Computational approaches for mapping the human connectome

263 views

Published on

Describes open challenges and ongoing work for mapping the human functional connectome and identifying inter-individual variation in the connectome that maps to phenotype and clinical outcomes. Also describes open science initiatives to help scientists from disparate backgrounds to become involved in this research.

Published in: Science
0 Comments
1 Like
Statistics
Notes
  • Be the first to comment

No Downloads
Views
Total views
263
On SlideShare
0
From Embeds
0
Number of Embeds
16
Actions
Shares
0
Downloads
9
Comments
0
Likes
1
Embeds 0
No embeds

No notes for slide

Computational approaches for mapping the human connectome

  1. 1. Computational approaches for mapping the human connectome R. Cameron Craddock, PhD Director, Computational Neuroimaging Lab Nathan S. Kline Institute for Psychiatric Research Director of Imaging, Center for the Developing Brain Child Mind Institute March 30, 2016
  2. 2. Functional Magnetic Resonance Imaging (fMRI) An fMRI time-course is formed by the rapid acquisition of MR images which are sensitive to the “blood oxygen level dependent” (BOLD) contrast 1. Hemoglobin, the protein which transports oxygen in blood, contains four heme molecules, each with an atom of iron 2. Deoxy-hemoglobin is paramagnetic and creates a magnetic gradient that dephases the MRI signal 3. Oxy-hemoglobin is diamagnetic and does not affect the MRI signal
  3. 3. Hemodynamic Response 1. Initially neuronal oxygen consumption increases the amount of deoxy-hemoglobin and the MR signal decreases 2. Blood flow increases, bringing more oxygenated blood to the area than is required, resulting in a signal increase 3. When neuronal activity ceases, the signal returns to baseline after a brief undershoot
  4. 4. Brain Mapping with fMRI
  5. 5. Resting State Functional Connectivity Biswal et al. MRM 1995 Intrinsic activity is ‘‘ongoing neural and metabolic activity which is not directly associated with subjects’ performance of a task’’-Raichle TICS 2010
  6. 6. Intrinsic Connectivity Networks De Luca et al, 2006 Independent Component Analysis Kelly et al, 2007 Functional Connectivity Seeding
  7. 7. Functional Connectivity Analysis 1. Data are preprocessed 2. Individual level FC maps are generated by ROI correlation with rest of brain, ICA, cross-correlation between several ROIs, or other method 3. FC maps are compared between groups feature-by-feature (voxel- by-voxel) using t-tests
  8. 8. Data Driven ROI Atlas Craddock et al. Human Brain Mapping 2012.
  9. 9. Functional Connectivity Graphs Courtesy of Dr. Xinian Zuo
  10. 10. The Human Connectome • The sum total of all of the brain’s connections – Structural connections: synapses and fibers • Diffusion MRI – Functional connections: synchronized physiological activity • Resting state functional MRI • Nodes are brain areas • Edges are connections Craddock et al. Nature Methods, 2013.
  11. 11. Discovery science of human brain function 1. Characterizing inter-individual variation in connectomes (Kelly et al. 2012) 2. Identifying biomarkers of disease state, severity, and prognosis (Craddock 2009) 3. Re-defining mental health in terms of neurophenotypes, e.g. RDOC (Castellanos 2013)
  12. 12. Diagnosing Depression • SVC successfully learned patterns of functional connectivity capable of predicting MDD from HC – Uncovered differences not discovered by t-test analysis • Feature selection substantially improved the prediction accuracy of SVC – Methods that incorporate reliability performed the best • Method requires selecting and localizing ROIs – Problematic if there is no previous research, introduces experimenter bias/error
  13. 13. Predicting Intrinsic Brain Activity Multivariate model of brain activity xn = b0 + bv v¹n å xv +x Underdetermined problem: solved using support vector regression or other regularized regression / dimensionality reduction method Craddock et al. NeuroImage 2013.
  14. 14. Nonparametric prediction, activation, influence and reproducibility resampling Predicted Time Course Observed Time Course Features Dataset 1 Observed Time Course Features Dataset 2 Model Estimation Model Estimation wixi+b i Prediction Prediction Accuracy Reproducibility Prediction wixi+b i Predicted Time Course Prediction Accuracy Network Model Network Model B A
  15. 15. Prediction Accuracy • Measure of the generalization ability of a model • Can be interpreted as a measure of the information content in the model about the region being modeled p(xn x1...xv ) » I(xn x1...xv )
  16. 16. Reproducibility • Measures the Signal-to-Noise ratio of the model SNR @ 1+r 1-rStrother, S. C. et al. NeuroImage 2003
  17. 17. Predicting Intrinsic Brain Function
  18. 18. Intra-individual variation
  19. 19. Intra-individual variation Reproducibility PredictionAccuracy 0.840.860.880.900.92 0.30 0.35 0.40 0.45 0.50 Lobe Frontal Occipital Parietal Subcortical Temporal A Reproducibility PredictionAccuracy 0.840.860.880.900.92 0.35 0.40 0.45 Functional Hierarchy Heteromodal Limbic Paralimbic Sensory−Motor Subcortical Unimodal B
