Cooperative contributions of structural and functional connectivity to successful memory in aging

Abstract

Understanding the precise relation between functional connectivity and structural (white matter) connectivity and how these relationships account for cognitive changes in older adults are major challenges for neuroscience. We investigate these issues using an approach in which structural equation modeling (SEM) is employed to integrate functional and structural connectivity data from younger and older adults ( n = 62), analyzed with a common framework based on regions connected by canonical tract groups (CTGs). CTGs (e.g., uncinate fasciculus) serve as a common currency between functional and structural connectivity matrices, and ensure equivalent sparsity in connectome information. We used this approach to investigate the neural mechanisms supporting memory for items and memory for associations, and how they are affected by healthy aging. We found that different structural and functional CTGs made independent contributions to source and item memory performance, suggesting that both forms of connectivity underlie age-related differences in specific forms of memory. Furthermore, the relationship between functional and structural connectivity was best explained by a general relationship between latent constructs—a relationship absent in any specific CTG group. These results provide insights into the relationship between structural and functional connectivity patterns, and elucidate their relative contribution to age-related differences in source memory performance.

Cognition’s dependence on functional network integrity with age is conditional on structural network integrity

“…Structural connectivity refers here to the white matter pathways between regions, which organize to enable energy-efficient processing (Betzel & Bassett, 2018; Park & Friston, 2013) and can be assessed by generating streamline estimates from diffusion-weighted MRI (Hermundstad et al, 2013). Structural connectivity typically declines with age (Betzel et al, 2014; Lim, Han, Uhlhaas, & Kaiser, 2015) and often strongly accounts for age-related changes in cognition (Davis, Szymanski, Boms, Fink, & Cabeza, 2019; Klein et al, 2016; Matejko, Price, Mazzocco, & Ansari, 2013; Moeller, Willmes, & Klein, 2015; Ponsoda et al, 2017). In contrast, functional connectivity refers to the node-to-node interactions between neurophysiologically active regions and can be estimated from functional magnetic resonance imaging (fMRI) using the temporal covariance between brain regional activity measured by blood oxygen level-dependent (BOLD) signal (Rosazza & Minati, 2011).…”

Human immunodeficiency virus‐related decreases in corpus callosal integrity and corresponding increases in functional connectivity

“…Although individuals with callosal agenesis or who have had commissurotomies typically have a general decrease in interhemispheric functional connectivity (Johnston et al, 2008; Mancuso, Uddin, Nani, Costa, & Cauda, 2019; Pizoli et al, 2011; Roland et al, 2017; Uddin et al, 2008), the increased functional connectivity may be driven by localized increases in connectivity. In older adults, in which the CC is intact but damaged, there are both decreases and increases in functional connectivity in response to degraded white matter in different regions (Davis et al, 2019). This suggests that in PLWH, the specific damage done to the CCb may lead to an increase in functional connectivity between regions with direct CCb connections.…”

“…Following the methods described in Davis, Szymanski, Boms, Fink, and Cabeza (2019), gray matter tract groups were defined as regions connected to a common white matter tract. Regions were considered to be connected to the white matter tract if there were at least 10 overlapping voxels.…”

Episodic Past, Future, and counterfactual thinking in Relapsing-Remitting Multiple sclerosis

“…Information on the structural connectivity analysis and the construction of connectivity metrics for canonical tract groups are detailed in the Supplementary Material . Briefly, we follow the estimation of connectivity between pairs of regions comprising canonical fiber systems, i.e., Canonical Tract Groups (CTG), as outlined in Davis et al (2018) . This CTG algorithm summarizes connectivity in canonical, well-characterized fiber systems.…”

“…We examined only region pairs that are connected by canonical fiber systems, i.e., CTG ( Davis et al, 2018 ). This algorithm summarizes connectivity in canonical, well-characterized fiber systems.…”