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Functional and Anatomical MRI-Based Biomarkers for Classifying Groups and Individuals Peter A. Bandettini, Ph.D. Section on Functional Imaging Methods, Laboratory of Brain and Cognition, NIMH & Functional MRI Facility, NIMH/NINDS.

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slide1

Functional and Anatomical MRI-Based

Biomarkers for Classifying

Groups and Individuals

Peter A. Bandettini, Ph.D.

Section on Functional Imaging Methods,

Laboratory of Brain and Cognition, NIMH

&

Functional MRI Facility, NIMH/NINDS

slide2

Abstract: In recent years, two major trends have emerged in MRI and fMRI. The first is the push to use MRI and fMRI to classify individuals and assess individual variation, and the second is the combined use of fMRI and genetics information – using fMRI measurements as informative phenotypes. Both of these trends come together with the question of what MRI and fMRI information can be used and what might be most useful or informative. A wide range of information about individual brain structure and function can be derived from MRI and fMRI. In this lecture, I will survey the literature on what useful individual-specific information has been derived as well speculate on the potential of MRI and fMRI for individual classification associated with individual genetic and behavioral differences across healthy and clinical populations. I will also attempt to answer the question of whether there exists sufficient “effect-size to noise” as well as powerful enough algorithms for robustly characterizing individual traits from MRI and fMRI scans.

slide3

MOTIVATION 1

Alzheimer’sdisease: 2 people out of 10concernedbeyond the age of 80; dependencyoccurswithin 3 to 5 yearsafter the disease has appeared.Depression: the second mostcommon condition in the world according to the WHO: itconcerns6 per cent of the population in the Western world.

Cerebralvascular accidents: the first cause of motordisabilities in adults.75 per cent of victimssufferfromresidualdisability.Parkinson’sdisease: second cause of motordisability. It affects 2 out of 1,000 people.

Multiple sclerosis: concernsmainlyyoung people and leads to a loss of autonomy in 30 per cent of cases.

Epilepsy: 50 million people concerned in the world of whichalmosthalfbebeforeage 10. The social and familial repercussions are lifelong.

motivation 2 data federation integration

Number of Peer Reviewed

Publications on the Brain /yr

  • Reality check
  • Data and knowledge is growing exponentially
  • Data and knowledge is increasingly fragmented
  • Benefits for society seem to be decreasing (diagnostic accuracy, treatments, drugs)
  • Economic burden increasing rapidly to unsustainable levels
  • What we lack
  • No integration plan
  • No data curation plan
  • No plan to link across levels
  • No plan to transfer knowledge from animal to human
  • No plan to go beyond symptom-based classification of diseases

MOTIVATION 2 - DATA FEDERATION & INTEGRATION

2012

slide6

Individual Assessment with fMRI

  • We can see activation in single runs (on or off).
  • We can see parametric modulations in activation.
  • We can see differences in activation that are correlated with performance, behavior, perception, conscious state, intent, etc..
  • We can “decode” fMRI signal: infer a mental process by assessment of fMRI dynamics or activation pattern.
slide7

Decoding by eye…

What is this person doing?

Left then right finger tapping : 1991

slide8

While group difference studies are ubiquitous, those that demonstrate the classification of individuals into groups based on their activation maps, dynamics, are much less common.

  • Handedness (or language dominance)
  • Gender
  • Sensorimotor characteristics
  • Differences and cognitive or personality traits
  • Differences in psychological state
  • Differences in physiologic state
  • Neurologic differences
  • Developmental differences
slide9

Anatomic MRI has been extremely successful clinically, where fMRI has made almost no inroads.

  • Why?
  • Quick, relatively easy, individual assessment with high specificity and sensitivity to physical pathology. (high effect size to noise ratio)
slide12

Effect Size / (Noise & Variability) > 10

  • We also have a clear gold standard with which to compare
slide13

Typical fMRI Studies

Group 1

Group 2

Gold standard measures are not always clear: (i.e. DSM-IV, V codes)

slide14

Individual genotypes very effective gold standards.

Comparison of two groups of normal individuals with differences in the Serotonin Transporter Gene

slide15

fMRI and MRI ARE exquisitely sensitive to individual traits.

A few examples of MRI-derived information as it correlates individual characteristics….

slide17

BOLD magnitude in dorsal striatum predicts video game learning success

2011

Dorsal striatum

slide20

Decision making

FA: Visual Choice Reaction Time

GM density: Response Conflict

Pre-SMA & striatum connection strength:

Speed - Accuracy tradeoff ability

slide21

Conscious Perception

Posterior superior parietal lobe size (negative correlation):

Switching between competing percepts

V1, 2, 3 surface area

(negative correlation):

Ability to see illusions

BA 10 size: Metacognition

slide22

Personality Is Reflected in the Brain's Intrinsic Functional Architecture

Published: November 30, 2011

DOI: 10.1371/journal.pone.0027633

Personality Is Reflected in the Brain's Intrinsic Functional Architecture

Published: November 30, 2011

DOI: 10.1371/journal.pone.0027633

Resting State: Personality Type

Adelstein et al. PLOS one, DOI: 10.1371/journal.pone.002763

slide23

Intelligence

Personality

slide24

Elements of a Classification Pipeline

  • Training Data Set.
    • Scan a very large number of well characterize subjects.
  • Feature extraction from raw data and dimensionality reduction.
    • Find the most informative measures and features from fMRI and/or anatomy
  • Minimize or Better Characterize noise and variability.
  • Maximize the effect size
    • Paradigm development & clear gold standard development
  • Model training and optimization.
    • Teach an algorithm to use the information to allow differentiation.
  • Application to test data.
  • Apply the learned rule to new data.
slide25

What measures can we obtain with MRI and fMRI?

