MRN fMRI Course Lecture 3.4 (1h): Group ICA

1. Vince D. Calhoun, Ph.D. Chief Technology Officer & Director, Image Analysis & MR Research The Mind Research Network Associate Professor, Electrical and Computer Engineering,Neurosciences, and Computer Science The University of New Mexico MRN fMRI CourseLecture 3.4 (1h): Group ICA

3. Using ICA to analyze fMRI data of multiple subjects raises some questions: How are components to be combined across subjects? How should the final results be thresholded and/or presented?

4. Group ICA Sub N Sub 1 ICA ICA ?

5. Group ICA Approaches

6. Approach 1 • Separate ICA analysis for each subject [V. D. Calhoun, T. Adali, V. McGinty, J. J. Pekar, T. Watson, and G. D. Pearlson, "FMRI Activation In A Visual-Perception Task: Network Of Areas Detected Using The General Linear Model And Independent Components Analysis," NeuroImage, vol. 14, pp. 1080-1088, 2001.] • Must select which components to compare between the individuals Sub N Sub 1 ICA ICA ?

7. 15 “events” … Example Press buttons (1-4) to indicate choice 1 2 3 4 0 15.4 31.5 47.0 300 Time (seconds)

8. SPM Results N=10 P<0.05 corrected • SPM revealed a large network of areas including: • frontal eye fields • supplementary motor areas • primary visual • visual association • basal ganglia • thalamic, and an • (unexpectedly) large cerebellar activation • bilateral inferior parietal regions were deactivated (not shown)

9. ICA Results N=10 Z>3.1 • ICA revealed a large network of similar areas including: • frontal eye fields (blue) • supplementary motor areas (green w/ outline) • primary visual (red) • visual association (red) • thalamic (red) • basal ganglia (green w/ outline) • a large cerebellar activation (red) • bilateral inferior parietal deactivations (not shown) • ICA also revealed areas not identified by SPM including: • primary motor (green) • frontal regions anterior to the frontal eye fields (blue) • superior parietal regions (blue)

10. ICA: Single Subject The ICA maps from one subject for the visual and basal ganglia components are depicted along with their time courses (basal ganglia in green and visual in pink) Note that the visual time course precedes the motor time course

11. Event-Averaged Time Courses • Time courses from selected voxels in the raw data (a) and time courses produced by the ICA method (b). • In all cases the time courses are event-averaged (according to when the figure was presented) within each participant and then averaged across all ten participants. • Voxels from the raw data were selected by choosing a local maximum in the activation map and averaging the two surrounding voxels in each direction. • Dashed lines indicate the standard error of the mean.

12. Approach 2 • Group ICA (stacking images) • [V. D. Calhoun, T. Adali, G. D. Pearlson, and J. J. Pekar, "A Method for Making Group Inferences From Functional MRI Data Using Independent Component Analysis," Hum. Brain Map., vol. 14, pp. 140-151, 2001.] • [V. J. Schmithorst and S. K. Holland, "Comparison of Three Methods for Generating Group Statistical Inferences From Independent Component Analysis of Functional Magnetic Resonance Imaging Data," J. Magn Reson. Imaging, vol. 19, pp. 365-368, 2004.] • Components and time courses can be directly compared Sub 1 ICA Sub N Sub 1 Sub N

13. Back-reconstruction Data ICA Subject i X A A1 Ai Si Subject 1 S_agg Subject N AN Group ICA

14. Simulation Nine simulated source maps and time courses were generated, followed by an ICA estimation. The red lines indicate the t<4.5 boundaries

15. Are the data separable? (Simulation) • A natural concern is whether the back-reconstructed maps from individual subjects will be influenced by the other subjects in the group analysis • This simulation was performed in which one of the nine “subjects” had a structured, source #2 map (whereas all of the nine “subjects” had a similar, source #1 map). • As one can see, in this example, the back-reconstructed ICA maps are very close to the individual maps and there appears to be little to no influence between subjects

16. The Stationarity Assumption Stationary source S common to all five “subjects” Sources S1-S5 differing across the five “subjects” • The ICA estimation requires the data to be stationary across subjects • Some signals in the data (e.g. physiologic noise) will most likely *not* be stationary • However it is reasonable to assume the signal of interest (fMRI activation) will be stationary • A simulation was performed to examine how non-stationary sources would affect the results • One stationary signal (fMRI activation) and one non-stationary signal were simulated for a five-subject analysis • The ICA results reveal that the fMRI activation is preserved S S1 S2 S3 S4 S5 ICA results source #1 source #2

17. Evaluation of Group ICA Methods E. Erhardt, S. Rachakonda, E. Bedrick, T. Adali, and V. D. Calhoun, "Comparison of multi-subject ICA methods for analysis of fMRI data," in Proc. HBM, Barcelona, Spain, 2010.

18. Comparison of multi-subject ICA methods for analysis of fMRI data E. Erhardt, S. Rachakonda, E. Bedrick, T. Adali, and V. D. Calhoun, "Comparison of multi-subject ICA methods for analysis of fMRI data," in Proc. HBM, Barcelona, Spain, 2010.

