WELCOME EVERYONE TO THIS NIA
VIRTUAL WORKSHOP TITLED
HARNESSING COMPUTATIONAL
APPROACHES TO ADVANCING AIMING
AND ALZHEIMER'S DISEASE AND
ALZHEIMER'S DISEASE RELATED
DEMENTIA RESEARCH.
MY NAME IS MATT SUTTER A PROGRAM
DIRECTOR IN THE DIVISION OF
NEURO SCIENCE WHICH ALONG WITH
THE DIVISION OF AGING BIOLOGY
AND BEHAVIORAL AND SOCIAL
RESEARCH ARE CO-SPONSORING THIS
WORKSHOP.
FOR THOSE WHO MAY NOT HAVE
PARTICIPATED OR VIEWED AN NIA
WORKSHOP BEFORE, THESE WORKSHOPS
ARE USED BY NIA PROGRAM STAFF
AND INSTITUTE LEADERSHIP TO
BETTER UNDERSTAND THE STATE OF
THE ART SCIENCE AND PARTICULAR
TOPIC AREAS AND HEAR FROM
LEADING RESEARCHERS, AND
IMPORTANTLY UNDERSTANDING THE
GAPS IN THE CURRENT LANDSCAPE
AND OPPORTUNITIES FOR
DEVELOPMENT.
AS SUCH, THE AGENDA HAS TIME FOR
DISCUSSION BETWEEN OUR WORKSHOP
PARTICIPANTS TO BEST PROBE THE
URGE ENTER QUESTIONS AND UNMET
OPPORTUNITIES FOR THE FIELD.
THE GOAL OF THIS WORKSHOP IS IS
TO EXPLORE COMPUTATIONAL
APPROACHES TO ADVANCING OUR
STUDY OF AGING AND AD/ADRD.
THERE'S BEEN A RAPID DEVELOPMENT
OF FIELDS FROM COMPUTATIONAL
BIOLOGY TO IMAGING AND MODELING
AND SYSTEM AND BIO-INFORMATICS
AND EXTENDED TO NEUROSCIENCE AS
WELL WHERE COMPUTATIONAL SCIENCE
IS AN INCOMPETENTER DISCIPLINARY
FIELD FOR ANNAL DECISION OF
MULTI SCALE MODEL AND NEURO
FUNCTION AND MORE ADVANCES IN
MACHINE LEARNING ARTIFICIAL
INTELLIGENCE AND LARGE-SCALE
DATASETS HAVE FACILITATED AN
EXPLOSION OF RESEARCH ACROSS
LEVELS OF BIOLOGY AND BEHAVIOR.
AS REFLECTED, COMPUTATIONAL
APPROACHES SPAN LEVELS OF
ANALYSIS FROM MOLECULES THROUGH
CELLS AND NETWORKS TO COGNITION
AND BEHAVIOR.
THIS WORKSHOP WILL EXPLORE
CURRENT AND POTENTIAL
APPLICATIONS OF COMPUTATIONAL
APPROACHES TO THE STUDY OF AGING
AND AD/ADRD TO HELP US BETTER
UNDERSTAND KEY QUESTIONS.
WITH THAT, I'D LIKE TO NOW
INTRODUCE Dr. AMY KELLEY, WHO
HAS SOME OPENING REMARKS TO
SHARE.
>>Amy Kelley: THANK YOU, MATT.
AND WELCOME, EVERYONE.
AND I'M HAPPY TO JOIN YOU FOR
THIS EXCITING WORKSHOP VIRTUALLY
AND AS YOU KNOW AND MATT STATED,
THE GOAL IS TO EXPLORE THE
COMPUTATIONAL APPROACHES TO
ADVANCE THE STURDY OF AGING AND
ALZHEIMER'S AND RELATED
DEMENTIA.
THIS WORK VERY COLLIERLY
ALLIANCE WITH NIA'S MISSION TO
UNDERSTAND THE NATURE AND AGING
AND AGING PROCESS AND DISEASES
AND CONDITIONS THAT ARE
ASSOCIATED WITH GROWING OLDER,
ALL IN ORDER TO EXTEND THE
HEALTHY AND ACTIVE YEARS OF LIFE
FOR OLDER ADULTS.
THE FOCUS OF TODAY'S WORKSHOP
CUTS ACROSS ALL OF OUR RESEARCH
AREAS SO GENETICS, BIOLOGICAL,
CLINICAL, BEHAVIORAL, SOCIAL AND
ECONOMIC RESEARCH ON AGING.
AND NIA IS COMMITTED TO
ADVANCING THE MOST INNOVATIVE
AREAS OF SCIENCE BY INVESTING IN
PROJECTS AND INFRASTRUCTURE THAT
APPLY CUTTING EDGE APPROACHES TO
FILL CRITICAL GAPS AND RESEARCH
AND COVER REAND FACILITATING THE
BROAD RESEARCH DATA AND THE
RESOURCE THAT'S ARE DEVELOPED
WITH PUBLIC FUNDS, WHICH IS
CENTRAL TO OUR COMMITMENT.
IN THAT SPIRIT, I WANTED TO
SHARE WITH YOU JUST A COUPLE OF
EXAMPLES OF NIA-FUNDED PROGRAMS
IN THIS REALM.
THE FIRST IS PROJECT CALLED
TARGET ENABLEMENT TO ACCELERATE
THERAPY DEVELOPMENT FOR
ALZHEIMER'S DISEASE.
OR TREAT AD.
THIS DEVELOPS HIGH-QUALITY
RESEARCH TOOLS AND TECHNOLOGY
THAT IS NEEDED TO VALIDATE AND
ADVANCE THE NEXT GENERATION OF
DRUG TARGETS FOR ALZHEIMER'S.
THE DATA, THE RESEARCH
METHODOLOGIES AND THE
COMPUTATIONAL AND EXPERIMENTAL
TOOLS ARE DISSEMINATED OPENLY
AND FREE OF CHARGE TO THE
BROADER RESEARCH COMMUNITY FOR
NOT USE AND DRUG DISCOVERY AND
RESEARCH THAT WILL HELP BETTER
UNDERSTAND THE COMPLEX BIOLOGY
OF THE DISEASE.
ANOTHER EXAMPLE IS THE NEW NIA
ARTIFICIAL INTELLIGENCE AND
TECHNOLOGY COLLABORATORIES.
AITC.
THESE ARE DEDICATED TO HELPING
AMERICANS LIVE LONGER, HEALTHIER
LIVES THROUGH THE APPLICATION OF
ARTIFICIAL INTELLIGENCE AND
EMERGING TECHNOLOGIES.
THE AITC WILL SERVE AS A
NATIONAL RESERVE TO PROMOTE
DEVELOPMENT AND IMPLEMENTATION
OF AI APPROACHES ASK TECHNOLOGY
THROUGH PIPPA CROSS INDUSTRY,
HEALTHCARE SYSTEMS, AND VARIOUS
OTHER STAKEHOLDERS UNDER THREE
AITC COLLABORATORIES WHICH ARE
CENTERED AT JOHNS HOPKINS
UNIVERSITY OF MASSACHUSETTS
AMHERST AND UNIVERSITY OF
PENNSYLVANIA ALONG WITH A
COORDINATING CENTER THAT IS
MANAGED BY ROSALIE AND
ASSOCIATES.
THOSE ARE JUST TWO EXAMPLES OF
WORK THAT NOT NI NIA IS SUPPORTS
CASE.
WE RECOGNIZE DATA SHARING.
THIS IS A WAY TO ENSURE THE
RIGOR, REPRODUCIBILITY AND
TRANSPARENCY OF RESEARCH
FINDINGS, SHARING SCIENTIFIC
DATA ALSO CAN ACCELERATE
BIOMEDICAL RESEARCH BY ENABLING
VALIDATION OF RESEARCH ADULTS
AND PROMOTING DATA REUSE FOR
ADDITIONAL RESEARCH STUDIES.
MANY OF YOU AND I'M CERTAIN ARE
AWARE OF NIH'S NEW POLICY FOR
DATA MANAGEMENT AND SHARING.
IT CAME INTO EFFECT EARLIER THIS
YEAR.
IT REQUIRES ALL NIA FUNDED
RESEARCHERS TO SUBMIT A PLAN
PRIOR TO THEIR AWARD THAT
OUTLINES HOW THEIR SCIENTIFIC
DATA GENERATED BY THEIR RESEARCH
WILL BE MANAGED AND SHARED.
THE POLICY REALLY EMPHASISES
GOOD DATA MANAGEMENT PRACTICES
AND IT IS ESTABLISHING
EXPECTATIONS FOR MAXIMIZING THE
SHARING OF SCIENTIFIC DATA BEN
RATED FROM PUBLICLY FUNDED
RESEARCH.
NIA IS WORKING ON GUIDANCE AND
RESOURCE THAT'S WILL SUPPORT
INVESTIGATORS SO THEY CAN SAFELY
AND EFFICIENT LEHMAN AGE AND
SHARE THE DATA FROM THEIR
STUDIES.
WE HAVE NIA RESOURCE FORCE DATA
SPECIFIC TO ALZHEIMER'S AND THE
BROADER AGING RESEARCH
COMMUNITIES SO ONE OF THE
EXAMPLES IS THE NIA DATA STORAGE
THAT IS IS CO-OPERATIVE
AGREEMENT THAT IS BETWEEN NIA
AND THE UNIVERSITY OF
PENNSYLVANIA.
ALSO THE NIA KNOWLEDGE PORTAL,
WHICH IS CREATED THROUGH THE
ACCELERATED MEDICINE PIP AND
AGING RESEARCH BIOBANK AND ALL
OF THESE AND THE NEW DATA
SHARING POLICY IN GENERAL WE
EXPECT TO OPEN A WIDE RANGE OF
NEW OPPORTUNITIES FOR
RESEARCHERS TO CONDUCT
COMPUTATIONAL RESEARCH WITH
THESE EXISTING DATA SOURCES.
SO, AS MENTIONED, TODAY'S
WORKSHOP IS A COLLABORATIVE
EFFORT.
IT WAS ORGANIZED BY STAFF FROM
NIA DIVISION OF NEUROSCIENCE,
THE DIVISION OF BEHAVIORAL AND
SOCIAL RESEARCH AND AGING
BIOLOGY.
YOU ALL ARE SPEAKERS AND
PARTICIPANTS, AND BRING TOGETHER
A WIDE RANGE OF EXPERTISE AND
PERSPECTIVES TO DISCUSS THE
POWER OF COMPUTATIONAL
APPROACHES TO ANALYZING
MOLECULAR SYSTEMS, LARGE-SCALE
BRAIN SYSTEMS, NOT COMPLEX
BEHAVIORAL PATTERNS AND MANY
OTHER TOPICS.
IT'S ALSO, WE'RE HOPING AN
IMPORTANT OPPORTUNITY TO HEAR
FROM THE COMMUNITY ABOUT THE
RESIZE AND STRENGTHEN AND
ACCELERATE AND WE HOPE OVER THE
NEXT TWO DAYS WE HOPE WE CAN
EXPLORE THIS SPACE TOGETHER AND
THESE DISCUSSIONS SPARK NEW
COLLABORATIONS BETWEEN
COMPUTATIONAL AND DATA
SCIENTISTS AND RESEARCHERS
ACROSS AGING RESEARCH
DISCIPLINES THAT WILL ADDRESS A
RANGE OF IMPORTANT RESEARCH
QUESTIONS RELATE TODAY AGING AND
NEURO DEGENERATION.
THANK YOU FOR JOINING US AND
CONTRIBUTING TO THIS IMPORTANT
DISCUSSION AND ALONG WITH YOU,
I'LL LOOK FORWARD TO HEARING THE
SPEAKERS AND DISCUSSIONS TODAY
AND LEARNING HOW WE CAN BETTER
HARNESS COMPUTATIONAL APPROACHES
OF AGING AND ALZHEIMER'S AND
RELATED DEMENTIAS.
MATT THANK YOU SO MUCH, Dr.
KELLY.
I JUST WANTED TO SHARE A FEW
ADDITIONAL LOGISTICAL DETAILS
BEFORE WE GET STARTED.
I WILL REMIND SPEAKERS TO PLEASE
KEEP THEIR LINES MUTED UNLESS
SPEAKING.
IN ADDITION, PLEASE TURN YOUR
VIDEO ON DURING THE DISCUSSIONS
DISCUSSION AND AT THE END OF THE
WORKSHOP.
DURING THE TALKS, PLEASE KEEP
YOUR VIDEO OFF UP LESS YOUR
PRESENTING.
THIS IS JUST FOR THE BROADCAST
FEED.
PLEASE USE -- IF YOU HAVE A
QUESTION OR COMMENT DURING THE
DISCUSSIONS, PLEASE USE THE
RAISE HAND FEATURE IN ZOOM SO WE
KNOW TO CALL ON YOU OR DROP A
QUESTION IN THE CHAT AND DON'T
FORGET TO LOWER YOUR HAPP HAND U
ASKED YOUR QUESTIONS.
SPEAKERS WILL HAVE 20 MINUTES
FOR THE HOCKEY AND WE'LL HOLD
QUESTIONS FOR DISCUSSION SESSION
AT THE END OF EACH SESSION.
NIA STAFF WILL PROVIDE TIME
REMAINDER AND AT THE END OF THE
20 MINUTES AND WE WILL CUT YOU
OFF AND.
FOR EVERYONE VIEWING THIS
MEETING AND APPROXIMATE THE LIVE
FEEDBACK FORM AND WE WILL TRY TO
BRING THESE QUESTIONS UP DURING
THE DISCUSSION QUESTION.
DURING THE DISCUSSION SESSIONS,
OR IF THE QUESTION IS MORE
SPECIFIC TO A PARTICULAR
SPEAKER, WE MAY FORWARD THAT
QUESTION TO THE INDIVIDUAL FOR
FOLLOW-UP AFTER THE MEETING.
PLEASE UNDERSTAND WE MAY NOT
HAVE TIME TO GET TO EVERY
QUESTION.
FOR THOSE OF YOU WHO ARE
REGISTERED OR MAY BE UNABLE TO
VIEW THE WORKSHOP LIVE, WE WILL
RECORD THE WORKSHOP AND NOTIFY
YOU THROUGH THE EVENT BRIGHT
REGISTRATION WHEN THE RECORDINGS
ARE AVAILABLE.
ONE LAST NOTE, I WOULD LIKE TO
RECOGNIZE THE OTHER NIA PROGRAM
STAFF WHO WORKED WITH ME OVER
THE LAST FEW MONTHS AND HAVE
BEEN IPSOS-REID M INSTRUMENTAL S
TOGETHER.
MY COLLEAGUES Dr. AMANDA
DEBAUTISTA AND CAREER SAINT
CLARK AND Dr.LY NODE AND
BEHAVIORAL AND SOCIAL RESERVE
LUKE STOKAL, JEANNINE SIMMONS
AND ELIZABETH .
Dr. DAVE FRANKOUSKI WHO SPENT
A LOT OF TIME AND EFFORT
MANAGING THE DETAILS FOR THE
WORKSHOP AND WILL HELP RUN
THINGS THE NEXT TWO DAYS.
WITH THAT, WE'LL TRANSITION INTO
OUR FIRST SESSION A BIT EARLY.
I WILL PASS THINGS OVER TO THE
DEAN GLADYSHEV.
>>THANK YOU, MATT.
WELCOME EVERYBODY TO THE FIRST
SESSION.
OUR EXCITING FIRST SESSION IS
COMPUTATIONAL ANALYSIS OF
MOLECULAR SYSTEMS.
MY NAME IS VADIM GLADYSHEV.
I'M AT HARVARD MEDICAL SCHOOL
AND OUR SECOND SPEAKER WILL BE
NAN HOA FROM UNIVERSITY OF
CALIFORNIA SAN DIEGO AND HE WILL
TELL US ABOUT ENGINEERING
LONGEVITY OF SINGLE CELL AGING
AFTER A BREAK WE'LL HAVE PAOLA
SEBASTIANI FROM TUFTS MEDICAL
CENTER TELLING US ABOUT
COMPUTATIONAL WORKING THIS -- IF
I UNDERSTAND CORRECTLY AND OUR
FINAL SPEAKER ALREADY ALEXEY
TERSKIKH AND HE WILL TELLS US
ABOUT MICROSCOPING IMAGING OF
HETEROGENEITY.
SO I ENCOURAGE YOU TO PLACE
QUESTIONS IN THE CHAT SO WE WILL
KEEP THE QUESTIONS FOR THE
DISCUSSION SESSION.
SO, I'M THE FIRST SPEAKER AND I
THINK WE'LL JUST SHARE MY OF
SCREEN AND BEGIN THE
PRESENTATION.
>>SO HE WILL TILL YOU ABOUT
AGING TODAY AND FIRST, JUST, HOW
WE APPROACH THIS QUESTION OF
QUANTWANTING AGING.
WANT
FYIQUAQUESTION OFQUANTIFYING AGE
SCALE SPINNING MANY SPECIES OF
MAMMALS AND TRYING TO UNDERSTAND
HOW NATURE CHANGES LIFESPAN AS
WELL AS ACROSS THIS STUDY IN
MICE WHERE THE ANALYZE WHAT
DIFFERENT INTERVENTIONS THAT
EXTEND LIFESPAN IF THEY ARE
COMMON IN ONE AND WHAT ARE THE
ACTUAL BASIC PRINCIPL PRINCIPLEE
LIFESPAN.
THIS IS LONGEVITY AND HAVE THE
POTENTIAL OF THE SYSTEM TO LIVE
LONG AND THIS SIGNATURE CAN BE
USED TO IDENTIFY A NEW
INTERVENTIONS BOTH IN
INTERCEPTIONS AND THAT MIGHT
INDUCE LONGEVITY IN CELLS,
TISSUES AND ORGANS.
AT THE SAME TIME, WE ALSO WORK
WITH AGING AND SO OF COURSE THE
POTENTIAL TO LIVE LONG AND AGING
PROGRESSION THROUGH A LIFE
COURSE IS KIND OF OUR -- SO
HERE, WE DEVELOP BIOMARKERS
CLOCKS, GENETIC CLOCKS THAN
QUANTIFY AGING, AND ALSO CAUSAL
BIOMARKERS WHICH RESULTS IN
ACTIONABLE BIOMARKERS OF AGING.
WE USE THEM A LOT AND ALSO A
THIRD COMPONENT IS RENEW NATION
OF SIGNATURES WHEN WE INDUCE THE
TRADITIONAL SYSTEM FROM THE
OLDEST STATE TO THE YOUNGEST
STATE.
THIS ALLOWS US TO TARGET AGE
INTERVENTIONS AND TEST VARIOUS
APPROACHES HERE BOTH GENETIC,
FARM CO LOGICAL AND NUTRITIONAL
AND TODAY BECAUSE IT'S SHORT, I
WILL MOSTLY FOCUS ON WANT FYING
AGING ON THIS PART OF THE SLIDE.
WE'LL START IN A WAY.
ABOUT 10 YEARS AGO AND AS FAR AS
I KNOW, STEVE HORVATH WAS THE
FIRST ONE TO BASICALLY DESCRIBE
A EPIGENETIC BIOMARKER AND THIS
IS HIS FAMOUS PAPER IN 2013 AND
SINCE THEN, THERE HAVE BEEN MANY
CLOCKS PUBLISHED LIKE GRIM AGE,
PINOT AGE, AND MANY, MANY OTHER
CLOCKS.
OF COURSE NOW IT'S JUST
IMPOSSIBLE TO FOLLOW ALL THE
LITERATURE OF TENS OF THOUSANDS
OF PAPERS.
IT'S A REVOLUTION.
HE CON HAVE YOU CONTRIBUTED ON E
SIDE.
BUT OF COURSE, OTHER LABS ALSO
DEVELOPED QUITE A FEW CLOCKS IN
BLUE HERE, ARE PAPERS FROM OUR
LAB AND THERE ARE OTHER PAPERS
FROM OTHER LABS.
THERE ARE OTHER CLOCKS AS WELL
BUT THE MICROBIOME AND THERE ARE
MANY CLOCKS CURRENT AND
AVAILABLE.
THIS IS JUST AN ILLUSTRATION OF
HOW IT WORKS.
THIS WAS OUR FIRST CLOCK WE
DESCRIBED IN THE MOUSE WHERE WE
SEQUENCES ABOUT 200 MICE BY
METHODS CALLED -- FROM VERY
YOUNG TO 35 MONTHS OLD MOUSE.
AND BY APPLYING MACHINE
LEARNING, FROM MINIMUM YUN OF
SCIENCE BY SEQUENCING THE
IDENTIFY MICE WHICH PRESENTED A
MATHEMATICAL MODEL WITH
BIOLOGICAL AGE TO THE MOUSE SO
FOR EXAMPLE, HERE, YOU CAN SEE
THE ACTUAL AGE AND PREDICTIVE
AGE AND THERE'S A GOOD
CORRESPONDENCE IN HERE AND IN
THIS IN RED THERE ARE FOUR
GROUPS OF MICE WHICH WERE
SUBJECTED TO CALORIE RESTRICTION
AND WE CAN SEE BASED ON THIS
CLOCK WE CAN SEE THESE MICE ARE
YOUNGER, BIOLOGICALLY YOUNGER
AND OF COURSE THEY ALSO LIVE
LONGER.
AND THIS IS NOT AN EXAMPLE WHERE
AGE MATCHED MICE COMPARED BUT IN
THE GROUND AND ALSO GROWTH FROM
YOU CAN SEE THAT THE KNOCK OUT
MOUSE WAS YOUNGER AND THIS
MOUSE LIVES 40% LONGER.
AND FINALLY, WHEN WE APPLY THIS
CLOCK TO OTHER SYSTEMS, FOR
EXAMPLE, IN HERE, WE COLLECTED
FIBER BLASTS FROM THE KIDNEY AND
FROM LUNG AND WE PROGRAMMED THEM
INTO INDUCE THE IMPORTANT STEM
CELLS AND YOU CAN SEE THAT THE
AGE DROPPED TO ZERO AND IT IT
MEANS THAT AGING CLOCK, EVEN
THOUGH IT WAS DEVELOPED FOR THE
ADULT AGES IT TICKS DURING THE
DEVELOPMENT AS WELL AND IT'S
CORRECTLY PREDICTING THE AGE OF
ZERO FOR THESE EMBRYONIC LIFE
CELLS.
SO, THIS OF COURSE THERE ARE
MANY CLOCKS LIKE THIS BASED ON
CLOCK.
BUT NOT, WE RECENTLY DEVELOPED
ANOTHER TYPE OF CLOCK WHICH IS
IS A FLEXIBLE SCALABLE FRAMEWORK
FOR PREDICTION AT A SINGLE CELL
EVOLUTION.
DEVELOPED BY ALEX TRAPP A
TALENTED MEMBER OF OUR LAB.
THE WAY IT WORKS, IT ACTUALLY
WORKS AT SINGLE CELL LEVEL.
HERE WE APPLY THIS TO THE SCH
FOR SINGLE CELL AGE AND TO CELLS
IN THE MOUSE.
SO FOR EXAMPLE, THIS IS A MOUSE
WHICH IS THREE TO SIX MONTHS OLD
AND THIS INDIVIDUAL THIS IS THE
ACTUAL AGE OF THE MOUSE AND YOU
CAN SEE THAT THERE'S A QUITE
NICELY PREDICT THE AGE OF
INDIVIDUAL CELLS AND THIS IS A
YOUNGER MOUSE, FOUR-MONTH-OLD
MOUSE, THIS IS FOUR MONTHS MARK
AND THIS IS ALSO THE QUANTIFY
AND BIOLOGICAL AGE AND FROM THIS
IT APPEARS AND EVEN THOUGH MANY
PROGRESS THROUGH AGING AND SOME
CELLS WHICH SEEM TO AGE FASTER
HERE WE FIND SOME CELLS AND SOME
CELLS THEY SHOW INCREASED
BIOLOGICAL AGE COMPARED TO OTHER
CELLS AND ON THE OTHER HAND WE
FOUND THAT SOME CELLS,
PARTICULARLY STEM CELLS, THEY
AGE SLOWER.
MUSCLE STEM CELLS.
THEY ACTUALLY SHOW REDUCED
BIOLOGICAL AGE COMPARED TO OTHER
CELLS IN THE TISSUE.
THERE'S THE BIOLOGICAL AGE.
IT CREATES COMPLEXITY AND IT'S
GOING TO NEW THINKING ABOUT THE
TISSUE AGING AND CELL AGING AND
IT REQUIRES DEVELOPMENT OF NEW
METHODS.
ANOTHER EXAMPLE HERE IS THE
PLACE WHERE HE HAVE UNDER REVIEW
WHERE WE FOUND SEVERE STRESS
INCREASES BIOLOGICAL AGE AND
WHEN THE STRESS IS RELIEVED, THE
AGE IS DECREASED.
WE THINK IN TERMS OF THAT
BIOLOGICAL AGE IS NOT STARTED,
IT'S NOT CHRONOLOGICAL AGE THAT
GROWS THIS AGE BUT INSTEAD IT'S
FLUCTUATES SOME DEPENDING ON THE
CONDITION.
AND THE FULL STRESS OF SEVERE
STRESS WE DESCRIBED IN THIS
STUDY IS IS A MAJOR SURGERY LIKE
FOR EXAMPLE HIP FRACTURE, IS
OFTEN A EMERGENCY SURGERY OR
COVID INFECTION HERE IS NOT JUST
INFECTION BUT WHEN SOMEONE GOES
TO THE HOSPITAL TO THE ICU TO
THE VENTILATOR AND AT THAT POINT
YOU SEE THE BIOLOGICAL AGE IS
INCREASED AND WHEN THE RETURN
RECOVERS IT'S DECREASED.
PREGNANCY THEY SAW THE INCREASED
AGE IN THIRD TRIMESTER AND
DECREASES POSTPARTUM.
WE CONNECT YOUNG AND OLD MICE
AND THEY OBSERVED THAT THE YOUNG
MOUSE ACTUALLY THE BIOLOGICAL
AGE IS INCREASED VERY
SIGNIFICANTLY WHEN THIS MICE
ESTABLISH COMMON CIRCULATION BUT
WHEN WE SEPARATE THE MICE, THEN
THE BIOLOGICAL AGE ALSO
RECOVERS.
SO, WE DON'T CALL IT
REJUVENATION IN THIS PARTICULAR
CASE BECAUSE WE DON'T KNOW.
WE JUST SAY IT'S A BIOLOGICAL
AGE IS NOT STATIC, IT'S
FLUCTUATES AND SEVERE STRESS
MIGHT INCREASE IT.
BUT ACTUALLY, SPEAKING OF
REJUVENATION, WE HAVE A STUDY
WHERE WE WANT TO APPLY OUR
CLOCKS TO SEE WHETHER THE AGE
COULD BE ACTUALLY REVERSED
WHETHER ORGANISMS CAN BE
REJUVENATED AND WE CONNECT A
YOUNG MOUSE AND AN OLD MOUSE SO
THAT THEY ESTABLISH COMMON
CIRCULATION AND THIS IS WORK IN
COLLABORATION WITH JEAN WHITE AT
DURING UNIVERSITY.
AFTER THREE MONTHS, WE SEPARATE
THEM AND FOLLOW WHAT HAPPENS
WITH THIS MICE.
AND APPLY THE CLOCKS.
FIRST WE FIND THAT ACTUALLY THE
MICE, THE OLD MICE, AFTER THEY
ESTABLISHED A COMMON CIRCULATION
WITH THE YOUNG MOUSE THEY LIVE
LONGER.
AND ALSO, WHEN WE APPLY THE
CLOCKS, THEN WE ALSO FIND THAT
THEY ARE TRULY REJUVENATED AND
THIS IS IN THE LIVER, IN THE
BLOOD AND THIS IS JUST ONE
EXAMPLE OF THE CLOCK THAT I SHOW
HERE BASED ON THE REDUCE
REPRESENTATION OF SEQUENCING BUT
WE ALSO APPLIED RA BASED CLOCKS
IN COLLABORATION WITH STEVE
HORVATH AND IT SHOWS ALL THE
SAME.
IT'S VERY, VERY DRAMATIC AND
VERY STRONG EFFECT OF
REJUVENATION.
SO, NOW I WANT TO BRING ISSUE.
IT'S ALL GOOD AT THE AGING CAN
BE QUANTIFIED.
HOWEVER, THERE ARE MANY
CHALLENGES.
I WILL DISCUSS EVEN MORE ABOUT
THESE CHALLENGES BUT FOR NOW,
LET ME BRING THIS CHALLENGE.
I SO USE MY AND TO GO WE COMPARE
A YOUNG MAN AND A OLD MAN AND
WHAT DO WE OBSERVE HIS AGE.
WE OBSERVE THAT FOR EXAMPLE THE
HAIR BECOMES GRAY AND ALSO
THERE'S A HAIR LOSS.
BUT THIS CHANGES KIND OF NOT
CAUSAL.
SO THEY JUST HAPPEN AND THEY
DON'T REALLY INFLUENCE AGE PER
SE.
BUT THEN WE ALSO OBSERVE, FOR
EXAMPLE, INCREASED USE OF
GLASSES OR ALSO HEARING AID OR
MAYBE SOME OTHER SITUATIONS IN A
WHEELCHAIR OR WE COULD QUANTIFY
LIKE NUMBERS OF VISITS TO DOCTOR
OFFICES AND ALL THE PEOPLE MORE
OFTEN GO DOCTOR OFFICE AND WE
COULD USE THIS FEATURES TO
DEVELOP A CLOCK.
WE WOULD PREDICT THE AGE
BASICALLY ON THE FEATURES LIKE
GLASSES, HEARING AID, VISITS TO
DOCTOR USING A WHEELCHAIR AND SO
ON BUT THEN, IF YOU LET'S SAY
FIRE ALL DOCTORS, BASED ON THIS
CLOCK, EVERYBODY WILL BE YOUNG
BECAUSE NO ONE GOES TO DOCTORS,
THERE'S NO ONE USES GLASSES BUT
OF COURSE IT'S NOT TRUE.
SO THIS IS BECAUSE WE WHEN
DEVELOP THE CLOCKS THESE
FEATURES, THEY HAVE THE AGING
PROCESS AND THEY DO NOT -- THEY
ARE NOT AGING PER SE SO, IT'S
IMPORTANT FOR US TO SEPARATE AGE
CHANGES INTO PER SE AND NEUTRAL
CHANGES IN ADOPTIVE CHANGES AND
THIS IS JUST ANOTHER EXAMPLE
THAT WORK IN PROGRESS.
A STUDENT IN THE LAB, HE APPLIED
OF METHODS OF COMBINED WITH
CHANGES TO IDENTIFY OF CPG SIZE
IN DELENATIONS CAUSAL TO AGING.
WE ARE DIVIDE A SUBSET WHICH ARE
MORE CAUSAL MEANING THAT THEY
ACTUALLY WORKED TOWARDS USING
LESS.
BUT ALSO, IDENTIFIED PROTECTIVE
SIDES ADAPTIVE SIDES THAT
INCREASE THE LIFESPAN AND THEN
USING THESE SITES, THEY MADE
SEPARATE CLOCKS AND WE CALLED
THEM AGE FOR DAMAGING.
AND THE OTHER ONE ADOPT AGE
FORAY CAPTIVE CHANGES AND BOTH
OF THEM WORK WELL.
THEY CAN PREDICT THE AGE BASED
ON THESE CLOCKS.
YOU CAN SEE THEM AGE,
DAMAGE-BASED CLOCK SHOWS
REJUVENATION WHERE ADOPTED AGE
DOES NOT.
SO, AGAIN, IT'S A CHALLENGE IN
THE FIELD OF HOW WE QUANTIFY AND
WHEN WE CAN APPLY THESE CLOCKS
IN THE DIRECTION FROM YOUNG TO
OLD BUT ALSO FROM OLD TO YOUNG.
ANOTHER EXAMPLE WE FOUND
RECENTLY AND THIS IS ANOTHER
EXAMPLE OF REJUVENATION, WE
FOUND THAT AT THE LEVEL OF COP
EXCEPTION, THAT THE ZYGOTE IS
ZERO AND IT'S STABLE AND THEN
IT'S REDUCED TO THE LEVEL OF THE
STAGE THEY CALL IS IS GROUND
ZERO WHEN IT'S PREDICTED
BIOLOGICAL AGE IT'S THE LOWEST
AND THEN AGING BEGINS HERE AND
OF COURSE DEVELOP PRESSES AND
ALSO WE OBSERVED IN INCREASED
BIOLOGICAL AGE AND WE'VE DONE IT
IN MICE, WE'VE DONE IT WILL IN
HUMANS AND QUANTIFY THE SINGLE
CELL LEVEL AND WE ALSO HAVE MANY
SCRIPT IN (INAUDIBLE).
AND THEN IF YOU APPLY OUR SINGLE
CELL CLOCK IN THIS CASE, WE FIND
THAT DURING MOUSE DEVELOPMENT
FROM 4.5 TO 7.5 DAYS AND THIS
LEVEL OF 80 PLUS WE OBSERVE
REJUVENATION YOU CAN SEE THEY
SHOW REDUCED AGE FROM 4.5 TO 7.5
DAYS.
IF WE LOOK AT THE INDIVIDUAL
CELL TYPES FOR EXAMPLE, THEY ALL
ARE RENEW NATUR JUVE NATURED.
IT SHOWS FOR EXAMPLE IT'S A
PLACENTA LATER AND AND THIS TAKE
BIOMARKER HELPS US IN THEM.
AND WE RECENTLY -- WHEN YOU IT'S
VERY HARD WHEN YOU TRY IT.
IT'S LESS STABLE THAN SO IT'S
MORE DIFFICULT N THIS CASE THIS
CLOCK IS BASED MORE THAN 3,000
SAMPLES AND MANY OF THEM FROM
DATABASES AND A HUGE META
ANALYSIS AND ALSO WE HAVE FOR
MANY OF THE THESE SAMPLES THE
SEQUENCE PARTICULARLY ESSENTIAL
INTERVENTIONS THROUGH THE
PROGRAM SUPPORT BY NIA AND SO IF
YOU COULD ADJUST FOR LIFESPAN.
THERE'S A GOOD CORRESPONDENCE
WITHIN THE PREDICT AND ACTUAL
AGE OF THE MICE.
WHEN YOU APPLY THE AGE TO
DATASET YOU SEE REJUVENATION OF
EMBRYONIC DEVELOPMENT.
NOW, I'D LIKE TO BRING THE
ISSUES HERE SO WE HAVE
MEASUREMENTS FOR FRAIL SEE, CELL
COMPOSITION, SYSTEMIC CHANGES
AND HOW DO WE TRAIN THE CLOCKS
AND DO WE TRAIN THEM FOR LOSS OF
FUNCTION, FORAY APPEARANCE OF
DISEASES FOR MORTALITY THESE ARE
ALL MANIFESTATION OF THE AGING
PROCESS AND WHILE THEY'RE
RELATED TO ONE ANOTHER THEY'RE
NOT THE SAME.
