH THE BLUE -- THE
PURPLE CURVE IS ESSENTIALLY THE
MODELED TIME COURSE FOR THE BOLD
SIGNAL.
SO THIS IS KIND OF WHAT WE
UNDERSTAND TO BE HAPPENING AS A
RESPONSE OF THE BLOOD
OXYGENATION LEVEL IN RESPONSE TO
A NEURAL ACTIVITY.
SO THE TIME COURSE CAN BE
DIVIDED INTO THREE PHASES.
THERE'S FIRST AN INITIAL
DECREASE DUE TO THE BURST OF
OXIDATIVE METABOLISM THAT
PRECEDES A RISE IN THE CEREBRAL
BLOOD FLOW AND ASSOCIATED INFLUX
OF OXYGEN.
THE CEREBRAL BLOOD FLOW AND
CEREBRAL -- CEREBRAL BLOOD FLOW
REALLY INCREASES PRETTY
DISPROPORTIONATELY DUE TO THE
METABOLIC RATE OF OXYGEN UPTAKE,
SO THE AMOUNT OF DEOXYHEMOGLOBIN
IN THE BLOOD WILL DECREASE AND
THE BOLD SIGNAL RISES TO A PEAK
AT ABOUT 5 TO 6 SECONDS SO THAT
GIVES YOU KIND OF A SENSE OF THE
TEMPORAL COURSE OF THE F PLY
SIGNAL.
AS THE CEREBRAL BLOOD FLOW AND
CEREBRAL METABOLIC RATE OF
OXYGEN DISSIPATE, THEN THE BOLD
SIGNAL DECLINES TOWARD BASELINE
AND MAY EVEN GO UNDER -- FALLING
BELOW THE BASELINE, FORMING THIS
POST STIMULUS UNDERSHOOT THAT
PEAKS AT AROUND 15 SECONDS.
SO NEXT SLIDE, PLEASE.
SO IN TERMS OF THE DATA
CHARACTERISTICS OF FMRI, WE
MEASURE THE CHANGE IN THE
HEMODYNAMICS DUE TO A STIMULUS.
AND THAT FUNCTIONAL ACTIVITY IS
REFLECTED IN THE SIGNAL OF
INDIVIDUAL VOXELS WITHIN THE
GREY MATTER OVER TIME.
SO FUNDAMENTALLY IN BOLD FMRI,
WHAT WE'RE ACQUIRING IS A SERIES
OF IMAGES AT DIFFERENT POINTS IN
TIME, AND THIS IS IN DISTINCTION
TO STRUCTURAL IMAGING, SAY, FOR
EXAMPLE, THIS T1 WEIGHTED
STRUCTURAL IMAGE SHOWN ON THE
LEFT, THAT IS A STATIC IMAGE
THAT USUALLY HAS ACQUIRED A VERY
HIGH SPATIAL RESOLUTION AND WILL
BE DISCUSSED LATER IN THIS PANEL
BY DR. GRIEVE.
GREVE.
SO GOING BACK TO THE FMRI DATA
IS WE TYPICALLY ACQUIRE A VOLUME
EVERY 1 TO 4 SECONDS AND THE
TEMPORAL RESOLUTION REALLY VAIR
VARIES BASED ON THE SCANNER
CAPABILITIES, ITS FIELD
STRENGTH, GRADIENT STRENGTH.
SO THAT'S ONE CONSIDERATION.
THE OTHER IS THE SPATIAL
RESOLUTION, SO EACH VOXEL IS
BETWEEN 1 TO 4 MILLIMETERS IN
SIZE, TYPICALLY 2 MILLIMETERS IF
NOT LARGER.
SO YOU CAN SEE THE SPATIAL
RESOLUTION OF FMRI IS STILL
FAIRLY LIMITED, AND THAT'S
FUNDAMENTALLY OF RELEVANCE IN
THE SENSE THAT IT GIVES YOU AN
IDEA OF HOW READILY IDENTIFIABLE
FEATURES MIGHT BE IN THE REST OF
THE FACE AND IN THE HEAD VOLUME,
NOT JUST IN THE BRAIN.
SO THESE ARE WHAT WE'RE
INTERESTED IN IN TERMS OF THE
FMRI DATA.
NEXT SLIDE, PLEASE.
SO FMRI ALSO MEASURES AND IS
SENSITIVE TO OTHER FEATURES OF
THE SIGNAL.
SO WHAT ELSE IS IT SENSITIVE TO?
WELL, IT'S SENSITIVE TO VARIOUS
NOISE SOURCES.
THESE INCLUDE THERMAL NOISE FROM
THE INSTRUMENT ITSELF AND THE
MEASUREMENT, AND THEN ALSO FROM
THE SUBJECT OR THE PARTICIPANT,
IT'S SENSITIVE TO PHYSIOLOGICAL
NOISE.
AND BY THAT, I MEAN
CONTRIBUTIONS FROM CARDIAC,
RESPIRATORY AND INTRAVASCULAR
FLUCTUATION SAYING THE CEREBRAL
BLOOD VOLUME, CEREBRAL BLOOD
FLOW.
SO THESE FACTORS ARE ALSO
MEASURED BY FMRI.
FINALLY THERE ARE SUBJECT
SPECIFIC EFFECTS THAT MAY OCCUR
DURING THE MEASUREMENT THAT THE
FMRI SIGNAL ALSO PICKS UP ON AND
HAS TO BE KIND OF ACCOUNTED FOR
IN OUR STATISTICAL ANALYSIS AND
MODELING OF THE SIGNAL, AND THAT
INCLUDES SUBJECT-SPECIFIC
EFFECTS, INCLUDING MOTION, AND
SO OVERALL THE FMRI SIGNAL IS
ESSENTIALLY THE AGGREGATE OF THE
ACTIVITY THAT WE'RE INTERESTED
IN, THE NEURAL ACTIVITY WHICH
HAS REFLECTED THROUGH THE
HEMODYNAMIC EFFECTS AND ALL OF
THESE POTENTIAL NOISE SOURCES
WITHLY CARRY INFORMATION THAT IS
SUBJECT-SPECIFIC, BUT THEY MAY
BE LESS READILY PARSED OUT.
SO NEXT SLIDE, PLEASE.
SO IN TERMS OF THE TYPES OF DATA
THAT WE GENERATE FROM FMRI AND
NOW I'M TALKING ABOUT THE TYPES
OF ANALYSES WE CAN DO TO THIS
DATA.
IT REALLY DEPENDS IN SOME WAYS
ON HOW THE F PLY MEASUREMENT IS
DONE, AND BY THAT, WE CAN
CLASSIFY INTO TWO MAJOR TYPES.
TASK-BASED FMRI WHICH REQUIRES
THE PARTICIPANT TO ENGAGE IN A
SPECIFIC TYPE OF BEHAVIOR OR
RESPONSE AS COMPARED TO A
CONTROL OR BASELINE CONDITION.
SO FROM TASK-BASED FMRI, THIS IS
A VERY TYPICAL TYPE OF OUTPUT
THAT WE CALCULATE A TASK-BASED
ACTIVATION MAP.
THIS ESSENTIALLY IS GENERATED
THROUGH STATISTICAL ANALYSES
THAT IDENTIFY AREAS OF BRAIN
ACTIVATION RELATIVE TO A CONTROL
STATE.
SO HERE I'M SHOWING RESULTS FROM
A MOTOR MAPPING TASK THAT WAS
PUBLISHED THROUGH THE HUMAN
CONNECTOME PROJECT AND I WOULD
SAY AS AN ASIDE THAT THE HUMAN
CONNECTOME PROJECT WHICH OUR
GROUP AT MGH AS WELL AS MANY
OTHERS ACROSS THE COUNTRY AND
THE WORLD HAVE BEEN INVOLVED IN
WAS REALLY A SEMINAL NEURAL
IMAGING STUDY IN THE SENSE THAT
IT WAS ONE OF FIRST TO SHARE
DATA AT A VERY LARGE SCALE.
1200 HEALTHY YOUNG ADULTS.
SO THIS DATA IS NOW ONLINE, MADE
POSSIBLE THROUGH THE NIH, AND SO
THESE ARE RESULTS OF MOTOR
MAPPING FROM THAT DATASET ON THE
GROUP LEVEL AND ALSO SHOWING
SORT OF THE DISTRIBUTION SORT OF
PARTICIPANTS SHOWING ACTIVATION
ABOVE A CERTAIN STATISTICAL
THRESHOLD.
THE OTHER TYPE OF DATA THAT IS
GENERATED BY FMRI IS RESTING
STATE FMRI DATA, AND THIS TYPE
OF RESTING STATE DATA IS REALLY
DONE WHILE -- IS ACQUIRED AS THE
PATIENT OR THE PARTICIPANT IS AT
REST.
SO ESSENTIALLY NOT ENGAGING IN
ANY SPECIFIC TASK.
SO THERE ARE SEVERAL WELL-KNOWN
CANONICAL NETWORKS THAT ARE
KNOWN TO BE ACTIVE AT REST.
THIS IS AN EXAMPLE OF PARTS OF
THE DEFAULT MO NETWORK, ONE OF
THESE FROM A SINGLE 15 MINUTE
RUN FROM A SINGLE SUBJECT, ALSO
TAKEN FROM THE HUMAN CONNECTOME
PROJECT.
SO IN RESTING STATE FMRI, WE
MEASURE THE SPONTANEOUS LOW
FREQUENCY FLUCTUATIONS IN THE
BOLD SIGNAL, WHICH CAN ALSO BE
USED TO CHARACTERIZE BRAIN
FUNCTION AT AN INDIVIDUAL AS
WELL AS A GROUP LEVEL.
SO THESE ARE THE TYPES OF DATA
THAT FMRI TYPICALLY ACQUIRES.
THERE'S ALSO A WHOLE HOST OF
ANCILLARY DATA THAT WE ACQUIRE
TO AID IN THE INTERPRETATION OF
THE FMRI SIGNALS.
THESE INCLUDE ANATOMICAL MRI,
WHICH IS -- USUALLY TAKES THE
FORM OF SAY A T1 WEIGHTED HIGH
RESOLUTION, SAY 1-MILLIMETER
ISOTROPIC STRUCTURAL IMAGING AND
THAT'S MANY TIMES USED FOR
COREGISTRATION WITH THE FMRI
DATA AND THEN CAN BE USED, SAY,
FOR SEGMENTATION AND, SAY, ROI,
REGION OF INTEREST-BASED
ANALYSES.
I'LL INCLUDE AMONGST THE TYPES
OF DATA THAT ARE COMMONLY
ACQUIRED ALONGSIDE FMRI TO
FUSION MRI, AND I DO THIS IN
SOME WAYS BASED ON OUR
EXPERIENCE FROM THE HUMAN
CONNECTOME PROJECT.
SO DIFFUSION MRI HELPS YOU INFER
INFORMATION ABOUT STRUCTURAL
CONNECTIVITY AND TISSUE
MICROSTRUCTURE, AND THAT CAN BE
USED TO EXPLORE
STRUCTURE-FUNCTION RELATIONSHIPS
IN THE LIVING HUMAN BRAIN.
PHYSIOLOGICAL DATA IS OFTEN
ACQUIRED ALONGSIDE THE FMRI DATA
AND THIS PROVIDES MEASURES OF
CARDIAC AND RESPIRATORY FUNCTION
TO HELP IDENTIFY AND CONTROL FOR
THOSE VARIABLES CONTRIBUTING,
SAY, TO PHYSIOLOGICAL NOISE,
THEIR INFLUENCE OVERALL ON BRAIN
ACTIVITY.
AND THEN FINALLY, IN TASK-BASED
FMRI, WE WILL ACQUIRE BEHAVIORAL
DATA DURING THE TASK TO PROVIDE
CONTEXT FOR INTERPRETING THE
FMRI DATA.
SO THE TYPES OF INFERENCE THAT
CAN BE MADE FROM FMRI, FMRI
REALLY IS KIND OF TRADITIONALLY
THOUGHT TO IDENTIFY AREAS OF THE
PLAIN THATBRAIN THAT ARE ACTIVAG
VARIOUS TASKS, PERCEPTION,
COGNITION AND DIFFERENT TYPES OF
ACTION.
AND SO FMRI DATA IS JUST A VERY
BRIEF KIND OF SYNOPSIS, CAN
PROVIDE INFORMATION ON NEURAL
CORRELATES OF K DECISION-MAKING,
ATTENTION, LANGUAGE PLAPPING WE
OFTEN USE THIS CLINICALLY FOR
PRE-SURGICAL PLANNING, AS WELL
AS EMOTIONS.
SO ONE OF THE CAVEATS THAT MUST
BE KIND OF EMPHASIZED THROUGHOUT
IS THAT IN FMRI, WHAT WE'RE
MEASURING IS THE HEMODYNAMIC
RESPONSE, AND THIS IS AN
INDIRECT MEASURE OF NEURAL
ACTIVITY, AND AS SUCH, WE NEED
TO UNDERSTAND ESSENTIALLY THE
OTHER -- THAT THIS MAY BE A
SOMEWHAT BIASED MEASURE OF
NEURAL ACTIVITY ITSELF.
SO THE EXTENT TO WHICH THAT
NEUROVASCULAR COUPLING DEPENDS
ON NEURAL ACTIVITY WILL REALLY
KIND OF MODULATE THAT FMRI
SIGNAL, AND THIS REALLY
UNDERSCORES THE NEED FOR OTHER
TYPES OF DATA, INCLUDING
NEUROPHYSINEUROPHYSINEUROPHYSIO,
ELECTROPHYSIOLOGY, TO UNDERSTAND
EXACTLY WHAT WE'RE MEASURING
INSIDE A SCANNER.
IN ADDITION, WE ALSO NEED TO
ACCOUNT FOR THE TYPES OF
TECHNICAL LIMITATIONS, INCLUDING
SPATIAL AND TEMPORAL RESOLUTION
LIMITS AND SCAN/RESCAN
REPRODUCIBILITY.
THESE ARE ALL CONSIDERATIONS
THAT NEED TO BE FACTORED IN,
REGARDLESS OF THE MEASUREMENT
TYPE OF THE MEASUREMENT FOR
THESE TYPES OF DATA.
NEXT SLIDE, PLEASE.
SO ONE THING I WILL EMPHASIZE IN
TERMS OF THE REAL VALUE THAT
FMRI AND MRI IN GENERAL OFFERS
FOR HUMAN BRAIN IMAGING AND KIND
OF NEUROSCIENCE IS THAT REALLY
MRI IS ONE OF THOSE FEW
MODALITIES THAT WE CAN USE TO
ACCESS INFORMATION IN THE LIVING
HUMAN BRAIN ON A SIZE SCALE
COMMENSURATE WITH WHOLE BRAIN
KIND OF ACTIVITY.
SO WE RECOGNIZE ONE OF THE
ENORMOUS CHALLENGES OF
UNDERSTANDING HOW THE BRAIN
WORKS AND HOW IT'S STRUCTURED,
IS THAT SORT OF UNDERSTANDING
THAT COMPLETE MATRIX OF
STRUCTURAL AND FUNCTIONAL
CONNECTIONS IN THE BRAIN REALLY
REQUIRES US TO BE ABLE TO LOOK
AT THE WHOLE BRAIN
SIMULTANEOUSLY WHILE
UNDERSTANDING AS WE DRILL DOWN
FURTHER AND FURTHER INTO THE
VOXEL WHAT'S ACTUALLY
CONTRIBUTING TO THAT SIGNAL.
AND I WOULD ARGUE THAT TOOLS
LIKE MRI DO OFFER THAT SPATIAL
RANGE TO BE ABLE TO GET TO THAT
INFORMATION, AND THE RESOLUTION
IS ACTUALLY NOW IMPROVING TO
THE DEGREE THAT WE CAN MEASURE
DOWN INTO THE SUBVOXEL LEVEL
USING CONTRASTS LIKE DIFFUSION
MRI AND ALSO FUNCTIONAL
MEASUREMENTS IF THE SPATIAL
RESOLUTION IS SUFFICIENTLY HIGH.
AND THIS HAS BEEN A FUNDAMENTAL
ADVANCE IN CONTRIBUTION FROM THE
BRAIN INITIATIVE IN TERM OF
INVESTING IN NEUROTECHNOLOGY
THAT WILL ENABLE US TO GET DOWN
TO THAT INFORMATION AT THE
MESOSCOPIC LEVEL ON THE ORDER OF
HUNDREDS OF MICRONS.
SO ULTIMATELY, THESE TYPES OF
TECHNOLOGIES ARE ALLOWING US TO
BRIDGE THIS GAP IN SIZE SCALES
AND WILL BE PROVIDING US
INFORMATION THAT'S COMING ONLINE
NOW, THAT REALLY WILL KIND OF
TRANSFORM THE WAY THAT WE LOOK
AT HOW THE HUMAN BRAIN IS
STRUCTURED AND HOW IT FUNCTIONS
IN LIVING INDIVIDUALS.
AND SO KEEPING THAT PERSPECTIVE
IN MIND, WE'VE BASICALLY BEEN
PREPARING FOR HOW TO INTERPRET
THESE TYPES OF DATA THROUGH OUR
OWN SORT OF TECHNOLOGY
DEVELOPMENT GOALS AND PROJECTS.
AT THE MGH, WE'RE ACTUALLY IN
THE PROCESS RIGHT NOW OF
INSTALLING A ONE OF A KIND NEXT
GENERATION HUMAN CONNECTOME
PROJECT.
WE CALL IT CONNECTOME 2.0,
BECAUSE THIS IS THE NEXT
GENERATION CONNECTOMIC SCANNER
FOR HUMAN IMAGING, AND IT WILL
ENABLE US TO REALLY IMAGE DOWN
TO THAT MICROSCOPIC SCALE IN
LIVING HUMANS.
SO WE'RE DOING THIS THROUGH A
WHOLE HOST OF ADVANCES IN MRI
HARDWARE AND ANALYSIS
TECHNOLOGY, INCLUDING DEVELOPING
THE STRONGEST GRADING THAT'S
EVER BEEN USED FOR HUMAN
IMAGING.
SO I WON'T GO INTO THE DETAILS
OF THIS ACQUISITION TECHNOLOGY,
BUT SUFFICE IT TO SAY IT'S
HAPPENING RIGHT NOW ACROSS THE
STREET FROM MY BUILDING AND
ESSENTIALLY ONE OF THE REASONS
I'M NOT JOINING HERE TODAY IN
PERSON.
BUT WHY DO WE NEED SUCH --
BASICALLY SUCH INSTRUMENTS AND
ADVANCES IN HARDWARE?
SO ONE OF THE REASONS
FUNDAMENTALLY IS THAT WE
RECOGNIZED THROUGH ACTUALLY OUR
WORK ON THE ORIGINAL CONNECTOME
PROJECT THAT HIGHER GRADIENT
STRENGTHS AND SLEW RATES AS WELL
AS COMPLEMENTARY EFFORTS AT
HIGHER MAGNETIC FIELDS ARE
NEEDED TO ACHIEVE HIGHER SPATIAL
RESOLUTION AND REALLY ENABLE US
TO INFER BRAIN STRUCTURE AND
FUNCTION DOWN TO THAT MESOSCOPIC
SCALE.
AND BY PUSHING THE LIMITS OF
WHAT IN VIVO MRI CAN DO IN TERMS
OF INTAIC SPATIAL AND TEMPORAL
RESOLUTION, WE'LL ULTIMATELY BE
ABLE TO INFER HOW THE BRAIN IS
STRUCTURED AND HOW IT CHANGES
ACROSS SCALES, BEHAVIORS AND
PATHOLOGY WITHIN INDIVIDUALS.
SO AS PART OF THIS INITIATIVE,
I'VE ALREADY MENTIONED OUR ROLE
IN THE HUMAN CONNECTOME PROJECT.
WE BUILT THE FIRST CONNECTOME
MRI SCANNER TO DO THIS AS PART
OF THE HUMAN CONNECTOME PROJECT
BASICALLY TO MAP ALL THE MAJOR
WHITE MATTER PATHWAYS IN THE
LIVING ADULT HUMAN BRAIN.
WE'VE ALSO USED THAT SAME
SCANNER TO GENERATE SEVERAL
UNIQUE DATASETS THAT WE HAVE NOW
SHARED TO THE SCIENTIFIC
COMMUNITY AND ESSENTIALLY WE
HAVE THOUGHT ABOUT MANY OF THESE
CONSIDERATIONS IN THE CONTEXT OF
DATA SHARING.
SO WHY HAVE WE CHOSEN TO SHARE
OUR DATA?
WELL, ONE OF THE REASONS IS AS
ARTICULATED EARLIER BY NINA AND
BY SASKIA, LARGER SAMPLE SIZES
ARE GOING TO BE BETTER FOR
SCIENTIFIC DISCOVERY.
SO IT WILL REALLY HELP US TO
MEASURE SMALL EFFECTS BETTER,
AND ALSO ENHANCE REPRODUCIBILITY
REPLICATION BASICALLY OPEN UP
THE SCIENTIFIC ENDEAVOR TO MORE
INDIVIDUALS.
THE SPECIFIC ROLE THAT WE'VE
FOUND IN OUR ROLE IN DEVELOPING
NEXT GENERATION HUMAN IMAGING
TECHNOLOGY FOR THE BRAIN
INITIATIVE -- SORRY, I THINK --
I'M NOT SURE WHICH SLIDE YOU
GUYS ARE ON.
ADVANCE TO THE NEXT SLIDE IF YOU
HAVEN'T ALREADY.
PERFECT.
FOLLOWING TO THE BENEFITS OF
SHARING MRI DATA.
SO ESSENTIALLY WE WANT TO
PROVIDE ACCESS TO UNIQUE
DATASETS GENERATED FROM THESE
ONE OF A KIND INSTRUMENTS TO
REALLY SHOWCASE WHAT WE CAN DO
WHEN WE PUSH THE LIMITS OF WHAT
CAN BE SEEN IN THE LIVING HUMAN
BRAIN USING SUCH UNIQUE SCANN
SCANNERS.
THESE SPECIALIZED IMAGING
CAPABILITIES HAVE REQUIRED
EFFORTS THAT ARE KIND OF AKIN TO
WHAT WAS USED FOR THE HUMAN
GENOME PROJECT, ESSENTIALLY
LARGE SCALE FIVE-YEAR PROJECTS,
MULTIMILLION DOLLAR BUDGETS.
THESE INSTRUMENTS ARE NOT
READILY REPLICATED AND WE WANTED
TO MAKE SUCH IMAGING
CAPABILITIES AVAILABLE TO THE
BROADER RESEARCH COMMUNITY, AND
DATA SHARING IS REALLY ONE OF
THOSE WAYS IN WHICH WE CAN DO SO
FAIRLY EASILY, MAKING THAT DATA
ACCESSIBLE TO NEUROSCIENTISTS
ACROSS THE RESEARCH SPECTRUM.
BUT WE ALSO NEED TO BE MINDFUL
OF THE RISKS OF SHARING
FUNCTIONAL AS WELL AS DIFFUSION
MRI DATA FROM SUCH UNIQUE
INSTRUMENTS.
SO IN PARTICULAR, FMRI DATA IS
CONSIDERED HIGHLY PERSONAL.
IT CAN REVEAL SENSITIVE
INFORMATION ABOUT INDIVIDUALS,
INCLUDING INFORMATION ABOUT
THEIR THOUGHT PATTERNS, THEIR
EMOTIONAL STATES, PERFORMANCE ON
VARIOUS TASKS, AND ALSO
POTENTIALLY RISKS FOR DEVELOPING
SPECIFIC DISORDERS.
I THINK THERE ARE SOME
MITIGATING FACTORS TOWARD THESE
RISKS.
FOR ONE, AT PRESENT, THE
INFERENCES THAT ARE MADE FROM
FMRI ARE GENERALLY MADE AT THE
GROUP LEVEL AN NOT WITHIN
INDIVIDUALS, SO THAT WILL HELP
WITH SOME OF THE PRIVACY CONC
CONCERNS, AT LEAST FOR NOW, BUT
SOMETHING TO DEFINITELY BE
THINKING ABOUT ON THE HORIZON.
THE OTHER KIND OF POINT I'VE
EMPHASIZED TECHNICALLY IS THAT
UP UNTIL NOW, THE FMRI DATA HAS
BEEN GENERALLY LOWER IN SPATIAL
RESOLUTION THAN, SAY, ANATOMICAL
IMAGING DATA, WHICH DOUG WILL
TALK MORE ABOUT, AND SO AS SUCH,
WE HAVEN'T NECESSARILY THOUGHT
OR HAVEN'T HAD TO NECESSARILY
REMOVE IDENTIFYING ANATOMICAL
INFORMATION IF ALL THE DATA THAT
WE'RE PROVIDING IS REALLY ABOUT
THE BRAIN AND IT'S NOT PROVIDED
AT A VERY HIGH SPATIAL
RESOLUTION.
SO SOME OTHER CONSIDERATIONS FOR
DATA SHARING IN TERMS OF THE
RISKS.
AS SASKIA MENTIONED, THERE ARE
RISKS FOR PRIVACY TO THE
INDIVIDUAL.
THEIR RISK OF MISINTERPRETATION
IN TERMS OF THE TYPES OF
CONCLUSIONS THAT ARE DRAWB ABOUT
SUCH DATA, AND I WANT TO KIND OF
REALLY EMPHASIZE THIS POTENTIAL
RISK THIRDLY ABOUT
STIGMATIZATION.
WE HAVE TO THINK CAREFULLY ABOUT
THE RESPONSIBLE USE OF VARIABLES
RELATED TO, SAY, RACE,
ETHNICITY, GENDER, AND SEX TO
AVOID THIS POSSIBLE
STIGMATIZATION, PARTICULARLY AS
THE BRAIN INITIATIVE IS ALSO
VERY INTERESTED IN ENHANCING
DIVERSE PERSPECTIVES AND MAKING
SURE THAT DIVERSE POPULATIONS
ARE INCLUDED NOT JUST IN THE
SCIENTIFIC ENDEAVOR AND
INVESTIGATION BUT ALSO IN THE
PARTICIPATION AND CONTRIBUTION
TO DATA GENERATING KIND OF
STUDIES.
SO WITHOUT THE PROPER ETHICAL
GUIDELINES IN PLACE ON HOW TO
MEASURE AND REPORT SUCH DATA
ABOUT SUCH VARIABLES, I THINK WE
WILL BE SOMEWHAT LIMITED IN
TERMS OF OUR ABILITY AND WE'LL
HAVE TO BE VERY CAREFUL ABOUT
HOW WE REPORT SUCH DATA.
SO IN SUMMARY, I WOULD SAY THAT
FMRI DOES PROVIDE UNIQUE DATA
ABOUT FUNCTIONAL ACTIVATION IN
THE LIVING HUMAN BRAIN.
I DO THINK THAT THE BRAIN
INITIATIVE HAS INVESTED HEAVILY
IN ADVANCING MRI ACQUISITION
HARDWARE AND ANALYSIS.
THIS WILL INCREASE
SPATIOTEMPORAL RESOLUTION SO WE
NEED TO THINK CAREFULLY ABOUT
HOW THAT DATA WILL BE SHARED.
THEN FINALLY ALL THE DATA
SHARING EFFORTS THAT ARE
CURRENTLY UNDERWAY, I
FUNDAMENTALLY BELIEVE IF WE WANT
THIS TO BENEFIT EVERYONE, THEN
IT DOES REQUIRE RESEARCH
PARTICIPATION BY ALL.
AND THIS IS THE MESSAGE AND KIND
OF THE OVERALL MISSION THAT
WE'VE UNDERTAKEN IN A SEPARATE
BRAIN INITIATIVE NEUROETHICS
PROJECT, IT'S AN R01 PROJECT
THAT I'M LEADING ALONGSIDE
FRANCES SHEN WHO'S PART OF THE
WORKING GROUP, JONATHAN JACKSON
HERE AT THE MGH.
WE'RE REALLY LOOKING TO DEVELOP
IN PARALLEL STRATEGIES TO
IMPROVE PARTICIPATION IN NEXT
GENERATION HUMAN NEUROIMAGING
RESEARCH BY UNDERREPRESENTED
POPULATIONS, BUT THEN ALSO
DEVELOPING GUIDELINES, SAY, IN
PARTNERSHIP WITH THE NEUROETHICS
WORKING GROUP AND STAKEHOLDER
GROUPS ABOUT HOW TO BEST USE
THAT DATA AND ENGAGE
PARTICIPANT.
PARTICIPANTS.
SO I'LL LEAVE IT AT THAT, AND
THANK YOU ALL FOR YOUR
ATTENTION.
[APPLAUSE]
>>THANK YOU SO MUCH, DR. HUANG.
THANK YOU ALSO FOR GOING FIRST,
AND WE CONTINUE TO FIGURE OUT
AND KIND OF HONE IN ON HOW
SUCCESSFULLY TO HAVE A SMOOTH
HYBRID MEETING.
WE HAVE TIME FOR MAYBE ONE OR
TWO QUESTIONS, EITHER IN PERSON
OR VIRTUAL FOLKS?
>>DR. HUANG, THAT WAS GREAT.
WE DID GET THE AUDIO WORKING
LOUD AND CLEAR SO WE HEARD
EVERYTHING GREAT.
IN TERMS OF THE AXES THAT SASKIA
KIND OF LINED UP FOR US AS A
FRAMEWORK FOR OUR THOUGHTS IN
HER INTRODUCTION,
REIDENTIFIABILITY, AND
INFERENCES THAT CAN BE DRAWN,
INFERENCES, YOU MENTIONED A
NUMBER OF TIMES FUNCTIONAL MRI,
TELLING US HOW PEOPLE THINK.
SO THOSE INFERENCES OF COURSE
CAN BE QUITE STRONG.
REIDENTIFIABILITY.
YOU MENTIONED THIS AT THE END.
DO YOU THINK MRI, FMRI
TECHNOLOGY IS AT A POINT OF
TRANSITION FROM BEING
POTENTIALLY VERY DE-IDENTIFIABLE
TO BEING MORE AND MORE EASILY
REIDENTIFIABLE WITH THESE
CHANGES IN RESOLUTION, ET
CETERA, THAT YOU MENTIONED?
>>THAT'S A GREAT QUESTION.
THANKS SO MUCH, SAMEER.
I THINK WE STILL HAVE A WAYS TO
GO, MEANING THAT IT'S NOT JUST A
MATTER OF ALSO THE SPATIAL AND
TEMPORAL RESOLUTION.
I THINK IT'S ALSO A MATTER OF
HOW MUCH SIGNAL YOU'RE SEEING
FROM AREAS THAT ARE MORE READILY
IDENTIFIABLE, FOR EXAMPLE, THE
BRAIN SO I WOULD SAY THAT
CONTRAST MECHANISMS LIKE FMRI
AND I'LL ALSO PUT DIFFUSION MRI
IN THAT CAMP, THEY'RE NOT VERY
GOOD AT LOOKING AT THE SORT OF
OTHER STRUCTURES AROUND THE HEAD
SIMPLY BECAUSE THE SIGNAL IN
THOSE AREAS ARE REALLY
ESSENTIALLY -- THEY CAN BE
MASKED OUT, SOMETIMES THEY'RE
JUST NOT GOING TO BE SEEN VERY
WELL, SO I THINK THAT'S AT LEAST
A VERY KIND OF NEED IMRAT
PRACTICAL CONSIDERATION.
I HAVE THAT ESPECIALLY AS A
NEURORADIOLOGIST.
I DO THINK, YES, MOVING FORWARD,
I MEAN, THE OTHER QUESTION IS,
HOW INTERPRETABLE ARE THE DATA
ON THE INDIVIDUAL LEVEL AS
OPPOSED TO THE GROUP LEVEL?
I THINK THIS IS SOMETHING THAT
WILL NEED TO BE DETERMINED.
BECAUSE ONE OF THE, I THINK,
GOALS OF INCREASING SPATIAL AND
TEMPORAL RESOLUTION ARE IN
IMPROVING SENSITIVITY OVERALL,
IS TO BE ABLE TO MAKE INFERENCES
EVENTUALLY ON THE INDIVIDUAL
LEVEL.
AND SO AT THAT POINT, YOU REALLY
DO THEN NEED TO WEIGH
POTENTIALLY THE ABILITY TO
IDENTIFY SOMEBODY BASED OP THEIR
INDIVIDUAL DATA, VERSUS THE
BENEFIT OF GAINING INFORMATION
AT THAT INDIVIDUAL LEVEL, AND I
THINK FOR THAT, IT WOULD BE MORE
LIKELY TO BE RELEVANT, SAY, FOR
APPLICATIONS TO DISEASE.
I WOULD SEE THAT THE BENEFIT
WOULD BE MORE -- WOULD OUTWEIGH
YOUR KIND OF RISKS FOR KIND OF
PROVIDING INFORMATION ABOUT THE
INDIVIDUAL, BUT THEN I WOULD SAY
THAT WOULD BE FOR A DIFFERENT
PURPOSE, AND WOULDN'T
NECESSARILY BE FOR SCIENTIFIC
RESEARCH ANYMORE, AT LEAST ALONG
THE LINES OF WHAT BRAIN IS
HOPING TO ACHIEVE.
>>DR. WANG.
>>THANK YOU.
THAT WAS A REALLY FASCINATING
TALK AND I ENJOYED LEARNING MORE
ABOUT THE BACKGROUND.
I DID HAVE A QUESTION JUST ABOUT
THE TEMPORAL NATURE OF THAT.
NOW BEING ABLE TO LOOK AT HIGHER
RESOLUTION WILL BE IMPORTANT BUT
ALSO THE INTERACTION AMONG
DIFFERENT NEURONAL CELLS AND
DIFFERENT TYPES OF CELLS.
WHAT DO YOU THINK THE PROSPECTS
ARE FOR GETTING DOWN TO A
MILLISECOND RESOLUTION OR EVEN
FASTER THAN THAT FOR FMRI WORK?
>>I THINK WE'RE DEFINITELY
HEADING THAT WAY.
THERE'S BEEN REALLY FASCINATING
WORK DONE ACTUALLY ON FAST FMRI
BY SOME OF MY COLLEAGUES AND
COLLABORATORS HERE AT THE
MARTINO CENTER, AS WELL AS
ELSEWHERE.
JOHN PELLIMENI, LAURA LEWIS,
THEY ARE DOING FAS FMRI
MEASUREMENTS NOW THAT ARE ON THE
ORDER OF HUNDREDS OF
MILLISECONDS.
IT'S AN INCREDIBLE TOOL BECAUSE
YOU CAN ACTUALLY START TO SEE
VARIATIONS IN PHYSIOLOGY THAT
YOU WOULDN'T BE ABLE TO SEE WITH
YOUR CONVENTIONAL FMRI
MEASUREMENTS SO TO US I THINK
IT'S OPENED UP A WHOLE NEW
AVENUE OF SCIENTIFIC DISCOVERY
BUT AT THE SAME TIME, I THINK IT
WILL START TO PROVIDE -- LIKE
OPEN UP QUESTIONS REGARDING ALSO
POTENTIAL RISKS, I SUPPOSE.
>>ANOTHER QUESTION FROM IN THE
HOUSE HERE.
>>HI.
THANKS FOR YOUR TALK, SUSIE.
IT'S ROBERT BERRY.
I HAVE A QUICK QUESTION
REGARDING THE KIND OF DATA FOR
MRI.
AND THIS IS MAYBE A GENERAL
QUESTION FOR THE ENTIRE SESSION
AND ALSO WHAT DOUG IS GOING TO
TALK ABOUT IN A FEW MINUTES.
BUT WHEN WE TALK ABOUT SHARING
MRI DATA, IS IT ONLY THE
RECONSTRUCTED IMAGING DATA OR
THE RAW DATA ACQUIRED BY THE
SCANNER?
I'M ASKING BECAUSE THIS IS
SOMETHING I'M STRUGGLING WITH
MYSELF FOR GENERAL KNOWLEDGE,
THE SCANNER ACQUIRES HUNDREDS OF
GIGABYTES AND TERABYTES OF DATA.
AND THEN WHEN WE RECONSTRUCT IT,
IT GETS DOWN TO JUST A COUPLE
HUNDRED MEGS.
THAT'S MORE COMPLICATED WITH
REGARDS TO SHARING LARGE AMOUNTS
OF DATA AND ALSO INCREASES THE
RISK OF REIDENTIFICATION.
SO JUST WONDERING IF YOU HAVE
ANY THOUGHTS ON THIS, AND ALSO
FOR MAYBE DOUG'S TALK IN A
LITTLE BIT, ESPECIALLY WOULD YOU
WANT TO MAYBE RE-EVALUATE
RECONSTRUCTION ALGORITHMS, YOU'D
WANT TO PROBABLY -- THE RAW DATA
RATHER THAN THE RECONSTRUCTED
DATA.
>>THANKS SO MUCH, ROB, FOR THAT
GREAT QUESTION.
I THINK FOR A LOT OF THESE
QUESTIONS, I WOULD ALSO TURN TO
SOME PRECEDENCE FROM THE HUMAN
CONNECTOME PROJECT.
SO WITH THE HUMAN CONNECTOME
PROJECT, I BELIEVE THEY DID
RELEASE PRE-PROCESSED AS WELL AS
PROCESSED DATA, AND I WOULD SAY
THAT THE BENEFITS OF RELEASING
RAW DATAVERSE US PROCESS DATA
REALLY DEPEND ON WHAT TYPES OF
RESEARCH ARE TO BE KIND OF
BENEFITED FROM THOSE TYPES OF
DATA.
IN SOME WAYIN WAYS, RAW DATA ISE
VALUABLE TO CERTAIN GROUPS OF
RESEARCHERS, SAY TO THOSE WHO
ARE DOING TECHNICAL
METHODOLOGICAL DEVELOPMENT.
IT DOES COME AT THE COST OF
LARGER DATA SIZES.
IT ALSO MAY COME WITH MAYBE
REDUCED RISK FOR
IDENTIFIABILITY, AND THE REASON
I SAY THAT IS IT'S HARDER IN
GENERAL TO INTERPRET -- LIKE
TAKING RAW CASE-BASED DATA,
RIGHT NOW I DON'T KNOW OF ANY
WAY TO KIND OF IDENTIFY PEOPLE
BASED ON THAT, ALTHOUGH WE KNOW
FROM THE WAY WE DO IMAGERY
CONSTRUCTION THAT THERE'S A VERY
STRAIGHTFORWARD WAY TO DO T IT'S
JUST I DON'T THINK THAT THAT'S
AS WIDELY KNOWN.
SO IT'S A GREAT QUESTION.
I THINK THE HUMAN CONNECTOME
PROJECT DID RELEASE BOTH
PREPROCESSED AS WELL AS
UNPROCESSED DATA, AND THAT WAS
SO THAT IT WOULD REALLY MAKE THE
DATA WIDELY APPLICABLE TO THOSE
RESEARCHERS WHO WANTED TO WORK
WITH THESE DIFFERENT TYPES OF
DATA.
>>THANKS VERY MUCH, DR. HUANG.
REMINDER WE'LL BE DOING THE
DISCUSSION IN AN HOUR AND A HALF
FROM NOW, FURTHER QUESTIONS AT
THAT POINT, DR. HUANG, HOPEFULLY
YOU CAN STICK AROUND FOR THAT
DISCUSSION.
DR. LORNA QUANDT IS ASSOCIATE
PROFESSOR OF EDUCATIONAL
NEUROSCIENCE AT GALLAUDET
UNIVERSITY, SHE COMPLETED A
POSTDOCTORAL FELLOWSHIP AT THE
UNIVERSITY OF PENNSYLVANIA.
HER CURRENT RESEARCH IN THE
ACTION AND BRAIN LAB AT
GALLAUDET UNIVERSITY FOCUSES ON
THE NEURAL SUBSTRATES OF ACTION
PERCEPTION RELATED TO VISUAL
LANGUAGE AND VISUAL LEARNING.
THROUGH EEG AND OTHER
TECHNIQUES, DR. QUANTITY STUD QS
THE FUNCTIONAL SENSORY MOTOR
LANGUAGE AND ATTENTIONAL
NETWORKS IN THE BRAIN.
DR. QUANDT.
>>HELLO.
THANK YOU FOR HAVING ME.
I WILL WAIT FOR SLIDES OR
SOMEONE CAN -- OKAY.
GREAT.
PROGRESS.
OKAY.
SO THANK YOU FOR THAT GREAT
INTRODUCTION, AND THANK YOU SO
MUCH FOR HAVING ME HERE TODAY.
I AM HERE FROM GALLAUDET
UNIVERSITY IN WASHINGTON, D.C.
JUST A FEW MILES FROM WHERE WE
ARE RIGHT NOW.
AS I WAS THINKING ABOUT WHAT TO
BRING TO TODAY'S WORKSHOP, I WAS
EXCITED TO THINK ABOUT EEG FROM
A LITTLE BIT OF A DIFFERENT
PERSPECTIVE AS TYPICALLY MY WORK
IS MORE RESEARCH-FOCUSED AND
LESS INTENSE FOCUS ON THE ETHICS
OF THE METHODOLOGY, BUT
NEUROETHICS IS AT THE CORE OF
ALL THE WORK WE DO, SO IT'S A
GOOD REMINDER TO CONTINUE
ENGAGING IN THESE QUESTIONS AS
WE PURSUE OUR RESEARCH QUESTI
QUESTIONS.
SO WITH EEG, I IMAGINE MY
AUDIENCE IS GENERALLY FAMILIAR
WITH THE METHODOLOGY ITSELF, AS
IT'S BEEN AROUND SINCE THE
1950s AND COMPARED TO SOME
OTHER METHODOLOGIES, NOT AS MUCH
AS CHANGED WITH THE PRINCIPLES
OF EEG IN THOSE DECADES SINCE WE
FIRST STARTED RECORDING
ELECTRICAL ACTIVITY FROM CATS
AND THEN FROM HUMANS.
EEG IS ESSENTIALLY A ELECTRICAL
RECORDING OF CONTINUOUS
WAVEFORMS REPRESENTING THE
ONGOING ELECTRICAL ACTIVITY IN
THE BRAIN, AND EVEN WITH MOST OF
TODAY'S MORED A VANED
METHODOLOGIES, WE HAVE THE SAME
BASIC PRINCIPLES UNDERLYING EEG.
WE HAVE DIFFERENT NUMBERS OF
ELECTRODES THAT LAY ON THE
SCALP, AND RECORD THE SIGNALS
THAT HAVE TRAVELED FROM THE
BRAIN THROUGH THE CORTEX,
THROUGH THE LAYERS SURROUNDING
THE BRAIN, THROUGH THE SKULL AND
THE SCALP, AND ON TO OUR
ELECTRODES.
WE MAY HAVE AS FEW AS ONE OR TWO
ELECTRODES OR AS MANY AS SEVERAL
HUNDRED ELECTRODES THAT GIVE US
INFORMATION ABOUT THAT ONGOING
ELECTRICAL ACTIVITY IN THE BRA
BRAIN.
HERE YOU CAN SEE JUST A SAMPLE
OF WHAT IT LOOKS LIKE.
WE'RE ALL FAMILIAR WITH THE
WAVEFORMS, AND UNDER ANY EEG
ANALYSIS, WE STILL HAVE THE SAME
SORT OF BASIC PRINCIPLE OF
ELECTRICAL ACTIVITY ABOUT THE
VOLTAGE CHANGES IN THE BRAIN,
RECORDED AT A CERTAIN NUMBER OF
ELECTRODES.
TYPICALLY WE ALSO COLLECT OTHER
TYPES OF DATA, SO PERHAPS
BEHAVIOR, PERHAPS RESPONSES TO A
TASK, SPEED, INFORMATION ABOUT
REACTION TIMES, MAYBE GROUP
LABELS OR MEDICAL INFORMATION IS
ASSOCIATED WITH THAT DATA.
AND FOR ANYONE WHO'S EVER TAKEN
A COGNITIVE NEUROSCIENCE CLASS,
YOU'RE FAMILIAR WITH THE IDEA OF
SETTING FMRI AND EEG AGAINST
EACH OTHER, SO WE HAVE VAST
DIFFERENCES IN SPATIAL
RESOLUTION.
EEG DOES NOT HAVE VERY GOOD
SPATIAL RESOLUTION.
YOU ARE LIMITED TO THE NUMBER OF
EE LOAK TROADZELECTRODES YOU'REA
FROM.
AND AT THE SAME TIME, EVEN WITH
HUNDREDS OF ELECTRODES, THE
SIGNALS ARE BEING DISTORTED
GREATLY BY TRAVELING THROUGH THE
BRAIN MATTER ITSELF AND THEN
THROUGH THE MANY INTERWING
LAYERS BETWEEN THE SIGNAL AND
THE CORTEX.
SO OF COURSE THERE ARE MANY WAYS
TO APPLY ALGORITHMS AND DO
SOURCE LOCALIZATION TO KNOW MORE
ABOUT THE SPATIAL STRUCTURE OF
YOUR DATA.
BUT THE FACT REMAINS THAT EEG
DOES NOT INFORM US MUCH ABOUT
THE STRUCTURE OR THE PHYSICAL
APPEARANCE OF THE BRAIN.
IN CONTRAST, EEG IS INHERENTLY
VERY HIGH TIME RESOLUTION, SO
TIMICALLY WE GATHER AT LEAST
1,000 DATA POINTS EVERY SECOND
AT A RATE OF 1,000 HERTZ, OR UP
TO 5,000 OR MORE HERTZ.
TIME RESOLUTION IS NO PROBLEM
FOR EEG, IN CONTRAST TO FMRI.
SO WITH THE CHARACTERISTICS OF
THAT DATA, WE MIGHT THINK, OKAY,
WE HAVE SPIKEY WAVEFORMS THAT
SHOW US ELECTRICAL ACTIVITY
ABOUT WHAT'S HAPPENING AT
DIFFERENT ELECTRODES ON THE
SCALP.
SURELY, THAT MUST NOT BE VERY
IDENTIFIABLE OR SURELY THAT MUST
NOT TELL US MUCH, EXCEPT FOR
WHAT WE WANT TO KNOW FROM OUR
RESEARCH STUDY.
HOWEVER, THAT IS NOT REALLY THE
CASE, AND TODAY WE'LL LOOK A
LITTLE MORE CLOSELY AT HOW THAT
ASSUMPTION MIGHT BE CHALLENGED.
I'LL ALSO MENTION THAT SIMILAR
TO FMRI, AS WAS DISCUSSED
EARLIER, WE MIGHT HAVE
TASK-BASED EERNLG, EEG, BUT WEY
HAVE RESTING STATE EEG OR WE'RE
LOOKING AT SOMETHING LIKE THE
DEFAULT MODE NETWORK TO LOOK AT
THE SORT OF ONGOING REGULAR
EVERYDAY BEHAVIOR OF THE BRAIN
WITHOUT RELATING TO A SPECIFIC
EXPERIMENTAL TASK.
WITH EEG RECORDINGS, WHAT
INFERENCES CAN WE MAKE TODAY,
NOW?
MOST COMMON MEDICAL APPLICATION
OF EEG IS STILL EPILEPSY AND
SLEEP.
SO A TRAINED RESEARCHER, A
SCIENTIST, A DOCTOR, MAY BE ABLE
TO EVEN MAKE ASSUMPTIONS ABOUT
EPILEPTIC BEHAVIOR OR PARTICULAR
SLEEP ACTIVITIES JUST WITH THE
BARE EYES.
BEYOND THAT INHERENT IN THE DATA
ARE QUALITIES THAT COULD EASILY
INFORM US ABOUT THE PRESENCE OF
EPILEPSY OR SLEEP-RELATED
ACTIVITIES.
THOSE ARE WELL-ESTABLISHED AND
SORT OF A CLEAR EXAMPLE OF WHAT
MIGHT BE HIDING IN OUR EEG
SIGNAL.
WE ALSO MIGHT HAVE INFORMATION
THAT IS A ATTACHED TO THE RAW
EEG SIGNAL SUCH AS LABELS,
PERSONAL IDENTIFYING INFORMATION
OR GENERAL GROUP CHARACTERISTIC.
SO YOU MAY DE-IDENTIFY YOUR
DATA, BUT YOU MAY RETAIN THE
LABELS OF SEX OR GENDER OR GROUP
PARTICIPATION LABEL OR OTHER
SORT OF META INFORMATION THAT
MIGHT HELP US LABEL THE DATA IN
A MEANINGFUL WAY.
MORE INTERESTINGLY, WE CAN ALSO
LOOK AT THE CHARACTERIZATION OF
RESTING STATE OR TASK-RELATED
NEURAL O OSCILLATORY ACTIVITY IN
THE WRAIN.
THE
BRAIN.
SO IF WE GATHER OUR EEG DATA AND
THEN WE UPLOAD IT OR SHARE IT
WIDELY, WHAT MIGHT ONE INFER
FROM THE DATA ITSELF?
WE CAN SEE THE RELATIVE POWER IN
DIFFERENT FREQUENCY BANDS, WE
CAN LOOK AT ALPHA ACTIVITY, BETA
ACTIVITY, DIFFERENT TYPES OF
RHYTHMS MAY HAVE DIFFERENT POWER
STRUCTURES IN THE EEG DATA THAT
CAN BE EASILY SORT OF REVEALED
AND ANALYZED, AND INTERESTINGLY,
THESE ONGOING RESTING STATE
NEURAL OSCILLATIONS DO APPEAR TO
BE CONSISTENT OVER TIME WITHIN
PEOPLE.
SO WE START TO SEE A HINT OF THE
IDEA THAT THERE'S SOMETHING IN
THE EEG DATA THAT MAY BE
PROVIDING US MORE INFORMATION
THAN JUST THE ELECTRICAL
ACTIVITY IN RESPONSE TO A TASK
OR ONGOING DEFAULT MODE, BUT
SOMETHING THAT MIGHT BE LIKE A
BIOMETRIC OR A KEY TO
IDENTIFYING OR RATHER
DE-IDENTIFYING -- REIDENTIFYING
THE DATA.
SO THIS PARTICULAR AREA OF
INQUIRY ABOUT WHETHER WE CAN
REIDENTIFY OR AUTHENTICATE
PERSONHOOD FROM EEG DATA HAS
GAINED A LOT OF TRACTION IN
RECENT YEARS.
SO THE IDEA OF USING EEG AS A
BIOMETRIC MIGHT LOOK LIKE
SOMETHING WHERE WE REFRAME EEG
FROM BEING SORT OF A GRAPHICAL
OUTPUT OF ELECTRICAL DATA ONLY,
BUT RATHER THINKING ABOUT IT
MORE LIKE A FINGERPRINT OR AN
EYE RUSS PATTERN IN YOUR EYE,
YOUR UNIQUE FACIAL STRUCTURE
THAT ALLOWS YOU TO UNLOCK YOUR
EYE PHONE.
SOMETHING THAT MIGH -- I FOA IPG
THAT MIGHT BE UNIQUE AND
CONSISTENT OVER TIME.
IN 2015, THIS IDEA WAS
INVESTIGATED WITH 88% ACCURACY
ON MATCHING PERSON TO PERSON
ACROSS EEG COLLECTION TIME
POINTS, AND SINCE THEN, THE
RESEARCH IS CONTINUING TO
ACCELERATE ALONG THOSE LINES.
SO WHEN I WAS ASKED TO THINK
AHEAD, THINK FIVE YEARS IN THE
FUTURE, THINK 10 YEARS IN THE
FUTURE, WHAT COULD WE BE DOING
WITH EEG DATA, AND WHAT PEOPLE
MIGHT BE GETTING FROM THAT DATA,
I THINK WILL LOOK VASTLY
DIFFERENT FROM WHAT WE IMAGINE
WE CAN DO WITH EEG DATA TODAY.
SO VERY RECENTLY, THERE'S BEEN A
LOT OF WORK IN USING DEEP
LEARNING MODELS TO IDENTIFY
THOSE UNIQUE CHARACTERISTICS OF
EEG THAT ARE UNIQUE TO EACH
INDIVIDUAL PERSON.
AND APPLY THESE MODELS TO EEG
DATA WHICH MIGHT ALLOW FOR
PERSON IDENTIFICATION WITHIN OR
ACROSS STUDIES.
SO THAT SORT OF PERSON
IDENTIFICATION COULD BE DONE IN
A RESEARCH CONTEXT, OR ONE AREA
OF REALLY RAPIDLY ACCELERATING
INQUIRY HAS BEEN USING THIS IN
APPLIED SETTINGS.
SO THERE ARE BODIES OF
RESEARCHERS OUT THERE NOW WHO
ARE CONSIDERING THE USE OF EEG
IN ADDITION TO OR AS A
REPLACEMENT FOR THINGS LIKE I
MENTIONED BEFORE.
FINGERPRINT SCANNERS OR FACIAL
RECOGNITION.
SO IN A WAY, IT MIGHT SEEM LIKE
A REAL STRETCH OF THE
IMAGINATION TO THINK ABOUT USING
EEG AS AN IDENTIFICATION TOOL IN
THE REAL WORLD.
10 YEARS AGO, I PROBABLY WOULD
HAVE LAUGHED IF YOU SAID THAT I
WOULD BE CARRYING FACIAL
RECOGNITION TECHNOLOGY IN MY
PURSE AND USING THUMBPRINT
RECOGNITION EVERY DAY, SO I'M
REMINDING MYSELF THAT WHAT I
THINK MIGHT BE CRAZY IN FIVE OR
10 YEARS COULD WELL BE
INTEGRATED SEAMLESSLY INTO OUR
EVERYDAY LIFE.
SO IT'S WORTH THINKING ABOUT
THESE POSSIBILITIES EARLY BEFORE
WE, YOU KNOW, REALIZE THAT THIS
RESEARCH HAS PRESENTED SERIOUS
RISKS AND WE HAVE BEEN UNAWARE
OF THOSE RISKS.
SO WE CAN IMAGINE THE POTENTIAL
FOR EEG TO BE USED MORE IN EEK
LOGICALLY VALID RESEARCH
CONTEXTS, AND THEN POTENTIALLY
TO MAKE IT OUT OF THE LAB.
SO IT DOES STILL SEEM CRAZY
BECAUSE EEG REQUIRES A FAIR
AMOUNT OF SETUP, ALTHOUGH IT'S
NOT AS COMPLEX OR REQUIRING AS
EXPENSIVE INVESTMENTS OF
RESOURCES AS SOMETHING LIKE
FMRI, SO NOW THERE ARE
COMMERCIALLY AVAILABLE TOOLS
THAT CAN RECORD DECENT QUALITY
EEG.
