Modularity of rsfMRI networks across aging, sexes, and processing speed

Abstract

AbstractBackgroundFunctional brain networks shift and adapt with task and environmental demands throughoutneural development and aging, which are both known to have gender specific profiles. The modularity ofthese networks has been shown to decrease with healthy aging but there is debate surrounding sexdifferences in aging and how dimorphisms relate to neurodegenerative pathologies such as Alzheimer’s(AD). This study used resting state functional magnetic resonance imaging(fMRI) to examine sexdifferences in network modularity in mild cognitive impairment (MCI) and healthy control (HC) groups toexplore its potential as a clinical biomarker of a prodromal AD.MethodParticipants included 90 MCI subjects age 55 to 77 (M = 68.19 years), and 127 HC subjects age45 to 84 (M = 66.53 years). Resting state fMRI scans were analyzed using standard preprocessing anddouble regression pipeline in FSL (version 6.0). BOLD time series were extracted from 280 brain regionsusing the Power atlas. Brain Connectivity Toolbox was used to compute the modularity graph theory metric.Generalized linear mixed modeling was used to examine the modularity metric Q as a function of sex andTrails‐A score in HC and MCI.ResultA significant interaction between sex and Trails‐A score was found (Wald c2=4.7, p=.031) withinthe HC group. Males showed a decrease in modularity with increased processing speed on Trails‐A in linewith previous findings, whereas females showed no association. No significant interactions were found forMCI groups.ConclusionThese results confirm previous work correlating modularity with task‐specific connectivityand performance. Future analyses should focus on divergences between sexes in brain network modularityto inform differential diagnoses of AD.