Resting-State Network Topology Differentiates Task Signals across the Adult Life Span

Abstract

Brain network connectivity differs across individuals. For example, older adults exhibit less segregated resting-state subnetworks relative to younger adults (Chan et al., 2014). It has been hypothesized that individual differences in network connectivity impact the recruitment of brain areas during task execution. While recent studies have described the spatial overlap between resting-state functional correlation (RSFC) subnetworks and task-evoked activity, it is unclear whether individual variations in the connectivity pattern of a brain area (topology) relates to its activity during task execution. We report data from 238 cognitively normal participants (humans), sampled across the adult life span (20–89 years), to reveal that RSFC-based network organization systematically relates to the recruitment of brain areas across two functionally distinct tasks (visual and semantic). The functional activity of brain areas (network nodes) were characterized according to their patterns of RSFC: nodes with relatively greater connections to nodes in their own functional system (“non-connector” nodes) exhibited greater activity than nodes with relatively greater connections to nodes in other systems (“connector” nodes). This “activation selectivity” was specific to those brain systems that were central to each of the tasks. Increasing age was accompanied by less differentiated network topology and a corresponding reduction in activation selectivity (or differentiation) across relevant network nodes. The results provide evidence that connectional topology of brain areas quantified at rest relates to the functional activity of those areas during task. Based on these findings, we propose a novel network-based theory for previous reports of the “dedifferentiation” in brain activity observed in aging.SIGNIFICANCE STATEMENT Similar to other real-world networks, the organization of brain networks impacts their function. As brain network connectivity patterns differ across individuals, we hypothesized that individual differences in network connectivity would relate to differences in brain activity. Using functional MRI in a group of individuals sampled across the adult life span (20–89 years), we measured correlations at rest and related the functional connectivity patterns to measurements of functional activity during two independent tasks. Brain activity varied in relation to connectivity patterns revealed by large-scale network analysis. This relationship tracked the differences in connectivity patterns accompanied by older age, providing important evidence for a link between the topology of areal connectivity measured at rest and the functional recruitment of these areas during task performance.

Hub distribution of the brain functional networks of newborns prenatally exposed to maternal depression and SSRI antidepressants

“…Only nodes at the 75th percentile and above were considered as hub regions and were included in a multivariate GLM model (Chan, Alhazmi, Park, Savalia, & Wig, 2017). An overview of the inclusion and exclusion of mother/infant dyads in the study scores as covariates in the model.…”

“…To determine whether a node could be considered a hub region, we examined the difference in the group average of positive and negative weights to delineate quartiles in our data. Only nodes at the 75th percentile and above were considered as hub regions and were included in a multivariate GLM model (Chan, Alhazmi, Park, Savalia, & Wig, 2017). For GLM analyses, Bonferroni-Holm methods (Abdi, 2010a) were used to correct for multiple comparisons.…”

Age‐related changes in fiber tracts in healthy adult brains: A generalized q‐sampling and connectometry study

“…According to these studies, a connectometry analysis detects a greater number of tract changes correlated with study factors than DTI. The connectometry analysis used in this study differed from the traditional connectomic analysis, which tracks the connectivity patterns throughout the brain, whereas connectometry located the connectivity in local tracts that was significantly correlated with age and showed great sensitivity …”

Structure–function multi‐scale connectomics reveals a major role of the fronto‐striato‐thalamic circuit in brain aging

“…Complex network approaches have highlighted the key role played by several network features in aging and brain diseases, such as network hubness, node efficiency, network modularity, and hierarchical organization. The effects of aging on network modularity have shown a decrease in network segregation along the lifespan (Chan, Park, Savalia, Petersen, & Wig, 2014;King et al, 2017;Song et al, 2014), a mechanism supporting the loss of functional specialization at the cognitive level (Chan, Alhazmi, Park, Savalia, & Wig, 2017).…”