WHEN THEY BUILT THE CLOCKS, THEY
BUILT THEM IN DIFFERENT WAYS AND
THERE'S CURRENTLY NO HOW EXACTLY
THEY SHOULD DO IT AND SECOND,
HOW DO WE RELATE AGING AT THE
LEVEL OF SINGLE CELL, TISSUE OR
THE CELL OF THE ORGANS.
THIS IS AN UNI INVOLVED
QUESTION.
ANOTHER ONE IS AGING ADAPTATION.
I SHOWED YOU A SLIDE.
ADAPT TAV AND DAMAGING CHANGES
AND THE DEVELOPMENT PROCESS IS
ALSO END UP IN THE CLOCKS AND
DEVELOPMENT FEATURES AND IT'S
THE SIMULATOR AND IT'S A MAIN
PROCESS IN THE ORDER AND HOW
THEY AND MID MANY AIR THEY'RE
DIFFERENT AND IT'S DEVELOPMENTAL
GENES AND IN THE PRO MOTORS AND
ENHANCER AND THE CHANGES FROM
YOUNG TO OLD ARE NOT THE SAME AS
THE TRANSITION FROM THE OLD TO
THE YOUNG STATE AND SO WHETHER
THE CLOCKS TO BE APPLIED IN ONE
CASE MAYBE NOT THE ACTUALLY
APPLICABLE IN THE OTHER CASE.
AND FINALLY, IT'S VERY EXPENSE
IDF TO ANALYZE SO BEGIN
ADDRESSING THAT, PERFORM A
CONSORTIUM IN FACT WE ANNOUNCED
IT JUST YESTERDAY.
THIS INVOLVES ABOUT 25 LESS HERE
AND MANY SENIOR PEOPLE, LEADERS
IN THE FIELD, MANY OF THEM WHO
DEVELOPED BIOMARKERS AND MANY
USE BIOMARKERS.
HERE, LUIGI, STEVE HORVATH, AND
MANY OTHERS THIS IS A COMMUNITY
EXECUTIVE COMMITTEE AND ALSO
HERE WE USE THE CLOCK BASE AND
THIS IS ON-LINE AND FORM TO HELP
EXPLORE AND ANALYZE BIOLOGICAL
AGE ACROSS GEODATABASE SO IT
COULD BE USE FOR COMMUNITY.
IN THE END, WHAT WE HAVE.
WE HAVE A BIOMEDICAL MARKER
REVOLUTION.
ABILITY TO QUANTIFY ASPECTS OF
AGING.
TEST INTERVENTIONS.
OPPORTUNITIES FOR MAJOR
ADVANCES.
YET WE HAVE MANY CHALLENGE AND I
OUTLINED 10 CHALLENGE THIS IS MY
SLIDES BUT I AM SURE THERE ARE
MORE.
THAT'S IT, THIS IS MY CURRENT
LAB.
I TOLD YOU, ABOUT SEVERAL
STUDIES AND THEY HAVE BEEN DONE
BY JESSE, ALEXEY, AND ALSO
ALBERT AND OTHER PEOPLE
CONTRIBUTED TO GO OTHER
PROJECTS.
THEY ARE FUNDED BY NIA AND OF
COURSE, WE ARE GRATEFUL FOR THAT
AND ALSO PREVIOUS LAB MEMBERS
ALEX, DAVID AND MITCHELL ALSO
THE KEY TO DEVELOPMENT OF CLOCKS
AND ALSO THE APPLICATION.
SO THANK YOU, VERY MUCH.
THIS IS IS THE END OF MY
PRESENTATION.
AND I THINK WE COULD NOW MUCH
SWITCH AND GO TO THE SECOND TALK
AND REMEMBER THERE WILL BE NO
QUESTIONS AFTER EACH TALK AND
NEXT PRESENTER IS IS NAN HOA.
ARE YOU READY?
>>YES, YES.
>>LET'S SEE.
SO, I'M THE ORGANIZER FOR THE
OPPORTUNITY SO IN THIS
PRESENTATION, IN THIS TALK I
WANT TO PRESENT OUR RECENT WORK,
COM YOU TATE TIVE SINGLE CELL
WITH MODEL TO UNDERSTAND HOW TO
REGULATE AGING PROCESS AND
IMPORTANT MODELS CAN BE USED TO
GUIDE THE DESIGN OF
INTERVENTIONS, TRAGEDIES TO PRO
MOT LONGEVITY.
SO, THE SYSTEM STUDY AND
REPLICATING EMBEDDING USED THIS
IS A GENETICALLY ATTRACTABLE
MODEL FOR TEST IN MAMMALS SUCH
AS STEM CELLS TO REPLICATE TIVE
IS THE NUMBER OF MODERN CELL
DIVISIONS BEFORE AND THE THAT
INFLUENCE LONGEVITY AMONG NOT --
TO UNDERSTAND HOW THIS INTERACT
WITH ONE ANOTHER AND HOW THIS
INTERACTIONS CHANGE DYNAMICALLY
TO DRIVE THE AGING PROCESS.
SO WE DEVELOPED WISE WHICH TRAPS
SINGLE MODERN CELLS AT THE
BOTTOM OF FINGER SHIP CHAMBERS
ALLOWING THEM TO BUFFED
CONTINUOUSLY AND NOW THE SINGLE
TIM LER AND WHOLE LOOK AT THE
WHOLE AGING OF THE MOTHER CELL.
THIS IS BUDDING AND TO EACH
BUDDING GENERATIONS DOWNWARD AND
STARTS AGAIN RATING THIS
ELONGATED SUGGESTING THE MOTHER
IS IS REALLY SICK AND EVENTUALLY
IT WILL DIE.
WE CAN TRACK THE CELLULAR
CHANGES DURING A LARGE NUMBER OF
SINGLE CELLS.
THE ISOGENIC USE OF CELLS AND
IT'S TWO DIFFERENT TYPESFUL
CHANGES DURING AGING.
HALF OF THE CELLS AND ALL THE
ELONGATED DAUGHTERS BEFORE THAT
WHILE ANOTHER HALF OF THE CELLS
PRODUCE SMALL RUNT DAUGHTERS AND
WE CALLED THEM MODE 1 AND MODE 2
AGES.
MODE 2 AGES FEATURED A MORE SLOW
DOWN OF CELL SEPARATE AND THE
SHELTER LIFESPAN COMPARED TO
MODE 1 AGING.
THEY FURTHER ATTRACT DIFFERENT
AND OBSERVE THE NUCLEAR OLI
BECAME ENLARGED AND FRAGMENTED
YESTERDAY MODE 1 CELLS AND NOT
IN MODE 2 CELLS AND THE MIKE OWE
CHONDRIAL IS NOT IN MODE 1 CELLS
SO THIS SHOWS THAT A DIFFERENT
MODES OF AGING BY TARGETED
DIFFERENT OREGON OREGON ELSE.
THE AGING DEPEND ON THE NUCLEAR
ENLARGEMENT IS RELATED IT'S
SILENCING AND NOT MEDIATED BY A
CONSERVE THE LIFE IN TWO
REPRESSES RECOM BIN EIGHTING THE
RDA AND THE MONITOR RDNA
SILENCING IN CELLS WE PROMOTER
AS RDNA REGION BECAUSE THE
REPORTER IS REPRESSED BY
SILENCING SO A DECREASE IND
INDICATES IN-HOUSE AND SILENCING
WHILE AN INCREASE REDUCES THOSE
IT HAS BEEN PROPOSED THE
DEFICIENCY OF A MAJOR FUNCTIONAL
FORM OF ERA MAY BE A T FACTOR IN
THE TRAPS DESCRIPTIONAL COMPLEX
IS REQUIRED OF A MIGHT OWE CON
AGREAL FUNCTION AND TO MONITOR
DURING AIMING WE USE THE NUCLEAR
AN CORED INFRARED.
IT DEPENDS ON A DEGRADATION AND
IT CORRELATES WITH CELLULAR
LEVELS SO A HIGH SIGNAL HAS A
LEVEL AND THE LOCAL IS IS IND
INDICATES A LOW LEVEL AND SO TO
DETERMINE HOW THOSE HAVE
PATHWAYS, OPERATES, TOGETHER, TO
DRIVE THE PROCESS INTRODUCE BOTH
THE RDA AND GFR REPORTER INTO
THE SAME CELLS.
SIMULTANEOUSLY TRYING TO ASSERT
TO HAVE ACTIVITIES DURING AGING.
HERE IN THIS SLIDE, I'M GOING TO
SHOW REP TENTATIVE MOVIES OF
SINGLE AGING CELLS AND THE AGING
TRAJECTORIES QUANTIFY FROM THIS.
HERE IN THIS PREVIOUS SPACE, THE
PERCENTAGE OF LIFETIME FROM 0%
TO 100%.
THE SIGNAL AND THE RDNA SIGNAL.
LET'S START WITH THE CELL.
AS WE CAN SEE DURING THE CELLS
SHOWED THE GRADUALLY INCREASED
THE SIGNAL AND NUCLEAR IRP
SIGNAL.
SO, NOW LET'S LOOK AT THE MODE 2
CELLS.
VERY DIFFERENT FROM MODE 1 CELLS
WHICH SEE A SHARP DEPLETION.
SO NEXT LET'S LOOK AT THE WHOLE
POPULATION OF EYE OWE AGAIN I
CAN CELLS AND HERE THE RIGHT
IMPORTANCE I FOUND THOSE CELLS
AND THE BLUE ARE FROM MODE 2
CELLS AND WE CAN FURTHER PROJECT
THE INITIAL STAGE OF OLDER CELLS
AND THE TRAJECTORIES CAN BE
SHOWING YOU THE SPACE AND THE
STATES OF OLDER CELLS CAN BE
PROJECTED OF ANOTHER CELL AS WE
CAN SEE HERE, ISOGENIC CELLS
TIGER WOODS REPORTERS THEY WORK
EARLIER IN LIFE AND PROGRESS
OVER TWO DIRECTIONS AND MODE 1
AGING ENDING THEIR LIES WITH A
LOWER TO HIGH STATE COP CYSTS OF
NUCLEAR OLDER DECLINE AND MODE 2
AGING ENDED WITH LOW STAGE
CONSISTS IN THE -- THIS RESULTS
REALLY SHOW THAT IDENTICAL CELLS
AGE TOWARDS DISCRETE STAGE WITH
UNCORRELATED ACTIVITIES
SUGGESTING POTENTIAL INHIBITION
BETWEEN CIRCLE AND A HALF THAT
IS EARLIER LIFE OF THE ORGANS OF
AGING.
WE CONFIRM THAT THIS ANALYSIS
AND IN ADDITION TO THAT, THEY
START TO HAVE REGULATIONS FROM
THE PREVIOUS.
BASED ON THIS, WE DEVELOPED A
SIMPLE METH MAD CAL MODEL
SIMULATING HOW THEY HAVE CHANGED
DURING AGING.
WE CAN INHIBITION AS WELL AS THE
ULTRA-REGULATION.
TO MAKE THINGS EASIER, THE LESS
TARGET THE SYSTEM FOR FOR
INHIBITION.
THIS IS A MODEL SIMULATION
SHOWING HOW HAP CHANGES OVER
TIME.
THESE ARROWS POINT TO THE
DIRECTION WHERE THE SYSTEM IS
MOVING TOWARDS.
THIS BLUE HORIZONTAL LINES ARE
CALLED SIR 2, THE CHANGE IS
ZERO.
THIS RIGHT ARE HAN MEANING THE
RATE OF CHANGE OF HAP IS ZERO ON
THESE LINES.
AND WHERE THEY INTERSECT, IT'S
CIRCLES ARE CALLED UNSTABLE
FIXED POINTS.
THESE ARE THE POINTS THAT THE
SYSTEM WILL ESCAPE FROM.
THIS CLOSE TO THE CIRCLE AS
STABLE THESE ARE THE POINTS
WHERE THE SYSTEM WILL GO TOWARDS
AND THE SYSTEM REACH THESE
POINTS THEY WILL STOP THERE.
SO HERE DEPENDING ON WHERE THE
SYSTEM STARTS, IT CAN MOVE TO
WHERE THE FOUR DIFFERENT STEADY
STATES, THE SLOW STATES AND THE
HIGHS START TO LOW STATE AND THE
LOW START TO HIGH STATE AND SO
IF THE CASE OF CELLS, WHEN THE
INHIBITION IS ADDED THE THE IN--
IN THIS REGION ELIMINATES THE
STABLE FIXED PARTS AND TO
ADDRESS THE NATURE OF AGING IN
SINGLE CELLS, THEY HAVE WORK OF
OUR MODEL AND COMMUTED A
POTENTIAL LANDSCAPE AND SOLVING
THE CORRESP CORRESPONDENCE AND F
THOSE STABLE FIX POINTS OF THE
DETERMINISTIC SYSTEM CORRESPONDS
TO A LOCAL VEIL ON THE LANDSCAPE
WITH REFLECTING PROBABILITY.
THE HIGHER THE PROBABILITY AND
IN THIS WAY, AGING OF EACH
SINGLE CELL CAN BE VIEWED AS A
FOR DIFFERENT VALUES ON THE
LANDSCAPE.
IT CORRESPONDS TO THE LOW HAP1
AND THE ANNUAL STAGE OF MODE 2
AGING.
WE FURTHER PERFORM THE GENETIC
AND TWO-FOLD OVER EXPRESSION
START TO SHOW THE VERY
INTERESTING RESULTS.
IN ADDITION TO MODE ONE AND MODE
TWO AGING, SO TO OVER EXPRESSION
GENERATED A NEW MODE OF THAT
HAVE ENDED WITH A HIGH STATE AND
WE CALL IT THE STORY AGING IT
CAN NORMAL CELLS SEPARATE
THROUGHOUT THE ENTIRE LIFESPAN
AND VERY DIFFERENT FROM MODE ONE
AND MODE TWO CELLS.
OUR MODEL IS INTEREST BY
TWO-FOLD AND IT'S INCREASED
ACTIVITY CAN PARTIALLY
COUNTERACT THE INHIBITION
LEADING TO THE EMERGENCE OF A
NEW STABLE FIX ON THE AND THE
NUS VEIL ON THE LANDSCAPE.
SO THE TOWARDS THESE HIGH HAP
STATES CORRESPONDS TO THE AGING
THAT WILL BE OBSERVED
EXPERIMENTALLY.
UNDER THE GUIDANCE OF OUR MODEL,
WE FURTHER PERFORMED ADDITIONAL
GENETIC MANIPULATIONS AND WE
REACHED THE LARGE FRACTION OF
CELLS WHO EFFECTIVELY PROMOTE
LONGEVITY OF THE WHOLE
POPULATION AND I DON'T HAVE TIME
TO TALK ABOUT THAT IN DETAIL
TODAY, INSTEAD I WILL FOCUS ON
OUR MOST RECENT UNPUBLISHED
REPORT SO LET'S GO BACK TO OUR
MAUD AND IT'S EACH SINGLE CELL
CAN BE VIEWED AS THEY WERE
GENERATION FOR A DIFFERENT
STEADY STATES AND AND THE LOW
START TO HIGH HALF STATES LEADED
TO NUCLEAR AND THE.
>>WE TRY TO HAVE FROM THE
INHIBITION TO FEEDBACK LOOP AND
IT CAN LEADS TO SUSTAIN
OSCILLATIONS IN THE LEVELS AND
IN WHICH THE SYSTEM KEEPS MOVING
ALONG THIS BLACK CIRCLE AND WILL
NEVER COMMIT TO THE AND.
>>BY HALF AND HALF OF IT TOO
WITH A PROPER VALUES THIS MODEL
CAN PRODUCE SUSTAIN OSCILLATIONS
IN CERTAIN LEVELS AND THE
SIMULATION AND ON THE BATTLE AND
WE FOUND THAT OSCILLATIONS ARE
FAVORED BY A STRONG INDUCTION OF
BY HALF AND THIS WILL SEARCH AS
HERE ANDS.
>>TO ENABLE A REPRESSION AND
INTO A FEEDBACK LOOP.
THESE ABUNDANCE IN
REPRESENTATIVE CELLS AND WE
OBSERVED THAT ALMOST OLD
ENGINEER CELLS SHOWED
OSCILLATIONS DURING AGING AND
THE CONTROL CELLS SHOW SUCH OLE
ISATIONS.
AND THE IMPORTANTLY, OUR
OSCILLATING SHOWED DRAMATICALLY
A END ITING THE LIFESPAN AND
WITH 8% INCREASE COMPARED OF
THAT THE CELLS AND THIS IS THE
MOST PRONOUNCED IT SHOWS THE
LONGEST LIFESPAN AND IN ADDITION
OUR OSCILLATOR SHOWED A FASTER
CELL SEPARATE COMPARED TO THE
OTHER LONG LIVED MUTANTS END
INDICATING THAT IT HAS A
HEALTHIER CELLULAR STATE DURING
AGING.
SO BASED ON OUR MODEL, IT
PREVENTS CELLS WHO DEPLETION SO
THEY MONITOR THE RNDA I'LLENS
AND THE ABUNDANCE IN THE OS
LATER CELLS USING THE NUCLEAR
REPORTER THAT WE INTRODUCED
PREVIOUSLY.
HERE IN THIS MAP, EACH ROW
REPRESENTS THE TIME TRAY OF A
SINGLE CELL THROUGHOUT THE
ENTIRE LIFESPAN.
THE COLOR REPRESENTS THE
INTENSITY.
IT IT TWO SHOW FLUORESCENT
CHANGES OF THE TWO REPORTERS
FROM THE SAME CELLS AND VERY
SIMILAR TO WHAT WE SHOWED
BEFORE, ABOUT HALF OF THE CELLS
ARE COMMITTED TO MODE AND ENDED
THEIR LIFE WITH A CONTINUOUS
SILENCING MODELS AND THE OTHERS
ARE COMMITTED AND ENDED THEIR
LIVES WITH A PRONOUNED
DEPLETION.
IN CONTRAST, THE MAJORITY OF THE
OLE OS LATER CELLS SHOWS PULSES
OF RNDA AND SIGNALS THROUGHOUT
THEIR ENTIRE LIFESPAN WITHOUT A
TO EITHER (INAUDIBLE) SO THIS
RESULTS WERE CONSISTENT WITH OUR
PREDICTION DEMONSTRATING THAT
THE OSCILLATOR CAN ENABLE A
BALANCE BETWEEN RDNA SILENCING
AND THEREFORE IT PROMOTE LON
GEOFF TLONGEVITY, I HOPE OUR STS
PROVIDE A CONVINCING EXAMPLE TO
SHOWCASE THE POWER OF SYSTEM
BIOLOGY AND COMPUTATIONAL
APPROACHES FOR ENABLING NEW
DISCOVERIES IN AGING BIOLOGY.
AND THE IMPORTANTLY, I WANT TO
HIGHLIGHT THE FRAMEWORK THAT WE
ESTABLISHED THROUGH THIS WORK
CENTERS ON EFFECT IT TIVE
INTEGRATION OF EXPERIMENT AND ED
MODELING THAT CAN DRIVE
INNOVATIONS IN INTERVENTION
DEVELOPMENT.
FINALLY I WANT TO ACKNOWLEDGE
OUR COLLABORATOR, JEFF, LAUREN
AND LANCE OVER THE PAST SEVEN
YEARS WE HAVE BEEN WORKING VERY
CLOSELY TOGETHER AND AGING AND
OUR CURRENT GOAL IS TO EXPAND
OUR APPROACH TO STUDY THE
DYNAMICS IN THE CELL AGING AND I
ALSO WANT TO THANK NIA FOR THE
SUPPORT TO OUR EFFORTS.
SOL, THANK YOU, VERY MUCH FOR
YOUR ATTENTION.
>>THANK YOU.
THIS IS REALLY GREAT.
AMAZING DISCOVERY.
CURRENTLY, WE HAVE 1:06 AND THE
NEXT -- WE HAVE A 10 MINUTE
BREAK SO LET'S BREAK FOR 10
MINUTES AND THEN WE WILL STARTED
A FEW MINUTES EARLY JUST EX
EXACTLY 10 MINUTES FROM NOW.
1:16.
THANK YOU, EVERYBODY.
>>LET'S BEGIN THE
SECOND PART OF SESSION ONE.
OUR NEXT SPEAKER IS PAOLA
SEBASTIANI.
>>THANK YOU FOR THE OPPORTUNITY
TO PRESENT THIS WORKSHOP AND I'M
GOING TO TALK ABOUT
COMPUTATIONAL CHALLENGES IN
STUDIES OF EXTREME LONGEVITY SO
I'M SWITCH TO GO TALK ABOUT OLD
PEOPLE AND EXTREMELY OLD PEOPLE
MORE THAN 200 PEOPLE PAST THE
AIMING OF 110 AND THE LONGER
FAMILY STUDIES AT NIH STUDY WITH
SINGLE GEOFF TEE AND AS A ROLE
OF 350 FAMILIES AND SUBJECTS AND
WITH A ROLE OF 1400 SEN TIN AR
ANS IN THEIR OFFSPRING AND WE'RE
MAKING MAJOR EFFORTS AN AND IT'A
SMALLER STUDY AND THE GOAL IS TO
ENROLL 350 CENTENARIANS AND ON
THE STUDY, THE RELATIVES THE
GOAL IS TO COLLECT BIOLOGICAL
SAMPLES, MOST OF THE STUDIES
HAVE A UNIFORM THEY COLLECT
BLOOD SAMPLES FOR DNA STRUCTURE
AND GENETIC STUDIES AND TRANCE
TOPIC STUDY AND PLASMA AND
LONGEVITY OMICS IS ADDED A
COMPONENT OF THE MICROBIOME WITH
SAMPLES THAT IS ON GOING.
IN ADDITION, ALL THE STUDIES AND
DATA COLLECTIONS SO WE COLLECT A
LOT OF DATA ON VICTIMS AND MANY
AGING TRAITS AND THE
PARTICIPANTS HAVE FOLLOWED
ANNUALLY AND SO FAR, IN TERMS OF
OMICS, WE HAVE GENERATED GENOME
DATA AND GENOME SEQUENCING
PARTICIPANTS IN THE NEXT WE HAVE
GENERATED MET TABLOMICS AND WE
HAVE MUCH MORE DATA AND GENOME
SEQUENCES ON EVERYBODY AND ON
MORE THAN 2,000 PARTICIPANTS AND
ALSO RNA DATA AND WE'RE
GENERATING DATA ON A FEW HOW
PARTICIPANTS IN SEQUENCING SO
THERE'S A LOT OF THAT WE'RE
COLLECT AND A LOT OF DATA THAT
WE'LL GENERATE IN THE NEXT
COUPLE OF YEARS AND ONE OF THE
QUESTIONS WHY DO WE LOOK AT
CENTENARIANS AND WHEN WE LOOK,
WE COLLECT ALL THE OMICS DATA.
SO IN THE -- IN ACCIDENT
PHYSICAL AND COGNITIVE
CONDITIONS AND THEY MAINTAIN THE
PHYSICAL AND COGNITIVE FUNCTIONS
TO THE INTRODUCTION OF THE
LIFESPAN AND THE FISH YOU AND
GENETICS AND UNMODIFIABLE RISK
FACTORS SO IF YOU HAVE IDENTIFY
THE GENETIC MUTATION THAT'S
ALLOW PEOPLE TO LIVE LONGER, THE
ONLY SOLUTION FOR EVERYBODY TO
CHOSE YOUR PARENTS WELL.
WE CAN'T REALLY DO SO IN THE
FAST FEW YEARS AND TRANSLATION
OF THESE GENETICS AND THE GOAL
IS TO IDENTIFY THIS MOL IKE LAR
THAT IS AND TRY TO UNDERSTAND
THE PROTECTED CORRELATED ARE
HAVING THE GOOD GENETIC
MUTATIONS AND MOLECULAR CAN BE
MODIFIED SO THE WHOLE PLAN HERE,
THIS SUGGESTS THAT WHAT WE'RE
TRYING DO IS TO IDENTIFY TARGETS
FOR INTERVENTIONS FOR ANTIAGING
THERAPEUTICS.
THE PROBLEM, HOWEVER, IS
COMPLICATE AND WHAT WE'RE TRYING
TO DO IS BE IN ORDER TO IDENTIFY
GOOD INTERCEPTIONS WE'RE TRYING
TO BUILD MODEL THAT'S CAN
CONNECT ALL THESE THINGS
TOGETHER SO WE CAN USE IT FOR
UNDERSTANDING MECHANISMS
PREDICTING OUTCOMES AND WE HAD
PREDICT MORE THAN ONE THING AT
ONCE.
AND SO, WHERE ARE WE NOW?
SO FAR, WE HAVE COLLECTED PIECES
OF PUZZLE FOR AGAIN THE TICKS WE
A GROWING CATALOG ASSOCIATED
WITH THEM, THE ABILITY OF PEOPLE
TO LIVE TO OLD AGES.
WE ARE WILL BE STARTING TO
COLLECT PROTECTIVE VARIANTS THAN
CORRELATE WITH THE ABILITY OF
CENTENARIANS TO AGE, MAINTAINING
GOOD COGNITIVE FUNCTION AND JUST
TWO WEEKS AGO WE PUBLISHED THIS
ARTICLE THAT IDENTIFIED
PROTECTIVE VARIANTS THAT ALLOW
INDIVIDUALS TO AGE, MAINTAINING
GOOD PROCESSING SPEED.
AND SO THERE'S MUCH MORE THAT WE
ARE DISCOVERING AND WE WOULD BE
PUBLISHING SOON.
WE HAVE CONNECTED THESE DOTS
HERE.
WE HAVE IDENTIFIED MOLECULAR
SIGNATURES OF THE GENETIC
VARIANTS WITH LONGEVITY WE HAVE
IDENTIFIED MOLECULAR SIGNATURE
OF THE ALLELE THAT IS ALSO
CORRELATES WITH MAINTAINING GOOD
COGNITIVE FUNCTION AS PEOPLE
AGE.
AND WE HAVE RECENTLY SHOWN THAT
APO IS A MA A BA ALCOHOLICS IS D
WE HAVE DONE THE SAME FOR OTHER
OMICS.
SO WE ARE WORKING WITH THE
TRANSCRIPT TOMIC OF AGING AND
SAME THING WE HAVE LOOKED AT
PROTEOMICS OF AGING AND
LONGEVITY AND THE SAME TRAITS.
THE EMERGING PICTURE IS THERE
ARE MANY BIOMARKERS OF AGING.
THE TRANSCRIPT TOMB TRANCETOM IE
UNIQUE OMICS IN SEN SEN TEN ARD
WHAT THIS CAUSE AND EFFECT OF
LIVING TO EX EXTREME OLD AIMING.
WE HAVE MANY PIECES OF THE
PUZZLE BUT HONESTLY WE'RE FAR
FROM HAVING THE BIG MAUD ARE NOL
WORKING.
HOW DO WE CONNECT THESE DOTS?
NOW, WE HAVE BEEN WORKING FOR A
COUPLE OF YEARS ON USING VISION
NETWORKS FOR MODELING THE
COMPLEX SET OF INTERACTIONS
WITHIN GENETICS AND MULTI
PHENOTYPES IN OBSERVATIONAL
STUDIES.
SO BASBAYESIAN NETWORKS ARE MULI
VARIABLE AND UNVARIABLES AND
THESE RANDOM VARIABLES CAN
REPRESENT RISK FACTORS,
OUTCOMES, OMICS AND THE
ADVANTAGE OF BAYESIAN NETWORKS
IS COMPOSE A COMPLEX INTO
FACTORS BY LEVERAGING MARGINAL
INDEPENDENCE.
THIS MEANS WE HAVE A COMPLEX
MODEL THAT FACTORED INTO SMALL
MODEL AND 'S YEAR TO LEARN FROM
DATA AND BE USEFUL FROM
REASONING SO WHEN WE PUT THE
MODEL TOGETHER, WE CAN USE IT
FOR PREDICTING FROM PRESERVATION
OF THE NETWORK AND SOME OF THOSE
IN THE NETWORK WE CAN PREDICT
OUTCOMES AND WE CAN IDENTIFY
WHAT THE MOST LIKELY PATH TO
PARTICIPATE OUTCOMES, ET CETERA.
AND THERE ARE MANY VARIATIONS
FROM NETWORKS AND THAT I SHOULD
MENTION AND SOME OF MY
COLLABORATORS CALLED MACHINE
HEARING BECAUSE IN TERMS OF THE
ABILITY TO MODEL COMPLEX
SYSTEMS, THEY HAVE A LOT OF
ADVANTAGES.
WE CAN ALSO RELY ON AWARD FOR
LEANING STRUCTURE AND PARAMETER
OF NETWORKS AND ALSO FOR THE
DEVELOPMENT OF PROBABLE ALICE
TICK REASONING AND SOME ARE WE E
MODELS THAT CAN USE FOR STROKE
AND PATIENT WITH SICKLE CELL
ANEMIA AND SEVERITY OF THE
DISEASE, ET CETERA.
SO A LOT HAS BEEN DONE.
WHEN I LOOK AT THIS CARTOON
HERE, THIS WAS DONE IN 2005 AND
IF 2005, WORKING THE SET OF 250
WAS A BIG DEAL.
NOW WE TALK ABOUT 50 MILLION AND
THE OMICS DATA ARE REALLY
MASSIVE SO A LOT OF WORK NEEDS
TO BE DONE AND TO ADAPT TO MAKE
THEM FASTER AND BE VISIBLE AND
TO THE TASK WE HAVE AND IT'S
SOMETHING ELSE THAT I HAS BEEN
LOOKED AT GENERATING FOR AI AND
NOT SO MUCH AI FOR OBSERVATIONAL
DATA AND A LOT OF THE DATA WE
WORK IT ARE NOT RANDOM SAMPLES
SO WE WORKED WITH OBSERVATIONAL
STUDIES WHICH THEY'RE ON A
SPECIFIC STUDY DESIGN THAT HAS
BEEN USED AND WE HAVE LONG
STUDENT NAL CONVERSATION AND WE
ALWAYS HAVE MISSING DATA SO
THERE ARE A LOT OF CHALLENGES
THAT THEY ARE DESIGNED TO
OVERLOOK SOMEONE'S STRATEGIES
AND THERE'S A LOOK AT VARY A LOT
OF THE SOME OF THE OMICS
SUMMARIES WE WORK WITH.
IT'S A BIG CHALLENGE THAT WE
HAVE AND WE FEEL A LITTLE BIT IS
TO PUT TOGETHER CLOCKS OR SCORES
AND PLATFORM AND CAN BE USED SO
THE WORK NEEDS TO BE DONE TO
ADAPT THE SO I'LL GO THROUGH A
COUPLE OF EXAMPLES AND THE
WORKING WITH DATA WE NEED TO USE
SOME DATA REDUCTION TECHNIQUES
AND SO WE WORK WITH THEM SIMILAR
WITH THE REDUCTION THAT USE
CLOCKS AND NOT SO MUCH FOR THE
CLOCKS PER SE BUT TO BE ABLE TO
INCOMPETENT GREAT DATA AND THIS
SEEMS TO BE AN ADVANTAGEOUS
APPROACH COMPARED TO OTHER
REDUCTION
TECHNIQUES TO PRODUCE SOME THAT
ARE NOT EASY TO INTERPRET AND BY
ADAPTING THE WORKING GENETICS
THAT HAS BEEN VERY SUCCESSFUL
WITH THE DEVELOPMENT OF THE RISK
SCORE WE STARTED TO DEVELOP
OMICS SCORES SO IT'S VERY
MENTIONED INTRODUCED AND SCORES
AND I USED THE WORD SCORE
BECAUSE THESE ARE NOT REALLY
ONLY LINKED TO AGING BUT ANY
AGING TRAIT AND THIS IS VERY
HARD TO GET GENERATED RESULTS
WITH OUR PLATFORM INDEPENDENCE
OR STUDY INDEPENDENCE AND THERE
IS NEED FOR STANDARDS.
EVEN WITH THE LIMITATIONS IF WE
TAKE A VISION NETWORK ALGORITHM
WITH ALL THE DATA TOGETHER, WE
ENDS UP WITH THE NETWORK AND
THIS EXAMPLE IS AN EXAMPLE OF A
NETWORK AND WITH A LOT OF
CONNECTIONS IF WE START USING
SOME DATA SUMMARY TECHNIQUES WE
GET TO AN IMAGE THAT IS MUCH
MORE UNDERSTANDABLE AND IT'S
MORE INTUITIVE SO IF WE
SUMMARIZE SOME OF THE OMICS DATA
WITH MULTIPLE TRADES WE CLEAN UP
A LITTLE BIT THE PICTURE AND
THERE'S A LOT AND IT HAS TO BE
DONE AND A LOT OF THINKING.
WE NEED TO THINK MORE WAYS TO
SUMMARIZE THAT WE HAVE A LOT OF
PHENOTYPE ON INDIVIDUALING
INDIVIDUALS AND AS AN EXAMPLE,
FOR THOSE OF YOU INVOLVED IN
HUMAN STUDIES AND RELATED TO
COGNITION OR ALZHEIMER'S, WE
TYPICALLY ADMINISTER MANY, MANY
PSYCHOLOGICAL FOR PARTICIPANTS
AND THESE PSYCHOLOGICAL TESTS
ARE NOT INDEPENDENT THEY'RE
RELATED TO EACH AND THEY'RE
TRYING TO CAPTURE THOSE HIDDEN
STATES OF COGNITION THAT ARE NOT
EVIDENT AND THEY'RE NOT
MEASURABLE AND SO ONE OF THE
THINGS WE HAVE DONE WHICH IS
JUST ACCEPTED FOR PUBLICATION IN
THE JOURNAL OF ALZHEIMER'S
DISEASE IS TO USE A ANALYSIS TO
SEE WHETHER THERE ARE COGNITIVE
SIGNATURE THAT ARE BASED ON
TESTS AND CAN BE USED TO
INDIVIDUAL INTO THOSE WHO ARE
AGING BY MAINTAINING EXCELLENT
COGNITIVE FUNCTIONSEL MULTIPLE
DOMAINS AND WHILE OTHER
INDIVIDUALS HAVE GOOD MEMORIES
AND WITH OTHER COGNITIVE
FUNCTIONS AND IN TERMS OF THERE
ARE CLEAR FACTORS AND SOME OF
THE EXTREME HAVE RELATED TO
GENETICS SO TO HAVE A GOOD
GENETIC VARIANTS ASSOCIATED WITH
THE COGNITIVE PERFORMANCE, SEEMS
TO BE ASSOCIATED WITH THE
ABILITY TO MAINTAIN MULTI DOMAIN
GOOD COGNITIVE PERFORMANCE.
AND ANOTHER CHALLENGE THAT IS
BEING -- IT'S NOT BEEN ONE OF
THE CENTER PHONES IN MACHINE
LEANING IS THAT WHAT DO WE DO,
HOW DO WE LEARN THE NETWORKS
FROM CORRELATED DATA?
IN STUDIES OF LONGEVITY, MOST OF
OUR STUDIES INCLUDE THE RELATED
PEOPLE.
SO WE CAN'T REALLY IGNORE THE
RELATEDNESS ON INDIVIDUALS.
WE HAVE TO DO SOMETHING.
