}Correlate/Cluster Subject N

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1 : fmri Course 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 Using 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? 2010 fmri Course 2 Group Group Approaches Sub 1? Sub N a Combine Single b Temporal c Spatial d Pre-Averaging 5 e Concatenation 3,7,5 Concatenation 6,5 Common Spatial Subject s 1,4 Common Temporal Unique Spatial Common Spatial Unique Spatial Unique Temporal Unique Temporal Common Temporal }Correlate/Cluster Subject N Time Voxels : Subject N Single subject maps } Single subject components* Back reconstruction Time Voxels Subject N Subject (avg) GIFT MELODIC Brain Voyager Time Subs Tensor 2,7 Common Spatial Common Temporal Subject Parameter Voxels 1) Calhoun VD, Adali T, McGinty V, Pekar JJ, Watson T, Pearlson GD. (2001): fmri Activation In A Visual-Perception Task: Network Of Areas Detected Using The General Linear Model And Independent Component Analysis. NeuroImage 14(5): ) Beckmann CF, Smith SM. (2005): Tensorial extensions of independent component analysis for multisubject FMRI analysis. NeuroImage 25(1): ) Calhoun VD, Adali T, Pearlson GD, Pekar JJ. (2001): A Method for Making Group Inferences from Functional MRI Data Using Independent Component Analysis. Hum.Brain Map. 14(3): ) Esposito F, Scarabino T, Hyvarinen A, Himberg J, Formisano E, Comani S, Tedeschi G, Goebel R, Seifritz E, Di SF. (2005): Independent component analysis of fmri group studies by self-organizing clustering. Neuroimage. 25(1): ) Schmithorst VJ, Holland SK. (2004): Comparison of three methods for generating group statistical inferences from independent component analysis of functional magnetic resonance imaging data. J.Magn Reson.Imaging 19(3): ) Svensen M, Kruggel F, Benali H. (2002): of fmri Group Study Data. NeuroImage 16: ) Guo Y, Giuseppe P. (In Press): A unified framework for group independent component analysis for multi-subject fmri data. NeuroImage fmri Course fmri Course 4 Approach 1 Separate 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 , 1088, 2001.] Must select which components to compare between the individuals Example Press buttons (1-4) to indicate choice Sub 1 Sub N ? 15 events Time (seconds) 2010 fmri Course fmri Course 6 1

2 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) SPM Results N=10 Z>3.1 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) also revealed areas not identified by SPM including: primary motor (green) frontal regions anterior to the frontal eye fields (blue) superior parietal regions (blue) Results 2010 fmri Course fmri Course 8 : Single Subject The 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 Event-Averaged Time Courses Time courses from selected voxels in the raw data (a) and time courses produced by the 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. p 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 fmri Course fmri Course 10 Approach 2 Group (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 , 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 , 368, 2004.] Components and time courses can be directly compared Sub 1 Data X A 1 A Group S_agg Back-reconstruction 1 A i Subject i S i Sub N Subject N A N Sub 1 Sub N 2010 fmri Course fmri Course 12 2

3 Simulation Nine simulated source maps and time courses were generated, followed by an estimation. The red lines indicate the t<4.5 boundaries 2010 fmri Course 13 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 maps are very close to the individual maps and there appears to be little to no influence between subjects 2010 fmri Course 14 The Stationarity Assumption Stationary source S Sources S1-S5 S5 common to all five differing across the subjects five subjects Evaluation of Group Methods S S3 results S1 S4 source #1 S2 S5 source #2 The 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 results reveal that the fmri activation is preserved 2010 fmri Course 15 E. Erhardt, S. Rachakonda, E. Bedrick, T. Adali, and V. D. Calhoun, "Comparison of multi-subject 16 Comparison of multi-subject methods for analysis of fmri data Comparison of multi-subject methods for analysis of fmri data DIFF STR G3 E. Erhardt, S. Rachakonda, E. Bedrick, T. Adali, and V. D. Calhoun, "Comparison of multi-subject 17 E. Erhardt, S. Rachakonda, E. Bedrick, T. Adali, and V. D. Calhoun, "Comparison of multi-subject 18 3

