Graph Analysis of Age-Related Changes in Resting-State Functional Connectivity Measured with fNIRS

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Abstract
Resting-state functional connectivity (rsFC) provides insight into the intrinsic organization of brain networks and is increasingly recognized as a sensitive marker of age-related neural changes. Functional near-infrared spectroscopy (fNIRS) provides a portable and cost-effective approach to measuring rsFC, including in naturalistic environments. However, the sensitivity of fNIRS to age-related alterations in rsFC topology remains poorly characterized. Here, we applied graph-based analysis to fNIRS data acquired during rest from 57 healthy participants divided into two groups: 26 young adults (18-30 years) and 31 older adults (50-77 years). Despite observing significantly attenuated low-frequency oscillation (LFO) power in older adults (5-6 fold reduction, p < 0.001), network analysis revealed counterintuitively higher connectivity in this group. Specifically, older adults exhibited 50-86% greater degree density, 16-43% higher clustering coefficients, and 31-75% greater global efficiency compared to young adults. These patterns were consistent across all canonical resting-state networks (sensorimotor, auditory, visual, frontoparietal, default mode) and all hemoglobin contrasts. While network decay rates were slower in older adults, the coexistence of reduced oscillatory and increased connectivity may reflect vascular rather than purely neural contributions. Overall, our findings validate fNIRS as a sensitive tool for detecting age-related changes in network organization, while highlighting the importance of disentangling neural and vascular signal components in aging research.

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