Brain network analyses of diffusion tensor imaging for brain aging

The approach of graph-based diffusion tensor imaging (DTI) networks has been used to explore the complicated structural connectivity of brain aging. In this study, the changes of DTI networks of brain aging were quantitatively and qualitatively investigated by comparing the characteristics of brain network. A cohort of 60 volunteers was enrolled and equally divided into young adults (YA) and older adults (OA) groups. The network characteristics of critical nodes, path length (L-p), clustering coefficient (C-p), global efficiency (E-global), local efficiency (E-local), strength (S-p), and small world attribute (sigma) were employed to evaluate the DTI networks at the levels of whole brain, bilateral hemispheres and critical brain regions. The correlations between each network characteristic and age were predicted, respectively. Our findings suggested that the DTI networks produced significant changes in network configurations at the critical nodes and node edges for the YA and OA groups. The analysis of whole brains network revealed that L-p, C-p increased (p < 0.05, positive correlation), E-global, E-local, S-p decreased (p < 0.05, negative correlation), and sigma unchanged (p >= 0.05, non-correlation) between the YA and OA groups. The analyses of bilateral hemispheres and brain regions showed similar results as that of the whole-brain analysis. Therefore the proposed scheme of DTI networks could be used to evaluate the WM changes of brain aging, and the network characteristics of critical nodes exhibited valuable indications for WM degeneration.

Automated summary

The study utilized a graph-based diffusion tensor imaging network approach to investigate the changes in brain aging, revealing significant differences in network characteristics between young adults and older adults on a whole-brain level.