Age-Related Changes In Resting-State And Task-Activated Functional Mri Networks

Resting-State Networks (RSNs) shown in functional magnetic resonance imaging (fMRI) have been consistently and reliably identified. Amongst these, the Default Mode Network (DMN) has been most well researched and shown to have age-related decrease in functional connectivity and negative consequences for cognition. There are two other distinct RSNs, Salience Network (SN) and Executive Control Network (ECN), shown to co-activate during fMRI tasks. The SN has been suggested to be correlated with cognitive decline in healthy aging, however, the age-related dynamics between these three RSNs are not well understood. The current study examined the DMN, SN and ECN during resting-state fMRI in young and elderly from Japan and Singapore using data-driven independent component analysis (ICA) and functional network connectivity (FNC). We further investigated if the functional connectivity of the DMN and SN varied across tasks of different cognitive demands between young and elderly. Interestingly, the elderly had increased intrinsic activity that deviated from the expected DMN, SN and ECN, and increased functional connectivity within the anterior SN relative to the young during resting-state fMRI. For task fMRI, the elderly showed decreased activation in the primary networks of visual and motor processing, and increased task related activity for higher cognitive processes. However, the DMN and SN for task fMRI revealed consistent increased activity shifted outside the expected regions for the elderly. Difference in functional connectivity between young and elderly was varied across tasks. The elderly had marginally less number of correlated component pairs compared to the young, suggesting a decline in functional network integrity in aging. The current study demonstrated that resting-state data could be combined across two sites using ICA, as well as the use of DMN and SN as reliable networks to examine age-related changes in rest and task fMRI. Understanding the dynamics of these networks in relation to aging will provide potential neuroimaging markers for enhancing cognition, as well as detecting pathological decline.