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

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.

Alzheimer’s disease: 2 people out of 10 concerned beyond the age of 80; dependency occurs within 3 to 5 years after the disease has appeared.   Depression: the second most common condition in the world according to the WHO: it concerns 6 per cent of the population in the Western world. 

Cerebral vascular accidents: the first cause of motor disabilities in adults.75 per cent of victims suffer from residual disability. Parkinson’s disease: second cause of motor disability. It affects 2 out of 1,000 people. 

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

Epilepsy: 50 million people concerned in the world of which almost half bebefore age 10. The social and familial repercussions are lifelong.

MOTIVATION 1

MOTIVATION 2 - DATA FEDERATION & INTEGRATION

Number of Peer Reviewed Publications on the Brain /yr

2012

Reality check1. Data and knowledge is growing 

exponentially2. Data and knowledge is 

increasingly fragmented3. Benefits for society seem to be 

decreasing (diagnostic accuracy, treatments, drugs)

4. Economic burden increasing rapidly to unsustainable levels

What we lack5. No integration plan6. No data curation plan7. No plan to link across levels8. No plan to transfer knowledge 

from animal to human9. No plan to go beyond symptom-

based classification of diseases

How much can you tell about an individual using MRI and fMRI?

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.

Left then right finger tapping : 1991

Decoding by eye… What is this person doing? 

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)GenderSensorimotor characteristicsDifferences and cognitive or personality traitsDifferences in psychological stateDifferences in physiologic stateNeurologic differencesDevelopmental differences

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)

Effect Size / (Noise & Variability) > 10

Group 1 Group 2

Clinical Anatomic Imaging of Tumors/Lesions

Effect Size / (Noise & Variability) > 10

We also have a clear gold standard with which to compare

Group 1 Group 2

Typical fMRI Studies

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

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

Individual genotypes very effective gold standards.

fMRI and MRI ARE exquisitely sensitive to individual traits.

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

fMRI – derived retinopy maps correlate with measures of visual acuity

2011

Dorsal striatum

BOLD magnitude in dorsal striatum predicts video game learning success

Biol Psychiatry 2011: 70: 866-872

FA: Visual Choice Reaction Time

GM density: Response Conflict

Pre-SMA & striatum connection strength: Speed - Accuracy tradeoff ability

Decision making

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

Conscious Perception

Resting State: Personality  Type

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

Intelligence Personality

Elements of a Classification Pipeline

1. Training Data Set.Scan a very large number of well characterize subjects.

2. Feature extraction from raw data and dimensionality reduction.Find the most informative measures and features from fMRI and/or anatomy

3. Minimize or Better Characterize noise and variability.

4. Maximize the effect size Paradigm development & clear gold standard development

5. Model training and optimization. Teach an algorithm to use the information to allow differentiation.

6. Application to test data.Apply the learned rule to new data.

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..

Elements of a Classification Pipeline

1. Training Data Set.Scan a very large number of well characterize subjects.

2. Feature extraction from raw data and dimensionality reduction.Find the most informative measures and features from fMRI and/or anatomy

3. Minimize or Better Characterize noise and variability.

4. Maximize the effect size Paradigm development & clear gold standard development

5. Model training and optimization. Teach an algorithm to use the information to allow differentiation.

6. Application to test data.Apply the learned rule to new data.

Sources of Variability Across Subjects

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

SC NL KB

JL HG EE

CC BK BB

Individual activations from the left hemisphere of the 9 subjects

group Extensive Individual Differences in Brain Activations During Episodic Retrieval

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

group Extensive Individual Differences in Brain Activations During Episodic Retrieval

KBNLSC

HGJL

BBBKCC

EE

Individual activations from the right hemisphere of the 9 subjects

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

Group Analysis of Episodic Retrieval

Subject SC 

Subject SC 6 months later

These individual patterns of activations are stable over time 

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

Neuro-vascular coupling variability with aging

Response to modified Stroop task

Response to Hypercapnia

...leads to a potential underestimation of neuronal activity in older adults

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

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

What’s in the time series noise?

Elements of a Classification Pipeline

1. Training Data Set.Scan a very large number of well characterize subjects.

2. Feature extraction from raw data and dimensionality reduction.Find the most informative measures and features from fMRI and/or anatomy

3. Minimize or Better Characterize noise and variability.

4. Maximize the effect size Paradigm development & clear gold standard development

5. Model training and optimization. Teach an algorithm to use the information to allow differentiation.

6. Application to test data.Apply the learned rule to new data.

How do we extract these individual differences accurately and robustly?

Group 1 Group 2

?

Multidimensional Classification

Resting State Classification

ControlSchizophreniaBipolar

Default Network Connectivity Predicts Conversion to Dementia in Subjects at Risk

48

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)

* 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

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.

Putamen - Sensorimotor hypo-connectivity

Elements of a Classification Pipeline

1. Training Data Set.Scan a very large number of well characterize subjects.

2. Feature extraction from raw data and dimensionality reduction.Find the most informative measures and features from fMRI and/or anatomy

3. Minimize or Better Characterize noise and variability.

4. Maximize the effect size Paradigm development & clear gold standard development

5. Model training and optimization. Teach an algorithm to use the information to allow differentiation.

6. Application to test data.Apply the learned rule to new data.

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.