Prepared for Neuroimaging+ Meeting on June 6th, 2013 by Nicco Reggente

1. Decoding Unattended Fearful Faces with Whole-Brain Correlations: An Approach to Identify Condition-Dependent Large-Scale Functional ConnectivitySpiro P. Pantazatos, Ardesheer Talati, Paul Pavlidis, Joy Hirsch Prepared for Neuroimaging+ Meeting on June 6th, 2013 by Nicco Reggente

2. Their Task Blue Red Yellow Equalized for luminosity

3. Analysis Workflow Outline • Select ROIs • Correlations • Atlas-Based with Singular Value Decomposition Extraction • GLM peak coordinates -- 6mm Spheres (49 ROIs) • Dosenbach meta-analysis nodes (160 nodes) • Beta Estimates • (summary measures of activation in response to each condition) • Voxel based • Prepare for Classification • Establish Features • Correlations • Beta Values

4. ROI Round-Up • Harvard-Oxford Bilateral (Cortical+Sub-Cortical) Parcellations + AAL • Only voxels shared by all subjects • Yielded 135 Atlas-Based-Regions • Each Subject • 283 TRs were extracted using Singular Value Decomposition • Top 2 Eigenvariates from each atlas-based region (270 nodes) • (also did this for GLM spheres/Dosenbach)

5. Classification Preparation • Correlations • Concatenate the eigenvariates according to the conditions of interest. Put all Fearful side by side for each . • 4 blocks of each. Each block had 10 TRs. • Regions in Figure are R1 through RN • Compute time-course correlation for each region to every other region. • R1_TR1-R1_TR2-R1_TR_3R2_TR1-R2_TR2-R2_TR_3 • R to Z, Demean, Take The Lower Triangle • Betas • From The GLM. 1 Beta per Voxel for each block. • No ROIS. Whole Brain • Time Course of Betas Instead of Bolds • Use both 2x2x2 and upsize to 4x4x4

6. Pattern Analysis • 36,315 features • Feature Select • Correlations • T-Test to filter for feature selection on TRAINING ONLY • Iteratively use a range of the |top-most| t-value features as the predictors for the with-held test examples. • Train on all, but only TEST on the |top-most| n features • Eliminate bias • Betas • Feature Select in same manner as Correlations for Un-Biased • Additionally: F-test Feature Select F>N with cluster threshold of 20 and p<.01 which yielded 4000 features • This was meant to serve as an “upper bound” for this type of classification in order to compare to the Correlation approach. • SVM using Spider • LTOCV across subjects • Assessment of Feature Contributions • Rank by total number of times that feature was included and then sort by absolute value of the average SVM weight.

7. Results • Correlation Classification Accuracies • Dosenbach Nodes: 63-73% with 75-130 features • GLM: 76-86% with 80 to 140 features • Atlas Based with SVD: 90-100% with 15-35 features • Beta Values Classification Accuracies • 76% with 1900 features • 92% with biased feature selection yielding 4000 features

8. Results Cont. • Right Angular Gyrus  Left Hippocampus = Connection with the most weight. • Thalamus = Most positive functional connectivity node for fear. • Sum of SVM weights • Thalamus positively modulated functional connections bilateral temporal gyrus and right insula • Many areas are in the vicinity of the STS. • Also ran a GLM over the summary time course of eigenvariates. • The resulting beta-weights were used as features in the same manner as before and yielded 69-79% accuracy with 40 to 150 features.

9. Interpretations With Literature • Amygdala Activity greater for explicit vs. implicit Fusar-Poli P. (2009) J Psychiatry Neurosci • Amygdala-Fusiform Interactions are modulated by emotional faces Fairhall, SL. (2007) Cereb Cortex • Right Angular Gyrus  Mentalizing/ Inferring Thought/ Feelings of others. Spreng RN.(2003) Brain Res • Left Hippocampus plays a role in autobiographical memory retrieval Everyone • Cerebellum involved during emotive processing Fusar-Poli P. (2010) J Clin Neurosci 17: 311–4. • Superior Temporal Sulcus and Middle Temporal Gyrus are primary regions for processing, specifically, the emotional expressions of faces. Haxby (2010) Jvis • Thalamus plays a role as a hub integrating cortical networks during the evaluation of the biological significance of affective visual stimuli. Pessoa, L. (2010) Nat Rev Neurosci Integration of autobiographical memory with mentalizing during implicit perception of emotional faces Functional connectivity between Thalamus and STS play a prominent role, together during implicit fear perception.

10. How We Did It • 264 nodes from Control-related systems in the human brain by J.D Power and S.E Petersen • Meta-analysis found 165 areas. Additional 99 using fc-Mapping techniques • Used stimulus controlled fMRI data from Westphal, Reggente, Rissman RPM study that looked at Reasoning, Memory, and Perception.

11. The “RPM” Task

12. Searchlight MVPA Reason Analogy Vs. Memory (Old) with Source Correct 100% of Subjects had significantly above chance (63.2 average) performance when the searchlight(3mm) was centered on voxel with coordinates [-42, 40, 6] Created An Additional ROI for fcMVPA of a 6mm sphere around this voxel.

13. Masking And Potential Analyses Memory Reasoning • First, Take the Lower Triangle of the Correlation Matrix • Mask by network • Mask by Within exclusion • i.e only allowing fronto-polar to talk to DMN, but not to itself. • From To • i.e from Fronto Polar to DMN and DAN for Memory vs. Perception and see which network is recruited more

14. Forced Choicewith penalized logistic regression likelihoods Standard Classification Evidence for Reasoning Evidence for Memory Evidence for Perception Held Out Correlation Patterns(RMP) Forced Choice Classification Idea Final Forced Choice Stringent Forced Choice For Across Subject Accuracy Determination. Only allow a subject to be 100% right or 0%.

15. Results • What’s significant? • Binomial Inverse Function. • Need 14 out of 20 Subjects Correct with Stringent Forced Choice (70%) • p<=.05

16. Top Features • Have the classifier output the weights of the logistic regression for each condition. • Rank Them. (Top N) • Network Distribution. • Pretty Pictures.  Searchlight Node Network Recruitment

17. Visualization (Top 20 Connections) Searchlight Node To 264 Fronto Polar to remaining 248

18. In Closing • Very powerful method that is being used sparingly (Dosenbach, Pantazatos, Shirer). • Outputs are very insightful for differential recruitment mechanisms and seeing the boost in performance with particular nodes. • More than just blocks. Can handle event-related. • Very well commented code. • Share with UCLA community! • Thanks! • Petersen/Power for the ROIs • Jesse Rissman • Andrew Westphal • Jesse Brown for the visualization backbone

19. Questions / Limitations • Any association between two brain regions could be the result of a spurious association with a third brain regions. • Thalamus most important? • Oh really? • Wouldn’t it be better to gauge importance from the functional connectivity average it has before classification? Just a pure, non eigenvalue approach (or even resting state) and then compare that to see the weight it gets assigned. If it’s already highly connected, then it means its not necessarily task-dependent. Whichever region had the GREATEST increase from intrinsic functional connectivity to assigned weight is most temporally matched.