SO WE INTRODUCED THE MEDALS FOR
LEANING VISION NETWORKS FROM
RELATED INDIVIDUALS IN THE PAST
AND ONE OF THE CHALLENGES THIS
MANTLE CAN BE SPECIFICALLY SOUND
BUT COMPUTATIONALLY INEFFICIENT
SO WE'RE WORKING ON DEVELOPING
METHODS TO LEARN NETWORKS FROM
CLASSIC DATA BUT THESE ARE MAJOR
CHALLENGES IN COMPUTATIONAL
CHALLENGE AND IT IT'S A ROAD
BLOCK IF WE DEVELOP THESE
COMPLEX MODELS.
LOOKING AHEAD.
ONE OF THE BIG CHALLENGES WE
FACE IS THIS, WITH THE HUMAN
STUDIES, WE COLLECT GENETIC
MUTATION, WE CAN MEASURE
MOLECULAR DATA, AND EPIGENETIC
DATA, ET CETERA, AND WE TAKE A
SNAPSHOT OF ONE POINT AND TIME
AND WE HAVE A SYSTEM AND THE
SYSTEM IN WHICH THINGS CHANGE
AND CONSTANTLY CHANGE SO CHANGES
IN MOLECULAR THAT DETERMINE
OTHER CHANGES AND FEEDBACK
GROUPS.
SO, EVERYTHING IS COMPLICATED BY
THE EXPOSURE, LEFT THEM EXPOSURE
TO RISK FACTORS AND SO AS WE
COLLECT MORE AND MORE DATA, ONE
OF THE BIG CHALLENGES IS
SHIFTING INTO BUILDING DYNAMIC
MODELS WHICH IS ANOTHER LEVEL OF
REAL COMPLICATION AND AS AND AS
WAS MENTIONED AT THE BEGINNING,
WITH THE NEW POLICY AT THE NIH,
WE ARE THE STATUS OF GONG GIVE
TEE HAVE COMMIT TODAY SHARE DATA
WITH' LET PROGRAM AND IT'S PART
OF THE KNOWLEDGE PORTAL AND WE
ARE SUBMITTED DATA TO BE SHARED
BUT IN A COUPLE, IN I WOULD SAY
IN A FEW MONTHS, WE WILL START
POPULATING THIS PROGRAM WITH A
LOT OF STUDIES AND A LOT OF DATA
FROM LONGEVITY STUDIES.
SO THE DATA ARE NOT HERE YET AND
NOT ALL BUT IT WILL HAPPEN SOON.
SOMETHING THAT HOWEVER SUBPOENA
A CHALLENGE IS, THEY NEED TO
HAVE BENCHMARK DATA.
BENCHMARK DATA FOR EVALUATION SO
I DON'T KNOW THAT -- I DON'T
THINK I HAVE THE TIME HERE TO GO
THROUGH THIS BUT MAYBE I CAN USE
THE TIME OF THE DISCUSSION TO
TALK ABOUT OUR EXPERIENCE WITH
SYNTHETIC DATA.
SO THERE'S A GROWING INTEREST IN
GENERATING SYNTHETIC DATA THAT
ARE A STATISTICAL FAITHFUL
REPRESENTATION OF REAL DATA AND
IT CAN BE USED AS A BENCHMARK.
I'M GOING TO CONCLUDE HERE WITH
A LIST OF MY COLLABORATOR AND I
WANT TO EMPHASIZE THE STUDY WHO
WORK WITH US AND THE SUPPORT OF
THE ON GOING STUDIES AND THANK
YOU, VERY MUCH.
WE HAVE ONE MINUTE TO SPARE.
>>THANK YOU.
YES, IT'S GREAT.
THANK YOU, VERY MUCH.
I THINK WE ARE READY TO MOVE TO
THE FOURTH TALK, ALEXEY ARE YOU
READY?
>>YES.
>>SHARE YOUR SCREEN.
>>EVERYBODY SEE?
>>IT'S GREAT.
>>THANK YOU.
>>OKAY.
THANK YOU VERY MUCH FOR THE I AM
VATATION.
I APPRECIATE THE OPPORTUNITY TO
SHARE SOME RESENT DATA FROM THE
LAB.
SO, FOR THE NEXT 20 MINUTES OR
SO, I WOULD LIKE TO PRESENT A
APPROACH THAT WE HAVE BEEN
DEVELOPING FOR A NUMBER OF YEARS
NOW THAT IS BASED ON IMAGING AND
IN PARTICULAR IMAGING OF
EPIGENETIC INFORMATION IN THE
CELL.
SO MOST OF US WOULD BE ABLE TO
LOOK AT HUMAN FACE AND
APPROXIMATELY TELL AN AGE.
IT'S BASED ON SOME ASSESSMENTS
OF THE SKIN AND WHAT WE'RE
REALLY DOING IS QUICKLY
ASSESSING THE BIOLOGICAL AGE OF
A PERSON BASED ON A FACIAL
FEATURES.
SO, WHAT WE'RE TRYING TO SO IN
THE LAB IS DO SOMETHING SIMILAR
WITH CELLS AND THE QUESTION WAS,
CAN WE LOOK AT A CELL AND
PARTICULARLY IN THE EPIGENETIC
LANDSCAPE OF A CELL AND TELL HOW
OLD THE CELL IS.
SO, BY WAY OF INTRODUCTION, IT'S
I THINK COMMONLY WELL UNDERSTOOD
NOW THAT AGING IS A COMPLEX
PHENOMENON BUT A LOT OF THIS HAS
SOMETHING TO DO WITH EPIGENETIC.
BASICALLY THE SAME SET OF GENES
THAT ARE FAITHFULLY MAIN TAPED
TO FUNCTION EARLY IN LIFE NOW
ARE FAILING AND GIVEN SOME
NUMBER OF GENETIC MUTATIONS, BY
AND LARGE, I THINK THE APP'
GENETIC REGULATION IS FAGE WITH
AGE AND MEMBER OF PUBLICATIONS
ATTESTING FOR THIS RECENT PAPER,
HAS A LOT OF PUBLICITY, I
BELIEVE, SUGGESTING THAT THE
LOSS OF EPIGENETIC INFORMATION
IS THE CAUSE OF AGING.
THIS IS TO BRING HOME THE POINT
THAT IS REALLY FUNDAMENTAL FOR
THE TALK AND OUR APPROACH,
NAMELY THAT THE GLOBAL LANDSCAPE
OF EMMY AGAIN MODIFICATION, IF
APPROXIMATE YOU TAKE ALL THE
INFORMATION TOGETHER AND IMAGE
THEM IN 3D, CON FURS A STATE
WHICH IS ESSENTIAL TO IDENTITY
AND THAT IS A IDENTITY WE'LL TRY
TO IMAGE IN UNDERSTAND THAT THE
PROJECTORY WITH AGING.
SO, AS VERY IMPORTANT QUANTITY
TATING BIOLOGICAL FUNCTIONAL AGE
OF KEY IMPORTANCE, MUCH MORE
IMPORTANT THAN POTENTIALLY
KNOWING CHRONOLOGICAL AGE WHICH
DOES FOR US REALLY WELL AND A
NUMBER OF TOOLS AND APPROACHES
HAVE BEEN DEVELOPED WITH TIME
SUCH AS -- AND METH LAYING MARKS
AND MENTIONED SEVERAL TIMES.
DNA METHYLATION CLOCKS BUT IT
MIGHT HAVE LIMITATIONS TOO IN
PARTICULAR, WITH UNDERSTANDING
THE BIOLOGICAL DETERMINANTS AND
ALSO EXPANSIVE AND SOME
PRACTICAL ASPECT THAT COULD BE
IMPROVED.
SO, WE CAME UP WITH THE IDEA OF
IMAGING OF EPIGENETIC AGING AND
COINED THIS TERM AS AN APPROACH
OF IMAGING.
NOW, A COUPLE OF POINTS I'D LIKE
TO UNDERLINE HERE IS, WHICH I'LL
ADDRESS LATER DURING THE TALK,
BUT BASICALLY THAT IS APPROACHED
THAT ESSENTIALLY BASED ON
DETERMINING THE DISTANCE METRICS
BETWEEN THE INDIVIDUAL
MEASUREMENTS WITHOUT ANY PRIOR
ASSUMPTION OF CONNECTIVITY SUCH
AS AGING FOR EXAMPLE AND IT'S A
SINGLE CELL RESOLUTION BY DESIGN
AND THEN WE MOVE MORE IN
ANALYSIS AND SO THAT EXTRACTS
SOME ADDITIONAL INFORMATION
COMPARED TO THE APPAL SIS THAT
MOSTLY PRESENTED TODAY.
AND IT EMPLOYS ALL POSSIBLE
EPIGENETIC IT'S IN NATURE AND
LIMITED ONLY BY NUMBER OF COLORS
ORP WASHES YOU CAN DO IN
SIMULTANEOUSLY MEASURE AND ONE
THING THAT I HIGHLIGHTED HERE
WHICH I'M NOT GOING TO BE
ADDRESSING TODAY, IT'S A HOT
TOPIC IN THE LAB WE WORK ON BUT
IMAGELY IS UNIQUELY SUITABLE FOR
AGING WHICH IS HARD TO DO WITH
THE MOUSE CONVENTIONAL TECHNIQUE
AND AGAIN, THE POINT BROUGHT
BEFORE, IT'S QUITE COST
EFFECTIVE.
JUST TAKE AN IMAGE.
SO, HOW DO WE DO THIS?
WELL, WE'LL START WITH THE CELLS
AND MOST OF THE START IT WOULD
BE THE THICK CELLS STAINED WITH
CONVENTIONAL ANTIBODY SUCH AS
METHYLATION AND IT'S DONE ON A
INSTRUMENT AND FORMATS AND AFTER
WHICH WE EXTRACT FEATURES AND
CONVERT IMAGES INTO ESSENTIALLY
SERIES THE TABLES.
WE STARTED WITH USING
CONVENTIONAL FEATURES SUCH AS
METRICS AND EXTRA ADVERTISE
TICKS AND MOST OF THE WORK WILL
BE HERE PRESENTED FROM THOSE
FEATURES AND WE TALKED ABOUT 250
FEATURES PER CHANNEL OF IMAGING.
NOW, MORE RECENTLY WE ACTUALLY
DEVELOPED OUR OWN FEATURES BASED
ON PROBABILITY DIFFERENT
EPIGENETIC AND IT TAKES
ADVANTAGES OF THE IMAGING AND
COLOCALLIZATION.
SO THAT'S FOLLOWED BY
COMPUTATIONAL APPROACHES AND
DIFFERENT MACHINE LEANING
APPROACHES AND WE PUBLISHED HAD
NISH WOULD BE WHEN WE
DEMONSTRATED THE USEFULNESS OF
THIS APPROACH TO IDENTIFY
DIFFERENT CELL TYPES AND DRUG
SCREENING AND ET CETERA AND
THAT'S BEEN PUBLISHED A FEW
YEARS AGO AND THE COMPOUNDS AND
WE HAVE AYAR I'VE THAT MOST OF
THE DATA WAS PRESENTED RIGHT NOW
FOCUSING ON AGING.
SO, WE STARTED WITH PETER AS A
COLLABORATOR HERE AT INSTITUTE
AND LOOKING AT FOR EXAMPLE
SINGLE CELL IN THE BLOOD.
THIS IS AN EXAMPLE OF THE
MODULATION STEVE PAIKINING WHEN
WE LOOKED AT A YOUNGER MOUSE, AN
OLD MICE, TWO YOUNG AND TWO OLD
MICE, FOR EXAMPLE, AND ASK THE
QUESTION, CAN WE SEPARATE THEM
INTO SINGLE CELL LEVEL JUST BY
USING IMAGING.
SO THE PORTION THAT I MENTIONED
BEFORE, WAS USED.
WE IMAGED THE MARK AND
CONSTRUCTED THE FEATURES AND
THIS IS A SIMPLE ANALYSIS THAT
SUGGESTED THAT IN DEED YOUNG AND
OLD CELLS CAN BE SEPARATED.
WE USE ADDITIONAL CD3 MARK TO
FOCUS ON T CELLS ANDED TO AVOID
CELL WITH AGE.
ON THE SINGLE CELL YOU CAN SEE
VISUALLY YOU CAN CELL WILL BE
SEPARATED AND AGAIN IT SUGGESTS
A REALLY GOOD SEPARATION.
WE THEN FOLLOW THAT WITH
EXPERIMENT WHEN WE ISOLATE IT SO
SITES FROM SEVERAL YOUNG AND OLD
MICE FIVE AND 20 MONTHS OLD MICE
AND THIS WAS IMAGE WITH A COUPLE
OF MARKS PRESENTED HERE AND SO,
WE PRESENTED THEM IN THIS
DIAGRAM WHERE WE ESSENTIALLY
MEASURED THE DISTANCE FOR EACH
CELL, WE MEASURED FROM THE OLD
AND DISTANCE FROM THE YOUNG
CELLS.
AND NOW EACH DOT HERE NOW
INDICATED IS A MOUSE AND WE
CONDUCTED THISSER PERMENT THREE
TIMES TO SEE HOW STABLE AND
REPRODUCE ABLE.
THE YOUNG ANIMAL AND OLD ANIMAL
ARE CLUSTERED IN THE
CORRESPONDING CONNERS SUGGESTING
THEY'RE ENOUGH TO SEPARATE YOUNG
AND OLD CELLS BY IMAGING.
NOW, FALLING ON THAT, WE LOOKED
AT A COLLECTION OF CELLS
ISOLATED FROM MICE, IN
PARTICULAR THEY CD3 SUBSET
MODULATED FROM MICE SPAN
INTERESTED 53 DAYS TO 68 DAYS
AND WE DID THE SAME PLOT AND THE
SAME APPROACH AND WE ESSENTIALLY
PLOT EVERYTHING SEPARATED BY A
DISTANCE FROM THE YOUNG AND OLD
AND EACH DOT IS A REPLICA AND
THERE'S A COUPLE OF MICE PER
TIME POINT AND A NUMBER OF REP
REPLICAS.
YOU CAN SEE THAT THERE SEEMS TO
FALL ON THIS LINE THAT CONNECTS
YOUNG AND OLD MICE.
SO, IF YOU PROJECT THE DATA ON
THAT AXIS IT'S THE LOWER PART
WHERE THIS EPIGENETIC AXIS WITH
THE MICE PROGRESS ALONG THIS
EPIGENETIC AXIS AND THE DISTANCE
IS THE COMPONENT THAT IS TO THE
AXIS.
SO, WE ASKED THE QUESTION, WILL
THAT PROGRESSION ALONG THAT AXIS
CORRELATE WITH AGE AND THAT'S A
SIMPLE CORRELATION THAT WE HAVE
HERE BOTH FOR THE PERIL RAL
BLOOD AND ALL THE CD3 POSITIVE
COM PART OF THIS.
IT'S IMPORTANT TO BRING THAT
THIS IS REALLY NARROW AND
REGRESSION OR ANY OTHER CHANGED
OF THE DATA.
THIS IS SIMPLY PLOTTING ONE SET
AGAINST THE OTHER DATASET SORT
OF AS IT IS.
THE IMAGING VERSUS CHRONOLOGICAL
AGENT AND YOU SAID IT EMERGES
AND IT'S NOT THE SUPER STRONG
IT'S DECENT AND DEFINITELY
SIGNIFICANT.
SO, YOU MAY THINK THAT THIS IS
SORT OF A APPROACH TO ANALYZING
THE AGING OR PROGRESSION ALONG
EPIGENETIC AGING SO WE TURNED
THE APPROACHES OR TERMINAL
APPROACHES TO LOOK AT THIS
QUESTION.
SO, FIRST WE JUST USE THE
REGULAR PC ANALYSIS AND PLOTTED
THE SIGNATURES ALONG THE COM
POPE ANT.
AND YOU CAN APPRECIATE THAT THE
PC1 COM PHONE SEPARATES THE CELL
TYPES BY THE NEGATIVE AND
POSITIVE WHERE THE COMPONENT
REALLY EXTRACTS THE AGING AXIS
FROM THE DATA.
VERY SIMILAR TO WHAT YOU HAVE
JUST SEEN BEFORE WITH THE
EPIGENETIC AGE AXIS
CONSTRUCTION.
FURTHER, WE COLLABORATE WITH THE
COLLEAGUE AND COLLATE RATE OR
AND USED THE HYPERBOLIC SPACE
WHICH IS VERY INTERESTING SPACE
AND I USE IT LATER AND I TALKED
TO APP EMERGENCE EMBEDDING WHICH
CAN HANDLE MUCH BETTER
DEMENTIONALITY THAT COULD BE
PROBLEMATIC AND IN SPACE AND
SUITABLE FOR THE ORGANIZED
BIOLOGICAL DATA AND YOU WILL SEE
IN SECONDLY IT'S A OF STATE OF
EXPANSE AND THE CONNECTION
BETWEEN THE TWO DOTS AND SPACE
NOT LOOK TO POSITIONS AND NO
MORE LINEAR STRAIGHT LINE WITH
THIS LINE.
SO WE PROJECTED BOTH CD3 AND
POSITIVE AND NEGATIVE STYLES OR
AGE LABELED STYLES IN THE
HYPERBOLIC SPACE AND WE RECEIVED
IT IN A SIMPLE OF THE EPIGENETIC
TRAJECTORY WITH AIMING.
I GUESS THIS IS SORT OF WHAT IS
IS CONVINCING TO US TO US THAT
IMAGING OF THE LANDSCAPE IN THE
CELLS RESULTED IN THIS EMERGENCE
OF THE TRAJECTORY WITHOUT ANY
REGRESSION OR ANY OTHER
MATHEMATICAL RATHER THAN JUST
PLOTTING AND EMBEDDING THEM IN
THE PC ANALYSIS INCLUDING SPACE
OR HYPERBOLIC SPACE.
SO THIS TRAJECTORY IS PRESENT IN
THE CELLS.
WE ALSO LOOKED AT DIFFERENT
SOLID ORGANS AND THAT'S AN
EXAMPLE OF THE FIBER ORGANS THAT
WE ANALYZED, SUBSTANTIALLY LESS
AGE GROUPS, THIS IS 15 AND 20
SOMETHING MONTHS AND WE SAW THE
PROGRESSION AND EMERGENCE OF
THIS TRAJECTORY IN THE SOLID
ORGANS WITH AN INTERESTING
COUPLE OF VEINERS AND IT WAS
KNOWN THAT BRAIN HAS THE LOWER,
SLOWER AGE ACCELERATION JUDGED
BY THE CLOCK AND ORGAN LIVING
AND IT WOULD SHOW THAT SO WE
ALSO OBSERVED THAT PROGRESSION
OF THE BRAIN IS SLOWER COMPARED
TO OTHER ANALYZED AND ALSO,
SORRY ABOUT THIS, AND ALSO WE
LOOKED AT CORRELATIONS BECAUSE
ALL THESE ORGANS WERE EXTRACTED
FROM THE SAME ANIMALS SO WE WERE
ABLE TO ASK THE QUESTION WERE
SHE'S OF THESE ORGANS AGE KIND
OF IN THE CORRELATED FASHION AND
FOR THE BRAIN AND HEART BUT NOT
OTHER ORGANS AND IT'S
INTERESTING BECAUSE THERE'S SOME
INDICATION IN DEED THAT BRAIN EX
HEART CAN HAVE A SIMILAR
MOLECULAR PATHWAY IN PARTICULAR
AND INDEXES ASSOCIATED WITH THE
BRAIN AND AGING AND HEART STUDY
RECENTLY.
SO, THERE WAS A DESCRIPTIVE APP
ALF AGE AND WE ASKED WHETHER THE
AGE DEFINED BY IMAGING WOULD
CORRELATE WITH THE FUNCTIONSAL
KNOWN TO EFFECT HEALTH AND SO WE
LOOK AT TWO THINGS, WHICH IS THE
THERAPY TREATMENT WHICH IS KNOWN
TO ACCELERATE AGING AND AGAIN WE
COMPARE IT HERE CHRONOLOGICAL
IDENTICAL MICE TREATED FOR THREE
MONTHS AND SIMILAR TO THE
PUBLISHED SYSTEMIC AND SINGLE
DOSE AND THEN WE LOOK AT TWO
MONTHS, TWO WEEKS LATER, IN
ISOLATED AND WE OBSERVED THAT
THE DOCS TREATED MICE HAVE ALL
THE SIGNATURE COMPARED TO
UNTREATED MICE IN AGE CONTROL.
WITH ANDERSON WE ASKED CALORIC
RESTRICTION WOULD RESULT IN THE
SLOWING DOWN OF EPIGENETIC
SIGNATURES AND SO WE GOT SAMPLES
WITH ROWS A LYNN AND OBSERVED
THAT CHANGE.
WE WANT TO ASK WHETHER IT
CORRELATES WITH INDIVIDUAL
FUNCTION BECAUSE WE'RE LOOKING
AT INDIVIDUAL SIGNATURES SO WE
DID EXPERIMENT WITH AMANDA
ROBERTS WHERE WE LOOKED AT THE
CHRONOLOGICAL IDENTICAL MICE AND
20 MICE AND WENT THROUGH A
NUMBER OF BEHAVIOR METABOLIC
TESTS AND MEASURED THE LANDSCAPE
AND SKELETAL MUSCLE AND ASKED
THE QUESTION, CAN WE ANALYZE THE
BEHAVIOR AND CORRELATE BEHAVIOR
OF THESE MICE WITH THEIR
EPIGENETIC SIGNATURES SO THE
TRAJECTORY OF CORRELATE WITH
READOUTS AND WE OBSERVED IN NINE
CLUSTERS OF THESE ORGANS IN SO
IT'S VERY INTERESTING THAT THEY
REALLY REFLECT OF THE LOCO MOTOR
AND METABOLIC FIX.
YOU CAN SEE AVERAGE RAILING WITH
SHIFT OR PAR PARTICIPATE TO THE
SIGNATURES FOR THE YOUNGESTEST G
YOUNGEST AND THE SAME WILL
PARTICIPATE WITH THE OPPOSITE
WITH OLDER STATES SO WE THINK
THAT IT CORRELATES WITH
INDIVIDUAL ORGANIZE APP AND
METABOLIC READOUTS AND WORKING
OUT THE PREDICTION POSSIBILITIES
AND THE LAST THING I WANT TO
QUICKLY GO OVER IS HETEROGENEITY
AND SO BECAUSE WE'RE IMAGING A
NUMBER OF CELLS, WE CAN NOW OF
ADDRESS ORIGINAL A TEE BETWEEN
INDIVIDUALS CELLS.
USING THE VARIANTS AND WHAT WE
FOUND IS IS HIT TREMENDOUS HETEE
CAN SPLIT THIS BY MARK AND CELL
TYPES AND WE OBSERVED THAT THE
HETEROGENEITY AND COMPONENT AND
IT'S MORE STABLE AND BECAUSE OF
THE HYPERBOLIC FUNCTION HAS IT
CAN PREDICT THE MAXIMUM LIFESPAN
LIMIT FOR MICE BASED ON, FOR
EXAMPLE, CD3 POSITIVE CELLS
WHICH IS INTERESTINGLY BETWEEN
THE MAXIMUM LIFESPAN IN THE LAB
AND OBSERVED IN THE WILD TYPE
MOUSE.
SO, IT MAKES IT VERY REASONABLE
I THINK SO I WANT TO THINK ABOUT
IT AND THE HETEROGENEITY AS A
REALLY TRAVELING THROUGH TIME
AND SPACE WHERE ALONG THAT
TRAJECTORIES, FOR EXAMPLE, IT'S
TWO DIFFERENT ORGANS AND
ORGANISMS AND THE RETRO GENEITY
INCREASE WITH AGE AND WITH THE
CELLS AND UNTIL IT REACHES THE
MAXIMUM POSSIBLE VARIANTS AND
COMPARABLE WITH LIFE AND THEN
THIS VARIANCE COULD BE AT A
DIFFERENT TIME IN THE TRAJECTORY
AND IN DIFFERENT INDIVIDUALS AND
THAT IS HOW OF WE TRIED TO
RECONCILE THOSE AGE TRAJECTORIES
AND INCREASE THE CHROMATIN
HETEROGENEITY.
SO TO SUM IT UP, THE FINAL
POINTS, SO EXISTENCE OF THIS
EPIGENETIC THAT CAN REVEAL BY
IMAGING THAT EP GENETIC AGE WITH
A LIFESPAN AND WE SHOW THAT
INDIVIDUAL EPIGENETIC FEATURES
AND SKELETAL MUSCLE OF THE
IDENTICAL MICE CORRELATE WITH
ORGANISMAL AND IT'S VERY
INTERESTING IN A ZAR CANYON
INDEPENDENT WAY AND THE INCREASE
WITH AGE AND PROVIDING AN
ESTIMATED OF THE SPY LAN AND I
DID MENTION WE ARE WORKING ON
LIFE IMAGING OF EPIGENETIC
LANDSCAPE.
I'M VERY FORTUNATE TO HAVE A
TALENTED GROUP OF INDIVIDUALS
WORKING WITH ME IN THE LAB AND
IT'S A COLLABORATION WITH MY
COLLEAGUE.
AND SCRIPTS, AND THANK YOU FOR
NATIONAL AGING FOR FUNDING
SUPPORTING THIS WORK.
>>THANK YOU.
SO, THIS CONCLUDES THE
PRESENTATION PART OF THE SESSION
AND NOW WE HAVE WHAT IS IN THE
PROGRAM, DISCUSSION OF SESSION
1.
WE CAN NOW HAVE A DISCUSSION.
SO, BOTH PRESENTERS COULD ASK
QUESTIONS AND ALSO PARTICIPANTS
IN THE ZOOM AND THEN I HOPE THAT
COLLEAGUES COULD ALSO TELL US
ABOUT THE QUESTIONS FROM OTHER
PARTS OF THE AUDIENCE.
I SEE A LOT OF QUESTIONS HAVE
BEEN ASKED ALREADY IN THE CHAT
SO MAYBE WE CAN GO AND MAYBE
ALEXEY COULD YOU ASK -- I SEE
YOU HAVE A QUESTION MAYBE YOU
CAN JUST TELL YOURSELF YOU ARE
ON MUTE.
>>THE OS SELLATOR IMPROVED THE
LIFESPAN AND NOT THE MAXIMUM AND
I JUST TAUGHT ABOUT ASKING YOU,
CAN YOU COMMENT ON THAT WHAT
WOULD BE YOUR THOUGHT OF
INCLUDING THE MAXIMUM IS THAT
POSSIBLE IN THE EAST?
>>THIS IS A GREAT QUESTION.
SO, I GUESS OUR CURRENT
HYPOTHESIS FOR THIS ENGINEERING
WE'RE ONLY LOOKING AT TWO MAJOR
FACTORS AND THE SILENCING PATH
WAY AND THE OTHER IS
MITOCHONDRIAL PATHWAY SO THERE
HAS TO BE OTHER FACTORS WE HAVE
NOT CONSIDERED OR WE HAVE NOT
CHARACTERIZED DURING THE AGING
PROCESS.
IT OF SEEMS LIKE OUR OSCILLATOR,
REALLY PROVIDED GOOD CONDITION
FOR BOTH PATHWAYS HOWEVER THE
CONTROLS STILL DIE.
THIS MUST BE SOME OTHER PATHWAYS
THAT IS IS NOT EFFECTIVE SO THIS
MAY BE WITH THE LIFESPAN WE CAN
SEE AND WHILE THE OTHER FROM THE
SCREEN MAY ALREADY FACTOR THAT
PATHWAY SO THAT WE SEE SIMILAR
MAXIMUM PATHWAYS.
SO IF THEY CAN IDENTIFY MORE
PATHWAYS, MORE FACTORS, I WOULD
THINK WE CAN FURTHER EXPAND THE
LAB.
SO THAT'S JUST THE IDEA.
>>THANK YOU.
>>I SEE THAT YOU PUT WET TO ME.
IF YOU WANT TO ASK?
>>YES, I WAS GOING TO ASK YOU
ABOUT THE IF THERE'S A LIMITED
WINDOW OF TIME THAT THAT'S
EFFECTIVE?
>>WE TRIED FOR THREE MONTHS AND
ONE MONTH AND THE EFFECT BASED
ON THE CLOCKS IS MUCH STRONGER
WHEN WE DO IT FROM THREE MONTHS
AND IN ONE MONTH, WE STILL SEE
THE REGENERATION AS WELL BUT NOT
AS STRONG.
INTERESTINGLY, WHEN THE MICE AND
LOOK FOR LIFESPAN WE DO OBSERVE
CHANGES IN LIFESPAN MEANING THAT
THE MICE LIVE LONGER AND
HOWEVER, THE EFFECT IS SMALLER
RELATIVE TO THE CHANGES IN
BIOLOGICAL AGE SO MY IMPRESSION
IS THIS EFFECT DIMINISHES OVER
TIME.
IT'S STILL SIGNIFICANT ENOUGH TO
OBSERVE DIFFERENCES LATE IN LIFE
BUT IT'S MUCH STRONGER AT THE
TIME WHEN THE MICE ARE SEPARATED
AND FADES AWAY.
IT'S NOT MORE EXPERIMENTS NEED
TO BE DONE IN THAT DIRECTION.
SO --
>>CAN I ASK ANOTHER QUESTION?
>>OF COURSE, YEAH.
>>BECAUSE I NOTICED IN YOUR
CLOCK AND ALSO OTHER CLOCKS FROM
ALEXEY, THIS PHENOMENON, WE'VE
COME ACROSS TOO THAT IF YOU TRY
TO BUILD THE TRUST WITH THE
OMICS CLOCK, AND MOST OF THE
TIME YOU END UP WITH A
PREDICTING A YOUNGER PEOPLE TO
BE OLD EVERY AND PREDICTING
OLDER PEOPLE TO BE YOUNGER.
THERE'S A BUY AS AND WE'VE SEEN
THIS IN MANY STUDIES AND IN OUR
ANALYSIS TOO.
CONSTANT ATTEMPT TO GET RID OF
THIS BIAS.
AND WITH THE BIAS WE SEE A
BIOLOGICAL ISSUE THAT THERE ARE
A LIMIT IN WHAT CAN COULD WITH
OMICS OR MODULATION DATA FOR
PREDICTION OF BIOLOGICAL AGE.
I DON'T KNOW IF APPROXIMATE YOU
GUYS HAVE NOTICED THE SAME
PROBLEM THAT THERE'S A CLEAR,
CLEAR BIAS THAT HAPPENS AND IT'S
HARD TO THINK OF A PATH.
>>I AGREE.
WE HAVE THE SAME ACTUALLY.
IT'S KIND OF CONSISTENTLY.
AS YOU SAID, IT IT'S HARD TO
DISTINGUISH WHETHER THIS IS
REALLY DUE TO SLOW AGING OR KIND
OF HETEROGENEITY IN THE
POPULATION OF MORE RAPIDLY AGING
INDIVIDUALS THAT THEY DIE AND
THEN IT'S A SELECTION.
SO SURVIVAL.
IT'S UNCLEAR BUT IT'S A GOOD
QUESTION OF THE CLOCK SO
ACTUALLY, THE STRUCTURE OF THE
POPULATION OR IT MIGHT BE BOTH.
THAT IS MY IMPRESSION.
I WANTED TO JUST MAKE A GENERAL
COMMENT BECAUSE I WAS REALLY
EVERYBODY PUBLISHED DATA AND
SOME OF THE EXCITING PAPERS
COMING OUT.
I THINK ALSO I THINK THAT THE
COMMON THEME WAS THAT EVERYBODY
HAS MASSIVE DATA AND IT MAKES
SENSE BECAUSE OF AGING IS A
SYSTEMIC PROCESS AND I THINK WE
COME TO REALIZE THAT BASICALLY
COMPUTATIONAL TOOLS AND MASSIVE
DATA IS ESSENTIALLY A PART OF
THE STUDIES CURRENTLY IN THE
FIELD.
ALSO, I NOTICED EVERYBODY
COMMENTED ON (INAUDIBLE) IN
AGING AND IT'S ANOTHER COMMON
FEATURE SO, ALSO CHALLENGES AND
EVERYBODY OF COURSE HIGHLIGHTED
CHALLENGES.
I THINK WE AGREE TO DISCUSS SOME
CHALLENGES.
I WOULD LIKE TO GIVE THE
MICROPHONE TO DAVE JONES,
BECAUSE HE HAS OPERATIONS IN THE
CHAT AND I SEE A RAISED HAND.
DAVE.
>>THANK YOU, VERY MUCH.
THIS IS JUST TO -- YOU OVER
ESTIMATE YOUNGER INDIVIDUALS EX
THIS IS BIAS TOWARDS THE MEAN
TRAINING DATASET AND THERE'S
DIFFERENT COMPUTATIONAL
APPROACHES TO DEALING WITH THAT
AND I DON'T THINK THE CONSENSUS
IN THE FIELD REGRESSING OUT THAT
EFFECT AND THEN USING THE
CORRECTED AGE GAP TO SEE HOW
THAT'S RELATED TO BIOLOGY IS THE
APPROACH WE'RE TAKING NOW BUT
THERE MAY BE OTHER, BETTER
SOLUTIONS TO THAT.
I THINK THAT'S A VERY IMPORTANT
AREA OF CONVERSATION FOR THE
FIELD.
I GUESS THE QUESTION I HAVE IN
THE CHAT IS KIND OF RELATED TO
THAT.
AND THAT WHEN I THINK ABOUT
THESE BIOLOGICAL CLOCKS, I DO
THINK ABOUT DIFFERENT SCALES AND
WE ARE USING DIFFERENT DATA,
DIFFERENT SCALES TO BUILD THEM.
AND YOU MENTIONED VERY POOR DATA
TO USE LIKE CLASSES AND VISITS
TO THE DOCTOR OFFICES, AND THAT
MAKES ME THINK OF GOOGLE FLU
TRENDS IF ANYONE REMEMBERS THAT.
THAT WAS A VERY POOR WAY TO
BUILD A DATA FOR THE SAME REASON
AND THEY CAN PREDICT CEASE OPALU
FOR SEARCHES IN GOOGLE HOW OFTEN
PEOPLE SEARCHED FOR FLU.
IT FIT WELL UNTIL IT DIDN'T.
I THINK THE SAME SORT OF
PROBLEMS HERE USES GLASSES TO
PREDICT AGE.
YOU ARE NOT MEASURING THE THING
OF INTEREST.
AND SO, YOU CAN MEASURE
DIFFERENT THINGS AT DIFFERENT
SCALES LIKE OUR COLLEAGUES IN
CARDIOLOGY WILL PREDICT AGE
BASED ON THE ECG AND THEY CAN
ACTUALLY SHOW A REJUVENATION TOO
IF THEY DO A HEART TRANSPLANT
THAT OLDER AGE WILL RETURN BACK
TO NORMAL SO IT'S A RELEVANT
PIECE OF INFORMATION TO H ESTIME
BIOLOGICAL AGE.
THE QUESTION I HAD IN THE CHAT
WAS, IS THIS A PRIVILEGED SCALE
WHICH WE SHOULD DEVELOP THESE
CLOCKS OR IS THERE A GENERAL
FRAMEWORK FOR THIS EFFORT IN
GENERAL?
