Brain Network Modularity Predicts Exercise-Related Executive Function Gains in Older Adults

Abstract

Recent work suggests that the brain can be conceptualized as a network comprised of groups of sub-networks or modules. The extent of segregation between modules can be quantified with a modularity metric, where networks with high modularity have dense connections within modules and sparser connections between modules. Previous work has shown that higher modularity predicts greater improvements after cognitive training in patients with traumatic brain injury and in healthy older and young adults. It is not known, however, whether modularity can also predict cognitive gains after a physical exercise intervention. Here, we quantified modularity in older adults (N = 128, mean age = 64.74) who underwent one of the following interventions for 6 months (NCT01472744 on ClinicalTrials.gov): (1) aerobic exercise in the form of brisk walking (Walk), (2) aerobic exercise in the form of brisk walking plus nutritional supplement (Walk+), (3) stretching, strengthening and stability (SSS), or (4) dance instruction. After the intervention, the Walk, Walk+ and SSS groups showed gains in cardiorespiratory fitness (CRF), with larger effects in both walking groups compared to the SSS and Dance groups. The Walk, Walk+ and SSS groups also improved in executive function (EF) as measured by reasoning, working memory, and task-switching tests. In the Walk, Walk+, and SSS groups that improved in EF, higher baseline modularity was positively related to EF gains, even after controlling for age, in-scanner motion and baseline EF. No relationship between modularity and EF gains was observed in the Dance group, which did not show training-related gains in CRF or EF control. These results are consistent with previous studies demonstrating that individuals with a more modular brain network organization are more responsive to cognitive training. These findings suggest that the predictive power of modularity may be generalizable across interventions aimed to enhance aspects of cognition and that, especially in low-performing individuals, global network properties can capture individual differences in neuroplasticity.

From Evaluation to Prediction: Behavioral Effects and Biological Markers of Cognitive Control Intervention

“…Intervention studies on cognitive control have primarily focused on CCT and group interventions [9]; however, exercise training has also been reported to have positive effects on brain function and behavioral performance. Consistent with findings from cognitive training and group interventions, higher levels of brain modularity were associated with greater improvements in exerciserelated cognitive control [10,114].…”

“…The extent to which cognitive control can be affected by training or other means is an important question, as is the question of which criteria should be adopted to evaluate the effects and efficacy of a chosen intervention. Since cognitive control intervention may affect functional changes both with and without structural alterations [8][9][10][11][12], in this review, we do not strictly distinguish the intervention gains derived from plasticity or flexibility. We will discuss intervention improvements at three levels.…”

A set of functionally-defined brain regions with improved representation of the subcortex and cerebellum

“…These studies have examined both structural and functional network organization in a wide variety of samples, including healthy young adults (Power et al, 2013;Zanto and Gazzaley, 2013), developmental cohorts (Gu et al, 2015;Nielsen et al, 2018;Rudolph et al, 2017), older adults (Baniqued et al, 2018;Gallen et al, 2016), and a plethora of neurological and psychiatric populations (Gratton et al, 2018a;Greene et al, 2016;Sheffield et al, 2015;Siegel et al, 2018). We have gained a better understanding of typical and atypical human brain organization from these efforts.…”

“…These two ROI sets sample the cortex well, representing a diverse set of brain areas that can be organized into functional networks. Many investigators have used them to describe functional brain organization in a variety of healthy samples (Power et al, 2013;Zanto and Gazzaley, 2013), lifespan cohorts (Baniqued et al, 2018;Gallen et al, 2016;Gu et al, 2015;Nielsen et al, 2018;Rudolph et al, 2017), as well as populations with neurologic and psychiatric diseases (Gratton et al, 2018a;Greene et al, 2016;Sheffield et al, 2015;Siegel et al, 2018). However, the first set (264 volumetric ROIs) under-samples subcortical and cerebellar structures, as only 17 ROIs are non-cortical, and the second set (333 parcels) is restricted to the cortex only, similar to other popular ROI sets, e.g.…”

Evaluation of different cerebrospinal fluid and white matter fMRI filtering strategies—Quantifying noise removal and neural signal preservation

“…In fact, the numbers of PCA components are often taken as “fixed” (Caballero‐Gaudes and Reynolds, ) in fMRI‐based neuroimaging. For example, five CSF and five WM PCA signals are used as the default settings in the CONN toolbox (https://www.nitrc.org/projects/conn) and in many other studies using different software (e.g., Baniqued et al, ); there are also studies involving other fixed numbers of these nuisance PCA‐based signals, for example, a recent fMRI FC study by Tillman (Tillman et al, ) used three CSF and three WM signals.…”