From correlation to communication: Disentangling hidden factors from functional connectivity changes

卷:7

Article

关键词: BRAIN NETWORKS;    HEMODYNAMIC-RESPONSE;    FMRI DATA;    ORGANIZATION;    DYNAMICS;    CORTEX;    MODEL;    SEGMENTATION;    REVEALS;    TRIAL;   

DOI  :  10.1162/netn_a_00290

来源: SCIE

【 摘 要 】

While correlations in the BOLD fMRI signal are widely used to capture functional connectivity (FC) and its changes across contexts, its interpretation is often ambiguous. The entanglement of multiple factors including local coupling of two neighbors and nonlocal inputs from the rest of the network (affecting one or both regions) limits the scope of the conclusions that can be drawn from correlation measures alone. Here we present a method of estimating the contribution of nonlocal network input to FC changes across different contexts. To disentangle the effect of task-induced coupling change from the network input change, we propose a new metric, communication change, utilizing BOLD signal correlation and variance. With a combination of simulation and empirical analysis, we demonstrate that (1) input from the rest of the network accounts for a moderate but significant amount of task-induced FC change and (2) the proposed communication change is a promising candidate for tracking the local coupling in task context-induced change. Additionally, when compared to FC change across three different tasks, communication change can better discriminate specific task types. Taken together, this novel index of local coupling may have many applications in improving our understanding of local and widespread interactions across large-scale functional networks.