>>THAT'S A VERY IMPORTANT
QUESTION.
MAYBE I'LL ASK PAOLA, IF YOU
WOULD LIKE TO.
>>IT SEEMS TO BE A EXTREMELY
DIFFICULT QUESTION.
I -- HONESTLY, THE ANSWER IS I
DON'T KNOW.
WE'RE TRYING.
WE DON'T KNOW WHAT WILL BE THE
RIGHT ANSWER HERE.
I UNDERSTAND SOMETIMES THERE'S A
LOT OF CHALLENGE AND WE HAVE
ECONOMIC MEASURE, RIGHT.
AND WE SAY IT'S JUST TRIAL AND
ERROR TO TRY TO SEE WHAT WORKS
BEST.
I DON'T REALLY HAVE A STRONG
POINT OF VIEW HERE.
I AM STILL TRYING TO UNDERSTAND
WHAT THIS IS IS THE RIGHT
ANSWER.
SO I DON'T KNOW IF ANYBODY
ANSWERS THAT ARE MORE FOCUSED
THAN A CLEAR ANSWER YES, WE
SHOULD GO IN THIS DIRECTION AND
AT THE MOMENT I'M NOT.
I DON'T HAVE A CLEAR ANSWER FOR
THAT.
THERE'S PHYSIOLOGY OF INTEREST
AT DIFFERENT SCALES WHICH SHOULD
BE MODELED AND I THINK THERE'S
PROBABLY A OF FRAMEWORK FOR
MODELING ACROSS THEM AND BETWEEN
THEM AND I THINK THAT'S A MAJOR
CHALLENGE FOR THE FIELD TO FIND
OUT WHAT THAT IS AND THE BEST
WAY TO DO THAT.
>>SO, I WANT TO GIVE UNEXAMPLE.
LAST YEAR WE HAD A CONFERENCE
AND ON AGING.
AND AT THE GAVE A QUESTIONNAIRE
TO THE PARTICIPANTS ASKING
SEVERAL REALLY MOST BASIC
QUESTIONS IN THE FIELD LIKE WHAT
IS AGING?
WHAT CAUSES AGING?
WHAT DOES AGING BEGIN?
BUT QUESTIONS LIKE THIS.
AND WHAT WAS MAZING TO ME WHAT
IT REVEALED IS THAT THE ANSWERS
IS THAT SCIENTISTS IN THE FIELD,
THEY VIEW AGING IN DIFFERENT
WAYS.
THEY THINK THAT AGING IS A
INCREASED CHANCE OF DYING WITH
AGE.
IT WAS 10 DIFFERENT CATEGORIES
LIKE THIS AND OF COURSE, IF IT
BUILT BY MARKERS OF AGING, YOU
WOULD BUILD THEM FOR VARIOUS
DIFFERENT MANIFESTATIONS OF
AGING SO THEY WOULD BE
DIFFERENT.
AND SO TO ME IT'S A VERY SERIOUS
CHALLENGE BECAUSE MAYBE NOT SO
MUCH DISCUSSED.
SO WHEN THEY SAY OF THEY KIND OF
DISCUSS OMICS AND WHATEVER,
METHODS AND THEY CONDUCT THE
CLOCKS BUT THEY TRAIN THEM FOR
WHAT IS AGING ACTUALLY?
SO TO ME, THIS IS A KEY QUESTION
AND EVERYBODY IS COMPLETELY
DIFFERENT DEFINITION, DIFFERENT
VIEWS ON AGING.
SO ALEXEY, DO YOU WANT TO
CONTINUE TAKING THIS VERY
SERIOUS QUESTION?
>>SURE.
I'M HAPPY TO COULD BE I OBJECT .
WE DON'T USE TRAINING OR
DIGRESSION WITH IMAGING.
REALLY IMAGE THE SIGNATURES AND
PLOT OR INQUIRE ONE OF THE
SIGNATURES ARE GOOD FOR AND SO
WE SAW THAT THEY'RE CORAL EIGHT
WITH CHRONOLOGICAL AGE AND AS I
SHOWED THEIR CORRELATED WITH
LOCOMOTION AND METABOLIC
PROGRESS SUCH AS MAXIMUM CO2
WHICH IS CONVENTIONALLY KNOWN AS
THE CAPACITY.
AND SO, WE ALSO OBSERVED THOSE
SIGNATURES HETEROGENEITY
INCREASE WITH AGE WHICH I GUESS
REFLECTION OF THE SECOND
DYNAMICS AND YOU WILL HAVE AN
INCREASE OF DISORDER AND I GUESS
IF YOU MEASURE IT CAREFULLY, YOU
WOULD MAYBE FIND MANY DIFFERENT
IN PARTICULAR AND THE LANDSCAPE
OR THE STATE OF THE CELLS IN
SINGLE CELL PROBABLY DECAY OR
ALTER INDEPENDENTLY AND THERE
FOR DIVERGE AND THAT'S WHAT
WE'RE MEASURING.
SO, I GUESS, WE TRY TO CON UP
WITH WAY TO IS TO ADDRESS THE
PROBLEM.
>>ABOUT WHAT DO YOU THINK.
>>DIFFERENT SCALES IN AGING.
>>YEAH, YEAH.
I THINK THAT'S VERY SENTIMENTAL
QUESTIONS.
SO AS YOU CAN SEE FROM OUR WORK,
IT'S VERY BASIC SINGLE CELL
CHANGES.
SO, HOW TO DEFINITION AND HOW TO
CONNECT IT AND HOW TO GO ABOUT
SCALE AND HOW TO CONNECT IT TO
TISSUE AGING, ANIMAL AGING AND
DIFFERENT TIME SCALES THERE AND
WHAT KIND OF DATA WE NEED AND
WHAT KIND OF DATA WE CAN
GENERATE IT AND I THINK THERE'S
A DEFINITELY A VARIOUS STRIKE IN
QUESTIONS FOR YOU LIKE VERY
CHALLENGING AND HOW TO EACH
STATE UNIQUE BECAUSE WE TRACK
THEIR LIFE SPANS THROUGHOUT THE
WHOLE LIFESPAN OF EVERY CELL AND
WE CAN SEE WHAT IS GOING ON
THERE.
IT'S A GOOD STARTING POINT BUT
WHEN WE THINK ABOUT GOING TO A
SLIGHTLY MORE COMPLEX SYSTEMS
AND SUDDENLY WE DON'T KNOW WHAT
TO DO EXPECT, RIGHT, WHAT KIND
OF DATA WE CAN GENERATE AND IT'S
LIMITED THERE.
TO ME THIS IS THE BIGGEST
CHALLENGE FOR THESE MODELS IN
THE IF APPROXIMATE WE CAN
GENERATE I THINK IT'S A PROCESS
AND WE JUST HAVE TO SEE WHAT
KIND OF DATA WE CAN GENERATE IN
AND IT'S BASED ON THAT WE CAN
GENERATE MODELS TO HOPEFULLY MAP
THESE MODEL TO UNDERSTAND THE
AGING PROCESS.
I DON'T KNOW WHETHER I ANSWERED
THE QUESTION BUT THAT'S REALLY
THE KEY CHALLENGE IN OUR STUDY
NO.
>>THANK YOU.
SPEAKING OF CHALLENGES,
BEFORE -- I JUST WANTED TO ASK
MANDY BAUTISTA TO ASK BECAUSE
SHE HAS A NICE QUESTION ABOUT
THE CHALLENGES AND IT'S TIME TO
ASK THAT QUESTION.
>>THANK YOU.
ABSOLUTELY.
YOU'VE ALREADY BEEN TOUCHING ON
THIS IN THE CONVERSATION A
LITTLE BIT.
I'D LIKE TO PUSH YOU A LITTLE
EACH OF YOU, TO TELL ME AND TELL
ALL OF US, IF YOU HAD TO CHOSE
JUST ONE, WHAT WOULD YOU PICK AS
THE MOST IMPORTANT CHALLENGE TO
OVERCOME THAT UNIQUE TO MODELING
AGING?
USING THESE TIMES OF
COMPUTATIONAL APPROACHES.
SECONDLY, WHAT WOULD IT TIKE DO
THAT?
SO WHAT WOULD IT TAKE TO
OVERCOME THAT CHALLENGE?
IT'S NOT AN EASY QUESTION BUT
I'M THROWING IT OVER TO YOU TO
SEE WHAT YOUR THOUGHTS ARE.
>>O. WOULD WOULD LIKE TO BEGIN
ANSWERING?
>>I CAN.
I CAN GIVE MY TWO CENTS.
I WORK WITH HUMAN DATA, I THINK
A CHALLENGE TO EVERYTHING WE DO
IS, AS I MENTIONED EARLIER, WE
MEASURE THINGS ONE POINT IN
TIME.
WE MEASURE ONE THING AND WE TRY
TO UNDERSTAND A VERY COMPLEX
SYSTEM.
SO THE BIG CHALLENGE IS THAT TO
TRANSITION INTO MODELING
DYNAMICAL SYSTEM BECAUSE WE'RE
LOOKING AT THE DYNAMICAL SYSTEM
WITH A LOT OF FEEDBACK GROUPS
AND WHAT COULD WE NEED TO DO
THIS, WE NEED TO HAVE MORE DATA.
WE NEED MORE DATA, TEMPORAL
DATA.
BECAUSE, AND THEN SAMPLING
ANOTHER QUESTION.
HOW OFTEN?
HOW MUCH -- HOW OFTEN IN TIME
YOU WANT TO MEASURE THINGS AND
SERVE ON THE CHANGED.
TO ME THE BIG CHALLENGE WE SEE
IS THE NEED TO MODEL A DYNAMICAL
SYSTEM AND WE REALLY DON'T HAVE
ENOUGH DATA NOW BUT ALSO WE
DON'T HAVE THE TOOLS TO BE ABLE
TO DO THIS.
SO THERE ARE COMPUTATIONAL
EVIDENCE SO THE INPUT IS REALLY
LACKING.
AND TO ME, IT'S REALLY THE MOST
DIFFICULT THING TO ACHIEVE.
BUT WE ARE GOING TO ADD A
LIMITED VISION AND UNDERSTANDING
IF WE JUST WORK WITH THE CROSS
SECTIONAL DATA IN HUMANS AND OF
COURSE WITH AN MA'AM AND OTHER
EXPERIMENTS ONE CAN GENERATE A
LOT OF LONGITUDINAL DATA AND
MEASURE THINGS ASSUMING INFINITE
YOU CAN MEASURE THINGS IN ANYWAY
SAMPLE FRAMEWORK.
>>I SEE YOU.
WHO WOULD LIKE TO IT?
>>SURE.
I THINK ONE OF -- THE MOST
IMPORTANT THING IS YOU POINTED
OUT IS BEING THAT TRYING TO
UNDERSTAND THE COWSAL COWS CAUSY
AND WHAT IS THE MOST IMPORTANT
AND THE CULTURAL PHENOMENON AND
FOR THAT, TWO THINGS COULD BE
HELPFUL IS IT'S IMPORTANT TO DO
LONGITUDINAL STUDIES AND OBSERVE
THE SYSTEM IN PROCESS
DYNAMICALLY AND SECOND, WE CAN
THINK ABOUT USING THE TEST
CAUSALITY AND TO DETERMINE
WHETHER THE TIME SERIOUS, ONE
TIME SERIES CAN PREDICT ANOTHER
AND SAYING IF THERE'S TWO
PHENOMENON CO REGULATE AND THEN
ONE OCCURS EARLIER AND IT'S
POTENTIALLY THE CAUSE THERE'S
ENOUGH STATISTICAL POWERS TO DO
THIS SINCE 1969 WHEN THEY
PROPOSED THAT IT'S BEEN REFINED
AND OPTIMIZED.
NOW USING ECONOMICS AND OTHER
AREAS TOO.
WHERE IT IS HARD TO DO THE SOME
IT'S EASIER BUT WHEN IT'S IN
HUMAN STUDIES FOR EXAMPLE, IT'S
HARD AND CAUSALITY COULD BE
HELPFUL.
>>THANK YOU.
>>YEAH, I AGREE WITH PAOLA AND
ALEXEY.
I THINK DATA IS THE KEY.
THE DATA, THE TOOL THAT WE CAN
USE TO MIRROR THE SYSTEM
DYNAMICALLY AND ALSO PERTURBED
THE SYSTEM.
WITH THAT WE CAN START BUILDING
ON DYNAMIC MODEL MECHANISTIC
MODEL.
USE IT AGAIN AND IT'S A UNIQUE
SYSTEM BUT GOING A LITTLE BIT UP
TO THE ANIMALS IS A PROBLEM SO I
THINK TECHNOLOGY IS WHAT WE
NEED.
MERITTOUS OF THIS DATA.
>>YEAH, SO PAYI MAYBE I CAN ALO
ANSWER.
BASICALLY I'D LIKE TO REFER TO
THIS -- I MENTIONED THE SURVEY
WE GAVE AND THEY ACTUALLY ASKED
A QUESTION WHAT'S IS THE
IMPORTANT ANSWER TO THE QUESTION
IF THE FIELD AND THE MAJORITY OF
PEOPLE SAY THAT THE MOST
IMPORTANT IS ACTUALLY TO
UNDERSTANDING AGING.
I FULLY AGREE.
IN FACT, WHEN I TRY TO DEFINE
WHAT WE DO WE ULTIMATELY TRY TO
UNDERSTAND AGING.
IT'S IMPORTANT REALLY WE DEFINE
IT.
WE DON'T HAVE TO HAVE A FORMAL
DEFINITION, BUT JUST IN ESSENCE
WHAT IS IT BECAUSE THE VIEW IS
IN A COMPLETELY DIFFERENT WAYS
AND IF YOU UNDERSTAND, THEN YOU
COULD TARGET IT AND I THINK
ULTIMATELY THIS IS THE MOST
IMPORTANT.
HOW WE COME TO THAT I DON'T
KNOW.
THE NIA SHOULD TAKE A
PARTNERSHIP MAYBE FORM NEW WAYS
OR INITIATIVES.
YOU ARE THE PEOPLE WHO KNOW HOW
TO DO IT, YEAH.
>>I MEAN, IF WE HAD THE ANSWER,
RIGHT, WE WOULDN'T BE HERE.
>>YOU ARE RIGHT.
>>THANK YOU.
THE NEXT QUESTION AND I THINK
THAT IS THE MOST IMPORTANT
QUESTION, WHEN YOU HAVE THAT
ANSWER, YOU CAN THEN KNOW HOW
YOU ARE COLLECTING YOUR DATA,
WHY YOU ARE COLLECTING IT AND
WHAT SCALE, HOW ARE YOU MODELING
IT AND WHY YOU ARE MODELING ITEL
AND WHY YOU ARE INTERVENING.
IF YOU NEED THIS ANSWER, AND I
THINK LET'S SAY YOU DID THINK OF
IT AS DYNAMIC PHENOMENON
MAINTAINING BALANCE FOR FUNCTION
AT A CERTAIN SCALE OR LEVEL, I
THINK IT'S REASONABLE AND
EVERYTHING THAT HANCE SHOWED US
WHAT APPLY AND YOU WANT TO AVOID
THE UNHEALTHY FACTOR STATES AND
FIND WAYS TO GET PEOPLE INTO
HEALTHY LIMIT CYCLES.
EACH SCALE OF THE FUNCTION YOU
ARE INTERESTED IN.
>>YEAH.
>>THANK YOU.
>>I'D LIKE TO SWITCH A LITTLE
BIT OF DISCUSSION AND ACTUALLY
ASK NAN A QUESTION.
IN YOUR TALK, YOU DIDN'T PUT IT
IN THIS WAY, BUT THE WAY WHEN
YOU PRESENTED YOUR WORK, I WAS
THINKING ABOUT AGING AND IT'S
NOT BEEN REALLY DISCUSSED IN THE
FIELD BECAUSE WE THINK TISSUE
AGING AND CELL AGING AND HERE
IT'S LIKE YOUR WORK REALLY
IMPLIES THAT THERE'S ALSO A SUB
CELLULAR SYSTEMS SYSTEMIC AGING
WHAT'S DO YOU THINK ABOUT IT?
>>YOU MEAN LIKE SUBCELLULAR'.
>>THEY MAY CHANGE IN DIFFERENT
WAYS?
>>THAT'S RIGHT, THAT'S RIGHT.
THEY ARE THINKING ABOUT AND THEY
MAY HAVE TO SAY THE SAME
FRAMEWORK AND SO THIS IS
ACTUALLY WHAT WE HAVE IS REALLY
THINK ABOUT IS THAT HOW THIS
GENETIC NETWORKS CAN UNDERLINE
THIS DIFFERENT PATHS AND EVEN AT
A SINGLE CELL LEVEL SO THIS IS
IS IDENTICAL CELLS WITHOUT ANY
OF THE COMPLETE FOR THE CONSTANT
AND THEY JUST THEY WORK IN THEIR
AGING PROCESS AND I THINK THERE
ARE I THINK THAT'S VERY
INTERESTING BEHAVIOR THAT WE
SAW.
THINKING ABOUT EXPANDING AGAIN
TO SEE THAT IN THE SAME TISSUE,
OLDER CELLS AS THEIR
HETEROGENEITY AND TALK ABOUT THE
EEP GENETIC IF THE CELLS AND
SIMILAR AGING THAT IS INCREASED
AND IT'S FANTASTIC ALTERATIONS
AND WAS UNDERLINE IS AND THE
TISSUE AND HOW DIFFERENT THAT
THEY ARE AND ARE THEY CAUSING
DIFFERENT WAYS TO AGE?
I THINK THAT'S A PERSPECTIVE
THAT THEY GET INTO STUDY AIMING.
I DON'T KNOW WHETHER IT ANSWERS
YOUR QUESTIONS BUT THIS IS OUR
PERSPECTIVE.
FROM THE SIMPLEST POSSIBLE UNION
FROM AND TRY TO MODEL IT AND
TROY TO MODEL THE DYNAMICS AND
THE MECHANISMS OF IT AND BUILD
ON IT TO SEE IF WE CAN
UNDERSTAND A MORE COMPLEX SYSTEM
AND IT'S WHAT WOVE BEEN THINKING
OF DOING.
>>I'D LIKE TO ASK NEXT, I'D
LIKE TO ASK JANINE SIMMONS SHE
HAS A QUESTION IN THE CHAT IF
YOU WOULD BE ABLE TO ASK A
QUESTION.
>>I THINK I WAS MOSTLY
RESPONDING TO DAVE JONES BUT I
WANTED TO SEE SUB SECOND UP TO
DECADES AND GENERATION PERHAPS?
>>AND I DID IT AND THE QUESTION
IS COULD SIMILAR MODELS BE
APPLIED IF WE HAD SOMETHING
WORKING AT ONE TEMPLE SCALE
COULD WE APPLY A SIMILAR MODEL
AND EXPECT TO APPLY A SIMILAR
MODEL AT ANOTHER SCALE
MEANINGFULLY OR DOES EACH HAVE
TO BE CREATED SEPARATELY?
>>WE WOULD LIKE TO BEGIN
ASKING.
SOMETIMES WE DISCUSS IN THE LAB
WE ARE YOUNGER IN THE MORNING
THAN IN THE OPENING AT THE SCALE
OF THE DAY AND MAYBE AT NIGHT
ORORNOT SO IT'S UNCLEAR HOW TO
ACTUALLY ADDRESS IT BECAUSE IT
WOULD NOT WORK AT THAT SCALE.
YOU ARE RIGHT SO SOME BIOMARKERS
THEY WOULD WORK AT DIFFERENT
SCALES AND OF COURSE IT'S
WORKING AND IT REFLECTS MORE OF
THE BIOLOGY THAN AGING THIS THIS
BECOMES ANOTHER CHALLE CHALLENG.
EVEN IF IT'S A SMALLER SCALE
IT'S SOMETHING THEY HAVE NOT
BEEN DOING.
>>I SEE AN AND DAVE.
>>I WOULD NOT ANSWER THAT
QUESTION BECAUSE THAT'S
GIGANTIC, HOWEVER, IT'S CLOSER
TO AN ANSWER BECAUSE THE IDEA
WOULD GET PRINCIPLE, UNIVERSAL
PRINCIPLES THAT CAN BE APPLIED
AT DIFFERENT SCALES.
OF COURSE THEY NEED NUMERICAL
ADJUSTMENTS BUT THEY CAN EXPAND
THEIR WHOLE HIERARCHY OF SCALES
AND THE TALK WOULD BE DEVELOPING
A LITTLE BIT ON NOT THESE
TOPICS, NOT OF COURSE THE
MOLECULAR LEVEL BUT JUST HEARING
THE COMMENTS ON WHAT YOU SAID
THAT YOU MENTAL STUDY STATES AND
I THINK THAT THIS IS EXACTLY
WHAT WE SEE AT HIGHER SCALES AND
CHANGES IN THEY ATTRACT OR TO
CELL ORGANIZE CRITICALITY AND
WHICH IS A SCALIST SO THAT I
MEAN IT'S JUST AN ADDITION
POSSIBILITY TO APPROACH THIS
TYPE OF ISSUES.
>>I AGREE ALSO, LIKE APPLYING
PRINCIPLES IS A APPROACH.
I'D LIKE TO ASK DAVE TO MAKE A
COMMENT OR A QUESTION.
DAVE JONES.
>>I JUST WANT TO REINFORCE THAT
I THINK THE ANSWER IS A COMPLEX
SYSTEMS APPROACH OR DYNAMICS
SYSTEM APPROACH.
YOU CAN GO ACROSS SCALES AND IT
WILL BE UNIQUE ASPECTS AT EACH
SCALE.
IF YOU DO HAVE MODELS AT EACH
SCALE AND YOU UNDERSTAND HOW YOU
MIGHT NEED TO RELATE THEM IN
THIS UNIFIED FRAMEWORK YOU WOULD
CROSS SCALES.
AND I REALLY THINK IT'S
IMPORTANT TO CONSIDER SCALES AND
ALL THE WORKS AND JUST THINKING
ABOUT SLEEP-WAKE CYCLES, IT'S A
PHENOMENON AND WE NEED THAT TO
MAINTAIN BALANCE IN OUR LIVES.
AND THERE ARE CERTAINLY
ENVIRONMENTAL FACTORS WHICH
WE'LL THROW THAT OFF.
YOU COULD EVEN WAKE SOMEONE UP
EVERY NIGHT AND THAT'S GOING TO
THROW THEIR SLEEP-WAKE CYCLES
OFF.
AND THEN THAT'S GOING TO CHANGE
THE CHEMISTRY AND THE CRYPTOMICS
AND LOTS OF THINGS BUT YOUR
INTERVENTIONS AREN'T THAT SCALE
AND YOU DON'T WANT TO INTERRUPT
THE PROTEINS AND YOU WOULD WANT
TO INTERVENE AT THE SHA SLEEP-WE
CYCLE TO FIX THAT PROBLEM.
WE NEED UNDERSTANDING THE WAY
THESE DIFFERENT THINGS ARE
CONNECTED IN A COMPLEX SYSTEM
AND OS TORI DYNAMICS.
>>THANK YOU, DAVE.
IT'S AN IMPORTANT COMMENT.
I WOULD LIKE TO ASK ACTUALLY
MATT AND ALSO HEL HELP LENNY IF
THERE ARE QUESTIONS FROM THE
AUDIENCE?
I DON'T KNOW WHAT IS THE WAY FOR
THEM TO ASK THOSE QUESTIONS
BECAUSE WE DON'T SEE THEM HERE.
>>SO FAR THERE ARE NO
ADDITIONAL QUESTIONS.
>>OKAY.
>>THAT WE HAVE RECEIVED.
>>FOLKS FROM THE VIEWING THE
LIVESTREAM DO HAVE QUESTIONS,
THERE'S THE LIVE FEEDBACK FORM
THAT WILL GET SENT TO DAVE WHO
IS MONITORING THOSE AND WE'LL
PASS THEM THROUGH.
>>WHILE WE WAIT FOR OTHER
QUESTIONS, WE CAN CONTINUE OUR
DISCUSSION.
AND I SEE ALSO THAT NAN HAD A
QUESTION FOR ALEXEY.
>>THAT'S ALL RIGHT.
THANK YOU.
SO, FIRST, I WANT TO ALSO I CALL
I DO THINK THAT THE AT LEAST IT
CAN BE APPLIED TO DIFFERENT
SCALES DEFINITELY.
IT REALLY DEPENDS ON WHAT KIND
OF DATA WE HAVE AND WHAT KIND OF
QUESTIONS WE ARE ASKED.
SO, AT LEAST AGAIN, AS I JUST
MENTIONED, I'M VERY INTERESTED
IN THE INCREASE THAT AGAIN EYE
TEE THAT WE SAW AND I WANT TO
SEE WHETHER IT'S TOO SPECIFIC
AND WHAT'S THE CAUSE OF IT AND
WHETHER IT RELATES TO THE
DIFFERENCE IN PHENOTYPES OR
PHYSIOLOGY DIFFERENCE?
SO, BECAUSE IT'S FANTASTIC.
IT WAS SERVI SERVICE AS A MATRIO
AGING.
>>IT'S WONDERFUL.
>>WE INCREASE THAT UNIVERSAL'
AND EVERY TISSUE LOOKED AT.
IT'S I THINK, WE DIDN'T HAVE
MUCH OF THE DATA WHERE WE
CORRELATE ORGAN AND TISSUE MY
PREDICTION IT WILL CORRELATE
WITH INCREASE OF HETEROGENEITY
AND IT'S A WELL-KNOWN PHENOMENON
WHERE MOST OF THE MEASUREMENTS
WOULD INCREASE IN AGE.
WE ORIGINALLY WERE DOING THIS
AND I DIDN'T HAVE A CHANCE TO
PRESENT THIS DATA BUT THIS
HETEROGENEITY COULD BE MEASURED
AND IN PARTICULAR IN DIFFERENT
TISSUE BECAUSE IMAGING IS
BEAUTIFUL AND OF COURSE KNOWN TO
WORK ON SECTIONS AND WHAT NOT SO
WE CAN MEASURE HETEROGENEITY
WITHIN INDIVIDUAL TISSUE
OBSERVED FOR EXAMPLE WE WORK
WITH COCHLEA WHERE THE CELLS,
THEY'RE VERY WELL DEFINED AND WE
CAN LOOK AT THE YOUNG AND OLD
CELLS IN THE SAME LOCATION SO WE
KNOW WE'RE LOOKING AT THE SAME
250 CELLS THAT MAINTAIN THE
FUNCTION FOR THE HEARING
FUNCTION AND WE KNOW THE HEARING
FUNCTION IS LAST WITH AGE AND WE
CAN SEE DRAMATIC INCREASE IN
HETEROGENEITY OF THOSE HAIR
CELLS WITH RESPECT TO THE
EPIGENETIC LANDSCAPE.
SO THAT'S SOMETHING THAT WE CAN
APPLY FROM MULTIPLE ORGAN AND
TISSUE AND REALLY UNDERSTAND HOW
AGING HAPPENS IN THREE DA
MENTION WITH RESPECT TO
HETEROGENEITY AND THE FUNCTION
OF CELLS AND TISSUE AND ORGANS
AND CELL SAMPLES LIKE NEURONAL
CONNECTIONS WITH CELLS ET CETERA
WHETHER THIS FUNCTIONALLY
CONNECTED CELL SIM POLLS AGE AT
THE SAME RATE AND WHETHER IT'S
CORRESPONDING TO THE FUNCTIONSAL
AT THE TIMER OWE GENEITY AND
SYSTEM.
>>SO, WHAT COULD BE THE CAUSE
OF IT?
IS IS THAT A CONSEQUENCE OF
AGING OR IT HAS SOME PROCESS
THAT DRIVE IT?
>>IT'S A MILLION DOLLAR
QUESTION.
[LAUGHTER]
>>I GUESS IT WOULD BE ENDLESS
DEBATES.
MY SIMPLE ANSWER IS,
POTENTIALLY, THE SAME SECOND
DYNAMICS THAT IS UNIVERSALLY
EXIST ALL OVER AND NOW PEOPLE
HAVE DISCUSSION AND PAPERS
APPEARING THAT IT PROBABLY HAS
THE DERMIS TICK SENSE DEALING
WITH THE QUANTUM ENTANGLEMENT
THAT YOU CAN ENTANGLE PRODUCTS
AND IT'S ENERGY LOST DURING THE
ENTANGLEMENT AND ESSENTIALLY IT
MOVES IN ONE DIRECTION AND
EVERYTHING ELSE IS A
CONSEQUENCE.
HOW EXACTLY IT HAPPENS THAT'S
WHAT WE TRY TO ANSWER AND
PHRASING IT ELEGANTLY IF WE CAN
UNDERSTAND THE CAUSES AND
MECHANISMS IS IS VERY, VERY
IMPORTANT.
>>THANK YOU.
>>THIS THANKS TO THE SCALE AND
BECAUSE EVEN IF YOU LOOK AT THE
SYSTEM AT THE CELLULAR LEVEL,
YOU ARE NOT LOOKING AT THE RIFLE
COURSE OF CELLS.
YOU ARE JUST THINKING, ONE
SNAPSHOT, ONE POINT IN TIME, AND
SO YOU ARE GOING TO HAVE YOUNG
CELL, AVERAGE AGE CELL, OLD
CELL, AND SO, I DON'T KNOW THAT
WE KEEP MAKING THE SAME -- WE GO
BACK TO THE SAME ISSUE THAT
WE'RE NOT LOOKING AT THE
DYNAMICS AND I DON'T KNOW IF WE
HAVE THE TOOLS TO EVEN DO THE
LIFE COURSE EXPERIMENT, AT A
CELLULAR LEVEL.
WE SEE HOW THINGS EVOLVE OVER
TIME BECAUSE THE PROBLEM WE HAVE
WHEN WE START LOOKING AT THE BIG
SYSTEM IN HUMANS, IT'S JUST
THAT'S NOT POSSIBLE.
BUT A LEVEL OF CELLS, MAY BE
IT'S POSSIBLE AND YOU CAN THINK
OF AGING AS -- NOT AGING, I'M
NOT TRYING ANSWER WHAT IS AGING.
IT'S A BIG QUESTION.
BUT WHAT I THINK IF YOU ZOOM IN
EVEN IN A SYSTEM AND YOU GO ON,
YOU THINK AT A HIERARCHAL
STRUCTURE WHERE YOU HAVE AGING
AT A DIFFERENT SCALE, AS YOU GO
DOWN TO THE CELLULAR LEVEL IT'S
CLEAR THINGS HAPPEN FASTER SO
THERE'S A TURNOVER THAT IS MUCH
FASTER THAN THE 1020 YEARS OF
INDIVIDUALS AND WE ARE REALLY
TRYING TO MODEL THIS HIERARCHY
OF EVENTS THAT THEN TRANSLATE
INTO A WHOLE SERIOUS OF EVENTS
OVER TIME SO WE GO FROM CHANGES
THAT OCCUR WITHIN SECONDS,
HOURS, I DON'T KNOW, TO THINGS
THAT HAPPEN OVER A HUNDRED
YEARS.
>>YEAH.
>>THANK YOU.
>>OKAY.
ALEXEY.
>>YEAH, THANK YOU, I JUST
MENTIONED THAT I DIDN'T SHOW THE
DATA BUILDING THE GENETIC ALLEN
GUINEARED PROBE WHICH CAN ENABLE
YOU TO FOLLOW THE CHANGING OF
THE LANDSCAPE AND WE ARE
ACQUIRING A LOT OF MOVIES AND WE
CAN FOLLOW, FOR EXAMPLE, THE
DIVISION OF THE DIFFERENT
CONDITIONS AND WE CAN FOLLOW THE
PROPER DEBATE AND A LOT HAS BEEN
DONE IN CULTURE THAN IN TISSUE
CULTURE SITUATIONS.
WE WOULD LOVE TO DO THIS IMAGING
IN VIVO WITH A WINDOW THAT CAN
BE INSTALLED IN A LIVE ANIMAL
AND THE SCALE OF COURSE WOULD BE
A CHALLENGE BECAUSE IT'S HARD TO
IMAGE OVER MONTHS.
WE IMAGE LONGITUDINALLY THE
BLOOD SAMPLES AND WE CAN SEE
EVEN IF WE MEASURE THE SAME
MOUSE EVERY MONTH, THEN THERE'S
A DRAMATIC, SUFFICIENT
EPIGENETIC I CAN MOUSE AND OVER
A COUPLE WEEKS WE ACCUMULATE
SUFFICIENT CHANGES TO -- THOSE E
MICE AND IN REAL LIFE, THE
CHANGES OCCUR PROBABLY IN A
DAILY BASIS THAT CAN BE
MEASURABLE THROUGH THE
EPIGENETIC LAND SCALE.
>>I HAVE A QUESTION FOR YOU.
WHEN YOU KIND OF THINK OF YOUR
NEW METHOD AND COMPARE IT TO
CLOCKS, WHICH WOULD BE THE MOST
COMPARABLE?
IS IT LADIES AND GENTLEMEN
PROBABL -- WHAT WOULD BE THECLO?
>>THAT'S A QUESTION GREAT.
WE DON'T HAVE APP ANSWER YET.
WE ACTUALLY PROPOSED SOME OF
THESE STUDIES, YOU KNOW, WE'LL
SEE WHAT THE STUDY SECTION THINK
ABOUT THIS.
THE BEST WOULD BE TO COMPARE TO
ALL OF THEM OR THE MAJORITY OF
THEM AS MUCH AS WE CAN MEASURE.
THE QUESTION OF HOW MUCH CLOCKS
YOU CAN BUILD THE MEASURE AND I
BELIEVE THE SINGLE CELL AGE THAT
YOU DEVELOP RECENTLY WOULD BE
ONE OF PARTICULAR INTEREST
BECAUSE OUR IMAGING IS BASED ON
SINGLE CELLS SO I THINK THEY'LL
BE INTERESTED TO CORRELATE AND
THEN UNDERSTAND HOW THIS SPACE
OF SINGLE CELLS METHYLATION
CLOCK AND NOT IMAGING SORT OF
CORRELATES AND CAN IDENTIFY THE
SAME GROUPS AND SO IT MIGHT BE
THE ONE THAT WOULD BE MY
FAVORITE.
WE NEED DO THE EX PE EXPERIMENT.
>>THANK YOU.
I SEE ANY RAISED HANDS.
>>SO, YES, FIRST FOR PEOPLE WHO
WATCH AND LISTEN, THROUGH VIDEO
CAST THERE'S A SEND LIFE
FEEDBACK BUTTON FOR QUESTIONS.
AND WE HAVE A QUESTION FROM THE
VIDEO CAST.
THANK YOU FOR THIS SESSION FROM
A BROAD PERSPECTIVE, WHAT IS THE
MAIN GOAL OF ACCURATELY
MEASURING AND UNDERSTAND AGING?
WOULD WE PERHAPS IMPROVE
KNOWLEDGE AND FURTHER TREATMENT
AND QUALITY OF LIFESPAN, THANK
YOU.
>>THAT'S A GREAT QUESTION.
WHO WOULD LIKE TO ANSWER?
