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Methods for Whole-Brain Comparisons of Resting State Functional Connectivity Stephen J. Gotts

Methods for Whole-Brain Comparisons of Resting State Functional Connectivity Stephen J. Gotts Laboratory of Brain and Cognition NIMH/NIH Bethesda, MD. Functional Connectivity of Spontaneous Activity at Rest (i.e. "Resting State"). Functional Connectivity of Spontaneous Activity at Rest

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Methods for Whole-Brain Comparisons of Resting State Functional Connectivity Stephen J. Gotts

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  1. Methods for Whole-Brain Comparisons of Resting State Functional Connectivity Stephen J. Gotts Laboratory of Brain and Cognition NIMH/NIH Bethesda, MD

  2. Functional Connectivity of Spontaneous Activity at Rest (i.e. "Resting State")

  3. Functional Connectivity of Spontaneous Activity at Rest (i.e. "Resting State") • very popular (easy and fast to administer) • subjects passively view a fixation cross • fluctuations in spontaneous activity (< .1 Hz) are correlated throughout the • brain in a spatially restricted manner

  4. Functional Connectivity of Spontaneous Activity at Rest (i.e. "Resting State") • very popular (easy and fast to administer) • subjects passively view a fixation cross • fluctuations in spontaneous activity (< .1 Hz) are correlated throughout the • brain in a spatially restricted manner For review: Fox & Raichle(2007). Nat Rev Neurosci Power, Schlaggar, & Petersen (2014). Neuron

  5. Problem: How can we do brain-wide testing in fMRI?

  6. Problem: How can we do brain-wide testing in fMRI? Typical methods for Task-based fMRI:

  7. Problem: How can we do brain-wide testing in fMRI? • Typical methods for Task-based fMRI: • Multiple regression to get Beta Weights per condition/subject

  8. Problem: How can we do brain-wide testing in fMRI? • Typical methods for Task-based fMRI: • Multiple regression to get Beta Weights per condition/subject • Test Beta Weights in a voxel-wise manner across subjects (using t-tests, ANOVA, correlation, etc.)

  9. Problem: How can we do brain-wide testing in fMRI? • Typical methods for Task-based fMRI: • Multiple regression to get Beta Weights per condition/subject • Test Beta Weights in a voxel-wise manner across subjects (using t-tests, ANOVA, correlation, etc.) • threshold statistic and correct for voxel-wise comparisons using cluster-size from Monte Carlo simulations or FDR

  10. Problem: How can we do brain-wide testing in fMRI? • Typical methods for Task-based fMRI: • Multiple regression to get Beta Weights per condition/subject • Test Beta Weights in a voxel-wise manner across subjects (using t-tests, ANOVA, correlation, etc.) • threshold statistic and correct for voxel-wise comparisons using cluster-size from Monte Carlo simulations or FDR • This holds for single whole-brain tests and can be adjusted for multiple tests on the same data using Bonferroni on the corrected p-value

  11. Problem: How can we do brain-wide testing in fMRI? • Typical methods for Task-based fMRI: • Multiple regression to get Beta Weights per condition/subject • Test Beta Weights in a voxel-wise manner across subjects (using t-tests, ANOVA, correlation, etc.) • threshold statistic and correct for voxel-wise comparisons using cluster-size from Monte Carlo simulations or FDR • This holds for single whole-brain tests and can be adjusted for multiple tests on the same data using Bonferroni on the corrected p-value • But what to do for functional connectivity studies?

  12. Problem: Brain-wide testing for functional connectivity?

  13. Problem: Brain-wide testing for functional connectivity? The number of comparisons explodes for voxel-wise tests. Every voxel with every voxel is a lot of tests for 40,000 voxels:

  14. Problem: Brain-wide testing for functional connectivity? The number of comparisons explodes for voxel-wise tests. Every voxel with every voxel is a lot of tests for 40,000 voxels: 40,000 x 39,999 / 2 ≈ 8.0 x 108 tests

  15. Problem: Brain-wide testing for functional connectivity? The number of comparisons explodes for voxel-wise tests. Every voxel with every voxel is a lot of tests for 40,000 voxels: 40,000 x 39,999 / 2 ≈ 8.0 x 108 tests P < .05/(8.0 x 108) = 6.125 x 10-11 (Bonferroni)