4 G3 Default Mode Group Maps STR E. Erhardt, S. Rachakonda, E. Bedrick, T. Adali, and V. D. Calhoun, "Comparison of multi-subject 19 Methods Scan Parameters 9 slice Single-shot EPI FOV = 24cm, 64x64 TR=1s, TE=40ms Right Thickness = 5/.5 mm 360 volumes acquired Preprocessing Timing correction Left t (secs) Motion correction Normalization Smoothing An 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 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 2010 fmri Course 20 Are the data separable? (fmri experiment) Comparison with GLM Approach R L 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 analysis, or (c) calculated from an analysis performed on each subject separately. A transiently task-related component is depicted in green. The results between the two 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 estimation) 2010 fmri Course 21 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 , fmri Course 22 Sorting/Calibrating A second-level or group analysis involves taking certain parameters (estimated by ) such as the amplitude fit for fmri regression models, or voxel weights, and testing these within a standard GLM hypothesis-testing testing framework Comp# R 2 Subject Reg1 Reg fmri Course 23 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 results," in Proc. HBM, Barcelona, Spain, fmri Course 24 4

5 Result 1: AOD and rest data produced highly similar networks Result 2: Though similar TCNs were identified for AOD and rest, spatial and temporal task modulation was induced Comp# Comp# Description Corr Oddball Rest A: Default mode B: Motor C: Sup parietal D: Medial visual E: Left lateral frontoparietal F: Lateral Visual G: Temporal H: Cerebellum I: Temporal J: Frontal K: Right lateral frontoparietal L: Anterior cingulate Description Tar Nov A: Default mode (1.4e-9) (5.6e-6) B: Motor 4.62 (2.3e-4) 1.11 (1.0) C: Sup parietal 2.51 (8.9e-2) (6.5e-3) D: Medial visual 1.09 (1.0) 0.12 (1.0) E: Left lateral frontoparietal 2.41 (1.1e-1) 1.21 (1.0) F: Lateral Visual (5.4e-4) (1.9e-3) G: Temporal (6.2e-12) 7.76 (1.1e-8) H: Cerebellum 4.09 (1.1e-3) (7.4e-2) I: Temporal (1.2e-15) 9.30 (1.1e-10) J: Frontal (8.1e-2) (1.2e-2) K: Right lateral (6.3e-15) (2.1e-3) frontoparietal V. D. Calhoun, K. A. Kiehl, and G. D. Pearlson, "Modulation of Temporally Coherent Brain Networks Estimated using at Rest and During Cognitive Tasks," Hum Brain Mapp, vol. 29, pp , 838, V. D. Calhoun, K. A. Kiehl, and G. D. Pearlson, "Modulation of Temporally Coherent Brain Networks Estimated using at Rest and During Cognitive Tasks," Hum Brain Mapp, vol. 29, pp , 838, fmri Course fmri Course 26 Example of spatial sorting 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 2010 fmri Course 27 A.Garrity, G.D.Pearlson, K.McKiernan, D.Lloyd, K.A.Kiehl, and V.D.Calhoun, "Aberrant 'Default Mode' Functional 2010 fmri Course Connectivity in Schizophrenia," to appear Am. J. Psychiatry, to identify Default Mode Network Healthy Schizo Spatial Sorting: Example 2 Classification of Schizophrenia Mapping the brain via intrinsic connectivity Healthy vs Schizo (N=26/26) +Symptoms Patients Controls A.Garrity, G.D.Pearlson, K.McKiernan, D.Lloyd, K.A.Kiehl, and V.D.Calhoun, "Aberrant 'Default Mode' Functional 2010 fmri Course Connectivity in Schizophrenia," to appear Am. J. Psychiatry, fmri Course 30 5