>>I DON'T THINK THERE'S A
UNIQUE ANSWER.
MAYBE IT'S MORE SUITABLE TO
ANSWER THIS.
>>MY TAKE ON IT NOT IS THAT IT
NEEDS TO QUANTIFY NEEDS FOR
EVERYTHING WE STUDY.
WE MUST QUANTIFY, FOR EXAMPLE,
THE SIMPLEST WAY IS
INTERVENTIONS AND JUST TEST
INTERVENTIONS.
FIND ASSOCIATIONS AND BIO BANKS
FOR EXAMPLE AND UNDERSTAND THE
RELATIONSHIP BETWEEN AGING AND
DEVELOPMENT OR AGING AND BIOLOGY
AND JUST BASICALLY QUANTIFYING
IN ESSENCE EVERYTHING.
SO, THAT'S WHY I THINK THIS IS
SO EXCITING AND ADVANCE THAT THE
ABILITY TO QUANTIFY EVEN ALL OF
THE CHALLENGES THAT I WOULD LINE
IN MY TALK WHICH IS REALLY
ADDRESSING AND WE STILL DON'T
UNDERSTAND EXACTLY WHAT THE
MEASURE AND HOW THE BEST WAYS TO
TRAIN THE CLOCKS BUT STILL IT'S
IMPORTANT.
JOHN, ALEXEY AND NAN WHAT'S IS
YOUR TAKE ON IT?
>>I ABSOLUTELY AGREE WITH YOU.
QUANTIFICATION, YOU ONLY KNOW
THINGS AS MUCH AS YOU CAN
MEASURE THEM.
IF YOU CANNOT MEASURE YOU CANNOT
KNOW.
IT'S A FUNDAMENTAL THING AND THE
PRECISION OF YOUR KNOWLEDGE
DIRECTLY CORRELATING OR DEPEND
ONG PRECISION OF YOUR
MEASUREMENT BUT FOR THE
PURPOSES, IN ONE DATA WILL
FIGURE OUT BETTER HOW TO DO AND
APPLY AND REJUVENATION YOU STILL
NEED TO MEASURE IT AND
PARTICULAR MEASURE VERY
ACCURATELY.
SO AGAIN, MEASUREMENT I THINK IS
ONE OF THE MOST IMPORTANT THING
THAT WE CAN DEVELOP AND PUSH
FORWARD OF COURSE WITH THE AIM
AND THE FUNDAMENTAL QUESTION.
>>SO, YEAH, FOR US, I THINK THE
PURPOSE OF MODELING IS REALLY
FIRST TRYING TO UNDERSTAND THE
CONSISTENT AND THE BASIC
MECHANISM LIKE FOR EXAMPLE, WE
OBSERVE ALL OF THIS INTEREST AND
BEHAVIOR AS WE SEE LIKE AND WE
CAN SEE IF WE DO CHANGES THEN
THERE WILL BE SPACE, WHATS GOING
ON THERE AND THE MODEL REALLY
HELP US TO UNDERSTAND ALL OF
THERE'S A INHIBITION AND
FEEDBACK AND SO THAT WE CAN HAVE
STABLE PARTS AND AND THAT KIND
AND THE MECHANISM THAT WE
OBSERVED DURING THE PROCESS AND
TRY TO UNDERSTAND THE MECHANISM
AND THAT'S THE FIRST GOAL.
AND IT FUELS SO THE MODEL CAN
HETCH US O TO PREDICT US AND
HELP WITH THE INTERVENTION
STRATEGIES FOR EXAMPLE, IN YEARS
WHAT WE CAN DO IS WE CAN EVEN
REVER THIS INHAD BIRTHS AND THE
MODEL WILL PRO DICTIONARY
TPRO PREDICT AND ITWILL NOT MAKE
CELL CAN LIVE LONGER AND WE CAN
TEST IT AND I THINK THAT'S BIAS
TO UNDERSTAND THE PROCESS AND
HOW IT WORKS AND TO PERTURBED IT
AND CHANGE IT SO WE CAN HAVE
LONGER LASTING AND THAT'S THE
GOAL.
THIS IS A PROOF OF PRINCIPLE BUT
THE GOAL IS TO EXTEND THAT I
THINK.
>>THANK YOU.
>>SO, I WONDER IF THERE ARE
ADDITIONAL QUESTIONS FROM
LIVESTREAM?
>>NO QUESTIONS RIGHT NOW.
BUT WE STILL HAVE A FEW MINUTES
SO IF ANY QUESTIONS COME THROUGH
WE'LL RELAY THEM.
>>I HAVE A QUESTION TO PAOLA,
ACTUALLY, SO YOU ARE MORE THAN
ANYTHING ELSE THE STUDY OF LONG
LIVE PEOPLE AND APPLIED OMICS.
WHAT ARE THE BEST METHODS MAYBE
BEST OMICS METHODS THAT WOULD
PREDICT THE OUTCOMES LIKE LATER
IN LIFE BEFORE CENTENARIANS.
>>UM -- I CAN -- SO THE ANSWER
IS I DON'T KNOW YET IF THERE'S
THE BEST METHOD TO PREDICT IT
OUTCOMES LIKE LIFESPAN, BUT
SOMETHING THAT I HAVE NOTICED IS
THERE IS A -- LET ME PREAMBLE.
WITH SOME OF THE OMICS WE'RE
LIMITED WHAT WE MEASURE.
WE MEASURE ONLY SOMETHING THAT
IS WE HAVE A LIMITED MEASUREMENT
SET FOR META BOL OMICS WE
MEASURE JUST A SMALL HANDFUL OF
EVERYTHING THAT HAPPENS.
CORRELATE IT TO AGING AND
LONGEVITY, A LOT OF THINGS
CHANGE IN A DRAMATIC, DRAMATIC
WAY.
MUCH MORE THAN WHAT WE SEE
CHANGING WITH THE TRANSCRIPTOMIC
AND THE GENETICS IS THE HARDEST
THING.
SO WE HAVE LOOKED FOR YEARS TO
SEE WHAT THE GENETIC VARIANTS
THAT ARE BIG PLAYERS AND IT'S
TRUE THAT WE HAVE NOT BEEN ABLE
TO MEASURE CORRUPTS WELL SO WE
DON'T HAVE THE RIGHT DATA.
WE HAD A LOT OF CHALLENGES WITH
THE MEASURES GENETICS AND THINGS
THAT DETERMINE THE ABILITY TO
LIVE LONG AND WE SEE MAJOR,
MAJOR EFFECTS THAT WE DIDN'T
EXPECT TO THE POINT WHEN YOU
LOOK AT IT I DID SOMETHING WRONG
BECAUSE IT'S POSSIBLE THAT
EVERYTHING CHANGES AND YOU
REALIZE WHAT WE'RE MEASURING AND
WHAT WE THINK IS CHANGING WITH
AGE SO THAT'S WHY EVERYTHING IS
CHANGES.
THERE'S A LOT OF LIMITATION IN
WHAT WE CAN MEASURE IN TERMS OF
IN METABOLOMICS AND IT'S A BUY
AS UNANSWERED AND WE CAN MEASURE
DATA AND WE SEE LESS PREDICT OR
IN TERMS OF LONGEVITY AND IT'S
MUCH LESS SO I'M REALLY POSSIBLE
AND I LOVE TO SEE METABOLOMICS.
YOU DO BLOOD TEST AND GLUCOSE
MARKER AND THAT'S THE LEVEL OF
HOW PEOPLE ARE DOING.
I THINK THERE'S A LOT TO BE
DISCOVERED WITH THE MET -- IT'S
A GOOD ASSESSMENT OF HOW PEOPLE
ARE AGING.
>>WE HAVE A LAST MINUTE SO I
WONDER IF ANYBODY HAS ANY LAST
IMPORTANT QUESTIONS AND IF NOT,
I WOULD LIKE TO FINISH WITH ALL
OF THE PARTICIPANTS ACTUALLY
STATING WHAT DO YOU THINK IS THE
MOST IMPORTANT IN THIS CONTEXT?
JUST VERY BRIEFLY.
LIKE ONE OR TWO SENTENCES.
WHAT IS YOUR WISDOM?
IN THE SPACE OF COMPUTATIONAL
ANALYSIS IN MOLECULAR SYSTEMS.
THAT IS OUR SESSION.
>>IF I -- I DON'T WANT IT TAKE
OVER BUT TO ME THE BIG CHALLENGE
I HAVE HAD IS AS WE REALLY GOT A
LOT OF DATA THE INTEGRATION IS
REALLY DIFFICULT.
IT'S REALLY HARD.
YOU SAY I DO INTEGRATION IT'S
VERY, VERY HARD.
THAT'S A LOT OF CHALLENGES WITH
THE DATA THAT -- I'M NOT SAYING
THAT THERE'S NOT SOLUTION, IT'S
JUST MUCH HARDER THAN WE
ANTICIPATED.
ALEXEY, WHAT'S YOU'RE WISDOM.
>>I THINK THE INTEGRATION OF
DIFFERENT DATASETS AND KNOWLEDGE
FROM SINGLE CELL AND MEASUREMENT
THAT WE CAN DO TO UNDERSTAND HOW
IT EFFECTS THE FUNCTION OF THE
ORGAN AND ORGANISM AND
INTEGRATING THIS DATA IN A
CONTEXT OF AGING, WHICH OF THIS
FUNCTION ARE CAUSAL.
>>THANK YOU, NAN.
YOU WILL BE THE LAST ONE JUST
BRIEFLY.
>>SO JUST, I THINK ABOUT THE
SCALE.
I MEAN, I THINK WE TALK A LOT
ABOUT THE SCALES AS SOMETHING
LEVEL TO THE TISSUE TO THE
ANIMAL ANDEL WHERE THE
PRINCIPLES CAN BE APPLIED TO THE
VISH YOU AND I THINK ALL THIS
ADDITIONAL DATA AND THIS WILL BE
THE NEXT BIG QUESTION TO US.
YES.
>>THANK YOU.
I GIVE THE NIA COLLEAGUES AND I
WOULD MAYBE CALL THE COFFEE
BREAK NOW.
>>YES, I JUST WANTED TO SAY
QUICK THANK YOU FOR SHARING AND
ALL OUR PANELISTS AND DISCUSS
APARTDISCUSSANTS FOR ILLUMINATID
EXCITING SESSIONS AND I WILL
HAVE A LARGER SUMMARY AT END OF
THE DAY AND YES, THEY'RE
TRANSITIONING TO A BREAK.
THANK YOU SO MUCH.
>>THE COFFEE BREAK IS GOING TO
WORK FOR FOLKS, THIS IS AN
OPPORTUNITY FOR THE PANELISTS TO
INTERACT CASUALTY AND WE'LL GO
TO A ZOOM BREAKOUT SESSION AND
SO THIS WILL NOT BE LIVESTREAMED
SO GRAB SOME COFFEE AND TAKE A
BREAK IF YOU NEED TO BUT THEN
COME BACK AND IT'S AN
OPPORTUNITY TO CHAT WITH EACH
OTHER INFORMALLY.
THOSE OF YOU ON THE LIVESTREAM,
WE'LL BE ON BREAK UNTIL 3:30.
WE'LL THROW UP A SLIDE SO YOU
HAVE THAT.
WELCOME BACK FOLKS FROM THE
BREAK.
IF YOU WERE ON THE LIVESTREAM,
AND FOR THOSE OF US WHO ARE IN
COFFEE HOUR, WE HAD A ROUSING
DISCUSSION ABOUT SORT OF SLEEP
AND THE PURPOSE AGING.
I WILL PASS THE BALL OVER TO
DAVID JONES AT THE MAYO CLINIC
WCLINICWHO WILL LEAD OUR NEXT S.
>>David Jones: THANK YOU, VERY
MUCH.
I'M DAVE JONES, A BEHAVIORAL
NEUROLOGIST PRACTICING AT THE
MATE MAYO CLINIC.
DIRECTOR OF ARTIFICIAL
INTELLIGENCE AND IT'S MY
PLEASURE TO INTRODUCE EARL
MILLER FROM MASSACHUSETTS I AM
SORRY MASS -- AND HE WILLSET THS
LARGE-SCALE INFORMATION OF THE
BRAIN WHICH IS A VERY EXCELLENT
FRAMEWORK FOR THE REST OF THE
SESSION AND THEN ANA SOLODKIN
WILL TALK ABOUT BIOLOGICAL MODEL
THAT'S CAN GENERATE PATTERNS AT
THIS SCALE AND RELATING THE
DEMENTIA AND WE'LL TAKE A BREAK
AND THEN I'LL TALK ABOUT SOME
CLINICAL APPLICATIONS OF USING
PATTERNS AT THIS SCALE AND IN A
CONCEPTUAL FRAMEWORK THAT YOU
CAN USE TO REASON ABOUT AND TO
BE USED IN THE CLINIC AND
CONCLUDE WITH DANI BASSETT
TALKING ABOUT HOW WE MIGHT BE
ABLE TO CONTROL THESE.
THERE'S SOME RELATIONSHIP
BETWEEN CONTROL THEORY AND THE
SCALE GIVING US A FRAMEWORK FOR
INTERVENTIONS.
>>I'LL SHARE MY SCREEN.
HERE WE GO.
ALL RIGHT.
HELLO, EVERYBODY, THANK YOU FOR
THE INVITATION TO SPEAK.
I'M NOT GOING TO SPEAK ABOUT
AGING, I'M HERE TO SORT OF GIVE
YOU MY VIEW OF WHERE WE ARE WITH
SCIENCE AND ORGANIZATION OF THE
BRAIN ON A LARGE-SCALE.
PART OF THAT IS TO EXPLAIN HOW
OUR VIEW OF THE BRAIN IS CHANGED
OVER THE PAST COUPLE DECADES.
SO, ABOUT WHAT OUR LAB DOES IS
STUDY EXECUTIVE BRAIN FUNCTION,
PLANNING AND FOCUSING ATTENTION
ET CETERA SO FOURTH AND WE STUDY
ELECTRICAL SIGNALS IF THE BRAINS
IN NON HUMAN PRIMATES.
WE JUST COMPUTATIONAL VOLUME TO
MAKE SENSE OF THESE SIGNALS AND
WE WANT TO UNDERSTAND COGNITION.
IT'S DYSFUNCTION AND DISEASES.
SUCH AS AUTISM, SCHIZOPHRENIA,
ADHD.
NOW, IN EMPLOYING THESE STUDIES,
WE BEGAN ABOUT 20 YEARS AGO OR
SO TO BEGIN USING NEW MULTIPLE
ELECTRODE TECHNOLOGY AND THIS IS
SIGNIFICANT BECAUSE IN THE
20th CENTURY, WHEN I WAS A
GRADUATE STUDENT, WITH A SINGLE
ELECTRODE WITH I LESS PART OF
THE FRAMEWORK AND WE HAD TO
FIGURE OUT THE BRAIN'S PARTS BUT
IN FACT IN GENERAL, WE FOCUSED
ON THE BRAIN'S PARTS INDIVIDUAL
BRAIN AREAS AND SPIKING OF
SINGLE NEURONS AND WE THOUGHT
THE AND THERE'S COMPLEXITY GOING
ON SO NOW WE HAVE THE TECHNOLOGY
TO RECORD SEALS OF HUNDREDS OF
MILLIONS OF NEURONS
SIMULTANEOUSLY.
AND THIS IS BEGUN TO INCREASE
OUR UNDERSTANDING OF THE BRAIN
AND A NETWORK LEVEL.
IT IS IS EMERGENT PROPERTIES AND
THESE ARE PROPERTIES IN
MECHANISMS THAT EMERGE AND YOU
CAN SEE WITH THE PARTS INTERACT
AS A WILDER WHOLE.
YOU CAN'T SEE THEM ONE PART AT A
TIME.
I'LL SHOW YOU AN EXAMPLE OF ONE
OF THEM IN A MINUTE.
WE USED TO THINK ABOUT SPIKES.
THINK ABOUT SPIKES TRYING TO
EXPLAIN HOW THE BRAIN MOVES
ABSTRACT CONCEPTS AND
PRINCIPLESES.
WE TALKED MONKEYS, SHAPE
CATEGORIES LIKE CATS VERSUS DOGS
OR WE TALKED TO RECOGNIZE SMALL
NUMBERS 1-5.
THEY DON'T LEARN AT THE LEVEL OF
RULES AND PRINCIPLES AND IT'S
WHAT YOU EXPECT AND WE WANT TO
SHOW HOW THE IT'S TOP DOWN AND
PRODUCE TOP-DOWN CONTROL.
NOW, WE DID HER EXPERIMENT AND
WE FOUND IT UNUSUAL AND IT'S
UNUSUALLY AND IT'S A COUPLE
DECADES AGO.
WE USED SO WE HAVE A BETTER
PICTURE OF THE NEURONS LOOKED
LIKE AND WE FOUND AND RECORDED
IN THE PREFRONTAL CORTEX AND WE
FOUND THESE EFFECTS, CAT VERSUS
DOG, NUMBERS, UP VERSUS DOWN,
SAME VERSUS DIFFERENT IF 30% OR
40% OF THE NEWERRANCE WE
SELECTED AND THAT IS A LOT OF
NEURONS AND IT'S LIKE A TASK
TOOK OVER THE BRAIN AND THE
PROBLEM WITH THAT AT THE TIME
WAS THE 20th CENTURY PARADIGM
SAID THAT EVERY NEURON SHOULD
HAVE UP WITH PENSION AND THEY DO
THEIR THING BY COMBINING THEIR
SIGNAL BY VIRTUAL OF HOW THEY'RE
WIRED TOGETHER AND YOU START
WITH SMALL NEURONS IN THE BACK
OF BRAIN AND DECORRECT THE VISH
ACTUAL AND AND IT'S WIDELY
ASSUMED, I BELIEVE, THAT THE
NEURONS DON'T HAVE ONE PENSION
ESPECIALLY IN HIGHER CORTICAL
AREAS AND THERE ARE MULTI
FUNCTIONAL LEVEL A PROPERTY
CALLED MIXED SELECTIVITY.
NOW WHAT IT MIXED SELECTION
ACTIVITY IS THE WAY WE USED TO
THINK ABOUT THE BRAIN IS WE
THOUGHT NEURONS ARE SIMPLE AND
SPECIALIZED WITH ONE FUNCTION
ONLY.
THEY MIGHT DETECT SINGLE
FEATURES LIKE EDGES THAT PIECE
TOGETHER TO SOMETHING SO THIS
EXAMPLE IS NEURONS THAT COMBINE
COLOR FORM AND ROLLING INTO THE
ROLLING BALL AND IT MIGHT
COMBINE FEATURES BUT THEY MAKE
SENSE TOGETHER.
IN CONDUCTING THESE EXPERIMENTS,
USING THESE HIGHER PRINCIPLES
WE'RE TEACHING THE ANIMAL WE
FOUND THAT PROPERTY CALLED MIX
SELECTIVITY IT DID NOT FIT THIS
TRADITIONAL MOLD.
THESE NEURONS WERE SELECTIVE FOR
A COMPLEX COMBINATIONS WITH
FEATURES WITH NO OBVIOUS
RELATIONSHIP.
AND IF YOU STUDY THEM MULTIPLE
TASK YOU TRAIN THE ANIMAL TO DO
DIFFERENT TRAFFICS YOU FIND THE
SAME NEURON WITH DIFFERENT
FUNCTIONS, IN DIFFERENT TASKS
AND WE WEREN'T RANDOM AND BACK
TO THE SAME TASK THE SAME
CONTEXT AND NEURONS AND THEY DID
THE SAME THING AND IT'S A REALLY
DIFFERENT THINGS IN DIFFERENT
SITUATIONS.
THE NETWORKS AND THE INFORMATION
AND THE GRAIN HAS THIS
INFORMATION AT ITS FINGER TIPS.
THIS MAKES THE BRAIN SMARTER
AND A HIGH DID MENTIONAL
REPRESENTATION SPACE WHERE
NEURAL REPUTATIONS GET
COMPLICATED AND CONTAIN MORE
INFORMATION SO YOU CAN DO MORE
HIGH-LEVEL STUFF WITH THAT.
AND ALSO FOR REASONS I WON'T
EXPLAIN, IT MAKES THE BRAIN GO
ESSENTIALLY CAPACITY UNLIMITED
BY DISTRIBUTING THE INFORMATION
ACROSS MANY DIFFERENT NEURONS SO
NOW THE BRAIN CAN STORE MUCH
MORE INFORMATION.
AND ALL THIS MINGLING
INFORMATION MEANS THAT THE
BRAIN, YOU CAN LEARN THINGS
QUICKLY AND YOU HAVE FLEXIBILITY
TO CHANGE YOUR MIND WHEN MOMENT
TO MOMENT.
HOWEVER, THE PROBLEM IS WITH MIX
SELECTIVITY, IT'S WHY THEY
ACCEPTED AND PEOPLE ARE FOR ALL
OVER THE BRAIN AND NOT THE
CONTEXT AND BUT MIXED SELECTION
ACTIVITY HAS A NEW WAY OF
THINKING ABOUT THE BRAIN.
WE USED TO THINK EVERY NEURON
HAD ONE FUNCTION AND WE USED TO
THINK THAT ANATOMY IS DESTINY
AND THEY'RE WIRED TOGETHER.
SO EVERY PERCEPTION, THOUGHT,
ACTION, EVERYTHING YOUR BRAIN
DOES HAS A UNIQUE NETWORK AND
ENSEMBLE.
I HAVE TWO NETWORKS HERE.
THE BLUE THOUGHT AND THE RECEIPT
THOUGHT IS ANOTHER NETWORK AND
EVERY OTHER THOUGHT YOU MIGHT
HAVE.
BUT THE PROBLEM IS MIXED
SELECTION ACTIVITY MEANS
NEWERRANCE ARE NOT MEMBERS OF
VIGNETTE WORKS AND MEMBERS OF
MULTIPLE NETWORKS.
SO, WE HAVE YOU ARE RED AND BLUE
TO ONE OVERLAPPING NETWORK AND
NOW SHARING COM ON ELEMENTS OF
THESE MULTI FUNCTIONAL MIXED
SELECTIVITY NEURONS BUT IF
ANATOMY IS DESTINY HOW DOES IT
WORK?
THEY ACTIVATE ONE ANOTHER AND
CREATE A JUMP BELL OF THOUGHTS,
RIGHT.
IF I WANT TO THINK THE RED
THOUGHT HERE, IF I TRY TO
ACTIVATE A RED NEURON ACTIVITY
WILL TRAVEL TO OTHER RED NEURONS
THAT GO TO THE MIXED SELECTION
ACTIVITY AND MULTI FUNCTIONAL
NEURONS AND WE ACTIVATE THE BLUE
NETWORK.
HOW DOES YOUR BRAIN SELECT
INDIVIDUAL ENSEMBLES FROM THIS
OVERLAPPING ANATOMY WITH THESE
NEWERRANCE THAT SHARE COM
ELEMENTS.
THIS IS TWO ENSEMBLE.
ONE SOLUTION IS IT'S A NEURAL
OWES I WILLTATION DYNAMICS IN
CORTEX.
WE AUTOMATIC KNOW ABOUT BRAIN
WAVES.
I'LL USE IT SHORT HAND FOR NEURO
OSCILLATIONS AND A LARGE-SCALE
ORGANIZATION OF NEURAL ACTIVITY.
SO WHAT YOU SEE HERE IS A CROWD
DOING THE WAVE.
HERE IS THE AND FROM LEFT TO
RIGHT, RIGHT, THIS IS WAY FOR
BRAIN TO SELF-ORGANIZE ITSELF
AND LOOKS LIKE A CROWD CAN DO AT
A STADIUM.
BUT IN THE PAST, WHEN WE FOCUSED
ON SINGLE NEURONS AND WHAT THEY
DO, BRAIN WAVES WERE DISMISSED
LIKE THE HUMMING OF A CAR
ENGINE.
HUMMING REFLECTS THE ENGINE
RUNNING BUT IT DOESN'T MAKE THE
ENGINE RUN.
IF YOU THINK ABOUT IT, IF A
STADIUM CROWD CAN SELF-ORGANIZE
WITH THE FEW RULES, LIKE STAND
UP ON MY LEFT AND IS IT
LARGE-SCALE ORGANIZATION LIKE
YOU SEE HERE ISN'T THAT WHAT IS
NEEDED FOR TOP DOWN EXECUTIVE
CONTROL?
I MEAN, YOU KNOW, YOU CAN'T SEE
THESE WAVES ON INDIVIDUAL
NEURONS IF I MISSED AND AROUND
THIS CROWD AND LISTEN ONE VOICE
AT A TIME AND IT'S ONLY THIS
HIGHER LEVELS ORGANIZATION WHERE
WE SEE ALL THE PARTS WORKING
TOGETHER DO WE SEE THIS KIND OF
MARK RETAIL AND IT'S THE
ORGANIZATIONS YOU NEED FOR TOP
DOWN EXECUTIVE CONTROL AND IT
REQUIRES GETTING LARGE BITS OF
YOUR CORTEX OUT OF THE SAME
BEIGE DIRECTED TOWARDS THE TASK
AT HANDS.
AND IT'S SOMETHING CALLED
REPRESENTATIONAL DRIFT.
NEURONS ARE EXTREMELY FICKLE.
THEY COME AND GO.
IF YOU RECORD FROM 100 NEURONS
OR A THOUSAND NEURONS OR A
MILLION NEURONS AND REPEAT THE
SAME CONDITION 10, 20, A MILLION
TIMES IN A ROW, THEORETICALLY,
YOU WOULD GET THE DIFFERENT
PATTERN OF NEURONS EVERY SINGLE
TIME.
RIGHT, BECAUSE THINK OF THE
INDIVIDUAL NEURONS AND THE BRAIN
IS BEING LIKE THE WORLD'S
LARGEST ORCHESTRA.
AND THE NEURONS ARE INDIVIDUAL
PLAYERS.
BUILDING, THE ORCHESTRA IS
PLAYING THIS MEL BEE BUT
INDIVIDUAL PLAYERS COME AND GO.
THEY WALK IN, WALK OUT, JOIN AND
LEAVE THE SONG.
SO HOW DO YOU ORGANIZE THE
BRAIN?
HOW DO YOU PRODUCE MACRO SCOPE
ORGANIZATION FOR TOP DOWN
CONTROL ON THE LEVEL OF
INDIVIDUAL PLAYERS?
YOU CAN'T DO IT IN.
IT NEEDS TO BE ON THE LEVEL OF
THE HIGHER ORGANIZATION.
AND WE THERE WAS A PAPER
PUBLISHED RECENTLY SHOWING THAT
YOU CAN ACTUALLY -- I TALKED
ABOUT SPIKE IN LFPs AND FIVE
FEET OUTSIDE YOUR BRAIN BUT
ELECTRIC CALL FIELDS THAT
COMBINE ACTIVITY OF SPIKES IN
LFPs THAT PRODUCE MIRROR
ELECTRIC FIELDS THAT HOVER NEAR
YOUR BRAIN'S CONNECTIONS NEAR
ALL YOUR DENDRITES AND STUFF AND
WE FOUND YOU CAN READ THE
CONTEXT OF WORKING MEMORY, NOT
FROM JUST FROM THE LFPs AND
THE ELECTRIC FIELD LEVEL AND
WHAT IS COOL ABOUT THIS, IS THAT
THERE'S LITTLE OR NO
REPRESENTATIONAL DRIFT.
IT THIS CASE AT THIS LEVEL, YOU
ARE HEARING THE LONG PLAY AND
YOU ARE NOT GETTING THE
REPRESENTATIONAL DRIFT THAT
INDIVIDUAL PLAYERS COMING AND
GOING.
DOESN'T IT SOUND LIKE A USEFUL
LEVEL FOR ORGANIZING BRAIN
ACTIVITY AND ISN'T IT EASIER TO
CONTROL A STABLE REPRESENTATION
THAN ONE THAT FICKLE.
HOW DO YOU ORGANIZE THAT KIND OF
ACTIVITY AT LEVEL OF INDIVIDUAL
NEURONS.
AND THE VERY LEAST YOU NEED OF A
GAZILLION GATES ON A GAZILLION
SYNAPSIS NEVER MIND THE NOT
PROBLEM WITH THIS REPRESENT
REPRESENTATIONAL DRIFT.
SO COULD THINK ABOUT THIS, YOU
ORGANIZE NETWORKS BY BRAIN
WAVES, NEURONS THAT HUM TOGETHER
WATER TOGETHER.
AND WHAT THIS MEANS IS BRAIN A
MANATOMY IS NOT DESTINY BUT POSA
LOT.
IT'S MORE LIKE THE RODENT
HIGHWAY SYSTEM.
INFRASTRUCTURE.
IT'S SAYS WITH THE TRAFFIC THE
NEURAL SIGNALS OF SPIKES WHERE
THEY COULD GO BUT YOUR THOUGHTS
MOMENT TO MOMENT ARE WHERE THE
TRAFFIC ACTUALLY IS FLOWING FROM
MOMENT TO MOMENT.
AND THAT IS WHAT WE NEED THESE
BRAIN WAVE PATTERNS, THE
PATTERNS OF RESIDENTS DIRECTING
THE TRAFFIC ON THE
INFRASTRUCTURE OF THE YOUR
BRAIN.
AND THERE EVIDENCE FOR THIS.
YES.
NOW PLENTY.
AND HERE IS AN EXAMPLE OF A
STUDY BY TIM BUSHMAN WHEN HE WAS
IN MY LAB.
HE IS A PROFESSOR AT PRINCETON
AND HE HAD THE ANIMALS SWITCHED
BACK AND FOURTH BETWEEN TWO
DIFFERENT RULES AND THE ANIMAL
HAD THE VIEW OF VERTICAL OR
HORIZONTAL BAR AND THE ANIMAL
NEEDED TO TELL US WHAT COLOR THE
BAR WAS OR WHAT ITS ORIENTATION
WAS AND WE CUE THE ANIMAL TO
SWITCH BACK AND FOURTH BETWEEN
THESE TWO RULES.
WHAT IS SHOWN HERE IS IS A
PREFRONTAL CORTEX AND THIS IS
ANTERIOR, POSTERIOR, VENTRAL AND
DOR SAL AND ON THAT HUMAN
PRIMATE FANS AND EACH OF THESE
CIRCLES IS A RECORDING SITE AND
WHAT THE LINES CONNECT IS WHERE
WE SAW SIGNIFICANT INCREASES IN
APPROXIMATE LFP SYNCHRONY WHEN
THE ANIMAL WAS FOLLOWING THE
COLOR RULE AND VERSUS THE
ORIENTATION RULE.
WE LOOKED ACROSS ALL FREQUENT
SEN SEE.
WHAT WE SEE WITH THIS MODEL WITH
DIFFERENT PATTERNS, DIFFERENT
NETWORKS, FOR ONE ROLE VERSUS
THE OTHER ALL BASED ON THESE
SYNCHRONIZED LFP DYNAMICS.
DIFFERENT BRAIN FREQUENCIES
CARRY DIFFERENT TYPE OF
INFORMATION.
IN THE BACK OR FRONT OF THE
BRAIN THEY USE, THEY RIDE ON
GAMMA, HIGH FREQUENCY GAMMA
OSCILLATIONS BUT THE LOW
FREQUENCY TOP DOWN IS FILTER AND
REGULATE SENSORY FOSTERING THEY
RIDE FROM THE FRONT OF THE BRAIN
TO THE BACK OF THE BRAIN AND
THESE LOW FREQUENCY ALPHABET TA
OSCILLATIONS AND WE THINK IT
EXPLAINS THINGS LIKE THE SENSORY
OVERLOAD SEEN IN AUTISM AND
WE'RE WORKING ON METHODS AND NEW
TECHNOLOGY AND TO REBALANCE
THEM.
AND THESE OSCILLATIONS CAN BE
PRODUCED VERY PRECISE EFFECTS OF
CORTEX TO HERE IS A EXAMPLE.
IT STANDS STILL LIKE A JUMP
ROPE.
THEY DON'T BOUNCE AROUND CORTEX
RANDOMLY THEY TRAVEL IN SPECIFIC
PATTERNS AND THEY HAVE PADS AND
CHANGE DIRECT WITH COGNITIVE
DEMANDS AND THEY'RE SUPER USEFUL
BECAUSE THEY BUFFER INFORMATION
ABOUT TIME AND RECENT NETWORK
ACTIVITIES AND WHAT THE BRAIN
NEEDS TO DO COMMUTATION IF YOU
TAKE A VIDEO OF A POND THAT I
DROPPED FOUR PEBBLES UP WITH AT
A TIME I COULD TAKE A STILL
PHOTOGRAPH OF THAT VIDEO AND YOU
COULD TELL ME HOW MANY PEBBLES
HAVE DROPPED WHERE THEY DROPPED
AND HOW LONG AGO THEY DROPPED.
THAT'S HOW TRAVELING WAVES RINGS
THE BRAIN.
THERE'S A PARADIGM SHIFT IN OUR
UNDERSTANDING OF THE BRAIN.
COGNITION IS COMPLEX AND DYNAMIC
AND ARYTHMIC AND IT'S ORGANIZED
BRAIN ACTIVITY AND PLAY KEY
ROLES IN COGNITION ASK
CONSCIOUSNESS.
THANK YOU.
>>David Jones: THANK YOU, EARL.
I THINK I'M LOCKED OUT ON THE
VIDEO.
THANK YOU, EARL AND THAT LAYS
THE FOUNDATIONS GREAT FOR GOING
THE OTHER DIRECTION.
>>THANK YOU FOR THE INVITATION
AND LET ME GO TO A DIFFERENT
DIRECTION BUT I THINK THAT WE'RE
GOING TO CONVERGE HERE BECAUSE
EARL MENTIONED SOME VERY
FUNDAMENTAL ASPECTS OF BRAIN
FUNCTION.
HOWEVER, YEAH, I'M GOING TO GO
TO THE OTHER DIRECTION IN THE
SENSE THAT MY INTEREST IS IN
CLINICAL RESEARCH IN DEMENTIA
AND I'M GOING TO START WITH A
RHETORICAL QUESTION.
WHAT CAN WE NOT CURE ALZHEIMER'S
GIVEN THE FACT THAT THERE'S
AMPLE STUDIES THAT GO FROM THE
MOLECULAR SO THE CELLULAR TO THE
NETWORK AND THE BEHAVIORAL
LEVEL.
I'M GOING TO COME BACK TO THESE
QUESTIONS LATER ON.
THE FACT, HOWEVER, IS THAT --
>>ARE YOU SHARING SLIDES
BECAUSE WE'RE NOT SEEING THEM.
>>YEAH.
OKAY.
LET ME TRY AGAIN.
>>LET ME TRY AGAIN.
>>I SEE YOUR SCREEN NOW.
>>OH, GOOD.
ALL RIGHT.
OKAY.
>>THANK YOU.
>>SO AT BEGINNING IT WAS A FEW
PLATITUDES SO COMING BACK HERE
CERTAINLY THE MODELING
APPROACHES HAVE BEEN ABLE
RAPIDLY AND I WOULD NOT GO NOW
TO THIS IS LIFE BECAUSE ACTUALLY
NAN TALKED ALREADY IN A SPECIAL
TYPE OF MODELING WHICH IS THE
BIOPHYSICAL CAL OR BIOLOGICALLY
BASED COMPETITION IN MODELS THAT
WE DO SIMULATIONS BASED ON BRAIN
ACTIVITY IN SIL I CAN OWE.