  20. 20. Inter-subject prediction • 480 subjects – 69 DZ twin pairs – 80 MZ twin pairs – 200 Non-siblings • Train on one individual, test with another – Intra individual – Between siblings (MZ, DZ) – Age and sex matched non-siblings
  21. 21. Global prediction accuracy
  22. 22. Regional Differences
  23. 23. SVR Training
  24. 24. Tracking Intrinsic Connectivity Networks
  25. 25. Amount of Training
  26. 26. Predicting the Future
  27. 27. RT Neurofeedback of the Default Mode Network (DMN)
  28. 28. ICN Competition Fox MD PNAS 2005
  29. 29. Exp. Design Class Training Labels Training run Time-Labeled Scans Image Recon and SVM Classification Image DataData Acquisition Stimulus Presentation Stimulus Conventional FMRI Test Data Classifier Output Testing Run Real-Time Tracking RSNs LaConte, et al. (2007) Hum Brain Mapp. 28: 1033-1044 Stephen LaConte August 19, 2009
  30. 30. Stimulus seen by volunteer Updated fMRI results Motion tracking and correction Intensity (brightness) of a single voxel, changing during stimulus conditions Controller interface for display parameters
  31. 31. RT Neurofeedback of DMN • Test hypothesis of DMN dysregulation in depression, ADHD, aging, etc …
  32. 32. Preprocessing Skullstripping (3dSkullStrip) Linear Registration to MNI (flirt) Segmentation (fast) Anatomical Acquisition (T1 MPRAGE 4m 30s) 1 - 2.5min 12s 30s Coregister EPI to T1 (flirt+BBR) Write DMN template into EPI space Write WM+CSF mask into EPI space 30s 1s 1s Mask Acquisition (EPI 4m 30s) Calculate mean (3dTstat) Calculate mask (3dAutomask) 1s 1s Resting State (Training) Scan (EPI 6m) Motion correction to mean EPI (3dvolreg) Nuisance variable regression (3dDetrend) Spatial smoothing (3dmerge) Spatial regression to extract DMN time course (fsl_glm) Support vector regression training (3dsvm) 13s 2s 2s 32s 6-20s Indicates data dependency • Online preprocessing can be performed in ~ 5 minutes, most of which can occur in parallel with acquisition
  33. 33. Online Denoising • fMRI activity is confounded by intensity modulations induced by head motion, physiological noise, scanner drift, … • Implemented RT denoising in AFNI to remove contributions of confounds – Nth order polynomial – Global mean – Mask average time series (i.e. WM, CSF) – Motion parameters (6 or 24 regressor models) – Spatial smoothing • Adds ~ 5 ms of delay
  34. 34. DMN Modulation Task
  35. 35. Modulating the DMN−2−1012 0 100 200 300 400 Best Subject Worst Subject TR Z−scoreDMNActivity −20246 0 100 200 300 400 TR Z−scoreDMNActivity
  36. 36. Results 0.00.10.20.30.40.50.6 3 1 7 13 6 9 5 10 11 8 4 2 12 Subject Accuracy Feedback No feedback FB NOFB 0.10.20.30.40.50.6 1 2 1 2 Scan Number Accuracy p = 0.055p = 0.68 Accuracy was measured from Pearson’s correlation between task paradigm and DMN activity extracted after post-processing.
  37. 37. Behavioral Correlates Measures that were significantly associated with DN regulation include (p<0.05, FDR corrected): the affect intensity measure (AIM), ruminative responses scale (RRS), and the imaginal processes inventory.
  38. 38. Enhanced and suppressed craving: Classification in real time PEER, LaConte S.M. et al. ISMRM 2006, Sathian K et al. NeuroImage 2011.
  39. 39. Principles of Open Neuroscience Data, tools and ideas should be openly shared -The Neuro Bureau Manifesto http://www.neurobureau.org
  40. 40. Neuroimaging meets big data
  41. 41. C-PAC • Pipeline to automate preprocessing and analysis of large-scale datasets • Configurable to enable “plurality” – evaluate different processing parameters and strategies • Automatically identifies and takes advantage of parallelism on multi- threaded, multi-core, and cluster architectures • “Warm restarts” – only re-compute what has changed • Open science – open source
  42. 42. 1000 subjects, ~400 ADHD, 600 Typically Developing Children
  43. 43. ABIDE Preprocessing DPARSF CBRAIN CCS
  44. 44. Quality Assessment Protocol • Spatial Measures – Contrast to Noise Ratio – Entropy Focus Criterion – Foreground to Background Energy Ratio – Smoothness (FWHM) – % Artifact Voxels – Signal-to-Noise Ratio • Temporal Measures – Standardized DVARS – Median distance index – Mean Functional Displacement – # Voxels with FD > 0.2m – % Voxels with FD > 0.2m http://preprocessed-connectomes-project.github.io/quality-assessment-protocol/
  45. 45. Quality Assessment Protocol (2) • Implemented in python • Normative datasets to help learn thresholds for quality control – ABIDE – CoRR http://preprocessed-connectomes-project.github.io/quality-assessment-protocol/
  46. 46. High Res Anatomical
  47. 47. Diffusion Imaging
  48. 48. Acknowledgments • CMI/NKI – Michael Milham, MD, PHD – Zarrar Shehzad – Stan Colcombe, PhD • Virginia Tech Carilion Research Institute – Stephen LaConte, PhD – Jonathan Lisinski, MS • Siemens Medical – Keith Heberlein, PhD – Chris Glielmi, PhD • Research Funded in part by a NARSAD Young Investigator Award and NIMH R01MH101555 Thank You!

×