SO IF THOSE TYPES OF TOOLS CAN
BE SCALED FURTHER TO BECOME MORE
INTEGRATED WITH DAILY LIFE, THEN
WE REALLY COULD SEE A TRANSITION
OF EEG FROM RESEARCH TOOL AND
MEDICAL DIAGNOSTIC TOOL INTO
SOMETHING THAT IS MORE
EVER-PRESENT.
I CAN'T QUITE IMAGINE WHAT THAT
WOULD LOOK LIKE IF WE HAVE TO,
YOU KNOW, DO A QUICK EEG SCAN
BEFORE WE ACCESS THE NUCLEAR
CODES OR SOMETHING, BUT IT
REALLY IS SOMETHING THAT PEOPLE
ARE WORKING ON NOW.
AS ONE RESEARCH PAPER I READ
SAID, A GREAT THING ABOUT THE
POTENTIAL FOR EEG AS AN
AUTHENTICATION TOOL IS THAT
UNLIKE FINGERPRINTS OR FACIAL
RECOGNITION, YOU CAN'T DO EEG
AUTHENTICATION WITH A DEAD
PERSON, SO THIS MIGHT BE A BIG
BENEFIT TO THE OTHER LESS SECURE
FORMS OF IDENTIFICATION BASED ON
BIOMETRICS.
SO AUTHENTICATION IS THIS SORT
OF INTERESTING APPLIED IDEA.
I DID A LITTLE SEARCH TO JUST
DEMONSTRATE THAT THIS IDEA IS
BEING PURSUED MORE ACTIVELY IN
VERY RECENT YEARS AND HAS BECOME
VERY HIGHLY SUCCESSFUL IN
RESEARCH SETTINGS, SO IF YOU
RECORD EEG DATA FROM A PERSON
AND YOU PUT IT INTO A TRAINING
DATASET AND LATER YOU TEST
AGAINST THAT DATASET, YOU CAN
ACHIEVE ALMOST 100% ACCURACY
USING THESE DEEP LEARNING MODELS
THAT IDENTIFY THE KEY UNIQUE
FEATURES IN THE EEG AND USE
THOSE FEATURES TO REIDENTIFY THE
DATA WHEN IT'S SEEN WITHOUT A
LABEL LATER.
OKAY.
ASIDE FROM THIS IDEA OF USING
EEG AS A UNIQUE SORT OF KEY
BIOMETRIC, WE CAN ALSO THINK
ABOUT THE MORE TYPICAL THINGS
THAT ONE MIGHT EXPECT WE COULD
GET FROM EEG DATA.
WE CAN LOOK AT THINGS LIKE
EMOTION CHARACTERIZATION, STATE
OF MIND, NOT COMPLETELY USING A
REVERSE INFERENCE, BUT WE CAN
MAKE SOME ASSUMPTIONS AND
DECISIONS ABOUT TASK-RELATED
COGNITIVE PROCESSES, EMOTIONS,
PERHAPS SOME NEUROLOGICAL
CONDITIONS BESIDES EPILEPSY,
SLEEP DISORDERS, DRUG USE CAN
CHANGE THE ONGOING EEG ACTIVITY
IN A WAY THAT IS POTENTIALLY
READY TO BE REIDENTIFIED, SO IF
THERE'S POTENTIAL INFORMATION IN
THE DATASET ABOUT THESE THINGS,
EMOTIONS, SUBSTANCE USE,
DIAGNOSES, THESE ALL POSE A
SIGNIFICANT RISK TO THIS EEG
DATA THAT WE MAY NOT HAVE
REALIZED AS RESEARCHERS AND
CERTAINLY AS LAY PEOPLE THAT
THIS EEG DATA MAY CONTAIN THESE
HIGHLY SENSITIVE PIECES OF
INFORMATION.
WE CAN'T SEE THEM WITH OUR BARE
EYES, WHICH IS ONE REASON THAT
IT'S NOT QUITE AS INTUITIVE AS
FMRI OR STRUCTURAL MRI, WHERE
WE'RE SEEING PICTURES OF
PEOPLE'S BRAINS.
THAT IS EASY TO UNDERSTAND HOW
YOU MIGHT REIDENTIFY THAT DATA,
BUT EEG, IT CONTAINS ALMOST
NOTHING THAT IS MEANINGFUL WITH
JUST YOUR EYES.
BUT WITH THESE ADVANCED DEEP
LEARNING MACHINE LEARNING
TECHNIQUES THAT ARE BEING
DEVELOPED MORE RECENTLY, THEY
CAN READ THAT DATA AS IF IT WERE
VISUALLY APPARENT THE WAY THAT
STRUCTURAL MRI IS VISUALLY
APPARENT TO US.
SO A SORT OF TAKEAWAY THAT I SEE
FROM THIS MORE RECENT SHIFT IN
RESEARCH IS REALLY THAT JUST
BECAUSE WE CANNOT, WITH OUR OWN
EYES AND BRAINS, MAKE SENSE OF
THE RICHNESS OF EEG DATA, DOES
NOT MEAN THAT IT'S NOT THERE AND
READY TO BE CAPITALIZED UPON.
SO THERE ARE MANY RISKS PRESENT
IN THIS SORT OF SHIFT TOWARDS
UNDERSTANDING EEG FOR THE RICH
DATASET THAT IT IS.
IT'S POSSIBLE THAT IF
AUTHENTICATION FOR EEG WERE TO
CATCH ON IN ANY SECTOR
WHATSOEVER, THAT PUBLICLY
UPLOADED EEG DATA COULD EASILY
BE USED TO FALSIFY THOSE
AUTHENTICATION RECORDS.
WE ALSO SEE PRIVACY CONCERNS, SO
IN THE INTRODUCTORY PAPER THAT
SASKIA WAS SHOWING US, THINGS
LIKE INSURERS, IF THEY WERE TO
GAIN ACCESS TO YOUR EEG DATA,
WHAT WOULD YOU MAKE OF THAT?
IF THERE WERE SENSITIVE PIECES
OF INFORMATION IN A WORKFORCE
THAT WERE, YOU KNOW, OF INTEREST
TO FOREIGN GOVERNMENTS AND
REQUIRED EEG AUTHENTICATION, WE
SEE HERE THAT THE RISK FOR THAT
TYPE OF FALSIFICATION IS VERY
HIGH.
WITH THE SORT OF ONGOING
INVESTMENT IN DEEP LEARNING
MODELS AND USING THOSE MODELS TO
GET MORE INFORMATION FROM THESE
RICH DATA SETS, WE CAN ALSO
EXPECT MORE ADVANCES IN WHAT CAN
BE DRAWN FROM THE DEEP LEARNING
MODEL.
SO MORE EMOTION
CHARACTERIZATION.
BETTER PERSON IDENTIFICATION
LINKED WITH DIAGNOSES OR EVEN
EMOTIONAL PROFILES.
IF YOU CAN TELL SOMETHING ABOUT
A PERSON'S TYPICAL REACTIVITY TO
STRESS BY LOOKING AT THEIR EEG,
IS THAT SOMETHING THAT WE WOULD
WANT TO SHARE?
TO WHAT LEVEL DOES THE
INFORMATION PRESENT IN OUR
ONGOING EEG FEEL PERSONAL TO US?
DO YOU WANT YOUR RESTING STATE
EEG TO BE UPLOADED AND USED FOR
ANYTHING?
I'M NOT SURE.
AND IT'S HARD TO SAY RIGHT NOW
WHAT IS POSSIBLE IN THE YEAR
2023 WILL NOT BE THE SAME
LIMITATIONS AS WHAT'S POSSIBLE
IN 2033.
WE CAN SEE AND SAY WITH
CERTAINTY THAT WHATEVER WE CAN
DO TODAY WILL BE ECLIPSED BY
THINGS THAT FUTURE GENERATIONS
OF RESEARCHERS FIGURE OUT.
WE ALSO HAVE SORT OF THE
TAKEAWAYS WITH THIS IDEA THAT
EEG DATA DOES CONTAIN UNIQUE
NEURAL SIGNATURES.
IMPORTANTLY, THEY'RE THERE
WHETHER OR NOT THEY'RE PART OF
THE INTENDED ANALYSIS.
SO YOU MAY RUN A STUDY THAT HAS
NOTHING TO DO WITH INDIVIDUAL
DIFFERENCES.
IT MAY HAVE NOTHING TO DO WITH
DIAGNOSES OR EMOTIONAL
PROFILING.
HOWEVER, THAT DATA MAY BE
INHERENTLY ENCAPSULATED WITHIN
THE EEG DATA THAT YOU ARE
SHARING.
SO WHEN I UP LOAD RAW EEG FILES
TO A REPOSITORY, WHAT ELSE AM I
UPLOADING THAT I MIGHT NOT BE
AWARE OF WHEN I UPLOAD THAT
DATA, THINKING THAT I'VE
DE-IDENTIFIED IT?
I'VE REMOVED ANY, YOU KNOW,
HIGHLY SENSITIVE PERSONALLY
IDENTIFYING INFORMATION FROM
THAT DATA.
OR SO I THINK.
SO WE NEED TO CONSIDER WHAT IT
LOOKS LIKE TO FUTURE-PROOF
SHARED EEG DATA.
WHAT CAN BE REMOVED, WHAT COULD
BE TRANSFORMED IN THE DATA,
PERHAPS, TO REMOVE ANY
IDENTIFYING CHARACTERISTICS THAT
WE DON'T WANT.
PERHAPS THERE'S ROOM FOR A
CONVERSATION ABOUT WHAT CAN BE
GAINED FROM SHARING PROCESSED
DATA, FILTERED DATA, REMOVE
METADATA, SAVE ONLY A KEY OR A
FINGERPRINT THAT IDENTIFIES THE
KEY FACTORS THAT YOU'RE
INTERESTED IN.
HOW CAN WE MAKE SORT OF BALANCE
THAT WE SAW ON THAT SCALE IN THE
INTRO SLIDES, HOW CAN WE MAKE
SURE THAT THAT SCALE IS TIPPING
TOWARDS THE BENEFITS AND THAT
THE POTENTIAL RISKS OF CURRENT
AND FUTURE DEIDENTIFICATION,
REIDENTIFICATION, MAY BE
LESSENED.
WE ALSO WANT TO THINK ABOUT THIS
PARTICULARLY IN THE CONTEXT OF
MARGINALIZED POPULATIONS AND
COMMUNITIES.
SO FOR PEOPLE WHO HAVE
NEUROLOGICAL DISORDERS,
PSYCHIATRIC DISORDERS, PEOPLE
WHO HAVE DIAGNOSES THAT MIGHT
PREVENT THEM FROM GETTING HEALTH
INSURANCE, PEOPLE WHO MAYBE DO
NOT WANT THEIR DATA TO BE USED
IN THE FUTURE TO, YOU KNOW, ACT
AS THEM IN AN AUTHENTICATION
SCENARIO.
HOW CAN WE THINK ABOUT DIFFERENT
GROUPS HAVING DIFFERENT
REACTIONS TO THIS INFORMATION
AND WANTING TO PRESERVE DATA
SHARING WHILE NOT PUTTING
MARGINALIZED GROUPS AT HIGHER
RISK.
SO I DON'T HAVE ANSWERS TO ALL
OF THESE QUESTIONS, SHOCKINGLY,
BUT I'M READY TO DISCUSS THEM
AND EXCITED TO HEAR EVERYONE'S
THOUGHTS.
THAT CONCLUDES MY TALK.
THANK YOU SO MUCH.
[APPLAUSE]
>>DR. QUANDT, THANK YOU VERY
MUCH, THAT WAS FANTASTIC.
I THINK WE HAVE TIME FOR ONE
QUESTION.
>>THANKS SO MUCH FOR THAT
THOUGHTFUL ENGAGEMENT.
ON THE NEURAL SIGNATURES PIECE
OF IT THAT YOU WERE TALKING
ABOUT, MY UNDERSTANDING, AND IT
MAY BE OUTDATED, IS THAT FOR
NOW, AUTHENTICATION IS POSSIBLE
BUT IDENTIFICATION IS NOT.
IS THAT STILL THE CASE ON THE
CURRENT RESEARCH, WHICH HAS
SLIGHTLY DIFFERENT RISKS, RIGHT,
WHICH IS TO USE IT FOR
AUTHENTICATION MODELS VERSUS
USING IT FOR IDENTIFICATION
MODELS.
>>SORT OF THE HIGHLY ACCURATE
IDENTIFICATION IS WITHIN A
RESEARCH CONTEXT, SO OF COURSE
YOU HAVE A POOL OF DATA THAT A
MODEL HAS BEEN TRAINED UPON, SO
YOU FEED IN YOUR TRAINING DATA
AND THEN YOU'RE ASKING MATCH, SO
THAT'S MORE AUTHENTICATION.
>>NOT IDENTIFICATION.
>>YEAH, SO I THINK THAT NOW MY
UNDERSTANDING, AND I DO NOT DO
AUTHENTICATION RESEARCH, SO IF
ANYONE ELSE DOES, PLEASE STEP
IN.
MY UNDERSTANDING OF THE
AUTHENTICATION RESEARCH IS THAT
IDENTIFICATION IS DEFINITELY THE
GOAL.
BUT THE SORT OF HIGHLY ACCURATE
IS STILL LIKE YOU MENTIONED
WITHIN AUTHENTICATION, AND SO
IDENTIFICATION WOULD REQUIRE
MUCH MORE DATA AND POTENTIALLY
WOULD BE OF MORE RISK, I THINK
WITHIN LIKE A SORT OF CLOSED
ENVIRONMENT, SO IF YOU WORKED AT
A FACILITY WHERE EVERYONE HAD TO
HAVE THEIR EEG SAVED ON SITE,
THEN THAT WOULD BE AN
AUTHENTICATION CHALLENGE, RIGHT?
>>ALL OF THE RESEARCH STUDIES
TO DATE HAVE BEEN DONE ON
AUTHENTICATION, WHICH IS
MATCHING, SO YOUR FACE I.D., ALL
OF THOSE ARE AUTHENTICATION
MODELS RATHER THAN UNIQUE
IDENTIFICATION MODELS.
>>RIGHT.
>>SO IDENTIFICATION WOULD
REQUIRE THAT YOU HAVE, YOU'RE
RIGHT, HUGE DATASET, BUT WE
WOULD ALSO NEED TO KNOW THAT
NEURAL SIGNATURES ARE UNIQUE
ACROSS BILLIONS OF PEOPLE RATHER
THAN WITHIN A CLOSED DATASET OF
MATCHING TO INDIVIDUALS.
AND THEN THE SECOND QUESTION IS,
MOST OF IT, IT LOOKED LIKE, IS
NOT RESTING STATE, BUT IT'S
FUNCTIONAL, MEANING THAT THE
ACCURACY GOES DOWN SUBSTANTIAL Y
IF YOU'RE DOING RESTING STATE
OUGHT THEN THECATION RATHER THAN
DOING FUNCTIONAL AUTHENTICATION
LIKE A SINGLE LITTLE DITTY IN MY
HEAD, THEN I RECORD THAT AND
THEN -- THAT REQUIRES THE COUP
COOPERATION OF THE INDIVIDUAL TO
ENGAGE IN FUNCTIONAL BIOMETRICS.
SO IS THERE MORE RECENT
RESEARCH, MINE IS A YEAR
OUTDATED AT LEAST, THAT SUGGESTS
THAT RESTING STATE
AUTHENTICATION IS HIGHLY
ACCURATE AS WELL?
>>I DON'T THINK IT'S HIGHLY
ACCURATE YET, BUT I DID -- I
HAVE BEEN LOOKING AT SOME OF THE
EFFORTS IN THAT SPACE TO COMPARE
AGAINST WHAT YOU MENTIONED OF
BEING TASK-BASED, DO THIS TASK
OR PROCESS IN YOUR HEAD COMPARED
TO DO WHATEVER YOU WANT.
I THINK IT'S A KEY DIFFERENCE.
I DON'T THINK THE PROBLEM HAS
BEEN SOLVED FOR RESTING STATE
YET.
AND SOME OF THE RESEARCH THAT
I'VE LOOKED AT HAS STATED THAT
THAT'S A POTENTIAL UPSIDE OF
THIS AS AN AUTHENTICATION
TACTIC, BECAUSE YOU CAN MAKE
SURE THAT NO ONE IS DOING IT
UNDER DURESS OR IT REQUIRES
BUY-IN.
>>IT'S NOT JUST BUY-IN.
FUNCTIONAL BIOMETRICS ARE FAR
MORE SECURE BECAUSE YOU CAN
EASILY CHANGE THEM, THEY REQUIRE
ACTIVE PARTICIPATION AGAINST THE
INDIVIDUAL AND SO MOST SECURITY
MEASURES THAT LOOK TO TRY TO
EMPLOY BIOMETRICS LOOK FOR
FUNCTIONAL BIOMETRICS OR THE
GOLD STANDARD RATHER, BUT
RESTING BIOMETRICS WOULD SUGGEST
A DIFFERENT LEVEL OF RISK,
REIDENTIFICATION WITHIN UP
LOADED AND SHARED EEG --
>>BY NO MEANS TODAY CAN YOU
TAKE UPLOADED TASK-RELATED EEG
AND, YOU KNOW, HACK INTO
SOMEONE'S BETA VERSION OF AN
AUTHENTICATION SYSTEM.
SO THE SYSTEMS ARE NOT THERE YET
SUCH THAT THEY PLACE A HIGH RISK
ON REPOSITORIES OF EEG DATA.
SO YOU'RE DEFINITELY RIGHT, FROM
MY UNDERSTANDING, THAT THERE IS
A LARGE AMOUNT OF SPACE TO BE
FILLED BETWEEN WHAT PEOPLE ARE
SHOWING IN A HIGHLY CONSTRAINED
ENVIRONMENT RIGHT NOW COMPARED
TO WHAT COULD BE POSSIBLE WITH
MUCH MORE DATA, MUCH MORE
FINESSE ABOUT TASKS OR RESTING
STATE SORT OF PARADIGMS, SO
YOU'RE RIGHT, WE'RE NOT THERE,
AND I DON'T MEAN TO SOUND AN
ALARM BELL ABOUT THINGS THAT ARE
POSSIBLE TODAY.
WHICH MIGHT ONLY BE POSSIBLE IN
A LAB OR HIGHLY CONTROLLED
ENVIRONMENT.
THERE'S A LOT LEFT.
>>THANK YOU.
WITH APOLOGIES, WE NEED TO MOVE
ON.
>>THANK YOU, THANK YOU.
[APPLAUSE]
>>WE WILL HAVE TIME AT THE END
AFTER ALL SPEAKERS FOR FURTHER
DISCUSSION.
SO I'M DELIGHTED TO INTRODUCE
OUR NEXT SPEAKER, DR. DOUGLAS
GREVE, ASSISTANT PROFESSOR OF
RADIOLOGY AT HARVARD MEDICAL
SCHOOL.
HE RECEIVED A PH.D. IN COGNITIVE
AND NEURAL SYSTEMS FROM BOSTON
UNIVERSITY.
DR. GREVE'S RESEARCH IS FOCUSED
ON THE ANALYSIS AND INTEGRATION
OF MULTIMODAL MRI.
HIS PRIMARY EXPERTISE IS IN THE
ANALYSIS OF FMRI AT THE TIME
SERIES AND GROUP LEVELS.
HE HAS ALSO RESEARCHED METHODS
TO QUANTIFY, CALIBRATE AND
REMOVE SITE-SPECIFIC ARTIFACTS
FROM FMRI AND LED A PROJECT
USING NEAR INFRARED SPECTROSCOPY
OR NIRS TO DENOISE FMRI DATA.
DR. GREVE.
>>THANK YOU.
CAN YOU HEAR ME OKAY?
>>YES, WE CAN HEAR YOU.
>>OKAY.
AND MY SLIDES ARE GOOD?
>>YES.
>>OKAY.
GOOD.
SO I'LL BE TALKING TODAY ABOUT
STRUCTURAL BRAIN IMAGING.
AND THIS IS BASICALLY WHAT WE
MEAN BY STRUCTURAL BRAIN
IMAGING -- SORRY, ONE MORE
THING.
CAN YOU SEE MY MOUSE AS I MOVE
IT AROUND?
>>YES, WE CAN.
>>OKAY.
SO IN SOME WAYS STRUCTURAL BRAIN
IMAGING IS MUCH SIMPLER THAN THE
EEG AND THE FMRI AND THE DTI
THAT HAVE BEEN DISCUSSED
BEFOREMENT THBEFORE.THEY'RE BASS
OF THE BRAIN.
THE ANALYSIS IS THEY LOOK AT THE
BRAIN AND YOU LOOK FOR
ANOMALIES.
SO THIS IS A T1 WEIGHTED IMAGE,
T2, FLAIR, P.D.
THESE ARE ABOUT 1-MILLIMETER
RESOLUTION SO THIS IS AT THE
HIGHEST OF THE HIGH RESOLUTION
THAT YOU'LL SEE IN BRAIN IMAGING
STUDIES.
IT'S ALSO WHOLE HEAD AND 3D, SO
WHAT THAT MEANS IS THAT EVEN
THOUGH I'M SHOWING YOU
INDIVIDUAL SLICES HERE, WHAT
YOU'RE ACTUALLY COLLECTING ARE A
SERIES OF SLICES AND SO YOU CAN
SEE EVERYTHING IN ALL THE
DIFFERENT PLANE, AND IT'S WHOLE
HEAD, MEANING THAT EVEN IF YOU
MIGHT JUST WANT THE BRAIN,
YOU'RE GOING TO GET BASICALLY
EVERYTHING LIKE SKULL AND SCALP
AND SKIN, AND FACE IMPORTANTLY.
SO THERE'S GOING TO BE A LOT
MORE INFORMATION IN THERE THAN
JUST THE BRAIN.
IN ADDITION, WITH THE DATA YOU
ALSO GET THE TIME AND DATE AND
PLACE OF WHERE THE SCAN WAS
ACQUIRED.
AND THESE SCANS ALSO MIGHT BE
ACQUIRED LONGITUDINALLY SO YOU
MIGHT HAVE SOMEBODY COME IN FOR
A SCAN, FIVE YEARS IN A ROW, FOR
EXAMPLE.
THERE ARE MASSIVE MRI DATA
SHARING EFFORTS THAT HAVE
ALREADY BEEN HINTED AT, SO THESE
ARE JUST A FEW OF THEM.
SO YOU HAVE SOMETHING LIKE THE
UNITED KINGDOM BIOBANK, WHICH IS
TARGETING I THINK 100,000
DIFFERENT PEOPLE, AND THESE
THOUSANDS AND THOUSANDS OF CASES
HAVE ALREADY BEEN RELEASED,
THERE'S A SITE CALLED OPEN
NEURO, WHERE PEOPLE WILL TAKE
THEIR DATA AND UPLOAD TO THE
SITE TO MAKE THAT PUBLICLY
AVAILABLE, AND THEY SAY THAT
THEY HAVE OVER 33,000 CASES SO
FAR.
GENERALLY NIECE ARE NORMAL
LOOKING BRAINS LIKE TUMORS OR
LARGE STROKES OR RESECTIONS OR
LIKE NORMAL PARTS OF THE BRAIN.
SO SASKIA POINTED OUT MANY
BENEFITS ALREADY.
MACHINE LEARNING IS VERY DATA
HUNGRY, SO HAVING THESE HUGE
NUMBERS AS I JUST SHOWED ON THE
PREVIOUS PAGE CAN BE VERY
IMPORTANT.
WE CAN CREATE NORMATIVE DATA, SO
WHEN YOU GO IN AND YOU GET A
SCAN, THAT CAN BE COMPARED TO
SOME DATABASE TO SAY THAT YOU
ARE ONE OR TWO OR THREE STANDARD
DEVIATIONS AWAY.
IT ALSO PROVIDES A DIVERSITY OF
SUBJECTS AND SCANNERS AND
PROTOCOLS WHICH CAN BE IMPORTANT
FOR ANALYSIS AND DRAW
CONCLUSIONS FROM DATA.
IF YOU HAVE A HUGE NUMBER OF
SUBJECTS, YOU MIGHT BE ABLE TO
FIND LIKE A LARGE NUMBER OR
RELATIVELY LARGE NUMBER OF RARE
SUBJECTS.
YOU CAN REANALYZE DATA TO TEST
NEW HYPOTHESES RATHER THAN
COLLECTING NEW DATA.
YOU CAN CONDUCT PILOT STUDIES
FOR YOUR R01.
AND YOU CAN TEST NEW ALGORITHMS
ON THIS.
AND AS HAS ALREADY BEEN STATED,
NIH NOW REQUIRES THAT WE HAVE A
DATA SHARING PLAN FOR OUR
NEUROIMAGING DATA.
SO THERE ARE SEVERAL RISK
FACTORS, MOST OF WHICH HAVE
ALREADY BEEN DISCUSSED SO ONE IS
CAN AN INDIVIDUAL BE DIAGNOSED
WITH A CONDITION OR DISORDER
FROM THE IMAGES BY THEMSELVES.
THE SECOND THING IS CAN AN
INDIVIDUAL BE IDENTIFIED FROM
THE IMAGES?
SO WE'LL TALK ABOUT BOTH OF
THESE RISKS.
SO WHEN WE GET AN ANATOMICAL
IMAGE, AND I MADE MY LIVING
ANALYZING ANATOMICAL IMAGES LIKE
THIS, WHAT WE TYPICALLY DO IS
SOME TYPE OF ANATOMICAL ANALYSIS
WHERE WE PROVIDE -- CREATE SOME
KIND OF SEGMENTATION.
SO EACH COLOR THAT YOU SEE HERE
IS A DIFFERENT BRAIN STRUCTURE.
AND THIS IS A FAIRLY COARSE
SEGMENTATION, WE ACTUALLY HAVE
ONES THAT ARE MUCH FINER THAN
THAT.
SO FOR EACH ONE OF THESE
STRUCTURES, WE CAN GET THE SIZE
AND THE SHAPE OF THAT PARTICULAR
STRUCTURE FOR THIS INDIVIDUAL
PERSON.
AND SO WE CAN DO STUDIES WITH
THIS, FOR EXAMPLE, HERE THIS
YELLOWISH STRUCTURE IS CALLED
THE HIPPOCAMPUS, IT'S ABOUT THE
SIZE OF YOUR PINKIE, AND IF YOU
DON'T HAVE IT, YOU CAN'T FORM
NEW MEMORIES.
THIS IS SOMETHING THAT'S OFTEN
DEVASTATED IN ALZHEIMER'S
DISEASE.
SO THIS IS AN EXAMPLE STUDY
WHERE WE TOOK 100 SUBJECTS, 50
CONTROL SUBJECTS AND 50
ALZHEIMER'S SUBJECTS, AND WE
EXTRACTED OUT THE SIZE OF THE
HIPPOCAMPUS AND HERE YOU CAN SEE
ON AVERAGE, THE CONTROLS HAVE A
LARGER HIPPOCAMPUS THAN THE
ALZHEIMER'S DISEASE SUBJECTS DO.
AND SO THIS IS REALLY THE BREAD
AND BUTTER OF STRUCTURAL
NEUROIMAGING STUDIES.
THEY MIGHT BE MUCH MORE
EXPANSIVE, THEY MIGHT USE
DIFFERENT DISEASES, AND IT MIGHT
HAVE A LOT LARGER NUMBERS, BUT
THIS IS BASICALLY HOW IT WORKS.
SO IF YOU LOOK THROUGH THE
LITERATURE, YOU'LL FIND MANY
DISEASES AND DISORDERS, SO I
ALREADY TALKED ABOUT
ALZHEIMER'S, HUNTINGTONS,
PARKINSON'S, MULTIPLE SCLEROSIS,
SCHIZOPHRENIA, MAJOR DEPRESSIVE
DISORDER, ADDICTION.
PROBABLY ANY DISEASE OR DISORDER
HAS BEEN STUDIED IN THIS WAY,
AND THEY PROBABLY FOUND SOME OF
THESE TYPES OF GROUP EFFECTS.
SO AS SUSIE POINTED OUT, THERE'S
THIS DIFFERENCE BETWEEN DOING A
POPULATION STUDY AND DOING AN
INDIVIDUAL STUDY.
SO WHAT I WAS JUST DESCRIBING
WAS A POPULATION STUDY.
AND POPULATION STUDY JUST SHOWS
THAT BRAIN ANATOMY TENDS TO BE
DIFFERENT IN SUBJECTS WITH A
DISEASE COMPARED TO A DIFFERENT
DISEASE OR COMPARED TO A HEALTHY
CONTROL.
THAT'S REALLY ALL THIS PICTURE
IS SHOWING.
SO SOME INDIVIDUALS WITH THE
DISEASE MAY HAVE THE EFFECT, AND
OTHERS WON'T.
SOME INDIVIDUALS WOW WITHOUT THE
DISEASE MAY HAVE THE EFFECT.
THE EFFECTS CAN BE VERY SUBTLE
AND FOUND USUALLY WITH SENSITIVE
STATISTICAL TESTS.
MANY DISEASES CAN HAVE THE SAME
TYPE -- AS YOU GET OLDER, YOUR
HIPPOCAMPUS WILL SHRINK AS WELL,
SO THAT'S JUST SIMPLY AGE,
MIMICKING THE EFFECT OF
ALZHEIMER'S.
SO WHAT WE'RE MOSTLY INTERESTED
IN IN TERMS OF RISK IS WHAT WE
CALL THE DIAGNOSIS PROBLEM.
SO DOES THE INDIVIDUAL HAVE A
DEELZ OR DISORDER AND CAN WE
TELL THAT FROM THE IMAGE.
SO HERE I HAVE TAKEN THESE 100
SUBJECTS AND I'VE SORTED THEM BY
THEIR HIPPOCAMPAL VOLUME, AND
THE RED ARE THE CONTROL SUBJECTS
AND THE BLUE ARE THE A.D.
SUBJECTS.
YOU CAN SEE THAT A.D. SUBJECTS
TEND TO BE ON THE LEFT SIDE, THE
SMALLER SIDE OF THE HIPPOCAMPAL
VOLUME AND THE CONTROLS TEND TO
BE ON THE RIGHT SIDE.
AND SO THE WAY THAT YOU WOULD
TRY TO DO SOME KIND OF DIAGNOSIS
FROM THIS IS THAT YOU WOULD JUST
CREATE A CUTOFF AND YOU'D SAY
EVERYBODY ON THE LEFT, I'M GOING
TO DIAGNOSE THEM AS A.D.,
EVERYBODY ON THE RIGHT, I'M
GOING TO SAY IS HEALTHY.
AND OF COURSE THIS WOULD LEAD TO
A LOT OF FALSE POSITIVES AND
FALSE NEGATIVES.
BUT IN RECENT YEARS, THERE'S
BEEN A HUGE IMPROVEMENT IN THE
ABILITY TO DO THIS TYPE OF
DIAGNOSIS DUE TO THE EMERGENCE
OF MACHINE LEARNING.
SO THIS PAPER BY INGLESE 2022
SHOWED THAT SENSITIVITY AND
SPECIFICITY OF AROUND 95 TO 98%,
OR A.D. DIAGNOSIS VERSUS
CONTROL.
AND THAT IS -- I'M SUPER
IMPRESSED BY THIS, HAVING DONE
THIS FOR A LONG TIME.
HAVING SAID THAT, OTHER DISEASES
ARE PROBABLY GOING TO BE MUCH
HARDER.
A.D. HAS A VERY PROFOUND EFFECT
UPON THE BRAIN.
IT IS NOT SUBTLE.
AND YOU MIGHT BE ABLE TO KIND OF
LOOK AT A BRAIN AND LOOK AT A
CONTROL BRAIN AND SAY THAT BRAIN
HAS ALZHEIMER'S DISEASE.
SO OTHER DISEASES ARE PROBABLY
GOING TO BE MUCH HARDER.
AND DISTINGUISHING BETWEEN
DISEASE IS GOING TO BE HARDER
STILL.
HOWEVER, IN THEORY, IF YOU CAN
SEE IT IN A POPULATION, IT MUST
BE THERE IN SOME OF THE
INDIVIDUALS.
SO I DON'T THINK THE RISK GOES
AWAY JUST BECAUSE THE DISEASE
MIGHT BE SUBTLE.
PREDICTING WHO'S GOING TO COME
DOWN WITH THE DISEASE IS ALSO
GOING TO BE MUCH HARDER.
BUT THIS COULD BE MADE EASIER IF
THE DATA ARE LONGITUDINAL, SO
THEY HAVE LIKE SEVERAL SCANS OF
THE PERSON.
AND FINALLY, THE DIAGNOSIS MIGHT
BE INCLUDED AS PART OF THE DATA
RELEASE.
SO FOR EXAMPLE, ON THE PREVIOUS
PAGE WHERE I SHOWED THE
DIFFERENT DATA RELEASES, I TALK
ABOUT ADNI.
THAT WILL ALSO -- ADNI JUST
RELEASES AND TELLS YOU WHAT THE
DIAGNOSIS IS FOR EACH
INDIVIDUAL.
SO THE SECOND AND PERHAPS BIGGER
RISK FACTORS CAN THE INDIVIDUAL
BE IDENTIFIED FROM THE IMAGES.
AND SO DEIDENTIFICATION IS A
STANDARD PRACTICE BEFORE DATA
RELEASES, AS HAS ALREADY BEEN
DESCRIBED.
BUT THERE'S LULLS A SITUATION
WHERE THE IDENTITY MIGHT NOT BE
REMOVED PROPERLY, LIKE THE NAME
OR THE ADDRESS OR SOME OTHER
TYPE OF IDENTIFYING INFORMATION
MIGHT JUST EXIST AS TEXT.
THE FACES MIGHT NOT BE REMOVED
OR DEFACING IS SOMEHOW REVERSED.
THERE ARE OTHER IDENTIFIABLE
ASPECTS OF THE IMAGES.
THERE MIGHT BE METADATA, AS I
MENTIONED BEFORE, OFTENTIMES THE
SCANS WILL INCLUDE A TIME, DATE
AND PLACE OF THE SCAN.
SO IF YOU KNEW THAT THIS PERSON
WAS AT THIS PLACE, THIS
PARTICULAR TIME, YOU MIGHT BE
ABLE TO IDENTIFY THAT PERSON.
UNUSUAL DEMOGRAPHICS, FOR
EXAMPLE, IF YOU HAD A
110-YEAR-OLD SUBJECT IN YOUR
DATA, IT MIGHT BE THE CASE THAT
THERE AREN'T A WHOLE LOT OF
110-YEAR-OLDS OUT THERE, SO IT
WOULD BE FAIRLY EASY TO FIND OUT
WHO THAT PERSON WAS.
AS I SAID BEFORE, THIS IS A VERY
WELL-KNOWN PROBLEM AND NIH
SPONSORED WORKSHOPS VERY SIMILAR
TO THIS ON DEIDENTIFICATION A
FEW MONTHS AGO.
SO CREATING DATA OR CREATING A
FACE FROM MRI DATA AS I SAID
BEFORE, WHEN YOU COLLECT
STRUCTURAL INFORMATION, YOU'RE
NOT JUST GETTING THE BRAIN,
YOU'RE GETTING THE ENTIRE HEAD,
INCLUDING THE FACE, AND IT'S
SUPER DOOPER EASY TO GO AND
CREATE ONE OF THESE SURFACES
FROM THE FACE AND SPLAYED IN 3D,
AS I'VE SHOWN HERE.
THEN THE WORRY HERE IS THAT IF
THIS INFORMATION IS IN THE
IMAGE, THEN YOU CAN JUST TAKE
THIS IMAGE, POP IT INTO GOOGLE
SEARCH, AND IT WILL COME OUT
WITH THE PERSON WHOSE SCAN THIS
IS.
SO I DON'T THINK IT'S QUITE THIS
EASY, BUT IT PROBABLY GOING TO
GET QUITE THIS EASY.
SO I THINK THIS PROBABLY LIKE
THE MAJOR RISK WHEN IT COMES TO
RELEASING DATA AND IDENTIFYING
THEM.
SO PEOPLE HAVE DEVELOPED
DEFACING ALGORITHMS, AS SASKIA
MENTIONED BEFORE.
THIS REALLY INVOLVES GOING IN
AND CHANGING THE PIXEL DATA, AND
THEN WHEN YOU HAVE A DATA
RELEASE, YOU RELEASE THIS DATA
THAT HAS THE PIXELS CHANGE
RATHER THAN THE ORIGINAL DATA.
AND THEN WHEN PEOPLE TRY TO
CREATE A FACE OUT OF IT, YOU GET
THIS KIND OF AVERAGE FACE HERE
THAT IS UNRELATED TO THE
ORIGINAL FACE.
AND HERE WE'VE REMOVED THE EARS
AS WELL.
THERE ARE POTENTIALLY OTHER
IDENTIFIABLE INFORMATION INSIDE
OF THE IMAGE.
SO AS I SHOWED BEFORE, HERE'S
THE T1 ANATOMICAL AND THIS IS
OTHER SEGMENTATION OF IT.
AND SO WHAT WE'RE ALSO
SEGMENTING IN HERE IS NOT JUST
THE BRAIN, BUT THE SKULL.
AND SO YOU CAN EXTRACT LIKE A
VERY DECENT-LOOKING SKULL OUT OF
THESE MR IMAGES.
YOU CAN SEE THAT THE INDIVIDUAL
TEETH IN THERE, AND IF SOMEBODY
REALLY WANTED TO, YOU COULD
PROBABLY FIND OUT EXACTLY WHAT
EACH TOOTH LOOKED LIKE, THE
RESOLUTION IS THAT GOOD.
SO IF SOMEBODY HAS ACCESS TO
SOME OTHER TYPE OF PRIVATE
HEALTH INFORMATION, THEN THEY
MIGHT BE ABLE TO IDENTIFY THEM.
SO IF THEY HAD LIKE SOME KIND OF
DENTAL RECORDS OR IF THEY KNEW
SOMEBODY HAD A TUMOR OR A STROKE
OR SOMETHING LIKE THAT, IT MIGHT
BE POSSIBLE TO IDENTIFY THEM OR
IF THEY HAD SOME KIND OF ANOMALY
IN THEIR BRAIN LIKE THEY DIDN'T
HAVE A CORPUS CALLOSUM.
ANOTHER QUESTION IS WHETHER
THERE'S ACCESS TO OTHER BRAIN
IMAGES.
SO BRAINS ARE LIKE A
FINGERPRINT, AND SO I'M NEAR
100% SURE IF YOU HAD A BRAIN
IMAGE FROM SOMEBODY IN ONE OF
THOSE BIG DATA RELEASES, YOU
COULD, WITH 100% ACCURACY,
FIGURE OUT WITH PERSON THAT WAS.
THE BRAINS ARE LIKE A
FINGERPRINT.
AND THEY DON'T HAVE TO BE VERY
HIGH RESOLUTION.
SO LIKE THESE LOW RESOLUTION
BRAINSTEM, LIKE SUSIE WAS
TALKING ABOUT, YOU KNOW, TWO,
THREE, FOUR, MAYBE EVEN
FIVE MILLIMETERS, YOU CAN MATCH
IT AGAINST THAT BRAIN PRETTY
EASILY.
AND DISTINGUISH THAT BRAIN FROM
SOMEBODY ELSE'S BRAIN.
SO TO SUMMARIZE, THERE'S
SUBSTANTIAL BENEFITS TO SHARING
DATA, AND IN FACT IT'S REQUIRED,
AND VARIOUS TOOLS LIKE OPEN
NEUROARE MAKING IT EASY TO DO.
AND I THINK IT'S IMPORTANT
AROUND VERY BENEFICIAL TO
OVERALL HUMAN HEALTH.
THE RISKS ARE ABOUT WHETHER YOU
CAN DIAGNOSE OR PREDICT SOME
KIND OF DISEASE OR DISORDER FROM
AN INDIVIDUAL SUBJECT, AND
WHETHER YOU CAN IDENTIFY THE
SUBJECT OR NOT.
SO AS WE'VE SEEN, IT IS POSSIBLE
TO DETERMINE DIAGNOSES FROM SOME
DISORDERS FROM AN INDIVIDUAL
FROM BRAIN IMAGES, AND THE
DIAGNOSIS MAY OR MAY NOT BE
ACTUALLY IN THE DATA RELEASE.
DEIDENTIFICATION IS A VERY
WELL-KNOWN PROBLEM, AND WE HAVE
VERY GOOD TECHNOLOGY NOW TO LOOK
FOR BOTH TEXT-BASED AND
IMAGE-BASED IDENTIFICATION
FEATURES.
I'D SAY THE THING THAT KEEPS ME
UP AT NIGHT WHEN I THINK ABOUT
THE FUTURE AS TO WHETHER THE
DE-FACING IS GOING TO BE
REVERSIBLE.
SO WE REMOVE A FACE, YOU THINK
OF SOMETHING LIKE A FORENSIC
PATHOLOGIST WHO MIGHT HAVE A
SKULL, AND THEN THEY TRY TO
RE-CREATE WHAT THE FACE OF THAT
PERSON LOOKED LIKE WHO HAD THAT
SKULL.
SO IS THAT KIND OF INFORMATION
GOING TO BE -- IS THAT IN THERE
SOMEWHERE AND WE JUST HAVEN'T
BEEN ABLE TO FIGURE OUT HOW TO
DEFEAT IT.
I WOULD SAY THAT'S PROBABLY THE
HIGHEST RISK AT THIS POINT.
OF COURSE ARE THERE OTHER WAYS
TO IDENTIFY BESIDES A FACE?
LIKE MATCHING WITH BRAIN IMAGES.
HAVING SAID THAT, THESE THINGS
ARE HARD.
IT'S HARD TO DIAGNOSE AND
IDENTIFY PEOPLE IN THESE IMAGES.
THESE ARE OFTEN VERY SUBTLE
ANATOMICAL CHANGES, OFTEN IT'S
QUITE COMPUTATIONALLY EXPENSIVE
TO TRY TO DEVELOP THESE TOOLS
WHICH MIGHT -- SOME NEFARIOUS
ACTOR MIGHT HAVE TO DEVELOP
THEIR OWN, AND ALSO REQUIRE
QUITE A BIT OF EXPERTISE.
OF COURSE THESE THINGS ARE
BECOMING A LOT EASIER, AND THERE
SEEMS TO BE NO END TO THE
MISCHIEF THAT PEOPLE WITH
MACHINE LEARNING EXPERIENCE CAN
GET INTO.
OKAY, THANK YOU.
[APPLAUSE]
>>THANK YOU SO MUCH, DR. GREVE,
FOR THAT PRESENTATION.
WE HAVE TIME FOR ONE QUESTION.
>>THANK YOU.
THAT WAS GREAT.
YOU TALKED ABOUT WHAT KIND OF
INFERENCES WE CAN DRAW FROM THE
DATA IN TERMS OF DISEASE STATES
AND PREDICTIONS OF DISEASES.
HOW ABOUT INFERENCES THAT ARE
OUTSIDE OF MEDICAL INFERENCES,
LIKE NON-DISEASE-RELATED
INFERENCES?
>>YOU KNOW, I'M NOT SURE IF YOU
HAVE AN EXAMPLE IN MIND.
THAT WOULD BE HELPFUL.
>>THINGS LIKE BEHAVIOR,
INFERENCES ABOUT INDIVIDUALS'
MENTAL STATES, THINGS LIKE THAT.
MAYBE MORE FUNCTIONAL.
>>I THINK STRUCTURALLY, THAT'S
GOING TO BE A REALLY HARD THING
TO TRY TO DO.
YOU KNOW, I THINK IT GOING TO BE
THINGS LIKE IF SOMEBODY HAS LIKE
A SUPER SMALL HIPPOCAMPUS,
THEY'RE PROBABLY GOING TO BE
SOMEBODY WITH A MEMORY DISORDER.
AND THAT'S GOING TO CREATE
VULNERABILITY.
BUT THAT'S ALSO LIKE MORE OF A
DIAGNOSIS.
>>THANKS VERY MUCH AGAIN, AND
WE'LL PICK THIS UP IN THE
DISCUSSION.
I'D LIKE TO INTRODUCE OUR NEXT
SPEAKER, DR. JENNIFER COLLINGER,
ASSOCIATE PROFESSOR IN THE
DEPARTMENT OF PHYSICAL MEDICINE
AND REHABILITATION AT THE
UNIVERSITY OF PITTSBURGH WITH
SECONDARY APPOINTMENTS IN THE
DEPARTMENT OF BIOENGINEERING AT
THE UNIVERSITY OF PITS WURG BURG
UNIVERSITY OF PITTSBURGH,SHE REN
BIOENGINEERING FROM THE
UNIVERSITY OF PITTSBURGH.
HER RESEARCH FOCUSES ON THE USE
OF NEUROPROSTHETICS TO RESTORE
FUNCTION FOR INDIVIDUALS WITH
UPPER LIMB PARALYSIS OR LOSS.
SHE'S DEVELOPED AN INTRACORTICAL
INTERFACE TECHNOLOGY FOR
INDIVIDUALS WITH TETRAPLEGIA.
HER WORK ALSO INCLUDES
NONINVASIVE IMAGING FOR
MEASURING NEUROPLASTICITY.
>>THANKS FOR THE INTRODUCTION.
THANKS EVERYBODY FOR ALLOWING ME
TO BE HERE TO TALK ABOUT
BRAIN-COMPUTER INTERFACES.
ARE YOU GUYS ABLE TO HEAR ME
OKAY AND SEE MY SLIDES?
>>PERFECT ON BOTH, YEAH.
>>OKAY, GREAT.
SO IN MY LAB, WE'RE DEVELOPING
BRAIN-COMPUTER INTERFACE AND
THAT'S THE TYPE OF DEVICE THAT
I'M GOING TO TALK TO YOU ALL
ABOUT TODAY.
WE SPECIFICALLY USE WHAT'S KNOWN
AS A BIDIRECTIONAL SENSORY MOTOR
BRAIN-COMPUTER INTERFACE SO I'LL
WALK YOU THROUGH THIS SCHEMATIC
TO GIVE YOU AN IDEA OF WHAT I
MEAN BY THAT.
SO IN OUR LAB, WE'RE RECORDING
FROM THE MOTOR CORTEX, MANY LABS
DO THE SAME.
AND WE TRY TO RECORD ACTIVITY
FROM MULTIPLE CHANNELS, DECODE
THOSE PATTERNS OF ACTIVITY AND
TURN THAT INTO A CONTROL SIGNAL.
THIS MIGHT BE FOR A ROBOTIC ARM
OR HAND LIKE I'M SHOWING HERE,
IT CAN BE FOR A COMPUTER CURSOR,
IT CAN BE FOR DECODING SPEECH TO
RESTORE COMMUNICATION.
AT THE SAME TIME, YOU CAN RECORD
FROM SENSORS IN THE ROBOT OR THE
COMPUTER ENVIRONMENT, DETECT
SENSORY INFORMATION SUCH AS HOW
HARD THE FINGERS ARE SQUEEZING
ON THIS BALL, AND THEN TURN THAT
INTO STIMULATION PATTERNS THAT
CAN BE PLAYED THROUGH ELECTRODES
THAT ARE IMPLANTED AND
SOMATOSENSORY CORTEX SHOWN HERE
IN RED.
AND SO THAT'S WHAT WE MEAN BY
BIDIRECTIONAL.
WE'RE GETTING INFORMATION OUT OF
THE BRAIN AND ALSO POTENTIALLY
PUTTING INFORMATION IN THROUGH
STIMULATION.
WE'VE ALREADY HEARD A LITTLE BIT
ABOUT THE DIFFERENT TYPES OF
NEURAL INTERFACES SO I WON'T
DWELL ON THAT TOO MUCH HERE.
THE TYPE OF DEVICE THAT WE USE
IN OUR LAB IS AN INTRACORTICAL
DEVICE, AND WHAT I MAYBE WANT TO
HIGHLIGHT HERE IS THAT IT'S VERY
SMALL, SO CURRENT DEVICES THAT
ALLOW YOU TO RECORD FROM SINGLE
CELLS IN THE BRAIN, SINGLE
NEURONS IN THE BRAIN OR VERY
SMALL GROUPS OF NEURONS, HAVE
VERY LIMITED SPATIAL COVERAGE
AND SO YOU'RE REALLY ONLY
GETTING A SNAPSHOT OF A SMALL
AREA OF THE BRAIN.
AS YOU MOVE TOWARDS
NON-IMPLANTED DEVASE VICES LIKE
EEG, YOU START TO GET BROADER
SPATIAL COVERAGE.
YOU'RE NOW NO LONGER RECORDING
FROM SINGLE NEURONS BUT GETTING
BROADER COVERAGE OF THE BRAIN.
SO THAT DOES IMPACT THE TYPE OF
INFORMATION THAT WE HAVE ACCESS
TO AND POTENTIALLY THE RISK
CONSIDERATIONS AND POTENTIAL USE
CASES FOR THESE DIFFERENT
DEVICES.
SO LET'S TALK A LITTLE BIT ABOUT
HOW WE READ MOVEMENT INTENTION
FROM THE BRAIN.
AND I'LL FOCUS ON MOVEMENT.
IT'S FAIRLY SIMILAR IF YOU'RE
TRYING TO RESTORE COMMUNICATION
AS WELL.
BUT FOR ALL OF THESE STUDIES
THAT ARE DEVELOPING
BRAIN-COMPUTER INTERFACES WITH
IMPLANTED TECHNOLOGY, THE FIRST
STEP IS HAVING TARGETED
PLACEMENT OF THESE ELECTRODES
FOR RECORDING NEURAL ACTIVITY.
SO WE IMPLANT IN MOTOR CORTEX,
THERE ARE OTHERS THAT ARE
TARGETING MORE PREMOTOR OR
PARIETAL AREAS OF THE BRAIN, BUT
AGAIN WE'RE TRYING TO PLACE
THESE ELECTRODES IN AREAS THAT
ARE FAIRLY WELL UNDERSTOOD AND
CARRY INFORMATION SPECIFICALLY
ABOUT THE TASK THAT WE ARE --
AND FUNCTION THAT WE'RE TRYING
TO RESTORE.
SO IN OUR CASE, ARM AND HAND
FUNCTION.
THEN THE DEVICE NEEDS TO BE
CALIBRATED.
SO TYPICALLY WHAT THAT INVOLVES
IS HAVING A PERSON FOLLOW ALONG
WITH MOVEMENTS IN A VIRTUAL
ENVIRONMENT OR IMAGINE
PERFORMING DIFFERENT MOVEMENTS
OR IMAGINE SPEAKING AND TRYING
TO RECORD NEURAL ACTIVITY ALONG
WITH THAT TASK-RELATED
INFORMATION SO THAT WE CAN
FIGURE OUT THE RELATIONSHIP
BETWEEN BRAIN ACTIVITY AND
MOVEMENT.
AND THEN WHILE THE DEVICE IS
BEING USED, THE IDEA IS TO
DECODE THESE MOVEMENT PARAMETERS
IN REALTIME.
SO IN MANY CASES, WE'RE TRYING
TO DECODE, SAY, THE N POINT
VELOCITY OF THE EFFECTOR.
SO MOVING ARM AROUND IN SPACE,
OPENING AND CLOSING THE HAND, IF
YOU'RE CONTROLLING A COMPUTER
CURSOR, YOU MIGHT BE DECODING
THE X AND Y UP AND DOWN LEFT AND
RIGHT MOVEMENT OF THAT COMPUTER
CURSOR WHEN THEY WANT TO CLICK,
THOSE ARE THE TYPES OF THINGS
WE'RE DECODING.
AND DURING ALL OF THESE
DIFFERENT STEPS, WE'RE RECORDING
THAT NEURAL INFORMATION, THE
TASK-RELATED DATA, AND THEN
WE'RE TRYING TO ESTIMATE WHAT
THEIR MOVEMENT INTENTION IS,
WHAT WERE THEY TRYING TO DO
DURING THAT TASK.
ON THE SENSORY SIDE, AGAIN, IT
STARTS WITH TARGETED PLACEMENT
OF THE ELECTRODES FOR
STIMULATING.
I THINK IT'S EVEN MORE CRITICAL
ON THE SENSORY SIDE.
THERE'S A VERY STRONG SPATIAL
ORGANIZATION IN THE BRAIN AS TO
WHICH AREAS ARE RESPONSIBLE FOR
RECEIVING INPUT FROM THE
PERIPHERY.
SO IN OUR CASE, WE TARGET AN
AREA OF SOMATOSENSORY CORTEX
THAT TYPICALLY RECEIVES TACTILE
INPUTS FROM THE HAND.
THEN YOU NEED TO DELIVER
PATTERNS OF MICRO STIMULATION TO
THESE IMPLANTED ELECTRODES TO
CONVEY SENSORY INFORMATION, AND
SO THERE'S LOTS OF DIFFERENT
WAYS TO ENCODE THIS, BUT
TYPICALLY STIMULATING THROUGH A
GIVEN ELECTRODE GENERATES A VERY
FOCAL SENSATION WITH FAIRLY
WELL-KNOWN PROPERTIES THAT IF
YOU STIMULATE WITH A HIGHER
LEVEL OF CURRENT, IT GENERATES A
MORE INTENSE SENSATION.
SO THE SCHEMATIC IS SHOWING IF
YOU'RE GRASPING GRASPING AN OBJE
FORCE GOES HIGH, THEN GOES DOWN
AND THERE'S DIFFERENT WAYS OF
STIMULATING.
YOU CAN INCREASE STIMULATION
AMPLITUDE, LINEARLY, OR THERE'S
MORE BIOMIMETIC APPROACH THAT
EMPHASIZES THE CONTACT POINTS,
BUT THE IDEA IS THE SAME, YOU'RE
CONVEYING TEMPORAL INFORMATION
ABOUT THE ENVIRONMENT.
AND PARTICULARLY IN THIS EARLY
PHASE OF RESEARCH AND DEVICE
DEVELOPMENT, WE'RE OFTEN
DOCUMENTING THE PERCEPTUAL
QUALITY, PSYCHOMETRIC PROPERTIES
OF THESE SENSATIONS, WHAT DOES
IT FEEL LIKE, FOR EXAMPLE, AND
THE IMPACT ON PERFORMANCE.