  16. Problem: Brain-wide testing for functional connectivity? The number of comparisons explodes for voxel-wise tests. Every voxel with every voxel is a lot of tests for 40,000 voxels: 40,000 x 39,999 / 2 ≈ 8.0 x 108 tests P < .05/(8.0 x 108) = 6.125 x 10-11 (Bonferroni) False Discovery Rate might work (if most p-values are low), but not for more selective differences in typical sized datasets

  17. Problem: Brain-wide testing for functional connectivity? The number of comparisons explodes for voxel-wise tests. Every voxel with every voxel is a lot of tests for 40,000 voxels: 40,000 x 39,999 / 2 ≈ 8.0 x 108 tests P < .05/(8.0 x 108) = 6.125 x 10-11 (Bonferroni) False Discovery Rate might work (if most p-values are low), but not for more selective differences in typical sized datasets Other options:

  18. Problem: Brain-wide testing for functional connectivity? • The number of comparisons explodes for voxel-wise tests. Every voxel with every voxel is a lot of tests for 40,000 voxels: • 40,000 x 39,999 / 2 ≈ 8.0 x 108 tests • P < .05/(8.0 x 108) = 6.125 x 10-11 (Bonferroni) • False Discovery Rate might work (if most p-values are low), but not for more selective differences in typical sized datasets • Other options: • Predefined Regions of Interest • - but might not capture the full picture

  19. Problem: Brain-wide testing for functional connectivity? • The number of comparisons explodes for voxel-wise tests. Every voxel with every voxel is a lot of tests for 40,000 voxels: • 40,000 x 39,999 / 2 ≈ 8.0 x 108 tests • P < .05/(8.0 x 108) = 6.125 x 10-11 (Bonferroni) • False Discovery Rate might work (if most p-values are low), but not for more selective differences in typical sized datasets • Other options: • Predefined Regions of Interest • - but might not capture the full picture • Methods that decompose the data into smaller numbers of elements, • such as ICA • - requires some assumptions about the nature of the data

  20. Today's Talk

  21. Today's Talk Two different whole-brain approaches that are more purely statistical (based in cluster-size correction), with fewer a priori assumptions about network structure:

  22. Today's Talk • Two different whole-brain approaches that are more purely statistical (based in cluster-size correction), with fewer a priori assumptions about network structure: • Using average "connectedness" (centrality)

  23. Today's Talk • Two different whole-brain approaches that are more purely statistical (based in cluster-size correction), with fewer a priori assumptions about network structure: • Using average "connectedness" (centrality) • Testing every voxel as a seed (without averaging)

  24. Average Connectedness (Centrality)

  25. Average Connectedness (Centrality) Compress the all-to-all voxels problem into a single map of "connectedness" for each subject (per condition) "Connectedness" = r = 1 r-val (df=134) 0 Seed Voxel Σ rSeed,i N i 8 min % signal change Z=+17 L L time (TRs)

  26. Average Connectedness (Centrality) Compress the all-to-all voxels problem into a single map of "connectedness" for each subject (per condition) "Connectedness" = r = 1 r-val (df=134) 0 Seed Voxel Σ rSeed,i N i 8 min % signal change Z=+17 L L time (TRs) * a la Bob Cox and his AFNI group

  27. Average Connectedness (Centrality) Compress the all-to-all voxels problem into a single map of "connectedness" for each subject (per condition) Group Average Connectedness (per condition): 0.25 Rz' 0 X=-2 Z=-11

  28. Average Connectedness (Centrality) Compress the all-to-all voxels problem into a single map of "connectedness" for each subject (per condition)

  29. Average Connectedness (Centrality) Compress the all-to-all voxels problem into a single map of "connectedness" for each subject (per condition) Pro: Preserves a lot of the spatial resolution in the data, Regardless of the group comparison, has a shot at finding "under" or "over-connected" voxels Con: Might miss more spatially restricted effects and mixtures of under/over-connection

  30. Example: Autism (ASD) vs. Typically Developing (TD)

  31. Altered Functional Connectivity in Autism Spectrum Disorders (ASD) 31 High-Functioning ASD adolescents • Using DSM-IV criteria + ADI, ADOS • "Triad" of impairments: • Impaired social functioning • Restricted interests/repetitive behaviors • Language/communication impairments 29 Typically Developing (TD) controls

