Tracking whole-brain connectivity dynamics in the resting-state

Transcription

1 Tracking whole-brain connectivity dynamics in the resting-state Supplementary Table. Peak Coordinates of ICNs ICN regions BA t max Peak (mm) (continued) BA t max Peak (mm) X Y Z X Y Z Subcortical networks ITG (93) Caudate (57) L inferior temporal gyrus R caudate nucleus R inferior temporal gyrus L caudate nucleus PreCG (56) Putamen (8) L precentral gyrus R putamen R precentral gyrus L putamen L supplementary motor area Putamen (3) MCC (9) R putamen Bi middle cingulate cortex L putamen pinsula (7) Thalamus (5) R insula R thalamus L insula L thalamus L IPL (76) Auditory networks L inferior parietal lobule STG (99) L middle frontal gyrus L superior temporal gyrus L inferior temporal gyrus R superior temporal gyrus MiFG (47) STG (98) R middle frontal gyrus R superior temporal gyrus L middle frontal gyrus L superior temporal gyrus IFG (88) Somatomotor networks R inferior frontal gyrus PreCG (2) L inferior frontal gyrus R postcentral gyrus ainsula (2) L postcentral gyrus R insula R cerebellum (VI) L insula L cerebellum (VI) R medial frontal gyrus R PoCG () IPL (59) R postcentral gyrus R inferior parietal lobule L cerebellum (VI) L inferior parietal lobule L PoCG (4) MiFG (36) L postcentral gyrus R middle frontal gyrus R cerebellum (VI) L middle frontal gyrus L supplementary motor area SMA (29) SMA (58) Bi supplementary motor area Bi supplementary motor area R STG+IFG (66) ParaCL (37) R superior temporal gyrus Bi paracentral lobule R inferior frontal gyrus PoCG (45) PHG (8) L postcentral gyrus R ParaHippocampal gyrus R postcentral gyrus L ParaHippocampal gyrus ParaCL (3) Default mode networks Bi paracentral lobule Precuneus (2) SPL (9) Bi precuneus R superior parietal lobule Bi middle cingulate cortex L superior parietal lobule Precuneus (44) Visual networks Bi precuneus MTG (77) PCC (39) L middle temporal gyrus L posterior cingulate R middle temporal gyrus R posterior cingulate LingualG (79) PCC (28) R lingual gyrus Bi posterior cingulate cortex L lingual gyrus R AG (64) Cuneus (6) R angular gyrus Bi cuneus R Precuneus CalcarineG (46) R superior frontal gyrus L calcarine gyrus ACC (26) R calcarine gyrus Bi anterior cingulate cortex Cuneus (27) L AG (75) Bi cuneus L angular gyrus FFG (43) L Precuneus L fusiform gyrus R angular gyrus R fusiform gyrus L superior frontal gyrus R MOG (82) L medial frontal gyrus R middle occipital gyrus MiFG+SFG (48) L MOG (89) L middle frontal gyrus L middle occipital gyrus L superior frontal gyrus MOG (8) R middle frontal gyrus R middle occipital gyrus L MTG+IFG (87) L middle occipital gyrus L middle temporal gyrus SOG (6) L inferior frontal gyrus L superior occipital gyrus Cerebellar networks R superior occipital gyrus R CB (2) Cognitive control networks R cerebellum (crus 2) R IPL (67) L cerebellum (crus 2) R inferior parietal lobule L CB (7) R middle frontal gyrus L cerebellum (crus 2) R inferior frontal gyrus CB (32) R inferior temporal gyrus Bi cerebellum (VI)

