DECODE: DEcomposition of COupled and DEcoupled Brain States: Separating Structure-Coupled and Structure-Decoupled Functional Organization in Alzheimers Disease
Brain functional activity emerges within an underlying structural scaffold. While structural network connectivity provides a relatively stable anatomical backbone for large-scale communication, functional interactions are high-dimensional and temporally dynamic, forming recurrent patterns that can be represented in a latent space of transient functional states. Yet, how these transient functional states are organized and the extent to which they remain constrained by structural network connectivity or become relatively decoupled from structural organization over time remain poorly understood. To address this gap, we introduce DECODE (DEcomposition of COupled and DEcoupled Brain States), a data-driven latent-space framework that uses independent component analysis (ICA) and multimodal data to decompose resting-state dynamic functional network connectivity into a lower-dimensional representation of transient functional states. These states capture temporally varying structure-coupled and structure-decoupled patterns using diffusion MRI-derived structural network connectivity priors. Structure-coupled states reflect functional configurations more strongly anchored to major structural motifs, whereas structure-decoupled states represent configurations less constrained by the identified structural organization, while potentially remaining within structurally defined dynamical bounds. DECODE identifies reproducible structure-coupled and structure-decoupled states, with mean squared error reconstruction of dynamic information < 0.1 and adjusted R-squared = 0.4. Applied to Alzheimers disease (AD), DECODE revealed stronger group differences (FDR-corrected p-value 0.05) in both connectivity organization and temporal dynamics for decoupled states. These findings suggest that functional states less constrained by identified structural organization may be more vulnerable to AD-related disruption, whereas structure-coupled states may be relatively preserved. More broadly, DECODE provides a reproducible and generalizable framework for quantifying how functional dynamics are shaped by, or diverge from, structural organization by incorporating a latent representation of the structural connectome as an informative prior. Our findings established structural and functional alignment variation by distinguishing dynamic functional patterns closely coupled with structure from those that deviate from it.