SO IF THEY'RE USING THE DEVICE
TO CONTROL A ROBOTIC ARM, DOES
IT ACTUALLY IMPROVE OR CHANGE
THEIR ABILITY TO USE THAT
DEVICE.
LET'S DIG IN A LITTLE BIT INTO
THE TYPES OF NEURAL DATA THAT WE
CAN RECORD FROM INTRACORTICAL
ELECTRODES.
SO AGAIN, HERE FOCUSING MOSTLY
ON MOTOR AND SOMATOSENSORY
CORTEX, WHICH ARE KNOWN TO HAVE
A STRONG SPATIAL ORGANIZATION
WHERE DIFFERENT BODY PARTS ARE
MORE GENERALLY REPRESENTED IN
THTHIS DISORDER WITH LEGS
REPRESENTED MORE MEDIALLY
TOWARDS MIDLINE, HAND LATERALLY
AND THEN FACE.
WE IMPLANT TWO ELECTRODES.
IF YOU ZOOM IN ON ONE OF THESE
SQUARES HERE THAT'S REPRESENTING
AB ACTAN ACTUAL ARRAY OF ELECTR.
EACH OF THESE BLACK BOXES IS
SHOWING RAW DATA RAW FILTER DATA
RECORDED FROM A SINGLE ELECTRODE
AND WE'RE ACTUALLY LOOKING AT
OVERLAPPING ACTION POTENTIALS.
SO WHEN YOU ZOOM IN ON ONE OF
THESE BOXES HERE YOU CAN SEE
THERE'S ACTUALLY TWO DIFFERENT
NEURONS THAT ARE BEING RECORDED
FROM BECAUSE THERE'S A UNIQUE
SHAPE TO THESE NEURONS.
AND IT'S THE RATE AT WHICH THESE
FIRE THAT PEOPLE ARE OFTEN
INTERPRETING IN ORDER TO DECODE
MOVEMENT.
IF WE ZOOM IN A LITTLE BIT ON
THE DIFFERENT TYPES OF DATA PROS
SOMETHING, FOR EACH OF THE
CHANNELS THAT WE HAVE IMPLANTED,
WE'RE RECORDING RAW VOLTAGE
DATA.
WE OFTEN ARE TRYING TO LOOK AND
IDENTIFY WHETHER AN ACTION
POTENTIAL HAS OCCURRED SO WE'RE
LOOKING FOR THESE DEVIATIONS
FROM A BASELINE VOLTAGE AND SO
IF WE SEE THAT ACTION POTENTIAL,
WE THRESHOLD AND MARK THAT AN
ACTION POTENTIAL HAS OCCURRED
AND THEN TYPICALLY IN ORDER TO
INTERPRET THIS DATA, WE NEED TO
BIN THOSE TOGETHER TO SAY HOW
MANY TIMES DID THIS PARTICULAR
CHANNEL FIRE AND WE CAN USE
THOSE TO ESTIMATE THE FIRING
RATES OF NEURONS.
SO DEPENDING ON THE DEVICE AND
BANDWIDTH AND SETTINGS, YOU
MIGHT BE SAVING DATA AT THIS RAW
VOLTAGE STAGE OR MORE AT THE
BINNED FIRING RATE STAGE.
BCIs ARE USED FOR A VARIETY OF
THINGS, TRYING TO RESTORE
FUNCTION.
SOME EXAMPLES INCLUDE ROBOTIC
ARM CONTROLS, SO HERE ARE
PARTICIPANTS CONTROLLING THE
REACH AND GRASP OF THIS ROBOT IN
ORDER TO PERFORM A FEEDING TASK.
THIS IS AN EXAMPLE OF USING A
BCI FOR COMPUTER CONTROL.
IT'S ACTUALLY A PORTABLE SYSTEM
THAT CAN BE USED OUTSIDE OF THE
LAB AND IN HOME ENVIRONMENTS.
AND AGAIN, THERE THE IDEA IS TO
ESSENTIALLY REPLACE A COMPUTER
MOUSE WITH DECODED BRAIN
ACTIVITY.
HERE'S AN EXAMPLE FR WHERE THEYE
ACTUALLY ABLE TO DECODE VERY
DETAILED HANDWRITING MOVEMENTS
IN ORDER TO RESTORE
COMMUNICATION.
THEN FINALLY ANOTHER EXAMPLE
FROM CHANG'S LAB WHERE THEY'RE
ACTUALLY DECODING SPEECH
DIRECTLY, SO TRYING TO DECODE
THESE DIFFERENT LETTERS.
SOMETHING THAT WAS NEAT ABOUT
THIS PARTICULAR SCHEMATIC IS
THAT THEY'RE SHOWING THAT FIRST
THEY WANT TO DETECT THAT THE
PARTICIPANT IS ACTUALLY MAKING
AN ATTEMPT TO SPEECH, THEN
DECODE THE LETTERS, AND THEN
THERE'S A MOTOR COMMAND SO A
VERY ROBUST GRASP-RELATED
ACTIVITY THAT CAN BE USED TO
DEACTIVATE THAT SO THE
PARTICIPANT WOULD HAVE CONTROL
OVER WHEN IT'S ON AND OFF.
ON THE SENSORY SIDE, AS I
MENTIONED WE'RE TRYING TO
DOCUMENT A LOT ABOUT WHAT THE
PARTICIPANT IS FEELING, SO
HERE'S AN EXAMPLE WHERE THE
PARTICIPANT RECEIVES STIMULANT
THROUGH FOUR DIFFERENT
ELECTRODES.
EACH CIRCLE IS ESSENTIALLY
SHOWING WHERE THAT SENSATION
FELT LIKE IT WAS ORIGINATING
FROM THEIR OWN HAND, AND THAT
TENDS TO BE VERY STABLE FROM DAY
TO DAY.
WE MIGHT ALSO DOCUMENT HOW
INTENSE THAT SENSATION FEELS, SO
THIS GRAPH IS SHOWING AS YOU
INCREASE THE STIMULATION
AMPLITUDE, THE INTENSITY AND
REPORTED INTENSITY OF THAT
INCREASES LINEARLY.
WE MAY ASK FOR MORE DESCRIPTIVE
TERMS ABOUT WHAT THE SENSATION
FEELS LIKE, SO THIS WORD CLOUD
IS JUST REPRESENTING THE
FREQUENCY WITH WHICH THEY USE
THESE DIFFERENT DESCRIBTIVE
WORDS TO DESCRIBE STIMULATION
ACROSS THE DIFFERENT ELECTRODES.
AND FINALLY, AS I MENTIONED, WE
ALSO MAY DOCUMENT THE IMPACT ON
PERFORMANCE.
SO DOING SAME TASK, PICKING UP
THIS OBJECT, MOVING TO A NEW
SURFACE WITH FEEDBACK, AND
WITHOUT.
OKAY.
SO WHAT TYPES OF DATA ARE WE
ACTUALLY SAVING WHEN WE'RE USING
A BCI OR CONDUCTING BCI
RESEARCH?
OBVIOUSLY YOU'RE SAVING THE
NEURAL DATA, IT CAN BE IN
VARIOUS FORMS.
SAVING INFORMATION ABOUT THE
DEVICE, SO EACH PARTICULAR STUDY
SITE AND EVEN PARTICIPANT MAY
HAVE A SLIGHTLY DIFFERENT DEVICE
CONFIGURATION, SO EXACTLY WHERE
IT WAS IMPLANTED, HOW MANY
CHANNELS IT HAS, WAS THERE
SENSORY FEEDBACK PROVIDED OR
NOT.
WE'RE SAVING MOVEMENT
KINEMATICS, SO EITHER MOVEMENT
KINEMATICS THAT THEY'RE TRYING
TO FOLLOW ALONG WITH OR MOVEMENT
KINEMATICS THAT ARE DECODED FROM
THE BRAIN.
IT'S POSSIBLE THAT THE BCI COULD
SAVE COMPUTER USAGE STATISTICS
OR A TEXT THAT'S GENERATED IN
THE CASE OF A COMMUNICATION BCI.
AND WE ALSO WOULD BE SAVING
SENSORY RESPONSES WITH THE DATA.
IN TERMS OF THE INFERENCES THAT
ARE KIND OF NOW BEEN PROVEN TO
BE POSSIBLE, IT'S DEFINITELY
POSSIBLE TO DECODE THE MOVEMENT
INTENTION.
SO WHAT IS THE PARTICIPANT
TRYING TO DO WITH THE END
EFFECTOR?
IT'S POSSIBLE TO DECODE LANGUAGE
DIRECTLY FROM DIFFERENT AREAS OF
THE BRAIN.
IT DOES RELY OBVIOUSLY ON HAVING
A ROBUST TRAINING SET, BUT THAT
IS POSSIBLE.
AND THEN WE CAN ALSO INFER JUST
FROM THE DATA ITSELF WOULD THE
EXPECTED SENSORY PERCEPTION IS.
SO WE KNOW STIMULATION ON THIS,
YESTERDAY IT FELT LIKE IT WAS
COMING FROM THE INDEX FINGER, WE
CAN EXPECT NEXT TIME WE
STIMULATE THAT CHANNEL, YOU
WOULD PROBABLY GENERATE THAT
SAME SENSATION.
IN TERMS OF WHAT MIGHT BE
POSSIBLE OR WHAT'S ON THE
HORIZON, I DEBAID BAITED A
LITTLE BIT PUTTING THIS
ENVIRONMENTAL INFORMATION AND
BEHAVIOR INTO CURRENT
INFERENCES, BUT WE CAN DETECT
CERTAIN CONTEXTS OF BCIUs.
FOR EXAMPLE, WHEN A PARTICIPANT
IS REACHING TOWARDS AN OBJECT
VERSUS NOT, ACTIVITY ACTUALLY
LOOKS DIFFERENT, AND WE COULD
FIND THAT SIGNATURE AND PULL OUT
THAT INFORMATION ABOUT THE
ENVIRONMENT.
BCI'S BENEFIT FROM KNOWING A LOT
OF INFORMATION ABOUT THE CONTEXT
AND ENVIRONMENT, SO I IMAGINE
THAT THERE WILL BE A DESIRE TO
COLLECT ADDITIONAL SENSOR
INFORMATION, CAMERAS, YOU KNOW,
OTHER MICROPHONES THAT MIGHT
ACTUALLY IMPROVE OUR ABILITY TO
DECODE, BUT THEN THAT WOULD
INCREASE THE AMOUNT OF DATA
THAT'S INCLUDED WITH THAT.
AS THEY'RE USING THE BCI DEVICE
JUST LIKE ANY WEARABLE DEVICE,
YOU WOULD HAVE INFORMATION ABOUT
COMMON PATTERNS OF BEHAVIOR AND
ACTIVITIES THAT THEY ARE
PERFORMING WITH THE DEVICE.
WHETHER WE COULD DECODE ANY
HEALTH BIOMARKERS, I DON'T THINK
THIS HAS PARTICULARLY BEEN SHOWN
IN BCI DATA, AND ONE REASON FOR
THAT IS THAT IT'S, AGAIN, VERY
LOCALIZED COVERAGE.
WE'RE STILL IN THE POINT OF VERY
SMALL NUMBERS OF STUDY
PARTICIPANTS, SO IT'S DIFFICULT
TO GENERALIZE AND BUILD THAT
ROBUST TRAINING SET.
THESE ARE IMPLANTED DEVICES THAT
I'M TALKING ABOUT TODAY, SO
AGAIN, IT'S NOT SOMETHING THAT
IS BEING DONE WITHOUT OUR
PARTICIPANTS' KNOWLEDGE, AND SO
IT'S NOT REALLY BEING USED FOR
THAT PARTICULAR PURPOSE BUT I
IMAGINE AS THE NUMBER OF
PARTICIPANTS GROWS, IT COULD BE
POSSIBLE.
SIMILARLY IT MAY BE POSSIBLE TO
DETECT CERTAIN COGNITIVE STATES.
ARE THEY FATIGUED, ARE THEY IN
PAIN, ARE THEY IN DISTRESS.
WE'LL BE LIMITED A LITTLE BIT
WITH CURRENT DEVICES BECAUSE OF
THE LIMITED SPATIAL COVERAGE.
CERTAINLY WE HEARD IN THE
EARLIER TALKS THAT WITH BROADER
BRAIN COVERAGE DETECTING THESE
COGNITIVE STATES IS SOMETHING
THAT IS DONE WITH PRETTY HIGH
ACCURACY, BUT FOR THIS LOCALIZED
IMPLANTED BCI, I THINK THAT'S A
LITTLE BIT ON THE HORIZON.
KNOWING EXACTLY WHAT SENSORY
PERCEPTION WILL BE GENERATED
THROUGH A FUTURE STIMULUS STILL
REQUIRES A BIT OF WORK.
THERE ARE SOME CHANGES FROM DAY
TO DAY.
IT MAY DEPEND ON WHETHER THEY
ARE USING THE BCI FOR MOTOR
CONTROL AT THE SAME TIME OR NOT.
SO I THINK THERE'S A LOT MORE
WORK TO DO TO KNOW EXACTLY HOW
PROVIDING SENSORY FEEDBACK COULD
BE PERCEIVED EITHER CONSCIOUSLY
OR SUBCONSCIOUSLY BY THE
PARTICIPANT.
AND ADDING SENSORY FEEDBACK
COULD POTENTIALLY MODULATE OUR
ABILITY TO DECODE THEIR
INTENTION AND WHAT THEY'RE
TRYING TO DO.
SO STIMULATION CAN ACTUALLY
CHANGE ACTIVITY IN MOTOR CORTEX
AS WELL.
IT MAY POTENTIALLY HAVE AN
IMPACT ON EFFECTIVE STATES.
I THINK, AGAIN, IF WE'RE
TARGETING AREAS THAT ARE
PRIMARILY, LET'S SAY, CUTANEOUS
REGIONS OR GENERATE SENSATIONS
OF TOUCH, THE LIKELIHOOD THAT
IT'S GOING TO HAVE AN IMPACT ON
THEIR GENERAL EFFECTIVE STATE OR
HIGHER ORDER COGNITIVE STATE IS
PROBABLY SMALL, BUT IT'S
SOMETHING TO BE CONSIDERED.
AND I'LL JUST CLOSE WITH MAYBE
FOUR DIFFERENT CONSIDERATIONS
THAT I TEND TO FOCUS ON WHEN YOU
THINK ABOUT THE RISKS OF DATA
SHARING.
NUMBER ONE IS THAT PARTICIPANTS
HAVE A RIGHT TO PRIVACY, SO I DO
THINK THEY SHOULD BE CHOOSING
WHICH DATA ARE SAVED, WHICH ARE
SHARED, AND WITH WHOM.
RIGHT NOW FOR BRAIN-COMPUTER
INTERFACES, IT'S REALLY AT THIS
EARLY FEASIBILITY CLINICAL TRIAL
RESEARCH STAGE, AND SO THEIR
ABILITY TO MAKE THESE SELECTIONS
KIND OF HAPPENS AT THE INFORMED
CONSENT PROCESS STAGE.
EVENTUALLY HAS DEVICES GET
COMMERCIALIZED, IT WOULD BE MORE
OF A CONSUMER AGREEMENT OR
REGULATION FROM TOP-DOWN THAT
WOULD HAVE TO PRESERVE THIS
RIGHT TO PRIVACY.
WE'VE BEEN DISCUSSING A LOT THE
POTENTIAL FOR HARM.
SO AGAIN THE CURRENT DEVICES
THAT HAVE FAIRLY LIMITED SPATIAL
COVERAGE AND RECORDING FAIRLY
LIMITED INFORMATION ABOUT THE
BEHAVIOR AND WHAT THEY'RE USING
THE DEVICES FOR.
BUT THERE'S A DESIRE CERTAINLY
TO INCREASE THE NUMBER OF
CHANNELS THAT WE HAVE ACCESS TO,
TO IMPROVE AND BROADEN THE
SPATIAL COVERAGE, AND SO THAT
WILL HAVE AN IMPACT OF THE
FUTURE CAPABILITIES, AND OF
COURSE RESPONSIBILITY RE-USE OF
THIS DATA HAS A MUCH LOWER RISK
OF HARM THAN ANYONE WHO HAS ILL
INTENTIONS, SO THAT CAN BE A
LITTLE DIFFICULT TO PREDICT, AND
I THINK THAT'S WHAT THIS
WORKSHOP IS REALLY TRYING TO BE
AWARE OF, AND MAKE SURE THAT
WE'RE COGNIZANT.
THE POTENTIAL TO REIDENTIFY.
SO THE DATA ITSELF, I SHOWED YOU
IS REALLY JUST A TABLE OF RANDOM
NUMBERS, SO TO THE CASUAL
OBSERVER, I THINK IT WOULD BE
VERY DIFFICULT TO REIDENTIFY
DIFFERENT FROM THE FACIAL
RECONSTRUCTION AND MRI THAT WE
SAW.
HOWEVER, THERE ARE VERY LIMITED
NUMBERS OF BCI STUDIES THAT ARE
HAPPENING RIGHT NOW IN LIMITED
NUMBERS OF PARTICIPANTS WHO
SOMETIMES BECOME IDENTIFIABLE
AND MAY BE POSSIBLE TO REI'D
FILE IF YOU HAVE ACCESS TO OTHER
HEALTH-RELATED INFORMATION AND
SO WHEN WE HAVE THESE SMALL
NUMBERS OF PARTICIPANTS, THOSE
MAYBE REQUIRE SPECIAL
CONSIDERATIONS.
AND THEN AGAIN, KIND OF TRYING
TO BALANCE ALL OF THESE
POTENTIAL RISKS WITH THE
POTENTIAL BENEFIT.
SO STUDY PARTICIPANTS ARE
SIGNING UP TO PARTICIPATE IN
THESE EARLY FEASIBILITY TRIALS
BECAUSE THEY REALLY HAVE A
DESIRE TO CONTRIBUTE TO
SCIENTIFIC KNOWLEDGE IN THE
DEVELOPMENT OF DEVICES THAT WILL
HELP OTHERS IN THE FUTURE, SO I
THINK THERE'S A RESPONSIBILITY
TO MAKE SURE WE ARE USING THIS
DATA EFFECTIVELY IN ORDER TO
PUSH THOSE GOALS FORWARD.
SO THAT'S ALL.
I'LL TAKE ANY QUESTIONS.
[APPLAUSE]
>>THANKS, DR. COLLINGER, THAT
WAS GREAT.
WE HAVE TIME FOR ONE QUESTION.
GO FOR IT.
>>THANK YOU.
THAT WAS A GREAT PRESENTATION
AND REALLY INFORMATIVE.
CAN YOU HELP ME UNDERSTAND JUST
ON THE PRIVACY FRONT, YOU TALKED
ABOUT THE DIFFERENCE BETWEEN
STORING THE RAW VOLTAGE OR THE
BINS DATA.
IN WHAT INSTANCES WOULD YOU
STORE THE RAW DATAVERSE US THE
BIN DATA, AND IS THERE ANY
DIFFERENCE IN, LIKE, FUTURE
ABILITY TO QUERY THOSE TWO
DATAS?
LIKE DOES THE RAW DATA, IF
THERE'S NEW INFERENCES THAT
COULD BE DRAWN, TO BE RETURNED
TO IN A WAY THAT THE BIN DATA
COULDN'T BE OR THE EQUIVALENT
THAT YOU COULD LEARN FROM KIND
OF BOTH SETS OF DATA?
>>YEAH, I THINK IN TERMS OF
WHETHER THE DATA ARE SAVED,
THERE'S PROBABLY A FEW
CONSIDERATIONS.
ONE IS JUST SAVING THAT FULL RAW
DATA DOES REQUIRE A LOT OF SPACE
AND BANDWIDTH.
SO IN DESIGNING CLINICAL
DEVICES, AND WIRELESS DEVICES, I
THINK PEOPLE ARE MOVING MORE
TOWARDS SAVING LIMITED DATASETS.
AND THAT BAND DATA.
IT REALLY DOES CONTAIN MOST OF
THE INFORMATION FOR THE PRIMARY
PURPOSE, RIGHT, WHICH IS
RESTORING AND DECODING MOVEMENT
OR LANGUAGE.
THE HIGHER ORDER COGNITIVE
VARIABLES MAY ACTUALLY COME FROM
FULL BANDWIDTH DATA AND LOOKING
AT LOCAL FIELD POTENTIALS SO
LOOKING AT SORT OF POPULATION
LEVEL ACTIVITY THAT MAY GIVE YOU
MORE INFORMATION INTO THOSE
HIGHER ORDER STATES WHICH IS NOT
NECESSARILY THE PRIMARY GOAL OF
THE DEVICES IN TERMS OF RE --
>>JUST TO MAKE SURE I
UNDERSTAND, THAT MEANS THE BINS
DATA, YOU WOULD ONLY BE ABLE TO
USE FOR THE INFERENCES FOR LIKE
THE PURPOSES YOU WERE MAKING THE
INFERENCES FROM THE RAW DATA,
BUT IF YOU WERE TRYING IN THE
FUTURE FOR NON-STUDY-RELATED
QUESTIONS, YOU COULD RETURN TO
THE RAW DATA, YOU COULD STORE IT
FOR ADDITIONAL QUERIES BEYOND
WHAT'S IN THE BIN DATA.
DO I HAVE THAT RIGHT?
>>WELL, I THINK IN THE BIN
DATA, IT IS POSSIBLE TO DECODE
SOME OF THOSE THINGS.
SO THE EXAMPLE I GAVE OF KNOWING
WHETHER YOU'RE REACHING TOWARDS
AN OBJECT OR EMPTY WORK SPACE,
THAT CAN COME FROM THE BIN DATA.
I THINK IT'S MORE THESE HIGHER
ORDER COGNITIVE VARIABLES SO
THAT IT POSSIBLE WOULD BE MORE
IN THAT FULL BANDWIDTH DATA.
>>GREAT.
THANK YOU.
>>ALL RIGHT, THANK YOU AGAIN,
DR. COLLINGER.
[APPLAUSE]
ALL RIGHT.
OUR FINAL SPEAKER FOR THIS PANEL
IS DR. DORIS WANG, ASSOCIATE
PROFESSOR OF NEUROSURGERY AT THE
UNIVERSITY OF CALIFORNIA AND
SAN FRANCISCO.
SHE RECEIVED HER M.D. AND PH.D.
FROM UNIVERSITY OF CALIFORNIA
SAN FRANCISCO.
DR. WANG AND HER TEAM STUDY THE
BRAINNET WORK PHYSIOLOGY OF
PATIENTS WITH MOVEMENT DISORDERS
INCLUDING PARKINSON'S DISEASE,
ISOLATED DYSTONIA, AND ESSENTIAL
TREMOR.
THE GOAL OF THIS WORK IS TO
UNDERSTAND THE PHYSIOLOGICAL
SIGNATURES OF MOTOR AND
NON-MOTOR SYSTEMS THAT UNDERLIE
THESE DISORDERS, IN ORDER TO
RESTORE DISEASE CIRCUITS TO
THEIR NORMAL STATES USING
NEUROMODULATION.
DR. WANG.
>>THANK YOU SO MUCH FOR
INVITING ME.
CAN EVERYBODY HEAR ME AND SEE ME
OKAY?
>>YEP, GREAT ON BOTH.
>>WONDERFUL.
ALL RIGHT.
SO TODAY I WILL TALK ABOUT,
FIRST OF ALL, HERE'S AN OUTLINE
OF WHAT I WILL SHARE TODAY.
FIRST OF ALL THE TYPE OF DATA I
COLLECT FOR MY RESEARCH AND
SECOND, INFERENCES THAT CAN BE
DRAWN FROM THE DATA.
FINALLY, SOME THOUGHTS ABOUT
RISKS OF SHARING THE DATA THAT I
COLLECT SPECIFICALLY FROM MY
RESEARCH.
SO FIRST A LITTLE ABOUT WHAT I
STUDY.
SO ONE MAJOR PART OF MY RESEARCH
EFFORT IS TO UNDERSTAND GAIT AND
BALANCE DISTURBANCES IN
PARKINSON'S DISEASE PATIENTS
FROM A NEUROPHYSIOLOGY
PERSPECTIVE.
SO GAIT AND BALANCE DISORDERS IN
PARKINSON'S IS ONE OF THE MAJOR
CAUSES OF MORBIDITY AND
MORTALITY.
IT CAN BE MARKED BY DIFFICULTY
INITIATING WALKING.
THE INABILITY TO MAINTAIN
RHYTHMIC, NORMAL STEPS, AND ALSO
DIFFICULTY IN ADAPTING OUR
STEPS.
ADJUSTING OUR STEP SIZE, -- THIS
CAN LEAD TO A LOT OF FALLS AND
ISSUES WITH MOBILITY IN PATIENTS
WITH PARKINSON'S DISEASE.
UNLIKE A LOT OF THE OTHER
SYMPTOMS OF PARKINSON'S, LIKE
TREMOR, RIGIDITY, SLOWNESS IN
MOVEMENT, THE CURRENT GOLD
STANDARD OF TREATMENT WHICH IS
DOPAMINE CURRENTLY DO NOT DO A
WHOLE LOT IN TERMS OF TREATING
ADVANCED STATE PROBLEMS LIKE
WHEN PATIENTS FREEZE, SO
SUDDENLY WHEN THEIR FEET GET
STUCK, THEY CAN'T MOVE WHILE
THEIR BODY IS STILL PROPELLING
FORWARD, OR WHEN THEY HAVE
POSTURE INSTABILITY, SO WHEN
THEY TURN -- TRANSITION FROM
SITTING TO STANDING, FOR
INSTANCE, THEY TEND TO -- FIRST
TAKING A STEP BACK, I THINK ONE
OF THE ISSUES WE HAVE IN
TREATING SOMETHING ADVANCED --
IS BECAUSE WE ACTUALLY DON'T
QUITE UNDERSTAND THE NEURAL
PROCESSES THAT CONTROL THIS
DYNAMIC PROCESS OF GAIT AND BALL
ABC.
SO AS YOU CAN IMAGINE, GAIT IS A
VERY COMPLEX TASK THAT REQUIRES
PRECISE MOVEMENTS ACROSS
MULTIPLE LENSES, INCLUDING THE
ARMS, LEGS, FROM BOTH SIDES OF
HER BODY, AND HOW THE BRAINNET
WORK CONTROLS SUCH ACTIONS IS
ACTUALLY REALLY POORLY
UNDERSTOOD.
AND UNTIL RECENTLY WE DON'T
ACTUALLY HAVE A GREAT WAY OF
STUDYING THIS JUST DUE TO
METHODOLOGICAL CONSTRAINTS WITH
DEVICES.
WHILE IMAGING IS GREAT IN TERMS
OF UNDERSTANDING ALL THE NEURAL
NETWORK CHANGES, BUT PEOPLE
AREN'T REALLY WALKING IN THE
FMRI SCANNER, FOR INSTANCE.
AND THEN WITH EEG, THERE'S
PROBLEMS OF EXTERNAL ELECTRODES
AND DEALING WITH ARTIFACTS
GENERATED BY -- AND KIND OF
OVERALL, MY GOAL IS TO ACTUALLY,
USING SOME OF THE ADVANCED
TECHNOLOGY THAT WE HAVE
AVAILABLE, TO PERFORM
INTRACRANIAL RECORDINGS, USING
COMPLETELY IMPLANTED DEVICES TO
UNDERSTAND THE CORTICAL AND
SUBCORTICAL NEURAL ACTIVITIES
ASSOCIATED WITH NORMAL AS WELL
AS PATHOLOGICAL GAIT PATTERNS,
AND POSSIBLY TO USE THIS
ADVANCED STIMULATION TECHNOLOGY,
AS DR. COLLINGER MENTIONED, KIND
OF THESE -- BY USING THESE
EMBEDDED IMPLANTED SYSTEM TO
CHANGE STIMULATION, AND ADAPT
THAT TO WHAT WE'RE DOING DIE NO,
MA'AMICALLY, TO RESTORE NATURAL
GAIT PATTERNS.
SO TAKING A STEP BACK, KIND OF
IN TERMS OF THE EXACT SIGNALS
AND THE TYPE OF REPORTINGS I'M
PERFORMING.
SO ON THE NEUROPHYSIOLOGY SIDE,
I USE A COMBINATION OF SURFACE
ELECTRODE PADDLES UNDERNEATH THE
DURA, AND ALSO I RECORD FROM
DEEP BRAIN STIMULATION LEADS, SO
THESE ARE ELECTRODES PENETRATING
THE BRAIN, AND WHAT THEY BOTH
ENCAPTURE ARE THESE FIELD
POTENTIAL ACTIVITIES.
SOMETIMES KNOWN AS LFPs.
BASICALLY THEY'RE KIND OF THESE
SQUIGGLY LINES WE SEE SIMPLY ON
EEG RECORDINGS AS WELL.
SO THE AVERAGE ELECTRICAL
ACTIVITY RECORDED EITHER FROM
THE SURFACE OF THE BRAIN OR FROM
NEURONS SURROUNDING THE
ELECTRODE IN THE DEPTH OF THE
BRAIN.
AND THEY MOSTLY REPRESENT
SUBTHRESHOLDS, SO THESE ARE
ACTUALLY NOT NECESSARILY
NEURONAL FIRING PATTERNS, BUT
AVERAGE FLUCTUATIONS ACROSS A
HUNDRED IF NOT THOUSANDS CAN BE
ON THE SIZE OF AN ELECTRODE.
SO THESE FLUCTUATIONS OF FIELD
REPORTINGS THAT WE SEE AND --
CHANGES ACROSS TIME.
AND NEURAL FIELD POTENTIALS FORM
THESE KIND OF WAVEFORM, WE
TYPICALLY CALL THESE NEURAL
OSCILLATIONS.
AND NEURAL OSCILLATIONS ARE
IMPORTANT BECAUSE THEY CAN
PROVIDE A POWERFUL WINDOW FOR US
TO ENTER OUR BRAIN CIRCUIT AND
WHAT IT'S DOING AND IT'S BEING
INCREASINGLY RECOGNIZED AS A WAY
FOR ANATOMICALLY DISTINCT AREAS
OF THE BRAIN TO COMMUNICATE WITH
ONE ANOTHER.
THESE -- FOR SPIKING ACTIVITY.
SO WHILE IN ITSELF, THESE
ACTIVITY ARE PROBABLY NOT
REPRESENTATIVE OF ACTUAL
NEURAL -- BUT AVERAGE CHANGES OF
THESE CONTRIBUTIONS CAN INCREASE
THE LIKELIHOOD OF NORMAL
NEURAL -- AND IT REPRESENTS AN
IMPORTANT MECHANISM FOR, AGAIN,
THESE SEGREGATED BRAIN REGIONS
TO COMMUNICATE WITH EACH OTHER,
SO FROM SUBCORTICAL AREAS SUCH
AS THE BASAL GANGLIA, WHICH IS
WHAT I STUDY, TO MOVE M OF THE
MOTOR CORTEX, AND THEIR WIDE --
AS WELL.
INCREASINGLY WE'RE TRYING TO
FIGURE OUT, DETECT MEANING OR
DRAW MEANING FOR THESE NEURAL
OSCILLATIONS.
AND IN TERMS OF MOVEMENT
DISORDERS, THE CURRENT THINKING
IS THAT BASED ON DECADES OF
ANIMAL MODELS AS WELL AS HUMAN
STUDIES, IS THAT WE KIND OF --
IN THE DISEASE STATES, THE LACK
OF DOPAMINE NEURONS IN THE BRAIN
AND PARKINSON'S DISEASE, CAN
INVOKE THESE OSCILLATIONS WHICH
NORMALLY EXIST IN THE BRAIN, AND
THEY JUST TRANSIENTLY FOR
COMMUNICATION PURPOSES ARE OUT
OF CONTEXT, SO THEY HIJACK THESE
NORMAL RHYTHMS, AND THAT IN
ITSELF CAN PRODUCE SOME OF THE
SYMPTOMS ASSOCIATED WITH
PARKINSON'S DISEASE.
SO FOR INSTANCE, THESE KIND OF
LOW FREQUENCY OSCILLATION --
THAT'S KIND OF -- ACTIVATION OF
ANTAGONIST AND AGONIST, MOST OF
WHICH -- PATIENT ARE KIND OF
CRAMPING, LIMBS STUCK IN AN
UNUSUAL POSITION, AND THEN, YOU
KNOW, THIS FREQUENCY RANGE AS
WELL AS OTHER FREQUENCY RANGES,
HAS BEEN ASSOCIATE WITH TREMOR.
SLOW -- HAS BEEN ASSOCIATED WITH
HYPERSYNCHRONIZATION IN THE BETA
RANGE AND INVOLUNTARY -- THAT
HAS BEEN ASSOCIATED WITH THIS
NARROW BAND, 60 TO 80 HERTZ OF
DYSKINESIA.
THE MOTOR AREA OR BETWEEN THE
TWO AREAS.
SO THAT'S ONE TYPE OF
MEASUREMENTS I COLLECT FOR MY
RESEARCH.
THE OTHER TYPE I COLLECT IS --
KINEMATIC ME SAWYER USING
ACCELEROMETERS.
SO ONE THING I DO IN THE
LABORATORY SETTING SINCE I'M
STUDYING GAIT, WE USE A VARIETY
OF UNITS, THESE ARE SENSORS THAT
WE REPLACE ALL OVER THE
PATIENT'S BODY, A THREE
DIMENSIONAL REPRESENTATION OF
THEIR SKELETON AND TRACK
MOVEMENTS OVER TIE. IN ADDITION
TO THIS, EACH ONE OF THE SENSORS
PROVIDES EXCEL ROM TRI DATA,
JOINT ANGLE DATA, AND I ALSO USE
SURFACE EMG, SO THESE ARE
ELECTROMYOGRAPHY DATA, SO WE
CAN, AGAIN, CHECK WHERE THE
PATIENT IS LIFTING THEIR FOOT
AND STEPPING DOWN, AND ALSO
THESE FOUR SENSORS, PRESSURE
SENSORS, AS WELL.
INCREASINGLY KIND OF TAKING
RESEARCH OUTSIDE OF THE
LABORATORY, ONE REALLY NEAT
THING THAT WE'RE DOING IS NOW
BEING ABLE TO RECORD POUR OWN ON
ACTIVITY AS WELL AS COLLECT
MOVEMENT DATA IN THE PATIENT'S
HOME.
SO ONE OF THE DEVICES I EMPLOY
FOR HOME RECORDING IS ACTUALLY
THESE DEVICES, IT'S CALLED A
ROLLOVER -- IT'S A SORT OF ANKLE
BRACELET, SO WE PLACE THEM
AROUND PATIENTS' FEET.
THERE IS ACCELEROMETERS THAT
CONTRACT ACCELERATION AND
THIS -- I'M DOING THIS IN
PARTNER WITH A COMPANY -- START
A COMPANY CALLED [INAUDIBLE], IN
WHICH THEY DEVELOP THEIR --
ALGORITHMS, -- RECORDING OF
KINEMATIC MEASUREMENTS, LIKE
ACCELEROMETER DATA FOR EACH ONE
OF THE FEET.
HOW MANY STEPS A PERSON IS
TAKING, AT WHAT TIME, STRIKE
TIME, VELOCITY, SYMMETRY, HOW
MUCH TIME THEY'RE STANDING IN
STANCE BASE VERSUS SPACE BASE
AND THEN FROM THESE COLLECTIVE
MEASURES THEY CALCULATE AN
AVERAGE AND A -- RISK FOR EACH
PATIENT.
THIS HAS BEEN VALIDATED IN
HEALTHY ADULTS, OLDER PATIENTS,
AND NOW STARTING TO KIND OF
VALIDATE THIS FOR PATIENTS WITH
PARKINSON'S DISEASE.
BESIDES JUST THESE TYPE OF
MEASUREMENTS, SOME OF THIGH
COLLEAGUES HAVE -- PATIENTS'
BRAIN RECORDINGS WITH APPLE
WATCHES, WHICH CAN TRACK TREMOR,
POSSIBLY TRACT DYSKINESIA AND
MAYBE EVEN SLOWNESS OF MOVEMENT,
LACK OF MOVEMENT, AND NOW EVEN
WITH THE I PHONE, THERE ARE TONS
OF APPS WHICH CAN TRACK MOVEMENT
BESIDES JUST STEPPING, BUT ALSO
STEP TIME.
SO THERE IS A VARIETY OF ALL
THESE EXTERNAL WEARABLE DEVICES
THAT ARE BEING INCREASINGLY USED
IN PATIENTS' -- THAT TRACK
ACTIVITY.
GOING A STEP FURTHER, HOW CAN WE
EXTRACT INFORMATION FROM THE
EXTERNAL WEARABLE DEVICES, TO
MEASURE -- SO ONE WAY THAT WE DO
THIS IS BY SYNCHRONIZING THE
BUILT-IN ACCELEROMETER DEVICE.
SO AGAIN, THE DEVICES THAT I USE
ARE THE INVESTIGATIONAL -- RC
PLUS, THEY'RE AVAILABLE FOR
RESEARCH USE ONLY, SO THEY'RE
NOT COMMERCIALLY AVAILABLE
DEVICES, BUT THESE DEVICES NOT
ONLY SEND SIMULATION TO THE
BRAIN, SO PERFORMS FUNCTIONS
LIKE A NORMAL SIMULATOR BUT --
POTENTIALS UP TO FOUR
DIFFERENT - -- FROM WHICH WE CAN
THEN SYNCHRONIZE WITH EXTERNAL
WEARABLE DEVICES AND
ACCELEROMETER, TO MAKE SENSE OF
THE TATA.
DATA.SO NOW, HOW IS THIS INFORMN
BEING USED?
FIRST I'LL TALK ABOUT IT FROM A
RESEARCH PERSPECTIVE.
SO ONE THING, AGAIN, I'M TRYING
TO UNDERSTAND HOW THE NEURAL
NETWORKS FROM THE CORTEX AND
BASAL GANGLIA DYNAMICALLY CHANGE
DIFFERENT PHASES OF THE CYCLE.
THIS IS AN ANIMATION OF HOW WE
WALK, BASICALLY STARTING WITH
HEEL STRIKE, WHEN WE SWING
BACK -- AND THEN WE HAVE THESE
DOUBLE SUPPORT TIMES AND THEN DO
IT OVER AGAIN WITH THE OTHER
LIGHT.
SO FROM OUR RESEARCH, WE'VE
ACTUALLY BEGUN TO DECODE
BASICALLY INDIVIDUALIZED
SIGNATURES, AND HOW MEDICATION
AND STIMULATION -- THIS IS WORK
DONE BY -- THESE JUST KIND OF
SPE TSPECTROGRAMS, AND THEN THER
CORTEX, AND LOOKING AT TWO
FREQUENCY BANDS, ONE'S ALPHA,
ONE IS BETA.
SO LOW MEDITATION STATE, SO EACH
LINE HERE REPRESENTS BASICALLY
ONE SINK SINGLE GAIT CYCLE.
YOU CAN SEE FIRST OF ALL
BASICALLY THE LENGTH OF THE GAIT
CYCLE AND -- IT HIGHLY VARIABLE,
SO IN TERMS OF THE TIMING OF THE
HEEL STRIKE TOLL BOX ARE SUPER
VARIABLE, BUT ON MEDICATION,
EARLIER IN THE DISEASE COURSE
WITH THIS PATIENT, WE FIRST OF
ALL SEE FROM THE KINEMATIC DATA
THAT THE -- THE TIMING OF THESE
EVENTS BECOME A LOT MORE -- LESS
VARIABLE.
AND THAT'S ONE OF THE
MEASUREMENTS OF IMPROVING GAIT
FUNCTIONS.
IN ADDITION TO THE TIMING OF
THESE GAIT EVENTS, THE OTHER
THING WE SEE IS THE NEURAL
SIGNAL BECOMES MORE HIGHLY
SYNCHRONIZED, SO THIS -- MAYBE I
IDENTIFY THE SIGNATURE OF MORE
NORMAL WALKING FOR THIS PATIENT.
AND THEN BESIDES DETECTING KIND
OF NORMAL OR MORE NORMAL WALKING
STATES IN PATIENTS, WHAT WE CAN
DO IS DETERMINE PATTERNS OF
REALLY -- SO THIS IS THIS SAME
PATIENT PERFORMING ONE OF HER
TASKS IN THE LABORATORY, JUST
WALKING BACK AND FORTH AL A
STRAIGHT LINE.
YOU CAN SEE DURING THE TURNS,
THERE'S SHOULD FELLING.
RIGHT HERE THERE'S A TIME POINT
WHERE THE PATIENT FREEZES.
AND YOU CAN TELL THAT BY BOTH
ACCELEROMETER DATA AS WELL AS
BRAIN DATA.
SO LOOKING AT THE CORTEX, EVEN
THE BASAL GANGL GANGLIA, -- SO G
NEURAL DATA, AND NOW WITH
ADVANCED MACHINE LEARNING
METHODS AND A.I., WE CAN
PROBABLY COLLECT HUNDREDS OF
HOURS OF THIS DATA AND TRY TO
DETECT WHEN THE PATIENT IS ABOUT
TO FREEZE, WHEN THEY'RE ABOUT TO
FALL.
HOPEFULLY USING THE SIGNAL ON
THE SIGNATURE, WE CAN ACTUALLY
PREVENT FALLS IN THE FUTURE.
SO THIS DATA IS REALLY
INTERESTING FROM A RESEARCH
PERSPECTIVE.
RIGHT NOW WE'RE GETTING A BETTER
UNDERSTANDING OF WHAT HAPPENS TO
THE BRAIN DURING THE MORE NORMAL
GATE.
CAN WE USE THIS TO IMPROVE THE
FUNCTIONS OF PATIENTS IN
PARKINSON'S DISEASE?
SO ONE STRATEGY WE HAVE IS USING
THE DATA THAT WE COLLECT
ACTUALLY IDENTIFIED
PATIENT-SPECIFIC BIOMARKERS OF
WHEN THEY'RE SWINGING THEIR LEFT
LEG VERSUS RIGHT LEG.
AGAIN, BECAUSE GAIT IS A COMPLEX
AND DYNAMIC PROCESS, PERHAPS ONE
OF THE REASONS CURRENT
STIMULATION -- DBS DOESN'T HELP
WITH IT, IT DOESN'T ALLOW FOR
THIS DYNAMIC CHANGE IN BRAIN
SPACE.
SO ONE IDEA WE HAVE IS ACTUALLY
USING THESE BIOMARKERS OF GAIT
EVENTS TO STIMULATE THE LEFT AND
THE RIGHT -- THIS IS WHAT WE
BEGAN TO TEST IN SOME OF THE
PATIENTS.
SO USING THEIR NEURAL DATA, WE
HAVE NO IDENTIFIED BIOMARKER, SO
THESE ARE PRETTY LOW FREQUENCY
AMPLITUDES FROM EITHER THE MOTOR
CORTEX OR DRIVEN BY THE BASAL
GANGLIA.
WE TESTED THIS OUT JUST LAST
MONTH, IN WHICH WE CAN ACTUALLY
ALTER BRAIN STIMULATION
AMPLITUDE BETWEEN KIND OF A
NORMAL HIGHER AMPLITUDE AND
LOWER AMPLITUDE, KIND OF
SYNCHRONIZE THAT WITH THE DEEP
CYCLES.
SO THIS IS KIND OF THE STATE
DETECTOR SHOWN BY THE GREEN LINE
HERE AND THIS IS REALTIME
MONITORING OF THE BRAIN SIGNAL,
WHEN IT REACHES OR CROSSES THAT
THRESHOLD, WE RAPIDLY CHANGE
STIMULATION STATE.
AND WE'RE ABLE TO CAPTURE ABOUT
I THINK 60 TO 70% FIDELITY.
WE'RE TRYING TO OBVIOUSLY
OPTIMIZE THAT, BUT IT JUST
PROVES THAT IT'S POSSIBLE TOO
USE PATIENTS' BRAIN SIGNALS
THEMSELVES TO -- AND THIS IS
JUST ONE STRATEGY.
EXTERNAL WEARABLE DEVASES, FOR
INSTANCE, WANT TO INTEGRATE WITH
THE SYSTEM TO CHANGE
STIMULATION.
AND THEN THE OTHER THING THAT WE
NEED ARE TO IMPLEMENT THIS FULLY
IS TO IDENTIFY BIOMARKERS TO
DIFFERENTIATE WHAT PATIENTS ARE
DOING AT HOME.
SO WHETHER THEY'RE WALKING
VERSUS SITTING BECAUSE AS YOU
CAN IMAGINE, SOME SETTINGS MIGHT
BE BETTER WITH HAND AND ARM
MOVEMENT, SOME ARE BETTER FOR
WATCHING.
SO AGAIN, THIS IS JUST USING
DETECTED OPEN CONTROL ACTIVITIES
OF LEFT BRAIN VERSUS RIGHT
BRAIN, AND USING A SIMPLE,
LINEAR ANALYSIS, WE CAN DETECT
WITH 90% ACCURACY WHETHER A
PATIENT IS PAWING OR SITTING.
SO IN TERMS OF INFERENCES WE CAN
MAKE ABOUT WHAT THE PATIENT IS
DOING AT HOME, SO AS I MENTIONED
BEFORE, WE'RE -- WE HAVE PATIENT
STREAM THEIR NEURAL DATA TO AN
EXTERNAL DEVICE THAT'S STORED IN
THE CLOUD, AND THEN ALSO CAPTURE
A LOT OF THE GATE KINEMATICS
FROM THESE ROVER HEALTH ANKLE
BRACELETS.
FROM THIS, EVEN LOOKING AT THE
RAW DATA ACCELEROMETER, SO THIS
IS ONE OF THE RAW DATA FROM ONE
OF THE ANKLE -- BETWEEN THE
SHEER AMPLITUDE OF THE
ACCELERATION AS WELL AS THE
HEADING INFORMATION, WHICH IS
PROVIDED BY THE DEVICE, WILL
GET -- THE PATIENT IS PROBABLY
KIND OF WALKING IN A STRAIGHT
LINE, AND THEN DURING THESE
MOMENTS WHERE THERE'S LOWER
ACCELERATION AMPLITUDE, THE
PATIENT IS PROBABLY TURNING.
SO THIS IS JUST A REALLY EARLY
TOEARLYTIP OF THE ICEBERG DIVE T
PATIENT MOVEMENT, STATES AT
HOME, AND NOW WITH MULTIMODAL
SENSOR, WE CAN PROBABLY JUST --
MAKING INFERENCES MORE ABOUT
MAYBE EVEN THE STABILITY OF
WALKING, THE POSTURE OF WALKING,
AND THIS IS JUST BASED OFF OF
KINEMATIC DATA ALONE.
SO IN THE FUTURE, RIGHT NOW
WE'RE JUST DETECTING AND TRYING
TO DO CODE PHASES OF THE DATA
CYCLE, BUT WE'RE TRYING TO DRAW
INFERENCES BASED ON THE NEURAL
ACTIVITY HOW NEURAL ACTIVITY CAN
TELL US ABOUT -- THAT INCREASE
STABILITY FROM JUST THE NEURAL
SIGNALS ALONE.
AND THEN ALSO, THE -- BECOMING
MORE OF ITSELF, THE I R THE INTO
WALK, TO STOP, TO TURN, TO
ADAPT.
AND THIS WOULD I A LOW THESE
KIND OF SMART ADAPTIVE
PARAMETERS THAT CAN PREVENT A
PROBLEM BEFORE IT ARISES IN THIS
PATIENT POPULATION.
AND RIGHT NOW WE'RE JUST LOOKING
AT LOWER BODY MOTOR CONTROL, BUT
WITH THE ELECTRODES PLACED IN
DIFFERENT LOCATIONS THROUGHOUT
THE BRAIN, WE MIGHT ACTUALLY BE
ABLE TO DRAW MORE INS FRENSES,
COORDINATION OF ARM AND LEGS,
CAN OCCUR, USING THE SETUP.
SO IN TERMS OF RISK OF DATA
SHARING, SO FIRST OF ALL TAKING
A STEP BACK, A LOT OF THIS HAS
BEEN MENTIONED BEFORE, FIRST OF
ALL WHY IS IT NECESSARY TO SHARE
NEUROSCIENCE DATA?
YOU KNOW, A LOT OF TIMES NOW, ES
WESHLY WITH THIS KIND OF
INTRACRANIAL RECORDINGS, THE
PATIENT POPULATION WE'RE
STUDYING, THE NUMBER OF PATIENTS
WHO CAN PARTICIPATE IN THESE
STUDIES IS LOW AND ACCESS TO
DEVICES IS ALSO LOW.
SO IN MY STUDY, WE HAVE
RECRUITED FIVE PATIENTS, NOW SIX
FOR THIS, AND TO BE ABLE TO DRAW
THIS TO A WIDER SCALE, YOU NEED
MULTICENTER STUDIES AND
RESEARCHERS TO COLLABORATE WITH
ONE ANOTHER.
AND A WE KNOW, ONE OF THE MAJOR
FACTORS THAT HAS HINDERED
SCIENTIFIC PROCESS IS DATA
REPRODUCIBILITY.
SO AS THESE -- TO ESTABLISH, NOW
WE REQUIRE THIS DATA SHARING TO
BE ABLE TO DRAW INFERENCES FROM
ACROSS DIFFERENT SENATORS, AND
WE CAN SHARE DATA AND SO WILL
LEAD TO GREATER SCIENTIFIC
PROGRESS AND ALSO TO MAKE SURE
THAT WE'RE DOING THE SCIENCE
CORRECTLY, RIGOROUSLY AND
REPRODUCIBLY.
AND ALSO, YOU KNOW -- HAS
ENABLED US TO COLLECT A LARGE
AMOUNT OF DATA FROM
INDIVIDUAL -- SO OUT OF THE FIVE
STUDY SUBJECTS IN VARIOUS
STAGES, I THINK -- COLLECTED
OVER 300 HOURS OF DATA FROM
THESE PATIENTS.
SO THIS IS A HUGE AMOUNT O, WE N
ACCELERATE THE SPEED AND THE
SCALE OF DATA PROCESSING.
AND NOW WITH THESE NOVEL
SOFTWARE AND ALGORITHMS, WE
BASICALLY HAVE ENTERED -- SO
IT'S NECESSARY TO SHARE THE DATA
BUT IN A, I GUESS, RESPONSIBLE
FASHION.
AND I THINK THE OTHER THING IS
THAT A LOT OF THESE COMPANIES
NOW ARE BECOMING AVAILABLE
OUTSIDE OF THE RESEARCH, SO FOR
INSTANCE, THE KCH COMPANY I PARR
WITH, ROVER HEALTH, THEY'RE
FDA-APPROVED DEVICE AND ANYBODY
CAN GET ACCESS TO THIS SO
PATIENTS CAN BUY THESE DEVICES
AND IT WILL TELL THEM ABOUT
THEIR FALL RISK, IT CAN TELL
THEM SOMETHING ABOUT THEIR GAIT.
SAME THING WITH THE NUMBER OF
APPS COMING UP ON THE IPHONE,
WITH APPLE WATCH.
SO THERE'S A HUGE, I GUESS,
DIRECTION AND MOVEMENT TOWARDS
DIRECT-TO-CONSUMER APPLICATIONS
FOR NEURAL DATA, KINEMATIC DATA
AND THERE'S A LOT OF CONCERNS
BUT ALSO A LOT OF USES FOR THIS
APPLICATION.
SO I THINK IN TERMS OF OVERALL,
THE ABILITY TO SHARE
NEUROSCIENCE DATA CAN PROMOTE
PROGRESS IN A CASE THAT WAS NOT
POSSIBLE BEFORE, AND FROM THIS
WE CAN ALSO UNDERSTAND
MECHANISMS OF DISEASE FUNCTION.
AND IN TERMS OF POTENTIAL RISKS
OF SHARING THIS DATA, SO OF
COURSE DISCLOSURE OF PERSONAL
SENSITIVE INFORMATION S
DIAGNOSIS, YOU KNOW, ABOUT THEIR
NEUROLOGICAL DISORDER MAY BE
POSSIBLE.
AND THEN REVERSE INFERENCE OF
NEURAL RECORDINGS COULD UNVEIL
COGNITIVE AND AFFECTIVE STATES.
RIGHT NOW I'M JUST CAPTURING
MOVEMENT AND MOTOR STATES, BUT
NOT MYSELF BUT MY COLLEAGUES
HAVE BEEN STUDYING HOW THESE
OSCILLATIONS CAN BE
REVERSE-ENGINEERED OR INFERRED
TO HELP PATIENTS' DEPRESSION,
MOOD AND OTHER STATES.
AND ONE OF THE PROJECTS I
HAVEN'T MENTIONED IS I'M
ACTUALLY USING THE SAME SETUP
AND STUDYING MOTOR LEARNING.
SO IT CAN REFLECT A LOT ABOUT A
PATIENT'S COGNITION, EVEN FROM
JUST LIMITED SAMPLES THAT I'M
COLLECTING FROM THE MOTOR CORTEX
AND PREMOTOR AREAS.
AND OF COURSE THIS RAISES
CONCERN THAT WHO HAS ACCESS TO
THIS INFORMATION.
FOR INSTANCE, IF IT'S KINEMATIC
DATA AND THE BRAIN DATA GETS IN
THE HANDS OF INSURANCE
COMPANIES, CAN THEY DENIED
INSURANCE APPROVAL FOR PATIENTS
OR COVERAGE FOR PATIENTS WITH
PARKINSON'S DISEASE IF SOMEBODY
IS AT HIGH RISK FOR FALLS?
AND THEN THE OTHER THING IS
DESAI FERRING ONE'S COGNITIVE
AND AFFECTIVE STATES, AND THE
ABILITY TO ALTER THESE WITH
NEUROMODULATION TECHNOLOGY DOES
RAISE ETHICAL CONCERNS ABOUT ARE
WE DOING SOMETHING
UNINTENTIONALLY FOR THE PATIENT?
SO IN TERMS OF OVERALL RISKS OF
DATA IN ITS CURRENT FORM AND I
THINK IN THE FUTURE IS THAT I
THINK OVERALL, THIS DATA WITHOUT
CONTEXT IS ACTUALLY PRETTY
MEANINGLESS.