BASICALLY IT'S IT TO DO REVERSE
ENGINEERING IN THE HUMAN BRAIN
AND AND THIS IS WHAT I WANT TO
SHOW YOU NOVEL ON HOW WE
APPROACH THIS ISSUE.
SO, WE ACTUALLY HAVE BEEN
WORKING IN THIS KNOWN FORMAT
TICKS PLATFORM NOW FOR 20 YEARS,
NOT THAT THE PLATFORM TOOK THAT
LONG WH WHAT WE DO IS SIMULATE
NATIVE BRAIN SIGNALS.
THESE BRAIN SIGNALS CAN BE
EPILEPTIC SEIZURES YOU NAMED IT
AND THE IDEA IS OKAY, WHAT ARE
THE OPERATIONS IN THE BRAIN THAT
ARE GENERATING THIS BRAIN
SIGNALS.
SO WE TAKE THE DI DIMENSION
REDUCTION THAT IT'S THE RESULT
OF TWO DIFFERENT CATEGORIES OF
INTERACTIONS AND THE FIRST ONE
THAT IS THE EASIEST TO
UNDERSTAND IS THAT THESE SIGNALS
ARE GOING TO BE RELATED TO THE
INTERACTION, GENERATED BY THE
INTERACTION BETWEEN AGENTS.
HOW CAN WE ASSIST BIOPHYSICAL
PARAMETERS ASSOCIATED WITH THIS
INTERACTION.
WHAT WE DO IS WE BASED THE
GENERATIONAL PARAMETERS BASED ON
A STRUCTURAL CONNECTIVITY FROM
IMAGING WHEN IT'S GETS DONE AND
IT'S THE ONLY I AM PUT THAT WE
INCLUDE IN THE CALCULATIONS OF
BRAIN DYNAMICS SO THAT IS UP
WITH OF THE ASPECTS.
THE SECOND ONE IS IT WOULD BE
IDEA TO HAVE THE ABILITY LIKE
EARLY TO HAVE A AN ELECTRODE
REGARDING INDIVIDUAL AREAS WHERE
WE CANNOT DO THAT SO WHAT WE DO
IS ACTUALLY USE LOCAL MODELS
THAT HAVE BEEN DEVELOPED IN THE
LAST 200 YEARS OF NEUROSCIENCE
AND IT'S ASSOCIATED WITH
BIOPHYSICAL PARAMETERS AT THE
REGIONAL LEVEL SO IF WE
INCOMPETENT GREAT THESE TWO TYPE
OF ELEMENTS WE CAN SIMULATE
NATIVE SIGNALS.
HOW DO WE VALIDATE THESE
SIGNALS?
WELL, WE ARE HAVING A SOLUTION
FROM THE SIGNALS WE CAN SIMULATE
FMRIEG, MEG OR INTRA CORTICAL
RECORDINGS FOR EPILEPSIES FOR
INSTANCE AND WHO WORK AT THIS
LEVEL AND IN HERE I JUST HAVE A
REPRESENTATION OF A FUNCTIONAL
CONNECTOME THAT CAN BE DIFFERENT
REGIONS FROM SIMULATED SIGNALS
AND WHAT WE DO IS TO COMPARE IT
TO THE I AM PE SIGNALS OBTAINEDE
SAME INDIVIDUAL THAT HAS THE
DISCONNECT TOMB.
THESE TWO HAVE TO MATCH.
IF THEY DON'T MATCH WE HAVE TO
COME BACK TO THE MODEL TO TAKE
PARAMETERS UNTIL THESE TWO FIT.
IF THEY FIT WE HAVE A GOOD MODEL
AND THEM THE REVERSE AND TO SHOW
YOU WHAT THIS MEANS AND I'M
GOING TO SHOW YOU SOME RESULTS
ON INITIAL STUDY THAT WE CAN DO
AS PROOF OF CONCEPT AND ON
ALZHEIMER'S DISEASE SO WE HAD
127 NOT INDIVIDUALS AND WITH
COGNITIVE A ZEST MEANT
CATEGORIZED THEM FROM HEALTHY
CONTROLS AND MODERATE OR MILD
ALZHEIMER'S DISEASE AND WE DID
THIS TYPE OF MODELING AND UNDER
TWO DIFFERENT PARADIGMS.
ONE, WE DID A WHOLE BRAIN
MODELING AND THE SECOND WE DID
JUST A MODELING IN LIMERICKS OF
NETWORK THAT INCLUDED REGIONS
THAT ARE ASSOCIATED WITH THE
BRACK AND BRACK STAGES FOR
ALZHEIMER'S THAT WILL THE
INITIAL STAGES WENT TO THREE SO
INCLUDED IN NETWORK AND SIPPING
YOU LATE AREAS AND AND THE LIKES
SO WE HAVE JUST EIGHT REGIONS
AND EIGHT HEMISPHERE FOR THIS
CASE AND I TELL YOU THE PUNCH
LINE IS THAT THE SIMULATION THAT
WE COM PAINED IN THE NETWORK
RESULTED IN ARE SENSITIVE
MEASURES ON THE CHANGES THAT ARE
PROCURING IN THE ALZHEIMER'S
DISEASE AND BASICALLY, WHAT THE
PARAMETERS THAT CHANGE THE MOST
FROM CONTROLS TOEL ALZHEIMER'S
WERE TWO.
ONE, AT THE LOCAL DYNAMICAL
LEVEL WHERE WE SAW A REDUCTION
IN HIB TORI OVER COUPLING SO IN
ADDITION WAS DOWN LOCALLY AND WE
HAVE ALSO AN CHANGE IN ONE OF
THE PARAMETERS ASSOCIATED WITH
THE INTERACTIONS AND GLOBAL
COUPLING AND I WILL EXPLAIN THAT
A LITTLE BIT BETTER AND
BASICALLY IT'S ALMOST YOU CAN
SEE BY BIOLOGICALLY AND
EFFECTIVE OF THE BRAIN BECAUSE
IT CHANGES THE GAIN OF THE
WEIGHTS OF THE STRUCTURAL
CONNECTIONS SO THOSE WERE THE
ONES THAT CHANGED THE MOST AND
HOWEVER, ONE OF THE MOST
INTERESTING ASPECTS THAT THE
RESULTED IN THIS SIDE, YOU CAN
NOTICE HERE IN THESE FIGURES,
THIS IS COMPARED TO THE WHOLE
BRAIN SIMULATION AND THE LIMBICS
OF NETWORK SHOW IN TERMS OF
THESE TWO PARAMETERS A HIGH
VARIANTS SO THIS IS JUST A
PLATEAU JUST VARIANTS OF THE AR
AM TERES IF THE DIFFERENT GROUPS
SO FOR THE MILD COGNITIVE
IMPAIRMENT, POPULATION, IT WAS
HUGE.
I CAN CANNOT PUT THE POINTER ON
THERE BECAUSE I HAVE PICTURES
THERE SO HERE IS THE COUPLING TO
THE RIGHT IS THE COUPLING AND
YOU SEE THE HUGE VARIANTS.
NOW THESE OF COURSE
STATISTICALLY ANNOYING BUT
CLINICAL WAS TAP TAS TICK AND WE
KNOW THAT THIS COHORT OF MCI IS
HETERO GENIOUS SO SOME OF THESE
INDIVIDUALS ARE GOING TO CONVERT
TO ALZHEIMER'S AND SOME ARE
GOING TO SAY SO THAT WAS QUITE
REWARDING SO THE OTHER RESPONSE
TO THIS STUDY WAS THE CO
VARIANTS BETWEEN THE MODEL
METRICS AND THE COGNITION WAS
MUCH HIGHER THAT THE CO VARIANTS
WITH THE FUNCTIONSAL
CONNECTIVITY ASK COGNITION SO
WE'RE GAINING SOME KNOWLEDGE IN
HERE SO AFTER THIS, WHAT WE
WANTED DO IS TO FOLLOW-UP WAS TO
DETERMINE HOW THESE PARAMETERS
ARE CHANGING THE PHYSIOLOGY OF
THE SYSTEM.
SO FOR THIS STUDY, WHAT WE DID
IS FOCUSED SIMPLY OUR MODELING
IN THE LIMBICS NETWORK AND
INSTEAD OF GOING THE WHOLE WAY,
WE FOCUSED ON THE LOCAL FILL
POTENTIALS WHERE THE SIGNAL OF
CHOICE TO BE MODELED AS A SIGNAL
AND THAT WAS EVEN BY THE
APPROPRIATE MODEL THAT IT GIVES
US THE ABILITY TO SIMULATE THESE
TYPES OF SIGNALS.
AND THE SECOND CHANGE THAT WE
DID WAS TO DO AN ADDITIONAL
EXTRAIFICATION OF THE HEALTHY
CONTROL CASES WE KEPT THE
SUBJECT AROUND THE MEAN AND THE
SUBJECTS THAT HAD A SUPERIOR
PERFORMANCE AND THE IDEA OF
DOING THIS WAS TO HAVE SOMEHOW A
GOLD STANDARD IN COGNITION AND
WITH THE HOPE THAT WE CAN
DETERMINE OR DETECT DIFFERENCE
THIS IS BRAIN DYNAMICS BETWEEN
THESE TWO GROUPS.
I TOLD YOU THE ONLY INPUT TO THE
THERE WAS A TREND THAT SEPARATES
SOMEHOW THE NORMAL CONTROLS IN
BLUE VERSUS THE SUPER NORMAL IN
GREEN.
SO, AFTER THAT WE STUDIED THE
PHYSIOLOGY OF THE SYSTEM BECAUSE
WE'RE ASSUMING THAT THE SIGNALS
ARE BASED ON THESE TWO
CATEGORIES OF LOCAL DYNAMICS AND
WE WANTED TO UNTANGLE THESE TWO
COMPONENTS AND THEY ARE WORRY WE
DID IF UP A QUESTION WITH THE
VIRTUAL BRAIN AND WE HAVE BRAIN
SIGNALS AND I WILL NOT WILL GO
TO ALL THE LIMITS HERE BUT THIS
IS REPRESENTING THE LOCAL
DYNAMICS AND THE FIRST THING WE
DID IS TO ELIMINATE THE GLOBAL
DYNAMICS EX JUST TO LEAVE THE
DYNAMICS ALONE AND WE DIMINISH
THE IT TO ZERO AND THAT MEANS
THAT ALL THIS IS GOING TO ZERO
AND LEAVING US WITH A LOCAL
DYNAMIC AND IN ADDITION THAT AND
BECAUSE WE WANTED TO CAPTURE THE
MOST INACCEPT GENERAL
OSCILLATIONS WE DECREASED THESE
NOISE WHICH ACTUALLY REPRESENTS
PHYSIOLOGICAL NOISE WHICH IS
FASCINATING TO A MINIMUM AND WE
OBTAINED AND I LOVE SHOWING,
SIMULATED RAW SIGNALS AND YOU
HAVE A REP SEN STAYING OF THE
TIME SERIOUS ON EACH ONE OF THE
LIMERICKS OF THE REGIONS AND AT
THE BOT YOU HAVE THE SUM OF THIS
ACTIVITY AND YOU CAN SEE REALLY
RANDOM ACTIVITY THAT DOESN'T
PRODUCE ANY.
WHAT WE HAVE AFTER THAT IS THE
INCREASING LITTLE BY LITTLE THIS
VALUE OF G IN FACT THAT THEN
INCREASING PROGRESSIVELY THE
VALUE OF THE STRUCTURAL
CONNECTION AND FOR EACH SINGLE
INDIVIDUAL IT'S NOT AN IDENTICAL
NUMBER BUT WHAT HAPPENS IS THAT
THERE'S A POINT WHERE THERE'S A
BUY FOR INDICATION WHERE THE
SYSTEM CHANGES THE STATES AND IT
HAS BEEN PROPOSED AND NOW
THERE'S A LOT OF DATA THAT
SUPPORTS THESE AND THAT COMPLEX
SYSTEMS ARE WORK WHAT IS THERE'S
HEALTH INTO THIS THAT PRODUCES
THE SYSTEM TO CHANGE
CONFIGURATIONS IN CONNECTIVITY.
SO, THE OLATIONS WERE ONLY AND
LOW GAM HAVE REACHING IN SOME
CASES MUCH POORER IN OTHERS SO
WE STUDY THEN THIS OSCILLATIONS
IN THOUSANDS GROUPS.
IT WAS TO SEE IF THERE WAS ANY
CHANGES OR IN THE RELATIONSHIP
OF THESE FREQUENCIES GROUPS AND
WHAT YOU HAVE HERE, IS THAT
SUPER NORMALS, TWO NORMAL
CONTROLS TO MCI TO ALZHEIMER'S,
SHOW A QUADIN SUPER NORMAL
CONTROLS AND THAT IN SOME CASES
HAVE TO BEFORE THEY SAY HYPO IN
SOME OF THE ALZHEIMER'S CASES.
THIS WAS MIMIC INTO EMPIRICAL
RESTING ESTATE AND ALSO HAVE
BEEN SHOWN IN APP MALL MODELS
AND I'M GOING TO JUST SKIP THIS
BECAUSE WE DON'T HAVE TIME AND
IT WAS JUST THE SIGNALS.
AND WHAT WE SAW ALSO WAS THAT
THIS OSCILLATIONS CAN BE IN
DIFFERENT LEVELS OF THE G THAT
KEEP DECREASING UNTIL THEY KILLS
APPEAR AND THE DIFFERENCE ON
THIS RANGE OF Gs ALSO HAS A
QUADRATIC REPRESENTATION ACROSS
GROUPS.
HOWEVER THE STATE OF THE BRAIN
WHEN THE IMAGING IS TAKING, IS
KIND OF SIMILAR BETWEEN THE
HEALTHY CONTROLS AND IT GOES IN
THE CLINICAL CASES.
THAT MEANS THAT AN MCI AND IN
ALZHEIMER'S DISEASE THE SYSTEM
IS OPERATING FAR FROM THE BUY
POINT.
IT'S RELATED TO COGNITION AND
BIFURCATION -- SO IN SUMMARY
WHAT I TRIED TO SHOW YOU IS WE
HAVE OF A WAY TO HAVE A
MULTI-SCALE METHOD TO ASSESS THE
DYNAMICS AT THE LEVEL OF THE
WHOLE BRAIN.
IT'S INDIVIDUALIZED AND WE CAN
DO THESE MEASURES CONSTANTLY
BECAUSE THE DYNAMICS ARE NOT,
THEY'RE GOING TO BE LEARNED BUT
IT'S SOMETHING THAT WE'RE
MEASURING, IT'S TRANSLATABLE, WE
CAN GO TO ANIMAL MOLDS AND WE
CAN GET INPUT FROM ANIMAL MODELS
AND TO TEST IN THE MODEL AND
MORE IMPORTANTLY, CLINICALLY,
THIS APPROACH CAN PRODUCE NEW
HYPOTHESIS TO DESIGNED
INTERVENTIONS AND THE SYSTEM CAN
ALSO BE USED TO TRY THERAPIES
AND WHY, BECAUSE IF WE DEVELOP A
NEW THERAPY, THEN WE CAN CHANGE
THE PARAMETERS ASSOCIATED WITH
THAT THERAPY AND SEE IF THE
DYNAMICS OF THE BRAIN ARE
CHANGED.
I THINK THAT'S RIGHT NOW, THE
ISSUE OF NOT HAVING INTER
INTERVENTIONS FOR ALZHEIMER'S IS
BECAUSE WE'RE MISSING THE
MECHANISMS SO THAT MEANS THAT
THE OPPORTUNITY TO CHANGE THE
PARADIGM IS NOW.
I THINK THAT THE OPPORTUNITIES
ALSO, THESE TYPE OF APPROACH CAN
MERIT WITH OTHER OBSERVATIONS AT
DIFFERENT SCALES AND ALSO I
THINK THAT BECAUSE WE INTO
EVIDENCE CRITICAL MASS OF THIS
TYPE OF STORIES, TO MAKE A REAL
CHANGE, THAT ALSO THERE'S APP
OPPORTUNITY FOR TRAINING
PROGRAMS TO THIS AND OTHER
APPROACHES.
THESE ARE PEOPLE THAT ACTUALLY
DID THE WORK THAT I PRESENTED
AND THERE WE GO.
SORRY, ONE MINUTE LATE.
>>David Jones: THANK YOU, VERY
MUCH.
I BELIEVE WE'RE GOING TO TAKE A
10-MINUTE BREAK NOW AND COME
BACK AT 4:10.
JUST A REMINDER, QUESTIONS CAN
BE PUT IN THE CHAT AND WE'LL
ADDRESS THOSE AT THE END OF THE
SESSION.
IT'S 4:10 SO I'LL SHARE MY
SCREEN HERE.
LET ME GET STARTED.
SO, I'LL BE TALKING ABOUT
CLINICAL UTILITY AND SOME
DISCLOSURE TEMPERATURE
DISCLOSURES BUT I WILL BE
TALKING ABOUT OFF-LABEL USE OF
FDG PET A MORTALITY THAT IS
COMMONLY USED IN THE SETTING
EVALUATION OF NEURODEGENERATIVE
DISEASE AND COVERED AND THE
CURRENT FDA APPROVAL OF FDG PET
DOES NOT EXTEND TO THIS USE CASE
AND CONSIDERED OFF LABEL.
THIS IS THE (INAUDIBLE) TO THEIR
WORK IT'S A MULTI DISCIPLINE ARY
TEAMWORK NORTH THE CLINICAL
PRACTICE AND WE HAVE A LOT OF
SUPPORT AND PARTNERS AND BUT WE
VIEW OURSELVES AS AN INTER
DISCIPLINARY TEAMWORKING IN
CLINICAL DATA STREAMS THAT'S
SPAN THESE THREE MAJOR ZONES,
PEOPLE, PLACES ASK DEVICES AND
IT'S ZONE ONE AND WE NEED TO
TRANSFORM DATA AND PUT IT INTO
SOFTWARE AND ALGORITHMS FOR
COMMUTATION AND PUT THAT INTO
APPLICATIONS TO DELIVER THEM
BACK TO THE POINT OF CARE.
ZONE 2 CONNECTING ALL THESE
PEACE AND ZONE THREE WE'RE DOING
OUR SOFTWARE ALGORITHM BUT
COMPUTATIONAL NEUROLOGY, THERE'S
THREE MAIN ASPECTS THE WAY I
THINK ABOUT IT AND YOU CAN HAVE
A COMPUTATIONAL THR THEORETICAL
FRAMEWORK AND TECHNIQUES APPLIED
TO CLINICALLY RELEVANT NEWER
LOGIC DATA TEAMS AND THEORY IN
COMPUTATIONAL MODELS OF BRAIN
FUNCTIONS AND IT FOCUSES ON ONE
AND TWO AND THIS IS REALLY
GROUNDING A PRAGMATIC SCIENTIFIC
PROCESS FOR MENTAL FUNCTIONS AND
THE GOALS SHOULD BE TO PROVIDE
SOME FRAMEWORK TO ASK QUESTIONS
ABOUT CLINICALLY RELEVANT MENTAL
FUNCTIONS AND YOU CAN THINK OF A
NEUROSCIENCE FRAMEWORK OF BEING
MENTAL FUNCTIONS, CORRESPONDING
TO BIO FISCAL DYNAMICS IN THE
BRAIN DURING AN ORGANISMS
INTRINSIC INTERACTIONS AND
BRINGING THIS FOR ADRD IF AGING
YOU WOULD ALL THIS AGE RELATED
DEGENERATIONS OF THESE FUNCTION
AND THEY CORRESPOND TO FAILURES
IN BIOPHYSICAL DYNAMICS
OCCURRING IN THE BRAIN DURING AN
ORGANISMS INTRINSIC ASK
EXTRINSIC INTERACTIONS LEADING
TO DISABILITY.
BUT THERE'S VARIOUS LEVELS OF
ABSTRACTIONS FOR THESE
BIOPHYSICAL CAL DYNAMICS.
THOSE CAN EXIST IN GENES,
MOLECULES, CELLS, CIRCUITS,
LARGE-SCALE SYSTEMS, MENTAL
FUNCTIONING, AND ACTIVITIES
LIVING.
THERE WAS A RECENT ARTICLE
PUBLISHED LAST WEEK WHICH I
THINK NICELY FRAMED THESE LEVELS
OF ABSTRACTIONS AND DIFFERENT
EXPLANATORY PROJECTS ACROSS
THEM.
AND HERE YOU HEARD ABOUT
MECHANISTIC MODELS AND WE'LL
TALK ABOUT HERE MAINLY, I WANTED
TO PAIN ADLs ARE THE THINGS I
SEE IN MY CLINIC.
HE THINK ABOUT THESE SORT OF
EXPLANATORY MODELS IN DE
DESCRIPTIVE TERMS WITHIN THIS
GLOBAL FUNCTIONAL STATE SPACE
WHICH I WILL TELL YOU MORE ABOUT
THAT COINCIDES WITH WHAT YOU SAW
FROM EARL MILLER AND REALLY THE
THING I'M OBSERVING AND USING
ARE LARGE-SCALE PATTERNS IN THE
BRAIN, CALLING THESE CERTAIN
MODES OF FUNCTION AND YOU CAN
THINK OF THIS USING THE TRAFFIC
ANALOGY THAT EARLY USED RATHER
THAN THE ROADS AND STREETS, WHAT
YOU ALSO NEED TO PLAN YOUR TRIP
THROUGH NEW YORK CITY IS
TRAFFIC.
YOU NEED TO KNOW TRAFFIC AND THE
MODE OF THE TRAFFIC AT THE TIME.
SO THEY ARE GLOBAL TRAFFIC
PATTERNS ON TOP OF IT BRAIN
STRUCTURE.
FUL USE THESE TO ANSWER CLINICAL
QUESTIONS ABOUT BRAIN BEHAVIOR
MAP SEWING HERE IS AN EXAMPLE OF
WHERE, AT END OF THE TALK, WE
CAN UNDERSTAND HOW WE USE THESE
GLOBAL PATTERNS TO DESCRIBE A
PATIENT AND YOU CAN CLEARLY
SEPARATE CLINICALLY NORMAL
INDIVIDUALS FROM A VARIETY OF
DEMENTIA SYNDROMES WITH HIGH
ACCURACY.
DESCRIBING LARGE-SCALE PATTERNS,
THIS IS DONE IN MULTIPLE WAYS.
MAYBE A DECADE AGO WE THOUGHT
MORE ABOUT NETWORKS BUT WE SINCE
STARTED THINKING ABOUT MAYBE
THESE WEREN'T NETWORKS BUT MORE
CO ACTIVATION PATTERNS AND WE'RE
USING TERMS LIKE FUNCTIONAL
GRADIENTS TO DESCRIBE THESE
THINGS.
OR MORE SPACIAL TEMPORAL
PATTERNS AND MAYBE THEY'RE
PROPAGATING WAVES.
MORE RECENTLY SUPER POSITION OF
STANDING WAVES DESCRIBED THESE
LARGE-SCALE PATTERNS.
BUT HERE IS THE HISTORICAL
ORIGINS OF THESE PATTERNS THAT
ARE OBJECTS OF OBJECT
INVESTIGATION IN NEUROSCIENCE IT
COMES FROM FDG PET IN YOU GO
BACK TO THE PAPER FROM 1983,
SHOWING VERY CARE TERRIS TICK
PATTERN OF METABOLISM, USING FDG
PET, THIS IS FROM 1983 AND THEY
ARE EARMARKED THIS SUGGESTED THE
ABNORMAL FUNCTIONAL BEHAVIOR
THAT DEMENTIA SUBJECTED HAD
RELATED TO DISTURBED FUNCTIONING
SURVIVING TISSUE THEN TO LOSS OF
BRAIN SUBSTANCE ITSELF AND I
KNOW IT'S ALSO THIS CAN OCCUR IN
MY CLINIC BECAUSE OF A PATIENT
HAS A STROKE OR A LESION, IT
CHANGES DRAMATICALLY AS DOES THE
FUNCTION.
THESE PATTERNS CONTINUED TO BE
RECOGNIZED IN THE '90s IN
CLINICAL SETTINGS.
YOU CAN SEE MAGNITUDE IMAGES AND
THIS IS DISEASE SEVERITY.
HERE IS IS IMAGE WHERE THE
BRIGHT COLORS INDICATE UPTICK
AND THEY ARE ALWAYS NOTED THAT
THERE'S THIS CURIOUS
PRESERVATION OF PRIMARY MOTOR
COURT SEES IN THESE PATTERNS AND
THIS IS SOMETHING THAT AS USED
BY CLINICIANS TO IDENTIFY
ALZHEIMER'S PATTERNS, IT'S A
STRANGE PRESERVATION OF PRIMARY
COURT SEES.
THIS PATTERN IS NOW BEEN
OBSERVED IN FUNCTIONAL
CONNECTIVITY AND YOU CAN ALSO DO
DECOMPOSITION OF FDG AND FIND
THE SAME PATTERN OF DEGENERATIVE
DISEASES SO IT'S SOMETHING ABOUT
THIS LARGE-SCALE PATTERN THAT IS
VERY IMPORTANT FOR THE PATTERNS
WE OBSERVED IN THE CLINIC.
SOON AFTER, MARCUS RAKEEL USED
FDG PET THAT CERTAIN AREAS OF
THE BRAIN ALWAYS WERE LESS
METABOLICALLY ACTIVE DURING
CERTAIN DEMANDING TASKS AND
COINED THE TERM DEFAULT MODE
NETWORK IT WAS ACCIDENTALLY SUBY
IDENTIFIED BY MRI AND TASK MRI
AND SHOWN TO BE ABNORMAL AND
ALZHEIMER'S DISEASE AND SAME
PATTERN.
SO, SINCE AFTER DESCRIBING THE
DEFAULT MODE NETWORK, OTHER
NETWORKS WERE DESCRIBED AND ON
THE RIGHT AND LEFT EXECUTIVE
NETWORKS AND THESE THINGS HAVE
MANY NAMES AND WE'LL GET TO THAT
AND HERE IS ANOTHER SPACIAL
DESCRIPTION AND THE MULTI DEMAND
SYSTEM CONTRASTING THAT WITH THE
LANGUAGE SYSTEM IT'S IMPORTANT
FOR THINKING ABOUT DIS EXECUTIVE
FORM OF ALZHEIMER'S VERSUS
LANGUAGE PRESENTATIONS OF
ALZHEIMER'S.
PEOPLE USE COGNITIVE CONTROL.
BUT IN GENERAL, THERE'S NOT A
CONSENSUS IN THE FIELD FOR THESE
LARGE-SCALE PATTERNS.
WHAT EXECUTIVE FUNCTION NETWORK
IS THE TITLE OF THIS ARTICLE.
PEOPLE USE TERMS LIKE COGNITIVE
DORRAL, EXECUTIVE CONTROL AND OF
WORK BEING MEMORY NETWORKS AND
THE SAME SPACIAL PATTERN GIVES
DIFFERENT FAILS IF YOU CLUSTER
OVER THE PATTERNS, THERE'S FOUR.
WE LOOK IN ALZHEIMER'S, HERE IS
SIX PATIENTS THAT I SAW AND MADE
THESE IMAGES ON THE LEFT OF THE
GLUCOSE UPTICK IN THEIR BRAIN
AND THESE IMAGES ARE OF TAU
PROTEIN AND YOU SEE THERE'S A
CORRESPONDENCE AND THIS PATIENT
HAD A MEMORY PREDOMINANT
DISORDER AND THIS PATIENT HAD A
VISUAL SPACIAL PREDOMINANT DIS
SCORED ORDERAND THIS PATIENT HAR
PREDOMINANT DISORDER AND SO HERE
IS SIX DIFFERENT TYPES OF
ALZHEIMER'S DISEASE AND IT'S
LIKELY A SPECTRUM OR CONTINUUM
BAYS YOU CAN SUBTYPE THESE
EXECUTIVE CASES AND YOU RECENTLY
USED DATA DRIVEN TECHNIQUES TO
DEFINE OR DIFFERENT TYPES OF
ALZHEIMER'S.
THESE ARE PATIENTS I SEE IN MY
CLINIC AND I KNOW THEY EXIST AND
THEY'RE VERY DIFFERENT SYMPTOMS
IF THE RIGHT SIDE OF THE BRAIN
OR THE LEFT SIDE OF THE BRAIN IS
WHERE THE PROBLEM IS AND WE WERE
ABLE TO SHOW THIS IN A
DATA-DRIVEN WAY AND OF PROPOSE A
FRAMEWORK FOR EXECUTIVE
FUNCTIONING WHICH IS DIFFERENT
THAN THE TRADITIONAL MODEL AND
WE THINK IT'S HELPING US
UNDERSTAND OUR PATIENTS BETTER
IF WE THINK ABOUT IT THIS WAY.
THIS ARE OTHER SYNDROMES, UNTIL
ALZHEIMER'S.
THEY'RE MORE FOCAL LIKE A
PROGRESSIVE AUDITORY VERBAL OR
PEER WORD DEAFNESS WHERE THEY
CAN HEAR SOUNDS JUST FINE BUT
THEY CANNOT UNDERSTAND SPOKEN
VERBAL LANGUAGE.
AND IT'S A FOCAL LEFT SIDED GEE
DEN RACE AND EVIDENT IN
STRUCTURAL MRI AND TAU PET.
AND IT INFORMATION IS IN A PDG
PELT.
WE HAVE NETWORKS WHICH CAN TAKE
AN FDG PET AND IMPUTE A TAU PET
SCAN.
AND ONE OF THE ADVANTAGES OF TAU
PET IS THAT IT CAN DIFFERENTIATE
THAT ALZHEIMER'S SPECTRUM FROM
THE FRONTAL TEMPORAL SPECTRUM
AND PDF PET CAN DO THAT AS WELL
AND IT CAN SYNTHESIZE THE TAU
PET AND FDG PET TELLS YOU ABOUT
INFORMATION THAT TAU PET DOES
NOT.
AND WE DID TRY TO UNDERSTAND AND
IT'S LEARNING THE STRUCTURE AND
PRINCIPLE GRADIENT WITH THE
SENSORY MOTOR COREY SEE IS
IMPORTANT AND WITH SO DEFINITELY
USING GLOBAL INFORMATION AND NOT
LOCAL TO SYNTHESIZE THE TAU PET.
SO, IT TELLS YOU ABOUT OTHER
FORMS OF DEGENERATION AND
THERE'S THE CINGULAR LET ISLAND
SIGN AND IT'S DIFFICULT FOR
PEOPLE TO IDENTIFY THESE
PATTERNS BUT THEY'RE THERE AND
CONTRAST ALZHEIMER'S AND WE
FOUND SIMILAR PATTERNS INDICATED
TDP43 AND A CONDITION CALLED
HIPPOCAMPAL SCLEROSIS AND IT'S
IN THAT PET SO WE CAN IDENTIFY
MIXED PATTERNS WHEN YOU HAVE FDF
PATTERN TO LOOKS A LITTLE BIT
MORE DLB LIKE AND WE'RE STARTING
TO DO THIS AND PEOPLE WHO HAD
PET DURING LIFE AND THEY COME TO
AUTOPSY AND WE CAN STAGE THEM
BOTH ON ALZHEIMER'S AND
PATHOLOGY, BRAIN STEM LOUIE BODY
PATIENTS YOU WOULD NEVER CROSS
THE ZERO LINE WHERE'S FOLKS WHO
HAVE A TRANSITIONAL OR PATHOLOGY
ANDLY LIKELY ALZHEIMER'S DISEASE
ARE BELOW THE LINE VERSUS HIGH
SO WE THINK WE WILL DO THIS.
A LOT OF TIMES YOU SEE
STRUCTURAL CHANGES IN THE BRAIN
AND YOU THINK IT'S A IF IT'S
CONNECTED SOUL SIDE AND THE
LONGITUDINAL FISHER AND YOU CAN
SEE IN THIS EXAMPLE, THAT IS NOT
FOLLOWING THESE PATTERNS WE'RE
TALKING ABOUT AND IT'S NOT
LIKELY A NEURODEGENERATIVE
DISEASE LIKE ALZHEIMER'S AND
IT'S LIKELY A PROBLEM OF
REGULATING CSF DYNAMICS AND
NORMAL PRESSURE HYDROCEPHALUS
AND THESE PATIENTS ARE CURRENTLY
POORLY RECOGNIZED AND TREATED
AND WE NEED TO DO A BETTER JOB
OF CARING FOR THEM.
YOU CAN SEE HERE, THIS CASE AS
DESCRIBED AS CLEAR POCKETS OF
SPINAL FLUID IN THESE AREAS AND
FDG PET BRAIN WAS NORMAL AND WE
DID A SHUTTING PROCEDURE AND YOU
CAN SEIZE IT HERE AND THERE'S
CLINICAL IMPROVEMENT AND
STRUCTURAL IMPROVEMENT.
THERE'S A IMPORTANT ROLE FOR FDB
PET IN THE VALUATION OF
PATIENTS.
WHETHER IT'S NEUROLOGICAL,
DEGENERATIVE OR NPH OR IT CAN
TELL YOU ABOUT 80% AND SPECTRUM
AND THIS ALLOWS TARGETED
EMERGENCY AND EXISTING
THERAPEUTICS AND COUNSELING
MANAGEMENT.
AND CLINICAL TRIALS.
WE'LL BE ABLE TO DO THAT WE CAN
USE A SIMPLE UNDERLINING
PATTERNS SO HERE WE DID A
DIMENSIONALITY REDUCTION AND TO
VARIABILITY AND ALL THESE
DIFFERENT PATTERNS WE CAN SEE
AND YOU ARE ONLY NEED ABOUT 10
DIFFERENT PATTERNS THAT WE
DESCRIBE EVERYTHING I JUST WAS
TELLING YOU ABOUT AND NOW YOU
CAN DESCRIBE PATIENT SCANS RAZ A
COMBINATION OF THESE 10 BRAINS
AND CLINICAL PRACTICE AND THAT
WERE EVALUATING WHERE WE'RE
USING MORE 50 AND IT DESCRIBES
THESE SAME PATTERNS AS WELL AND
YOU CAN RELATE THEM TO COGNITIVE
FUNCTION AND YOU CAN RELATE THEM
TO NEURAL NETWORKS AND YOU CAN
RELATE THEM TO PATIENTS SYMPTOMS
AND SYNDROMES.
HERE WE'RE SHOWING MEAT ANALYTIC
FUNCTION AND PATTERNS AND YOU
CAN LOOK AT THEM IN THE SAME
FRAMEWORK THAT WE'RE LOOK AT OUR
PATIENT'S BRAINS AND THE
DISTANCE AND IT'S PREDICTIVE OF
THEIR CLINICAL SYNDROME AND SO
WE CALL THIS THE GLOBAL
FUNCTIONAL STATE SPACE AND IT'S
A DIFFERENT WAY OF THINKING
ABOUT THESE LARGE-SCALE BRAIN
PATTERNS AND THE RELATIONSHIP TO
BRAIN FUNCTION AND.
