Dynamic regulation of brain network and muscle activity in upper limb force generation among older adults: A temporal dynamic graph Fourier transform approach

This study investigates the neural mechanisms underlying age-related declines in motor control by proposing a novel Temporal Dynamic Graph Fourier Transform (TDGFT) method. TDGFT integrates graph signal processing with dynamic brain networks analysis to characterize time-varying corticomuscular interactions in the spectral domain, thereby linking global and local brain connectivity patterns to motor behavior. Integrating functional near-infrared spectroscopy (fNIRS) and electromyography (EMG), we systematically examine the dynamic regulation of brain network and muscle activity in older adults and younger adults during elbow flexion tasks at 30% and 70% of maximum voluntary contraction (MVC). Sixteen older adults and sixteen younger adults are recruited for the study. Our findings reveal that older adults exhibit weaker dynamic regulation of brain regions during high-load tasks, accompanied by significantly increased constraints of structural brain networks on functional activity, reflecting a decline in cognitive control. Additionally, older adults rely on multi-regional brain coordination for motor control during low-intensity tasks, while reducing cognitive load to enhance motor efficiency during high-intensity tasks. By providing an interpretable spectral representation of corticomuscular dynamics, TDGFT advances the understanding of how aging reshapes motor-related brain connectivity. These findings may help identify changes of age-related motor decline and facilitate the design of individualized motor rehabilitation strategies for older adults.