Functional and Anatomical MRI-Based Biomarkers for Classifying Groups and Individuals

1. 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

2. 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.

3. 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.

4. 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

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

6. 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.

7. Decoding by eye… What is this person doing? Left then right finger tapping : 1991

8. 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

9. 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)

10. Effect Size / (Noise & Variability) > 10

11. Clinical Anatomic Imaging of Tumors/Lesions Group 1 Group 2

12. Effect Size / (Noise & Variability) > 10 • We also have a clear gold standard with which to compare

13. Typical fMRI Studies Group 1 Group 2 Gold standard measures are not always clear: (i.e. DSM-IV, V codes)

14. Individual genotypes very effective gold standards. Comparison of two groups of normal individuals with differences in the Serotonin Transporter Gene

15. fMRI and MRI ARE exquisitely sensitive to individual traits. A few examples of MRI-derived information as it correlates individual characteristics….

16. fMRI – derived retinopy maps correlate with measures of visual acuity

17. BOLD magnitude in dorsal striatum predicts video game learning success 2011 Dorsal striatum

18. Biol Psychiatry 2011: 70: 866-872

20. Decision making FA: Visual Choice Reaction Time GM density: Response Conflict Pre-SMA & striatum connection strength: Speed - Accuracy tradeoff ability

21. 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

22. 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

23. Intelligence Personality

24. 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.

25. 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..

26. 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.

27. Sources of Variability Across Subjects • Thermal • Scanner • Hemodynamics • Neuro-vascular coupling • Structure • Task strategy • Medication • Performance • Arousal/Motivation

28. 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

29. 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

30. 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

31. Neuro-vascular coupling variability with aging

32. Response to Hypercapnia Response to modified Stroop task ...leads to a potential underestimation of neuronal activity in older adults

33. 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

34. What’s in the time series noise? Bianciardi et al. Magnetic Resonance Imaging 27: 1019-1029, 2009

35. 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.

36. How do we extract these individual differences accurately and robustly?

37. Group 1 Group 2 ?

39. Multidimensional Classification

44. Resting State Classification

46. Control Schizophrenia Bipolar

47. 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.

48. 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

49. 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.

50. 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.