Soltanian-Zadeh, H.; Bashirgonbadi, A.; salehi, m.

Structural connectivity defines the network architecture supporting large scale brain dynamics, yet how this network constrains the temporal statistics of signals defined on it remains poorly understood. Prior work has reported associations between intrinsic timescales of resting-state fMRI and structural connectivity strength, but it is unclear which signal components primarily drive this relationship. Here, we adopt a graph signal processing framework to analyze intrinsic temporal properties of networked brain signals. Regional Blood Oxygenation Level Dependent (BOLD) activity is modeled as a graph signal supported on the structural connectome and decomposed via graph spectral filtering into low-frequency (structure-coupled) and high-frequency (structure-decoupled) components. Using diffusion MRI derived structural connectivity and resting-state fMRI from 100 unrelated participants of the Human Connectome Project, intrinsic timescales are quantified using relatively low-frequency power and related to node-wise structural connectivity strength while controlling for regional volume. We show that intrinsic timescales derived from structure-coupled signals exhibit robust positive associations with structural connectivity strength at both group and inter individual levels, whereas structure decoupled signals display substantially weaker coupling. Notably, slow structure decoupled dynamics are preferentially expressed in higher order association cortex. Graph spectral null models further demonstrate that these effects critically depend on the empirical organization of the structural network. Together, these results establish a graph spectral interpretation of structure timescale coupling, showing that network topology selectively constrains the temporal statistics of graph smooth neural activity.