Age-Related Differences in the Dynamic Architecture of Intrinsic Networks

Abstract

Correlations among low-frequency spontaneous fluctuations in the blood oxygen level-dependent (BOLD) signal reflect the connectivity of intrinsic large-scale networks in the brain. These correlations have typically been characterized over the entire timecourse (mean connectivity), but the mean correlations between regions vary dynamically. By focusing on the linear relationship between activity in network nodes within the default mode network (DMN), dorsal attention network (DAN), and fronto-parietal task control network (FPTC) captured by their intercorrelations, we demonstrate that this dynamic pattern of fluctuations reveals a detailed substructure, that this substructure is robust across individuals, and that the expression of specific factors is correlated with age. To do this, we conducted a chained P-technique factor analysis of the correlations in nonoverlapping temporal windows across N = 145 normal aging subjects (age 56-89). The expression of factors within the DMN, FPTC, and DAN was highly correlated with age: Decreased intercorrelations within nodes in each factor were correlated with advanced age. Although these findings converge with those from stationary analysis, the ability to quantify age-related changes in the coherence of fluctuating connectivity may yield more insights into age-related cognitive decline.

Associations between dynamic functional connectivity and age, metabolic risk, and cognitive performance

“…Therefore, the present study aimed to determine age-related differences in dynamic functional connectivity and their relation to cognitive performance in healthy adults. We hypothesized age differences in patterns of DMN dynamic connectivity as static connectivity differences within this network have been previously observed (Andrews-Hanna et al, 2007; Damoiseaux et al, 2008; Wu et al, 2011) and increased DMN variability has been noted in older adults (Madhyastha and Grabowski, 2014). Specifically, we hypothesized that time allocation among specific dynamic connectivity profiles would be age dependent and that older adults would devote less time to profiles dominated by strong connectivity within DMN, and between DMN and other networks, compared to younger counterparts.…”

“…This variability may reflect changes in neural activity related to cognitive and sensorimotor operations, as well as non-neuronal factors such as systemic physiological changes or spontaneous head motion. Previous work suggests that variability in hub region multi-network participation is lower (Schaefer et al, 2014), and variability within DMN dynamic functional connectivity is higher (Madhyastha and Grabowski, 2014) in older compared to younger adults. Furthermore, stability of functional connectivity increases with age in some regions (e.g.…”

“…We also hypothesized that time share of specific profiles would be related to cognitive performance, with more “youthful” patterns of dynamic connectivity linked to higher cognitive scores, analogous to associations observed in extant studies of static DMN connectivity and cognition (Sala-Llonch et al, 2012; Wang et al, 2010). Considering reported age-related decreases in dynamic hub-region network variability (Garrett et al, 2010; Schaefer et al, 2014) and increased variability in DMN component intercorrelation (Madhyastha and Grabowski, 2014), we also expected our dynamic analysis to capture age differences in the rate of switching between connectivity profiles. Specifically, we hypothesized older adults would switch profiles at a lower rate, which might reflect less-than-optimal cognitive processing.…”

Ageing and the resting state: is cognition obsolete?

“…Going forward, future dynamic connectivity work should determine how these different brain states relate to external task events (e.g., Bassett et al, 2011). There has been some suggestion that dynamic connectivity differs with age at rest (Madhyastha & Grabowski, 2014), and it would be interesting for future work to examine this possibility within the context of a task, the structure of which would enable one to predict when dynamic shifts should occur and the implications for cognition. Potentially even more informative are recent attempts to characterize age differences in effective connectivity during task performance (e.g., Legon et al, 2015; Waring, Addis, & Kensinger, 2013).…”

Dual Adjacency Matrix: Exploring Link Groups in Dense Networks

“…From this point, Tara used the prototype to visually assess the quality of the link groups. In a later session we combined Tara's own link clusters of three large-scale intrinsic brain zones [MG14] for her to explore a subset of individuals with Parkinson's Disease (PD) and age-matched controls. Research, including Tara's, suggests that the coupling of these zones may be dysregulated in PD.…”