SO I'M JUST SHARING THE NEURAL
DATA, ESPECIALLY THESE
OSCILLATIONS, WITHOUT
UNDERSTANDING WHERE IT'S COMING
FROM OR WHAT THE PATIENT OR
SUBJECT IS DOING.
THERE'S PROBABLY ONLY VERY
LIMITED INFORMATION SOMEONE CAN
EXTRACT FROM IT AND MAY EVEN BE
IMPOSSIBLE TO TELL WHETHER A
PATIENT HAS PARKINSON'S DISEASE
BASED ON THESE NEURAL
MODULATIONS.
BECAUSE AGAIN, IT ALL DEPENDS ON
THE CONTEXT.
IS IT BEING RECORDED IN AN
OFF -- BUT I THINK IF WE
COMBINED THE NEURAL DATA WITH
THE CONTEXT IN WHICH IT'S
COLLECTED, THEN IT BECOMES MORE
RISKY.
ALREADY, WE CAN DETECT, DECODE
WHETHER A PATIENT IS SITTING OR
STANDING AND KIND OF THE GENERAL
MOVEMENT STATE BASED ON THIS
NEURAL DATA, AND THIS IS VERY
PRELIMINARY DATA.
IF WE HAD USING A.I., MACHINE
LEARNING METHODS, DEEP LEARNING,
WE CAN PROBABLY DECODE MORE AND
INFER MORE THAN JUST MOVEMENT
BASED ON THIS DATA, THE
INTENTION TO MOVE OR THOUGHT
PROCESSES.
SO THAT BECOMES A LITTLE BIT
MORE RISKY.
AND THEN I THINK CURRENTLY,
ACTUALLY THE HIGHEST RISK, IT'S
KINEMATIC DATA THAT'S GENERATED.
WE'LL KIND OF USE COMPANY'S OWN
PROPRIETARY ALGORITHM TO
GENERATE A FALL RISK SCORE, I
THINK SOME OF THE OTHER STUDIES
IN TERMS OF MOVING PATIENTS'
NEURAL RESEARCH IN THE HOME
SETTING, AMBULATORY SETTING,
EMPLOYS, FOR INSTANCE, CAMERAS,
FACIAL RECOGNITION, SEGMENT OUT
WHOLE BODY MOVEMENTS OR USING
SHOES THAT THEY WEAR CAN TELL
MORE ABOUT WHAT THE PATIENT IS
DOING.
SO THESE THINGS, IF THE D THE DA
GETS IN THE WRONG HANDS OR
AGAIN, DATA BECOMES NOT PRIVATE,
CAN POTENTIALLY CAUSE A LOT OF
HARM, I THINK, TO THE INDIVIDUAL
AND ALSO TO THE PATIENT
POPULATION AS A WHOLE.
AND THEN WHETHER IT'S POSSIBLE
TO KIND OF HACK INTO THE DATA,
TO ACTUALLY ALTER BRAIN STATES
AND BEHAVIOR, I THINK IS ANOTHER
POTENTIAL RISK OF THIS.
RIGHT NOW, WE ARE USING
COMPLETELY EMBEDDED SYSTEMS, SO
AGAIN, EVERYTHING IS ENCLOSED,
SO ONCE WE PROGRAMMED IN THESE
CONTROL ALGORITHMS INTO THE
DEVICE, YOU KNOW, I'LL ASK
YOU -- UNLESS YOU EXTERNALLY
CHANGE IT, IT'S NOT -- BASICALLY
IT'S DIFFICULT TO CHANGE IT, YOU
KNOW, WITHOUT SOMEBODY LIKE
BEING THERE.
BUT I THINK THERE'S A LOT OF
STUDIES OUT THERE IN WHICH WE'RE
USING EXTERNAL WEARABLE DEVICES,
STREAMING THAT TO AN EXTERNAL
COMPUTER OR DEVICE THAT PROCESS
THE DATA AND THEN SEND
INFORMATION BACK TO THE
SIMULATION DEVICE.
SO THERE COULD BE A LOT OF --
AND POTENTIALLY HACKING IN TO
THE SYSTEM, CHANGING THE
ALGORITHM AND UNINTENTIONALLY
ALTERING STATES AND BEHAVIOR IN
THAT WAY, SO THAT CAN ALSO BE A
RISK TO THE PATIENTS.
SO I THINK OVERALL, WE'RE STILL
IN THE EARLY STAGES OF
INTERPRETING THESE DATA.
THESE ARE SMALL CLINICAL
STUDIES.
SO I THINK THE RISK OF
DE-IDENTIFYING PATIENTS INSTEAD
OF INFORMATION IS REALLY LOW
BASED ON DATA WE'RE COLLECTING
BUT AS SOMEBODY MENTIONED, WHO
KNOWS 10 YEARS FROM NOW WHAT
INFORMATION CAN BE
EXTRACTED/ANALYZED IN THE
FUTURE?
WE JUST DON'T HAVE A GREAT IDEA
OF THAT RIGHT NOW.
SO OF COURSE THERE'S A NEED TO
PROVIDE -- PROTECT PATIENTS'
PRIVACY AND HEALTH-RELATED
INFORMATION, AND ALSO THEY
PROBABLY SHOULD BE THINKING
AHEAD OF HOW THIS INFORMATION
CAN BE USED IN THE FUTURE AND
PROTECTED IN THE FUTURE.
SO WITH THAT, I'D LIKE TO THANK
MY LAB, MY WONDERFUL
COLLABORATORS AND MY FUNDING
SOURCES.
>>THANK YOU SO MUCH.
[APPLAUSE]
I'M GOING TO ASK ONE QUESTION
THAT WAS IN OUR VIRTUAL CHAT
FROM DR. SYD JOHNSON, A MEMBER
ATTENDING VIRTUALLY.
ARE THERE PARTICULAR RISKS WITH
UPLOADING DATA TO THE CLOUD AND
DOES THIS HAPPEN IN REALTIME?
>>THAT'S A GOOD QUESTION.
SO YES, SO USING THE NEURAL DATA
FROM RC PLUS S, IT DIRECTLY
UPLOADS TO THE CLOUD, BASE CL BY
THIS INFORMATION IS BEING
COLLECTED AND SENT.
THE COLLECTIVE DATA, IT DEPENDS
ON WHICH FORM.
SO IT'S STORED ON THE
ACCELEROMETER ITSELF, SO THE
PATIENT HAS TO KIND OF
PHYSICALLY PLUG NO A COMPUTER,
AND YES, THERE ARE DEFINITE
RISKS.
YOU CAN IMAGINE IF SOMEBODY WERE
TO HACK INTO THIS SYSTEM IN A
PATIENT'S HOME, THEY COULD
INTERCEPT IT.
CURRENTLY WE HAVEN'T HAD ANY
ISSUES.
>>THANKS, DR. WANG.
APPRECIATE IT VERY MUCH.
[APPLAUSE]
THERE'S A QUESTION IN THE CHAT
THAT IF YOU HAVE A CHANCE TO
RESPOND TO.
LET'S GET RIGHT INTO THE
DISCUSSION.
WE'RE A LITTLE BEHIND WI BUT THS
OKAY.
I WANT TO START OFF WITH
SOMETHING REALLY SPECIAL.
WE HAVE TWO INDIVIDUALS HERE WHO
ARE GOING TO PROVIDE A PATIENT
PARTICIPANT PERSPECTIVE.
-- COPELAND IS JOINING US
VIRTUALLY.
WANTED TO START OFF THE
DISCUSSION ASKING IF EITHER OF
THEM HAVE ANY THOUGHTS OR
REFLECTIONS ON WHAT WE'VE TALKED
ABOUT SO FAR.
CHRISTINE, NATHAN IS SUGGESTING
YOU GO FIRST, IF YOU'D LIKE.
>>SO I'M LISTENING IN AND I
THINK FIRST OFF, HE THINK WE ARE
MOVING INTO A NEW DIGITAL AND
DATA-DRIVEN AGE.
AND I THINK ONE OF THE THINGS
THAT REALLY NEEDS TO BE
CONSIDERED IS EDUCATING OUR
PATIENTS AND OUR GROUPS AROUND
DATA.
AROUND THE SIGNIFICANCE OF DATA.
WE'RE ASKING THEM AT CERTAIN
POINTS TO COLLECT DATA.
SORE HOW ARE THEY COLLECTING
DATA AND HOW ARE WE WORKING WITH
THEM IN THOSE WAYS.
I THINK ONE OF THE THINGS THAT I
WAS THINKING ABOUT IS WE'RE
TALKING ABOUT RISKS IS MENTAL
HEALTH IS PROTECTED BY
SUBPOENAS.
BUT WILL SOME OF THIS
INFORMATION NOT BE.
SO ONE OF THE INSTANCES THAT A
FRIEND OF MINE RAISED WHILE WE
WERE TALKING ABOUT THIS WAS A
WOMAN WHO HAS POSTPARTUM
DEPRESSION OR WHO HAS DEPRESSION
OR MAYBE A WOMAN WHO HAD AN
ABORTION AS WELL.
AND SO THAT COMBINATION OF
RECORDS IN THE STATE THAT OUR
COUNTRY IS COULD BE EXTREMELY
RISKY.
SO THOSE THINGS AND THEN
THINKING ABOUT, WHICH WE'LL TALK
MORE ABOUT TOMORROW, OUR RISKS,
BUT THEN HOW ARE THESE ALSO THE
INFORMATION AND THE DATA
BENEFITING THE PATIENTS
THEMSELVES IN THE COMMUNITIES
AND WHAT'S GOING BACK TO THEM IF
THERE IS INFORMATION FOUND IN
THESE THINGS.
SO I'LL LET NATHAN TAKE IT FROM
THERE, BUT I KNOW TOMORROW WE'LL
HAVE A GREAT DISCUSSION.
>>NATHAN, ANYTHING YOU'D LIKE
TO ADD?
MAYBE LATER.
>>OKAY.
SO I WOULD INVITE OUR PANELISTS
TO COME ON CAMERA, AND WE ARE --
OH, DID HE COME ON?
OKAY.
I'M GOING TO INVITE OUR
PANELISTS TO CONSIDER THE
FOLLOWING QUESTION, WHICH SASKIA
PREVIEWED EARLY ON THIS MORNING.
SO SHOULD BRAIN DATA BE THOUGHT
OF ON A SPECTRUM OR IN
CATEGORIES BASED OFF OF THE
AMOUNT OF RISK FROM SHARING
THEM?
AND IF YES, WHAT ARE THE
DETERMINANTS OF WHERE A
PARTICULAR KIND OF DATA MIGHT
FALL ON THE RISK SPECTRUM, FOR
EXAMPLE, IF IT'S
REIDENTIFIABILITY OR INFERENCES
THAT CAN BE DRAWN OR NATURE OF
THE DATA, JUST TO PROVIDE A FEW
EXAMPLES.
>>I WOULD SAY YES, I WOULD SAY
THAT THE BIGGEST RISK IS ABOUT
IDENTIFIABILITY OF THE DATA.
SO SOMETHING LIKE FMRI AND DTI
ARE NOT -- YOU'RE GOING TO HAVE
A HARD TIME, I THINK,
IDENTIFYING IT UNLESS YOU HAVE
SOME OTHER BHI, SO THE SCANS I
WAS SHOWING ARE -- IF YOU CAN
GET A FACE OUT OF THEM, YOU'RE
HALFWAY THERE TO REALLY BEING
ABLE TO IDENTIFY IT.
SO I THINK THAT THOSE -- THAT'S
PROBABLY THE HIGHEST RISK, AND
MAYBE WITH SOME OF THE OTHER
MODALITIES, YOU'RE MORE LIKELY
TO BE ABLE TO DIAGNOSE SOMETHING
THAT COULD BE COMPROMISING IN
SOME WAY.
>>MAYBE I'LL JUMP IN AND
HIGHLIGHT A COMMENT IN THE CHAT.
IT WAS FROM ELAINE COLLIER WHO'S
TALKING ABOUT ARE WE TALKING
ABOUT OPEN DATA SHARING OR
CONTROLLED ACCESS DATA SHARING?
AND SO THIS KIND OF GOES INTO
THE IDEA OF POTENTIAL FOR HARM,
SO IT'S NOT ONLY HOW SENSITIVE
IS THE DATA AND WHAT COULD YOU
ACTUALLY GET FROM IT, BUT WHO
WILL HAVE ACCESS TO THAT DATA
AND HOW ARE THEY INTENDING TO
USE IT, WHAT REGULATIONS ARE
THEY SUBJECT TO.
I THINK SHARING DATA IN A
RESEARCH CONTEXT IN A WAY THAT
IT'S USED RESPONSIBLY HAS VERY
DIFFERENT RISK PROFILE THAN
SHARING DATA PUBLICLY.
POTENTIALLY.
NOT THAT ANY -- NOT THAT WE
SHOULD DISMISS THE RISK THAT
SOMEBODY IS ABLE TO GET THEIR
HANDS ON DATA ONCE YOU START
SHARING IT, BUT I DO THINK THAT
THAT'S AN IMPORTANT
CONSIDERATION SO THE DEGREE OF
HARM, I WOULD WEIGH ALONG WITH
THE IDENTIFIABILITY.
>>IN TERMS OF JUST ANSWERING
THAT QUESTION OR CLARIFYING THE
QUESTION, I THINK I'D SAY BOTH,
I MEAN, BOTH ARE VERY MUCH UPON
US NOW OBVIOUSLY, SO YOU KNOW,
MAYBE IT'S DIFFERENT BECAUSE THE
REQUIREMENT FOR PUBLIC SHARING
ARE DIFFERENT THAN THAT OF NOT
PUBLIC SHARING.
BUT WE'RE ALL PARTICIPATING IN
BOTH TO AN INCREASING DEGREE.
MAYBE ONE THING TO THROW IN THAT
TIES SOME OF THESE -- WELL ALL
OF THESE TALKS TOGETHER IS THE
INCREASING MULTI-MODALITY NATURE
OF THE DATA.
SO SUSIE, LORNA, DOUG, YOU GUYS
TALKED ABOUT IMAGING IN EEG,
THESE ARE BECOMING MORE AND MORE
TIED TOGETHER, YOU HAVE TO
INTENTIONALLY SEPARATE THEM, IF
YOU'RE TRYING TO DE-IDENTIFY ONE
EU PULLING IN A BUNCH OF DATA OR
METADATA FROM MANY OTHERS.
JENNIFER, YOU TALKED ABOUT
BIDIRECTIONAL NEURAL INTERFACES.
THE MORE THESE ARE TIED WITH
BEHAVIORAL MONITORING, WHETHER
IT'S MOVEMENTS, THOUGHTS, YOU
KNOW, OTHER ACTIONS, EMOTIONS,
ET CETERA, IT'S BECOMING
INCREASINGLY IDENTIFIABLE.
SO WITH THE GREATER BENEFIT OF
THE MULTIMODAL NATURE COMES THE
GREATER RISK.
SO MAYBE -- YEAH, ADDITIONAL
THOUGHTS ABOUT HOW THESE CAN BE
STRATIFIED, BECAUSE I THINK WE
CAN MAYBE ALL AGREE THAT IT'S
NOT ALL THE SAME IN TERMS
OF DEGREE OF IDENTIFIABILITY,
IDENTIFIABILITY, DEGREE OF
INFERENCE, DRAWABILITY.
THIS IS ONLY GOING TO GET, I
THINK, INCREASINGLY CHALLENGING
OVER TIME AS WE, AGAIN, GO
MULTIMODAL AND HAVE ARTIFICIAL
INTELLIGENCE/DEEP LEARNING THAT
CAN PULL OUT EVEN MORE AND MORE
INFORMATION.
THOUGHTS.
>>I THINK OVERALL, AND JEN AND
I HAVE MENTIONED, IF YOU
SEGREGATE THE BEHAVIOR FROM I
GUESS LIKE NEUROPHYSIOLOGY DATA,
IT'S HARD TO DRAW INFERENCES,
BUT I THINK THERE ARE -- THE
RISK IS, ARE THOSE COMPANIES AND
THIRD PARTY, YOU KNOW, THIRD
PARTY PARTNERS WITH INDUSTRY AND
ACADEMIA, THAT ACTUALLY
SYNCHRONIZE ALL THE DATA
TOGETHER AND PUT IT IN A CALL
BASE SERVICE, THAT'S REALLY
USEFUL FOR RESEARCHERS RIGHT
NOW.
BUT ALSO YOU CAN THINK ABOUT THE
DANGER AND THAT TYPE OF SOFTWARE
AND DEVICE, THESE CLOUD-BASED
THINGS, THAT YOU CAN'T INTEGRATE
BOTH.
I THINK IT NEED TO BE REALLY
HIGHLY REGULATED, AND I THINK
THAT'S VERY, VERY RISKY.
>>AT THE SAME TIME, I THINK THE
BENEFIT OF THE DATA IS VERY
LIMITED IF YOU DON'T COMBINE
THESE DIFFERENT MODALITIES, SO
MAYBE WHILE THE DATA PROBABLY
DOES LION A CONTINUUM IN TERMS
OF SENSITIVITY, I FEEL LIKE YOU
COULD PROBABLY GET DOWN TO TWO
OR THREE CATEGORIES WHERE IT'S
JUST NOT SENSITIVE, DOESN'T NEED
MUCH IF ANY REGULATION, OR IT'S
A BENEFICIAL DATASET THAT HAS
BEHAVIOR, HAS NEURAL DATA AND AT
THAT POINT, WE NEED TO APPLY
REGULATIONS THAT STILL ALLOW FOR
INNOVATION AND DATA SHARING AND
THAT PROBABLY ENCOMPASSES THE
MAJORITY OF NEURAL DATA, AND
THEN PERHAPS THERE IS A MORE
EXTREMELY SENSITIVE -- HAS A
HIGH LIKELIHOOD OF
IDENTIFIABILITY THAT WOULD HAVE
ADDITIONAL REGULATION.
>>ANOTHER FEATURE THAT WAS
REALLY -- I FOUND INTERESTING,
LORNA, YOU TALKED ABOUT SORT OF,
I DON'T KNOW, PORTABILITY,
RIGHT?
SO YOU'RE NOT GOING TO BE
CARRYING AROUND MRI SCANNERS
NECESSARILY WITH YOU, OR USING
THEM MAYBE FOR AUTHENTICATION
ANY TIME SOON, BUT EEG
ABSOLUTELY, RIGHT?
YOU BROUGHT UP EXAMPLES ABOUT
THAT BEING DONE ALREADY.
SO THE MORE THAT THIS IS IN THE
PUBLIC DOMAIN AND JUST OUR
ROUTINE USE OF OPENING UP OUR
ELECTRONIC DEVICES, NOT JUST
WITH OUR EYES AND FACE BUT WITH
OUR BRAIN ACTIVITY PATTERNS, IT
SEEMS LIKE THE EASIER IT IS TO
USE IT FOR WHATEVER PURPOSES,
THEY'RE NOT INTENDED.
SO I THINK THAT'S JUST ANOTHER
SORT OF AXIS OF DISCUSSION OR
CONSIDERATION PERHAPS.
WHAT DO YOU THINK?
>>I CAN'T COMMENT -- DOES THIS
WORK?
OKAY.
MANAGING MY BEST IN THE HYBRID
WORLD.
SO ONE THING THAT MADE ME THINK
OF, SAMEER, EEG RIGHT NOW, WE
DON'T CASUALLY ENCOUNTER EEG IN
THE WILD, AND WE CERTAINLY DON'T
CASUALLY RECORD EEG TO ACCESS
INFORMATION.
BUT THERE IS ANOTHER EMERGING
TECHNOLOGY THAT MIGHT BE SORT OF
ON THE BORDERLINE OF WHETHER WE
CONSIDER IT NEURAL DATA, AND
THIS REALLY IS STARTING TO TOUCH
PEOPLE'S LIVES.
SO VIRTUAL REALITY HEADSETS ARE
ALREADY CAPABLE OF RECORDING EYE
TRACKING BEHAVIOR, AND MOTION
BEHAVIOR, AND WHEN WE USE THOSE
DEVICES, WE'RE NOT NECESSARILY
THINKING ABOUT THE FACT THAT
WE'RE GIVING OUR PERSONAL
INFORMATION OR PERSONAL DATA, SO
I KNOW SOMEONE MENTIONED MAYBE
SASKIA IN THE INTRODUCTION, WHAT
ABOUT THESE TYPES OF DATA THAT
ARE NOT DIRECTLY NEURAL BUT ARE
INFORMATIVE IN A SIMILAR WAY.
SOMETIMES WE CONSIDER EYE
TRACKING TO BE DIRECTLY
REFLECTIVE OF NEURAL STATES.
SO EVERY TIME SOMEONE PUTS ON A
CURRENT V.R. DEVICE, THEY'RE
GIVING SOME DATA, AT MINIMUM,
THEIR MOVEMENT DATA TO THE
COMPANY.
NOW MORE AND MORE DEVICES ARE
INCORPORATING EYE TRACKING.
YOU DON'T THINK ABOUT IT AS
GIVING YOUR DATA TO THE COMPANY
UNLESS YOU'RE A VERY
PRIVACY-AWARE CONSUMER.
IT IS NOT CRAZY TO THINK ABOUT
THE NEXT GENERATION OF VR
HEADSETS BEYOND APPLE'S NEW ONE
COMING OUT.
INCORPORATING EEG ELECTRODES
STRAIGHT INTO THE DEVICE.
THIS IS ON THE RADAR, I THINK,
FOR SOMETHING COMING UP, SO IN
10 YEARS, WE PUT ON OUR V.R.
DEVICES, WE'RE GIVING EYE
TRACKING, PROBABLY RECORDING
SOUND, MOVEMENT, LOTS OF DEGREES
OF FREEDOM, AND MAYBE THERE ARE
THREE ELECTRODES ON THE
FOREHEAD.
SO AS IT BECOMES EASIER TO BUILD
THESE TYPES OF DATA COLLECTION
INTO DEVICES THAT PEOPLE
ACTUALLY WANT TO USE, I THINK
YOU'RE RIGHT, I KIND OF FORGET
WHAT THE ACTUAL QUESTION WAS,
BUT IT'S -- FOR SOMETHING LIKE
FMRI, IT'S HARD TO ENVISION THE
FUTURE OF SORT OF ACCIDENTALLY
RECORDING FMRI THAT REVEALS TOO
MUCH ABOUT YOU, BUT IN OTHER
WAYS, IT REALLY IS COMING DOWN
THE PIKE SOON, AND WILL BE
HIGHLY INFORMATIVE, ESPECIALLY
FOR MARKETING PURPOSES, IT
BECOMES SORT OF A BUSINESS
ETHICS QUESTION, BUT ALREADY THE
COMPANY IS MAKING DEVICES LIKE
THAT ARE USING YOUR EYE AND BODY
MOVEMENTS TO IDENTIFY YOU AND
TARGET YOUR ALZHEIMER'S DISEASES
TO WHAT THEY THINK YOU WANT TO
SEE.
SO IF THEY CAN DO THAT NOW, WHAT
ABOUT ADDING THE EEGs INTO THE
HEADSET?
I DON'T KNOW.
>>I JUST WANT TO ASK A QUESTION
AS WE TALK ABOUT ALL OF THIS,
AND I THINK GIVING DATA FOR
RESEARCH IS ONE THING, BUT I
THINK A LOT OF PATIENTS ARE OUT
THERE AND DON'T KNOW WHO THEIR
DATA IS GOING TO, WHETHER IT'S
RESEARCH OR A COMPANY.
AND SO I WORK WITH AN
ORGANIZATION LIKE COLLECTIVE
WHERE WE'RE WORKING ON DIGITAL,
SO AT A POLICY LEVEL, WHERE IS
THE CONVERSATION AROUND AN
INDIVIDUAL'S DIGITAL RIGHTS WHEN
IT COMES TO THIS DATA?
I DON'T KNOW IF ANYONE HAS ANY
ANSWERS TO THAT.
>>I DID WANT TO CHIME IN AND
SAY I THINK DANIEL MOSLEY
COMMENT THE IN THE CHAT ABOUT
MAKING THESE FORMS OF DATA
AVAILABLE IN THE EMR RECORD.
I BELIEVE AS PART OF THE 21ST
CENTURY CURES ACT, ACTUALLY
PATIENTS DO HAVE ACCESS TO THEIR
OWN DATA, SO THEY ACTUALLY COULD
MAKE THEIR OWN DATA AVAILABLE IN
LIMITED FORMS.
THAT'S SOMETHING INTERESTING TO
THROW INTO THE MIX.
I GUESS TO ADD TO THE
SPECTRUM/CATEGORIZATION
QUESTION, I THINK THE TYPE OF
RESEARCH THAT IS BEING DONE ON
THE DATA IS ANOTHER FACTOR TO
CONSIDER, AND THIS WAS ACTUALLY
A SIDE CONVERSATION I WAS HAVING
WITH FRANCES SHEN BY EMAIL, ONE
OF MY COP.I.s ON THIS ETHICS
GRANT, BUT HE MENTIONED THERE'S
CERTAIN QUESTIONS THAT YOU ASKED
THAT ARE FAIRLY INNOCUOUS, LIKE
DOES SOMEBODY LIKE SUGA SUGARY L
VERSUS SOMETHING POTENTIALLY
MORE FOCUSED AND SENSITIVE
INFORMATION REGARDING THEIR
PERSONAL PREFERENCE, REGARDING
THEIR EMOTIONAL STATES.
SO I THINK THE NATURE OF THE
RESEARCH QUESTION, ESPECIALLY
FOR FMRI, COULD BE DEFINITELY
ONE FACTOR OR ONE CATEGORY TO
CONSIDER, ACKNOWLEDGING ALSO THE
DATA COULD HAVE OTHER EMBEDDED
INFORMATION THAT WAS NOT THE
EXPLICIT INTENTION OF THE
RESEARCHER TO EXTRACT BUT CAN
EITHER NOW OR IN THE FUTURE BE
EXTRACTIBLE.
AND THE OTHER THING WOULD BE THE
TEMPORAL ASPECT OF THE DATA, SO
WHAT ARE THE CURRENT LIMITATIONS
OF DATAVERSE US WHAT'S G WHAT'SO
BE COMING DOWN THE PIKE IN TERMS
OF DATA CAPABILITIES, SPATIAL,
TEMPORAL RESOLUTION OF THE DATA,
AND THAT'S SOMETHING TO FACTOR
IN ALSO, AND I THINK FOR THAT,
IN TERMS OF FUTURE-PROOFING IS
GOING TO ACTUALLY BE PRETTY
DIFFICULT TO PREDICT UNTIL WE
SEE THE FULL CAPABILITIES OF THE
DATA WE'RE ABLE TO GENERATE WITH
THESE NEW TECHNOLOGIES.
>>I JUST HAD A COMMENT TOO ON
THE DATA.
I THINK WE HAVE ACCESS TO OUR
EMRs.
WE DON'T HAVE ACCESS TO RESEARCH
OR THINGS THAT WE'VE
PARTICIPATED IN.
AND SO IF THAT INFORMATION COULD
BE RETURNED TO MY OWN CLINICAL
TEAMS, I THINK WE'D HAVE MORE
ANSWERS THAN WE WOULD WITH THE
TESTING WE'RE CURRENTLY DOING.
>>THANKS SO MUCH.
I WANT TO JUST POSE A QUESTION
FOR PEOPLE TO THINK ABOUT,
BECAUSE WE'RE A COUPLE MINUTES
OVER SO I WANT TO MAKE SURE THAT
PEOPLE HAVE LUNCH, BUT KIND OF
BRIDGING SOME OF THE QUESTIONS
THAT WE'RE SEEING IN THE CHAT
AND WHAT WE WANT PEOPLE TO BE
THINKING ABOUT HERE IS WE'VE
HEARD A LITTLE BIT ABOUT SORT OF
THIS BALANCE BETWEEN INNOVATION
AND ENABLING SHARING, SO WHAT
MIGHT NEED TO BE IN PLACE TO
ENABLE THAT TO HAPPEN, WHETHER
THAT'S SORT OF -- IF IT'S AROUND
CONTROLLED ACCESS, IF IT'S
AROUND SOMETHING POLICY-WISE,
FROM SOMEBODY IN THE CHAT, TO BE
ABLE TO ENSURE THAT SHARING
HAPPENS FOR BENEFIT.
SO PEOPLE CAN CONTINUE THAT
DISCUSSION OVER LUNCH OR IN YOUR
VIRTUAL ENVIRONMENTS.
I WANT TO THANK ALL OF OUR PANEL
ONE SPEAKERS VERY MUCH FOR
SPEAKING WITH US TODAY.
THANK YOU AGAIN SO MUCH.
[APPLAUSE]
SO WE ARE ON BREAK NOW UNTIL
2:00.
>>GOOD AFTERNOON, EVERYONE.
I THINK WE'RE GOING TO GO AHEAD
AND GET STARTED WITH THE
AFTERNOON SESSION.
SO THIS IS PANEL 2.
IT'S GOING TO BE MODERATED BY
MYSELF AND SASKIA, SO IN ARE ANY
DIFFICULT QUESTIONS, SASKIA IS
HERE TO ANSWER THEM.
AND THIS PANEL -- SO ONE THING I
WAS STRUCK BY THIS MORNING IS IN
HEARING ABOUT THE DIFFERENT
TECHNIQUES, THE DIFFERENCES IN
RESOLUTION AND TIME SENSITIVITY.
THE BRAIN FUNCTIONS ON MULTIPLE
DIFFERENT SCALES FROM MICRO
SECOND ACTION POTENTIALS TO
YEARS-LONG WORTH OF MEMORIES AND
EMOTIONS.
AND TAKING DIFFERENT MODALITIES
AND TRYING TO USE THEM
INDIVIDUALLY, YOU'RE NEVER GOING
TO CAPTURE ALL THOSE DIFFERENT
ASPECTS OF BRAIN BEHAVIOR.
SO ONE OF THE THINGS THAT IS
IMPORTANT IS SHARING OF
MODALITIES, MULTIPLE DIFFERENT
MODALITIES TO TRY TO UNDERSTAND
HOW THE BRAIN FUNCTIONS, IN
HOPES THAT THE TOTAL IS MORE
THAN THE SUM OF THE PARTS.
AND SO THIS SESSION IS DEVOTED
TO TRYING TO LOOK AT THAT,
TRYING TO UNDERSTAND HOW
MULTIMODAL ANALYSIS OF BRAIN
DATA MIGHT BE USEFUL IN
CATEGORIZING DATA THAT HAS MORE
IMPORTANCE OR PERHAPS MORE
SENSITIVITY IN TERMS OF OUR
OVERALL DISCUSSION FOR THE
MEETING.
AND JUST AS A BRIEF COMMENT ON
THIS, ONE OF OUR SPEAKERS IS DRS
"THE REVOLUTION WILL NOT BE
CONTROLLED."
IN THIS ARTICLE, HE ACTUALLY
DOES OFFER WAYS OF CONTROLLING
THE ISSUES OF NATURAL
STIMULATING OF SPEECH, BUT THE
TITLE IS PROVOCATIVE, AND I
THINK THAT'S WHAT WE SHOULD
THINK ABOUT IN TERMS OF THIS
PARTICULAR SESSION.
WITH THAT IN MIND, I'M GOING TO
BE DOING THE FIRST INTRO.
AND THE FIRST SPEAKER IS CONOR
RUSSOMANNO.
MR. RUSSOMANNO IS THE FOUNDER
AND CEO OF OPEN BCI.
HE RECEIVED A BACHELOR OF
SCIENCE IN CIVIL ENGINEERING AND
ENGINEERING MECHANICS FROM
COLUMBIA UNIVERSITY AND MASTER
OF FINE ARTS IN DESIGN AND
TECHNOLOGY FROM PARSONS SCHOOL
OF DESIGN.
AS FOUNDER AN CEO OF OPEN BCI, A
COMPANY DEDICATED TO OPEN SOURCE
INNOVATION OF HUMAN INTERFACE
TECHNOLOGIES, MR. RUSSOMANNO HAS
SPENT THE PAST DECADE OVERSEEING
THE DESIGN AND DISTRIBUTION OF
MORE THAN 40,000 TOOLS FOR
NEUROSCIENCE IN OVER 100
COUNTRIES AROUND THE WORLD.
I THINK IN THIS EFFORT, HE'S
HELPING TO DEMOCRATIZE THE
TECHNOLOGIES THAT ARE ASSOCIATED
WITH NEUROBIOLOGY, AND IN THAT
WAY, HOPEFULLY WILL BE ABLE TO
GENERATE DATA THAT'S CONSISTENT,
NORMALIZED AND UNDERSTANDABLE.
>>THANK YOU FOR THAT INTRO.
THAT WAS HELPFUL.
I CAN SKIP THE FIRST FIVE SLIDES
NOW.
HEY, EVERYBODY!
PLEASURE TO BE HERE.
IT'S A HUGE HONOR TO GET TO BE
IN THE MIDST OF THIS GROUP AND
THIS IS A VERY IMPORTANT TOPIC
TO ME SO IT'S A PLEASURE TO GET
TO SPEAK ON IT.
SO WHO AM I AND WHAT IS OPENBCI?
YOU JUST GOT A LITTLE BIT OF
THAT.
ABOUT ME, I COFOUNDED OPEN BCI.
MOST OF THE STUFF HERE YOU JUST
HEARD SO I'M GOING STO SKIP OVER
ALL OF IT.
THE ONE THING YOU DIDN'T HEAR IS
THE WAY I GOT INTERESTED IN THE
BRAIN WAS ACTUALLY THROUGH
CONTACT SPORTS.
I HIT MY BRAIN ENOUGH TIMES WITH
THE SIDE OF MY SKULL THAT I
STARTED TO GET CURIOUS ABOUT
WHAT HAPPENS WHEN YOU DAMAGE THE
HARDWARE ENOUGH.
SO IN GRAD SCHOOL AT PARSONS, I
STARTED BUILDING MY OWN BCI
HEADSETS.
IN THE BEGINNING THEY LOOKED
LIKE THIS.
THEY GET BETTER.
BUT IN THE BEGINNING I WAS
REALLY INTERESTED IN TRACKING MY
OWN EMOTIONS AND MY OWN
ACTIVITIES, AND SO I DISCOVERED
LOW COST EEG AND DECIDED I WOULD
TAKE MATTERS INTO MY OWN HANDS
AND SEE IF I COULD START TO
QUANTIFY MY OWN MOODS, SWINGS IN
MOODS, BUT ALSO IF CERTAIN
EVENTS OR ACTIONS, TAKING A NAP,
DRINKING A COFFEE, WERE HAVING A
QUANTIFIABLE IMPACTS ON MY EEG.
SO I BUILT THIS ONE-OFF
BLUETOOTH APP TO ANDROID PHONE
AND WITH A SINGLE CHANNEL EEG,
REALIZED IT'S VERY DIFFICULT TO
DO, ESPECIALLY TELEMONITORRING
WHERE YOU'RE WALKING AROUND NEW
YORK CITY TRYING TO RECORD EEG
WHILE, YOU KNOW, WALKING THROUGH
CARS AND TRAFFIC LIGHTS AND
THINGS LIKE THAT.
SO I BEGAN THINKING ABOUT HOW
CAN I USE EEG IN CONJUNCTION
WITH ONE OF MY FAVORITE TOPICS,
OR THINGS EVER, GAMING.
I PLAY A LOT OF GAMES, I LOVE
GAMES.
I'VE ALWAYS PLAYED GAMES.
SO FOR MY THESIS, I DECIDED I
WANTED TO TRY A KIND OF A CLOSED
LOOP NEURAL FEEDBACK EXPERIENCE
WHERE YOU'RE SUBCONSCIOUSLY
INFLUENCING THE PLOT OF A STORY.
SO THE IDEA IS THAT -- IT'S A
SHORT STORY WHERE YOU'RE A
HUMANOID ROBOT AND YOU'RE
EXPERIENCING SYMPTOMS OF
CONSCIOUSNESS IN THE FIRST
CHAPTER, AND THE MORE ATTENTION
THAT YOU PAY TO THE STORY, THE
MORE YOU BECOME CONSCIOUS.
THE MORE YOU START TO DISCOVER
FREE WILL AND THE SEMANTICS OF
THE LANGUAGE ACTUALLY CHANGE, SO
YOU ACTUALLY START TO USE
PRONOUNCE AND YOU START TO REFER
TO YOURSELF AS AN INDIVIDUAL.
IN THE OPPOSITE END OF THE
STORY, IF YOU'RE NOT PAYING
ATTENTION AND YOU'RE CHECKED
OUT, THE STORY KNOWS AND IT
TURNS YOU INTO A ROBOT.
YOU RECEDE BACK INTO
PROGRAMMATIC COMMUNICATION.
ANYWAY, SO OVER THE COURSE -- I
HAD ACTUALLY INSTALLED THIS AND
IT WAS ONLY EVER INSTALLED FOR A
WEEK, BUT I ACTUALLY GOT 100
PEOPLE TO PROVIDE FEEDBACK AND
EVEN THOUGH IT WAS EXTREMELY
CRUDE AND IT DIDN'T WORK THAT
WELL, I THINK IT WAS AN EARLY
CONCEPT, THIS WAS 10 YEARS AGO,
SO THIS WAS AN EARLY PROOF OF
CONCEPT ON THIS IDEA THAT NEW
FORMS OF ENTERTAINMENT CAN BE
VERY PERSONALIZED AND LISTEN AND
ADAPT TO THE INDIVIDUAL, AND I
DON'T THINK THAT'S JUST
ENTERTAINMENT.
I ALSO THINK IT APPLIES TO MANY
OTHER M ACATION APPLICATIONS AS.
THIS IS KIND OF HOW THE PLOT
WORKED, WHERE YOU GET BRANCHED
THROUGH THE STORIES, IT'S A
LITTLE BIT OF A HIERARCHY.
SO AFTER GRAD SCHOOL, I PARTED
INNED UP WITH ONE OF MY
PROFESSORS TO START OPEN BCI.
THE REASON WE STARTED IT IS
BECAUSE I FELT QUITE LIMITED IN
GRAD SCHOOL BY THE TECHNOLOGY AT
THE TIME.
MOST IMPORTANTLY ACCESS TO RAW
DATA.
SO THE TWO DEVICES THAT EXISTED
OUT THERE IN THE WORLD, BLACK
BOX, OBSCURED THE RAW DATA,
PREVENTING IT FROM REALLY BEING
A SCIENTIFIC TOOL.
SO IN ORDER TO GET ACCESS TO RAW
DATA, YOU HAD TO EITHER PAY
MONTHLY PREMIUM SUBSCRIPTION FOR
THE ENTERPRISE SOLUTION, OR PAY
TENS OF THOUSANDS OF DOLLARS FOR
RESEARCH-GRADE EQUIPMENT, AND
THEN YOU GET THE RAW DATA.
SO WE SET OUT TO BUILD OPENBCI,
WHICH WAS -- SHOW OF HANDS, WHO
HERE KNOWS ARDUINO ?R IT'S LIKE
LEGO FOR ELECTRONICS.
THEY WERE LIKE HEY, HOW DO WE
TAKE THE CUTE LITTLE
INSTRUCTIONS, AND THIS BUILDING
BLOCK MENTALITY AND APPLY TO THE
INDUSTRY OF ELECTRONICS SO WE
CAN GET YOUNGER PEOPLE
INTERESTED AT AN EARLIER AGE,
TURN IT INTO FUN AND NOT
NECESSARILY LEARNING.
WE TRIED TO IMPLEMENT A SIMILAR
MODEL FOR BCI.
THE WHOLE IDEA WAS HOW DO WE
TURN THIS INTO BUILDING BLOCKS
AND THEN CREATE ENGAGING
DOCUMENTATION AND TUTORIALS THAT
TEACH PEOPLE ONE WAY HOW TO USE
THE TECHNOLOGY BUT ACTUALLY OPEN
THE DOOR FOR THEM TO USE IT IN
DIFFERENT WAYS, EVEN WAYS THAT
WE DON'T -- THAT WE DIDN'T EVEN
KNOW OR EXPECT.
SO THAT WAS THE IMPETUS FOR
CREATING OPENBCI, WAS REALLY TO
RE-LOWER THE BARRIER OF ENTRY.
BUT MAKING SURE THAT THE TOOL
THAT WE'RE PUTTING INTO THE
WORLD OR THE PRODUCT IS STILL A
RESEARCH TOOL.
YOU HAVE ACCESS TO THE RAW DATA,
AND WE DON'T COLLECT IT, RIGHT?
ONCE IT'S YOURS, IT'S YOURS.
SO FAST FORWARD 10 YEARS, WE'VE
BEEN BUILDING HARDWARE AND
SOFTWARE FOR A DECADE NOW, WE'VE
SHIPPED PRODUCTS ALL OVER THE
WORLD.
WE'VE GOT OVER 300 CITATIONS
THAT ARE REFERENCING OR USING
OPENBCI EQUIPMENT AND THAT LIST
IS GROWING BY THE DAY.
A LOT OF THIS WAS SAID EARLIER.
THIS IS COOL, IT'S KIND OF AN
EVOLUTION OF -- IN THE BEGINNING
WE ACTUALLY STARTED AS A
SUBCONTRACT ON A DARPA
SOLICITATION.
SO THERE WAS A SOLICITATION TO
BUILD A LOW COST EEG D DEVICE FR
NON-TRADITIONAL USERS.
WE RAN THAT SOLICITATION AND
THEN A COMPANY WE WERE
SUBCONTRACTING TO DIDN'T GET
PHASE 2 SO WE WERE ABLE TO SPIN
OUT AND CREATE OUR OWN COMPANY,
WHICH IS PRETTY KOO. THEN OVER R
THE FIRST FIVE YEARS, WE
CONTINUED TO MAKE THE PRODUCTS
CHEAPER AND STILL THE ECOSYSTEM
OF PERIPHERAL DEVICES, BUT THEN
IN THE LAST FOUR YEARS, WE'VE
MOVED ON TO INTEGRATING THE
TECHNOLOGY THAT WE BUILT IN THE
FIRST FIVE YEARS INTO AR, VR AND
THE DOMAINS THAT WE ARE
PREDICTING ARE GOING TO BE THE
MOST INFLUENTIAL ON THE NEXT
MAJOR EVOLUTION IN COMPUTING
TECHNOLOGY.
SO WEARABLE COMPUTER, AR, VR, AI
ESSENTIALLY.
SO THIS IS PRETTY COOL, IT'S
JUST COMPANIES WE HAVE WORKED
WITH OR WHO HAVE PURCHASED OUR
EQUIPMENT OVER THE LAST DECADE.
AS YOU CAN SEE, THIS IS -- BCI
IS TRANSLATING INTO THE REAL
WORLD.
LIKE THESE COMPANIES ARE BUYING
THE EQUIPMENT, THEIR RESEARCH
LABS ARE WORKING WITH OUR
TECHNOLOGY, SOME OF THEM HAVE
PAID HUNDREDS OF THOUSANDS OF
DOLLARS FOR EQUIPMENT OVER THE
YEARS.
AND SO WE'RE SEEING -- 10 YEARS
AGO WHEN I FIRST STARTED OPEN
BCI, IT WAS THE WILD WEST.
IT WAS LIKE A HANDFUL OF
COMPANIES AND IT WAS A -- AT
LEAST IN THE LOW COST SPACE, IT
WAS VERY MUCH QUANTIFIED SELF OR
PEOPLE LIKE ME, JUST THESE
NEUROJUNKIES OR WHATEVER, BUT
THEN OVER THE LAST 10 YEARS,
WE'VE SEEN THIS AMAZING, YOU
KNOW, TRANSLATION, PEOPLE LIKE
MARK ZUCKERBERG, ELON MUSK USING
THE ACRONYM BCI FREELY LIKE WE
SHOULD KNOW WHAT IT MEANS.
SO IT'S BEEN REALLY, REALLY COOL
TO WATCH, AND I THINK THE
TAKEAWAY HERE IS WE'RE CLOSER
THAN WE THINK TO NEEDING THESE
POLICIES AND STANDARDS TO BE PUT
IN PLACE.
SO WE BUILT THE ULTRA CORTEX,
THE CYTON BOARD IS THE KIND OF
CHIP, THE CENTER MODULE THAT CAN
DO EEG, EMG, ECG, MANY DIFFERENT
TYPES OF ELECTRODERMAL -- SO IT
KIND OF THIS LIKE VERY
PROGRAMMABLE OPEN SOURCE LITTLE
MODULE THAT CAN RECORD MANY
DIFFERENT TYPES OF DATA FROM THE
BODY.
AND THEN OVER THE YEARS, OTHER
PEOPLE STARTED DEVELOPING
PERIPHERAL MODULES OR SIMILAR
MODULES THAT WE HELPED -- WE
HELPED THEM COME INTO EXISTENCE
AND THEN STARTED PUTTING THEIR
PRODUCTS INTO OUR STORE SO THAT
WE COULD INCLUDE NEW TYPES OF
SENSORS AND MODALITIES THAT WE
HADN'T BUILT OURSELVES.
SO THIS IS SOME OF THE SOFTWARE,
I'M NOT GOING TO GO INTO TOO
MUCH DETAIL HERE.
THESE ARE -- IN THE BEGINNING,
THIS, I THINK IS RELEVANT FOR
THIS GROUP, WHICH IS -- THIS IS
CALLED THE BRAIN INITIATIVE, BUT
I THINK IT'S SUPER IMPORTANT
THAT WE DON'T ENTER THE PROBLEM
SPACE WITH BLINDERS ON BUT THAT
WE ALSO ACKNOWLEDGE, RESEARCH
AND FUND THE PERIPHERAL
TECHNOLOGIES AND DOMAINS THAT
ACTUALLY MAKE THE BRAIN
RELEVANT.
SO IN THE TBINNING, WE STARTED
THE ONLY GOAL WAS TO BUILD AN
EEG DEVICE.
WE WANT TO PUT AN EEG DEVICE
INTO THE HANDS OF STUDENTS AND
MAKERS AND TECHNOLOGISTS, YADA,
YADA, YADA, AND WE STARTED DOING
THAT AND REALIZED ALL OF THEM
WERE DOING THE REST OF THIS.
EMG, EDA, PPG, EYE TRACKING, IN
CONJUNCTION WITH EEG.
AND THE TAKEAWAY HERE IS THE EEG
BY ITSELF IS VERY BORING AND
ACTUALLY IRRELEVANT.
THERE WAS ALMOST NOTHING -- NO
CONCLUSIONS YOU COULD DRAW FROM
EEG BY ITSELF, BUT THE MOMENT
YOU START LABELING AND ADDING
CONTEXT AND COLLECTING OTHER
DATA AT THE SAME TIME, THAT'S
WHEN EEG GETS REALLY, REALLY
INTERESTING.
SO THIS IS A BIG TAKEAWAY, IS
THAT MULTIMODAL, I WOULD SAY
10Xs -- 10 TO 1,000X IS THE
VALUE OF EEG DATA WHEN YOU
COLLECT IT IN CONJUNCTION WITH
THESE OTHER DATASETS.
THIS IS SOME OF THE WORK THAT'S
BEEN DONE WITH OUR TECHNOLOGY,
OUR KIND OF DERIVATIVE WORK
THAT'S COME OUT OF IT.
SO NOW THIS NEW EFFORT IS
VENTURE GALEA, I'M GOING TO KIND
OF JUMP AHEAD A LITTLE BIT.
SO WHAT WE REALIZED IS THAT THE
OTHER TAKEAWAY IS THAT IN
ADDITION TO MULTIMODAL, PEOPLE
ARE NOT JUST INTERESTED IN
RECORDING FROM THE BRAIN, BUT
ALSO MODULATING OR STIMULATING
THOUGHTS, EMOTIONS, FEELINGS, ET
CETERA.
AND SO I THINK THAT THIS DIAGRAM
IS REALLY THE BEST DIAGRAM ABOUT
THE BIG PROBLEM SPACE -- THAT
WE'RE IN FOR THE FUTURE, WHICH
IS THAT WE ALREADY HAVE THESE
A.I. AGENTS AND SYSTEMS THAT
OPERATE IN THE BACKGROUNDS OF
OUR COMPUTER THAT ARE OPTIMIZING
CLOSED LOOPS THAT WE HAVE WITH
OUR TECHNOLOGY.
THIS IS ONLY GOING TO BECOME
MORE AND MORE IMPORTANT AND MORE
AND MORE NUANCED AND LIKE MY
GRAPHIC NOVEL WHERE THERE'S A
SUBCONSCIOUS INPUT, YOU DON'T
KNOW HOW YOU'RE AFFECTING THE
STORY BUT YOU ARE, THAT TYPE OF
THING IS GOING TO CONTINUE TO
HAPPEN AND IT'S GOING TO BE
BAKED MORE AND MORE SEAMLESSLY
INTO OUR PRODUCTS.
SO I BELIEVE THAT THIS IS AN
INEVITABILITY AND I THINK THE
WEARABLES, THE V.R. HE HEADSETS,
THE VISION PRO, ALL OF THE
THINGS ARE TRENDING IN THIS
DIRECTION AND ALSO THEY'RE
LAYING THE FOUNDATION MAKE IT
EASIER FOR SENSOR COMPANIES LIKE
US TO JUST STICK OUR MODULES ON
FOR VERY LITTLE EXTRA COST AND
COLLECT THE DATA ANYWAY.
SO HERE WE'RE TRENDING TOWARDS
WHAT I LIKE TO REFER TO AS THE
OPERATING SYSTEM OF THE MIND.
WE'RE WORKING ON IT, OTHER
COMPANIES ARE WORKING ON.
COMPUTERS AS THEY ARE NOW ARE
DESIGNED FOR THE GENERIC
CUSTOMER.
BUT TECHNOLOGIES WILL BE
DESIGNED THAT WAY, EVEN MACHINE
LEARNING MODELS AND CLASSIFIERS
WILL BE DESIGNED FOR GENERIC
POPULATIONS BUT THEN WHEN YOU
HAVE YOUR OS OF THE MIND
COMPUTER, YOUR OPERATING SYSTEM
AND ALL OF THE CLASSIFIERS AND
SYSTEMS WITHIN IT WILL SLOWLY
ADAPT THEMSELVES TO YOU AND
THERE'S A TRANSFER LEARNING
THING THAT WILL HAPPEN WHERE THE
GENERIC MODEL WILL BECOME
RELEVANT INIRRELEVANT AND YOUR L
MODEL WILL PREVAIL IN TERMS OF
HOW IT'S -- BUT I THINK THAT
THERE'S A REALLY, REALLY
IMPORTANT LAYER THAT WE NEED TO
BUILD THAT DOESN'T EXIST YET,
WHICH IS THIS MENTAL VAULT OR
THIS BARRIER BETWEEN THE CLOSED
LOOP THAT EXISTS WITH YOU IN
YOUR APPLICATIONS INSIDE OF
TECHNOLOGY AND ANYBODY ELSE OUT
THERE, DOCTOR, COMPANIES,
RESEARCH INSTITUTIONS THAT WANT
ACCESS TO WHERE THE DATA IS
BEING RECORDED BUT ALSO THE
ABILITY TO TWEAK SETTINGS ON THE
OTHER END AND SEE -- WITH YOUR
KNOWLEDGE, SEE IF YOU CAN BE
INFLUENCED IN THIS CLOSED LOOP
DIRECTION IN A WAY THAT BENEFITS
YOU, THAT YOU CHOOSE.
SO THINK AT THE CORE OF THIS,
AND I'M REALLY SUPER, SUPER GLAD
THAT NINA INVITED ME HERE IS
THIS CONCEPT OF COGNITIVE
LIBERTY, WHICH I THINK IS REALLY
ONE OF THE CORE THEMES IN HER
BOOK.
THIS MUST BE, I BELIEVE, MAYBE
NOT EVERYBODY AGREE WITH THIS
BUT I BELIEVE THAT THIS SHOULD
BE THE PRIMARY DESIGN CONSTRAINT
IN ANY TECHNOLOGIES THAT WE
BUILD.
ESPECIALLY AS WE'RE GETTING INTO
THE CONSUMER LANDSCAPE.
SO GALEA IS THE THURNG WEEFL
BEEN THING WE'VE BEENWORKING ONE
YEARS.
IT REAL.
WE'VE BEEN USING THEM EVERY DAY.
WE SHIP THEM TO ALPHA PARTNERS
ALL OVER THE WORLD.
IT BRINGS ME RIGHT IN THE MIDST
OF ANNOUNCING GALEA, WE RAN A
500-PERSON SURVEY WITH SURVEY
MONKEY AND WE MADE SURE THE
GENDER SPLIT WAS EVEN, WE TRIED
TO BE AS UNBIASED AS POSSIBLE,
BUT I THINK THAT THIS -- EVEN
THOUGH THIS SURVEY IS CLOSE TO
TWO YEARS OLD NOW, I THINK THE
RESULTS ARE ACTUALLY STILL VERY
INTERESTING FOR THIS GROUP.
SO WE ASKED DO YOU USE ANY
DEVICES THAT USE BIOMETRICS TO
UNLOCK OR LOG IN, YOU KNOW,
FINGERPRINTS, FACE RECOGNITION,
ET CETERA, DO YOU FEEL MORE
SECURE USING BIOMETRICS OR
TRADITIONAL TYPE-IN PASSWORDS TO
UNLOCK PERSONAL DEVICES?
SO THESE WERE THE RESULTS.
WE'LL GO THROUGH THESE BECAUSE I
THINK SOME OF THESE ARE REALLY
INTERESTING.
85% SAID THAT THEY USE DEVICES
THAT LEVERAGE BIOMETRICS FOR
SECURITY.
53% OF PEOPLE SAID THAT THEY
FEEL MORE SECURE AND ONLY 25%
SAID THAT THEY WOULD RATHER
THEIR DEVICE USE A PASSWORD AS
OPPOSED TO BIOMETRICS.
SO I THINK HERE, YOU CAN SEE
THAT PEOPLE ARE, GENERALLY
SPEAKING, WILLING TO USE
BIOMETRICS AS OPPOSED TO A
PASSWORD.
AS WE MOVE ALONG, WE ASKED WOULD
YOU BE MORE OR LESS LIKELY TO
USE A DEVICE THAT SPECIFICALLY
USED YOUR BIOLOGICAL SIGNALS TO
IMPROVE DEVICE PERFORMANCE OR
IMPROVE YOUR MENTAL OR PHYSICAL
HEALTH.