2 A DVARS (std) EFFECT OF POST-PROCESSING STEPS ON MOTION-RELATED VARIANCE 3 2 Subject 24 Subject 52 Original TCs, all components Original TCs, just ICNs Regression of realignment parameters Regression + outlier removal B AVERAGE FC, ORIGINAL SC AUD SM VIS CC FD (mm) Frame # (TR = 2 s) DM CB SC AUD SM VIS CC C AVERAGE FC, FULLY POST-PROCESSED DM CB DVARS (std) 3 2 Subject 267 Subject correlation (r).62 FD (mm) Frame # (TR = 2 s) D DIFFERENCE (POST-PROCESSED ORIGINAL) FD (mm) DVARS (std) 3 Subject Subject 367 correlation (r) Frame # (TR = 2 s) Figure S. Effects of motion removal at the subject (A) and group levels (B-D). In (A), we display the framewise displacement (FD, bottom panel) computed from realignment parameters, and the root-mean-square of the temporal differential of components TCs (DVARS, top panel, see Power JD et al., 22) for the three example subjects (left column) and three additional subjects chosen at random (right column). From the large spikes in DVARS, it is clear that the original TCs of all components (solid blue line) as well as just ICN components (dotted blue line) are contaminated with motion-related variance. Regression of realignment parameters and their derivatives (green line) reduces motion influence in some cases, but additional outlier removal (black line) offers much greater improvement. The effect of post-processing can be seen in the difference between stationary FC matrices computed from the original TCs (B) and fully post-processed TCs (regression of realignment parameters and outlier removal) (C), as shown in panel (D). In agreement with (Power JD et al., 22), we observe motioninduced increases in short-range connections (e.g., between SM ICNs) and, to a lesser extent, decreases in longrange connections (e.g., between SC and VIS ICNs).

3 Subcortical Networks Component 57, Caudate 7.4 X = 2 mm Y = mm Z = 5 mm 7.4 Auditory networks Component 8, Putamen Component 99, STG X = 2 mm Y = 2 mm Z = 6 mm 7.4 X = 5 mm Y = 33 mm Z = 8 mm 38. X = 2 mm Y = 9 mm Z = 3 mm X = 5 mm Y = 3 mm Z = 2 mm Component 3, Putamen Component 98, STG X = 27 mm Y = 6 mm Z = 3 mm 76.4 X = 63 mm Y = 24 mm Z = mm 47.8 X = 3 mm Y = 3 mm Z = mm X = 57 mm Y = 2 mm Z = 3 mm Component 5, Thalamus 52. X = 9 mm Y = 8 mm Z = 6 mm 52. Figure S2. SMs of ICNs. ICNs are divided into the same groups shown in Figure 2 and are thresholded at t > 2, where one-sample t-statistics have been computed across the single-subject SMs. Sagittal, coronal, and axial slices are shown at the maximal t-statistic for clusters larger than 3 cm 3.

4 Somatomotor networks Component 2, PreCG Component 37, ParaCL X = 57 mm Y = 6 mm Z = 27 mm 74.8 X = 5 mm Y = 24 mm Z = 66 mm 65.8 Component 45, PoCG 52.4 X = 54 mm Y = 9 mm Z = 3 mm Component, R PoCG X = 57 mm Y = 24 mm Z = 36 mm X = 42 mm Y = 24 mm Z = 6 mm Component 4, L PoCG 57.6 X = 57 mm Y = 2 mm Z = 39 mm Component 3, ParaCL X = 42 mm Y = 24 mm Z = 57 mm 57.5 X = 8 mm Y = 9 mm Z = 66 mm 46.7 Component 58, SMA Component 9, SPL X = 5 mm Y = 3 mm Z = 48 mm 46.3 X = 2 mm Y = 45 mm Z = 63 mm 52.4

5 Visual networks Component 77, MTG 44.5 Component 27, Cuneus 62.6 X = 48 mm Y = 72 mm Z = 9 mm 44.5 X = 5 mm Y = 87 mm Z = 3 mm 62.6 Component 43, FFG X = 48 mm Y = 63 mm Z = 9 mm 52.5 Component 79, LingualG 5. X = 27 mm Y = 48 mm Z = 2 mm 52.5 X = 2 mm Y = 72 mm Z = 6 mm 5. X = 3 mm Y = 42 mm Z = 5 mm Component 82, R MOG X = 8 mm Y = 75 mm Z = 6 mm 47. Component 6, Cuneus 6.8 X = 36 mm Y = 84 mm Z = 6 mm Component 89, L MOG 47. X = mm Y = 84 mm Z = 24 mm Component 46, CalcarineG 66. X = 27 mm Y = 9 mm Z = mm 42.4 X = 9 mm Y = 69 mm Z = 9 mm 66.

