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. 2015 May 1:111:476-88.
doi: 10.1016/j.neuroimage.2015.01.057. Epub 2015 Feb 7.

Introducing co-activation pattern metrics to quantify spontaneous brain network dynamics

Jingyuan E Chen  1 , Catie Chang  2 , Michael D Greicius  3 , Gary H Glover  4
Affiliations

Introducing co-activation pattern metrics to quantify spontaneous brain network dynamics

Jingyuan E Chen et al. Neuroimage. .

Abstract

Recently, fMRI researchers have begun to realize that the brain's intrinsic network patterns may undergo substantial changes during a single resting state (RS) scan. However, despite the growing interest in brain dynamics, metrics that can quantify the variability of network patterns are still quite limited. Here, we first introduce various quantification metrics based on the extension of co-activation pattern (CAP) analysis, a recently proposed point-process analysis that tracks state alternations at each individual time frame and relies on very few assumptions; then apply these proposed metrics to quantify changes of brain dynamics during a sustained 2-back working memory (WM) task compared to rest. We focus on the functional connectivity of two prominent RS networks, the default-mode network (DMN) and executive control network (ECN). We first demonstrate less variability of global Pearson correlations with respect to the two chosen networks using a sliding-window approach during WM task compared to rest; then we show that the macroscopic decrease in variations in correlations during a WM task is also well characterized by the combined effect of a reduced number of dominant CAPs, increased spatial consistency across CAPs, and increased fractional contributions of a few dominant CAPs. These CAP metrics may provide alternative and more straightforward quantitative means of characterizing brain network dynamics than time-windowed correlation analyses.

Keywords: Brain dynamics; Co-activation patterns; Point process analysis; Resting state networks; Working memory.

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Figures

Fig. 1
Fig. 1
Illustration of CAP analysis in (Liu and Duyn, 2013) with the resting state datasets in this study (see section 2.2 Experiments and Analysis). (A) Conventional seed-based correlation map can be replicated by averaging time frames when the seed signal exhibits relatively large BOLD contrasts; (B) The scheme to generate CAPs. Different colors indicate different CAPs in temporal clustering.
Fig. 2
Fig. 2
Steps to extract the dominant CAP-set for each cluster number k. (1) extract k CAPs, and their temporal occurrence fractions, ‘TF’; (2) reorder the k CAPs in descending TF as CAP1, CAP2, ... CAPk; (3) compute {rms}1≤m≤k , spatial similarity of the weighted (by TF) average of the top-ranked CAPs with the overall frame average. Top-ranked CAPs {CAPj}1≤j≤n with {rn−1s<rthress} & {∏p≥nI{rps≥rthress}} (in red rectangle) comprise the dominant CAP-set at k.
Fig. 3
Fig. 3
(A) The group t-test result of the positive and negative correlations with respect to the DMN-seed at rest. (B) The group t-test result of the activation in response to the 2-back WM task. ECN-seed is a local maximum, indicated by white arrow.
Fig. 4
Fig. 4
(A) Variability (standard deviations) over the sequence of 1-min sliding-window correlations between each brain voxel and the DMN and ECN seeds, averaged across 21 subjects; (B) Spatial extent of grey matter voxels with higher linear correlation variations during rest compared to WM task vs. the other way around for each subject (the difference of variability is thresholded at 0.1). ‘R’ stands for ‘rest’, ‘W’ stands for ‘WM task’. Each blue straight line connects the percentage values (‘R>W’ and ‘W>R’) of a single subject.
Fig. 5
Fig. 5
(A) The spatial profiles of the dominant CAP-sets associated with DMN. Numbers identify CAPs in each set, (*) denotes the overall frame average. (B) The spatial similarity covariance matrices between the overall dominant REST-CAP-set and WM-CAP-set for DMN, and within each dominant CAP-set. (C) The spatial similarity between the “overall dominant CAP-sets” derived with ROI90 and ROI499w.
Fig. 6
Fig. 6
(A) The spatial profiles of the dominant CAP-sets associated with ECN. Numbers identify CAPs in each set, (*) denotes the overall frame average. (B) The spatial similarity covariance matrices between the overall dominant REST-CAP-set and WM-CAP-set for ECN, and within each dominant CAP-set. (C) The spatial similarity between the “overall dominant CAP-sets” derived with ROI90 and ROI499w.
Fig. 7
Fig. 7
(A) Silhouette scores as a function of cluster numbers. Higher silhouette scores correspond to higher similarity between cluster members, i.e. more appropriate clustering. Results indicate k = 2 is best for both DMN and ECN. (B) The spatial patterns of the 1st/2nd dominant CAPs derived from ROI90.
Fig. 8
Fig. 8
(A) Temporal fractions of the 1st dominant CAPs during sustained WM task compared to rest (p<0.05 *, p<0.00005 ***); (B) P values (group paired t-test, the temporal fraction of the 1st dominant CAP during WM task compared to rest) as a function of number of ROIs (in descending order of importance in differentiating two CAPs, see text 3.2.2 Temporal fractions of the 1st dominant CAP) included in the initial K-means clustering, where ‘-1000’ indicates the mean and standard deviation of t values associated with the 1000 K-means clustering results, and ‘combined’ indicates the t value of the synthesized clustering result from the 1000 K-means trials (see text 2.2.7 CAP analysis).
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