19. Comparison of multi-subject ICA methods for analysis of fMRI data GICA3 STR DIFF E. Erhardt, S. Rachakonda, E. Bedrick, T. Adali, and V. D. Calhoun, "Comparison of multi-subject ICA methods for analysis of fMRI data," in Proc. HBM, Barcelona, Spain, 2010.

20. Default Mode Group Maps GICA3 STR E. Erhardt, S. Rachakonda, E. Bedrick, T. Adali, and V. D. Calhoun, "Comparison of multi-subject ICA methods for analysis of fMRI data," in Proc. HBM, Barcelona, Spain, 2010.

21. + + Methods • Scan Parameters • 9 slice Single-shot EPI • FOV = 24cm, 64x64 • TR=1s, TE=40ms • Thickness = 5/.5 mm • 360 volumes acquired • Preprocessing • Timing correction • Motion correction • Normalization • Smoothing • ICA • An ICA estimation was performed on each of the nine subjects • Data were first reduced from 360 to 25 using PCA, the data were concatenated and reduced a second time from 225 to 20 using PCA • An ICA estimation was performed after which single subject maps and time courses were calculated • Group averaged maps were thresholded at t<4.5, colorized, and overlaid onto an EPI scan for visualization Right + Left t (secs) 0 90 180 270 360

22. Are the data separable? (fMRI experiment) • The same slice from nine subjects when the right (red) and left (blue) visual fields were stimulated, (a) analyzed via linear modeling (LM), (b) back-reconstructed from a group ICA analysis, or (c) calculated from an ICA analysis performed on each subject separately. A transiently task-related component is depicted in green. • The results between the two ICA methods appear quite similar and match well with the LM results as well (note that there may be small differences due to different initial conditions for the ICA estimation)

23. Comparison with GLM Approach R L V.D. Calhoun, T. Adali, G.D. Pearlson, and J.J. Pekar, "A Method for Making Group Inferences From Functional MRI Data Using Independent Component Analysis," Hum. Brain Map., vol. 14, pp. 140-151, 2001.

24. Sorting/Calibrating • A ‘second-level’ or group analysis involves taking certain parameters (estimated by ICA) such as the amplitude fit for fMRI regression models, or voxel weights, and testing these within a standard GLM hypothesis-testing framework

25. Prenormalization 1) No Normalization (NN), where data is left in its raw intensity units (Calhoun, 2001) 2) Intensity Normalization (IN), which involves voxel-wise division of the time series mean 3) Variance Normalization (VN), voxel-wise z-scoring of the time series (Beckmann, 2004). E. Allen, E. Erhardt, T. Eichele, A. R. Mayer, and V. D. Calhoun, "Comparison of pre-normalization methods on the accuracy of group ICA results," in Proc. HBM, Barcelona, Spain, 2010.

26. Result 1: AOD and rest data produced highly similar networks V. D. Calhoun, K. A. Kiehl, and G. D. Pearlson, "Modulation of Temporally Coherent Brain Networks Estimated using ICA at Rest and During Cognitive Tasks," Hum Brain Mapp, vol. 29, pp. 828-838, 2008.

27. Result 2: Though similar TCNs were identified for AOD and rest, spatial and temporal task modulation was induced V. D. Calhoun, K. A. Kiehl, and G. D. Pearlson, "Modulation of Temporally Coherent Brain Networks Estimated using ICA at Rest and During Cognitive Tasks," Hum Brain Mapp, vol. 29, pp. 828-838, 2008.

28. Example of spatial sorting

29. Example 1: ‘Default Mode’ Mask • Using wfu pickatlas to define mask using regions reported in Rachle 2001 paper • Posterior parietal cortex BA7 • Occipitoparietal junction BA 39 • Precuneus • Posterior cingulate • Frontal Pole BA 10 • Smooth in SPM with same kernel used on fMRI data • Sort in GIFT using spatial sorting A.Garrity, G.D.Pearlson, K.McKiernan, D.Lloyd, K.A.Kiehl, and V.D.Calhoun, "Aberrant 'Default Mode' Functional Connectivity in Schizophrenia," to appear Am. J. Psychiatry, 2006.

30. ICA to identify ‘Default Mode’ Network Healthy Schizo Healthy vsSchizo (N=26/26) +Symptoms A.Garrity, G.D.Pearlson, K.McKiernan, D.Lloyd, K.A.Kiehl, and V.D.Calhoun, "Aberrant 'Default Mode' Functional Connectivity in Schizophrenia," to appear Am. J. Psychiatry, 2006.

