The association between functional connectivity and cognition in older adults

Abstract

In this cross-sectional study of older adults, we investigated two fundamental brain networks whose coupling is known to be associated with cognitive aging and dementia risk, the Task Negative Network and the Task Positive Network. We investigated connectivity using data from functional magnetic resonance brain imaging (fMRI) with a working memory task in the Brain Health Study (BHS), a study nested within the Baltimore Experience Corps Trial. BHS participants (n=85) were socio-demographically diverse community-dwelling adults over the age of 60. We used a priori derived regions of interest from the literature, and developed a linear regression-based method using Pearson's correlations as measures of network connectivity and scores on neuropsychological tests, administered outside of the scanner. The cognitive domains assessed were executive functions (task switching and working memory) and memory (episodic memory), which are known to be associated with dementia risk. We found that memory was both positively and negatively associated with inter-network connectivity, depending on the sub-network, while executive functions were negatively associated with inter-network connectivity. Sex modified these results, indicating underlying biological differences in the associations between brain network connectivity and cognitive functions in men and women. These results provide support for the potential utility of this paradigm when applied to the analysis of fMRI data to detect pre-clinical changes in cognitive functions.

What Executive Function Network is that? An Image-Based Meta-Analysis of Network Labels

“…Further, this green cluster is also visually similar to Network 7 from the Yeo et al, 17-Network parcellation (Yeo et al 2011), created using their dorsal anterior prefrontal cortex seed (PFCda). While the label "Anterior Control Network" is not commonly used, it is not without precedence (Langenecker et al 2004;Khasawinah et al 2017). Other authors (e.g., Smith et al, 2009) have previously labeled visually similar networks "Executive Control Network", however, we intentionally did not want to re-use terms (e.g., "Executive") across multiple different clusters.…”

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

“…For the rs-fMRI analyses, we used a publicly available network parcellation of the brain defined by Yeo et al (2011) that has been commonly used in the resting-state literature (Betzel et al, 2014;Fjell et al, 2017;Khasawinah et al, 2017;Dixon et al, 2018;Dubois et al, 2018;Ruiz-Rizzo et al, 2019). The resting-state networks were projected into MNI152 space, and we specifically defined four of the networks (DMN, DAN, FPCN, and CON) as regions of interests (ROIs) for ROI-ROI functional connectivity analyses.…”

Persistent Homology-based Functional Connectivity Explains Cognitive Ability: Life-span Study

“…In neuroscience, researchers have used various methods to describe how the brain network functionally works during the performance of cognitive tasks or rest. For example, recent studies investigating the relationship between brain functional connectivity and cognitive abilities have mainly utilized average functional connectivity (over the whole brain or within or between networks) [14, 22, 24], the brain system segregation [11], and graph theory-based network topology measures [29]. In the current study, we introduced persistent homology (PH)-based functional connectivity measures and applied these measures to investigate how functional connectivity in the most efficient information flow structure explains cognitive abilities.…”

“…he relationship between resting-state functional networks and cognition [11, 14, 22, 24], cognitive aging [50], and cognitive reserve [43] has been widely investigated. Brain functional connectivity has been quantified in various ways; for example, average correlation within/between networks [22, 24] and brain system segregation [11, 14]. These measurements are calculated based on the assumption that the brain network structure is modular.…”