  • BOLD, Flow, Volume:
  • Location
  • extent
  • magnitude
  • shape
  • latency
  • post undershoot
  • transients within activation response
  • changes in activation over time
  • resting state correlation magnitude
  • resting state correlation extent
  • dynamics of resting state
  • ICA components
  • cortical hub sizes magnitudes locations
  • BOLD/flow ratio
  • Anatomy: gray matter density & volume
  • white matter
  • CSF
  • gyrification
  • diffusion tensor
  • fractional anisotropy
  • correspondence to EEG, MEG, PET, behavior
  • susceptibility weighted measurements (blood volume and iron)
  • Myelo-architecture
  • Spectroscopy: many molecules..
slide26

Elements of a Classification Pipeline

  • Training Data Set.
    • Scan a very large number of well characterize subjects.
  • Feature extraction from raw data and dimensionality reduction.
    • Find the most informative measures and features from fMRI and/or anatomy
  • Minimize or Better Characterize noise and variability.
  • Maximize the effect size
    • Paradigm development & clear gold standard development
  • Model training and optimization.
    • Teach an algorithm to use the information to allow differentiation.
  • Application to test data.
  • Apply the learned rule to new data.
slide27

Sources of Variability Across Subjects

  • Thermal
  • Scanner
  • Hemodynamics
  • Neuro-vascular coupling
  • Structure
  • Task strategy
  • Medication
  • Performance
  • Arousal/Motivation
slide28

group

Individual activations from the left hemisphere of the 9 subjects

SC

NL

KB

EE

JL

HG

BB

BK

CC

Extensive Individual Differences in Brain Activations During Episodic Retrieval

Courtesy, Mike Miler, UC Santa Barbara and Jack Van Horn, fMRI Data Center, Dartmouth University

slide29

Individual activations from the right hemisphere of the 9 subjects

SC

KB

NL

group

HG

JL

EE

BB

BK

CC

Extensive Individual Differences in Brain Activations During Episodic Retrieval

Courtesy, Mike Miler, UC Santa Barbara and Jack Van Horn, fMRI Data Center, Dartmouth University

slide30

Subject SC

Subject SC 6 months later

These individual patterns of activations are stable over time

Group Analysis of Episodic Retrieval

Courtesy, Mike Miler, UC Santa Barbara and Jack Van Horn, fMRI Data Center, Dartmouth University

slide32

Response to Hypercapnia

Response to modified Stroop task

...leads to a potential underestimation of

neuronal activity in older adults

slide33

Sources of Time Series Variability

  • Blood, brain and CSF pulsation
  • Vasomotion
  • Breathing cycle (B0 shifts with lung expansion)
  • Bulk motion
  • Scanner instabilities
  • Changes in blood CO2 (changes in breathing)
  • Spontaneous neuronal activity
slide34

What’s in the time series noise?

Bianciardi et al. Magnetic Resonance Imaging 27: 1019-1029, 2009

slide35

Elements of a Classification Pipeline

  • Training Data Set.
    • Scan a very large number of well characterize subjects.
  • Feature extraction from raw data and dimensionality reduction.
    • Find the most informative measures and features from fMRI and/or anatomy
  • Minimize or Better Characterize noise and variability.
  • Maximize the effect size
    • Paradigm development & clear gold standard development
  • Model training and optimization.
    • Teach an algorithm to use the information to allow differentiation.
  • Application to test data.
  • Apply the learned rule to new data.
slide37

Group 1

Group 2

?

slide46

Control

Schizophrenia

Bipolar

default network connectivity predicts conversion to dementia in subjects at risk
Default Network Connectivity Predicts Conversion to Dementia in Subjects at Risk

MCI

non-convertor

MCI

convertor

Difference

J. R. Petrella, F. C. Sheldon, S. E. Prince, V. D. Calhoun, and P. M. Doraiswamy, "Default Mode Network Connectivity in Stable versus Progressive Mild Cognitive Impairment," Neurology, vol. 76, pp. 511-517, 2011.

static fnc in fbirn schizophrenia data n 315 hc sz
Static FNC in fBIRN Schizophrenia Data (n~315 HC/SZ)

* Hyper: thalamus-sensorimotor

* Hypo: thalamus-(prefrontal-striatal-cerebellar)

Inversely related (less so in patients)

Sensorimotor region & cortical-subcortical antagonism co-occur with thalamic hyperconnectivity

dynamic states schizophrenia vs controls
Dynamic States: Schizophrenia vs Controls

Putamen - Sensorimotor hypo-connectivity

E. Damaraju, J. Turner, A. Preda, T. Van Erp, D. Mathalon, J. M. Ford, S. Potkin, and V. D. Calhoun, "Static and dynamic functional network connectivity during resting state in schizophrenia," in American College of Neuropsychopharmacology, Hollywood, CA, 2012.

slide50

Elements of a Classification Pipeline

  • Training Data Set.
    • Scan a very large number of well characterize subjects.
  • Feature extraction from raw data and dimensionality reduction.
    • Find the most informative measures and features from fMRI and/or anatomy
  • Minimize or Better Characterize noise and variability.
  • Maximize the effect size
    • Paradigm development & clear gold standard development
  • Model training and optimization.
    • Teach an algorithm to use the information to allow differentiation.
  • Application to test data.
  • Apply the learned rule to new data.
slide52

Some closing thoughts..

  • Individual Classification is likely the best chance for fMRI to make clinical inroads.
  • Rather than performing group studies with databases – perhaps effort to test individual classification with these (more “leave-one-out studies”).
  • Ultimately create practically useful fMRI/MRI classification databases that captures genetic, behavioral, developmental variability and that aid in diagnosis and outcome prediction.