Analysis of brain subnetworks within the context of their whole‐brain networks

Abstract

Analyzing the structure and function of the brain from a network perspective has increased considerably over the past two decades, with regional subnetwork analyses becoming prominent in the recent literature. However, despite the fact that the brain, as a complex system of interacting subsystems (i.e., subnetworks), cannot be fully understood by analyzing its constituent parts as independent elements, most studies extract subnetworks from the whole and treat them as independent networks. This approach entails neglecting their interactions with other brain regions and precludes identifying potential compensatory mechanisms outside the analyzed subnetwork. In this study, using simulated and empirical data, we show that the analysis of brain subnetworks within the context of their whole‐brain networks, that is, including their interactions with other brain regions, can yield different outcomes when compared to analyzing them as independent networks. We also provide a multivariate mixed‐effects modeling framework that allows analyzing subnetworks within the context of their whole‐brain networks, and show that it can better disentangle global (whole‐brain) and local (subnetwork) differences when compared to standard t‐test analyses. T‐test analyses may produce misleading results in identifying complex global and local level differences. The provided multivariate model is an extension of a previously developed model for global, system‐level hypotheses about the brain. The modified version detailed here provides the same utilities as the original model—quantifying the relationship between phenotypes and brain connectivity, comparing brain networks among groups, predicting brain connectivity from phenotypes, and simulating brain networks—but for local, subnetwork‐level hypotheses.

Using Low-Dimensional Manifolds to Map Relationships Between Dynamic Brain Networks

“…The slightly high standard deviation of classification results is most likely due to this small sample size. We chose to use a dataset that we had in house because our prior work (Mayhugh et al, 2016;Bahrami et al, 2019b) had demonstrated network differences for both the task and group, albeit for static networks. This smaller dataset also avoided the growth in the computational intensity of this methodology associated with increasing the number of networks.…”

Functional Brain Networks: Unique Patterns with Hedonic Appetite and Confidence to Resist Eating in Older Adults with Obesity

“…We used continuous scores of the PFS and WEL in our models. This two‐part model examines the probability (presence or absence) of network connections as well as the strength of existing connections (29,30). For the scope of this study, we focused solely on the strength of the network connections.…”

A mixed-modeling framework for whole-brain dynamic network analysis

“…. However, we are working on devising a procedure to allow for analyzing dynamics of local brain networks within the context of their whole networks as done for the original framework for static networks in (Bahrami et al, 2019a). Finally, future studies should conduct a thorough sensitivity analysis of our framework with respect to the parameters used in generating the dynamics networks and the parcellation scheme used.…”