69% OF PEOPLE SAID MORE THAN TWO
THIRDS SAID MORE LIKELY TO USE,
LESS THAN A QUARTER SAID LESS
LIKELY TO USE.
THEN WE ASKED ESSENTIALLY THE
SAME QUESTION, BUT INCLUDED
BRAIN ACTIVITY.
I'LL GO BACK A SLIDE.
EVEN MORE PEOPLE ARE INTERESTED
AND EVEN LESS PEOPLE ARE NOT
SURE.
SO THE TAKEAWAY HERE IS THAT
PEOPLE ARE EXCITED ABOUT THE
BRAIN.
OKAY, BIOMETRICS, COOL, BUT AS
SOON AS THE BRAIN IS INVOLVED,
PEOPLE ARE ACTUALLY -- GRANTED
THIS IS ONE SURVEY.
IT SEEMS PEOPLE ARE EXCITED
ABOUT THE USE OF BRAIN ACTIVITY
IN THEIR TECHNOLOGY.
BUT LET'S KEEP MOVING.
HOW CONCERNED ARE YOU -- HOW AM
I DOING ON TIME, BY THE WAY?
I'M CLOSE.
OKAY.
HOW CONCERNED ARE YOU ABOUT YOUR
ELECTRONIC DEVICES, READING YOUR
BIOMETRIC SIGNALS?
OKAY, SO 72% OF PEOPLE ARE
CONCERNED ABOUT -- WHICH IS --
IT'S TELLING.
IT'S INTERESTING THAT PEOPLE ARE
BOTH EXCITED AND CONCERNED.
RIGHT?
IT'S NOT -- LIKE THE EXCITEMENT
DOESN'T -- IT'S NOT UNIVERSALLY
PROPORTIONAL.
THERE'S BOTH SIMULTANEOUS
CONCERN AND EXCITEMENT FOR THIS
TECHNOLOGY.
17% OF PEOPLE WERE EXTREMELY
CONCERNED.
WE THEN ASKED THE SAME QUESTION,
BUT INCLUDED BRAIN DATA.
CONCERN WENT UP JUST THE SAME AS
INTEREST.
SO BIOMETRIC SIGNALS, COOL.
IF YOU INCLUDE BRAIN ACTIVITY,
PEOPLE ARE MORE INTERESTED BUT
THEY'RE ALSO MORE CONCERNED.
EXTREMELY CONCERNED ALSO WENT UP
AS WELL.
SO THEN I THINK THAT THIS ONE IS
REALLY INTERESTING.
WE ASKED WHAT REALLY MATTERS,
HOW CONCERNED ARE YOU WITH THE
FOLLOWING COMPANIES, I GUESS
AND/OR INSTITUTIONS, HAVING
ACCESS TO BIOMETRIC DATA LIKE
PULSE RATES, FINGERPRINTS AND
FACIAL RECOGNITION, ET CETERA?
NOT SURPRISINGLY, FACEBOOK,
PEOPLE ARE MOST CONCERNED ABOUT
FACEBOOK, AKA META, HAVING
ACCESS TO THE DATA, BUT THEN AS
YOU GET INTO TBOO GEL, APPLE,
MICROSOFT, AMAZON AND THESE
OTHER MAJOR CONSUMER TECH
COMPANIES, IT'S ALL KIND OF
COMPARABLE.
GOOGLE BEING SLIGHTLY HIGHER
THAN THE OTHER ONES.
I DON'T THINK THAT'S SURPRISING
BECAUSE FACEBOOK AND GOOGLE ARE
BOTH KNOWN TO BE COMPANIES WHO
MAKE MONEY ON DATA MODELS.
MOST OF THE MONEY THEY MAKE IS
ON SHARING AND SELLING YOUR
DATA.
SO THIS CHECKS OUT.
THIS TRACKS.
BUT I THINK WHAT'S SUPER
INTERESTING IS YOU PUT THE
GOVERNMENT ON HERE, YOU KNOW,
BROAD TERM, GOVERNMENT, CONCERN
IS ALMOST IDENTICAL TO --
BARRING GOOGLE AND FACEBOOK, THE
REST OF THESE MAJOR CONSUMER
TECHNOLOGY COMPANIES.
SO BROADLY SPEAKING, CONSUMER
SURVEY, CUSTOMERS DO NOT TRUST
THE GOVERNMENT JUST THE SAME WAY
THEY DON'T TRUST MAJOR TECH
COMPANIES.
IMPORTANT TO NOTE.
FACTORS, ON THE OTHER HAND,
INSURANCE AND HEALTH, INSURANCE,
A LITTLE BIT OF A DROPDOWN,
PEOPLE DON'T NECESSARILY TRUST
INSURANCE BUT THEY TRUST THEM
MORE THAN THE GOVERNMENT, WHICH
I FIND SURPRISING, AND AMAZON,
BUT THEN DOCTOR, SIMILARLY, SOME
PEOPLE DON'T TRUST THEIR
DOCTORS.
10% OF PEOPLE ARE EXTREMELY
CONCERNED WITH BRAIN DATA AND
BIOMETRIC DATA BEING SHARED WITH
THEIR DOCTORS.
FASCINATING.
AND OVER 50% ARE STILL
CONCERNED.
SO, THESE ARE THE KIND OF
RESPONSES WE GOT.
I THINK THESE COMPANIES JUST
WANT TO USE BIOINFO TO TARGET
ADS AND OTHER SERVICES THAT MAKE
MONEY AT THE EXPENSE OF PRIVACY
AND CONCERNS.
MIGHT BE DENIED INSURANCE
COVERAGE BECAUSE OF PRE-EXISTING
CONDITIONS.
FACEBOOK, AMAZON AND GOOGLE ARE
JUST NOT TO BE TRUSTED WITH THAT
MUCH DATA.
SECURITY AND IDENTITY THEFT ARE
THE BIGGEST CONCERNS.
SO ANYWAY, I'M GOING TO END
THERE, ENDING WITH THOSE SURVEY
RESULTS IS JUST KIND OF AN
INTERESTING SHED ON OTHER
PEOPLE'S PERSPECTIVES, BUT WHAT
WE'RE WORKING ON NOW IS FIGURING
OUT HOW TO PUT RESEARCH TOOLS
INTO THE WORLD THAT GIVE ACCESS
TO RAW DATA THAT PUSH THE
ENVELOPE OF WHAT'S POSSIBLE WITH
WHAT WE CAN LEARN, MULTIMODAL
SENSOR, ALL OF THIS STUFF IN
CONJUNCTION WITH THE EEG, ALL
NONINVASIVE, ALL WEARABLE, BUT
I'M VERY, VERY CONCERNED WITH
HOW THIS TECHNOLOGY COULD BE
USED.
AND I'VE FOUND AT LEAST
PERSONALLY, I THINK ONE OF THE
BEST WAYS THAT WE CAN COMBAT THE
MISUSE OF TECHNOLOGY IS THROUGH
TECHNOLOGY TRANSPARENCY AND
EDUCATION AND OPENNESS AND SO
THAT'S A HUGE IMPETUS BEHIND
OPENBCI.
BUT I DO THINK THAT WE ARE ON
THE BRINK OF REAL TRANSLATION,
I'M WO WORKING ON IT, OF A REAL
TRANSLATION OF THIS TECHNOLOGY
INTO THE CONSUMER LANDSCAPE.
AND SO COGNITIVE LIBERTY, MENTAL
VAULT AND THESE THINGS I THINK
ARE PARAMOUNT TO CONSIDER AS WE
BUILD THE SYSTEMS.
THANK YOU.
[APPLAUSE]
>>AT THE END OF THE FIRST
SESSION, WE HAVE TIME FOR ONE
QUESTION.
>>CONOR, WOULD YOU MIND GOING
BACK TO THAT SLIDE WHERE YOU S
SAY -- DIFFERENT KINDS OF
SENSORS --
[AUDIO BREAKING UP]
AND MY QUESTION FOR YOU IS IF
YOU COULD JUST UNPACK THAT A
LITTLE BIT.
>>SORRY, I SHOULDN'T HAVE --
I'VE GOT TO SIGN BACK IN.
>>OKAY.
SO WE'LL TRY TO IMAGINE IT IN
OUR BRAINS AND YOU CAN DECODE
IT.
BUT YOU HAD, I THINK, EEG, EMG,
LIKE FIVE OR SIX DIFFERENT
SENSORS ON THERE.
AND SO I'M GOING TO DO A
COMPOUND QUESTION SINCE I'M ONLY
ALLOWED ONE QUESTION WHICH IS,
ONE, IF YOU CAN UNPACK WHAT YOU
MEAN AS EEG ALONE IS BORING,
BECAUSE IT SEEMS LIKE THERE'S
STILL A GOOD BIT OF INFORMATION
THAT WE COULD GLEAN FROM THE
EEG, BUT THEN THE SECOND IS,
WHEN YOU SAY THE REAL POWER
COMES IN, THE REAL POWER TO DO
WHAT?
WHAT ARE THE ADDITIONAL
INFERENCES THAT CAN BE MADE BY
COMBINING THE DIFFERENT SENSORS
TOGETHER, AND WHAT'S THE KIND OF
INCREASED PRECISION OR INCREASED
INFERENCES OR DATA THAT YOU GET
BY DOING SO?
>> COOL, I'LL ANSWER THE LAST
ONE FIRST.
THEN I'LL WORK BACKWARDS.
SO WHEN YOU COMBINE -- EVEN
THOUGH WE STRIECH TO PUT TOOLS
INTO THE WORLD THAT PRODUCE RAW
DATA, WHAT WE ACTUALLY CARE
ABOUT IS NOT THE RAW DATA.
AND I THINK THAT THIS IS, YOU
KNOW -- WHEN WE'RE TRYING TO
SOLVE PROBLEMS AROUND SOMEBODY
LIVING WITH SEVERE DISABILITIES,
GIVING THEM FREEDOM AND AUTONOMY
TO CONTROL DEVICES AGAIN, OR
SOMEBODY TRYING TO RECOVER FROM
PTSD, YOU KNOW, FINDING THE
RIGHT COCKTAIL OF VR EXPERIENCES
AND MEDICATION TO HELP THEM
OVERCOME, I THINK THAT
THERE'S -- THE SENSOR FUSION AND
THE ABILITY TO TAKE RAW DATA AND
EXTRACT FIRST DERIVATIVE
METRICS, AND WHAT I MEAN BY THAT
IS THIS MODEL THAT I LIKE TO
USE.
IT'S RAW DATA, FIRST DERIVATIVE
METRICS, SECOND DERIVATIVE
METRICS.
FIRST DERIVATIVE METRICS ARE
THINGS LIKE POWER BANDS, FFT
FROM EEG, HEART RATE, HEART RATE
VARIABILITY, RIGHT?
IT'S LIKE -- BUT THEN THE SECOND
DERIVATIVE METRICS ARE MORE
PERSONAL.
IT'S LIKE STRESS, AROUSAL,
FATIGUE, COGNITIVE WORKLOAD,
THESE -- YOU KNOW, REAL EMOTIONS
AND FEELINGS AND INTERNAL STATES
THAT CAN BE DECODED.
SO WHILE THE BRAIN IS WHAT WE
CARE ABOUT BECAUSE WE KNOW IT'S
THE NUCLEUS OR THE KIND OF
CENTRAL TERMINAL OF THIS PROBLEM
TO UNDERSTAND THE FULL PROBLEM,
WE SHOULD BE -- WE NEED TO
LISTEN TO THESE OTHER MODALITIES
BECAUSE WHAT WE ACTUALLY CARE
ABOUT IS NOT THE BRAIN, BUT THE
BRAIN'S INTENTION.
SO FOR DECODING INTENTION AND
LOOKING AT REAL EMOTIONS, WE
FOUND THAT WHEN YOU LOOK AT EEG
DATA ALONE, IT'S ALMOST
IMPOSSIBLE, BUT WHEN YOU LOOK AT
ENVIRONMENTAL DATA, VR HEADSET
ARE GREAT BECAUSE THEY HAVE
ENVIRONMENTAL SENSORS LIKE EIGHT
CAMERAS, DEAF SENSORS,
MICROPHONES, EVERYTHING, YOU
COMBINE THAT WITH INWARD
POINTING TECHNOLOGY, NOW YOU
HAVE EXTREMELY RICH DATASETS TO
EXTRACT FIRST AND SECOND
DERIVATIVE METRICS WHICH I
BELIEVE IS WHAT WE TRULY CARE
ABOUT.
>>HAVE YOU GUYS PUBLISHED ON
THE DIFFERENCES BETWEEN LIKE
WHAT YOU -- SENSOR FUSION,
INTERPRETATIONS OF MENTAL,
EMOTIONAL KIND EXPERIENCES
VERSUS LIKE A SINGLE STREAM OF
DATA, MEANING EEG DATAVERSE US
THE SENSOR FUSION, THE INCREASED
ACCURACY OR THE DIFFERENCE OF
WHAT YOU'RE ACTUALLY ABLE TO
INTERPRET?
>>SO WE HAVEN'T AS A COMPANY
PUBLISHED BUT A LOT OF OUR
RESEARCHERS THAT WE'VE PARTNERED
WITH HAVE, AND ACTUALLY ONE OF
OUR ADVISERS, WE HAVE THREE OF
THEM, ONE OF THEM IS PAUL
SHEIDA, A LOT OF HIS WORK IS ON
THIS, WHICH IS LOOKING AT EEG
AND EYE TRACKING MODALITIES IN
TANDEM, AND WHAT THEY FOUND,
THEY RAN A BATTERY OR THEY RAN A
TEST WHERE THEY DID THE EXACT
SAME STUDY USING PEOPLOMETRY AND
PEOPLE DILATION, AND THEN
LOOKING AT AN EVENT-RELATED
POTENTIAL OR P300 SPIKE IN
PARIETAL LOBE AND THEY FOUND
THAT THE DATA, WHEN YOU LOOK AT
THE TWO TREND LINES, THEY MATCH
EACH OTHER.
THEY FOLLOW EACH OTHER.
AND WHEN YOU TAKE EITHER DATASET
BY ITSELF, YOU GET ABOUT 80%
EFFICACY, BUT THEN WHEN YOU
COMBINE THE TWO DATASETS
TOGETHER, YOU GET 96 H 96-PLUS.
I MIGHT BE OFF A LITTLE BIT WITH
THE NUMBERS, BUT ESSENTIALLY
THEY PROVED IN THE CONCLUSION
STATISTICALLY THAT YOU TAKE EYE
TRACKING AND EEG AND YOU PUSH
THEM TOGETHER AND FOR EXTRACTING
YOU KNOW, A RESPONSE TO A
STIMULI, YOU'RE WAY MORE
RELIABLE WITH BOTH AT THE SAME
TIME AS OPPOSED TO EITHER ONE
INDEPENDENTLY.
>>ACTUALLY I'M SORRY, WE
REALLY -- WE NEED TO MOVE ON.
SORRY.
>>CAN'T WE JUST FINISH THAT
THOUGHT, IT'S NOT SO MUCH THAT
IT'S BORING --
>>IT'S NOT BORING.
I SAY THAT --
>>THAT'S FINE.
I JUST WANTED TO MAKE SURE I
UNDERSTOOD.
>>THANK YOU.
>>THE EEG, THE ABILITY TO
DETECT STATES IN EEG IS
HORRIBLE.
BUT THE ADVANTAGE OF SEEING HOW
IT'S ASSOCIATED WITH PARTICULAR
BEHAVIOR.
SO YOU NEED THAT INTERACTION.
>>THANK YOU.
SO IT'S MY PLEASURE TO INTRODUCE
DR. ALEXANDER HUTH, UNIVERSITY
OF TEXAS AT AUSTIN.
HE RECEIVED HIS PH.D. IN
NEUROSCIENCE FROM THE UNIVERSITY
OF CALIFORNIA, BERKELEY.
HE USES COMPUTATIONAL METHODS TO
MODEL HOW THE BRAIN PROCESSES
LANGUAGE AND REPRESENTS MEANING.
USING FMRI, THE HUTH LAB RECORDS
HUMAN BRAIN RESUMM RESPONSES WHE
PEOPLE LISTEN TO SPEECH IN A
FORM OF STORIES OR PATHS THEY
BUILD CODING MODELS THAT PREDICT
THOSE RESPONSES BASED ON THE
AUDIO AND -- OF THE STORIES.
THE BEST ENCODING MODELS THEY
USED TO -- TO EXTRACT MEANINGFUL
INFORMATION FROM THESE STORIES.
DR. HUTH, WE LOOK FO FORWARD TO
YOUR PRESENTATION.
>>I'M REALLY EXCITED TO BE HERE
AND TALK TO YOU A LITTLE ABOUT
WHAT WE DO IN MY LAB.
SO I MIGHT HAVE TITLED THIS
TALK, WOW, YOU CAN DO THAT WITH
IF MRI?
BECAUSE THAT'S KIND OF POINT, WE
COULD DO A LOT MORE WITH FMRI
THAN WE THOUGHT WAS POSSIBLE
FNLT SO LET'S START WITH KIND OF
A BIG PICTURE IDEA.
I WAS WORRIED ABOUT HAVING TIME
TO INTRODUCE ALL THE IDEAS BUT
THANKFULLY THE TALKS THIS
MORNING DID A REALLY GREAT JOB
OF THAT.
SO CAN WE HELP A PERSON TO SPEAK
WITHOUT MOVEMENT?
SAY A PERSON IS UNABLE TO MOVE
IN ORDER TO SPEAK, CAN WE HELP
THEM DO THAT.
SO IN THE ORDINARY
CIRCUMSTANCES, WOULD YOU HAVE
SOMETHING HAPPENING IN YOUR
BRAIN, YOU'D USE THIS TO MOVE
YOUR MOUSE, TO MOVE YOUR
ARTICULATORS, MAKE AIR COME OUT
OF YOUR LUNGS THAT YOU MODULATE
INTO SOUNDS LIKE I'M DOING RIGHT
NOW AND THIS BECOMES SOME KIND
OF SPEECH OUTPUT, SOME KIND OF
IDEA INSIDE YOUR HEADS.
CAN WE LEAP FROG THIS PROCESS?
CAN WE GO STRAIGHT FROM BRAIN TO
OUTPUT?
SO THIS IS BASICALLY LIKE A
LANGUAGE BCI.
SO ONE QUESTION WE MIGHT HAVE
WHEN WE'RE TRYING TO DO THIS IS
WHAT STAGE OF LANGUAGE
PRODUCTION SHOULD WE ACTUALLY
LOOK AT?
WE HEARD A LITTLE THIS MORNING
FROM DR. COLLINGER ABOUT SORT OF
THE STATE OF THE ART IN MODERN
IMPLANTABLE BCIs, WHICH MOSTLY
FOCUS ON ESSENTIALLY THE LAST
STAGE OF LANGUAGE PRODUCTION.
SO WE THINK OF THIS FROM GOING
YOU HAVE AN IDEA INSIDE YOUR
HEAD, TURNS INTO AN INTENTION TO
SAY SOMETHING, A PLAN FOR HOW
YOU'RE GOING TO MOVE AND THEN
THE ACTUAL ACTIONS THAT OCCUR.
MOST CURRENT SYSTEMS ACTUALLY
WORK AT KIND OF THIS ACTION
STAGE.
SO THEY'RE READING OUT THE MOTOR
CORE TECH.
WHAT ARE THE ACTUAL COMMANDS
THAT YOUR BRAIN IS SENDING TO
YOUR MUSCLES IN ORDER TO CREATE
SPEECH TO COMMUNICATE?
THIS IS GREAT FOR SOME THINGS
AND IT'S GREAT WHEN YOU HAVE
HIGH TEMPORAL RESOLUTION AS YOU
DO WHEN YOU ACTUALLY PUT
ELECTRODES IN SOMEONE'S BRAIN
BUT IT'S LESS GREAT FOR OTHER
THINGS.
SO WITH SYSTEMS LIKE FMRI, WE
HAVE REALLY BAD TEMPORAL
RESOLUTION ON THE ORDER
OF SECONDS, THAT'S ALL WE'RE
GOING TO HAVE, THIS IS A PRETTY
LIKE FUNDAMENTAL LIMITATION.
THERE ARE WAYS -- YOU CAN DO
FASTER MRI AND THERE'S .01% OF A
SIGNAL THAT'S FASTER THAN THE
NORMAL SIGNAL.
BUT REALLY WHAT WE CAN WORK WITH
PRACTICALLY IS VERY SLOW WITH
FMRI.
AND ALSO THIS COULD BE NOT THE
BEST STRATEGY TO LOOK AT
ACTIONS.
IF, FOR EXAMPLE, THE PROBLEM THE
PERSON HAS IS NOT AT THIS STAGE
BUT IS AT SOME EARLIER STAGE.
MAYBE THEY CAN'T PLAN THE
MOVEMENTS TO SPEAK.
THEN DECODING THE ACTIONS IS OF
NO USE TO YOU.
SO IF SOMEBODY HAS, FOR EXAMPLE,
BROCA'S APHASIA, THEY'RE UNABLE
TO TURN THEIR IDEAS INTO WORDS.
THEY MIGHT STILL BE ABLE TO MOVE
THEIR MOUTHS EFFECTIVELY, THEY
CAN SPEAK THINGS THAT SOUND LIKE
LANGUAGE BUT MAYBE THEY CAN'T
ACTUALLY -- THERE'S A STEP HERE
THAT'S MISSING. SO WE'RE GOING
TO LOOK AT THE IDEA STAGE.
CAN WE DECODE DIRECTLY FROM THE
IDEAS?
INTO WORDS?
OKAY.
WE'RE GOING TO BE DOING AN FMRI
EXPERIMENT.
THIS IS NOT YOUR STANDARD FMRI,
WE DO THINGS A BIT DIFFERENTLY.
SO SO WE HAVE OUR SUBJECTS GO
INTO AN MRI SCANNER AND JUST LAY
THERE AND LISTEN TO PODCASTS.
THAT'S THE WHOLE EXPERIMENT.
THERE'S NOTHING ELSE, NO FANCY
TASKS, NO LISTENING TO BEEPS AND
PUSHING BUTTONS, NONE OF THE
NORMAL FMRI STUFF, JUST LAY IN
THE SCANNER AND LISTEN TO
PODCASTS.
IT'S A REALLY FUN EXPERIMENT TO
BE IN, I'M ONE OF THE -- WE
LISTEN TO PODCASTS ANYWAY, WHY
NOT DO IT IN AN MRI SCAN E IT'S
FINE, RIGHT?
SO WE HAVE ALL THIS DATA.
IT'S NATURAL SPEECH DATA.
SO IT'S JUST SPEECH, IT'S
SOMEBODY TALKING.
SO IT'S NOT CONTROLLED IN THE
SAME WAY THAT MOST EXPERIMENTS
ARE.
THE KIND OF STANDARD TOOLS,
STANDARD ANALYSES THAT PEOPLE
MIGHT USE TO ANALYZE FMRI
EXPERIMENTS, THEY JUST DON'T
APPLY IN THIS KIND OF SETTING,
WE HAVE TO HAVE A NEW SET OF
TOOLS.
THANKFULLY THIS SET OF TOOLS HAS
BEEN DEVELOPED OVER DECADE,
MOSTLY FOR STUDYING VISION IN
THE BRAIN, AND WE'RE APPLYING
THEM HERE FOR LANGUAGE.
WE USE WHAT WE CALL VOXEL-WISE
ENCODING MODELS.
I'M GOING TO TELL YOU A LITTLE
HOW THAT WORKS AND THEN GET INTO
THE DECODING.
SO THE BASIC IDEA HERE IS WE
HAVE TWO THINGS, THE HUMAN FMRI
RESPONSES WE MEASURED FROM THE
BRAIN AND WE HAVE LANGUAGE -- WE
KNOW WHAT THE WORDS ARE IN THE
STORY.
WE CAN ANNOTATE THE STORIES.
WE CAN WRITE DOWN WHAT ALL THE
WORDS ARE AND EXACTLY WHEN THEY
OCCUR.
WHAT WE DO IN OUR MODELING
PROCEDURE IS WE TAKE THESE WORDS
AND WE EXTRACT SOME KIND OF
FEATURES FROM THEM.
WE TURN THEM INTO A NUMERICAL
REPRESENTATION.
SO FOR EVERY SECOND OF THE
STORY, FOR EVERY REALLY
TWO-SECOND PERIOD OF THE STORY,
WE HAVE SOME VECTOR OF NUMBERS
THAT SAYS WHAT'S HAPPENING IN
THIS TWO-SECOND PERIOD.
THE BEST TOOL WE HAVE FOR THIS
RIGHT NOW, I WILL SHOW YOU IN A
MOMENT.
SO WE EXTRACT THIS SET OF
NUMBERS AND THEN WE ESSENTIALLY
JUST FIT A REGRESSION MODEL.
WE FIT THESE LARGE SCALE --
LINEAR REGRESSION MODELS SO
SIMPLE IN THE WORLD OF MACHINE
LEARNING BUT DEFINITELY BEYOND
KIND OF STANDARD REGRESSION,
THESE ARE MODELS WITH TENS OF
THOUSANDS OR HUNDRED THOUSAND
PARAMETERS.
THAT PREDICT THE BRAIN RESPONSES
BASED ON THE WORDS IN THE STORY.
SO WE'RE ALWAYS TRYING TO
PREDICT THE BRAIN RESPONSES.
IF WE KNOW THE STORE OOO A
PERSON HEARD, CAN WE GUESS HOW
THAT PERSON WOULD RESPOND TO THE
STORY?
CAN WE GUESS THE TIME COURSE FOR
EVERY VOXEL IN THE BRAIN?
TURNS OUT THIS WORKS QUITE WELL.
SO WHAT ARE THE ACTUAL FEATURES
THAT WE USE?
WE'VE HIT ON SOMETHING THAT
WORKS INCREDIBLY WELL AND HAS
REALLY TAKEN OUR FIL FIELD KINDF
BY STORM WHICH IS USING DEEP
NEURAL NETWORK LANGUAGE MODELS.
SO EVEN IF THAT PHRASE IS NOT
FAMILIAR TO YOU, YOU HAVE
PROBABLY HEARD OF GPT IN SOME
FORM.
GPT IS ACTUALLY WHAT WE ARE
USING HERE.
CHATGPT THAT YOU ARE ALL
PROBABLY FAMILIAR WITH IS
GPT3.5, THEY CALL IT.
WE'RE USING G PT1, SO THE KIND
OF OLD, OLD -- PRECURSOR OLD
BEING LIKE 2019, BUT PRECURSOR
TO THE MODERN CHATGPT.
SO THIS IS A BIG NEURAL NETWORK
MODEL.
WE FEED IN THE WORDS IN THE
STORY, IT READS THE STORY AS THE
PERSON IS READING THE STORY, AND
THEN WE EXTRACT WHAT'S HAPPENING
INSIDE THIS NEURAL NETWORK.
WE EXTRACT KIND OF WHAT'S
HAPPENING IN ITS BRAIN.
THEN USE THAT TO PREDICT WHAT'S
HAPPENING IN HUMAN'S BRAIN.
IT WORKS BETTER THAN ANY OTHER
TOOLS WE HAD FOR MODELING BRAIN
RESPONSES TO LANGUAGE.
IT'S ASTONISHING HOW WELL THIS
WORKS.
THERE'S A WHOLE WORLD OF DOZENS
OF PAPERS THAT ARE LOOKING AT
THIS NOW.
WHY THIS WORKS SO WELL IS AN
EXCITING AND WEIRD AREA OF STUDY
AND IF YOU ASK QUESTIONS, I'LL
TALK TO YOU ABOUT IT.
OKAY.
SO WE CAN DO THIS, WE CAN FIT
THESE MODELS THAT PREDICT FOR AN
ACTIVITY, WE CALL THIS AN
ENCODING MODEL.
BUT NOW CAN WE USE THIS TO ALSO
DO DECODING?
IF WE CAN PREDICT HOW THE BRAIN
WOULD RESPOND TO A STORY, CAN WE
ALSO TAKE THE BRAIN RESPONSES
AND FIGURE OUT WHAT WERE THE
WORDS THAT CAUSED THEM?
SO WHAT I'M GOING TO BE TALKING
TO YOU ABOUT HERE, THIS IS WORK
BY MY FAB LAS GRAD STUDENT JERRY
TANG.
SO WE HAVE THE STORY, THE
NATURAL STIMULI THAT ARE GOING
INTO THE PERS PERSON'S EARS, HE,
WE'RE READING OUT FMRI DATA FROM
THAT, AND THE ENCODING MODELS
THAT I WAS JUST TALKING ABOUT,
THEY TAKE THE STORY AND THEY
PREDICT THE FMRI DATA, SO THEY
GO FROM THE STORY TO THE FMRI
DATA, AND WE'RE ASKING CAN WE
ALSO DO THE REVERSE?
CAN WE TAKE THE FMRI DATA AND
DECODE THAT BACK INTO THE STORY?
SO WE CAN FRAME THIS IN THIS
WAY.
I'M GOING TO TALK A LITTLE ABOUT
BAY'S RULE HERE.
WE WANT TO FIND THE MOST LIKELY
STIMULUS GIVEN THE RESPONSES.
WE WANT TO FORMULATE THIS
PROBABILITY DISTRIBUTION WHICH
IS HIDING KIND OF BEHIND THE
TRANSCRIPT HERE, PROBABILITY OF
STIMULUS IF GIVEN RESPONSE.
WE CAN TRY TO OPTIMIZE IT, FIND
THE MOST LIKELY STIMULUS.
TURNS OUT THIS IS REALLY HARD.
THIS IS REALLY HARD TO DO
DIRECTLY.
AND THERE'S A LOT OF REASONS FOR
THIS, I'LL GIVE YOU KIND OF ONE
OF THE MAIN ONES, IT'S FMRI.
LIKE YOU HEARD THIS MORNING,
FMRI WORKS ON MEASURING
HEMODYNAMIC RESPONSES.
WE'RE MEASURING BLOOD FLOW,
WE'RE NOT MEASURING NEURAL
RESPONSES.
SO IF YOU HAVE A BURST OF NEURAL
ACTIVITY, SORT OF SMALL BURSTS
OF NEURAL ACTIVITY, THIS
ACTUALLY CAUSES BLOOD FLOW TO
RISE OVER THE COURSE OF TWO,
THREE, FOUR SECONDS, AND THEN
DECAY OVER ANOTHER FIVE OR
SIX SECONDS.
SO YOU GET 10 SECONDS OF KIND OF
MUSHY STUFF COMING OUT OF ONE
INSTANTANEOUS BURST OF NEURAL
ACTIVITY.
THIS IS A PROBLEM.
THERE ARE A LOT OF WORDS THAT
OCCUR IN 10 SECONDS.
ESPECIALLY IF YOU'RE TALKING AS
QUICKLY AS I AM RIGHT NOW.
SO EVERY BRAIN IMAGE THAT WE
MEASURE IS ACTUALLY INFORMATION
COLLAPSED ACROSS, I DON'T KNOW,
10, 15 SECONDS MAYBE?
THERE'S INFORMATION ABOUT 30 OR
40 WORDS PACKED INTO ONE BRAIN
IMAGE IN SOME KIND OF MESSY
JUMBLE.
THE SAME WORDS -- THERE'S A
STATISTICAL MESS HERE IN KIND OF
DISENTANGLING THIS.
BUT IT TURNS OUT THAT THERE'S
SOME PRETTY CLEVER IDEAS FOR HOW
TO GET AROUND THIS.
THE SOLUTION THAT WE USED IS
SOMETHING CALLED BAYESIAN
DECODING.
THIS, I THINK, WAS REALLY
DEVELOPED AND PUT IN THIS FORM
BY MY OLD COLLEAGUE IN HIS PAPER
FROM 2011 WHERE THEY WERE
ACTUALLY TO ACTUALLY DECODE
VIDEOS FROM SOMEONE'S BRAIN
WHICH IS PRETTY INCREDIBLE AND I
THINK NOT REALLY ECLIPSED TODAY,
AND THE IDEA IS WE'RE GOING TO
APPLY BAYES' RULE HERE.
WE WAS -- BAYES RULE SAYS YOU
CAN FACTORIZE THAT, YOU CAN FLIP
IT AROUND.
SO WE CAN TAKE IT AND
CHARACTERIZE THAT AS THE
PROBABILITY OF THE RESPONSE
GIVEN THE STIMULUS, MULTIPLIED
BY P OF S, JUST THE PROBABILITY
OF THE STIMULUS.
SO WHEN YOU MULTIPLY THESE TWO
TOGETHER, THEN YOU GET PF.
S GIVEN R.
SO WHY IS THIS EASIER?
SO P OF R, GIVEN S, THIS IS JUST
THE ENCODING MODEL WE ALREADY
HAVE.
THIS IS SAYING YOU FEED IN SOME
STIMULUS, HOW IS THE BRAIN GOING
TO RESPOND TO IT?
GREAT.
WE ALREADY HAVE A DECADE OF
WORKING ON THIS, IT WORKS PRETTY
WELL.
AND THEN P OF S, THIS IS JUST
OUR NEURAL NETWORK LANGUAGE
MODEL, THIS IS ACTUALLY WHAT
NEURAL NETWORK MODELS ARE
TRAINED TO DO.
THIS IS WHAT CH CHATGPT WAS TRAD
TO DO, GUESS THE NEXT WORD.
WHAT IS A REASONABLE SEQUENCE OF
WORDS AND WHAT IS A
NON-REASONABLE SILLY NONSENSE
SEQUENCE OF WORDS.
SO WE MATCHED THESE TWO THINGS
TOGETHER AND THAT BECOMES OUR
DECODER.
SO I'M NOT GOING TO GO INTO
DETAILS OF THIS, ASK ME
QUESTIONS IF YOU WANT.
LET ME SHOW YOU SOME EXAMPLES OF
HOW THIS LOOKS.
SO HERE THE ACTUAL STIMULUS OF
THE PERSON HEARD WAS I DIDN'T
KNOW WHETHER TO SCREAM, CRY OR
RUN AWAY, INSTEAD I SAID LEAVE
ME ALONE, I DON'T NEED YOUR
HELP.
WITWHAT THE MODEL PULLED OUT OF
THIS WAS SHE JUST SAID I TOLD
YOU TO LEAVE ME ALONE, YOU CAN'T
HURT ME.
SO I THINK THIS NICELY
ILLUSTRATE SOME OF THE -- BOTH
ADVANTAGES AND DISADVANTAGES OF
THIS APPROACH.
SO IT JUST MISSES SOME THINGS.
THE RUN AWAY AND I DON'T NEED
YOUR HELP ARE COMPLETELY MISSING
IN THE DECODED VERSION, IT SEEMS
LIKE IT KIND OF FIT, RIGHT, BUT
IT IS NOT ACTUALLY SOME OF THE
WORDS THAT WERE SAID.
THIS IS SOMETHING THAT IT DOES
ALL THE TIME.
IT GET THE WRONG PRONOUNS, SO
INSTEAD OF I SAID, SHE SAID,
THIS IS A WEIRD KIND OF ERROR
AND WE SEE IT SO MUCH THAT WE'RE
NOT REALLY SURE WHY IT HAPPENS,
WE'RE TRYING TO DIG INTO THAT
MORE.
BUT IT GET A SURPRISING NUMBER
OF THINGS CORRECT.
THE OVERALL KIND OF GIST FEELS
RIGHT.
IT'S A LITTLE SNIPPET OF A STORY
WITH THE SAME KIND OF CONTENT,
IT'S TELLING A SIMILAR KIND OF
STORY.
IT EVEN GETS SOME OF THESE EXACT
PHRASES CORRECT.
RIGHT?
SCREAM, CRY, SCREAM AND CRY.
LEAVE ME ALONE, LEAVE ME ALONE.
WHICH HONESTLY, I DON'T KNOW,
HAVING DONE FMRI FOR 15, 20
YEARS, THIS IS SHOCKING TO ME
THAT THIS WORKS.
FMRI SUCKS, BUT STILL, WE CAN DO
THINGS WITH IT.
LET ME SHOW YOU ONE MORE
EXAMPLE.
HERE THE ACTUAL STIMULUS --
AGAIN, THIS IS SOMEBODY
LISTENING TO A STORY AND WE'RE
DECODING WHAT THEY'RE HEARING.
THE ACTUAL EXAMPLE WAS I DON'T
HAVE MY DRIVER'S LICENSE YET AND
I JUST JUMPED OUT RIGHT WHEN I
NEEDED TO, SHE SAYS WHY DON'T
YOU COME BACK.
THE DECODED VERSION IS SHE HAS
NOT EVEN STARTED TO LEARN TO
DRIVE YET, I HAD TO PUSH HER OUT
OF THE CAR, I SAID WE WILL TAKE
HER HOME.
SO YOU KNOW, IT GETS SOME STUFF
APPROXIMATELY RIGHT.
I DON'T HAVE A DRIVER'S LICENSE
YET, SHE'S NOT EVEN LEARNED --
STARTED TO LEARN TO DRIVE YET,
NOTE THAT, LIKE, VERY FEW WORDS
ACTUALLY MATCH THERE, EACH EVEN
THOUGH THAT'S VERY MUCH THE SAME
SENTIMENT.
I JUST JUMPED OUT, PUSH HER OUT
OF THE CAR, VERY CLOSELY
RELATED, JUMPED OUT OF THE CAR,
THAT'S ACTUALLY IMPLICIT THERE.
WE DO GET, AGAIN, SOME EXACT
MATCHES, AND THIS IS A THING
THAT I THINK IS KIND OF
INTERESTING, I HAD TO PUSH HER
OUT OF THE CAR, THIS IS
SOMETHING THAT THE PERSON WHO'S
LISTENING TO THE STORY, THEY
KNEW THIS BECAUSE IT'S
RECOUNTING SOMETHING THAT
HAPPENED EARLIER IN THE STORY
BUT THE WORD DIDN'T ACTUALLY
APPEAR HERE.
MAYBE THIS IS CHANCE, MAYBE THIS
IS JUST LUCKY, BUT I THINK THIS
IS A NEAT INDICATION OF SOME
THINGS THAT THE MODEL CAN DO.
ALL RIGHT.
SO IN GENERAL, WHAT WE SEE IS
THAT OUR DECODER KIND OF
CAPTURES THE GIST, WHAT WAS THE
STORY ABOUT.
IT DOESN'T GET INDIVIDUAL WORDS
CORRECT BECAUSE AGAIN THEY'RE
HAPPENING QUICKLY AND FMRI IS
SLOW AND MESSY.
WE CAN LOOK THE A QUANTITATIVE
METRICS AND PERFORMANCE, THE
WORD ERROR RATE IS VERY HIGH,
90-PLUS%.
BUT FOR OTHER METRICS LIE BERT
SCORE, A MODERN METRIC THAT
TRIES TO MEASURE HOW MUCH IS THE
MEANING OF TWO PIECES OF TEXT
OVERLAP, WE'RE 20 STANDARD
DEVIATIONS AWAY FROM THE NULL
HYPOTHESIS, WHICH IS PRETTY GOOD
IF YOU ASK A PHYSICIST.
LET'S NOT BELABOR THIS.
LET ME TELL YOU ABOUT SOME COOL
THINGS WE CAN DO WITH THIS.
SO ONE OF THE THINGS WE THOUGHT
WHEN WE GOT THIS WORKING WAS
OKAY, YOU CAN DO IT WITH
SOMEBODY LISTENING TO A STORY.
CAN YOU DO IF THEY'RE JUST
THINKING A STORY?
LIKE IMAGINING TELLING A STORY?
SO WE HAD OUR SUBJECTS MEMORIZE
STORIES AND THEN PRACTICE
TELLING THEM.
AND THEN GO INTO THE SCANNER AND
TELL THEM WITHOUT SPEAKING THEM.
SO WE'D GIVE THEM QUEUES LIKE
START TELLING YOUR STORY NOW,
STOP NOW.
AND HERE ARE SOME EXAMPLES OF
HOW WELL THIS WORKED.
SO WHAT WE THINK THE PERSON WAS
IMAGINING WHEN THEY WERE
PRACTICING TELLING THE STORY,
THEY SAID LOOK FOR A MESSAGE
FROM MY WIFE SAYING SHE HAD
CHANGED HER MIND AND THAT SHE
WAS COMING BACK.
THE DECODED VERSION IS TO SEE
HER FOR SOME REASON I THOUGHT
MAYBE SHE WOULD COME TO ME AND
SAY SHE MISSES ME, CLEARLY,
AGAIN, KIND OF GETTING THE GIST.
I WON'T READ THROUGH THESE, BUT
YOU CAN SEE THAT THEY MATCH
PRETTY WELL.
QUANTITATIVELY, THIS IS WORSE
THAN LISTENING TO A STORY, LIKE
THE QUALITY IS A LITTLE BIT
WORSE.
BUT IT STILL WORKS QUITE WELL.
I WANT TO POINT THIS OUT ALSO AS
A REAL DIFFERENCE FROM OTHER
KIND OF LANGUAGE BCIs THAT
CURRENTLY EXIST.
SO THESE THINGS THAT, FOR
EXAMPLE, TRY TO DECODE THE
SPEECH ARTICULATIONS OR
HANDWRITING, THESE THINGS
GENERALLY LIKE THE PERSON HAS TO
ACTUALLY TRY TO DO IT.
THEY HAVE TO ACTUALLY TRY TO
SPEAK IN ORDER FOR THAT TO WORK,
EVEN IF THEY'RE UNABLED TO
SPEAK, DUE TO, SAY A STROKE,
THEY HAVE TO TRY IN ORDER FOR
THEIR MOTOR CORTEX TO BE ACTIVE
IN THE RIGHT WAY.
OUR SUBJECTS WERE JUST IMAGINING
HERE.
THEY WERE NOT TRYING.
THEY COULD SPEAK IF THEY WANTED
TO.
THEY'RE JUST LAYING HERE
THINKING A STORY INSIDE THEIR
HEADS AND YET THIS WORKED.
I THINK THAT'S A PRETTY BIG STEP
FROM WHAT WE COULD DO BEFORE.
YEAH, BLAH, BLAH, BLAH.
OKAY.
SO WHEN WE SAW THESE RESULTS, WE
WERE LIKE THIS IS A LITTLE
SCARY, MAYBE, MAYBE WE SHOULD
THINK ABOUT WHAT ARE THE
LIMITATION ON THIS, WHAT ARE THE
KIND OF BOUNDARIES, WHAT THINGS
CAN'T THIS DO, WHAT ARE THE
IMPORTANT QUESTIONS WE MIGHT
WANT TO ASK?
SO ONE -- WHY DID I TELL THIS IN
THE WRONG ORDER?
OKAY.
LET ME SHOW YOU THIS FIRST AND
THEN I'LL GET BACK TO THE
BOUNDARIES.
SO THIS WORKS WITH LANGUAGE, IT
WAS TRAINED ON LANGUAGE, BUT WE
THINK IT'S GETTING IT IDEAS.
THAT'S WHY IT'S ABLE TO CREATE
SOMETHING THAT'S LIKE THE GIST
OF THE STORY WITHOUT GETTING THE
EXACT WORDS CORRECT.
SO WHAT IF WE PUT IN SOMETHING
TO THE BRAIN THAT'S NOT
LANGUAGE?
BUT THAT STILL HAS IDEAS?
SORRY, IS THERE A WAY THAT WE
COULD HIDE THE TRANSCRIPT HERE
FOR A MOMENT?
I'M GOING TO HAVE LIKE A
TRANSCRIPT RUNNING ON THE BOTTOM
OF THE SLIDE THAT WE KIND OF
NEED TO READ.
I GUESS I COULD READ IT ON MY
SCREEN.
NEVER MIND.
WHATEVER.
I'LL JUST TRY THIS.
OKAY.
SO WE HAD OUR SUBJECTS WATCH A
SHORT VIDEO.
THIS IS JUST A SILENT VIDEO AND
THEN -- IT'S OKAY.
I'LL JUST READ IT.
SORRY.
DOES THIS -- OKAY.
IT'S FINE.
WE'LL FIGURE IT OUT.
SO WE HAD SUBJECTS JUST WATCH A
VIDEO.
NO SOUND, NO LANGUAGE, AND THEN
WE PUT THAT BRAIN DATA INTO OUR
LANGUAGE DECODER AS IF THEY WERE
LISTENING TO A STORY.
AND LET ME READ TO YOU WHAT IT
GAVE US HERE.
SO YOU'RE GOING TO SEE THE VIDEO
AND I'M GOING TO READ THE
TRANSCRIPT OF -- YEAH.
SO I KNOW YOU DID IT FOR ME AND
FOR YOU, THEN SHE TOOK MY HAND
AND HELD TO HER LIPS, SHE KISSED
IT, I SMILED AND SHE PULLED ME
IN FOR A HUG.
I GOT HER BACK FOR ABOUT HOURS I
HAD TO STOP THE BLEEDING AND
GAVE HER MY SHIRT TO PUT OVER
IT.
SO THIS DOES A -- ACTUALLY A
FAIRLY GOOD JOB AT JUST GETTING
AT THE IDEA, WHICH WE FOUND KIND
OF SHOCKING, AND REALLY AGAIN
SPEAKS TO THE HIGH LEVEL THAT
WE'RE GETTING AT HERE.
OKAY.
I'M GOING TO SKIP THIS IN THE
INTEREST OF TIME.
SUBJECTS CAN CONTROL WHAT WE'RE
READING OUT.
SUBJECTS CAN RESIST DECODING IF
THEY DON'T WANT TO WORK WHEN
LISTENING TO A STORY, THEY CAN
THINK ABOUT OTHER THINGS AND IT
STOPS WORKING WHICH IS AN ACTIVE
PROCESS BUT STILL THEY HAVE SOME
MODICUM OF CONTROL HERE.
AND IT TURNS OUT WE CAN DO THIS
FOR MANY DIFFERENT BRAIN AREAS.
YOU DON'T HAVE TO USE A SPECIFIC
BRAIN EA YOU CAN USE MANY
DIFFERENT THINGS.
AREA, YOU CAN USE MANY
DIFFERENT THINGS.
CURRENTLY ONE DATA IS REQUIRED.
WE'VE TRIED A LOT OF THINGS, A
LOT OF THE MORE MODERN METHODS
HERE.
IT HARD.
WE REALLY NEED A LOT OF DATA
FROM THE ONE PERSON, WHICH WE
CAN TALK MORE ABOUT LATER.
ONE SMALL THING TO ADD ON TO
THIS, SO GPT IS OLD.
RIGHT?
2019.
CAN WE DO BETTER WITH THESE
BETTER MODELS THAT WE HAVE
TODAY?
IT TURNS OUT YES, WE CAN WITH
THESE BIGGER MODELS.
OUR CURRENT MODELS, THIS IS OLD
GPT, THIS IS OUR 30 BILLION
PARAMETER MODELS, THESE JIE NAN
TICK MODELS WE HAVE TODAY.
WE'RE TRYING TO TRANSLATE THIS
INTO DECODING PERFORMANCE NOW
AND WE'LL SEE WHAT HAPPENS
THERE.
AND MAYBE AS THINGS GET BIGGER,
LIKE GPT4, THEY'RE WAY OUT OVER
ON THE RIGHT-HAND SIDE HERE.
ALL RIGHT.
I'LL SKIP THE CONCLUSIONS.
YOU ALL KNOW WHAT I WAS TALK
ABOUT.
IT WASN'T THAT LONG.
IT WORKS PRETTY WELL.
WE THINK IT CAN WORK BETTER.
WE THINK IF WE DO CLOSED LOOP
DECODING, REALTIME DECODING IT
WILL GET EVEN BETTER BECAUSE
NEURAL FEEDBACK ESSENTIALLY THE
PERSON CAN CHANGE WHAT THEY DO.
SO LET ME STOP HERE, THANK THE
PEOPLE INVOLVED IN THIS,
ESPECIALLY JERRY AND R.J., MY
GRAD STUDENTS.
THANK YOU.
[APPLAUSE]
>>THANK YOU.
I THINK I WANT TO KEEP TO ONE
REALLY SMALL QUESTION.
MAYBE HALF OF A NINA QUESTION OR
SOMETHING LIKE THAT.
>>SO WHEN YOU SHOWED THE
ENCODING MODEL, IT LOOKED LIKE
YOU SHOWED THAT YOU DO IT BASED
ON WORDS RATHER THAN PHRASES.
>>YEAH.
>>CAN YOU TALK ABOUT WHY IT'S
WORDS INSTEAD OF PHRASES?
>>SO IT ACTUALLY DOES OPERATE
ON PHRASES.
YOU KNOW, WE FEED IN THE WORDS
ONE AT A TIME, BUT THEN THE
MODEL KIND OF SEES MANY WORDS.
AND THIS IS ONE OF THE THINGS
THAT WAS A BIG ADVANTAGE OF
USING THESE NEURAL NETWORK
LANGUAGE MODELS, IS THAT THEY
CAN ACTUALLY PROCESS PHRASES
INSTEAD OF JUST INDIVIDUAL
WORDS, WHICH IS WHAT WE COULD DO
BEFORE.
AND IT'S A BIG LEAP UM.
THUP.THAT REALLY HELPS THINGS WK
WELL.
>>I ACTUALLY HAVE A QUESTION,
SPECULATIVE.
IF YOU COULD LOOK AT AN
INDIVIDUAL WHO'S IMPLANTED WITH
STEER YOWITHSTEREO EEG ELECTRODD
GIVE YOU MUCH GREATER TEMPORAL
RESOLUTION, DO YOU THINK YOU
COULD DO BETER?
>>I'VE ASKED HIM THIS.
>>WE'RE NOT SURE, WE'RE REALLY
NOT SURE.
I THIS IS A REALLY INTERESTING
QUESTION BECAUSE THE THING YOU
SACRIFICE WITH SAIR YOE EEG IS
COVERAGE.
WITH FMRI, IT'S SLOW, IT'S
CRUMMY IN A LOT OF WAYS.
>>YOU COULD DO THE EXPERIMENT
HERE.
YOU COULD JUST NOT USE PART OF
YOUR DATA AND --
>>WHICH ACTUALLY WORKS KIND OF
WELL.
YEAH.
BUT IT'S STILL -- THAT'S STILL
USING THOUSANDS OF VOXEL THE IN
EACH OF THESE AREAS WHEREAS
STEREO EEG IS GOING TO BE 100
TOTAL SIGNALS.
>>THERE WILL BE A TRADEOFF
BUT --
>>IT'S A GOOD QUESTION.
I THINK IT'S PLAUSIBLE.
WE'RE EXCITED TO TRY.
>>I'M GOING TO TURN US TO THE
LAST SPEAKER OF THIS PANEL AND
THEN I'M EXCITED FOR THE GENERAL
DISCUSSION.
SO IT'S MY PLEASURE TO INTRODUCE
DR. MICHAEL YOUNG, WHO IS A
NEUROLOGIST AND RESEARCHER AT
MASSACHUSETTS GENERAL HOSPITAL,
HARVARD MEDICAL SCHOOL.
HE IS THE ASSOCIATE DIRECTOR OF
THEIR NEURORECOVERY CLINIC.
DR. YOUNG RECEIVED AN M.D. FROM
HARVARD MEDICAL SCHOOL AND FILM
AND PHILOSOPHY FROM CAMBRIDGE.
DR. YOUNG COMPLETED NEUROLOGY
RESIDENCY AND A NEURORECOVERY
FELLOWSHIP AT MASS GENERAL
BIRMINGHAM SPECIALIZING IN
LONGITUDINAL CARE OF PATIENTS
RECOVERING FROM SEVERE BRAIN
INJURIES AND DISORDERS OF
CONSCIOUSNESS.
HIS RESEARCH FOCUSES ON
IMPROVING CARE SYSTEMS THROUGH
NEUROTECHNOLOGY, NEUROETHICS AND
NEUROSCIENCE.
I'M LACKING FORWARD TO YOUR
TALK, DR. YOUNG.
>>THANK YOU.
THANK YOU SO MUCH FOR THE
INTRODUCTION.
IT'S SUCH AN HONOR TO BE HERE,
AND TO BE LEARNING FROM
EVERYBODY.
I'M DEEPLY GRATEFUL TO THE
NEUROETHICS WORKING GROUP AND
THE ORGANIZERS FOR CONVENING US
ALL HERE TO DISCUSS THIS
INCREASINGLY GROWING AREA OF THE
ETHICAL DIMENSIONS OF NEURAL
DATA SHARING, WHICH IS A FIELD
WHOSE QUESTIONS ARE ENDLESSLY
INTRIGUING AND RAPIDLY
EXPANDING.
AS MY SLIDES COME UP, JUST BY
WAY OF BACKGROUND, MY INTEREST
IN THIS FIELD AND RELATED
RESEARCH IN NEUROETHICS TOOK
ROOT WHEN I WAS IN COLLEGE AND
LATER GRADUATE SCHOOL IMMERSED
IN PHILOSOPHY.
QUESTIONS RELATING TO THE MIND,
CONSCIOUSNESS, IDENTITY, HOW TO
CHARACTERIZE THESE CONCEPTS AND
HOW SOMETHING SO PHENOMENON RICH
SEEMED TO BE AT THE CORE OF
EFFORTS TO UNDERSTAND WHAT
MATTERS MOST IN THE HUMAN
EXPERIENCE WHICH IS A GOAL
THAT'S SHARED BY
PHILOSOPHERS,ET ETHICISTS,
NEUROSCIENTISTS, AND CLINICIANS.
AND WHEN I LATER ENTERED MEDICAL
SCHOOL AND RESIDENCY IN
NEUROLOGY, I WAS STRUCK BY HOW
SOME OF THESE CONCEPTS AND
QUESTIONS WERE FRAMED AND
APPROACHED IN A WAY THAT FORMS A
SEPARATE YET COMPLEMENTARY
MATRIX FOR THINKING ABOUT SOME
OF THESE DIFFICULT DILEMMAS THAT
PHILOSOPHERS HAVE BEEN GRAPPLING
WITH FOR AGES.