6 Robustness of modes The Challenge Accurate classification requires single-subject subject accuracy -> very stringent requirement! We cannot use knowledge of the diagnosis in the development of the classification algorithm.5 khz 1 khz tone, sweep, whistle Target Novel 2010 fmri Course fmri Course 32 Temporal Lobe Synchrony Supervised Classification Step 1: Select Training Group Step 2: Use 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 Temporal Lobe Synchrony in Schizophrenia t HC 1 HC N t t t Sz 1 Sz N Calhoun VD, Kiehl KA, Liddle PF, Pearlson GD: Aberrant Localization of Synchronous Hemodynamic 2010 fmri Course Activity in Auditory Cortex Reliably Characterizes Schizophrenia. Biol Psychiatry 2004; fmri Course 34 Temporal Lobe Synchrony in Schizophrenia Step 6: Form classification measure (average the values within each boundary and subtract) DF t, t, i i, IM t iim, t.* MSKH C SZ Step 7: Optimize group discrimination (using a sensible error metric) min Err t, t DF t, t, i0 DF t, t, i0 isz Step 8: Apply classification to new data ihc 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 Analysis All data entered into group analysis in GIFT 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 time course Statistical Analysis: Images: all subjects entered into voxelwise 1-sample t-test 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 Calhoun VD, Kiehl KA, Liddle PF, Pearlson GD: Aberrant Localization of Synchronous Hemodynamic 2010 fmri Course Activity in Auditory Cortex Reliably Characterizes Schizophrenia. Biol Psychiatry 2004; fbirn Phase II Data: fmri Course NCRR (NIH), 5 MOI RR ( ) 2006) and 1 U24 RR (2006 onwards) 36 6

7 Component 1: Bilateral Frontal/Parietal Component 2: Right Frontal, Left Parietal, Post. Cing. fbirn Phase II Data: fmri Course NCRR (NIH), 5 MOI RR ( ) 2006) and 1 U24 RR (2006 onwards) 37 fbirn Phase II Data: fmri Course NCRR (NIH), 5 MOI RR ( ) 2006) and 1 U24 RR (2006 onwards) 38 Component 3: Temporal Lobe Example 2: Simulated Driving Paradigm * Drive Watch fbirn Phase II Data: fmri Course NCRR (NIH), 5 MOI RR ( ) 2006) and 1 U24 RR (2006 onwards) fmri Course 40 Walter, Previous Work Our results suggest that simulated driving engages mainly areas concerned with perceptualmotor integration and does not engage areas associated with higher cognitive functions. Driving Watching 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 fmri Course 41 N=12 Baseline Simulated Driving Results * Drive Watch 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; i 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 2010 fmri Course Dynamics in Multiple Neural Systems During Simulated Driving," Hum. Brain Map.,, vol. 16, pp , 167,

8 SPM Results Interpretation of Results Calhoun, V. D., Pekar, J. J., and Pearlson, G. D. Alcohol Intoxication Effects on Simulated Driving: Exploring Alcohol fmri Course Dose Effects on Brain Activation Using Functional MRI. Neuropsychopharmacology fmri Course 44 Functional Network Connectivity (between groups) Key: A: Default FNC Software : ρ patient > ρ control : ρ control > ρ patient G: Temporal B: Parietal F: Frontal C: L. & M. Visual Cortical Areas E: Frontal Parietal Subcortical D: Frontal Temporal Parietal 2010 fmri Course fmri Course 46 Fusion Toolbox (FIT) FMRI Snapshots (movie) 500+ unique downloads Funded by NIH 1 R01 EB fmri Course 47 ERP (temporal) Components: T t t FMRI (spatial) Components: S s1 s FMRI Image Snapshot: M t T F TS t Calhoun, V.D., Pearlson, G.D., and Kiehl, K.A. (2006). Neuronal Chronometry of Target Detection: Fusion of 2010 Hemodynamic fmri Course and Event-related Potential Data. NeuroImage 30, N N 8

9 Target Stimuli Novel Stimuli SNPs rs rs7412 rs rs rs rs rs rs SNPs rs rs7412 rs rs rs rs rs rs rs Genes ADRA2A APOE ABCB1 TH ABCB1 MDH1 GNAO1 ADRA2A Genes ADRA2A APOE TH MDH1 ABCB1 APOE PIK3C3 PIK3C3 ADRA2A Demo 3 subject Sorting Component Explorer (split time courses, event-related average) Orthogonal Viewer Composite Viewer Examine Regression Parameters Taking Images/Timecourses from GIFT to SPM 2010 fmri Course fmri Course 50 Comparison of multi-subject methods for analysis of fmri data 2010 fmri Course 51 E. Erhardt, S. Rachakonda, E. Bedrick, T. Adali, and V. D. Calhoun, "Comparison of multi-subject 52 9