The Role of Resting-State Network Functional Connectivity in Cognitive Aging

Abstract

Aging is associated with disruptions in the resting-state functional architecture of the brain. Previous studies have primarily focused on age-related declines in the default mode network (DMN) and its implications in Alzheimer's disease. However, due to mixed findings, it is unclear if changes in resting-state network functional connectivity are linked to cognitive decline in healthy older adults. In the present study, we evaluated the influence of intra-network coherence for four higher-order cognitive resting-state networks on a sensitive measure of cognitive aging (i.e., NIH Toolbox Fluid Cognition Battery) in 154 healthy older adults with a mean age of 71 and education ranging between 12 years and 21 years (mean = 16). Only coherence within the cinguloopercular network (CON) was significantly related to Fluid Cognition Composite scores, explaining more variance in scores than age and education. Furthermore, we mapped CON connectivity onto fluid cognitive subdomains that typically decline in advanced age. Greater CON connectivity was associated with better performance on episodic memory, attention, and executive function tasks. Overall, the present study provides evidence to propose CON coherence as a potential novel neural marker for nonpathological cognitive aging.

Machine learning and individual variability in electric field characteristics predict tDCS treatment response

“…Specifically, the basal ganglia have been suggested to work conjunctively with the middle frontal gyrus to select information to be stored in working memory [78]. In addition, increased functional connectivity of the cingulo-opercular network (also referred to as the ventral attention/salience network) is associated with better performance on measures of fluid cognition (e.g., executive function) in older adults [79]. The SVM identified critical current features in brain regions previously associated with working memory performance and age-related cognitive decline, serving as an additional proof of principle for this approach.…”

The older adult brain is less modular, more integrated and less efficient at rest: A systematic review of large-scale resting-state functional brain networks across the adult lifespan.

“…Hence, a complementary approach to whole brain studies is to investigate specific regions or networks across the adult lifespan. Although a systematic review of the literature that focuses on specific networks or regions is beyond the scope of this review, it is worth noting that the large-scale network differences noted above with ageing have also been shown to occur within-networks of higher-order cognitive functions, namely, the default mode, cingulo-opercular, executive control salience and attention networks (Andrews-Hanna et el., 2007;Bagarinao et al, 2019;Bo et al, 2014;Chan et al, 2014;La Corte et al, 2016;Onada et al, 2013;Goldstone et al, 2016;Grady et al, 2016;Geerligs et al, 2015;Hausman et al, 2020;He et al, 2014;Mowinckel et al, 2012;Nashiro et al, 2017;Tsvetanov et al, 2016;Vij et al, 2018;Wen et al, 2020b;Zhai & Li, 2019).…”

Defining a Connectome-Based Predictive Model of Attentional Control in Aging

“…Changes in connectivity among canonical networks implicated in attentional control, such as the frontoparietal, salience, dorsal attention, and default mode networks, begin to appear as early as middle-age (Siman-Tov et al, 2017), and are observed across normal aging, mild cognitive impairment, and Alzheimer's disease (Dennis & Thompson, 2014;Esposito et al, 2018;Sheline & Raichle, 2013). Age-related deficits in attentional performance have been linked to certain functional alterations, including decreased integrity of the default mode (Andrews-Hanna et al, 2007;Damoiseaux et al, 2008), frontoparietal (Geerligs, Renken, et al, 2015), and salience networks (Hausman et al, 2020;Onoda et al, 2012), as well as loss of segregation between the default mode network and the dorsal attention (Avelar-Pereira et al, 2017) and frontal executive control networks (Ng et al, 2016). However, the majority of previous studies have relied on select, a priori regions or canonical networks, likely missing critical variance represented across multiple neural systems.…”