Brain Modularity: A Biomarker of Intervention-related Plasticity

Abstract

Interventions using methods such as cognitive training and aerobic exercise have shown potential to enhance cognitive abilities. However, there is often pronounced individual variability in the magnitude of these gains. Here, we propose that brain network modularity, a measure of brain subnetwork segregation, is a unifying biomarker of intervention-related plasticity. We present work from multiple independent studies demonstrating that individual differences in baseline brain modularity predict gains in cognitive control functions across several populations and interventions, spanning healthy adults to patients with clinical deficits and cognitive training to aerobic exercise. We believe that this predictive framework provides a foundation for developing targeted, personalized interventions to improve cognition.

Keywords: brain injury; cognitive control; cognitive training; executive function; functional connectivity; graph theory.

Figure 1 (Key Figure).. Brain network modularity predicts training-related cognitive gains.

(A) Brain modularity calculated at baseline is predictive of intervention-related cognitive gains across several interventions and populations. Specifically, individuals with higher brain network modularity have larger cognitive gains than those with lower brain network modularity. Toy brain networks are comprised of nodes (circles) and edges that connect them (lines). Nodes are colored according to their module membership; within-module connections are colored to match that of nodes in their own module, while between-module connections are colored black. The network with higher modularity (right side of graph) has many within-module connections and fewer between-module connections, while the network with lower modularity (left side of graph) has fewer within-module connections and many between-module connections. (B) Baseline brain modularity predicted cognitive control gains (on a composite of tasks) in TBI patients who participated in group-based attention training, but not those who participated in a control education intervention (adapted with permission from Wolters Kluwer Health, Inc.: [23]). (C) Baseline brain modularity predicted cognitive control gains (on the Test of Strategic Learning [31]) in healthy older adults who participated in group-based reasoning training, but not those who were in a no-contact control group (adapted from [24]). (D) Baseline brain modularity predicted cognitive control gains (on a composite of tasks) in healthy older adults who participated in exercise training, but not those who participated in a control dance intervention (after controlling for age, in-scanner motion, and baseline cognitive control functioning; adapted from [25]). It is important to note that the range of measured brain modularity values varied across studies due to differences in graph theoretical methodological decisions, such as the number of network nodes and edges and choice of atlas. Nevertheless, the relationship between baseline modularity and intervention-related gains was robust to varying analytic methods.

Figure I.. Functional brain network analysis pipeline.

(A) The brain is first parcellated into a set of brain regions to form network nodes (e.g., the Power et al. atlas [37]). (B) For functional network analyses, the time series of each node is then extracted. (C) The time series of every possible pair of brain regions is then correlated to form the network edges. (D) The network is then partitioned into sub-networks or modules. Finally, the modularity of the network can be calculated by comparing within and between module connections. Brain network images in (A) and (D) were visualized with the BrainNet Viewer [85].