>>THERE WAS A CLEAR RELATION IF
WE MODEL OVER WINDOW LATE AN ANR
THERE WAS ONE GLOBAL PATTERN IN
TRANSITIONING BETWEEN THEM
DESCRIBES WHAT WE TRADITIONALLY
CALL FUNCTIONAL CONNECTIVITY AND
AND WE'RE ABLE TO ACTUALLY
MONITOR PATIENTS NOW COMPARE
THEIR BRAINS AND THIS FUNCTIONAL
STATE SPACE TO OTHER PATIENTS
WE'VE SEEN AND HAVE DIFFER
CONDITIONS.
WHEN THEY IMPROVE CLINICALLY
WITH TREATMENT WE SEE CHANGE AND
THEY MOVE TO A NORMAL
NEIGHBORHOOD.
WE CAN TRACK PATIENTS OVER TIME.
THESE ARE GENETIC FTD PATIENTS
AND THEY'RE DESTINED TO GET
FRONTAL TEMPORAL DEMENTIA AND
THESE BLUE AREAS ARE NORMAL
LOOKING BRAINS AND THIS IS OUR
PATIENT WHEN THEY'RE NORMAL AND
THEY MOVE INTO AN ABNORMAL
NEIGHBORHOOD AND THAT CHANGE
ACTUALLY OCCURS BEFORE SYMPTOMS
ONSET AND WE CAN TRACK THAT VERY
PRECISELY BY MOVEMENT FROM THE
SPACE AND THIS SPACE.
>>SO THAT MEANS YOU LIKELY HAVE
LOUIE BIO DISEASE AS WELL.
SO THIS CHANGES FROM A
DISCONNECT PER A DIME TO A
POPULATION DYNAMICS PARADIGM AND
BOTH ACCOUNT FOR THESE
LARGE-SCALE BRAIN PATTERNS BUT
THEY HAVE DIFFERENT MECHANISTIC
EXPLANATION AND DEGENERATIVE
LARGE-SCALE BRAIN DYNAMICS YOU
WANT TO CONTROL JUST TO CHANGE
THESE THINGS.
SO, SOME SUGGESTIONS HERE IS
THAT WE HAVE THIS INFRASTRUCTURE
THAT CAN SUPPORT MEASURING,
MODELING AND INTERVENING ON
LARGE-SCALE BRAIN DYNAMICS AND
MENTAL FUNCTIONS AND SO THANK
YOU FOR YOUR ATTENTION AND I
KNOW I'M GOING OVER A LITTLE BIT
ON TIME.
AND I'LL TURN IT OVER TO DANI
BASSETT WHO WILL TALK ABOUT WAYS
NETWORK CONTROL RELATES TO BRAIN
METABOLISM.
THANK YOU SO MUCH MANY OF IT'S
SO GREAT TO BE HERE.
I WOULD LIKE TO GIVE MY THOUGHTS
OVER THE NEXT FEW MINUTES ABOUT
USING NETWORK CONTROL THEORY TO
MODEL THENNENNER JET TICKS OF
BRAIN FUNCTION AND I APOLOGIZE
FOR MY THROAT.
I'M GETTING OVER COVID.
SO, THE FUNCTION OF MANY
BIOLOGICAL SYSTEMS IS MADE
POSSIBLE BY A NETWORK ALONG
WHICH ITEMS OF INTEREST LIKE
NUTRIENTS, GOODS AND INFORMATION
IT'S COMPOSED OF REGIONS THAT
PERFORM SPECIFIC FUNCTIONS AND
ENGAGE IN PARTICULAR
COMMUTATIONS AND AND THOSE
REGIONS ARE CONNECTED AND OUR
LARGE-SCALE TRUCKS AND IT'S A
BUNDLE OF AXONS ALONG WHICH
INFORMATION BEARING THEY HAVE A
PATTERN OF CONNECTIVITY THAT
CONSTRAISTRAINS HAS FOCUSED ON A
VARIETY OF STATISTICAL MEASURES
CHARACTERIZE THAT PATTERNS OF
CONNECTIVE AND HOW IT CON
STRAINS THE PASSAGE OF
INFORMATION.
THEY RANGE FROM SOMETHING LIKE
THE CLUSTERING CO EFFICIENT AND
THE NUMBER OF TRY AGES AND THE
NUMBER OF CONNECTED TRIPLES TO
THE HUB ORGANIZATION AND THE
PRESENCE OF CAVITIES AND
SHORTCUTS AND MODULAR TEE OR
COMMUNITY STRUCTURE.
BUT IN EACH CASE, THE GOAL IS TO
UNDERSTAND HOW THAT PATTERN CON
STRAINS WHERE INFORMATION CAN
FLOW.
IN TURN, THAT PATTERN OF
INFORMATION FLOW DETERMINES THE
SORTS OF FUNCTIONS THAT THE
BRAIN CAN SUPPORT AND THEREFORE
UNDERSTANDING THE STRUCTURAL
NETWORK CONSTRAINTS OF THE BRAIN
AND IT'S ALTERATION DISEASE.
THEY HAVE EXPANDED THE
INVESTIGATION OF STRUCTURAL
CONSTRAINTS IN SEVERAL WAYS.
FIRST THE FIELD HAS SEEN AN
INCREASE IN THE SENSITIVITY OF
MEASUREMENTS AND SO TYPICALLY
WHAT IS USED FOR HUMANS IS
DIFFERENT FUSION WEIGHTED MRI
AND IT'S SENSITIVE TO MICRO
STRUCTURAL INTEGRITY AND
PROVIDES TRACK LOCATION AT FINER
AND FINER SPACIAL RESOLUTIONS.
THAT IS MADE POSSIBLE BY
INCREASED SCAN TONE, BY AN
INCREASING NUMBER OF DIFFUSION
DIRECTIONS AND EXTENSIONS ON THE
ALGORITHMIC SIDE OF THE CO NOT
YOUTATIONAL.
WE SEE THE BRAIN IN A MUCH
RICHER WAY.
IN ADDITION TO THESE
INCREASINGLY SENSITIVE
MEASUREMENTS THERE'S BEEN A
FOCUS ON DEVELOPING DATA
INFORMED COMPUTATIONAL MODEL
THAT ALLOW US TO INVESTIGATE
THEM DIFFERENTLY REPERTOIRE AND
ONE EXAMPLE OF SUCH A DATA
INFORMED COMPUTATIONAL MODEL IS
THE NETWORK CONTROL MODEL.
IT HAS PROVEN PARTICULARLY AIR
TRAFFIC CONTROL SYSTEMS AND
ROBOTICS AND THE IDEA OF NETWORK
CONTROL THEORY IS THAT WE HAVE A
NETWORK DYNAMICAL SYSTEM AND
HERE THAT IS SHOWN AS A TOY
EXAMPLE OF A NETWORK OF FOUR
NODES AND FOUR EDGES IN ADDITION
TO HAVING THAT NETWORK, WE ALSO
HAVE INPUT INTO THE SYSTEM FOR
THE BRAIN THAT SORT OF THING AND
INPUT HERE WE SEE AN INPUT INTO
X1, X2, AND WITH THOSE INPUTS
WHICH CAN VARY OVER TIME, THE
STATE OF THE SYSTEM CAN CHANGE.
SO INITIALLY PERHAPS THIS SYSTEM
IS SITTING AT THE ORIGIN WHERE
EACH OF THE NODES HAS AN
ACTIVITY LEVEL OF ZERO AND THEN
AFTER THESE INPUTS HAVE BEEN
INJECTED, THE STATE OF THE
SYSTEM IS MOVED TO A DIFFERENT
STATE, A DIFFERENT PLACE ON THE
STATE SPACE.
WHAT NETWORK CONTROL IS WHAT
SORT OF INPUTS CAN DRIVE THE
SYSTEM FROM SOME INITIAL STATE
OF TO SOME FINAL STATE AND WHAT
SORTS OF FINAL STATES ARE
POSSIBLE TO GOAT AND HOW.
NOW THE NETWORK CONTROL MODEL
STIMULATES PRECISELY HOW
ACTIVITY FLOWS ALONG STRUCTURAL
CONNECTIONS.
AND IT IS MADE POSSIBLE BY
DEFINING THE DYNAMICS OF THE
SYSTEM ACCORDING TO WHERE THE
STRUCTURAL CONSTRAINTS ARE.
THAT MODEL CAN HAVE VARYING
LEVELS OF COMPLEXITY BUT HERE IS
A SIMPLE ONE.
THE STATE OF THE BRAIN X AT TIME
T PLUS 1 IS EQUAL TO THE STATE
OF THE BRAIN AT TIME T TIMES SUM
PATTERN OF CONNECTIVITY WHICH IS
TYPICALLY THIS WEIGHTED AND WE
CAN DERIVE WHERE USE OF CAP APP
IS THE INPUT INJECTED INTO KAPPA
REGIONS AT TIME T AND IT'S A
MATRIX AND WITH ONES ALONG THE
SIGNAL AND WHERE THE INPUT IS
HERE AND.
>>AS PATTERNS OF ACTIVITY
ACROSS NEURAL UNITS AND THE
PATTERNS OF ACTIVITY CAN
REPRESENTS EVENTS OR OUR
EXPERIENCES OR CONSULTS AND
IMPORTANTLY, WE MOVE FROM ONE
STATE OF ACTIVITY TO ANOTHER IN
A WAY THAT TAKESSENNER GAO HE
AND THEN GEAR GEE IT TAKES TO
MOVE FROM ONE STATE TO ANOTHER
DEPENDS ON HOW ACTIVITY WITH
FLOW THROUGH THIS SYSTEM.
IT DEPENDS ON THE STRUCTURAL
CONSTRAINTS.
AND WHEN WE THINK ABOUT STATE
TRANSITIONS, WE NEED TO THINK
ABOUT NOT JUST THE PATTERNS OF
ACTIVATION THAT WE OPEN BUT ALSO
THE NETWORK UNDERNEATH OF IT AND
WE KNOW THAT ACTIVE NEURONS CAN
SPREAD ACTIVITY TO OTHER
NEWERRANCE AROUND THEM AND IT'S
EASIER IF THAT ARE CONNECTED.
THIS ALLOWS US TO THINK OF
THROUGH WHAT SORT ARE EASY
VERSUS DIFFICULT AND SO HERE FOR
EXAMPLE IF WE HAD A BRAIN, THAT
HAD A PATTERN OF CONNECTIVITY
LIKE THIS VERY MODULAR NETWORK
WITH ONE SET OF FIVE NODES BEING
INTERCONNECTED WITH ONE ANOTHER
AND ANOTHER SET WITH ONE ANOTHER
AND YOU WE WANT TO HAVE AN
ACTIVITY STATE WHERE THE FIRST
FIVE UNITS ARE ACTIVE AND THE
SECOND FIVE UNITS ARE NOT
PARTICULARLY ACTIVE THIS
COMBINATION IS PRETTY EASY TO
ATTAIN AND MAINTAIN AND THE
REASON IS THAT THIS ACTIVATION
STATE CAN CAPITALIZE ON THE
UNDERLYING NETWORK STRUCTURAL
AND SO WE JUST NEED AN EXCITE
TORI SIGNAL TO BE INJECTED INTO
THE FIRST MODULE AND INHIBITORY
SIGNAL IN THE SECOND MODULE AND
BECAUSE THERE'S ONLY ONE WEAK
LINE BETWEEN THEM THE ACTIVITY
CAN BE ISOLATED ACROSS THE TWO
MODELS SUCH AS THE ACTIVE ONE
DOESN'T ACTIVATE THE I INACTIVE
ONE.
HERE IS AN EXAMPLE OF.
>>YOU HAVE TO WORK UNDER THE
UNDERLYING STRUCTURE IN ORDER TO
REACH THIS ACTIVATION STATE.
HERE IS THE LAST EXAMPLE WHERE
WE HAVE THE MODULAR NETWORK
AGAIN WITH ONLY ONE CONNECTION
BETWEEN THE TWO MODULES AND THE
STATE THAT WE WANT TO GET TO IS
WHERE THE FIRST UNIT IS ACTING
AND THE SECOND UNIT IS NOT
ACTIVE AND.
>>IT HELPS US TO THINK ABOUT
WHICH STATE TRANSITIONS ARE EASY
VERSUS DIFFICULT.
NOT JUST WHICH STATES ARE EASY
TO GET TO.
SO, HERE IS AN EASY STATE
TRANSITION.
HERE WE HAVE ACTIVATION OF THE
FIRST FIVE DEACTIVATION OF THE
SECOND AND WE WANT TO SWITCH IT
SO THE FIRST FIVE ARE INACTIVE
AND THE SECOND FIVE ARE ACTIVE.
TO DO THIS ON THIS MODULAR
ACTIVE AND EXCITE TORI CONTROL
SIGNAL INTO THE SECOND MODULE SO
WE ONLY REALLY NEED TWO GENERIC
CONTROL SIGNALS.
IF CONTRAST IF WE WANT TO ENACT
THE STATE TRANSITION I'M SHOWING
59 BOTTOM WE NEED A LOT MORE.
SO, HERE WE HAVE THE MODULAR
NETWORK AGAIN AND THE STATE THAT
WE WANT TO START WITH, IS THIS
ON AND 06 BINARY STATE AND WE
WILL SWITCH IT THAT ANYTHING ON
IS OFF AND ANYTHING THAT WAS
OFFS IS NOW ON.
TO ENACT THIS STATE TRANSITION,
WE NEED TO INJECT EITHER AN
EXCITE TORI CONTROL SIGNAL AND
AGAIN INTO EVERY SINGLE NODE IN
THE NETWORK.
SO WHEN I SAY THAT A STATE
TRANSITION IS EASY, MANY.
>>THE BRAIN MAY MOVE ALONG THIS
TRAJECTORY FROM X TO SOME
MODELED GRAY SCALE IMAGE XT IN
CONTRAST TO MOVE TO SOME FINAL
STATE XT COLORED IN ROYAL PURPLE
HERE ACTIVATION OF THE VORTEX
AND WHAT SORTS OF INPUTS YOU
INJECT NOD WHICH REGIONS AND
WILL ALLOW THIS TRANSITION TO
OCCUR ASK IT LAWS US TO AND WE
HAVE A COST FUNCTION THAT TEN
ALLIESES WHAT IS REQUIRED AND
THE DISTANCE OF THE CURRENT
STATE XT FROM THE TARGET STATE
SO THIS IS THE MINIMIZATION
WE'RE USING AND WE USE ENERGY IS
TO MODEL THE FACT THAT THE BRAIN
IS UNDER MARKED BIOLOGICAL
CONSTRAINTS AND WE MINIMIZE
DISTANCE IS IT ALLOWS FOR A
RELATIVELY STRAIGHT TRAJECTORY
FROM AN NATIONAL STATE TO A
FINAL STATE RATHER THAN ALLOWING
THE BRAIN TO NEANDER ACROSS THE
STATE SPACE?
A WAY THAT IS INEFFICIENT.
SO, IT'S BEEN HAPPENING AND
ALSO, AN INCREASE IN FOCUS ON
THE DEVELOPMENT OF DATA INFORMED
COMPUTATIONAL MODELS.
I RAISED NETWORK CONTROL THEORY
AS ONE OF THOSE COMPUTATIONAL
MODELS OF INTEREST.
THE THIRD AND FINAL REASON THAT
RECENT EFFORTS HAVE EXPANDED THE
INVESTIGATION OF STRUCTURAL
CONSTRAINTS, IS A SHIFT IN OUR
EXPLANATIONS FOR COGNITION.
SO, FOR A LONG PERIOD OF TIME,
WE HAVE MADE OF SIGNIFICANT
PROGRESS IN THE FIELD MANY BY
PROVIDING AND EXCAVATING AND
REPORTING ACTIVITY-BASED
EXPLANATIONS FOR COGNITION.
SO, A CERTAIN KIND OF COGNITION
OR COGNITIVE PROCESS IS
EXPLAINED BY THE PATTERN OF
ACTIVATION THAT WE OBSERVE ON
THE BRAIN.
NOW, IN MORE RECENT YEARS, WE'RE
ALSO PROVIDING AND STUDYING A
COMPLIMENTARY TYPE OF
EXPLANATION WHICH IS A STRUCTURE
BASED EXPLANATION AND SO IN THAT
KIND OF STRUCTURE BASED
EXPLANATION, WE ARE SUGGESTING
THAT A DI DYNAMICAL PROCESS THAT
CONNECTS THE REGIONS INVOLVED
AND THEREFORE THE COGNITIVE
PROCESS IS NOT ONLY EXPLAINED BY
THE PATTERN OF ACTIVATION, BUT
ALSO BY THE RELATION BETWEEN
THAT PATTERN AND THE PATTERN OF
CONNECTIVITY UNDERNEATH.
TOGETHER, THESE THREING
EXPANSIONS OF MEASUREMENT,
MODELING AND CONCEPTUAL
APPROACHES ARE PROVIDING A
RICHER UNDERSTANDING OF
STRUCTURAL CONSTRAINTS ON HUMAN
BRAIN FUNCTION.
NOW I WANT TO PROVIDE YOU WITH
AN EXAMPLE OF THAT FINAL
CONCEPTUAL EXPANSION AND TO DO
THAT I'M GOING TO FOCUS ON
COGNITIVE EFFORT TO INDICATE THE
PREPORTION OF THE LIMITED
CAPACITY CENTRAL PROCESSING.
WE KNOW FROM PREVIOUS WORK THAT
COGNITIVE EFFORTS IS CORRELATED
WITH TASK DIFFICULTY AND TASK
KNOWLEDGE HE HAVE TEE AND
VARIETY OF INDIVIDUAL FACTORS
INCLUDING AGE AND NEURAL
DIFFERENCES HOWEVER WE ALSO KNOW
THAT COGNITIVE EFFORT IS NOT
IDENTICAL WITH THE VARIABLES SO
THE INTERESTING QUESTION FOR US
IS HOW CAN ONE MEASURE COGNITIVE
EFFORTS.
A FEW COMMON MEASUREMENTS
INCLUDE SELF-REPORT SO YOU CAN
ASK THE PATIENT HOW EFFORTFUL
DID THAT FEEL AND YOU CAN USE
AUTO KNOT I CAN RESPONSE OUTPUT
INCA DATING MORE COGNITIVE
EFFORT.
THERE ARE INTERESTING STUDIES
THAT HAVE USED DYNAMICAL
COMPLEXITY OF THE ACTIVITY TIME
SERIES TO EXPLAIN COGNITIVE
EFFORTS.
WHAT I'D LIKE TO SUGGEST IN THE
CONTEXT OF MY PREVIOUS REMARKS,
IS AN ALTERNATIVE APPROACH TO
STUDY THE COST OF ORCHESTRATING
COMPLEX IDENTITY DYNAMICS ON A
NETWORK AS OPERATIONALIZED BY
NETWORK CONTROL THEORY AND
SPECIFICALLY BY THE CONTROL
ENERGY ESTIMATE.
TO GIVE YOU EXAMPLES OF HOW THIS
WORKS, I WANTED TO SHOW YOU WORK
FROM A VISITING FELLOW IN THE
LAB.
HE WAS REALLY INTERESTED IN
ASKING HOW MUCH CONTROL ENERGY
REQUIRED TO TRANSITION FROM APP
ATTENTION DEMANDING STATE TO A
WORKING MEMORY STATE THAT
REQUIRES MORE COMMENTS OF EFFORT
AND HOW MUCH HOLDS EITHER OF
THOSE STATES.
TO ADDRESS THOSE TWO QUESTIONS,
HE GATHERED STRUCTURAL
CONNECTIVITY DATA FROM
INDIVIDUAL PARTICIPANTS AND THEN
ALSO FUNCTIONAL MAGNETIC IMAGING
DATA AS THE PARTICIPANTS WERE
PERFORMING AS THE TASK AND A TWO
BACK WORKING MEMORY TASK IN THE
SCANNER.
HE APPLIED A GENERAL LINEAR
MODEL TO EXTRACT A BETA RATES
WITH THE ACTIVATIONS THAT ARE
CHARACTERISTIC FOR THAT PERSON
FOR THEIR TIME IN THE ZERO BACK
TASK OR THEIR TIME IN THE TIER
BACK TASK.
HE WANTEDDED TO UNDERSTAND HOW
MUCH ENERGY IS REQUIRED TO GET
FROM THIS ZERO BACK TO THE TWO
BACK AND GIVEN THE PATTERN OF
STRUCTURAL CONNECTIVITY OF THAT
INDIVIDUAL.
NOW WE KNOW THAT PRECISE CONTROL
OF STATE TRANSITIONS AND STATE
PERSISTENCE IS REQUIRED FOR
OPTIMAL PERFORMANCE OF THIS TASK
INCLUDING TASK ACCURACY AND
SPEED.
SO HERE IS THE FIRST PIECE OF
DATA THAT I WANTED TO SHOW YOU,
STUDYING TRANSITIONING TO THE
WORKING MORE DEMANDING WORKING
MEMORY STATE AND ALSO BACK DOWN
TO THE ZERO BACK STATE ALONG THE
Y AXIS HERE IS IS THE CONTROL
ENERGY AND ALONG THE X AXIS AN
INDICATING THE TRANSITION OF
CENTER AND MERE WE'RE MOVING
FROM ZERO BACK TO THE TWO BACK
WORKING MEMORY STATE AND YOU SEE
A MARKED DEGREE OF CONTROL
ENERGY AND BY CONTRAST, THE
TRANSITION FROM THE CREDIT BACK
STATE TO THE ZERO BACK STATE
REQUIRES LESS CONTROL ENERGY AND
SO WHAT THAT MEANS, IS THAT
TRANSITIONING INTO THE CON NA
ACTIVELY MORE DEMANDING STATE
REQUIRES MORE CONTROL ENERGY
THAN THE OPPOSITE TRANSITION.
THAT IS CONSISTENT WITH OUR
NOTIONS OF COGNITIVE EFFORTS
SUGGESTING THAT CONTROL ENERGY
MAY BE A MARKER OF COGNITIVE
EFFORT.
HOW MUCH IS HOLD ONE OF THESE
ESTATES AND THE ZERO BACK WE'LL
ASK HOW MUCH ENERGY IS REQUIRED
TO STAY THERE.
NOW WE KNOWS THAT HOLDING A
PARTICULAR STATE FOR A COMPLEX
SYSTEM WILL DEMAND ENERGY AND AS
LONG AS YOU ARE SEEING THE
CONTROL ENERGY AND WE SEE THE
ZERO BACK STATE IS SIGNIFICANTLY
MORE STABLE THAN TWO BACK STATE
AND THIGHS CONTROL THEORY
MEASURES ARE BEHAVIORALLY
RELEVANT AND HELPS US TO
UNDERSTAND THE PARTICIPANTS SO
IN SUMMARY WHAT WE'VE DONE IS TO
HOW IT FLOWS THROUGH THE SYSTEM
AND INFLUENCING THE TYPES OF
COMMUTATIONS AND FUNCTIONS THAT
CAN BE PERFORMED.
I NOTE THAT RECENT EFFORTS HAVE
EXPANDED THE INVESTIGATION OF
THESE STRUCTURAL CONSTRAINTS IN
THREE WAYS.
FIRST, BUSINESS NOT INCREASING
USING INCREASINGLY SENSITIVE
MEASUREMENTS SO EXPANDING
DIFFUSION WEIGHTED IMAGES FOR
DIRECTIONS, LONGER SCAN TIMES
AND MORE SOPHISTICATED
ALGORITHMS OF RECONSTRUCTION.
SECOND, THERE'S CONCERTED EFFORT
TO HE CAN PANNED ON THE
DEVELOPMENT OF DATA INFORMED
COMPUTATIONAL MODELS AND I
MENTIONED NETWORK CONTROL THEORY
AS A MODEL THAT I'M EXCITED
ABOUT AND THAT I THINK IS
RELEVANT AND IMPORTANT IN
THINKING THROUGH THESE
STRUCTURAL CONSTRAINTS ON
ACTIVATION STATES AND THEIR
TRANSITION.
THE THIRD CONSTRAINTS IS A
CONCEPTUAL ONE AND IT'S WHERE
THE FIELD IS EMBRACING A
CONCEPTUAL SHIFT IN EXPLANATIONS
FOR COGNITION FROM
ACTIVITY-BASED EXPLANATIONS
SOLELY TO A COMBINATION OF
ACTIVITY BASED AND STRUCTURE
BASED EXPLANATIONS.
TOGETHER, THESE THREE SORTS OF
EFFORTS IN MODELING AND ON HUMAN
BRAIN FUNCTION.
I CLOSE THE TALK BY FOCUSING ON
AN EXAMPLE WHICH WAS THE STUDY
OF COGNITIVE EFFORT AND
ILLUSTRATED COGNITIVE
COMMUTATIONS WITHIN A FORMAL
THEORY OF NETWORKENNER JET TICKS
SO WITH THAT THANK YOU FOR
LISTENING.
I'LL STOP SHARING.
>>David Jones: THAT'S GREAT.
THANK YOU SO MUCH.
I THINK WE'RE GOING TO TAKE A
BREAK NOW AND RETURN AT 5:00
>>THERE HAVE BEEN A COUPLE
QUESTIONS IN THE VIDEO CAST OR
THE CHAT SO I CAN START THERE.
SO, Dr. COLLINS DO YOU WANT TO
ASK JURY QUESTION FOR PROFESSOR
BASSETT IF.
>>SURE.
THANK YOU FOR THE GREAT TALKS.
AND I WAS INTERESTING IF YOUR
TALK I WAS CURIOUS WHETHER, AS
YOU SAID, IT WAS A VERY BIG
ABILITY ACROSS PARTICIPANTS AND
SO I'M WONDERING HOW HOW USEFUL
THE SIGNAL IS TOLL MEASURE
COGNITIVE AND WHETHER IT'S NOISY
AND COMPARES TO THE VARIOUS THAT
YOU MIGHT KEPT WITH SIMPLE
MEASURES LIKE SELF-REPORT OR
OTHER NON BRAIN IMAGING DEPEND
DEPENDENT MEASURES.
>>IT WAS ENERGY THE ENERGY
REQUIRED FOR A STATE TRANSITIONS
TO COGNITIVE AND COGNITIVELY
DEMANDING TASK BUT THEN ALSO TO
EVALUATE THIS DUEL-STATE THEORY
OF DOPAMINE FUNCTION AND IT
SUGGESTS THAT D2 RECEPTORS
FUNCTION CAN ALTER THE ENERGY
LANDSCAPE ON WHICH THE BRAIN IS
MOVING AND CHANGE THE SO WE
DIDN'T COLLECT SELF-REPORT
MEASURES IN THIS STUDY AND AND
NATURALLY THROUGH GENETICS OR
THROUGH PHARMACOLOGICAL
MANIPULATION CAN EXPLAIN THE
ENERGY DIFFERENCE SO IT'S THE
DIRECTION THAT THE STUDY WENT
AND SO WE DON'T KNOW IF IF THIS
IS MORE CORRELATED THAN
SELF-REPORT AND THEY ALLOW US TO
TO CONNECT BRAIN TO NEURO
TRANSMITTER FUNCTION WHICH I
THINK IS EXCITING.
>>I ALSO IT LOOKS LIKE ROBERT
WILSON HAD A QUESTION.
>>GREAT TALKS.
I GUESS THIS IS THE OLDER
SPEAKERS AND JUST THINKING ABOUT
THESE LARGER SCALE NETWORKS AND
MAYBE THE STABILITY TEE THAT
PROGRESS OR MILLER TALKED ABOUT
OF THESE NETWORKS AND WITH
RESPECT TO THE DRIFT AND IS
THERE A SCALE A PREFERRED TIME
SCALE THAT WE TAUGHT TO THINK
ABOUT IN TERMS OF THESE NETWORKS
AND OR IS IT A HIERARCHY OF
SCALES AND A DIFFERENT
COMMUTATIONS IN BEHAVIORS
REPRESENTED AT DIFFERENT TIME
SCALES AND LANCE SCALES AND I
GUESS WITH A IT'S OF TIME AND OF
SPACE.
>>I'M HAPPY TO START IF THAT'S
OKAY.
>>WE THINK OF I WOULD NOT
FOLLOW THE TRAP OF THINKING THAT
OSCILLATIONS BANDS LIKE ALPHA
AND THEY DO SPECIFIC FUNCTIONS
AND IT'S LIKE SAYING SPIKE OF A
SPECIFIC FUNCTION.
IT DEPENDS ON WHERE THEY'RE
SPIKEING AND WHAT THEY'RE
SPIKING TO AND WHAT NETWORKS ARE
IN AND THESE DIFFERENT FREQUENCY
BANDS, FIRST OF ALL, IT'S ONE
THING THE FREQUENCY BANDS WERE
MADE 150 YEARS AGO AND ARE VERY
ARBITRARY AND THEY DON'T
CORRESPOND TO REAL TRUE
DIFFERENCES BETWEEN THEM.
THINK OF THEM NOT AS A -- THINK
OF THEM AS DIFFERENT ENERGY
STATES IN THE NETWORK.
WHEN THERE'S A LOT OF SPIKING
GOING ON THERE'S A LOT OF GAMMA
AND HIGH LEVELS OF SPIKING AND
AT LOWER LEVELS, BETA, ALPHA,
THERE'S MORE LOW-LEVELS OF
SPIKING AND MORE SLOW FREQUENCY
AND WE THINK BECAUSE OF THOSE
DIFFERENCES, THE LOWER
FREQUENCIES ARE MORE APPROPRIATE
OR BETTER USED FOR A TOP-DOWN
CONTROL MECHANISM BECAUSE YOU
GET THE WAVES ACTING ON OF A
LARGER SPECIAL SCALE SO THEY'RE
BETTER TO ORGANIZE THE SCALE YOU
NEED FOR TOP DOWN CONTROL AND IF
YOU LOOK AT FACTOR BALL BY
LESLIE KAY WHEN LOTS OF GAMMA
AND THE SPIKE LINES WITH THE
SNIFFS AND AND IT SHIFTS INTO
DATA BANDS AND IT'S VERY LOCAL
AND IT'S VERY MUCH THE FEET
FORWARD STUFF COMING IN AND
SWITCHES THE BETA AND SOMETHING
THE WHOLE FACTORY SYSTEM COULD
BE PART OF THE MOTOR SYSTEM OF
CINCO NICED AND THINK OF THIS AS
NOT HAVING A FUNCTION BUT
SWITCHING TO DIFFERENT ENERGY
STATES AND COMPATIBLE WITH
DIFFERENT FUNCTIONS.
>>AND I WOULD LIKE TO ASK A
FOLLOW-UP QUESTION TO DANI ABOUT
THIS AND IF YOU LOOKED AT THE
RELATIONSHIP BETWEEN COST
MEASURES AND FREQUENCY BANDS AND
SWITCH INTERESTING AN ALPHA
DOMINATED TO A DAMN A GOM
INNATED DOES THAT TELL YOU ABOUT
THE COST SWITCHING BETWEEN THOSE
STATES.
>>THE MAJORITY OF OUR WORK IN
THE SPACE SO FAR HAS USED
FUNCTIONAL MAGNETIC HOWEVER WE
DO HAVE ONE STUDY WHERE THE
CITIES WE WERE EXAMINING WERE
POWER ACROSS DIFFERENT REGIONS
AND IN PARTICULAR FREQUENCY
BANDS AND SO THAT IS A PAPER
THAT WAS PUBLISHED IN THE
RECORDS AND I DID SEE ANA'S
HANDOUT AND I DON'T KNOW IF YOU
WANT TO SAY A LITTLE BIT ON THIS
TOO.
>>YEAH, I WAS GOING TO COMMENT
THAT THE NETWORK BUT SINCE WE
ARE TALKING ABOUT THE ENERGY
ISSUE AND I ACTUALLY ALSO HAD A
QUESTION FOR YOU AND WHICH IS
HOW DO YOU SEE THESE ENERGY
CONSUMPTION THROUGH AGING AND
BECAUSE IN YOUR EQUATION, I
MEAN, YOU HAVE THE DISTANCE AND
THE CHANGES AND THE STRUCTURAL
CHANGES MAY BE VERY, VERY SUBTLE
AND THROUGH THE YEARS AND YET
THROUGH THE PROCESS AND SO SO
WHAT DO YOU THINK WOULD BE
RELEVANT CONSIDERATION TO
CAPTURE THESE FUNCTIONAL CHANGES
TA GET MODIFIED OVER A LONG TIME
IN SUBTLE WAYS?
>>I THINK THAT'S A GREAT
QUESTION TOO.
THE CONTROL ENERGY ESTIMATES ARE
ONES THAT DEPEND ON CHANGES IN
STRUCTURE WHICH CAN BE SMALL OR
CHANGES IN ACTIVATION PATTERNS,
RIGHT AND IT'S THE COMBINATION
OF THOSE TWO RAILROADS' ABLE
THAT'S INFORMS THE CONTROL
ENERGY ESTIMATE AND SO AS AIMING
OCCURS BECAUSE THERE ARE CHANGES
BOTH IN ACTIVATION PROFILES AND
IN STRUCTURE THE CONTROL ENERGY
WOULD SENSE THAT HOWEVER, I
THINK IT'S ALSO INTERESTING TO
ASK WHICH OF THOSE IS DRIVING
THE ESTIMATE MORE SO ONE WAY
THAT CAN YOU DO THAT IS TO USE A
TEMPLATE STRUCTURAL CONNECTIVITY
FROM A SET OF YOUNG, HEALTHY
ADULTS AND YOU CAN SEE HOW MUCH
ENERGY WAS REQUIRED FOR THESE
TRANSITION OWNS THAT NETWORK AND
IT WOULD GIVE YOU AN ESTIMATE OF
HOW MUCH THE CHANGED IN
ACTIVATION IS DRIVING YOUR
RESULTS IN CONTRAST TO WHEN YOU
USE PARTICIPANTS STRUCTURAL
CONNECTIVITY THEN YOU ARE
ASSESSING MORE THE ROLE OF
DIFFERENCES IF THE THE PATTERN
OF CONNECTIVITY DRIVING AND THE
CONTROL ENERGY ESTIMATE SO JUST
TO BACK UP TO YOUR BROADER
QUESTION IS HOW DO WE THINK
ABOUT CHANGES IN CONTROL ENERGY
WITH AGE WE HAVEN'T LOOKED AT
OLDER AGE YET BUT WE HAVE LOOKED
AT DEVELOPMENT AND WE SEE FOR
EXAMPLE, THAT IN YOUTH, BETWEEN
THE AIMS OF EIGHT YEARS AND # 2
YEARS OF AGE WE SEE A MARKED
DECREASE IN THE CONTROL ENERGY
REQUIRED TO ACTIVATE FRONTAL FOR
ITSAL CORTEX CONSISTENT WITH
INDIVIDUAL DIFFERENCES IN
EXECUTIVE FUNCTION SO FOR THE
YOUTH WHO C HIGHER LEVELS OF
EXECUTIVE FUNCTION WE SEE THAT
THEY REQUIRE LESS CONTROL ENERGY
FOR FRONTAL CORTEX DUE TO THE
CONSTRUCT SURE CONNECTIVITY SO
THAT IS CENTERING ASK DUGS TO US
THIS IS AN APPROACH THAT CAN BE
USED ACROSS TIME SCALES AND IN
THE LIFESPAN BUT WE HAVE NOT YET
APPLIED TO LATER AGE.