AND WHILE IT WOULD BE IMPOSSIBLE
TO FULLY CANVAS ALL OF THESE
ISSUES IN THIS SHORT TALK, WHAT
I HOPE TO PROVIDE IS A WINDOW
INTO HOW WE AT THE CENTER FOR
NEUROTECHNOLOGY AND
NEURORECOVERY AT MGH HAVE BEEN
CONSIDERING THESE ISSUES IN OUR
RESEARCH AND CLINICAL PROGRAMS
INVOLVING PLANTABLE
NEUROTECHNOLOGIES AND A
POTENTIAL ROAD MAP TO FOSTER
RESPONSIBLE SHARING AND USE OF
INTRACRANIAL NEUROPHYSIOLOGY
DATA ACROSS THE NEUROTECHNOLOGY
DEVELOPMENT LIFECYCLE IN ORDER
TO ENSURE THAT INNOVATION
PROCEEDS RESPONSIBLY WHILE AT
THE SAME TIME PROTECTING THE
RIGHTS, WELFARE, AND SAFETY OF
PERHAPS THE MOST IMPORTANT
PEOPLE IN NEURAL INTERFACE
RESEARCH, NAMELY, THE CLINICAL
TRIAL PARTICIPANTS AND PATIENTS.
JUST BY WAY OF BACKGROUND, AND
AACKNOWLEDGMENT AND DISCLOSURES,
NO FANGS DISCLOSURE, BUT I DO
ACKNOWLEDGE THAT THE SIT ON THE
IEEE NEUROETHICS WORKING GROUP
AND MEDICAL SUBCOMMITTEE AND
WHILE I WILL NOT BE SPEAKING IN
THAT CAPACITY TODAY, SOME OF THE
MATERIAL THAT I WILL PRESENT
WILL BE REFLECTIVE OF OUR
COMMITTEE'S COLLECTIVE WORK.
I'VE ALSO RECEIVED FUNDING FROM
THE NIH BRAIN INITIATIVE AND
ALSO BY WAY OF ACKNOWLEDGMENT,
REALLY NONE OF THE WORK THAT I'M
PRESENTING ON TODAY WOULD BE
POSSIBLE WITHOUT THE SUPPORT OF
AN AMAZING TEAM AT THE CENTER
FOR NEUROTECHNOLOGY AND
NEURORECOVERY, AND AMAZING
MENTORSHIP OF DR. LEE HOTBERG
AND DR. BRIAN EDLOW.
WE'LL BEGIN WITH AN OVERVIEW OF
IMPLANTABLE NEUROTECHNOLOGIES AS
POTENTIAL SOURCES OF SENSITIVE
NEURAL DATA.
WE'LL THEN TAKE A LOOK AT SOME
OF THE TEUNTSZ AND ETHICAL
CHALLENGES INVOLVED IN
COLLECTING AND SHARING NEURAL
DATA THAT ARE RECORDED BY
IMPLANTABLE NEUROTECHNOLOGIES
SPECIFICALLY, AND WE'LL CONCLUDE
WITH A VIEW TOWARD POTENTIAL
FUTURE DIRECTIONS, INCLUDING
NEUROETHICS INFORMED APPROACHES
TO NEURAL DATA SHARING, AND
COLLECTION.
IN CONSIDERING WHAT WE MEAN WHEN
WE ARE DISCUSSING NEURAL DATA
AND ITS POTENTIAL SOURCES, WE'VE
HEARD A LOT THIS MORNING, AND
IT'S IMPORTANT TO UNDERSTAND THE
CURRENT LANDSCAPE OF WHAT WE'RE
DEFINING AS IMPLANTABLE
NEUROTECHNOLOGIES.
IN MY WORK WITH THE WORKING
GROUP, WE'RE CONSIDERING ROUGHLY
FIVE CATEGORIES OR METHODS OF
NEUROTECHNOLOGY, EACH OF WHICH
PRODUCES NEURAL DATA WITH
DIFFERENT TYPES OF RISK.
THE FIRST CATEGORY ARE
NEUROTECHNOLOGIES FOR SENSING OR
RECORDING.
THESE ARE DEVICES THAT ARE
CAPABLE OF CAPTURING INFORMATION
WITH THE ACTIVITY OF THE BRAIN,
FOR EXAMPLE, EEG SIGNALS, OLD
SIGNAL, ACTIVITY-DEPENDENT BLOOD
FLOW OR OTHER KIND OF SIGNALS
THAT WE COULD CAPTURE AND DRAW
INFERENCES FROM ABOUT THE
NERVOUS SYSTEM.
SECOND, THERE ARE TECHNOLOGIES
TO STIMULATE OR MODULATE THE
BRAIN.
THESE ARE TECHNOLOGIES THAT ARE
CAPABLE OF ACTUATING OR
MODULATING THE ACTIVITY OF THE
NERVOUS SYSTEM AND CAN BE
EXTERNAL TO THE BRAIN LIKE TDCS
OR IMPLANTED TECHNOLOGIES LIKE
DEEP BRAIN STIMULATION OR VAGUS
NERVE STIMULATION.
THERE ARE CLOSED LOOP DEVICES
AND WE HEARD A LITTLE BIT ABOUT
THESE.
THESE ARE TECHNOLOGIES THAT
COMBINE SENSING WITH STIMULATION
WHERE THE RECORDED SIGNALS ARE
USED TO REGULATE MODULATION IN A
FEEDBACK CONTROL LOOP, OR THERE
ARE TECHNOLOGIES FOR DIRECT
PHYSICAL AND BIOLOGICAL
MODIFICATION THAT PHYSICALLY
ALTER THE NERVOUS SYSTEM BY
MODIFYING ITS PHYSIOLOGY.
EXAMPLES MIGHT INCLUDE FOCUSED
ULTRASOUND PULSATION OR
OPTOGENETICS WITH OPTICAL
STIMULATORS, AND FINALLY, ONE
MIGHT CONSIDER DEVICES FOR
AUGMENTATION AND FACILITATION.
THESE ARE NEUROTECHNOLOGIES THAT
SUPPORT OR EXPAND THE EXISTING
FUNCTION OF THE NERVOUS SYSTEM
AND MIGHT INCLUDE THINGS LIKE
ROBOTICS, EXOSKELETONS, RETINAL
IMPLANTS TO RESTORE VISION, OR
OTHER SIMILAR NEURAL
PROSTHETICS.
AND I'LL EMPHASIZE THAT WHILE
THERE'S OVERLAP BETWEEN
TECHNOLOGIES IN EACH OF THESE
CATEGORIES, UNDERSTANDING THESE
FUNCTIONS PROVIDES A HELPFUL
FRAMEWORK FOR NEUROETHICS
ANALYSIS IN BOTH CLINICAL AND
RESEARCH CONTEXTS, ESPECIALLY AS
IT PERTAINS TO NEURAL DATA CHECK
SHUP AND SHAR
COLLECTION AND SHARING.
A RAPID RISE OF RESEARCH
ACTIVITY AND FUNDING THROUGH
LARGE SCALE BRAIN INITIATIVES
AND INDUSTRY HAS CONTRIBUTED TO
OVER 16,000 PATENTS IN
HEALTH-RELATED NEUROTECHNOLOGY,
JUST BETWEEN 2008 AND 2016, WITH
SIGNIFICANT UPWARD TREND.
THESE INCLUDE PATENTS FOR
NEUROMODULATION DEVICES,
NEUROPROSTHETICS, NEUROIMAGING,
NEUROSENSING,
NEUROREHABILITATION AND
BRAIN-COMPUTER INTERFACES AND IN
AGGREGATE, REFLECT A RAPIDLY
GROWING FIELD THAT WILL
INEVITABLY YIELD INCREASINGLY
RICH NEURAL DATA.
MANY OF THESE INTELLECTUAL AND
FINANCIAL COMMITMENT ARE
BEGINNING TO ENTER AND IMPACT
CLINICAL PRACTICE.
THIS TABLE DISPLAYS JUST SOME OF
THE NEUROTECHNOLOGIES THAT HAVE
RECEIVED FDA APPROVAL IN
CHRONOLOGICAL ORDER.
IN 2002 WAS THE FIRST TAME AN
INTRACRANIAL DEVICE WAS
IMPROVED, MAINLY DEEP BRAIN
STIMULATION INITIALLY FOR TREMOR
AND LATER FOR DYSTONIA AND
OBSESSIVE COMPULSIVE DISORDER.
OVER THE YEARS, PROGRESS HAS
ACCELERATED WITH FDA APPROVALS
FOR RESPONSIVE NEUROSTIMULATION
DEVICES AND OTHER FORMS OF
NEUROMODULATION THAT WE'VE BEEN
SEEING OVER THE PAST SEVERAL
YEARS.
APART FROM THESE IMPLANTABLE
NEUROTECHNOLOGIES THAT HAVE
ALREADY BEEN APPROVED FOR
CLINICAL USE, THERE IS VIGOROUS
RESEARCH IN OUR CENTER AND
AROUND THE WORLD EXPLORING HOW
TO LEVERAGE INCREASING
CAPACITIES TO DETECT AND
INTERPRET NEURAL SIGNALS IN
ORDER TO RESTORE OR REHABILITATE
FUNCTION FOR PEOPLE WITH SEVERE
SPEECH AND MOTOR IMPAIRMENTS.
AND OUR CENTER UNDER THE
LEADERSHIP OF DR. LEE HOTBERG
HAS FOR MANY YEARS BEEN DEVOTED
TO THE DEVELOPMENT OF
TECHNOLOGIES TO RESTORE THE
INDEPENDENCE OF PEOPLE AFFECTED
BY NEUROLOGIC DISEASE THROUGH
BRAIN-COMPUTER INTERFACES.
WE'VE HEARD ABOUT -- COMPUTER
INTERFACES TODAY ALREADY,
INTENDED TO TRANSLATE SIGNALS
FROM THIS NERVOUS SYSTEM INTO
COMMANDS FOR EXTERNAL OR
INTERNAL DEVICES AND IN SO
DOING, BCIs AIM TO RESTORE OR
REHABILITATE LOST FUNCTIONS,
POTENTIALLY IMPROVING
COMMUNICATION, EMOTION,
INTERACTION WITH THE ENVIRONMENT
AND ACHIEVEMENT OF PERSONAL
GOALS FOR PEOPLE WITH SEVERE
NEUROLOGICAL IMPAIRMENTS.
AS BCIs ADVANCE, AND WHAT WE
SEE ON THE SLIDE HERE ARE JUST
SEVERAL OF THE MANY CLINICAL
TRIALS THAT ARE ONGOING IN THE
BCI SPACE, THE ETHICAL
CONSIDERATION SURROUNDING
PRIVACY, STORAGE AND SHARING OF
THE NEURAL DATA THAT THEY RECORD
ARE OF GROWING IMPORTANCE.
AND THIS IS ALONGSIDE MANY OTHER
ETHICAL QUESTIONS SURROUNDING
THINGS LIKE USER AGENCY,
RESPONSIBILITY, DECISION-MAKING
CAPACITY, PERSONAL IDENTITY,
ISSUES OF ENHANCEMENT, WHERE TO
DRAW THE BOUNDARY BETWEEN
RESEARCH AND CLINICAL PRACTICE,
AND ACCESS TO TECHNOLOGIES AND
DISPARITIES IN PARADIGMS OF
ACCESS IN CLINICAL TRIAL
ENROLLMENT.
NORMATIVE CONSIDERATIONS THAT
SURROUND NEURAL DATA CAPTURE AND
SHARING ARE MAGNIFIED, PERHAPS,
BY ACCELERATING ADVANCES IN
SENSOR TECHNOLOGIES, WHICH HAVE
MADE IT POSSIBLE TO RECORD FROM
LARGER NEURONAL ENSEMBLES AT
UNPRECEDENTED RESOLUTION IN
HUMANS, SUCH AS THIS ELEGANT
WORK LED BY SYD CASH AND ANG
L --USING SILICON NEUROPIXEL PRS
TO ACHIEVE LARGE SCALE NEURAL
RECORDINGS WITH SINGLE NEURON
RESOLUTION.
AS OUR ABILITIES TO SIM TAIN
JUSTLY RECORD NEURONS ACROSS
BRAIN REGIONS EXPANDS, THE
RICHNESS OF THE NEURAL DATASETS
PARTICULARLY WITH RESPECT TO
SENSITIVE INTERNAL COGNITIVE
STATE WILL FORESEE BLI GROW.
INNOVATIONS SUCH AS THESE ARE
PART OF AN EXPANDING SUITE OF
NEURAL RECORDING TECHNOLOGIES
WITH HIGHER RESOLUTION,
POTENTIALLY HIGHER SPATIAL
COVERAGE, STABILITY, AND
FUNCTIONALITY, AND WHILE THESE
TECHNOLOGIES ON THE ONE HAND
CARRY IMMENSE PROMISE TO ADVANCE
NEUROSCIENCE AND THE DEVELOPMENT
OF BCIs CAPABLE OF RESTORING
INDEPENDENCE TO PEOPLE WITH
SEVERE SPEECH AND MOTOR
IMPAIRMENTS, THEY ALSO ALLOW FOR
A INCREASINGLY RICH AND
SENSITIVE SET OF NEURAL DATA TO
BE RECORDED WHICH AMPLIFY THE
CHALLENGES WE FACE AS WE
CONTEMPLATE SOME OF THE
CHALLENGES AROUND HOW NEURAL
DATA OUGHT TO BE COLLECTED AND
SHARED.
IN STUDYING THE LANDSCAPE OF
WHAT NEURAL DATA COLLECTED
THROUGH IMPLANTED
NEUROTECHNOLOGIES MIGHT
CURRENTLY REVEAL OR IN THE
FUTURE COULD POSSIBLY REVEAL,
ONE MIGHT CONSIDER A VARIETY OF
DIMENSIONS, RANGING FROM
SOMEBODY'S LEVEL OF
CONSCIOUSNESS, AND THIS IS
ACTUALLY A AREA OF GROWING
CLINICAL INTEREST IN DETECTING
COVERT CONSCIOUSNESS IN PEOPLE
WHO APPEAR UNRESPONSIVE BY
BEDSIDE PHYSICAL EXAMINATION.
ONE MIGHT CONSIDER SLEEP HABITS,
DAY TIME ACTIVITY, PSYCHOACTIVE
MEDICATION, SENSORY PERCEPTIONS.
WE JUST LEARNED ABOUT LANGUAGE
AND COMMUNICATION, BE IT
CONSTRAINED OR UNCONSTRAINED,
AND PERHAPS AS THE SPATIAL
COVERAGE OF SPATIAL SENSORS
GROW, IN THE FUTURE, MOOD OR
OTHER INTERNAL EMOTIONAL STATES,
MEMORIES, PREFERENCES, BELIEFS
AND ELEMENTS OF IMAGINATION OR
INTROSPECTION.
AND FIGURING OUT HOW TO TRULY
DE-IDENTIFY DATASETS,
PARTICULARLY GIVEN THE RANGE OF
POTENTIAL INSITES THAT ONE MIGHT
IN THE FUTURE DRAW FROM THESE
DATA IS A KNOWN ETHICAL
CHALLENGE AND THERE ARE
CURRENTLY NO BROADLY ACCEPTABLE
STANDARDS FOR COLLECTING OR
SHARING INTRACRANIAL NERVE
PHYSIOLOGY DATA.
I'D ALSO HIGHLIGHT THAT THESE
RISKS ARE ACCENTUATED BY THE
REALITY THAT PARTICIPANTS IN
SMALL N NEURAL INTERFACE TRIALS
HAVE BEEN AND ARE STILL LIKELY
TO BECOME KNOWN TO THE WIDER
COMMUNITY.
IN THIS WAY, BCI DATA ARE
SOMEWHAT UNLIKE ECOG OR
NEUROIMAGING DATA WHICH ARE
COLLECTED FROM HUNDREDS OR
THOUSANDS OF PEOPLE IN CLINICAL
CONTEXTS ANNUALLY.
AND IT'S CRUCIAL TO INFORM
PARTICIPANTS OF THESE POTENTIAL
RISKS, ESPECIALLY AS THE SCALE
AND RESOLUTION OF NEURAL DATA
GROWS.
IN LIGHT OF THESE
CONSIDERATIONS, IT'S BECOME
CLEAR THAT NEURAL DATA RECORDED
FROM INTRACRANIAL DEVICES MAY BE
ETHICALLY DISTINCTIVE GIVEN
THEIR POTENTIAL TO REVEAL HIGHLY
PERSONAL OR SENSITIVE ASPECTS OF
COGNITION, IDENTITY AND BEHAVIOR
IN THE FUTURE, ESPECIALLY WHEN
ANALYZED IN CONJUNCTION WITH
CLINICAL, PSYCHOMETRIC OR
BEHAVIORAL DATA.
AND A LOOMING CONCERN IS THAT
ANY SUFFICIENTLY RICH NEURAL
DATASET MAY BECOME PERSONALLY
IDENTIFIABLE, EVEN IF SURFACE
LEVEL IDENTIFIERS HAVE BEEN
REMOVED.
THIS CONNECTS TO CHALLENGES IN
INFORMED CONSENT WHICH MAY BE
LIMITED INSOFAR AS IT'S NOT YET
FULLY UNDERSTOOD WHAT NEURAL
DATA COULD POTENTIALLY REVEAL IN
THE FUTURE ON HOW THEY MIGHT BE
USED.
LEARNING FROM CHALLENGES IN
STUDYING AND HANDLING GENOMIC
DATA WHERE SIMILAR CHALLENGES
WERE INITIALLY PERCEIVED, SOME
HAVE ADVOCATED FOR A NEURAL
INFORMATION NON-DISCRIMINATION
ACT, MODELED OFF OF THE GENETIC
INFORMATION NON-DISCRIMINATION
ACT, AND THIS IS SOMETHING THAT
SCHOLARS HAVE BEEN
CONTEMPLATING.
ONCE DATA ARE COLLECTED,
ADDITIONAL QUESTIONS ARISE
CONCERNING WHO OWNS NEURAL DATA.
ARE THE DEVELOPERS OR
RESEARCHERS THE OWNERS OF NEURAL
DATA COLLECTED?
OR MIGHT INDIVIDUAL USERS HAVE A
CLAIM OF OWNERSHIP AND HAVE A
SAY IN HOW DATA SHOULD BE SHARED
OR USED?
AND THE SCIENTIFIC REPORTING OF
NEURAL DATA IS A TOPIC THAT'S
CURRENTLY BEING DISCUSSED BY AN
IEEE WORKING GROUP ON REPORTING
STANDARDS FOR AN IN VIVO NEURAL
INTERFACE RESEARCH.
SOME OF YOU ATTENDED LAST WEEK'S
UNESCO INTERNATIONAL CONFERENCE
ON THE ETHICS OF NEUROTECHNOLOGY
WHERE THIS PUBLICATION WAS
ANNOUNCED THAT CALLS ATTENTION
TO AN INTERNATIONAL GOVERNANCE
VACUUM IN NEUROTECHNOLOGIES, AND
CALLS FOR A GLOBAL FRAMEWORK
THAT CAN OFFER GUIDANCE TO
REGULATIONS AIMED AT
SAFEGUARDING INDIVIDUALS IN
RELATION TO NEUROTECHNOLOGY,
PARTICULARLY RECOGNIZING THAT
TECHNOLOGIES THAT RECORD OR
TRANSMIT BRAIN DATA MAY INTRUDE
IN UNIQUE WAYS ON MENTAL PRIV
PRIVACY.
IN DEVELOPING A SYSTEMATIC
APPROACH TO NEUROETHICS-INFORMED
NEURAL INTERFACE DATA SHARING,
IT'S WORTHWHILE TO BEGIN WITH
CONSIDERATION OF ISSUES AS THEY
RELATE TO THE BASIC FRAMEWORK OF
BIOETHICAL PRINCIPLES, BUT AS
WE'VE SEEN, THERE ARE SOME
ISSUES THAT SIMPLY DO NOT MAP
NEATLY ON TO THE TRADITIONAL
PRINCIPLES OF BIOETHICS.
RESPECT FOR THE AUTONOMY AND
AGENCY OF PARTICIPANTS IN NEURAL
INTERFACE RESEARCH REQUIRES
BESPOKE MODELS OF INFORMED
CONSENT THAT ARE SENSITIZED TO
THE RAMIFICATIONS OF NEURAL
DATA, AND WITHIN THESE MODELS,
WE'VE ARGUED THAT RESEARCHERS
SHOULD BE EXPLICIT ABOUT
APPROACHES TO NEURAL DATA
OWNERSHIP AND SHARING AS A
ROUTINE PART OF A RIGOROUS
INFORMED CONSENT PROCESS.
TO SUPPORT THE AGENCY OF END
USERS, OPPORTUNITIES ALSO EXIST
FOR RESEARCHERS TO CONSIDER HOW
TO EMPOWER INDIVIDUALS WITH
GREATER CONTROL OVER WHEN AND
HOW INTRACRANIAL NEUROPHYSIOLOGY
DATA WILL BE RECORDED AND SHA
SHARED.
PRINCIPLES OF DESIGN AND USER
CENTERED DESIGN MAY ALSO BE
PURSUED TO ENSURE THAT IMPLANTED
NEUROTECHNOLOGIES AND METHODS OF
DATA RECORDING AND SHARING ALIGN
CLOSELY WITH PATIENTS' VALUES,
PRERCHS AND GOALS.
WHICH IS SOMETHING THAT COULD BE
LEARNED THROUGH ENGAGEMENT WITH
COMMUNITIES OF PATIENTS AND
PATIENT ADVOCATES.
CONSIDERING THE PRINCIPLES OF
BENEFICENCE AND NON-MALEFICENCE,
QUESTIONS RELATING TO THE
EXPECTED OR UNEXPECTED EFFECTS
OF RECORDING THE BRAIN NATURALLY
ARISE.
IN STRIKING THE RIGHT BALANCE
HERE, LARGE LEA UNKNOWN
POTENTIAL FUTURE HARMS MUST BE
BALANCED AGAINST PROSPECTIVE
BENEFITS AGAINST INDIVIDUALS,
THE NEUROSCIENTIFIC COMMUNITY
AND TO SOCIETY.
THE PRINCIPLE OF JUSTICE PROMPTS
FURTHER INQUIRY INTO TOPICS
SURROUNDING THE POTENTIAL STIGMA
OR DISCRIMINATION ON THE BASIS
OF INTRACRANIAL NERVE PHYSIOLOGY
DATA, EQUITY AND ACCESS TO
NEURAL DATA ENSURING IT DOES NOT
REMAIN SILOED AT ITS SOURCE,
WHILE PROTECTING THE PRIVACY OF
PARTICIPANTS AND
REPRESENTATIVENESS OF THE
DATASETS THAT ARE BEING
COLLECTED.
RESEARCHERS SHOULD ALSO BE
MINDFUL OF ISSUES PERTAINING TO
COMPENSATION FOR NEURAL DATA
COLLECTION AND USE.
ESPECIALLY IF AN ULTIMATE GOAL
IS DEVICE COMMERCIALIZATION.
IT'S IMPORTANT IN THIS PROCESS
TO ON THE ONE HAND EXHIBIT
RESPECT FOR PATIENTS AND
RECIPROCITY, WHILE ALSO BE
CAREFUL TO AVOID UNDUE
INDUCEMENT OR COERCION.
OPPORTUNITIES ALSO EXIST TO
DEFINE POST TRIAL
RESPONSIBILITIES PERTAINING TO
NEURAL DATA STEWARDSHIP AND HOW
TO RETURN POTENTIALLY SENSITIVE
AND SOMETIMES CLINICALLY
ACTIONABLE RESULTS TO
PARTICIPANTS AND HOW THESE
DISCOVERIES SHOULD BE MADE KNOWN
TO THOSE WHO HAVE SELFLESSLY
GIVEN THEIR DATA TO RESEARCH
PROGRAMS.
A LOT OF THE DISCUSSION TODAY IS
CENTERED AROUND SHARING OF DATA
WITH RESEARCH COMMUNITIES AND
ANOTHER AXIS TO CONTEMPLATE IS
HOW TO THINK ABOUT SHARING DATA
BACK TO PARTICIPANTS OR
SURROGATES.
FINALLY THERE ARE A WIDE RANGE
OF VERY INTRIGUING PHILOSOPHICAL
PUZZLES THAT COME UP IN THIS
FIELD RELATING TO THE IMPACT OF
DISCOVERIES FROM ANALYZING
NEURAL DATA ON NOTIONS OF
PERSONHOOD, IDENTITY AND
CONSCIOUSNESS, THE POTENTIAL
IMPLICATIONS OF THESE DATA ON
OUR PHILOSOPHICAL UNDERSTANDING,
OF AGENCY, VOLITION, LEGAL
NOTIONS OF RESPONSIBILITY AND
CULPABILITY, AND DEFINING THE
RELATIONSHIP BETWEEN NEURONAL
PROCESSES AND SUBJECTIVE IF HE
NO, MA'APHENOMENONLOGICAL EXPERR
QUALIA.
AND RECOGNIZING SOME OF THESE
CHALLENGES, EFFORTS TO DESIGN
ENHANCED MODELS OF INFORMED
CONSENT FOR NEURAL DATA SHARING
ARE CRUCIAL TO ENSURE THAT IT
DOES NOT PROCEED WITHOUT
COMPREHENSIVE AND CLEAR
UNDERSTANDING AMONG PARTICIPANTS
ABOUT THE POTENTIAL BENEFITS,
RISKS AND VOLUNTARINESS OF DATA
SHARING.
IN TANDEM, IT COULD ALLOW
PARTICIPANTS AN ONGOING AND
ITERATIVE VOICE IN HOW IF AT ALL
THEY WOULD LIKE THEIR NEURAL
DATA AND POTENTIAL INFERENCES TO
BE DRAWN FROM THEM TO BE
RETURNED BOTH INDIVIDUALLY AND
SHARED WITH THE BROADER
COMMUNITY, EITHER IN CONTROLLED
ACCESS, FEDERATED ACCESS OR OPEN
ACCESS REPOSITORIES.
THIS REFLECTS BROADER MOMENTUM
IN OUR HOPES TO ESTABLISH AN
OPEN ACCESS LIBRARY OF VETTED
AND BROADLY ACCESSIBLE TOOLS AND
ETHICS RESOURCES TO FOSTER
RESPONSIBLE TRANSLATION OF
NEUROTECHNOLOGIES AND NEURAL
DATA SHARING ACROSS THE
DEVELOPMENT LIFECYCLE.
THANK YOU SO MUCH.
THAT'S THE END OF MY TALK.
HAPPY TO TAKE ANY QUESTIONS, BY
NOW OR BY EMAIL.
[APPLAUSE]
>>THANK YOU SO MUCH, DR. YOUNG.
WE'LL HAVE A QUICK
PRESENTATION -- OR QUICK
QUESTION FOR DR. YOUNG, AND THEN
WE'RE GOING TO THE ENTIRE PANEL
DISCUSSION.
>>THANKS FOR THAT PRESENTATION.
CAN YOU GO BACK FOUR SLIDES?
GREAT, THANKS.
SO I AGREE WITH ALL.
IF I LOOK AT THE FIRST THREE
CATEGORIES, AUTONOMY AGENCY,
BENEFICENCE/NON-MALEFICENCE,
JUSTICE/NEURORIGHTS, THOSE SEEM
TO BE PRETTY FOR LACK OF A
BETTER TERM GENERIC.
I WOULD THINK THAT COULD APPLY
TO ANYTHING TO DO WITH HUMANS,
BUT IT'S REALLY YOUR FINAL BULL
HE ET, THE PHILOSOPHICAL ISSUES,
THAT EARN THE TITLE NEUROIN
NEUROETHICS.
CAN YOU COMMENT ON THAT?
IT SEEMS THAT THE KINDS OF DATA
THAT WE ARE OBTAINING, THEY'RE
SPECIAL, THEY'RE DIFFERENT,
BECAUSE THEY DO IMPACT OR
INFLUENCE WHO WE ARE AS
INDIVIDUALS.
SO CAN YOU MAYBE -- IF YOU
AGREE, COULD YOU EXPAND ON THAT
A LITTLE BIT?
>>THANK YOU.
AND I THINK THAT'S EXACTLY THE
RIGHT QUESTION TO BE ASKING.
THE PHILOSOPHICAL DIE MENGES OF
THE DATA THAT ARE BEING SHARED,
WHAT WE CAN LEARN FROM NEURAL
DATA ABOUT WHAT IT MEANS TO BE
HUMAN, WHAT IS CONSCIOUSNESS,
HOW DO WE DEFINE AND
OPERATIONALIZE OUR CONCEPTS OF
SELF, PERSONHOOD, IDENTITY,
THESE ARE ALL CRITICAL QUESTIONS
THAT I THINK CAN BE PERHAPS NOT
UNIQUELY BUT IN A VERY SPECIAL
WAY ADDRESSED BY INTRACRANIAL
NEUROPHYSIOLOGY DATA AND OTHER
FORMS OF NEURODATA.
AS WAS MENTIONED IN PREVIOUS
TALKS, COMBINING THESE DATA WITH
OTHER METRICS LIE BEHAVIOR AND
BEHAVIORAL OUTPUTS, I THINK WILL
BE CRUCIAL.
BUT TO THE EXTENT THAT THESE
DATA AND THE INFERENCES THAT
COULD BE DRAWN FROM THEM MIGHT
SHED LIGHT ON AGE-OLD
PHILOSOPHICAL QUESTIONS, I WOULD
ARGUE REALLY PUTS THESE DATA IN
A SPECIAL CATEGORY OF
CONSIDERATION WHEN IT COMES TO
ETHICAL ANALYSIS AND
PHILOSOPHICAL IMPLICATIONS.
>>OKAY.
THANK YOU.
AND NOW I'M GOING TO OPEN THE
FULL PANEL DISCUSSION AND FIRST
TURN IT TOWARDS OUR RESEARCH
PARTICIPANT MEMBERS WHO ARE
PARTICIPATING IN THIS WORKSHOP
TO ASK IF YOU HAVE INITIAL
THOUGHTS THAT YOU WANT TO SHARE
WITH THE GROUP BEFORE WE END UP
DISCUSSING UNTIL LATE IN THE
NIGHT.
OKAY.
YOU CAN THINK ABOUT IT AND
INTERRUPT US WHENEVER YOU FEEL
SO INCLINED.
SO LET ME THEN OPEN THE
DISCUSSION BY ASKING, SO THIS
WORKSHOP IS ABOUT -- PART OF THE
GOALS OF THIS WORKSHOP IS TO
IDENTIFY WHAT ARE OUR MOST HIGH
RISK TYPES OF DATA.
SO BASED ON WHAT WE'VE BEEN
DISCUSSING, WE'VE HEARD ABOUT
SEVERAL FEATURES OR PRACTICES
THAT MIGHT BE ASSOCIATED WITH
INCREASED RISK.
BUT I'VE ALSO HEARD COME UP AS A
THEME THROUGHOUT THIS DISCUSSION
HOW WE SHOULD MANAGE THOSE
RISKS.
SO WE'VE TALKED ABOUT POLICIES,
REGULATIONS, RIGHTS, THINGS LIKE
THAT.
NOT -- AND THOSE MAY HAVE A VERY
IMPORTANT ROLE IN THESE
CONVERSATIONS, BUT I WONDER
ABOUT THE THINGS WE COULD DO AS
A RESEARCH COMMUNITY, OTHER THAN
LOBBY OUR GOVERNMENTS FOR THOSE
RIGHTS OR OUR PROTECTIONS.
WHAT SHOULD WE BE FOCUSING ON?
SHOULD WE BE FOCUSING ON TRYING
TO PROTECT PEOPLE'S -- FROM
BEING REIDENTIFIED, SHOULD WE BE
FOCUSING ON CONTROLLING ACCESS
TO THE DATA?
WHAT SHOULD WE BE FOCUSING ON?
>>GIVEN THE WAY THAT THE LAST
PRESENTATION WENT, IT WOULD BE
USEFUL AS YOU'RE DOING THAT TO
THINK ABOUT IN THIS REGARD AS
DISTINCT FROM OTHER DATA
MANAGEMENT OR OTHER HUMAN
RESEARCH PROTECTION, IF THERE
NEEDS TO BE SOMETHING SPECIAL OR
UNIQUE IN ADDITION TO AND
BEYOND.
SO THERE WAS THROWING UP THERE
THE POSSIBILITY OF AND INFORMED
CONSENT DIFFERENCE.
THE ONLY THING I COULD SEE THAT
WAS DIFFERENT WAS AN INFERENCE
ABOUT MEMORIES.
THE REST OF IT ALL COULD BE
INFERRED FROM ANY OTHER DATA
THAT'S BEING COLLECTED ABOUT A
PERSON.
SO THINKING ABOUT WHAT SHOULD
FOR A RESEARCH COMMUNITY BE DONE
AND SHOULD IT BE CARVED OUT,
ESPECIALLY FROM OTHER HUMAN
RESEARCH PROTECTION OR NOT?
>>I CAN GET STARTED ON THAT
ONE.
FOR ME, I THINK IT'S IDENTIFY
IDENTIFIABILITY, IS JUST THE KEY
THING.
WHO CARES WHAT YOU CAN INFER
ABOUT SOMEONE IF YOU DON'T KNOW
WHO IT IS?
THAT SEEMS FULL STOP LIKE THE
MOST IMPORTANT THING.
ALSO, PROBABLY THE HARDEST TO
ACTUALLY KIND OF LOCK DOWN.
LIKE I THINK MAKING SOMETHING
FUNDAMENTALLY NOT IDENTIFIABLE
MEANS ALSO MAKING THAT DATA
USELESS.
MAYBE THAT'S A STRONG STATEMENT,
BUT I THINK IT'S HARD.
>>I'M GOING TO THROW OUT ONE
OBJECTION TO THAT, THAT IT MAY
NOT JUST BE IDENTIFYING AN
INDIVIDUAL, BUT IT COULD BE
IDENTIFYING A GROUP.
SO YOU COULD THINK OF A PROGRAM
THAT CAN BE USED TO IDENTIFY
COMMUNISTS VERSUS REPUBLICANS.
SO AS A GROUP, I THINK PEOPLE
COULD OBJECT TO HAVING THEIR
DATA USED.
>>I ACTUALLY THINK THE MORE I
LISTEN TO BOTH PANELS, THE MORE
I THINK IDENTIFIABILITY IS A
SORT OF RED HERRING ALMOST.
I DON'T THINK WE CAN PROTECT IT
THAT WELL, NUMBER ONE.
AND ALSO HERE ARE THESE THINGS
LIKE YOU'RE ACCESSING PEOPLE'S
IDEAS, RIGHT?
AND YOU TALKED ABOUT
IMPLICATIONS ON PERSONHOOD.
I MEAN, THOSE ARE BIG NOTIONS
THAT HAVE NOTHING TO DO WITH
IDENTIFIABILITY.
THOSE ARE THINGS THAT IT SEEMS
LIKE WE SHOULD ALL CARE ABOUT,
BUT DO WE WANT TO DO SOMETHING
WITH THE DATA TO PROTECT AGAINST
THAT?
I DON'T KNOW THE ANSWER.
I'M JUST RAISING THE QUESTION.
I DON'T THINK IDENTIFIABILITY IS
THE BIGGEST PROBLEM.
>>I THINK IT'S NOT JUST THAT
IT'S THE BIGGEST PROBLEM.
I THINK THAT IT'S SHOWN TIME AND
AGAIN TO FAIL, AND INCREASINGLY
IF NEURAL SIGNATURES BECOME
POSSIBLE, I THINK MY HOPE IS TO
MOVE BEYOND IDENTIFIABILITY AS
THE WAY IN WHICH WE TRY TO
PROTECT DATA BECAUSE I DON'T
THINK THAT HAS WORKED.
BUT THESE IDEAS ABOUT MEMORIES
OR INTENTIONALITY, YOU KNOW,
CONOR, YOU SAID EEG DATA IS
BORING.
I KNOW YOU DIDN'T MEAN IT
ENTIRELY, BUT ONE THING I HEAR A
LOT IS WHEN PEOPLE SAY, YOU
KNOW, RIGHT NOW YOU CAN LEARN A
LOT MORE FROM MY TEXT MESSAGES
AND MY PHONE AND MY GPS LOCATION
AND THE COMBINATION OF ALL OF
THOSE DIFFERENT CENTERS THAN YOU
COULD FROM ANY OF THE BRAIN
DATA.
YOU COULD MAKE INFERENCES ABOUT
MY IDEAS, MY MEMORY, MY
INTENTIONS, HOW LONG I SPEND ON
A TIK TOK VIDEO WILL GIVE YOU A
VERY ACCURATE ALGORITHMIC
INTERPRETATION.
I DON'T SURF TIK TOK, I'M JUST
SAYING, BUT THE POINT IS, LIKE,
WHAT IS IT BOTH TODAY AND IN THE
FUTURE THAT WE THINK THE
ADDITION OF BRAIN DATA GETS YOU?
WHAT IS THE UNEXPRESSED THROUGH
EVERYTHING ELSE, HEART RATE,
PUPILLARY RESPONSE, FACIAL
RECOGNITION, SWIPING, HOW LONG
YOU SPEND ON A VIDEO, ET CETERA,
ET CETERA, THAT BRAIN DATA, IF
THERE IS SOMETHING ADDITIONAL
THAT IT PROVIDES, THAT REQUIRES
SOME ADDITIONAL LEVEL OF
PROTECTION?
>>FOR ME, I WOULD SAY BEING
IDENTIFIED ISN'T THE BIGGEST
PROBLEM.
FOR ME, IT WOULD BE MORE AROUND
THE CONSENT, MAKING SURE I
UNDERSTAND IT, MAKING SURE THAT
IT IS A DYNAMIC CONSENT WHERE I
COULD PULL BACK MY INFORMATION
IF I FIND OUT SOME COMPANY IS
MISUSING MY INFORMATION.
SO FOR ME, IT'S NOT ABOUT BEING
IDENTIFIED.
I THINK THERE'S A LOT OF
PATIENTS OUT THERE WHO ARE HAPPY
TO BE IDENTIFIED BECAUSE THEY
THINK THAT IT MAY HELP FIND A
CURE OR SOMETHING FOR THEIR
DISEASE, BUT IT IS OUR CONSENT,
IT'S HOW OUR DATA IS BEING USED,
WHO'S USING IT AND IN WHAT
MANNER, AND THEN WHERE IS THE
DATA GOING AFTER THAT, RIGHT?
WE SEE A LOT OF COMPANIES THAT
ARE PACKAGING UP OUR DATA AND
RESELLING IT, SO HOW DO YOU
PROTECT US FROM THAT SIDE OF IT
AS WELL?
>>AGREED, AND TO ECHO SOME OF
THE POINTS THAT WERE RAISED
EARLIER, WHAT STRUCK ME FROM
MANY OF THE EARLIER TALKS WAS AN
EMPHASIS ON THE INCREASING
POTENTIAL DIAGNOSTIC UTILITY OF
SOME OF THESE DATASET, AND AS WE
THINK ABOUT COLLECTING DATA,
THAT MIGHT NOT NOW HAVE
DIAGNOSTIC IMPLICATIONS BUT
MAYBE IN 10 YEARS OR SOME
UNDEFINED FUTURE MIGHT HAVE SOME
IMPLICATIONS FOR WHAT SOMEBODY'S
BRAIN HEALTH MIGHT BE LIKE,
BRAIN DISEASES, HOW DO WE THINK
ABOUT RETURNING THOSE RESULTS TO
INDIVIDUALS AND DISCLOSING THAT
INFORMATION IN A RESPONSIBLE
WAY?
AND THAT'S SORT OF ANOTHER AXIS
OF DATA SHARING BEYOND SHARING
DATA WITH RESEARCH COMMUNITIES
AND THE PUBLIC THAT SHOULD BE
CONTEMPLATED PROACTIVELY.
>>CAN I ASK A QUESTION OF ALEX
ON THIS PIECE WHICH IS, ALEX,
YOU THREW OUT THE SLIDE THAT
DIDN'T GO INTO THE DETAILS ON
THE CLASSIFIER TRAINED ON ONE
PERSON COULD BE ACCURATELY USED
ON ANOTHER.
CAN YOU UNPACK THAT A LITTLE BIT
BECAUSE THIS IS ONE OF THE
THINGS I'VE BEEN CURIOUS ABOUT,
WHICH IS THE EXTENT TO WHICH IF
BRAIN DATA IS SHARED, WHAT CAN
BE INFERRED FROM IF YOU DON'T
HAVE THE CLASSIFIER MODEL?
AND MAYBE IT VARIES BY THE KIND
OF BRAIN DATA, BUT YOU KNOW, CAN
YOU EXTRACT MEMORIES IF YOU'RE
SHARING BRAIN DATA BUT YOU DON'T
HAVE THE CLASSIFIER, AND IS
THERE STILL VALUE IN SHARING THE
DATA ITSELF?
LIKE CAN RESEARCHERS USE IT FOR
THE PURPOSES IF THEY DON'T HAVE
THE CLASSIFIER THAT IS TRAINED
ON THE INDIVIDUAL OR THE DECODER
RING OR HOWEVER YOU WOULD PHRASE
IT ON THE PERSON THEMSELVES?
>>YEAH, SO THIS IS VERY MUCH
LIKE A STATEMENT ABOUT WHAT WE
CAN DO CURRENTLY AND WE REALLY
DON'T KNOW WHAT THIS IS GOING TO
LOOK LIKE IN THE FUTURE, RIGHT?
BUT AS WE HEARD A LOT THIS
MORNING, YOUR BRAIN STRUCTURE IS
LIKE A FINGERPRINT.
EVERYBODY'S BRAIN IS UNIQUELY
FOLDED AND EVEN BEYOND THAT, IF
YOU UNFOLDED THE BRAIN, BLEW IT
UP INTO A BALLOON AND TRIED TO
LINE IT UP ON OTHER PEOPLE'S
BRAINS, IT DOESN'T ACTUALLY LINE
UP.
EVERYBODY HAS A VISUAL CORTEX IN
THE SAME PLACE, EVERYBODY HAS AN
AUDITORY CORTEX IN THE SAME
PLACE, BUT THE DETAILED WHERE
EXACTLY ALL THE LITTLE
FUNCTIONAL AREAS ARE ENDS UP
BEING DIFFERENT IN EACH PERSON.
SO I THINK THERE ARE ACTUALLY
GOING TO BE STRONG FUNDAMENTAL
LIMITS ON WHAT YOU CAN INFER
FROM FUNCTIONAL DATA WHERE YOU
HAVE NO OTHER SORT OF REFERENCE
DATA FOR A PERSON.
I THINK -- I WOULD BE CONFIDENT
IN SAYING LIKE THERE IS AN UPPER
LIMIT, YOU WON'T BE ABLE TO GET
BEYOND SOME LEVEL ON THAT.
IF YOU HAVE A LITTLE BIT OF DATA
FROM SOMEONE, THEN THAT MIGHT BE
ENOUGH TO SORT OF HELP ALIGN
THINGS THAT MAKE IT WORK.
>>I'M GOING TO THINK OF THAT AS
A KEY, THERE'S A WHOLE BUNCH OF
BRAIN DATA THAT HAS ALSO BEEN
TRAINED -- OR COLLECTED FROM ME.
IS THERE STILL VALUE TO ANOTHER
RESEARCH EARLY IF YOU'RE TRYING
TO AGGREGATE BRAIN DATA, STUDY
IT, LEARN OTHER THINGS, IS THERE
STILL VALUE OF THE DATA WITHOUT
THE KEY, AND IS THE KEY SOME WAY
TO PROTECT THE INDIVIDUAL FROM
THE MOST SENSITIVE KINDS OF
INFERENCES THAT COULD BE DRAWN
ABOUT THEM INDIVIDUALLY?
>>THE SECOND QUESTION, I THINK
POTENTIALLY, YES.
IT'S HARD TO PUT A FIRM YES ON
THAT BECAUSE WE DON'T KNOW
WHAT'S GOING TO BE POSSIBLE IN
FIVE YEARS OR 10 YEARS.
SIMILARLY, I THINK CURRENTLY WE
DON'T REALLY HAVE A GREAT USE
FOR A TON OF DATA THAT'S
UNLABELED.
OR THAT WE DON'T HAVE THE KEY
FOR, WE DON'T KNOW WHAT THE
PERSON WAS THINKING OR WHAT THEY
WERE HEARING OR WHATEVER.
IT'S HARD TO USE EFFECTIVELY
RIGHT NOW.
BUT THAT DOESN'T MEAN THAT IT
ALWAYS WILL BE.
>>ACTUALLY WE'RE GOING TO GO TO
SOMEBODY WHO'S WATCHING ON
VIDEO.
KAREN, CAN YOU STATE YOUR
QUESTION?
>>HI.
THANKS.
I WONDERED -- I WANTED TO GO
BACK TO THE IMPORTANT FRAMING
THAT SASKIA INVITED US TO
CONSIDER, WHICH WAS HOW ARE WE
AS A RESEARCH COMMUNITY
ADDRESSING SOME OF THESE ISSUES.
AND I THINK MANY OF THE SPEAKERS
IN A PRIOR SESSION WERE OFFERED
SOME INTERESTING IDEAS AS WELL,
BUT FOR THIS SESSION, I'M
CURIOUS ABOUT MICHAEL YOUNG, YOU
BROUGHT UP CONSENT.
VERY SIMPLY, I WONDERED GIVEN
YOUR BACKGROUND HOW YOU HAVE
THOUGHT ABOUT EVEN SINCE COMING
ON BOARD TO THE TEAM, HOW YOU'VE
GONE ABOUT CONSENT PROCESSES
DIFFERENTLY, HOW YOU EXPLAIN
CERTAIN CONCEPTS AND WHETHER
YOU'VE CHECKED THE READABILITY
EVEN OF THOSE DOCUMENTS.
BECAUSE I KNOW AS WE'RE
CONCERNED ABOUT PATIENTS THAT
MANY OF THEM ARE NOT READABLE.
SO WRITING CLEARER OR MORE
INFORMATION ON CONSENT FORMS
WON'TING USEFUL IF THAT
INFORMATION ISN'T
COMPREHENDIBLE.
AND TO CONOR, YOU BROUGHT UP A
REALLY IMPORTANT ETHICS BY
DESIGN CONCEPT, KIND OF INVOKING
THE COGNITIVE LIBERTY TERM, AND
YOU TALKED ABOUT TRANSPARENCY IN
EDUCATION, AND I'M WONDERING, AS
YOU IN THE PIONEER OF THE FIELD
IN OPENBCI, WHAT YOU ALL HAVE
DONE TO FACILITATE COGNITIVE
LIBERTY IN YOUR ETHICS BY DESIGN
PROCESSES.
AND DR. HUTH, I WAS WONDERING,
HOW YOU DESIGN INTO YOUR
EXPERIMENT AND ETHICAL
CONSIDERATIONS ABOUT AUTONOMY
AND WHETHER INDIVIDUALS NEED TO
REALLY PARTICIPATE ACTIVELY TO
HAVE THEIR REPRESENTATIONS
DECODED.
BUT ONE OF THE KEY FRAMES OF
THIS SESSION WAS ALSO TO ASK
ABOUT IMPLICATIONS FOR THE
ABILITY TO DECODE SEMANTIC
REPRESENTATIONS AND EFFECTS OF
RACIALLY DISCRIMINATORY
PRACTICES.
AND I THINK -- JOHNSON THE OTHER
NEW MEMBER WHO'S ON HERE THAT
HAD A SIMILAR QUESTION.
SO I PUT THOSE OUT THERE JUST AS
QUESTIONS FOR HOW SOME OF THE
PRACTITIONERS ARE HANDLING SOME
OF THESE ISSUES AND THEY HAVE
BEST PRACTICES THEY CAN SHAI AND
IF ONE OR SOME OF YOU COULD
RESPOND, THAT WOULD BE HELPFUL.
>>THANK YOU, DR., FOR THE
INSIGHTFUL QUESTION.
I'M VERY MUCH IN TUNE WITH HOW
YOU FRAMED IT.
WE ARE ACTIVELY CONSIDERING HOW
TO ENHANCE THE INFORMED CONCEPT
PROCESS FOR PARTICIPANTS OR
PROSPECTIVE PARTICIPANTS IN
NEURAL INTERFACE TRIALS.
YOU MENTIONED A NUMBER OF
CONCEPTS THAT ARE REALLY
CRUCIAL, INCLUDING READABILITY,
UNDERSTANDING, THE EXPRESSION
AND TRANSPARENCY AROUND THE
UNCERTAINTY OF THE FUTURE OF HOW
NEURAL DATA WILL BE DEFINED,
BEING TRANSPARENT ABOUT WHERE
AND HOW NEURAL DATA WILL BE
SHARED.
WILL IT BE SHARED IN OPEN ACCESS
REPOSITORIES, IN CONTROLS ACCESS
REPOSITORIES, HOW MUCH AGENCY
MIGHT A PARTICIPANT RETAIN IN
THE FUTURE AS MORE IS LEARNED OR
MORE BECOMES INFERRIBLE ON THE
BASIS OF DATA THAT'S SHARED.
ALL OF THOSE ARE SOMEWHAT
DISTINCTIVE CONCEPTS THAT
INFORMED CONSENT PROCESSES NEED
TO PROACTIVELY CONSIDER AND
INCORPORATE BY DESIGN.
>>JUST TO CLARIFY, DR. YOUNG,
THAT'S WHAT YOU ALL ARE DOING
RIGHT NOW OR YOU THINK THOSE ARE
GOOD THINGS FOR PEOPLE TO DO IN
THE FUTURE?
>>SO THESE ARE THINGS THAT WE
ARE ACTIVELY CONSIDERING, AND
RIGHT NOW OUR PROCESS IS DEFINED
IN TANDEM WITH OUR IRB AND WE'RE
WORKING CLOSELY WITH ALL OF THE
ORGANIZATIONS THAT ARE INVOLVED
IN SETTING THE REGULATIONS AND
ETHICAL FRAMEWORKS INCLUDING THE
NTHENEWG.
>>I'LL JUMP IN TO ANSWER YOUR
QUESTION.
HEY, KAREN.
SO AS FAR AS THE OPENBCI AND OUR
ETHICS BY DESIGN AND THE
EMPHASIS ON COGNITIVE LIBERTY, I
THINK WE HAVEN'T FIGURED OUT A
GOOD WAY TO DO IT, WHICH IS WHY
WE DON'T COLLECT DATA.
ANY PHYSIOLOGICAL DATA.
SO WE SELL OUR TOOLS, YOU CAN
KIND OF THINK OF IT LIKE A
TELESCOPE, WHERE IT'S AN
EPHEMERAL DATASET, UNLESS YOU
DECIDE OTHERWISE.
SO WE HAVEN'T DONE ANY
APPLICATIONS OR SOFTWARE WHERE
WE'RE SOLICITING DATA BACK,
WHICH FROM A VALUE OF THE
COMPANY PERSPECTIVE, CERTAINLY
AT LEAST TO CERTAIN INVE INVESTS
DECREASES OUR VALUE OR PERCEIVED
VALUE AS A COMPANY BUT BECAUSE
WE HAVEN'T FIGURED OUT A GOOD
WAY TO DO IT, WE DON'T AND SO
THAT'S PART OF THE REASON I
WANTED TO PARTICIPATE IN THIS
CONVERSATION.
AND I'M REALLY GRATEFUL TO BE
HERE.
WHAT I BELIEVE IS THAT THE
EXISTING COMPUTERS, THE MAJOR
COMPANIES THAT HAVE BUILT THE
PLATFORMS WE HAVE TO USE TO DO
THIS, WE HAVE TOWN LEARN AND
BACK PEDAL A LOT OF THE
PROCESSES AND THE APPLICATIONS
THAT WE PUT IN PLACE IN ORDER TO
DO THIS ETHICALLY AT A CONSUMER
SCALE.
SO I DON'T THINK IT'S POSSIBLE
PRESENTLY WITH EXISTING
TECHNOLOGY TO REAP THE BENEFITS
OF SHARING YOUR DATA WHILE ALSO
PROTECTING YOURSELF AND YOUR
PRIVACY.
BUT I DO BELIEVE IT'S A SOLVABLE
PROBLEM.
INDIVIDUALS DATA ELEMENT CAN BE
PROCESSED AND LEVERAGED FOR
VALUE WITHOUT EXPOSING WHO THEY
ARE AS AN INDIVIDUAL.
I THINK IT'S A VERY DIFFICULT
CHALLENGE BUT I THINK IT IS
POSSIBLE.
>>THANK YOU, KAREN.
I ACTUALLY WANTED TO ASK YOU A
QUESTION OF THE GROUP AND THAT
IS YOU'VE ALL GENERATED DATA
THAT COULD POTENTIALLY BE
SHARED.
HAS THAT DATA BEEN SHARED?
HAVE PEOPLE ASKED FOR THE DATA
OR HAVE YOU ACTUALLY SHARED DATA
WITH OTHERS, BROUGHT THAT DATA
INTO YOUR OWN WORK?
WHAT'S AN EXAMPLE OF HOW THIS
HAS WORKED IN THE PAST?
>>I DON'T USE OTHER PEOPLE'S
DATA BECAUSE I THINK OUR DATA IS
BETTER.
WE SHARE ALL OF OUR DATA.
YEAH.
ALL OF IT'S PUBLIC.
WE THOUGHT ABOUT THIS, WE TALKED
TO SUBJECTS ABOUT THIS.
I'M ONE OF THE SUBJECTS WHOSE
DATA SK SHARED IN SUFFICIENT
SCALE TO ACTUALLY BUILD THESE
MODELS THAT DO THESE THINGS.
SO I DON'T KNOW.
IT'S SOMETHING WE THINK ABOUT
BUT I THINK THE BALANCE IS
CURRENTLY IN FAVOR OF DOING
INTERESTING THINGS WITH IT.
>>DO YOU HAVE PEOPLE TAKING
YOUR DATA AND TRYING TO USE IT
IN THEIR OWN EXPERIMENTS?
>>SO OUR SORT OF RELEASE OF
THIS DATASET WAS VERY RECENT.
I THINK THERE'S BEEN MAYBE ONE
OTHER PUBLICATION USING IT, BUT
HOPEFULLY THAT WILL PICK UP.