6 Cognitive control networks Component 67, R IPl 52.3 Visual networks continued Component 8, MOG X = 45 mm Y = 39 mm Z = 5 mm X = 3 mm Y = 93 mm Z = mm 7.8 X = 48 mm Y = 42 mm Z = 5 mm Component 93, ITG 42.9 X = 27 mm Y = 96 mm Z = 3 mm Component 6, SOG X = 5 mm Y = 63 mm Z = 9 mm X = 36 mm Y = 8 mm Z = 29 mm 47. X = 5 mm Y = 6 mm Z = 9 mm Component 56, PreCG 36.7 X = 39 mm Y = 75 mm Z = 36 mm X = 48 mm Y = 6 mm Z = 48 mm 36.7 X = 5 mm Y = 6 mm Z = 48 mm

7 Cognitive control networks continued () Component 47, MiFG 42.3 Component 9, MCC 37.5 X = 45 mm Y = 5 mm Z = 33 mm 42.3 X = 5 mm Y = 8 mm Z = 39 mm 37.5 Component 7, pinsula 5.5 X = 42 mm Y = 9 mm Z = 33 mm Component 88, IFG 39. X = 45 mm Y = 3 mm Z = 3 mm 5.5 X = 48 mm Y = 39 mm Z = mm 39. X = 45 mm Y = 6 mm Z = 3 mm Component 76, L IPL 5.6 X = 48 mm Y = 33 mm Z = 9 mm Component 2, ainsula 56.7 X = 42 mm Y = 5 mm Z = 5 mm 5.6 X = 33 mm Y = 24 mm Z = 6 mm 56.7 X = 48 mm Y = 33 mm Z = 2 mm X = 3 mm Y = 24 mm Z = 3 mm

8 Cognitive control networks continued (2) Component 59, IPL 49. X = 57 mm Y = 45 mm Z = 39 mm 49. Component 66, R STG+IFG 43.9 X = 57 mm Y = 45 mm Z = 42 mm Component 36, MiFG X = 57 mm Y = 48 mm Z = 5 mm Component 8, PHG X = 3 mm Y = 54 mm Z = 6 mm 45.6 X = 24 mm Y = 8 mm Z = 2 mm 37.2 X = 3 mm Y = 54 mm Z = 2 mm Component 29, SMA X = 2 mm Y = 8 mm Z = 2 mm 45. X = 5 mm Y = 5 mm Z = 6 mm 45.

9 Default-mode networks Component 64, R AG 62.6 Component 2, Precuneus 68.7 X = 45 mm Y = 66 mm Z = 42 mm 62.6 X = 6 mm Y = 72 mm Z = 39 mm 68.7 X = 24 mm Y = 33 mm Z = 54 mm X = mm Y = 24 mm Z = 3 mm Component 44, Precuneus 65.2 X = 6 mm Y = 63 mm Z = 39 mm Component 26, ACC 57. X = mm Y = 54 mm Z = 5 mm 65.2 Component 39, PCC 67.8 X = 5 mm Y = 45 mm Z = 3 mm 57. Component 75, L AG 56.7 X = 5 mm Y = 54 mm Z = 5 mm 67.8 Component 28, PCC 65.7 X = 48 mm Y = 66 mm Z = 33 mm 56.7 X = 5 mm Y = 36 mm Z = 27 mm 65.7 X = mm Y = 6 mm Z = 33 mm

