The Research Landscape of Dynamic Functional Connectivity in Parkinson's Disease: Systematic Review and Interactive Tool

Dynamic functional connectivity (dFC) analysis of functional Magnetic Resonance Imaging (fMRI) data has emerged as a powerful framework for characterizing the time-varying network disruptions underlying Parkinsons disease (PD). However, the rapid expansion of this field has introduced substantial methodological heterogeneity, creating a fragmented landscape that complicates knowledge synthesis and clinical translation. To address this, we performed a systematic review to map the conceptual and methodological diversity of dFC research in PD. Beyond a traditional static synthesis, we developed DynaPD, an open-source interactive application that allows researchers to dynamically explore study designs, analytical pipelines, and reported findings. Our synthesis of 37 eligible studies reveals a landscape defined by methodological contrast. While preprocessing strategies exhibited high variability (divergence), we observed a marked convergence on core analytical choices, specifically the use of Independent Component Analysis (ICA) for parcellation and the sliding-window technique coupled with k-means clustering for state identification. Regarding empirical findings, a consensus emerged on the relevance of specific dynamic features - namely dwell time, fraction time, and number of transitions - yet clinical interpretations varied, particularly concerning the implications of strongly connected versus sparsely connected states. We further identified stated limitations across the studies, including a predominance of cross-sectional designs, small sample sizes, and inconsistent reporting standards. These findings highlight the urgent need for longitudinal studies with extended acquisition protocols and standardized reporting frameworks. By providing both a systematic evidence synthesis and a living interactive tool, this work aims to consolidate the diverse dFC literature, fostering a more coherent and cumulative path toward precision neurology in PD.