  32. Altered Functional Connectivity in Autism Spectrum Disorders (ASD) 31 High-Functioning ASD adolescents • Using DSM-IV criteria + ADI, ADOS • "Triad" of impairments: • Impaired social functioning • Restricted interests/repetitive behaviors • Language/communication impairments 29 Typically Developing (TD) controls Groups matched on: AGE: ~17 (12-24) IQ: ~113 (85-143) Sex 95% male subjects

  33. Altered Functional Connectivity in Autism Spectrum Disorders (ASD) 31 High-Functioning ASD adolescents • Using DSM-IV criteria + ADI, ADOS • "Triad" of impairments: • Impaired social functioning • Restricted interests/repetitive behaviors • Language/communication impairments 29 Typically Developing (TD) controls Groups matched on: AGE: ~17 (12-24) IQ: ~113 (85-143) Sex 95% male subjects Scanned at rest with 3.5 sec TR for 8 min 10 sec with 1.7 x 1.7 x 3 voxels

  34. Altered Functional Connectivity in Autism Spectrum Disorders (ASD) How is functional connectivity altered in ASD ?

  35. Altered Functional Connectivity in Autism Spectrum Disorders (ASD) How is functional connectivity altered in ASD ? • Generalized disruption of all ‘circuits’ ? • ... or System-specific disruption ? • (e.g. circuits involved in social processing)

  36. Altered Functional Connectivity in Autism Spectrum Disorders (ASD) How is functional connectivity altered in ASD ? • Generalized disruption of all ‘circuits’ ? • ... or System-specific disruption ? • (e.g. circuits involved in social processing) • Increase in Local Interactions ? (**)

  37. The "Social Brain" (a la Brothers, 1990; Frith & Frith, 2007; Adolphs, 2009)

  38. Using Group Connectedness to Find Seeds ASD TD 0.25 X=-2 Rz' 0 Z=-11

  39. Using Group Connectedness to Find Seeds TD - ASD 4.0 Z=-16 t-val (df=58) X=-2 2.0 X=-27 X=+29

  40. Using Group Connectedness to Find Seeds TD - ASD 4.0 Z=-16 t-val (df=58) X=-2 2.0 • Seeds: • p<.05 • at least 100 voxels • Yields 14 Seeds X=-27 X=+29

  41. Seeds + Seed Tests --> 27 Total Regions of Interest Z=-14 X=-27 X=+30 L TD > ASD: Seed ROIs X=-43 X=+44 TD > ASD: Non-seed voxels (p<.001, corrected) X=-50 X=+55

  42. Seeds + Seed Tests --> 27 Total Regions of Interest Dorsal Rostral Caudal Ventral Z-coordinate Y-coordinate

  43. Seeds + Seed Tests --> 27 Total Regions of Interest Dorsal Rostral Caudal Ventral Z-coordinate Y-coordinate How do these areas relate to each other ?

  44. Visualizing ROI-ROI correlations with Multi-Dimensional Scaling and K-Means Clustering (K=3) TD ROI Dimension 2 Dimension 1

  45. Visualizing ROI-ROI correlations with Multi-Dimensional Scaling and K-Means Clustering (K=3) ASD ROI TD ROI Dimension 2 Dimension 1

  46. Visualizing ROI-ROI correlations with Multi-Dimensional Scaling and K-Means Clustering (K=3) ASD ROI Cluster 2 TD ROI Dimension 2 Cluster 1 Cluster 3 Dimension 1

  47. Visualizing ROI-ROI correlations with Multi-Dimensional Scaling and K-Means Clustering (K=3) ASD ROI Cluster 2 TD ROI % Variance Explained Number of Clusters (K) Dimension 2 Cluster 1 Cluster 3 Dimension 1

  48. % Variance Explained TD > ASD (t-val) Number of Clusters (K) P<.05 (Bonferroni-corrected) Dimension 2 P<.05 (uncorrected) Dimension 1 Z-coordinate X-coordinate Y-coordinate

  49. Cluster 2: Control/selection-retrieval Cluster 1: Social inference/affective Cluster 3: Social perception Form / motion

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