31. Spatial Sorting: Example 2 • Classification of Schizophrenia • Mapping the brain via intrinsic connectivity Patients Controls

32. Robustness of ‘modes’

33. The Challenge • Accurate classification requires single-subject accuracy -> very stringent requirement! • We cannot use knowledge of the diagnosis in the development of the classification algorithm

34. Temporal Lobe Synchrony • Supervised Classification • Step 1: Select Training Group • Step 2: Use ICA to extract temporal lobe maps • Step 3: Compute within-group mean images • Step 4: Subtract the mean images • Step 5: Set a positive and negative threshold … HC1 HCN ICA … Sz1 SzN Calhoun VD, Kiehl KA, Liddle PF, Pearlson GD: “Aberrant Localization of Synchronous Hemodynamic Activity in Auditory Cortex Reliably Characterizes Schizophrenia”. Biol Psychiatry 2004; 55842-849

35. Temporal Lobe Synchrony in Schizophrenia

36. Temporal Lobe Synchrony in Schizophrenia • Step 6: Form classification measure (average the values within each boundary and subtract) • Step 7: Optimize group discrimination (using a sensible error metric) • Step 8: Apply classification to new data Calhoun VD, Kiehl KA, Liddle PF, Pearlson GD: “Aberrant Localization of Synchronous Hemodynamic Activity in Auditory Cortex Reliably Characterizes Schizophrenia”. Biol Psychiatry 2004; 55842-849

37. Temporal Sorting: fBIRN SIRP Task • Methods • Subjects & Task • 28 subjects (14 HC/14 SZ) across two sites • Three runs of SIRP task preprocessed with SPM2 • ICA Analysis • All data entered into group ICA analysis in GIFT • ICA time course and image reconstructed for each subject, session, and component • Images: sessions averaged together creating single image for each subject and component • Time courses: SPM SIRP model regressed against ICA time course • Statistical Analysis: • Images: all subjects entered into voxelwise 1-sample t-test in SPM2 and thresholded at t=4.5 • Time courses: Goodness of fit to SPM SIRP model computed, beta weights for load 1, 3, 5 entered into Group x Load ANOVA fBIRN Phase II Data: www.nbirn.net; NCRR (NIH), 5 MOI RR 000827 (2002-2006) and 1 U24 RR0219921 (2006 onwards)

38. Component 1: Bilateral Frontal/Parietal fBIRN Phase II Data: www.nbirn.net; NCRR (NIH), 5 MOI RR 000827 (2002-2006) and 1 U24 RR0219921 (2006 onwards)

39. Component 2: Right Frontal, Left Parietal, Post. Cing. fBIRN Phase II Data: www.nbirn.net; NCRR (NIH), 5 MOI RR 000827 (2002-2006) and 1 U24 RR0219921 (2006 onwards)

40. Component 3: Temporal Lobe fBIRN Phase II Data: www.nbirn.net; NCRR (NIH), 5 MOI RR 000827 (2002-2006) and 1 U24 RR0219921 (2006 onwards)

41. Example 2: Simulated Driving Paradigm

42. Previous Work • Walter, 2001. Driving Watching “Our results suggest that simulated driving engages mainly areas concerned with perceptual-motor integration and does not engage areas associated with higher cognitive functions.” “our study suggests that the main ideas of cognitive psychology used in the design of cars, in the planning of respective behavioral experiments on driving, as well as in traffic related political decision making (i.e. laws on what drivers are supposed to do and not to do during driving) may be inadequate, as it suggests a general limited capacity model of the psyche of the driver which is not supported by our results. If driving deactivates rather than activates a number of brain regions the quests for the adequate design of the man-machine interface as well as for what the driver should and should not do during driving is still widely open.”

43. Watch Drive Baseline Simulated Driving Results N=12 Higher Order Visual/Motor: Increases during driving; less during watching. Low Order Visual: Increases during driving; less during watching. Motor control: Increases only during driving. Vigilance: Decreases only during driving; amount proportional to speed. Error Monitoring & Inhibition: Decreases only during driving; rate proportional to speed. Visual Monitoring: Increases during epoch transitions. * V. D. Calhoun, J. J. Pekar, V. B. McGinty, T. Adali, T. D. Watson, and G. D. Pearlson, "Different Activation Dynamics in Multiple Neural Systems During Simulated Driving," Hum. Brain Map., vol. 16, pp. 158-167, 2002.

44. SPM Results Calhoun, V. D., Pekar, J. J., and Pearlson, G. D. “Alcohol Intoxication Effects on Simulated Driving: Exploring Alcohol-Dose Effects on Brain Activation Using Functional MRI”. Neuropsychopharmacology 2004.

45. Interpretation of Results

46. Functional Network Connectivity(between groups) A: Default Key: : ρpatient > ρcontrol :ρcontrol > ρpatient B: Parietal G: Temporal C: L. & M. Visual Cortical Areas F: Frontal D: Frontal Temporal Parietal E: Frontal Parietal Subcortical

47. FNC Software

48. Fusion ICA Toolbox (FIT) 500+ unique downloads http://icatb.sourceforge.net Funded by NIH 1 R01 EB 005846

49. FMRI Snapshots (movie) Calhoun, V.D., Pearlson, G.D., and Kiehl, K.A. (2006). Neuronal Chronometry of Target Detection: Fusion of Hemodynamic and Event-related Potential Data. NeuroImage 30, 544-553.

50. 0.55 Target Stimuli 0.47 Novel Stimuli