Recent studies have shown that aging has a large impact on connectivity within and between functional networks. An open question is whether elderly still have the flexibility to adapt functional network connectivity (FNC) to the demands of the task at hand. To study this, we collected fMRI data in younger and older participants during resting state, a selective attention (SA) task and an n-back working memory task with varying levels of difficulty. Spatial independent component (IC) analysis was used to identify functional networks over all participants and all conditions. Dual regression was used to obtain participant and task specific time-courses per IC. Subsequently, functional connectivity was computed between all ICs in each of the tasks. Based on these functional connectivity matrices, a scaled version of the eigenvector centrality (SEC) was used to measure the total influence of each IC in the complete graph of ICs. The results demonstrated that elderly remain able to adapt FNC to task demands. However, there was an age-related shift in the impetus for FNC change. Older participants showed the maximal change in SEC patterns between resting state and the SA task. Young participants, showed the largest shift in SEC patterns between the less demanding SA task and the more demanding 2-back task. Our results suggest that increased FNC changes from resting state to low demanding tasks in elderly reflect recruitment of additional resources, compared with young adults. The lack of change between the low and high demanding tasks suggests that elderly reach a resource ceiling.
Keywords: adaptability; aging; eigenvector centrality; functional network connectivity; graph theory; resting state; selective attention; task dependent connectivity; working memory.
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