>>SO HIGH LEVEL DE-IDENTIFIED
SAFETY DATA HAVE BEEN SHARED BUT
THE FULL BANDWIDTH NEURAL DATA
HAVE NOT YET BEEN SHARED WITH
THE PUBLIC OUTSIDE OF THE
RESEARCHERS WHO ARE INVOLVED IN
THE WORK, WHICH IS PART OF WHY
THIS KIND OF CONVERSATION AND
DIALOGUE IS SO CRUCIAL, SO WE
COULD UNDERSTAND HOW TO BALANCE
THE GOALS OF FOSTERING
COLLABORATION, ADVANCING
NEUROSCIENCE, WHEEL AT THE SAME
, WHILE AT THE SAMETIME OPTIMALE
PRIVACY OF CLINICAL TRIAL
PARTICIPANTS.
>>I'M GOING TO GIVE US THREE
MORE MINUTES, WE'RE ALREADY KIND
OF OVERTIME AND I KNOW THERE'S A
LOT OF HANDS.
SO I DON'T KNOW HOWL TO FAIRLY
DO THIS.
BUT WE HAVE AT LEAST ONE NEW
MEMBER WHO'S ON THE LINE, SYD
JOHN SOFNL I'M GOING TO HAVE SYD
ASK HER QUESTION AND I WOULD
ENCOURAGE THE OTHERS TO TYPE OUT
YOUR QUESTIONS IN THE CHAT SO
THE SPEAKERS CAN MAYBE ANSWER
YOUR QUESTIONS IN THE CHAT IF WE
DON'T GET TO YOU.
MY APOLOGIES.
>>THANKS, SO SO .
I WAS REALLY INTERESTED IN THE
WAY THAT THEY'RE USING THESE
LARGE LANGUAGE MODELS TO SORT OF
DO THESE TRANSLATIONS OF BRAIN
DATA INTO LANGUAGE.
THERE'S A CODE OF ETHICS FOR
PROFESSIONAL TRANSLATORS WHICH
INCLUDES THAT THEY'RE GOING TO
AK RALTLYACCURATELY AND WITHOUTS
CONVEY THE MEANING OF A
COMMUNICATION THAT THEY'RE
TRANSLATING, AND I WONDER HOW WE
MIGHT BUILD THOSE KINDS OF
SAFEGUARDS INTO BCIs OR OTHER
DEVICES THAT WE MIGHT BE USING
IN THIS KIND OF CONTEXT.
FOR EXAMPLE, TO PROTECT PEOPLE
WHO MIGHT BE USING THEM FROM
HAVING THEIR WORDS BECOME RACIST
OR ABLEIST OR IN A VARIETY OF
OTHER WAYS, GIVEN THE CURRENT
EXPERIENCE WITH THESE LARGE
LANGUAGE MODELS, WHICH TEND --
APPARENTLY TEND TO TAKE ON SOME
OF THE WORST CHARACTERISTICS OF
THE TEXT THAT THEY'RE TRAINED
ON.
>>THANK YOU, THAT'S A GREAT
QUESTION.
I SAW YOU TYPE THAT OUT EARLIER
AND DIDN'T GET A CHANCE TO
RESPOND.
IT'S VERY INTERESTING THAT YOU
BRING UP TRANSLATORS.
I THINK THAT'S A WONDERFUL
PARALLEL.
BECAUSE I THINK IN MANY WAYS
LIKE TRANSLATION, CERTAINLY
THERE IS SOME CONTENT TO YOUR
THOUGHTS, THAT IT'S LIKE THE
EXACT THING THAT YOU WANT TO
REPRESENT OUT THERE IN THE
WORLD.
BUT LIKE IN OUR WORDS, THERE
MIGHT BE THINGS THAT ARE -- HAVE
MULTIPLE TRANSLATIONS, THAT HAVE
MULTIPLE DIFFERENT WAYS TO SAY
IT, THAT MAYBE DEPEND ON CONTEXT
AND HOW YOU INTERPRET SOMETHING.
SO THESE MODELS I THINK ARE
ALWAYS GOING TO HAVE SOME OF
THESE BIASES BUILT IN.
IN SOME WAYS THOSE BIASES, THERE
ARE HARMFUL BIASES, THERE ARE
ALSO HELPFUL BIASES, WHICH IS
HOW THE MODELS KIND OF HELP TO
FILL IN THE HOLES IN FMRI DATA,
WHICH IS HONESTLY FULL OF HOLES.
BUT I THINK THERE'S A LOT OF
GOOD WORK HAPPENING IN THE
MACHINE LEARNING NATURAL
LANGUAGE PROCESSING WORLD
BECAUSE PEOPLE HAVE REALLY
ACKNOWLEDGED THIS AS AN ISSUE
THAT THERE ARE RACIAL BIASES, ET
CETERA, IN THESE MODEL, AND SO I
THINK A LOT OF OUR WORK WITH
BRAIN DECODING CAN KIND OF PIGGY
BACK ON THAT WORK IN TRYING TO
BRING THESE BEST PRACTICES,
MODELS THAT HAVE HAD AS MUCH
BIAS REMOVED AS POSSIBLE INTO
THE WORLD OF BRAIN DECODING.
I HOPE THAT ANSWERS YOUR
QUESTION.
>>I'M SORRY, I'M AFRAID WE'RE
GOING TO HAVE TO END THE SESS
SESSION, SO SASKIA, I WANT TO
APOLOGIZE TO THE SPEAKERS, WE
WOULD VL LOVED TO HAVE HEARD
EACH OF YOU SPEAK FOR AN HOUR
AND ALL THE QUESTIONS IN CHAT
WE'RE NOT ABLE TO ADDRESS,
PLEASE DO SEND EMAILS OR PUT
THEM IN CHAT FOR ANSWERERRING.
BUT THANK YOU, AND WE'LL
RECONVENE AT 3:45, AFTER A BRIEF
BREAK.
>>IT'S MY PLEASURE TO
INTRODUCE OUR THIRD PANEL, WHICH
IS ABOUT THE POTENTIAL RISK TO
COMMUNITIES.
SO SO FAR TODAY, WE'VE TALKED A
LOT ABOUT ACTUALLY POTENTIAL
RISK TO INDIVIDUALS, SO I'M
EXCITED TO SEE THIS PANEL, WHICH
WILL CONSIDER HOW RISK TO
COMMUNITIES MAY ARISE FROM
SHARING BRAIN DATA, INCLUDING
THE EFFECTS OF RACIALLY
EXCLUSIONARY PRACTICES IN
COLLECTING AND MANAGING BRAIN
DATA, AND HOW THEY MIGHT INFORM
A SPECTRUM OF DATA SHARING
RISKS.
SO I JUST WANT TO POINT OUT, I
KNOW WE HAVE BEEN LIMITED IN OUR
ABILITY TO HAVE DISCUSSIONS
AFTER EACH OF THE SPEAKERS.
THERE'S APPARENTLY A LOT TO TALK
ABOUT, WHICH IS GREAT.
WE DO HAVE AT THE END OF
TOMORROW, WE'LL HAVE SMALLER
BREAKOUT GROUPS WHICH WILL ALLOW
US TO TP SOME OF THE IMPORTANT
CONVERSATIONS THAT YOU START.
SO WITHOUT FURTHER ADO, IT'S MY
GREAT PLEASURE TO INTRODUCE
MS. JOSLIN RICARD, RESEARCH
ASSISTANT AT YALE UNIVERSITY AND
PURSUING A PH.D. IN NEUROSCIENCE
AND STANFORD SCHOOL OF MEDICINE
STARTING THIS FALL.
MS. RICARD GRADUATED FROM THE
UNIVERSITY OF MINNESOTA TWIN
CITIES WITH A BACHELOR'S DEGREE
IN NEUROSCIENCE.
WITH A FOCUS ON SUBSTANCE USE
DISORDERS, MS. RICARD AIMS TO
UNDERSTAND HOW INEQUITY AND
DISADVANTAGE IMPACT BRAIN
FUNCTIONING.
HER WORK HAS BEEN PUBLISHED IN
NUMEROUS JOURNALS.
SHE WAS NAMED 2023 HENNESSY
SCHOLAR AT STANFORD UNIVERSITY
AND SHE'S ALSO THE RECIPIENT OF
THE FORD FOUNDATION FELLOWSHIP
AWARDED BY THE NATIONAL
ACADEMIES OF SCIENCE,
ENGINEERING AND MEDICINE.
THANK YOU FOR JOINING US.
WE LOOK FORWARD TO YOUR
PRESENTATION.
>>AWESOME.
THANK YOU SO MUCH FOR HAVING ME.
I AM SO EXCITED AND SO HONORED,
AND THANK YOU ESPECIALLY AS WELL
TO TH THE BACKGROUND TEAM FOR
PUTTING THIS ALL TOGETHER.
AS SASKIA SAID, I'M GOING TO BE
TALKING ABOUT CONFRONTING
EXCLUSIONARY PRACTICES WITHIN
NEUROIMAGING.
SO KIND OF IN THE UNDERSTANDING
OF THE BRAIN INSTITUTIONS AND
PEOPLE HAVE BEEN TAKING A LOT OF
STEPS TO SORT OF RECOGNIZE THE
CHALLENGES RELATED TO
INCLUSIVITY.
HOWEVER, LESS THOUGHT HAS BEEN
PERHAPS PAID ATTENTION TO THE
PROCESS OF SCIENCE FROM THE WAY
WE RECRUIT PEOPLE TO THE
TECHNOLOGIES WE USE AND THE
ANALYSIS THAT WE CONDUCT, CAN
ALL HAVE DOWNSTREAM MARKED
EFFECTS ON THE EQUITY AND
GENERALIZABILITY OF OUR SCIENCE.
AND THEN I THINK -- SO IN THIS
TALK TODAY, I'M GOING TO KIND OF
FOCUS ON THESE THREE REALMS, BUT
I THINK IT'S ALSO IMPORTANT TO
KEEP IN MIND THAT WHEN WE TRY TO
MITIGATE THESE BIASES THAT CAN
OCCUR IN ALL THESE STAGES, THAT
DISTRUST OF THE SCIENTIFIC AND
MEDICAL ESTABLISHMENT IS
STILL -- IS WELL DOCUMENTED AND
STILL PERSISTS FOR AS A BARRIER
FOR MARGINALIZED GROUPS TO BE IB
VOFLED IN OUR NEUROSCIENCE
RESEARCH, SO I THINK THAT'S JUST
IMPORTANT TO KEEP IN MIND TODAY,
BUT -- AND I KNOW THAT THERE'S A
LOT I WON'T BE ABLE TO COVER BUT
HOPEFULLY THAT WE CAN DISCUSS
FURTHER LATER AND THROUGHOUT THE
WORKSHOP.
SO WHAT I'M SHOWING YOU ARE TWO
BAR GRAPHS THAT HAVE THE
STATISTICS OF HUMAN NEUROSCIENCE
STUDIES WHERE PERCENTAGE IS ON
THE X AXIS AND WE HAVE RACE AND
ETHNICITY ON THE Y AXIS.
THESE DATA WERE ACQUIRED FROM
THE NIH'S INCLUSION STATISTICS
REPORT FOR THE YEARS 2018 TO
2021.
WHAT YOU CAN SEE FOR THESE LAST
FOUR YEARS OR THE YEARS WE HAVE
DATA ON ARE INCLUSION BY RACE
AND ETHNICITY HAVE BEEN
OVERWHELMINGLY WHITE AND
OVERWHELMINGLY NOT HISPANIC OR
LATINO.
AND AS A NEUROIMAGER, THIS
DEMOGRAPHIC PATTERN ALSO IS
NOTED ACROSS OUR LARGE SCALE
NEUROIMAGING COLLECTION EFFORTS,
AND SO ONE BEING THE U.K.
BIOBANK, IT'S A LARGE SCALE
BIOMEDICAL RESEARCH RESOURCE,
AND IT CONTAINS A LOT OF IN
DEPTH HEALTH INFORMATION FROM
AROUND HALF A MILLION U.K.
PARTICIPANTS, IT IS ALSO THE
LARGEST NEUROIMAGING DATABASE
COLLECTION IN THE WORLD AT
AROUND 50,000 PEOPLE WITH
IMAGING BUT AIMING TOWARDS
100,000.
BUT I'LL POINT YOU TO THE RED
BOX INDICATING THAT WHITE
INDIVIDUALS ACTUALLY MAKE UP
AROUND 9 # 5% OF THE TOTAL
PEOPLE RECRUITED FOR THE U.K.
BIOBANK.
IN ANOTHER EXAMPLE I'M SHOWING
YOU TWO MORE ADDITIONAL LARGE
SCALE NEUROIMAGING RESOURCES.
THE HUMAN CONNECTOME PROJECT,
HCP, WHICH COMPRISES ABOUT 76%
OF WHITE INDIVIDUALS, AND
ADOLESCENT BRAIN AND COGNITIVE
DEVELOPMENT STUDY, THE ABCD,
WHICH ENCOMPASSES AROUND 56% OF
WHITE AMERICANS.
ONE IMPORTANT NOTE HERE I WANT
TO MAKE IS THAT THE ABCD
PROVIDES RESEARCH MATERIALS IN
SPANISH FOR SPANISH-SPEAKING
FAMILIES, AND I THINK PROBABLY
AS A DIRECT RESULT OF THIS, YOU
SEE AROUND 20% OF THE ABCD
PARTICIPANTS BEING HISPANIC OR
LATINO.
WE KNOW THE U.K. IS
OVERWHELMINGLY WHITE, SO WHY
MIGHT AS A COMMUNITY WE EXPECT
DIFFERENT NUMB NUMBERS, BUT I K
IT'S IMPORTANT THAT EVEN IN
THESE INSTANCES WHERE THESE
SAMPLES ARE BROADLY
REPRESENTATIVE OF A PARTICULAR
COUNTRY LIKE THE U.K., SAMPLE
GENERALIZABILITY ISSUES CAN
BECOME AMPLIFIED WHEN WE ARE
UTILIZING DATASETS IN A GLOBAL
CONTEXT WHERE WHITE PEOPLE
REPRESENT AROUND 12% OF THE
WORLD'S POPULATION.
I THINK WE ALSO NEED TO JUST
CONSIDER THAT THE DECADES OF
EXCLUSION FROM OUR RESEARCH AND
EVEN BEYOND IN DIFFERENT TERMS,
THAT IT MIGHT BE TIME FOR US AS
NEUROIMAGERS TO OVERCOMPENSATE
IN OUR RECRUITMENT NOW, AND
ADDITIONALLY, THE NIH IS ONE OF
THE LARGEST SCIENTIFIC FUNDERS
IN THE WORLD AND SUCH -- IT IS
SUCH THAT OFTENTIMES THESE
INITIATIVES ARE ONLY AVAILABLE
FOR SCIENTISTS IN AMERICA, SO
PERHAPS AS A CONSEQUENCE, IT IS
PRIMARILY RESEARCH GROUPS WITHIN
WEALTHIER COUNTRIES LIKE THE
UNITED STATES THAT HAVE THE
RESOURCES TO CONDUCT THIS IN
VIVO IMAGING WORK.
SO WE ALSO NEED TO CONSIDER HOW
BIASES ARE ENTAILED IN OUR
METHODOLOGY THAT WE USE.
SO SOMETHING AS SEEMINGLY AS
INNOCUOUS AS AN INDIVIDUAL'S
HAIRSTYLE CAN PRESENT A
CONSIDERABLE BARRIER TO
RECRUITMENT.
IN THE EXAMPLE I'M SHOWING YOU
HERE ON THE SCREEN, YOU CAN SEE
EXTENSIONS THAT ARE THESE SMALL
METALLIC PIECES THAT ARE CLOSE
TO THE SCALP, AND THIS IS
SHOWING UP IN AN X-RAY IMAGE
HERE THAT WOULD POSSESS A SAFETY
RISK IF THEY WERE TO GO INTO AN
MRI, BUT YOU CAN SEE THIS IS
CLEARLY SHOWING UP IN OUR IMAGES
AN AN ARTIFACT.
IN AN MRI STUDY SPECIFICALLY,
THERE CAN BE DIFFICULTIES IN
FITTING A NATURAL HAIRSTYLE THAT
IS, FOR EXAMPLE, AN AFRO
DREADLOCKS BRAIDS INTO THE
CONSTRICTED SPACE OF THE HEAD
COIL, AND FOR THOSE THAT ARE UP
FAMILIAR WITH MRI, THE HEAD COIL
IS JUST WHERE THE PARTICIPANT
LAYS THEIR HEAD AND CAN OFTEN BE
CONSTRUCTED IN SPACE.
IN ADDITION, THINGS LIKE SEW-INS
OR EXTENSIONS WHICH ARE COMMON
STYLES WORN BY BLACK WOMEN CAN
CONTAIN METAL TRACKS THAT LINE
THE SCALP OR SOMETIMES IN OTHER
CASES, HAVE METALLIC THREADS
THAT ARE KIND OF WOVEN
THROUGHOUT THE BRAIDS.
AND AS A DIRECT -- AS A
DECORATIVE ELEMENT, AND THESE
TRACKS OR THESE SORT OF
DECORATIVE ELEMENTS CAN POSE A
SAFETY RISK IN THE MAGNETIC
FIELD OF THE ENVIRONMENT, AND
ALSO MAKE IT DIFFICULT FOR
RESEARCHERS TO USE -- TO COLLECT
USABLE STUDY DATA.
SO IN ONE EXAMPLE THAT I'M
SHOWING YOU HERE, WE SEE A
51-YEAR-OLD BLACK WOMAN WHO CAME
IN WITH BRAIDS, AND THIS BRIGHT
AURA ACTUALLY AROUND THE MRI,
WHICH IS WHERE I'M POINTING WITH
THE RED ARROWS.
CAME FROM A PASTE IN HER BRAIDS
THAT CONTAIN A HIGH IRON OXIDE
CONTENT.
AND ULTIMATELY PRODUCED THIS
ARTIFACT.
AND ANOTHER EXAMPLE HERE WHAT
I'M SHOWING YOU IS A 15-YEAR-OLD
BLACK CHILD THAT CAME IN WITH
TWISTS WHICH ARE A STYLE OF
BRAIDS, AND SIMILARLY HAD A
PRODUCT IN THEIR BRAIDS THAT
CONTAINED A SIMILAR IRON OXIDE
MAKEUP THAT PRESENTED THIS
ARTIFACT.
SO THESE MAGNETIC SUSCEPTIBILITY
ARTIFACTS CAN EMERGE FROM
PRODUCTS IN BLACK HAIR STYLES,
BUT UNFORTUNATELY, THE
CONSEQUENCES OF THESE DATA
COLLECTION AND THESE NUANCES ARE
OFTEN OVERLOOKED BY RESEARCHERS
AND CAN BE NEGLECTED IN TERMS OF
OPERATOR TRAINING.
SO WITH THIS IN MIND, THESE
BIASES CAN PERMEATE INTO OUR
ANALYSES AS A DIRECT RESULT OF
THESE ISSUES THAT PLAGUE OUR
RECRUITMENT AND OUR DATA
COLLECTION.
SO IF WE ARE ATTEMPTING TO
IDENTIFY BRAIN BEHAVIOR
RELATIONSHIPS AND THESE
ASSOCIATIONS THROUGH OUR
NEUROIMAGING BUT THESE DATA ARE
PRIMARILY DRIVEN BY MORE
REPRESENTED GROUP, WE ARE PRONE
TO INTERPRETATION BIAS.
SO HERE I'M SHOWING YOU WITH ONE
OF THESE LARGE NEUROIMAGING
DATASETS HCP, RESEARCHERS WERE
BUILDING PREDICTIVE MODELS OF
BEHAVIOR RELATED TO COGNITION.
HOWEVER, AS YOU CAN SEE, BLACK
AMERICANS HAD A LOWER ACCURACY
OR, IN OTHER WORDS, A HIGHER
LIKELIHOOD OF MISREPRESENTATION
OR MISCLASSIFICATION THAN WHITE
AMERICANS WHEN TRYING TO PREDICT
BEHAVIORS RELATED TO COGNITION.
SO IN SCIENCE, SUCCESSFUL
PREDICTIVE MODELS AND METHODS
SHOULD NOT ONLY ACHIEVE HIGH
ACCURACY, BUT ALSO HAVE AN EQUAL
LEVEL OF VALIDITY FOR DIFFERENT
SUBPOPULATIONS.
AND THIS IS IMPORTANT, I THINK,
WHEN YOU THINK ABOUT THE BIGGER
PICTURE OF, YOU KNOW,
RESEARCHERS AND SCIENCE AND WHY
WE DO THIS IN THE FIRST PLACE,
IS UTILIZING THESE PREDICTIONS,
YOU KNOW, FOR DIAGNOSTIC
PURPOSES, FOR EXAMPLE.
WE CAN REALLY EXACERBATE
EXISTING HEALTH DISPARITIES WHEN
OUR SCIENCE CAN SIGNIFICANTLY
AND BETTER PREDICT FOR A SUBSET
OF THE POPULATION.
SO WHAT I WAY AN W WANT TO HIGHT
IS PROVIDING ACCESSIBLE AND
DIVERSE STUDY MATERIALS.
SO IN MANY NEUROIMAGING GROUPS,
FACES ARE OFTEN USED TO ASSESS
EMOTIONAL PROCESSES.
HOWEVER, MOST AVAILABLE
DATASETS, FACE DATASETS
REPRESENT MINIMAL DIVERSITY AND
AS SUCH CAN COU CONFOUND OUR
INTERPRETATIONS OF OUR ANALYSES,
SO WONDERFULLY RESEARCHERS HAVE
ALREADY OPENLY PROVIDED THIS
OPEN RESOURCE FOR USE FOR
RESEARCHERS TO USING THEIR DATA
A RACIALLY DIVERSE STIMULUS SET.
AND HAVING A RACIALLY DIVERSE
STIMULUS SET NOT ONLY CAN BEGIN
TO SORT OF MITIGATE THESE
BIASES, BUT ALSO MAY BE USEFUL
IN RESEARCH THAT LOOKS AT THE
EFFECTS OF RACE AND ETHNICITY ON
EMOTIONAL AND SOCIAL PROCESSES.
ANOTHER OPPORTUNITY FOR RESEARCH
TO INCREASE RECRUITMENT IS
REMOVING METHODOLOGICAL BARRIERS
WHERE APPROPRIATE.
SO IN EEG NEUROIMAGING, ONE OF
THE MOST IMPORTANT THINGS IS
PROVIDING FLESH CONTACT WITH THE
ELECTRODE AND THE SCALP AND THAT
ALLOWS FOR PROPER RECORDING OF
BRAIN SIGNALS.
BUT BECAUSE OF THIS,
PARTICIPANTS WITH COARSER HAIR
AND OFTEN DARKER SKIN
PIGMENTATION ARE EXCLUDED FROM
OUR EEG NEUROIMAGING STUDIES DUE
TO THESE DIFFICULTIES.
SO TO ADDRESS THIS, ONE OF THE
WAYS THAT PEOPLE ARE ADDRESSING
THIS ARE WITH THIS SYSTEMIC
COLLECTION OR THE SYSTEMIC
COLLECTION BIAS IN EEG STUDIES,
RESEARCHERS HAVE DESIGNED
METHODS FOR OBTAINING HIGH
QUALITY EEG READINGS FROM
INDIVIDUALS WITH COARSE AND
CURLY HAIR, AND ONE OF THE WAYS
THAT THEY'RE DOING THIS IS
THROUGH A NEW TYPE OF BRAIDING
THAT ALLOWS FOR CONTACT -- FLESH
CONTACT WITH THE SCALP.
SO I ANOTHER WAY THIS CAN BE
DONE IS BY REMOVING FINANCIAL
BARRIERS WHERE APPROPRIATE.
ONE WAY RESEARCHERS ARE DOING
THIS IS THROUGH THE USE OF
THINGS THROUGH UBER AND LYFT TO
INCREASE RETENTION OF
PARTICIPANTS IN THEIR STUDIES,
AND YOU KNOW, AT ALL COSTS KIND
OF AVOIDING ANY SORT OF
OPPORTUNITY FOR PARTS PANTS TO
PAY THEIR WAY TO BE INVOLVED IN
OUR RESEARCH IS REALLY CRITICAL,
AND I KNOW OFTENTIMES WITH
INSTITUTIONS, WE'RE COMBATING
INSTITUTIONAL BUREAUCRACY IN
WHICH WE CAN'T AVOID
REIMBURSEMENT, BUT I THINK WHEN
WE CAN, WE SHOULD AVOID THIS
BURDEN FOR RESEARCH PARTICIPANTS
TO HOLD THESE COSTS WHENEVER
POSSIBLE.
AND THEN LASTLY, DEMOGRAPHIC
COLLECTION AND REPORTING.
SO THE ROLE OF RACE AND
ETHNICITY IN HUMAN NEUROSCIENCE
STUDIES AND PSYCHOLOGICAL
RESEARCH IS LARGELY NEGLECTED
AND OFTEN CORRECTED FOR.
AND SO MUCH SO THAT MOST
PUBLICATIONS FAIL TO REPORT THE
RACE OR ETHNICITY OF ITS STUDY
PARTICIPANTS.
AND ONE OF THE THINGS THAT
REALLY SHOCKED ME WAS THAT LESS
THAN 1% OF HUMAN COGNITIVE
NEUROSCIENCE IN PSYCHOLOGY
RESEARCH ARTICLES PUBLISHED FROM
1970s TO 2010s HAVE
HIGHLIGHTED RACE, AND I THINK AS
A SCIENTIFIC COMMUNITY, WE
SHOULD EXPLICITLY REPORT THESE
DEMOGRAPHICS, AND I THINK WE
ALSO NEED TO ACKNOWLEDGE THAT
THERE ARE DIFFERENT REGULATORY
DIFFERENCES THAT MAY CONSTRAIN
COLLECTION IN DIFFERENT
COUNTRIES, BUT I THINK WHERE
POSSIBLE, WE SHOULD.
AND I JUST ALSO WANT TO MENTION
THAT THIS LIST IS NOWHERE NEAR
EXHAUSTIVE.
I MEAN, I HIGHLIGHT THESE FEW
THINGS AS JUST POTENTIAL WAYS
THAT WE CAN MITIGATE THESE
ISSUES WE'RE FACING, BUT I DON'T
THINK THAT THIS LIST IS BY ANY
MEANS THE FINALIST.
SO I HOPE THAT I'VE BEEN ABLE TO
SHOW YOU SOME WAYS IN WHICH THE
MAKEUP OF OUR NEUROIMAGING
STUDIES ARE LIMITED, AND
ESPECIALLY AS WE MOVE FORWARD
AND REALLY THINKING DEEPLY OF
HOW WE CAN KIND OF SORT OF
CORRECT FOR THESE SORTS OF
THINGS.
AND THIS IMAGE THAT'S ON THE
SCREEN IS A VISUAL
REPRESENTATION OF HOW OUR
NEUROIMAGING FROM EEG AND MRI IS
LESS INCLUSIVE AND HOW WE LOSE A
LOT OF PEOPLE IN THIS PROCESS.
BUT I ALSO HOPE THAT I HAVE
CONVINCED YOU IN WAYS THAT I'M
ALSO EXCITED TO DISCUSS, OF WAYS
THAT WE CAN MITIGATE THERE'S
ISSUES, BY PROVIDING ACTIONABLE
ITEMS BOTH IN THE SHORT TERM AND
IN THE LONG TERM AS WE MOVE
FORWARD IN OUR HUMAN
UNDERSTANDING IN COLLECTING
THESE DATA IN OUR HUMAN
NEUROSCIENCE STUDIES.
SO LASTLY, I JUST WANT TO SAY
SPECIAL THANK YOU TO MY TEAMS
FOR THEIR PERSPECTIVES IN ALL OF
THIS WORK.
AND PARTICULARLY I JUST WANT TO
POINT OUT, JASMINE KWASA, THIRD
FROM THE LEFT, SHE IS ACTIVELY
WORKING TOWARDS MAKING EEG MORE
INCLUSIVE BY CONTACT WITH THE
SCALP IN INDIVIDUALS WITH DARKER
SKIN AND COARSER HAIR.
SO THANK YOU SO MUCH.
I AM LOOKING FORWARD TO THE
DISCUSSION PIECE.
[APPLAUSE]
>>THANK YOU SO MUCH.
ANY INITIAL QUESTIONS FOR
MS. RICARD?
>>DO WE HAVE ANY SENSE JUST OF
THE SCALE OF THE PROBLEM OF
EXISTING DATASETS, GIVEN THE
EXCLUSIONARY PRACTICES, YOU
KNOW, OVER TIME, WHETHER IT'S
HAIR OR OTHER WAYS IN WHICH
INDIVIDUALS HAVE BEEN
INCREASINGLY BURDENED TO BE PART
OF STUDY OR EXCLUDED FROM
STUDIES, DO WE HAVE ANY SENSE OF
THE IMPACT OF THE BIAS OF
EXISTING DATASETS AS A RESULT?
>>SO ONE OF THE THINGS I THINK
HAVE BEEN COMING OUT RECENTLY
ARE, IN PARTICULAR, THESE
PREDICTIVE MODELS WORKING BETTER
FOR OR ONLY BEING ABLE TO
PREDICT FOR CERTAIN SECTORS OF
THE GROUP, AND IN ONE CASE, IT
WAS OUT OF JAMA PSYCHIATRY THAT
RECENTLY PUBLISHED ON BEING ABLE
TO PREDICT SUICIDE RISK FOR
WHITE AND ASIAN PARTICIPANTS,
BUT NOT FOR BLACK AND AMERICAN
INDIAN/ALASKAN NATIVE, AND SO I
THINK ABOUT, YOU KNOW, THE
CONSEQUENCES OF THAT,
ESPECIALLY, YOU KNOW, IN 10
YEARS, WHEN WHAT ARE WE DOING
WITH FMRI, FOR EXAMPLE, WHEN WE
ARE AT SUCH HIGH STAKES OF
THINGS LIKE LIFE AND DEATH, I
THINK THESE SORT OF THINGS
CAN -- WE CAN LOSE PEOPLE IN THE
CRACKS.
SO I DON'T THINK WE HAVE A FULL
SENSE YET OF HOW MUCH WE ARE
LOSING, BUT I THINK WE'RE KIND
OF STARTING TO RECOGNIZE THAT
THERE IS REALLY A BIG ISSUE IN
THESE DATASETS, AND WAYS IN
WHICH WE CAN START TO KIND OF
BRING PEOPLE IN TO OUR SCIENCE.
AND ANOTHER THING I DIDN'T TALK
ABOUT BUT I THINK WHEN WE'RE
KIND OF GOING INTO THESE
COMMUNITIES AND INVITING PEOPLE
INTO OUR RESEARCH, I THINK ONE
REALLY BIG THING WHICH I THINK
IS MORE OF A LONG TERM THING,
PERHAPS, IN PEOPLE'S LABS IN
SCIENCE IS HAVING REPRESENTATION
AT ALL LEVELS.
I THINK WE GO INTO COMMUNITIES
WITHOUT ANY SORT OF
REPRESENTATION OF THE RESEARCH
TEAM, AND I THINK THAT, ON TOP
OF THE ALREADY EXISTING SORT OF
DISTRUST THAT'S BEEN BUILT OVER
CENTURIES IS A HARD THING TO
COMBAT WITH -- IF YOU'RE GOING
INTO THESE COMMUNITIES WITHOUT
THAT SORT OF REPRESENTATION.
>>THANK YOU FOR YOUR REALLY
INTERESTING TALK.
I WAS JUST WONDERING IF THE
EXCLUSION THAT YOU DESCRIBED,
THE DISPARITY, IS ACROSS ALL
TYPES OF NEURAL STUDIES, OR IS
IT SORT OF -- I KNOW YOU'VE DONE
IMAGING IN PARTICULAR, BUT SAY
PHARMACOLOGICAL STUDIES
ASSOCIATED WITH BRAIN WORK OR
NEUROLOGICAL WORK OR OTHER TYPES
OF STUDIES.
IS IT ACROSS THE BOARD OR
SPECIFIC?
>>YES, YES, DEFINITELY, I THINK
WE SEE THIS IN A LOT OF
DIFFERENT STUDIES.
NOT ONLY JUST AROUND
NEUROSCIENCE, BUT EXTENDING
ACROSS PSYCHOLOGY, I THINK
ANYWHERE WHERE PEOPLE ARE
INVOLVED, WE DO SEE THIS.
BUT I THINK IN THE LAST COUPLE
YEARS, IT'S REALLY BEEN
HIGHLIGHTED, ESPECIALLY IN THE
SENSE OF EEG, JUST BECAUSE OF
TRYING TO OPTIMIZE A HIGH
QUALITY SIGNAL WITH COARSER HAIR
AND DARKER SKIN, AS WELL AS
FNIRs AS WELL.
BUT I THINK AS OF MORE RECENTLY,
IN MRI, I THINK KIND OF MRI HAS
BEEN THAT ONE THING THAT
EVERYONE CAN BE INVOLVED IN, BUT
EVEN IN MRI, WE'RE LOSING PEOPLE
THROUGH THE CRACKS IN OUR
RECRUITMENT AND OUR METHODOLOGY.
SO I THINK THERE'S A LOT MORE TO
KIND OF BE DISENTANGLED, AND I
NING THERE ARE A LOT OF
QUESTIONS THAT WE STILL DON'T
KNOW THE ANSWERS ABOUT IN LIKE
TERMS OF WHAT SHOULD OUR SORT OF
EVERYDAY SAMPLE LACK LIKE IN OUR
NEUROIMAGING STUDIES, BUT I
THINK THOSE ARE ALL REALLY GREAT
QUESTIONS.
>>THANK YOU.
I KNOW THERE ARE SOME OTHER
QUESTIONS THAT ARE STILL IN THE
LINEUP, BUT I'LL ENSCOURGE PE
PEOPLE TO HOLD ON TO THEM AS WE
MOVE TO OUR SECOND SPEAKER OF
THIS PANEL, AND THEN WE CAN
DISCUSS IN THE PANEL DISCUSSION.
THANK YOU AGAIN, MS. RICARD.
SO IT'S MY PLEASURE TO INTRODUCE
DR. TSOSIE.
ESPECIALLY BECAUSE SHE'S JOINING
US ALL THE WAY FROM YOU AUSTRAL,
WHICH IS A VERY DIFFERENT TIME
ZONE.
SO A SPECIAL THANK YOU.
DR. TSOSIE IS AN ASSISTANT
PROFESSOR IN THE SCHOOL OF LIFE,
SCIENCE AND THE EVOLUTION AND
SOCIAL CHANGE AT ARIZONA STATE
UNIVERSITY.
SHE RECEIVED HER PH.D. IN
GENOMICS AND HEALTH DISPARITIES
AND AN MPH IN GENETIC
EPIDEMIOLOGY AND MA IN BIOETHICS
AT ARIZONA STATE UNIVERSITY.
SHE ALSO COMPLETED A
PRECEDENTIAL POSTDOCTORAL
SCHOLARSHIP AT ARIZONA STATE
UNIVERSITY.
HER RESEARCH FOCUSES ON THE
BIOETHICAL ENGAGEMENT OF THE
U.S. TRIBAL NATIONS AND GLOBAL
INDIGENOUS COMMUNITIES AND THE
GENOMIC AND DATA APPROACHES TO
ASSESS INDIGENOUS VARIATION
CONTRIBUTING TO HEALTH
INEQUALITIES THROUGH MODELING
AND REGRESSION METHODS.
THANK YOU SO MUCH FOR JOINING
US.
>>THANK YOU SO MUCH FOR THAT
KIND INVITATION.
[SPEAKING FOREIGN LANGUAGE]
I'M GOING TO GO THROUGH THIS AS
QUICKLY AS POSSIBLE BECAUSE
EVERYONE ALWAYS WANTS TO LEARN
ABOUT EVERYTHING, BUT HAS
ABSOLUTELY NO TIME TO ACTUALLY
DIGEST IT.
SO WE'RE GOING TO FOLLOW ON THAT
SAME TREND.
I WANT TO AGAIN HIGHLIGHT THAT
DESPITE EFFORTS TO INCREASE
DIVERSITY IN GENOMIC STUDIES
OVER THE PAST DECADES,
INDIGENOUS PEOPLE STILL
CONSTITUTE LESS THAN 1% OF
RESEARCH PARTICIPANTS.
AND THIS IS ACTUALLY FROM A
HISTORY OF A REEL REAL ENTRENCHD
HARM OF OUTSIDE RESEARCHERS
PERPETUATING ON TO COMMUNITY
MEMBERS.
WE HAVE HAD ETHICAL
CONTROVERSIES THAT ARE ROOTED IN
DIFFERENTS BEDIFFERENCES BETWEED
POLITICAL MISALIGNMENTS OF THE
RESEARCH FOR INDIGENOUS PEOPLES
AND ALSO THIS CENTRICITY OF
DISTRUST AND A LOT OF
DIFFERENCES IN INDIGENOUS AND
WESTERN VALUES AND APPROACHES IN
RESEARCH AND VALUE TO COMMUNITY
MEMBER, ESPECIALLY IN CONTEST
AND HYPELY STIGMATIZED SAMPLING
AS IT RELATES TO THE BRAIN.
I DO ALSO WANT TO HIGHLIGHT THAT
NOT ENOUGH PEOPLE UNDERSTAND
WHAT THE HISTORY OF INDIGENOUS
SOURCE BIOMARKERS ACTUALLY ARE
AND THE HISTORY OF THOSE
DATASETS.
SO RIGHT NOW, WE REALLY HAVE TO
LOOK TO LARGE SCALE DIVERSITY
PROJECTS THAT STILL HAVE THE
SAME AIM OF INCREASING DIVERSITY
OF UNDERREPRESENTED POPS,
BASICALLY THE HUMAN GENOME
DIVERSITY PROJECT WHICH HAS
ABSOLUTELY LAMBASTED BUY GLOBAL
INDIGENOUS POPULATIONS SUCH THAT
OVER 600 OF THEM APPEAL TO LAND
NATIONS ASKING FOR CESSATION OF
LARGE SCALE DIVERSITY PROJECTS
AS ROOTED IN THE GENOME.
DESPITE THIS, THOUGH, THE HTTP
PROJECT PUBLISHED IN 2005, THIS
PAPER SHOWING THEIR SUCCESSFUL
RECRUITMENT OF GLOBAL
POPULATIONS TO INCLUDE FIVE
INDIGENOUS GROUPS.
IF YOU LOOK AT THE MAP, THEY'RE
NOT IN THE U.S.
THEY ARE MEXICO, CENTRAL SOUTH
AMERICA, AND SOMETIMES IN AREAS
IN WHICH THE COLONIAL
AUTHORITIES DON'T EVEN RECOGNIZE
INDIGENOUS PEOPLE'S POWER TO
EXIST.
AND YET THIS IS ALSO THEIR WHERE
RESEARCHERS SOURCED THESE
DATASETS, REALLY CAUSING US TO
QUESTION WHETHER OR NOT WE ARE
JUST FURTHER CAPITALIZING ON
ALREADY DISENFRANCHISED PEOPLES.
THIS ALSO GETS US TO A POINT
THAT DSI IS ACTUALLY EMERGING AS
A TOP GLOBAL COMMODITY, AND WE
HAVE SEEN THIS IN THE NEWS TIME
AND TIME AGAIN, WHERE DIRECT TO
COP SEUMER DATASETS WHICH HAVE
AMASSED 20-PLUS MILLION MEMBERS
ARE NOW ABLE TO ALSO TURN AROUND
AND UTILIZE THOSE ASSETS AS
PROFIT.
ANCESTRY PROFITED $1 BILLION
EVERY HOLIDAY QUARTER UNTIL THEY
SOLD TO BLACKSTONE FOR
$4 BILLION.
23ANDME STARTED AS AN
ENTERTAINMENT COMPANY, GOT A
MASSIVE WRIST SLAP BY THE FDA
AND VERY RECENTLY HAS BEEN
SELLING EXCLUSIVE ACCESS TO GSK
FOR $300 MILLION TO DRUG FOR
MINE TARGETS.
SOME PEOPLE CYNICALLY STATE THAT
THAT WAS THE MODEL ALL ALONG,
BECAUSE THIS R. THERE REALLY
WASN'T THAT MUCH MONEY IN
GENETIC ANCESTRY TESTING.
AND THEN INDIVIDUALS AND
COMPANIES DON'T EVEN HAVE TO
PURCHASE OR -- COMPANIES DON'T
EVEN HAVE TO GET SEQUENCE DATA,
THEY CAN JUST GET IT FOR FREE
FROM INDIVIDUALS TO DRAW
THEIR -- DOWNLOAD THEIR OWN
DATA, COMPOSITE ON WEBSITES,
SOLD IT TWICE.
THERE'S THIS PUBLIC AND EVEN
AMONG SCIENTISTS THIS
MISCONCEPTION ABOUT NATIVE
AMERICAN AS AN IDENTITY AS
ROOTED IN BIOLOGY.
SO IN THE U.S., ACTUALLY
WORLDWIDE, INDIGENOUS PEOPLES
NEVER THOUGHT OF IT AS A RACE
CONSTRUCT.
IT WAS A SOCIOPOLITICAL ONE, AND
YET WE SEE THESE
CHARACTERIZATIONS ACROSS A
GENETIC DATASETS ACROSS THE
HUMAN GENOME DIVERSITY PROJECT,
THESE ARE THE ONLY AVAILABLE
GENOMIC DATASETS FOR IMPUTATION
AND WE HAVE AS FEW AS 30
INDIVIDUALS.
THESE ARE INDIVIDUALS WITH
DISTINCT GENETIC AND CULTURAL
HISTORIES COMPARED TO THOSE IN
THE U.S.
AND YET WE UTE THESE AS
REFERENCE POPULATIONS AND CREATE
A WHOLE LITANY OF GENOMIC
NARRATIVES THAT MAY NOT EVEN BE
APPROPRIATE.
I ALSO WANT TO HIGHLIGHT, TOO,
THE JUSTICE FACTOR OF THIS IS,
WHEN IN 2007, "THE NEW YORK
TIMES" REPORTER WENT BACK AND
BASICALLY ASKED WHAT WAS IT THAT
THEY PROMISED IN EXCHANGE FOR
THEIR DNA, AND IT WAS SAID WE
WERE PROMISED MEDICINES AND
THERAPEUTICS AND THEY WERE
SHOCKED WHEN THEY REALIZED THAT
IT TURNED INTO A BILLION DOLLAR
INDUSTRY OF THEIR CELL LINES,
AND THIS DID NOT REALLY REFLECT
WELL ON THE NIH, WHICH APPROVED
THE STUDY, AND AT THE TIME, THEY
STATED, WELL, THIS IS THE NORM.
THIS IS HOW SCIENCE IS NORMALLY
DONE.
IT'S PERFECTLY LEGAL, LEGIT
MOUNTAIN AND FOR THE BENEFIT OF
MANKIND.
THIS IS WHERE WE HAVE TO START
QUESTIONING WHO ARE WE ACTUALLY
BENEFITING WHEN WE'RE TALKING
ABOUT ENGAGING THESE
TECHNOLOGIES?
NOW, I SAY THAT THERE'S A DERTH
OF INFORMATION AND A LACK OF
AVAILABLE CELL LINES SPECIFIC TO
INDIGENOUS PEOPLES SO NOW THE
QUESTION IS, WHAT HAPPENS WHEN
WE START STACKING GENE EDITING
TECHNOLOGIES TO MODEL VARIATION
PRESENT IN INDIGENOUS
COMMUNITIES, AND START STACKING
IT FOR OTHER TECHNOLOGIES
ESPECIALLY RELATING TO ORGANOIDS
OR MODELING NEUROPSYCHIATRIC
CONDITIONS, THIS IS THE
TRAJECTORY OF RESEARCH AS WE'RE
SEEING IT AND AGAIN WE HAVE TO
ASK OURSELVES HOW RELEVANT IS
THIS IN TERMS OF BIOLOGY,
ESPECIALLY WHEN WE CONSIDER THE
WEALTH OF HEALTH DISPARITIES
THAT ARE ROOTED IN SOCIAL,
STRUCTURAL DISPARITIES AND ALSO
WHETHER OR NOT WE ARE JUST
FINDING NEW WAYS TO CIRCUMVENT
INDIGENOUS DATA SOVEREIGNTY, THE
RIGHT OF -- IT'S USUALLY
UNFORTUNATELY COLONIALLY DEFINED
IN THAT THE U.S. HAS ALREADY
ENTRENCHED AND ABROGATED TREATY
REGULATIONS AND ARBITRARILY HAS
DENOTED 574 TRIBAL NATIONS BUT
THEY ALSO DON'T RECOGNIZE THE
AUTHORITY OF STATE OR
UNRECOGNIZED TRIBAL PEOPLES OR
EVEN NATIVE HAWAIIANS AND OTHER
PACIFIC ISLANDERS AND ALSO URBAN
SUBURBAN AREAS, SO THIS IS,
AGAIN, ENTRENCHED VIOLENCE ON TO
INDIGENOUS PEOPLES BY NOT ONLY
ALLOWING THEIR AUTHORITY TO
SELF-GOVERN ALONG COLONIAL
LINES.
SO ABSENT REAL MEASURES FOR
DOING COMMUNITY-ENGAGED WORK,
IT'S AN ABSENT REAL FUNDING FOR
QUALITATIVE METHODS AND ACTUAL
COMMUNITY ENGAGEMENT, YOU KNOW,
RESEARCHERS ARE ABLE TO JUST GO
INTO URBAN AND SUBURBAN AREAS
LIKE PHOENIX AND JUST CIRCUMVENT
THESE COMMUNITY ENGAGED
APPROACHES AND ALSO THEIR OTHER
TECHNIQUES FOR CIRCUMVENTING
INDIGENOUS DATA SOVEREIGNTY.
TAKING DNA FROM UNPROTECTED
GROUPS.
NEURODIVERSITY PROJECTS LIKE THE
ALL-OF-US RESEARCH PROGRAM THAT
PUTS THEIR RESEARCH HUBS IN
URBAN AREAS LIKE TUCSON BUT NOT
IN ACTUAL PARTNERSHIP WITH
TRIBAL NATIONS OR FROM PUBLIC
HEALTH DATABASES.
I ALSO DO WANT TO HIGHLIGHT, WE
TELL OURSELVES THIS MYTH THAT
SIMPLY BY DE-IDENTIFYING DATA,
THAT WE'VE REMOVED THE RISK OF
REIDENTIFICATION, AND ESPECIALLY
FOR GENOMIC DATA, THAT'S
ACTUALLY NOT TRUE.
AND THEN WE ALSO HAVE TO REALIZE
THAT OUR M MODEL FOR STANDARD
INFORMED CONSENT LANGUAGE IS
OUTDATED.
IT'S BASED OFF OF A MODEL IN
WHICH RISKS AND BENEFITS WERE
VERY LOW ACTUALLY RELATED TO
TISSUE SAMPLING, WHEN THESE
PRINCIPLES WERE WRITTEN IN THE
70s.
NOW WE HAVE THE ABILITY TO NOT
ONLY IDENTIFY INDIVIDUALS USING
THOSE THREE PARAMETER, BUT NOW
BE ABLE TO START CREATING
INFERENTIAL STATEMENTS BASED ON
THAT PERSON'S ANCESTRY AND ALSO
THEIR KINSHIP TO SMALL EASILY
REIDENTIFIABLE GROUPS.
SO ETHICALLY THE STANDARD FOR
INDIVIDUAL INFORMED CONSENT IS
INADEQUATE, ESPECIALLY FOR
INDIGENOUS COMMUNITIES AND OTHER
FORMS OF BIOLOGICAL DATA.
THIS REALLY GETS TO THAT POINT
OF GROUP RISK.
THIS RISK IS COMPOUNDED FOR
SMALL EASILY IDENTIFIABLE GROUPS
OF THEIR OWN BUT WE DON'T FULLY
DISCLOSE TO MEMBERS OF THESE
COMMUNITIES THAT THEY ARE
SUBJECT TO GROUP RISK AND GROUP
HARM.
WHICH AGAIN IS A FAILURE OF
FULLY INFORMED CONSENT.
SO THIS IS SOMETHING THAT HE
WITH SHOULD BE THINKING ABOUT IN
OUR STANDARD PIPELINES FOR
GATHERING CONSENT AND WE REALLY
SHOULD THINK MORE ETHICALLY AND
NOT JUST LEGALLY, AND ALSO
REALLY QUESTIONING, AGAIN, THESE
NARRATIVES OF REALLY LIKE ONLY
LOOKING AT LEAST ALZHEIMER'S
DI -- LEASTADMIXED INDIGENOUS PD
THE FACT THAT INDIGENOUS PEOPLES
THAT -- THE ACT OF 1934 WHEN
THESE BLOOD QUANTUM RULES AND
THESE RACIAL CATEGORIES AND
THESE TRIBAL NATION CATEGORIES
WERE CREATED.
AND WE ALSO HAVE TO RECONCILE
THE FACT THAT IN BIOLOGY, WE
HAVE TOLD NARRATIVES USING
INDIGENOUS PEOPLE'S DNA ABOUT
ASSOCIATED CONDITIONS THAT
REALLY AREN'T ROOTED IN
GENETICS.
SO WE HAVE DR. ELER'S GROUP
CREATED OVER 120 PEER REVIEWED
ARTICLES IN PUBMED, IF YOU
SEARCH NATIVE AMERICAN GENETICS
AND ALCOHOLISM YOU CAN LOOK AT
THEM, IT SHOWS IT'S THE SAME OR
LESS IF TRIBAL COMMUNITIES,
AROUND THEN YOU SEE ALSO PAPERS
THAT LOOK AT QUESTIONS OF
SUICIDE IDEATION AND DRUG
ADDICTION HAS ROOTED IN GENETIC
PREDETERMINE NANT FACTORS.
SO WE ALSO HAVE TO CONSIDER THE
DATA ETHICS THAT WE ARE
GROUNDING OURSELVES IN, BECAUSE
ACROSS THE NIH, WE ADHERE TO THE
FAIR PRINCIPLES AND WE THINK
THIS IS PROMOTING JUSTICE AND
EQUITY BUT IT REALLY PROMOTING
EQUITY TO ACCESS AND HAS NOTHING
DO WITH OUR SOCIAL BENEFIT TO
THE COMMUNITIES THAT WE SHOULD
BE SERVING.
WHEN WE ASK COMMUNITY MEMBERS
WHAT IS IT THAT THEY ARE MOST
CONCERNED ABOUT PARTICIPATING IN
BIOLOGICAL RESEARCH, THEIR
NUMBER ONE QUESTION IS WILL MY
GENETIC DATA BE USED IN OTHER
STUDIES WITHOUT MY KNOWLEDGE OR
CONSENT?
THEY RATE THAT HIGHER ON WHETHER
OR NOT THE RESEARCH WILL BENEFIT
THEIR TRIBE OR INDIVIDUALS'
ABILITY TO DRAW THEIR OWN DATA,
THEY DON'T CARE ABOUT THAT.
WE WANT TO KNOW WHO'S LOOKING AT
THEIR DNA AND THEIR GENES.
WE HAVE ACTUALLY RECOGNIZED AND
HEARD FROM TRIBAL LEETDERS
THEMSELVES THAT THEY ARE NOT
NECESSARILY AGAINST BIOMEDICAL
RESEARCH PER SE.
THEY ARE ALSO AGAINST THE FACT
THAT THEY DON'T HAVE SAY AND THE
DOWNSTREAM DATA USES AND ACCESS
REQUESTS, ESPECIALLY WHEN
FEDERALLY FUNDED RESEARCH
REQUIRES THESE FEDERAL
DEPOSITION RULES, THAT DON'T
ALLOW INDIGENOUS NATIONS AND
PEOPLES TO ACTUALLY HAVE A SITE
AND OVERSIGHT IN THE -- WHO GETS
ACCESS TO THEIR DATA, BUT IF WE
CAN PROVIDE ALTERNATIVES THAT
ARE INDIGENOUS-LED OR THAT
INDIGENOUS PEOPLE ACTUALLY HAVE
A SAY IN THE PROCESS, THAT TRUST
PROCESS COULD ALSO LOOK
DIFFERENT.
WE ALSO HAVE TO ACKNOWLEDGE THE
HAVASU NATION, SUING THE ARIZONA
BOARD OF REGENTS, REALLY HAS
HIGHLIGHTED THE HARMS RELATED TO
BROAD CONSENTING, AND YET NOW
WE'RE IN AN OPEN DATA
ENVIRONMENT THAT WANTS TO DO
BROAD CONSENTING EVERYTHING, AND
THIS IS JUST PERPETUATING THE
SAME HARMS WE'VE SEEN IN THE
PAST.
IF YOU REALLY WANT TO LOOK AT
MORE SOPHISTICATED MODELS, WE
NEED TO LOOK AT MORE ADAPTIVE
FORMS OF CONSENT, AND THAT
REQUIRES AN E-DYNAMIC CONSENT
PORTAL, SO THAT WE CAN MANAGE
OUR OWN DATA BUT ALSO LICENSED
TO SOFTWARE TO OTHER -- FOR
FREE.
WE'VE ALSO STARTED STACKING
MACHINE LEARNING APPROACHES
RELATED TO BLOCKCHAINING, WHEN
YOU HAVE INDIGENOUS LED AND
COMMUNITY LED SEVERS YOU CAN DO
THIS.
WE CAN ACTUALLY NO LONGER ADHERE
TO THIS OLD OUTDATED MODEL OF
REQUIRING EVERYTHING TO BE DRAWN
DOWN TO LOCAL MACHINES.
AND THEN ALSO WE HAVE TO START
QUESTIONING OF OWNERSHIP OR WHO
ACTUALLY HAS THE AUTHORITY TO
CREATE AND MANDATE DATA SHARING
RULES?
IS IT THE TRIBAL NATIONS OR IS
IT THE FEDERAL GOVERNMENT?
AND WHAT HAPPENS IN THE SPACE OF
PUBLIC HEALTH RESEARCH, AND
THESE ARE JUST EMERGING
QUESTIONS THAT WE NEED TO
CONSIDER AND JUST TO GROUND IT A
LITTLE BIT MORE IN YOUR
DISCIPLINE WITH THE ABCD STUDY,
THERE WAS A HUGE AMOUNT OF
BACKLASH RELATED TO THE STUDY.
AND I ENCOURAGE YOU TO LOOK AT
THIS PAPER BY WHITE ET AL. BUT
REALLY WHAT CAME OUT OF IT IS
THERE NEEDS TO BE ASSURANCES
THAT THERE'S BENEFIT, NOT HARM.
