Comparative analysis of resting-state functional connectivity networks across different age groups using fNIRS

Resting-state functional connectivity (rsFC) is a fundamental aspect of neuroscience and has been extensively researched, particularly for cognitive and clinical purposes. In the past, we developed methods to characterize rsFC networks using functional near-infrared spectroscopy (fNIRS). However, the question of whether fNIRS is sensitive to changes in rsFC as a function of age hasn’t been addressed yet. In this work, we examined the different connectivity profiles obtained with fNIRS during rest in 57 healthy participants. We divided the individuals into two groups: young (N=26, 18 to 30 yo) and older adults (N=31, 50 to 77 yo). Functional connectivity was assessed using Pearson correlation coefficients and graph theory metrics. Across the group, we observed significant differences in global network properties between young and old adults, with the latter group exhibiting higher connection density, leading to higher clustering and global efficiency. These results are consistent with other neuroimaging techniques (EEG, fMRI), reinforcing the ability of fNIRS to probe rsFC reliably.