10 Default-mode networks continued Component 48, MiFG+SFG Cerebellar networks Component 2, R CB X = 33 mm Y = 75 mm Z = 39 mm 5.6 X = mm Y = 33 mm Z = 48 mm Component 87, L MTG+IFG 34. Component 7, L CB X = 36 mm Y = 6 mm Z = 42 mm 38.2 X = 57 mm Y = 39 mm Z = 3 mm 42.4 Component 32, CB 43.9 X = 54 mm Y = 8 mm Z = 2 mm X = 5 mm Y = 66 mm Z = 24 mm 43.9

11 .5 AUC.2.9 COM (Hz).22 SC AUD SM A AREA UNDER THE CURVE (AUC) OF FC SPECTRA, AVERAGED OVER SUBJECTS B CENTER OF MASS (COM) OF FC SPECTRA, AVERAGED OVER SUBJECTS VIS CC DM CB ventricular cortical WM ICNs ARTs CB WM motion vascular susceptibility SC AUD SM VIS CC DM CB ventricular cortical WM CB WM motion vascular susceptibility ICNs ARTs AUC of FC spectra between all ICNs t = 39.4, P =.3e COM of FC spectra between ICNs (Hz)...9 t = 27.7, P = 2.7e AUC of FC spectra between all ARTs COM of FC spectra between all ARTs (Hz) Figure S3. Differences in FC variability between ICNs and ARTs. FC spectra were evaluated in terms of the area under the curve (AUC) and the center-of-mass (COM), both non-parametric descriptors of the spectral shape that are robust to noise. FC oscillations between ICNs are larger variable (A, top) and are focused at lower frequencies (B, top). Comparing the FC spectral properties within subjects between all ICNs (average over quadrant II) and all ARTs (average over quadrant IV) yields highly consistent and significant differences (A-B, bottom).

12 k = 2 k = 3 k = 4 k = 5 k = 6 k = 7 k = 8 k = 9 k = 933 (64%) 7 (57%) 53 (5%) 248 (4%) 73 (39%) 3 (34%) 879 (29%) 82 (27%) 755 (25%) S 93 (36%) 975 (32%) 593 (2%) 497 (6%) 383 (3%) 243 (8%) 227 (8%) 25 (7%) 29 (7%) S2 34 (%) 295 (%) 283 (9%) 279 (9%) 265 (9%) 263 (9%) 233 (8%) 23 (8%) S3 67 (2%) 49 (6%) 387 (3%) 357 (2%) 225 (7%) 28 (9%) 83 (6%) S4 57 (7%) 487 (6%) 49 (4%) 393 (3%) 35 (2%) 39 (%) S5 37 (%) 289 (%) 243 (8%) 37 (%) 265 (9%) S6 423 (4%) 333 (%) 263 (9%) 249 (8%) S7 463 (5%) 299 (%) 279 (9%) S8 255 (8%) 225 (7%) S9 3 (%) S Figure S4. Cluster centroids for k = 2 to. For each k, the k-means algorithm was applied with 5 repetitions to the subject exemplar windows (326 instances). The gray rectangle highlights the clustering result presented in the main text. The total number and percentage of occurrences is listed above each centroid.