PARTNER EARLY AND OFTEN AND
KATION ABOUT MAKING
GENERALIZATIONS ABOUT INDIGENOUS
PEOPLES -- SAMPLING AND
HETEROGENEITY IN THE FACT THAT
IT'S A SOCIOCOMMIT -- NOT A
POLITICAL ONE.
THIS PAPER REALLY LOOKING AT
CREATING COMMUNITY CENTERED DATA
GOVERNANCE RULESCOULD BE
CONSIDERED FOR RE-USE AND
INTERPRETATION.
AND FINALLY, WE NEED TO NOT JUST
THINK OF ETHICALLY BUT THINK
BEYOND JUST LEGAL, WITHIN OUR
LEGAL BOUNDS, BECAUSE EVEN
THOUGH SAMPLES AND DATA ARE
TECHNICALLY CONSENTED IT DOESN'T
ALWAYS MEAN THAT PARTICIPANTS
ARE FULLY INFORMED.
RESEARCHERS DO NOT HAVE AN
IMPLICIT RIGHT TO ACCESS OR USE
OR RE-USE DATA.
WE SHOULD REALLY BE CONSTANTLY
ASKING OUR SERVETION DOES THIS
ACTUALLY TRULY BENEFIT
INDIGENOUS PEOPLES? AND NOT
JUST DECADES FROM NOW IN THIS
HAZY, FUZZY VIEW OF THE FUTURE,
BULL IS IT ACTUALLY GOING TO
BENEFIT US FIRST AND ALSO BEYOND
COMMERCIAL PARTNERS.
FINALLY JUST BECAUSE WE CAN DO
THE RESEARCH DOESN'T MEAN WE
NECESSARILY SHOULD YET.
THANK YOU SO MUCH.
[APPLAUSE]
>>OKAY.
THANK YOU SO MUCH.
LET'S START WITH A QUICK Q & A
FOR THE SPEAKER.
MAYBE ONE OR TWO QUESTIONS AND
THEN WE CAN TURN TO THE ENTIRE
PANEL.
>>THANK YOU.
IT'S REALLY INTERESTING TO HEAR
YOUR TAKE ON ALL OF THIS AND TO
SORT OF LEARN ABOUT HOW THIS HAS
DEVELOPED OVER THE LAST SEVERAL
DECADES.
BUT DO YOU SEE THINGS IMPROVING
WITH RESPECT TO GENOMIC DNA DATA
USE AND ACQUISITION?
IS IT IMPROVING OR IS IT STILL
AS BAD AS IT SEEMS TO BE?
>>BOTH.
I WILL SAY THAT EVEN FIVE YEARS
AGO, THE CONVERSATION RELATED TO
COMMUNITY ENGAGEMENT IN GINO
NIGENOMICWAS NON-EXISTENCE, ANDG
SANS OF INCORPORATING SOCIAL AND
STRUCTURAL DETERMINANTS OF
HEALTH IN THE CONVERSATION
RELATED TO HEALTH INEQUITIES WAS
SOMETHING THAT EXIS EXISTED IN R
DISCIPLINES, AND I THINK THIS
HAS A LOT TO DO WITH UP AND
COMING INDIGENOUS SCIENTISTS
OCCUPYING SPACES IN ACADEMIA
THAT THEY PREVIOUSLY HAVEN'T
BEEN A PART.
SO WE HAVE NOW THE LARGEST
COHORT OF INDIGENOUS TRAINED
SCIENTISTS IN ACADEMIA, BUT
THERE'S STILL THAT POWER
IMBALANCE THAT'S IN PLAY AND
THAT WE ARE STILL LARGELY -- AND
SOME OF US ARE MID CAREER, AND
WE ARE ACTUALLY -- IT FEELS LIKE
CONSTANTLY BUTTING HEADS WITH
THE OLD I HATE TO SAY VANGUARD
OF INDIVIDUALS THAT, YOU KNOW --
YOU KNOW, THERE WAS A WAY OF
DOING RESEARCH IN THE PAST THAT
HIGHLIGHTED RECRUITMENT MODELS
AND THE POWER DYNAMICS MORE
UNIT-DIRECTIONAL, AND THERE HAVE
BEEN A LOT OF CURRENCY AND POWER
AND AUTHORITY GIVEN TO SOME
INDIVIDUALS, AND JUST -- AN WE
HAVE TO JUST ACKNOWLEDGE THAT
JUST LIKE WE ADOPT NEW
TECHNOLOGIES IN OUR RESEARCH AND
ADOPT A NEW SEQUENCING TOOLS AND
HAVE IT AS PART OF A TOOLKIT,
ETHICS IS ALSO A PART OF OUR
TOOLKIT AND THOSE METHODS ALSO
NEED TO BE ADAPTED AND WE ALSO
NEED TO KEEP ABREAST OF THOSE
NEW INNOVATIONS AND APPROACHES.
SO I THINK IT'S A LITTLE BIT OF
BOTH, BUT I'M POSITIVE FOR THE
FUTURE.
THANK YOU.
>>THANK YOU, DR. TOE SEE.
, DR. TSOSIE.I WANTED TO FOLLOWT
INFORMED CONSENT THAT YOU SAID
IT DOESN'T WORK, IT'S
INSUFFICIENT OR INADEQUATE OR
SOMETHING LIKE THAT, WHICH I
TEND TO AGREE WITH.
I DON'T KNOW IF YOU HAD A CHANCE
TO HEAR SOME OF THE EARLIER
PRESENTATIONS TODAY, BUT THERE
WAS SOME FOCUS ON, YOU KNOW, WE
NEED TO MAKE INFORMED CONSENT
BETTER, BUT IT IS AN IMPORTANT
PART OF PROTECTING PEOPLE IN
RESEARCH.
TO YOU THINK THAT THAT'S
POSSIBLE?
AND PART OF THAT QUESTION, YOU
MENTIONED DYNAMIC CONSENT.
IS THE REASON THAT'S BETTER
BECAUSE IT GIVES INDIVIDUALS
MORE CONTROL OVER WHAT GET DONE
AFTER THE INITIAL CONSENT
PROCESS?
IS THAT THE REASON YOU THINK
THAT'S THE BEST MODE?
>>PART OF THAT.
SO THE GREAT THING ABOUT BEING
VIRTUAL AND AFAR IS THAT AS I
WAS LISTENING TO EARLIER
CONVERSATIONS, I COULD QUICKLY
UPDATE MY SLIDES.
SO THANK YOU TO THE EARLIER
PRESENTATION FOR INSPIRING
THAT -- I MEAN, IT'S ALREADY A
BLOATED SLIDE DECK, SO THANK YOU
FOR ALLOWING ME TO RAPIDLY GO
THROUGH THEM ALL, AND ALSO THE
PREVIOUS SPEAKER TALKING ABOUT
THE U.K. BIOBANK, WHICH IS
ACTUALLY THE MODEL OF DYNAMIC
CONSENT AND OUR SORT OF EE
DYNAMIC CONSENT PLATFORM WE'VE
BUILT.
I THINK PARTICIPANTS HAVE REALLY
CALLED FOR THIS AND IT'S ALSO
PART OF NOT JUST THE
TRUST-BUILDING RELATIONSHIP,
LIKE WHAT ARE RESEARCHERS
ACTUALLY DOING WITH MY DATA, BUT
IT'S ALSO PART OF THE EDUCATION
PROCESS, SO IN ALLIANCE WITH OUR
DATA MANAGEMENT PORTAL OF WHICH
PARTICIPANTS CAN ACTUALLY SE SEG
SEEISEE --THEY CAN UPDATE THEIR
PREFERENCES FOR TYPES OF STUDIES
AND THEY CAN CHANGE IT AT WILL.
AND THEN THE OTHER PART OF THAT
IS WE HAVE LIKE A COMMUNITY
EDUCATION PART OF WHAT IS THE
DNA, WHAT IS DATA SHARING, AND
WE CAN HAVE THOSE ON A MORE
PUBLIC FACING SITE.
SO RIGHT NOW WE AS LIKE A GLOBAL
GENERAL SCIENCE PUBLIC ARE VERY
UNFORTUNATELY NOT INFORMED AS
THEY SHOULD BE ABOUT THESE
THINGS.
AND THERE'S A KNOWLEDGE GAP THAT
IS BOTH -- I'M POSITIVE ABOUT,
BUT ALSO SCARED ABOUT,
ESPECIALLY WHEN WE'RE TALKING
ABOUT HOW POLITICS IS INNERVATED
AND HOW WE'RE TRAINING OUR UP
AND COMING DATA LEADERS.
AND SO I THINK JUST HAVING THOSE
OPEN COMMUNICATIONS BETWEEN
SCIENTISTS AND PARTICIPANTS, BUT
IN MORE REALTIME, IS SOMETHING
WE CAN AIM FOR.
SO I'M REALLY EXCITED FOR OUR
MANAGEMENT PLATFORM, BUT ALSO
TIED WITH OUR COMMUNITY
EDUCATION EFFORTS.
>>SO NOW I'M NOT JUST JUMPING
IN AND BEING THAT PERSON BUT
HAVE THE PRIVILEGE OF JUST
MODERATING THE Q & A FOR OUR
PANEL.
AND SO I WILL OPEN IT UP TO
EVERYONE IF THERE ARE IMMEDIATE
QUESTIONS, BUT ONE FRAMING FOR
THE QUESTIONS THAT WE HAVE
REALLY GOES BACK TO SASKIA'S
PRESENTATION EARLIER THIS
MORNING, THINKING ABOUT
COMMUNITY LEVEL HARMS AND GROUP
LEVEL HARMS RATHER THAN JUST
INDIVIDUAL HARMS AND TRYING TO
UNDERSTAND THAT, AND BOTH OF
THESE PRESENTATIONS, I THINK,
REALLY FOCUSED OUR ATTENTION ON
THOSE QUESTIONS.
BUT I WANT TO BRING IT BACK TO
THE UNIQUENESS OF BRAIN DATA FOR
COMMUNITY LEVEL HARMS AND TO TRY
TO UNPACK THAT A LITTLE BIT IN
THE CONVERSATION THAT FOLLOWS.
AND YOU'LL HEAR ME TODAY AND
TOMORROW AROUND THIS KIND OF
BEATING THE DRUM OF TRYING TO
UNDERSTAND EXACTLY WHAT THE
UNIQUE HARM IS, UNDERSTANDING,
OF COURSE, IN THE CONTEXT THAT
WE'VE HAD THE CONVERSATION THAT
WE'RE LOOKING AT MULTIMODAL
CENTERS, WE'RE LOOKING AT A LOT
OF OTHER DATA BEING INTEGRATED,
AND THIS IS ONE MORE PIECE OF
DATA.
BUT TRYING TO ISOLATE THIS SINCE
PART OF WHAT WE'RE TRYING TO
FIGURE OUT IN THIS CONVERSATION
IS THE EXTENT TO WHICH THERE'S
SOME UNIQUE GUIDANCE THAT NEEDS
TO BE GIVEN TO RESEARCHERS OR
SOME UNIQUE POLICY THAT SHOULD
BE ADOPTED.
SO LET ME TURN TO BOTH OF OUR
SPEAKERS FIRST TO SAY FOCUSING
IT IN THAT WAY, ARE THERE UNIQUE
RESTS OF THE COLLECTION, IN
PARTICULAR, THE SHARING OF BRAIN
DATA, THAT IS -- THAT WE SHOULD
BE THINKING ABOUT FROM COMMUNITY
OR GROUP HARM LEVEL, AND SECOND,
IF SO, AFTER HEARING AND
THINKING ABOUT EVERYTHING TODAY,
ARE THERE FEATURES OR SPECIFIC
KINDS OR ASPECTS OF BRAIN DATA
THAT ARE UNIQUELY RISKY FOR
COMMUNITIES OR GROUPS?
SO I'M GOING TO OPEN IT UP TO
BOTH OF YOU AND MAYBE DO WE
HAVE -- THIS IS MY -- MY BRAIN
DOES NOT WORK THIS WAY WELL.
I'LL START OVER HERE TO SEE IF
YOU HAVE THOUGHTS ON THAT TO
SHARE FIRST.
PART OF IT CAN BE AS YOU HAVE
DESCRIBED, RIGHT?
I MEAN, SO IT'S THE PARTICULAR
RISKS AS YOU OPINED ABOUT THE
RISK OF MISINTERPRETATION,
MISAPPLICATION OR EXCLUSION TO
GROUPS THAT CREATES HARMS AND
INTERPRETATION AND APPLICATION,
BUT I WONDER IF THERE'S KIND OF
MORE THAT YOU WANT TO SHARE ON
THAT QUESTION.
>>YEAH, DEFINITELY.
I THINK IN PARTICULAR WITH
ANALYSES THAT WE CONDUCT, I
THINK FOR ME, THAT FEELS LIKE
PERHAPS ONE OF THE BIGGEST
CHALLENGES IN TERMS OF PEOPLE'S
LIVES, AND I THINK M ACATIONS TO
PEOPLE'S LIVES, BUT I GUESS I'M
STILL CONSIDERING THAT GETTING
PEOPLE IN THE DOOR AT ALL IN OUR
BRAIN STUDIES, AND I THINK THAT
IS SOMETHING THAT WE REALLY NEED
TO CONSIDER BRINGING PEOPLE IN,
BECAUSE I DO THINK THERE IS
SOMETHING UNIQUE TO
UNDERSTANDING THE BRAIN THAT IN
PARTICULAR FOR RESEARCHERS, BUT
I THINK ALSO FOR EXAMPLE, LIKE
LIVING AS A BLACK AMERICAN AND
THE WAYS THAT PEOPLE ARE
TARGETED AND HARMED.
ONE EXAMPLE I THINK OF THE
CRIMINAL JUSTICE SYSTEM AND THE
WAYS THAT THIEF BEGU THEY'VE BEE
GENOMIC DATA TO KIND OF FIND
PEOPLE IN THE SENSE OF OTHER
SORTS OF OLD OR -- YOU KNOW, OLD
CRIMES, AND THAT IN PARTICULAR
SORT OF SCARES ME IN THAT WHAT
PEOPLE ARE DOING NOW AND
CONSIDERING THAT I FIND THAT
BRAIN DATA IS QUITE POWERFUL,
THAT IS ONE OF THE FEARS OF THE
FUTURE FOR ME IN PARTICULAR.
>>SO STIGMATIZATION, GOING BACK
TO CONOR'S STUDY EARLIER
THINKING ABOUT DISTRUST THAT
PEOPLE HAVE OF GOVERNMENT, I
SUSPECT THAT'S BECAUSE
GOVERNMENT IS LUMPED TOGETHER
AND PEOPLE ARE THINKING ABOUT
LAW ENFORCEMENT AND KIND OF
GOVERNMENT ACCESS AND USE AND
MISUSE AND IF WE THINK ABOUT THE
WAY A.I. HAS BEEN USED TO TARGET
AND CONTINUE TO TAKE --
EXACERBATE RACIAL BIAS AGAINST
BLACK DEFENDANTS FOR PAROLE OR
SENTENCING BASED ON THE VENEER
OF OBJECTIVITY YOU MIGHT EXPECT
THAT WE'LL HAVE SOMETHING
SIMILAR IF THERE'S GROUP LEVEL
DATA THAT'S DEVELOPED OR
AGGREGATED BASED ON THAT.
LET ME TURN TO KRYSTAL AND SEE
IF YOU HAVE THOUGHTS ON THAT
FIRST QUESTION ABOUT PARTICULAR
HARMS AT A GROUP LEVEL, THINKING
ABOUT IT FROM THE SPECIFIC LENS
OF, YOU KNOW, SOMETHING ABOUT
BRAIN DATA THAT WOULD SUGGEST
RESEARCHERS NEED TARGETED
GUIDANCE ABOUT HOW TO THINK
ABOUT SHARING GROUP LEVEL OR
COMMUNITY LEVEL HARMS.
>>YEAH, THERE HAVE BEEN MODELS
OF SUCCESSFUL REPATRIATION,
REMITRATION EFFORTS OF RETURNING
BIOLOGICAL SAMPLES BACK TO THEM,
BUT THEN STILL BROKERING A SORT
OF TRUST FULL RESEARCH
PARTNERSHIP.
IT IMPORTANT BECAUSE THERE'S A
CULTURAL HARM HERE IN THAT MANY
INDIGENOUS COMMUNITIES BELIEVE
THAT YOU NEED THE WHOLE BODY IN
ORDER TO BE -- INTERRED IN ORDER
FOR THAT SPIRIT TO RETURN TO THE
ANCESTRAL WORLD.
AND THE WAY THAT THESE EFFORTS
TO REPATRIATE HAVE BEEN DONE,
IT'S BEEN ALWAYS, YOU KNOW, WITH
THE CONTEXT OF USUALLY BLOOD OR
HAIR SAMPLES, AND YOU CAN TAKE
LIKE A TINY LITTLE SAMPLE OF
EACH OF THOSE TYPES OF -- SAMPLE
TYPES, AND THEN, YOU KNOW, ALSO
DERIVE A LOT OF SEQUENCE DATA,
OF COURSE WITH CONSENT.
BUT THAT TYPE OF MODEL IS A LOT
HARDER TO DO WITH LIKE BRAIN
TISSUE, BECAUSE, YOU KNOW, A
TINY LITTLE BIOPSY O IS NOT AS
INFORMATIVE AS A WHOLE ORGAN DEP
SIG, WHICH IS USUALLY WHAT THE
IDEAL ASK IS.
BUT THAT'S JUST A LOT MORE TO
REQUEST FROM INDIGENOUS PEOPLES.
AND THEN THERE'S ALSO THE
QUESTION NEUROIMAGING DATA.
SO IN TERMS OF SOME OF THE
MORPHOLOGICAL DATABASES THAT ARE
OUT THERE THAT HAVE IMAGING DATA
FROM INDIGENOUS PEOPLE, A LOT OF
TIMES THESE ARE FROM LARGELY
PIMA, AND I DON'T THINK PEOPLE
UNDERSTAND THAT THE OTHAM
PEOPLES IN THE SOUTHWESTERN
REGIONS IN THE U.S. HAVE HAD A
40-YEAR-LONG RELATIONSHIP WITH
THE NIH AND NIDDK FOR DIABETES
AND THEN THEY WITHDREW
THEMSELVES FROM THAT STUDY AT
THE START OF THIS CENTURY.
BUT THEY STILL HAVE LIKE -- A
HUGE FREEZER ROOM OF SAMPLES
THAT ARE TAKEN THAT HAVE
CONTINUED TO SORT OF BE THE
SOURCE OF ORIGINAL DATA 20 YEARS
AFTER THE COMMUNITY HAD REMOVED
THEMSELVES FROM RESEARCH.
DUE TO A LACK OF PERCEIVED
BENEFIT AND CONTINUED HARM.
SO AGAIN, THIS IS LIKE ARE WE
CONTINUING TO EXPLOIT THAT THIS
GROUP THAT HAS HI HISTORICALLY
REMOVED THEMSELVES, AND ON TOP
OF THIS, IT'S PARTICULARLY
MEXICAN OTHAM PEOPLE, SO SAME
RELATIVES, JUST ON THE OTHER
SIDE OF THE BORDER BECAUSE THE
OTHAM PEOPLE, UNFORTUNATELY THEY
PREDATE THE U.S. -- MEXICO-U.S.
BOUNDARY, AND THEN THEY HAVE
THIS JUST -- BY SECTOR PEOPLES
SO THE U.S. SIDE HAS FEDERAL
SOVEREIGNTY AND ARE ABLE TO HAVE
THEIR DECISION TO NOT
PARTICIPATE IN RESEARCH BE
RESPECTED, BUT THE MEXICAN SIDE
OF THE OTHAM PEOPLES, THEIR TATA
IS ALL OVER THE PLACE.
AND FOR THE OTHAM DATA THAT
PREDATE THEIR RESTRICTIONS ON
RESEARCH AND OTHAM ON THE
MEXICAN SIDE, WITHOUT THEIR
CONSENT OR KNOWLEDGE, WHICH IS
PROBLEMATIC, I HAVEN'T ENTERED
THE WHOLE AI, UTILITY ON TOP OF
THAT IMAGING DATA YET AND THERE
ARE A LOT OF QUESTIONS TOO ABOUT
KINSHIP AND STEWARTSHIP WITH
THESE CONSIDERED NON-THANK YOU
MAFNLT THERE'S A WHOLE RHETORIC
OF INDIGENOUS, SCIENTISTS AND
SCHOLARS WHO ARE EXPLORING THIS
QUESTION AND IT'S IN CULTURAL
ALIGNMENT WITH HOW WE REVIEW
SOVEREIGNTY AND THESE OTHER
FORMS OF DATA.
>>I THINK THAT RAISES A GREAT
QUESTION ABOUT THINKING ABOUT
ENGAGEMENT AS WE FOCUS ON THIS
QUESTION OF ARE THERE UNIQUE
HARMS FOR GROUPS OR ARE THERE
PARTICULAR GUIDANCE THAT WOULD
BE GIVEN TO RESEARCHERS, FOR
EXAMPLE, IT SOUNDS, KRYSTAL,
FROM YOUR COMMENTS, THERE COULD
BE DIFFERENT GROUPS THAT WOULD
HAVE DIFFERENT VIEWS WITH
RESPECT TO ONGOING USE OF DATA,
COLLECTION OF DATA, SHARING OF
DATA, THE RIGHT TO RESCIND THAT
DATA AND MAY HAVE A DIFFERENTIAL
RELAYS SHIP, VIS-A-VIS TISSUE
VERSUS IMAGING DATAVERSE US
OTHER DATA, AND THINKING ABOUT
THAT AND THINKING ABOUT MOVING
FORWARD TO CONCRETE ADVICE FOR
RESEARCHERS IN THIS CONTEXT, SO
THINKING ABOUT COMMUNITY
ENGAGEMENT TO INFORM THAT, AND
WHAT PROCESS WE MIGHT KIND OF
THINK ABOUT OR GROUPS THAT YOU
THINK WOULD BE CRITICAL TO COULD
SO OR HOW THOSE CONVERSATIONS
OUGHT TO HAPPEN.
I WONDER IF WE CAN REFLECT ON
THAT NOW BECAUSE THE GOAL, OF
COURSE, IS TO TAKE THESE
INSIGHTS AND TO DRIVE THEM INTO
ACTIONABLE RECOMMENDATIONS FOR
RESEARCHERS AND SO DO YOU HAVE
SORT OF IDEAS ABOUT COMMUN WHAT
COMMUNITY ENGAGEMENT OUGHT TO
LOOK LIKE OR COMMUNITIES IN
PARTICULAR THAT YOU THINK
CERTAINLY NEED TO BE ENGAGED AND
AT THE TABLE IN ORDER TO BE
HAVING THESE CONVERSATIONS AND
UNDERSTAND UNIQUE VIEWS THEY MAY
HAVE BRAIN DATA, DIFFERENT
MODALITIES OF COLLECTION AND USE
AND STORAGE AND SHARING OF THAT
DATA.
>>DO YOU MIND IF I ANSWER THAT
QUESTION FIRST?
>>PLEASE.
>>OKAY.
YEAH.
THERE'S AN INTERESTING ANALOGOUS
SITUATION IN PALEOGENOMICS
RESEARCH ON OUR ANCESTORS IN
THAT THERE'S A FEAR FROM THE
MUSEUM'S SIDE THAT THEY DON'T
DISCLOSE PUBLICLY BUT WE'VE
HEARD IN CIRCLES THAT MUSEUMS
DON'T WANT TO DISCLOSE WHAT'S IN
THEIR COLLECTIONS BECAUSE
THEY'RE FEARFUL THAT DESCENDANT
COMMUNITIES WILL RECALL THEIR
ANCESTORS BACK HOME, AND THEN
THAT WILL REMOVE SAMPLE FROM
STUDIES.
SO IT REALLY INTERESTING, WE
HAVE THIS OPEN DATA MOVEMENT OF
YES WE NEED ACCESS TO DATA BUT
THERE'S NOT A REAL COG SANS OF
WHAT THIS MEANS WHEN COMMUNITIES
ALSO HAVE ACCESS OPENLY AND
KNOWING WHAT'S IN COLLECTIONS
AND I THINK THAT THAT'S
SOMETHING I THINK EXISTS IN
OTHER FORMS OF BIOLOGY AND ALSO
IN BRAIN BANKING TOO, LIKE DO WE
WANT PEOPLE TO KNOW PUBLICLY
THAT THIS EXISTS?
AND HOW ARE YOU GOING TO BROKER
A CONVERSATION WHERE IT STARTS
WITH, WELL, DA DA DA, WE'VE HAD
YOUR PEOPLE'S SAMPLES FOR NOW X
NUMBER OF DECADES, AND WE JUST
HAD IT.
THAT'S A DIFFICULT FORM OF
CONVERSATION -- CONVERSATION
STARTER.
BUT IT'S A REALLY IMPORTANT ONE
TO HAVE.
I FORGET MY SECOND POINT BECAUSE
IT'S 6:30 A.M.
SASKIA, I'LL LET YOU RESPOND.
>>ONE OF THE THINGS I WAS GOING
TO SAY THAT I'VE SEEN THR DONE
REALLY WELL IN COMING OUT INTO
THE COMMUNITY IS THROUGH
COMMUNITY-BASED PARTICIPATORY
RESEARCH.
AND OFTENTIMES YOU SEE THIS A
LOT IN PUBLIC HEALTH, BUT IT
WORKS WELL BECAUSE YOU ARE
BUILDING TRUST WITH THE
COMMUNITY BUT THAT DOES TAKE
TIME AND I THINK IN THE -- IN
ACADEMIA, THAT'S OFTEN WHERE THE
CURRENCY -- OUR PAPERS AND
PUBLICATIONS AND PRESENTATIONS
AND WHETHER IT KIND OF FEELS
LIKE THERE'S THIS LACK OF TIME,
I DON'T THINK IT IS AS HIGHLY
VALUED IN KIND OF THE GRAND
SCOPE OF ACADEMIA, THOUGH I DO
THINK THAT SOME DEPARTMENTS ARE
INCENTIVIZING THAT AND I DO
THINK THAT IS ONE WAY FORWARD
FOR RESEARCHERS TO KIND OF WORK
WITH COMMUNITIES, YOU KNOW, IN
THE LONG TERM IN ESTABLISHING
THOSE RELATIONSHIPS.
>>JUST WANTED TO GIVE THE
RESEARCH PARTICIPANTS AN
OPPORTUNITY TO WEIGH IN.
>>I JUST HAD TWO COMMENTS
BECAUSE I THINK WE'RE TALKING
ABOUT THE COMMUNITY RISKS AND
THERE WERE TWO THAT I DIDN'T
REALLY HEAR ANYONE MENTIONING,
BUT ONE WAS LIKE THE GROUP
PROFILING AND ASTIGMATISM AROUND
IF WE START CLASSIFYING PEOPLE
AND THEN THE EXPLOITATION AND
TARGETING WE'RE GETTING FROM
THIRD PARTY COMPANIES SO AS WE
SHARE THIS DATA, I THINK THOSE
ARE TWO RISKS TO OUR COMMUNITIES
THAT WE HAVEN'T YET TOUCHED ON.
SO JU A THOUGHT.
>>SO THINKING ABOUT THAT,
FORGIVE ME I OF BEING CONCRETE T
THE END OF THE DAY, BUT THINKING
ABOUT THAT, WHAT ARE
RECOMMENDATIONS TO HOW TO TACKLE
IT, THINKING ABOUT DRIVING
TOWARD -- THE HOPE HERE IS THAT
WE'LL HAVE SOME GUIDING QUEST
QUESTIONS FOR RESEARCHERS,
POTENTIAL PRINCIPLES OR IDEAS OR
SPECIFIC RECOMMENDS THAT WE
MIGHT MAKE.
IDEAS?
>>FOR ME, I THINK IT'S
CONNECTING TOO WITH A LOT OF THE
GRASSROOTS ORGANIZATIONS BECAUSE
I THINK WE A LOT OF TIMES GO TO
NATIONAL ORGANIZATIONS TO LOOK
FOR RESOURCES WHEN SOME OF THOSE
ORGANIZATIONS AREN'T REALLY
HELPING THOSE GROUPS, SO REALLY
FINDING THOSE KOLs WITHIN THE
PATIENT COMMUNITIES AND PEOPLE
TO ACTUALLY HAVE THOSE
CONVERSATIONS WITH WHO ARE
EDUCATED AROUND THE BUSINESS
MODEL OF HEALTHCARE, I GUESS
YOU'D CALL IT, AND THEN REALLY
KIND OF -- BUT I THINK THERE IS
A LACK OF TRUST IN EVERY ASPECT
OF HEALTHCARE THAT WE HAVE HERE
AND THOSE CONVERSATIONS NEED TO
BE HAD IN A WAY WITH EACH OF
THESE DIFFERENT COMMUNITIES.
SO I THINK EVEN AS WE LOOK AT
ASIAN POPULATIONS, WE
CALCULATE -- I MEAN, WE
CATEGORIZE EVERYONE THAT'S ASIAN
INTO ONE HUGE CATEGORY, WHEN
WE'VE GOT SMALL TINY LITTLE
GROUPS THAT DON'T ASSOCIATE OR
EVEN CONNECT WITH OTHERS.
WE HAVE TO REALLY LOOK AT ALL OF
THESE DIFFERENT GROUPS INVOLVED
AND HOW WE COMMUNICATE WITH
THOSE GROUPS AND I THINK IT IS
REALLY BUILDING THAT TRUST AND
IT'S GOING TO TAKE TIME.
>>GREAT.
OTHER QUESTIONS?
I DON'T WANT TO KEEP JUMPING IN.
GO AHEAD.
>>I THINK IF WE'RE BEING
TRUTHFUL -- I'M SORRY, GO AHEAD.
>>PLEASE.
>>OH.
WHO'S SPEAKING?
ME?
>>KRYSTAL, GO AHEAD AND THEN WE
HAVE A QUESTION IN THE BACK OF
THE ROOM.
>>I THINK WE NEED TO BE
TRUTHFUL AMONGST OURSELVES AS
DATA SCIENTISTS TOO ABOUT THE
LAYERS OF INFERENTIAL
ASSUMPTIONS THAT WE KEEP
STACKING UPON OURSELVES AND THE
LIMITATIONS OF THOSE INFERENCE.
SO FOR INSTANCE, IF YOU LOOK AT
POPULATION GENETICS, IT'S ROOTED
IN MORE DEM KR THESE DEMOGRAPHYE
HAVEN'T REALLY QUESTIONED
WHETHER OR NOT THAT'S AN
ACCURATE WAY TO REFLECT THAT.
WE'VE JUST FOUND MORE TOOLS TO
SORT OF REIFY THOSE ASSUMPTIONS.
I KID YOU NOT, IF I WERE TO ASK
A GRADUATING PH.D. STUDENT IN
GENETICS WHAT AN EIFEN VALUE OR
VECTOR IS AND HOW EXACTLY
THEY'RE CALCULATING THE
PRINCIPLE COMPONENTS IN THEIR
PCA PLOTS THAT SEGREGATES ONE
GROUP VERSUS ANOTHER IN TERMS OF
DISTINCTIVENESS AND REALLY
QUESTION LIKE WHY ARE WE GATING
PEOPLE AND INDIVIDUALS BY A
SIMILARITY TO A REFERENCE GROUP
WHEN THE REFERENCE GROUP MAY NOT
EVEN BE RELEVANT OR REFLECTIVE,
AND WHAT DOES IT MEAN WHEN WE
DROP OFF INDIVIDUALS BECAUSE
THEY HAVE LIKE REAL LIVED
EXPERIENCES THAT ALSO ARE
VALUABLE FOR UNDERSTANDING OF
HEALTH INEQUITIES, AND WHEN WE
START THINKING ABOUT, WOW,
THERE'S A LOT OF LAWS IN HOW WE
DO POPULATION GENETICS, AND HOW
WE THINK ABOUT CATEGORIZATIONS
OF RACE AND ETHNICITY USING
THESE COVARIATES THAT WE PUT
INTO OUR LOGISTIC REGRESSION
MODEL, I THINK ONCE WE START
BEING TRUTHFUL ABOUT OURSELVES
ABOUT WHERE THESE ASSUMPTIONS
COME FROM, I THINK WE CAN REALLY
START UNPACKING, LIKE, HOW --
THAT THESE THINGS THAT WE CALL
CAPITAL T WITH THE TRUTH, ARE
ACTUALLY REALLY, REALLY SMALL,
LOWER CASE T WITH THE TRUTH, AND
ONCE WE HAVE ACCOUNTABILITY AND
VISIBILITY TO OURSELVES AND I
THINK WE CAN ACTUALLY START
COMMUNICATING TO THE COMMUNITIES
THAT WE'RE RESPONSIVE TO AND
ACTUALLY START BUILDING THAT
TRUST.
>>GREAT, THANK YOU.
>>MY NAME IS OLIVIA FROM SOUTH
AFRICA BUT I'M A POSTDOC HERE AT
THE NIH.
I'M INTERESTED IN THINKING ABOUT
SO AT THE NIH, WE HAVE SORT OF
RESPONSIBLE ETHICS TRAINING THAT
ALL SCIENTISTS NEED TO DO
ANNUALLY, AND I'M THINKING ABOUT
HOW WE COULD TRY TO FOSTER IN
TAKING IT BACK TO SASKIA'S
QUESTION EARLIER IN THE DAY IS
WHAT CAN WE DO AS RESEARCHERS,
HOW CAN WE IN DIFFERENT
INSTITUTIONS ENSURE THAT THESE
CONVERSATIONS ARE ACTUALLY
HAPPENING OR THE EDUCATION IS
ACTUALLY HAPPENING AMONGST
CLINICIANS, AMONGST SCIENTISTS,
AMONGST ENGINEERS AND PEOPLE WHO
ACTUALLY ARE WORKING WITH THIS
DATA TO BE ABLE TO MAKE SURE
THAT THEY ARE SENSITIZED TO
ACTUALLY INCLUDE WHO IS
PARTICIPATING IN THEIR STUDIES.
WHAT ARE THE DEMOGRAPHICS
THEY'RE REPORTING ON AND SOME
FOFORM OF ACCOUNTABILITY.
SO I DON'T KNOW IF THERE'S ANY
EXAMPLES FROM DR., HOW HAVE YOU
BEEN ABLE TO SENSITIZE THEM?
I LEARNED A LOT AND I DIDN'T
EVEN KNOW ABOUT SOME OF THESE
EXAMPLES AND CASES THAT ARE
REALLY ETHICALLY CHALLENGING.
HOW DO WE MAKE SURE THAT PEOPLE
KNOW ABOUT IT?
I KNOW THAT IN OTHER CONTEXTS
BESIDES BRAIN WORK, THERE'S OLD
AGE CASES THAT PEOPLE DON'T KNOW
ABOUT WIDELY BUT HOW DO WE START
TO SENSITIZE AND MAKE SURE THAT
PEOPLE ARE EDUCATED?
BOTH SCIENTISTS BUT ALSO THE
COMMUNITIES, DIFFERENT MODELS.
THANK YOU.
>>ONE MORE COMMENT BEFORE WE
MOVE TO END OF DAY WRAP UP, SO
GIVE THE PANEL AN OPPORTUNITY TO
EITHER COMMENT OR --
>>I THINK WE HAVE AT LEAST ONE,
MAYBE TWO MORE QUESTIONS HERE,
SO MAYBE WE CAN --
>>OKAY.
>>LET US DO A ROUND ROBIN HERE,
WHICH IS LET'S HAVE BOTH OF THE
QUESTIONS ON THE TABLE AND THEN
WE'LL INVITE OUR PANELISTS TO
PROVIDE FINAL WRAP-UP REMARKS
ACROSS ALL OF THEM.
>>THANKS FOR BRINGING UP THESE
TOPICS.
I THINK THEY REALLY INTERSECT
WITH WHAT NITA WAS BRINGING UP
EARLIER ABOUT WHAT WAS DIFFERENT
ABOUT THE NEURAL DATA.
THE NEURAL DATA IS, IN THE END,
IF YOU HAVE IT ALL, IT IS THE
PACE IS OF BASIS OF WHAT MAKES S
INDIVIDUALS WHICH MAKES US
DIFFERENT FROM OTHER
INDIVIDUALS, WHICH MAKES GROUPS
DIFFERENT FROM OTHER GROUPS, AND
ALL THOSE DETERMINANTS, AND YOU
CAN IMAGINE, YOU KNOW, LOTS OF
STUDIES IN THE FUTURE TRYING TO
UNDERSTAND THESE THINGS WHICH
START TO PARCEL PEOPLE INTO
GROUPS THAT THE GROUPS MAY NOT
BE COMFORTABLE WITH.
SO I THINK YOU BROUGHT UP A
REALLY INTERESTING IDEA OF
KEEPING THE RESEARCHERS AND THE
PARTICIPANTS IN THE LOOP OVER
THE LONG RUN AS OPPOSED TO JUST
AROUND THE STUDY THAT THEY
VOLUNTEER FOR, BUT HOW TO KEEP
THEM IN THE LOOP GOING FORWARD
TO KNOW WHAT THEY FEEL
COMFORTABLE WITH HAVING THAT
DATA USED FOR.
BECAUSE IT'S NOT SOMETHING THAT
THEY COULD HAVE ANTICIPATED
OFTENTIMES WHEN THEY JOINED THE
STUDY.
SO I GUESS THAT WOULD BE THE --
IT SEEMS TO ME THAT WOULD BE THE
FINAL SOLUTION IF IT WAS
FEASIBLE, IT'S THE FEASIBLITY OF
IT WHICH MAKES IT DIFFICULT TO
CONCEIVE OF, BUT I WONDER IF
MAYBE THAT'S SOMETHING TO
PURSUE.
>>I WAS ACTUALLY GOING TO
FOLLOW UP WITH CHRISTINE BECAUSE
I THOUGHT THE TWO COMMENTS, THE
TWO ISSUES THAT YOU RAISE THAT
HAVE NOT BEEN DISCUSSED, THE
STIGMATIZATION AND THEN THE
TARGETING, ARE BOTH REALLY
IMPORTANT ISSUES, AND AT SOME
LEVEL, SEEM TO -- ONE CAUSES THE
OTHER IN A CERTAIN WAY.
AND SO I WONDERED IF YOU HAD
IDEAS ABOUT HOW TO MAKE SURE
THAT THOSE ARE NOT HAPPENING.
S
>>SO I WOULDN'T SAY I HAVE THE
ANSWERS OR ANYTHING BUT TOMORROW
I DO HAVE A PRESENTATION WITH
ONE OF THE GROUPS I WORK WITH,
THE LIGHT COLLECTIVE.
WE'VE COME UP WITH OUR SEVEN
REQUIREMENTS FOR COLLECTIVE
DIGITAL RIGHTS, WHICH WE ASK
COMPANIES TO TAKE A LOOK AT IF
THEY'RE LOOKING TO WORK WITH
PATIENT DATA.
AND SOME OF THOSE THINGS THAT
ARE IN THERE IS FIDUCIARY
RESPONSIBILITIES IS HAVING A
PATIENT ON A BOARD AS WE GO INTO
A LIST OF ALL OF THESE DIFFERENT
THINGS THAT YOU CAN DO TO
INCLUDE PATIENTS IN THOSE
CONVERSATIONS AS WE MOVE FURTHER
INTO THE DISCUSSIONS AROUND
DATA.
SO I WILL HAVE A FULL KIND OF
THING THERE, BUT I WOULD SAY
REALLY THEY'RE ALSO CONNECTING
AGAIN WITH THOSE GROUPS THAT
REALLY KNOW WHAT'S HAPPENING IN
THOSE COMMUNITIES.
BECAUSE IF YOU LOOK AT SOME OF
THESE NATIONAL ORGANIZATIONS,
YOU ALSO SEE HUNDREDS OF SMALL
GRASSROOTS ORGANIZATIONS WHO
AREN'T GETTING THE HELP THEY
NEED FROM THOSE ORGANIZATIONS.
SO HOW DO YOU INCLUDE THOSE
PERSPECTIVES THAT ARE STILL
REALLY BEING LEFT OUT.
>>LET ME GIVE OUR PANELISTS
EACH A QUICK ONE-MINUTE OR SO
RESPONSE, THEN WE'RE GOING TO
MOVE IN TO WRAP-UPS FOR THE DAY.
I KNOW PEOPLE ARE LOSING ENERGY
BUT I JUST WANTED TO PUT A PLUG
IN FOR COMING BACK TOMORROW.
YOU HEARD THAT TEASER FOR
TOMORROW.
LOTS OF GOOD REASONS TO COME
BACK TOMORROW.
WHO WANTS TO START?
AND YOU DON'T HAVE TO.
YOU CAN LEAVE IF YOU FEEL LIKE
YOU'VE SAID YOUR PEACE.
>>I DO FEEL LIKE I'VE SAID MY
PEACE, I JUST WANT TO DOUBLE
DOWN ON WHAT PEOPLE HAVE SAID.
I THINK ESPECIALLY FROM THE
PARTICIPANTS' YOU KNOW,
EXPERIENCE, I THINK THESE ARE
REALLY IMPORTANT THINGS TO
CONSIDER.
SO THANK YOU ALL FOR BEING HERE.
>>KRYSTAL, ANY FINAL THOUGHTS?
>>SURE.
ONE, CITY TRAINING IS SOMETHING
A LOT OF INSTITUTIONS USE FOR
LIKE THEIR STANDARD IRB
REQUIREMENTS FOR RECEIVING
FEDERAL FUNDS.
THERE ARE ACTUALLY INDIGENOUS
AND ALSO COMMUNITY ENGAGEMENT
RELATED TRAINING MODULES
ACTUALLY THAT WE AS INSTITUTIONS
COULD HIGHLY RECOMMEND OR EVEN
IF YOU'RE WORKING WITH CERTAIN
POPULATIONS REQUIRE AN
INSTITUTION LEVEL OR EVEN A
FEDERAL LEVEL THAT THAT'S A
POSSIBILITY.
WE ALSO, I THINK IN ACADEMIC
DISCIPLINES AND DEPARTMENTS, WE
HAVE A PROBLEM THAT WE'VE BEEN
LIKE, OH, WE CAN GRADUATE A
PH.D. STUDENT IN BIOINFORMATICS
IN AS FEW AS THREE YEARS.
AND WHEN YOU DO THAT, AND YOU'RE
TRAINING STUDENTS WITH AN
ALREADY CLEANED DATASET AND, YOU
KNOW, HAVE A MODEL THAT JUST
NEEDS TO BE ADAPTED, YOU'RE NOT
REALLY ALSO TRAINING THEM IN
ETHICS.
I THINK WE NEED TO HAVE MORE
CONVERSATIONS ABOUT DATA ETHICS
IN CONJUNCTION WITH THE TOOLS.
OTHERWISE WE GET A LOT OF LIKE
SCIENCE RIGHT NOW HAS A HUGE
ETHICS WASHING PROBLEM, IN THAT
YOU GET THE LANGUAGE AND THE
VERNACULAR THAT SOUNDS SORT OF
ETHICSY BUT YOU'RE STILL DOING
THE SAME EX-TRABTIVE STUFF THAT
YOU'VE BEEN DOING, IT JUST
SOUNDS BETTER.
AND THEN JUST EDUCATION THAT'S
MORE COMMUNITY SPECIFIC AND
THERE HAS TO BE ALSO LIKE THIS
RECOGNITION THAT PEOPLE CAN BE
SCIENTISTS, DATA SCIENTISTS AND
ALSO BE ADD ROW VO ADVOCATES IR
COMMUNITY AS WELL.
THERE'S BEEN A HISTORY IN WHICH
THERE'S BEEN A BIT OF COMPLICITY
AND A BIT OF COERCION THAT WE'RE
JUST TRAINING SCIENTISTS OF
COLOR THAT LOOK LIKE, YOU KNOW,
WE'VE BEEN DOING THE SAME WORK,
BUT THEY JUST LOOK BROWN OR
BLACK OR -- AND WE NEED TO THINK
MORE ABOUT THAT YOU CAN HAVE
COMMUNITY ADVOCATES THAT ARE IN
THE SPACE, AND THAT'S WHY I'M
REALLY EXCITED TO CREATE
INDIGENOUS CENTERED DATA SCIENCE
WORKSHOPS THAT ARE RELATED TO
INFORMATICS, AND WE HAVE ONE
NEXT WE'RE ON HEALTH A.I., SO
YOU IS PORT FOR THOSE SUPPORT FF
ENDEAVORS AND SUPPORT FOR EARLY
CAREER SCIENTISTS TO ALSO HAVE
THOSE MEN INVENTORYSHIP
OPPORTUNITIES ACROSS DISCIPLINES
I THINK IS ESPECIALLY IMPORTANT
FOR SMALL, LESS VISIBLE
POPULATIONS.
THANKS.
>>MAYBE WE COULD WRAP UP OUR
THIRD PANEL AND WE HAVE TWO END
OF THE DAY CLOSING REMARKS.
ONE IS FROM OUR CO-CHAIRS WHO
HAVE THE PRIVILEGE OF GIVING
US -- ADD CHALLENGE OF GIVING US
A SUBSEQUENT WRAP-UP AND NITA
HAS CLOSING REMARKS IN TERMS OF
LOGISTICS.
>>I THINK WE'LL PROBABLY BE
BRIEF IN OUR SUBSTANTIVE, SO
I'LL MAKE JUST A COUPLE OF
STATEMENTS AND I'M SURE
CHRISTINE WILL ADD TO IT, BUT I
KNOW EVERYBODY HAS HAD A LONG
DAY AND THEY'VE BEEN LISTENING
ATTENTIVELY AND CLOSELY.
OUR GOAL IS TO BOTH START THE
DAY WITH A SET OF CLEAR FACTS,
RIGHT, AND THE REASON THAT THE
WORKSHOP WAS STRUCTURED THE WAY
IT WAS IS TO START WITH A COMMON
SET OF FACTS TO GET TO THE POINT
OF BEING ABLE TO MAKE INSIGHTS
ABOUT WHAT THE ROLE OF SHARING
IS WHEN IT COMES TO BRAIN DATA,
IT'S TO UNDERSTAND WHAT CAN
REALLY BE DONE, AND WHAT IS, IF
ANYTHING, DIFFERENT IF WHAT
COULD BE INFERRED, COLLECTED AND
SHARED, SO THE MORNING SESSIONS
REALLY BEGAN FOCUSING ON THAT
QUESTION, AND THEN MOVING TO A
PANEL FOCUSED ON MULTIMODAL
SENSORS AND PUSHING THE ENVELOPE
TO THE CURRENT GENERATION OF
BOTH WHAT'S POSSIBLE AND HOW
PEOPLE ARE THINKING ABOUT THAT,
THE FRAMEWORKS THAT THEY'RE
DEVELOPING AROUND THAT.
WITH THE GOAL OF UNDERSTANDING
BOTH INDIVIDUAL RISKS THAT CAN
ARISE FROM BRAIN DATA, AND BRAIN
DATA NOT JUST IN ISOLATION BUT
ESPECIALLY IF BRAIN DATA IS
BORING, BUT BRAIN TA DATA IN
CONTEXT OF THE FACT THAT IT
DOESN'T EXIST IN ISOLATION, IT
IS COLLECTED WITH A WHOLE LOT OF
OTHER INFORMATION, WHETHER
INFERENTIAL OR, YOU KNOW, ALL OF
THE REST OF OUR PERSONAL DATA
UNDERSTANDING THAT THE CONTEXT
IS BOTH RESEARCHERS BUT ALSO A
LOT OF COMMERCIAL APPLICATIONS
THAT ARE DEVELOPING FROM THE
SAME.
AND THEN LOOKING NOT JUST, OF
COURSE, AT INDIVIDUAL LEVEL
RISKS BUT GROUP LEVEL RISKS.
ALL OF THE GOAL OF THIS IS TO
BOTH UNDERSTAND THE PROBLEM
FULLY WITH A DEEP EVIDENCE-BASED
GROUNDING TO MAKETH CAL
RECOMMENDATIONS AND TO TRY TO
ASSESS OUT WHAT ARE THE
POSSIBILITIES BEYOND
DEIDENTIFICATION, WHETHER IT'S
INFORMED CONSENT, DYNAMIC
INFORMED CONSENT, WHETHER IT IS
MUCH BETTER UNDERSTANDING AND
ENGAGEMENT OF GROUPS AND
COMMUNITIES AND PATIENTS WHO CAN
HELP INFORM THOSE CONVERSATIONS,
ALL OF IT IS IDEALLY TO COME UP
WITH A MORE NUANCED WAY OF BEING
ABLE TO UNDERSTAND WHAT THE
RISKS AND BENEFITS OF SHARING
BRAIN DATA ARE AND HOW TO
SAFEGUARD AGAINST THOSE SO THAT
MOVING FORWARD, WE CAN REAP THE
BENEFITS OF ADVANCES IN NEURAL
DATA AND THE SHARING OF NEURAL
DATA WHILE MINIMIZING THE RISKS
THAT ARISE FROM IT.
SO TOMORROW WE'RE HOPING TO DIVE
INTO THAT IN MORE DETAIL AND
REALLY MOVE FROM WHAT IS A VERY
SOLID FOUNDATION FROM DAY ONE, I
HOPE, FOR EVERYONE.
I KNOW IT WAS INCREDIBLY
INFORMATIVE FOR ME, TO BEING
ABLE TO THEN START TO TRY TO
DEVELOP APPLICATIONS OF SO THEN
WHAT, HOW DO WE THEN TAKE WHAT
WE'VE LEARNED FROM DAY ONE,
IDENTIFY PARTICULAR PATHWAYS
FORWARD, AND BE ABLE TO COME UP
WITH SPECIFIC POTENTIALLY
RECOMMENDATIONS OR A SPECTRUM OF
SENSITIVITY OF DATA OR SPECTRUM
OF MODALITY, COLLECTION OF DATA,
SHARING OF DATA, IT MAYBE IT'S T
A SPECTRUM, MAYBE IT'S ONE BIG
THING.
I THINK WE'RE AGNOSTIC TO WHAT
THE ANSWER IS.
WE ARE JUST HOPEFUL THAT THERE
IS A SET OF RECOMMENDATIONS AND
ANSWERS WE CAN BEGIN TO GENERATE
FROM HERE AND TO BE ABLE TO USE
THIS WORKSHOP AS FUNDAMENTAL TO
INFORMING THEM.
SO I'LL HAND IT OVER TO
CHRISTINE TO MOVE US FROM
INTEREST.
THERE.
>>SO IT IMPOSSIBLE TO FOLLOW
NITA, I'D SAY.
YOU DID A GREAT JOB OF
SUMMARIZING.
I JUST WANT TO DO TWO THINGS.
I JUST WANT TO THANK ALL OF THE
PEOPLE WHO BRENTED TODAY BECAUSY
BECAUSE YOU GAVE US ALL GREAT
FOOD FOR THOUGHT IN TERMS OF
WHAT THE RISKS ARE AND WHETHER
OR NOT THERE ARE MEANINGFUL WAYS
TO SEPARATE THEM OR THINK ABOUT
THEM AS REQUIRING DIFFERENT
KINDS OF PROTECTIONS.
YOU ALSO GAVE US SOME IDEAS
ABOUT WHAT SOME OF THOSE
PROTECTIONS SHOULD BE, AND AS
YOU JUST HEARD FROM NITA,
TOMORROW WE'RE GOING TO CONTINUE
THAT CONVERSATION, SINK A LITTLE
DEEPER INTO THE INDIVIDUAL KINDS
OF RISKS THAT MIGHT OCCUR, HEAR
FROM SOME PARTICIPANTS AND
OTHERS WHO HAVE COLLECTED DATA
FROM PARTICIPANTS AND TRY TO
DECIDE WHETHER OR NOT THERE'S A
SPECTRUM OR WHETHER OR NOT THE
CURRENT WAYS THAT WE THINK ABOUT
PROTECTING INDIVIDUALS AND
COMMUNITIES FROM THE RISKS OF
SHARING BRAIN DATA ARE THE RIGHT
ONES, AND WHETHER WE NEED TO
EXPAND ON THOSE.
SO THAT'S THE TASK FOR TOMORROW,
AND TODAY WAS A BEAUTIFUL SETUP
FOR THAT TASK FOR TOMORROW.
I DO WANT TO CLOSE BEFORE WE DO
CLOSE BY THANKING NINA AND
SASKIA FOR ALL OF THE WORK THAT
THEY DID, PUTTING THIS TOGETHER.
AND ALL OF THE OTHER PEOPLE THAT
WORKED WITH THEM, SO DANA AND I
DON'T KNOW WHO ELSE, OTHER
PEOPLE WHO HAVE BEEN WORKING
WITH THEM TO MAKE THIS DAY
POSSIBLE.
AND I THINK IT WAS A VERY RICH
AND FRUITFUL CONVERSATION.
SO THANK YOU.
[APPLAUSE]
>>SO NOW THE CLOSE-CLOSE.
SO FOR THOSE OF YOU WHO ARE IN
PERSON, IF YOU CAME OVER IN THE
SHUTTLE, THERE IS NOW A SHUTTLE
OUTSIDE TO TAKE EVERYBODY BACK
TO THE HOTEL.
WE WILL CONTINUE THE
CONVERSATION INFORMALLY WITH A
GATHERING AT PIN STRIPES, WHICH
IS WALKING DISTANCE FROM THE
HOTEL.
THERE'S A NICE UPSTAIRS AND
ROOFTOP SECTION FOR PEOPLE TO
CONVENE THERE AND CONTINUE
DISCUSSING.
WE WILL RECONVENE HERE IN THE
SAME ROOM AT 10:00 A.M.
TOMORROW.
SO IF YOU ARE TAKING THE SHUTTLE
AGAIN TOMORROW AS YOU DID,
PLEASE BE OUTSIDE AT 9:10,
BECAUSE IT WILL DEPART SHARP AT
9:15.
AND IF YOU ARE CHECKING OUT
TOMORROW, YOU CAN BRING YOUR
BAGS WITH YOU ON THAT SHOULD
TELL AND SHUTTLEAND WE CAN STORE
BACK IF WE ASSUME YOU'RE HEADING
TO YOUR NEXT TRAVEL AFTER
TOMORROW'S DISCUSSIONS END.
THANK YOU, EVERYBODY.
SEE YOU ALL TOMORROW.
[APPLAUSE]