S THAT HAVE IS VERY INTERESTING
IF EIGHT TO 22 BECAUSE FOR WHAT
YOU SAY, IF IT'S EFFECTIVE
CONTROL AND THAT MAY BE RELATED
TO THE LEVEL OF MY LYNNATION OF
THE TRACKS THAT ARE TAP NORTH
AND SO YOU WONDER IF THAT
INCREASING MYELIN ALSO WILL
PRODUCE AND INCREASE IN
SYNCHRONY OF THE SIGNALS BECAUSE
NOW THIS TRACTS ARE FULLY
DEVELOPED.
>>IS COLDATED WITH THE
INDIVIDUALS EX THAT COULD BE
REALLY INTERESTING AND IT WOULD
ADDRESS THE QUESTION OF WHETHER
THE DEGREE OF MY LYNNATION IS IN
PART A FACTOR CONNECTED TO THOSE
THAT I'VE BEEN DESCRIBING.
>>I THINK A NAN HAD A RELATED
QUESTION THAT WOULD BE USEFUL
FOR THE GROUP.
>>THANK YOU, THIS IS EXACTLY
WHAT MY QUESTION IS GETTING AT.
WE'VE BEEN SPENDING TIME HERE
TALKING ABOUT CHALLENGES THAT
MAY BE UNIQUE TO MODELING THE
BRAIN AND USING COMPUTATIONAL
APPROACHES AND YOU KNOW, MY
QUESTION IS, REALLY, WHETHER
THERE ARE UNIQUE LIMITATIONS
THAT MIGHT EXIST IN THE CONTEXT
OF LOOKING AT THE AGING BRAIN
AND YOU JUST TOUCHED ON THAT
TALKING ABOUT HOW WITH AGING AND
IT COULD BE HARD TO DETECT AND
WHAT ELSE WOULD YOU MIND TALKING
MORE ABOUT PARTICULAR CHALLENGES
BUT ALSO LIMITATIONS AND COMING
UP TO APPLY THESE APPROACHES IN
AGING IN THE BRAIN?
>>OTHER PEOPLE HAVE DIFFERENT
ANSWERS BUT WE APPLY AGE
PREDICTION MODELS TO DATA LIKE
FDG PET AND YOU CAN USE A DEEP
NURRAL NETWORK AND WHAT HAVE YOU
TO PREDICT SOMEONE'S AGE AND
COMPARE IT TO THE CHRONOLOGICAL
AGE AND EXPLORE WHAT THE MODEL
IS USING TO MAKE THAT PREDICTION
AND THAT CAN TELL YOU SOMETHING
ABOUT THE BIOLOGICAL MECHANISMS,
THE SCALE AND THE PHYSIOLOGY OF
THE DATA THAT YOU ARE USING TO
ASK THAT QUESTION.
AND SO, WHEN WE DO THAT WITH FDG
PET, YOU KNOW, WHAT WE SEE IS IT
TRANSITION WHERE YOUNGER FOLKS,
THE PREDICTIONS MORE DEPEND
APARTMENT ON THE FRONTAL LOBE
AND OLDER PREDICTIONS IT DEPENDS
ON THE BACK AND THERE'S THIS
GRADIENT FROM FRONT TO BACK AND
IT IS THE SAME THING YOU SEE IN
FUNCTIONAL MRI ACTIVATION
STUDIES SO WHEN WE HEAR THE SAME
THING, TO STRUCTURAL MRI, YOU
CAN ALSO PREDICT SOMEONE'S AGE
AND THERE'S TOTALLY DIFFERENT
FEATURES IN THAT DATA THAT ARE
BEING USED TO MAKE THAT PREDICT
AND IT'S SSF DYNAMICS SO THE
PROBLEM WHEN THE CHALLENGE IS
WHEN YOU HAVE THESE BIOLOGICAL
AGE ESTIMATES FROM BRAIN DATA AT
THE SCALE, WHAT EXACTLY DOES
THAT GAP TELL YOU BETWEEN
BIOLOGICAL ASK PREDICTIVE AGE.
SOME THINGS ARE THE SAME AND
SOME THINGS ARE DIFFERENT AND
STRUCTURAL MRI IT'S CSF DYNAMICS
THAT ARE DRIVING THE PREDICTION
AND THEN FDG PET, IT'S BRAIN
ENERGY CHANGES WITH AGE THAT ARE
DRIVING PRIEST DICTION AND
HAVING FRAMEWORK TO UNDERSTAND
THE DIFFERENT THINGS FROM
MULTIPLE PERSPECTIVES IS A
CHALLENGE.
>>I HAVE A QUESTION FOR Mr.
JONES.
I'M WONDERING, SO WHEN YOU TALK
DIVIDED PATIENTS INTO LANGUAGE
ORP MEMORY OR SENSORY, YOU HAVE
FIRST CLASSES OF PATIENTS AND
I'M WONDERING IF THERE'S DEMAND
FROM THE CLINICAL SIDE FOR SIZE
AND ON THE BEHAVIORS SIDE AND
YOU DESIGN DIGITAL TESTS FOR
THOSE SPECIFIC THINGS AND
EVERYONE GETS THEM, THAT'S WHAT
WE NEED.
WE DON'T HAVE THAT AND UP WITH
OF THE CHALLENGES THAT WE
ACTUALLY LIKE THEIR EXECUTIVE
COGNITIVABILITY THEY CANNOT AND
IT'S ONE DOMAIN AND IT SHOWS ON
ANY SORT OF TEST YOU DO AND THE
TASK PERFORMING AND WE DON'T
HAVE GOOD ASSESSMENTS FOR THEM
AT ALL AND AND IT'S A BIG
CHALLENGE.
>>I WILL ECHO TO BE AWARE OF
THE NIH TOOLBOX WHICH IS MULTI
DIMENSIONAL BRIEF MEASURES
ASSESSING COGNITIVE EMOTIONAL
AND MOTOR SENSORY FROM AGES 3-85
WHICH WOULD MEET THE NEED FOR A
STANDARD SET OF MEASURES USED AS
A COMMON CURRENCY ACROSS DIVERSE
STUDY DESIGNS AND SETTINGS.
AND THERE'S A LINK IN THE CHAT
FOR FOLKS ON THE ZOOM CALL AND
WE'LL MAKE SURE TO ACCEPTED IT
OUT AND POST MATERIALS FOR FOLKS
WATCHING AT HOME.
>>YEAH, I WOULD LIKE COME BACK
TO AMANDA'S QUESTION AND IT'S A
QUESTION THAT COMES OVER AND
OVER ON MODELING.
MAYBE I HAVE A VERY SIMPLISTIC
NOTION OF IT BUT FROM A
PERSPECTIVE THE REAL WORLD IS
VERY MESSY AND PART OF THE ROLE
OF MODELING IS TO GET TO THE
BEAR BONES THAT ARE RELEVANT TO
A SPECIFIC SITUATION.
SO, IN THAT SENSE, IN THIS
USEFULNESS I THINK I COMPLETELY
AGREE WITH FAVORITE ONE OF THE
NET TRICKS THAT TESTFULNESS OF
THE MODEL THAT THE PREDICTIVE
VALUE OF THAT MODEL SO THAT IS
ONE WAY TO GO.
ON THE OTHER HAND I THINK THAT
ALSO THIS MODELS THEY CAN BE
GENERATING HYPOTHESIS OF THE
MECHANISMS AND IN THIS SENSE,
THE LINK TO THE BASIC RESEARCH
AND IT'S FUNDAMENTAL.
WHY, BECAUSE THE MODEL CAN'T BE
THEY GO THE GENERATION OR
HYPOTHESIS CAN MAKE TESTABLE TO
WHAT EXTENT THAT IT'S PROPER AND
ON THE OTHER, ALSO, BASIC
RESEARCH CAN FEEDBACK TO THE
MODELING AND THAT'S JUST LINKING
WITH THE FIRST SECTION OF THESE
WORKS THAT IS WHERE THIS
INTERACTION CAN BE INTEGRATED
DATA FROM THE MOLECULAR TO THE
WHOLE BRAIN.
>>I WILL ADD I AGREE.
A LOT OF WORK WE'RE DOING, A LOT
OF THE HYPOTHESIS WE'RE TESTING
ON THESE DYNAMICS THEY CAME FROM
COMPUTATIONAL MODEL -- YOU KNOW,
IT'S TOO HARD TO FIGURE OUT
COGNITION FOR FIRST PRINCIPLES,
YOU NEED MODELS TO TELL YOU WHAT
PROPERTIES AND YOU WOULD NEVER
THINK TO LOOK FORWARD TO BEGIN
WITH AND TO PROCESS OF REFINING
THE MODELS AND MAKING NEW
REPREDICTIONS AND IT'S ALL
BEAUTIFUL.
>>I GREATLY AGREE.
BRAIN DATA PEOPLE TAKE IT DOWN
TO PARTICLE PHYSICS.
WELL, WE'VE GOT OTHER QUESTIONS
TO ANSWER SO I TRY TO TAKE A
PRAGMATIC APPROACH AND THIS IS
THIS IS A MODEL YOU CAN USE TO
ASK QUESTIONS BUT OUR DATA AT
LEAST WITH PETS THERE'S A VERY
TIGHT CORRELATION BETWEEN THESE
PATTERNS THE COGNITIVE SYNDROMES
THAT PATIENTS HAVE SO THERE'S A
LINK ASK IT'S VERY IMPORTANT AND
HAVING MODELS EXPLAIN THAT LINK
ARE GOOD FOR ASKING QUESTIONS IT
DOESN'T MEAN THEY'RE CAPITAL T
TRUE.
>>THERE WAS A QUESTION FROM THE
VIDEO CAST SPECIFICALLY FOR
Dr. MILLER IN TERMS IF YOU
COULD RECLASSIFY OF FREQUENCY
BANDS THAT THEY HAVE MEANINGFUL
PHYSIOLOGICAL FUNCTIONS WOULD
YOU DO SO AND IF SO HOW?
>>WELL, I WOULD STICK TO
GENERAL TEE.
I WOULDN'T DIVIDE FREQUENCIES
THE INTO ALPHA VERSUS BETA.
THERE'S GOING TO BE A LOT OF
INDIVIDUALITY AND IT'S SHIPPED
AROUND FROM BRAIN TO BRAIN AND
THE LOW FREQUENCY VERSUS HIGH
FREQUENCY IS A BIG DINK TANKION
AND THE HIGHER FREQUENCIES ARE
SENSORY INFORMATION AND CREATING
MOTOR OUTPUT AND LOW FREQUENCIES
THAT CAN PUSH AROUND THAT WILL
PLAY MORE OF A ROLE IN
CONTROLLING GAMMA IS MAIN LOCAL
PHENOMENON BUT IT'S INFORMATION
AND THERE'S THOUGHT IT CROSSES
TO NETTA BARZ THETA HELPS IT TRG
DIS DANCES WE USE GAMMA AND
NETTA BARZILANETTHETA BUT IT'S S
BETWEEN FREQUENCIES THAT WE
SHOULD BE MORE FOCUSED ON.
O.
>>I HAVE A QUESTION FOR DAVE
AND MAYBE EARL YOU CAN WEIGH ON
THIS AS WELL.
DAVE, YOU SHOW COOL DATA ABOUT
MAKING PREDICTIONS USING FDG PAD
FOR COGNITION.
DO YOU THINK IT'S AS EASY TO
PREDICT POTENTIALLY ANYTHING
RELATE TODAY MOW BILL AND
MOBILITY TO DECLINE USING THOSE
SAME TOOLS?
>>SO, I THINK SO.
WE'RE WORKING ON THAT NOW AND
THIS IS, I WOULD LIKE TO MAKE
THINGS CONCRETE SO I JUST THINK
ABOUT MY PATIENTS BECAUSE THEY
ASK THIS QUESTION AND FOR
ALZHEIMER'S, SOMETIMES THOSE
PATIENTS ARE IN NURSING HOMES
WITHIN TWO YEARS AND I SEE IT
AND I KNOW THAT.
I SEE OTHER PATIENTS WHERE IT'S
10 YEARS OR LONGER SO I'LL START
OFF BY TELLING MY PATIENTS, THIS
IS A WIDE RANGE OF VARIABILITY
I'VE SEEN IN MY CLINIC AND THEY
WANT TO KNOW ABOUT THEM AND WE
HAVE A PAPER IF YOU CAN SUB
PHENOTYPE THIS AN ATOMICALLY AND
VERSUS A RIGHT FRONTAL OR LEFT
FRONTAL OR HETERO MOTTAL THOSE
HAVE DIFFERENT CLINICAL
TRAJECTORIES THAT CAPTURES THE
RATES WE BELIEVE AND THE YOUNG
ALZHEIMER'S PATIENTS ARE THINK
THAT'S GOING TO BE VERY
PREDICTIVE OF A RELATIVELY LESS
A BLESS I HAVE COURSE.
AND SO, HOW WILL WE DO THAT?
I WOULD TAKE ALL THE PATIENTS OF
THE FDG PET PATTERN AND MATCH IT
TO EVERYONE IN OUR DATABASE WHO
HAS THAT PATTERN AND WE HAVE
LONGITUDINAL DATA ON THEM AND I
COULD SAY WELL, EVERYONE ELSE
WHO HAS HAD BRAIN PATTERN LIKE
THIS IS THE CLINICAL OUTCOME AND
I THINK THAT'S GOING TO WORK AND
WE HAVEN'T DONE THAT YET.
ALSO, ONE OTHER IMPORTANT THINGS
IT'S ONLY IF THEY'RE A SYNDROME
AND THE DISABILITY IS CAPTURED
BY THE DIS TEASE FAN METING IN
THEIR FDG PEST AND IF THEY HAVE
A DISORDER MISSED.
THEY WILL GET DISABILITY FROM
NORMAL INAND IT'S JUST AN
EXAMPLE AND WE HAVE TO TAKE INTO
ACCOUNT THE PATIENTS ECONOMICS
TEX'' COP TEXTAND MULTIPLE FACTE
PREDICT THINGS LIKE THIS.
>>EARL, ANY THOUGHTS ON THAT
QUESTION?
>>I MEAN, LOOK, WE ARE A VERY
BASIC NEUROSCIENCE LAB.
WE DON'T STUDY PATIENT GROUPS
BUT WE HAVE A MODEL OF SENSORY
OVERLOAD OF AUTISM WHERE WE
THINK IT'S IMBALANCE BETWEEN THE
LOW AND HIGH FREQUENCY.
WHEN THEY'RE LOW AND HIGH AND
THE TOPPED DOWN CONTROL BASED ON
LOW FREQUENCIES CAN'T FILTER OUT
THE INWANTED SENSORY SIGNALS AND
YOU GET SENSORY OVERLOAD AND
IT'S CONSISTENT WITH THE
OBSERVATIONS OF EEG AND ASD
POPULATIONS.
WE'RE TRYING TO START A HAVE
HE'S OF INVESTIGATIONS WHERE
WE'RE GOING TO ACTUALLY
MANIPULATE THE RHYTHMS USING
STIMULATIONS AND IF WE CAN TURN
DOWN THE LOWER FREQUENCY CONTROL
RHYTHM WE SHOULD LET THE FOOD
GATES OF SENSORY INFORMATION
COME IN AND CREATE A MODEL OF
SENSORY OVERLOAD AND THEN FIX IT
BY TURNING UP THE RHYTHM AND SO
THAT PROPER FILTERING COMES
BACK.
SO, PATIENT POPULATIONS BUT
WE'RE HOPING TO ACTUALLY CREATE
A DISEASE LIKE MODEL USING BUY
VIRTUE OF MANIPULATING THE BRAIN
ROOMS.
>>I THINK THAT'S WHAT YOU JUST
SAID EARLIER IS GREAT AND I
THINK THAT IS REALLY TIES
TOGETHER I THINK OF DANI'S WORK
AND BRAIN ENERGETICS.
LET'S SAY WE KNOW THE BRAIN
CONFIGURATIONS OF OUR PATIENTS
IT WILL HELP DIAGNOSE AND
PROGNOSIS.
MAYBE IT WILL BE EASIER TO MAKE
THESE STATE TRANSITIONS, IT WILL
BE LESSENNER JET TICK AND
THEY'LL BE ABLE TO DO IT WHICH
MEANS THEY'LL GET IT TO PERFORM
EXECUTIVE FUNCTIONS AGAIN BEFORE
THEY WEREN'T.
SO EVEN IF YOU DON'T BELIEVE
THIS LEVEL IS CAUSAL, IT'S A
PARADIGM YOU CAN USE TO TREAT
PATIENTS EVEN IF YOU DON'T THINK
IT'S CAUSAL.
>>I BELIEVE IT IS CAUSAL.
IT'S HARD TO IMAGINE IT'S NOT.
IT'S A LARGE ORGANIZATION OF
BRAIN ACTIVITY.
TO THAT POINT, WE'RE DOING THESE
EXPERIMENTS USING NEW TECHNOLOGY
ULTRAFAST CLOSE LOOP STIMULATION
AND ELECTRICAL STIMULATION WHERE
EVERYBODY NOW AND ANIMALS IS
DOING GENETICS AND WHY ARE WE
DOING ELECTRICITY INSTEAD
BECAUSE WHEN WE FIGURE OUT HOW
THOSE SYSTEMS WORKS INSIDE THE
BRAIN WE CAN THEN REPLAY THE
RHYTHMS USING NON-INVASIVE
STIMULATION AND WORK ALONG
TREATMENT PLANS AND HAVE PEOPLE
REINFORCE THE RIGHT BRAIN RHYTHM
WHEN PEOPLE ARE IN TRAINING
SESSIONS THAT WILL REINFORCE THE
STRUCTURE OF THE BRAIN THAT DANI
TALKED ABOUT.
YOU WILL NEVER DO THAT BY
INJECTING A VIRUS INTO A HUMAN
BRAIN.
IT'S ALWAYS A RESEARCH TOOL.
USING ELECTRICAL STIMULATION IS
A PATH TOWARDS ACTUAL TREATMENT.
>>David Jones: THE ONLY WAY TO
GET US THERE IS WE NEED THEORIES
AND MODELS THATS OPERATE AT THIS
LEVEL.
THAT WILL GUIDE THE TREATMENTS
AND MEASURING A RESPONSES AND I
THINK THAT'S WHERE A LOT OF WORK
NEEDS TO BE DONE.
>>I CON AGREE MORE.
>>IF I CAN, I JUST HAVE -- IT'S
SELF-SERVING BUT WE HAVE A
PLATFORM AND IT'S BUILT TO BE
USED NOT ONLY IN HUMANS BUT ALSO
IN MACAQUE AND IN MICE AND RATS.
COMING BACK TO THE ISSUE OF --
IT'S POSSIBLE TO SIMULATE THE
TMS OR TTC STIMULATION IN THE
MODEL AND MODEL THE POTENTIAL
DYNAMICS THAT IT PRODUCES ORIGIN
IF THERE'S A PARADIGM THAT
SUGGESTS A TREATMENT, ALSO IT
CAN BE TESTED AT THE MODEL LEVEL
WITH MEASURING PROPER CHANGES IL
OF THE ELECTRIC FIELDS OR I MEAN
WE CAN MODEL AT ANY LEVEL.
"CAN FACILITATE THE TESTING OF
THE THERAPISTS.
>>I AGREE.
>>I GUESS JUST TO FOLLOW-UP ON
THAT AT THAT POINT ABOUT
SIMULATED DATA AND THE ROLE OF
SORT OF SIMULATED DATA WITH
REGARDS TO MODELING, YOU KNOW,
YOU TALKED ABOUT THAT AND PAOLA
TALKED ABOUT IT IN THE FIRST
SESSION AND I'M CURIOUS IF YOU
COULD OR OTHERS FEEL LIKE WHAT
IS THE STATE OF SORT OF
SIMULATED DATASETS IN SORT OF
SURVEYING THESE SORT OF
COMPUTATIONAL MODELING
APPROACHES?
ARE THE SIMULATIONS GOOD ENOUGH
OR DO WE NEED BETTER SIMULATION
AND DO WE NEED TO JUST.
>>WELL, THINK THAT ONE SIZE
DOESN'T FIT ALL BUT I WILL GIVE
YOU AN EXAMPLE OF WHEN
COLLABORATOR AND FRIEND VICTOR
GERSA WHO WAS INTERESTED IN
SIMULATING SEIZURES IN
EPILEPSIES CASES AND RIGHT NOW
THE SIMULATION -- HE IS LIVES IN
FRANCE AND SO THEY ARE RUNNING A
CLINICAL TRIAL WITH THE
SIMULATIONS EMBEDDED INTO THE
DETERMINATION OF THE SITE OF THE
ORIGIN OF THE SEIZURE BEFORE THE
NEURO SURGEONS COME ABOUT AND
DISSECT THE AREA.
BECAUSE THIS MODEL CAN BE SO
SENSITIVE THAT CAN BY PASS THE
EFFICACY OF FINDING THIS
LOCATION OF THIS SEIZURES BASED
ON CURRENT METHODS ON EEG AND
ALL THESE.
SO IT'S A CLINICAL TRIAL THAT IS
GOING ON RIGHT NOW.
SO, IT CAN BE, AGAIN, YOU KNOW,
THE MOLDS ARE NOT PERFECT AND
CAN BE EXTREMELY USEFUL.
AND THAT'S WHERE THE PREDICTIVE
VALUE I THINK COMES ABOUT THAT
IS RELEVANT.
IT'S MUCH MORE SPECIFIC RIGHT
NOW WE HAVE FOR THE LOCAL
DYNAMICS WE HAVE A LIBRARY OF
MAYBE 257 DIFFERENT MODELS AND
THEY GO FROM THE DYNAMICS AND
DEPENDING ON THE HYPOTHESIS THAT
ONE HAS, YOU KNOW, YOU CAN HAVE
CURRENTS IF THAT'S WHAT THE
INTEREST IS AND SO I THINK THE
QUESTION AGAIN IS, WHAT IS A
USEFUL MODEL.
>>I LOVE TO SHARE DATA WITH
MODELERS AND I HAVE A LARGE
CATEGORY OF DATA FROM ANIMALS
PERFORMING COGNITIVE AND
ELECTRODES ALL OVER THE BRAIN
AND LFPs AND SPIKES.
ANYONE INTERESTED WORKING
TOGETHER WITH THIS DATA, CONTACT
ME.
I MEAN THAT.
>>YES.
I MEAN IT TOO.
THIS IS AWESOME.
>>COOL.
>>I'D LIKE IT MAKE A COMMENT.
AS I HEARD MY NAME.
I DIDN'T HAVE TIME TO DISCUSS
THIS EARLIER AND I OF WORKED
WITH FOR A COUPLE OF YEARS WITH
COMPANY AND DEVELOPED SO
PHYSICAL INDICATES PROCEDURES
SIMULATION AND OF THE STATISTICS
AND LANGUAGE MEASURE PROCESSING
AND IT'S CALLED EACH AND AND USE
HAPPENING PROCESSING TOOLS TO
SIMULATE DATA FROM YOUR ORIGINAL
DATASET.
I WAS VERY SKEPTICAL AND THEY
HAD A REAL DATASET AND THEY GAVE
ME A SIMULATED DATASET THAT IS
STATISTICAL REPRESENTATION OF
THE ORIGINAL DATA AND THEY CAN'T
SIMULATE IF I GIVE THEM A
THOUSAND OBSERVATION, I CAN'T
SIMULATE A HUNDRED THOUSAND AND
HOW CAN THAT BE?
IT TURNS OUT, SO I STARTED VERY
SKEPTICAL AND I BELIEVE THAT WAS
POSSIBLE AND I HAVE TO SAY I'M
VERY IMPRESSED BY WHAT THEY GAVE
ME BACK.
SO REALLY DATASET AND THE GOAL
WAS TO CREATE WOULD BE SHARED
WITHOUT ANY RESTRICTIONS IT
TURNS OUT THAT NOW MY STUDENTS
ARE USING THEM FOR TUNING UP
THEIR MODELS BECAUSE THOSE ARE
AN INCREDIBLE RESOURCE THAT IS
ESSENTIALLY DATA WILL NOT HAVE A
PRELIMINARY AND YOU CAN DO IT AS
MUCH AS WANT AND FOR REALLY
VALIDATION OF MODELS AND THEY
SEEM TO BE TO FACILITATE TO
SHARED DATA AND WE SEE A LOT OF
MODELS THAT DEVELOP AND THIS
COULD BE A REALLY USEFUL
BENCHMARK DATA THAT CAN BE
SHARED WITH NO RESTRICTIONS AND
SO IT COULD BE AN INTERESTING
IDEA TO EXPLORE FURTHER.
>>I WAS VERY SKEPTICAL AND I'M
REALLY IMPRESSED BUT THAT.
>>EVEN IF THE SIMULATIONS ARE
GOOD FOR RESEARCH, I THINK OF AT
LEAST THERE'S A MATHEMATICAL
FORMULATION BEHIND ME THAT MAKES
THE SIMULATION VERY CONSISTENT
AND SO THAT DOESN'T MAKE IT TRUE
EX MAKES MAKES USEABLE THE
FACILITY USE THE DATA.
NOW --
>>WHAT I FIND SURPRISING OF
THIS APPROACH IS THAT IT DOES
NOT RELY ON MATHEMATICAL MODELS.
YOU KNOW, YOU CAN PROVE YOURSELF
THAT ARE RIGHT IF YOU SIMULATE
DATA CONSISTENT WITH YOUR
HYPOTHESIS AND THAT HAPPENS A
LOT.
THESE ARE REALLY BASED ON SAY
IT'S A EMPIRICAL WAY OF DATA
FROM A LARGE DATASET, DL DATA
AND THERE'S NO MATHEMATICAL
FORMULATION BEHIND IT.
WHICH AGAIN, I SEE SURPRISING
AND SKEPTICAL PHASES WHICH I
AGREE AND I WAS VERY SKEPTICAL
AND IT CAN BE DONE BUT THIS IS
SOMETHING TO EXPLORE AND JUST
DISTRACTING INFORMATION ON EX AM
POLLS FROM THE REAL DATA.
>>SO ONE OTHER THING WE PLAN TO
DO, I'M INVOLVED WITH THE DATA
COMMUNITY THAT IS PART OF THE
PORTAL THAT WE STORE AND SHARE
AND MAINTAIN DATA FROM STUDIES
AND ONE OF THE THINGS WE PLAN TO
DO IS ACTUALLY CREATE A
CHALLENGE IN A COUPLE OF YEARS
WITH SYNTHETIC DATA AND SO LET'S
SAFE WE CAN -- I THINK IT'S
GOING TO BE USEFUL FOR TUNING UP
MODELS AND HELPING WITH THE
COMPUTATIONAL STRATEGIES.
>>I HAD A QUESTION FOR YOU,
DANI, ABOUT THE DIMENSIONALITY
OF THE STATE SPACE THAT YOU ARE
CONTROLLING OVER.
IS THAT A LOW D SPACE AND HOW
LOW D?
>>YEAH, WE'RE USING N AS THE
DIMENSIONS SO N BEING THE NUMBER
OF BRAIN REGIONS WE'RE STUDYING
AND THAT'S A COUPLE HUNDRED
TYPICALLY.
IT'S NOT VERY LOW D.
>>DO YOU THINK YOU NEED ALL
THAT OR DO YOU THINK COULD YOU
HAVE A LOWER DIMENSIONAL
REPRESENTATION IN THE SAME
FRAMEWORK?
>>I THINK THAT YOU COULD USE A
LOWER DIMENSIONAL
REPRESENTATION.
WE HAVE ONE STUDY THAT WAS DONE
IN COLLABORATION WITH ANN
GRABIAL AND USE USING CELLULAR
IMAGING IN MACAQUE MONKEYS TO
UNDERSTAND WHETHER ENERGY IS
ALTERED AS THE MOP KEYS ARE
DEVELOPING HABITS AND MOTOR
HABITS.
AND IN THAT CASE, THE
DIMENSIONALITY IS LOWER.
SO I DON'T THINK THAT THERE'S
NOTHING ABOUT THE APPROACH THAT
REQUIRES US TO HAVE AS MANY
DIMENSIONS AS WE HAVE AND I
THINK AN INTERESTING QUESTION IS
WHAT IS THE DIMENSIONALITY
NEEDED TO UNDERSTAND GROUP
DIFFERENCES IF WE'RE TALKING
ABOUT CLINICAL CONDITIONS AND
VERSUS TO UNDERSTAND INDIVIDUAL
DIFFERENCES IN COGNITIVABILITY,
FOR EXAMPLE, AND WE DO NEED A
HIGHER SPACE AND I THINK IT'S AN
OPEN QUESTION AND IT DETOYS MORE
ATTENTION.
>>IT'S SOMETHING THAT WE
STRUGGLED AND IT SEEMS LIKE 107
AND IT'S VERY GOOD FOR
REPRESENTING THE CONCEPT OF
ALZHEIMER'S IN GENERAL AND WHEN
YOU GET TO INDIVIDUALS IN MAKING
MORE PRECISE PREDICTIONS,
ESPECIALLY TRACKING ONE
INDIVIDUAL OVER TIME AND IT
SEEMS MORE THAN THE ORDER OF 50
OR 60 BUT AFTER THAT WE DON'T
SEEM TO GET VERY MUCH MORE
INFORMATION.
AND WE COMPARE THAT TO USING THE
WHOLE BRAIN SCAN, USING ALL THE
EYES AND BREAKING UP AND IT
SEEMS MORE LIKE 60 LARGE-SCALE
BRAIN CAPTURES THAT BETTER.
YOU GET MORE NOISE IF YOU USE
THE WHOLE IMAGE AND SO WE WERE
SURPRISED THAT 60 IS STILL LOW I
THINK.
>>SURPRISEING AND SUPER
INTERESTING.
>>THE BRAIN SIMILARITY GRAPHS
OR SIMILARLY PATIENT WISE TO ONE
ANOTHER.
IT'S A PATIENT THE EDGE IS THE
COSINE SIMILARITY BETWEEN THESE
LOW DIMENSIONAL REPRESENTATION
SO IN THOSE GRAPHS, IT'S 50 TO
60.
RIGHT NOW IT'S 50 AND IF WE GO
60, 70, 80, THEY'RE NOT HELPING
MAKING THOSE PREDICTIONS THAT
YOU SAW AND ALSO TRACKING AND
IDENTIFYING AND ONLY NEED 650 OR
0650 OR60 AND NOT HELPED BY GETG
ANYMORE.
>>WE HAVE A COUPLE MINUTES LEFT
HERE AND ANY OTHER PARTICIPANTS
AND ANY QUESTIONS OTHERWISE I
WILL ASK EACH SPEAKER TO SORT OF
GIVE THEIR SORT OF ONE BIGGEST
SORT OF TAKEAWAY GAP OR
OPPORTUNITY HERE.
>>ANY PARTICULAR ORDER,
SPEAKERS.
>>WE CAN GO IN SPEAKING ORDER.
YOU SPOKE UP FIRST.
YOU START.
>>I WOULD SAY THE 21s
21st SEPTEMBER REIS THE
SEPTEMBER REOF UNDERSTANDING THE
BRAINS IMMERSION PROPERTIES AND
THEY WERE IGNORED FOR A LONG
TIME AND NOW WE UNDERSTAND
ENOUGH ABOUT THE PIECES AND WE
HAVE TO START LOOKING AT THAT
LEVEL.
>>ANA.
>>YES THE TIME IS RIGHT FOR A
CHANGE IN PARADIGM TAPPING
MECHANISMS AND NOT ONLY AGING
BUT ALSO BRAIN DYSFUNCTION AND I
THINK THAT THE APPROACH HAS TO
HAVE A PERMANENT FEATURE.
THINDIVIDUALATION OF THE MEASURS
AND IT'S A WAY OF PUSHING
TOWARDS PRECISION MEDICINE.
>>I THINK WE NEED ONTOLOGY FOR
THE LARGE-SCALE ORGANIZATION OF
THE BRAIN THAT WE ALL UNDERSTAND
AND AGREE ON AND RELATE TO
CERTAIN CLINICAL SYNDROMES AND
SYMPTOMS AND A MODEL OF THAT TO
MEASURE THAT AND INTERVENE ON
THAT.
>>I THINK THAT ONE OF THE
CHALLENGE THAT'S IS GOING TO BE
PRODUCTIVE IN EVALUATING OVER
THE NEXT COUPLE OF YEARS IS
UNDERSTANDINGENNER JET TICKS IN
THE BRAIN FROM DIFFERENT
PERSPECTIVES AND MAYBE TRYING TO
OF CONNECTION THEM TOGETHER AND
MEASUREMENTS AND MODELS AND I
THINK THAT THAT IS PROVIDING US
WITH THIS BEAUTIFUL CONSTRAINTS
ON THE SYSTEM CAN PUSH US
FORWARD.
WELL, I THANK EVERYONE IN THIS
SESSION FOR NOT A GREAT SET OF
TALKS AND DISCUSSION AND I WILL
PASS TO LENNY FOR CLOSING
REMARKS.
>>YES, I WOULD LIKE TO THANK
ALL TODAY'S PRESENTERS AND
DISCUSSANTS FOR AN EXCITING
DYNAMIC ELIMINATING FIRST DAY OF
TWO SESSIONS.
THEY REFLECTED IN THEIR TALKS
RAPID DEVELOPMENT OF SEVERAL
FIELDS AND INTERSECTION OF
COMMUTATION, AGING, SYSTEMS,
GENETICS, OMICS, IMAGING,
BIOLOGY, NEUROSCIENCE, MEDICINE
AND OTHER FIELDS.
WHILE THE CLUSTER BY THE TARGET
APPLICATIONS SYSTEM MANY
RESEARCH APPROACHES ARE LARGELY
INTER DISCIPLINARY.
LARGE VOLUMES OF GENERATED DATA
IN NECESSITATES MATTERS FOR
PREDICTIONS AND INTEGRATING IS
ESPECIALLY AND COULD BE THE
BUYICONTRIBUTION FORFIELDS AND L
LAYER OF PREDICTION AND
ANALYSIS.
WITH STEPS IN THE RIGHT
DIRECTION TOWARDS MORE ACCURATE
DESCRIPTION OF RE REAL WORLD
COMPLEXITY.
THIS COULD CREATE AGING AND
LONGEVITY AND PREVIOUSLY DEEMED
IMPOSSIBLE AND CONTRIBUTE TO A
STRATEGIC DIRECTIONS THROUGH A
BETTER UNDERSTANDING OF AGING
PROCESSES AND THEIR IMPACT ON
THE PREVENTION PROGNOSIS AND
LONGEVITY.
THANK YOU.
SO TOMORROW WE START AT 12:25
WITH SESSION 3.
COMPUTATIONAL ANALYSIS OF
COMPLEX BEHAVIOR.
>>THANK YOU, ALL.
SEE YOU TOMORROW.