Brain Connectivity in Late-Life Depression and Aging Revealed by Network Analysis

Objective

To use novel methods to examine age associations across an integrated brain network in healthy older adults (HOA) and individuals with late-life depression (LLD). Graph theory metrics describe the organizational configuration of both the global network and specified brain regions.

Methods

Cross-sectional data were acquired. Graph theory was used to explore diffusion tensor imaging–derived white matter networks. Forty-eight HOA and 28 adults with LLD were recruited from the community. Global and local metrics in prefrontal, cingulate, and temporal regions were calculated. Group differences and associations with age were explored.

Results

Group differences were noted in local metrics of the right prefrontal and temporal regions, but no significant differences were observed on global metrics. Local (not global) metrics were associated with age differently across groups. For HOA, local metrics across all regions correlated with age, whereas in adults with LLD, correlations were only observed within temporal regions. In keeping with hypothesized regions impacted by LLD, stronger hubs in right temporal regions were observed among HOA, whereas LLD individuals were characterized by robust hubs in frontal regions.
Conclusion

We demonstrate widespread age-related changes in local network properties among HOA with different and more restricted local changes in LLD. Although a preliminary analysis, different patterns of correlations in local networks coupled with equivalent global metrics may reflect altered local structural brain networks in patients with LLD.