13 A CLUSTER CENTROIDS FROM BOOTSTRAP RESAMPLES S S2 S3 S4 S5 S6 S7 63 (35%) 93 (6%) 28 (9%) 377 (2%) 44 (5%) 28 (9%) 393 (3%) 69 (35%) 27 (7%) 277 (9%) 353 (2%) 45 (5%) 35 (%) 4 (3%) 35 (34%) 237 (8%) 286 (%) 367 (2%) 369 (2%) 275 (9%) 44 (5%) 973 (32%) 24 (8%) 27 (9%) 365 (2%) 425 (4%) 3 (%) 455 (5%) 2 (34%) 263 (9%) 267 (9%) 39 (%) 429 (4%) 263 (9%) 425 (4%) B CLUSTER CENTROIDS FROM SPLIT-HALF ANALYSIS 535 (35%) 3 (7%) 2 (8%) 85 (2%) 235 (5%) 29 (8%) 29 (4%) HALF SAMPLE 5 SAMPLE 4 SAMPLE 3 SAMPLE 2 SAMPLE 489 (33%) 5 (8%) 45 (%) 87 (2%) 95 (3%) 59 (%) 29 (4%) HALF 2 Figure S5. Cluster centroids for bootstrap resamples (A) and split-half samples (B). In (A), subjects were resampled and the k-means algorithm was applied with 5 repetitions to the subject exemplar windows ( 3 to 4 instances depending on the sample). In (B), the subjects were split into two groups and the k-means algorithm was applied with 5 repetitions to the subject exemplars in that group ( 5 instances). The total number and percentage of occurrences is listed above each centroid.

14 A EXAMPLE FC TIMESERIES CalcarineG (46) to PreCG (56) CalcarineG (46) to MCC (9) CalcarineG (46) to pinsula (7) ORIGINAL DATA Correlation (z).5.5 subject time (s) B AVERAGE OF SUBJECT EXEMPLARS C CLUSTER CENTROIDS (k = 7) S S2 S3 S4 S5 S6 S7 3 (34%) 243 (8%) 265 (9%) 357 (2%) 49 (4%) 289 (%) 423 (4%) SR: CONSISTENT FOURIER PHASE SHIFT Correlation (z) time (s) 496 (6%) 4 (3%) 249 (8%) 458 (5%) 5 (7%) 473 (6%) 438 (4%) SR2: INCONSISTENT FOURIER PHASE SHIFT 674 (55%) (%) (%) 2 (%) 39 (34%) 232 (8%) 6 (2%) Correlation (z) time (s) Figure S6. Clustering results with surrogate datasets. (A) Demonstration of surrogate data creation from the original FC timeseries (top) using a consistent Fourier phase shift (SR, middle) and inconsistent phase shift (SR2, bottom). In SR, the FC timeseries maintain their phase relationships. In SR2, phase relationships (and thus covariance structures) are disrupted. Neither consistent nor inconsistent Fourier phase shifting alters the mean, variance, or temporal autocorrelation of individual FC timeseries, as demonstrated by the very similar average subject exemplars shown in (B) for original data (top) SR (middle) and SR2 (bottom). (C) Cluster centroids for original data (top, identical to Figure S4, k = 7), SR (middle), and SR2 (bottom). Clustering of exemplars from SR2 fails to replicate the centroids observed in original data and SR : two clusters (S2 and S3) are empty and all others strongly resemble the mean shown in (B). This suggests it is the patterns of covariance (preserved in SR), rather than distinctions in mean or variance, that drive the clustering.

15 A SIMULATED DYNAMIC NEURAL FUNCTIONAL CONNECTIVITY state state 2 state 3 state 4 state 2 + FC Amplitude Time (s) B CLUSTER CENTROIDS.6 centroid centroid 2 centroid 3 centroid 4.4 k = 4.2 Cluster validity index Number of clusters (k) C STATE TRANSITIONS Distance from centroid Time (s) centroid centroid 2 centroid 3 centroid 4 estimated state true state Figure S7. Validation of clustering approach with simulated data. (A) Simulated BOLD timeseries for ten nodes are generated under a model of dynamic neural connectivity where 4 states are possible (State 2 repeats). Simulation parameters (TR = 2 s, 48 volumes) are matched to experimental data; connectivity states are modeled after clusters observed in real data (e.g., Figure 5). (B) Windowed covariance matrices are estimated from the simulated timeseries and are subjected to k-means clustering with the L distance metric. The elbow criterion correctly identifies k = 4 clusters, and cluster centroids show high similarity to the true states. (C) The distance of each window to each cluster centroid. The assignment of individual windows to states is very accurate. Distances and state assignments are plotted at the time point corresponding to the center of the window.