Dynamic Mode Decomposition-Based FMRI Analysis for Parkinson's Disease Detection
Year: 2026
Type: conference-paper
Abstract: Resting-state fMRI (rs-fMRI) provides rich temporal dynamics of brain activity. Component analysis methods, such as principal component analysis (PCA) and independent component analysis (ICA), are widely used to extract functional patterns, but they yield largely static representations and do not explicitly model the temporal evolution of the fMRI signal. Here, we investigate Dynamic Mode Decomposition (DMD) as a dynamic extension of conventional component analysis for characterizing Parkinson's disease (PD)-related brain dynamics. DMD decomposes rs-fMRI time series into coherent spatial modes coupled with explicit temporal dynamics, enabling systematic interrogation of disease-sensitive spatio-temporal patterns. Experiments demonstrate that DMD-derived dynamic modes provide stronger discriminative power for PD detection than PCA/ICA-based representations, supporting the value of modeling temporal evolution in addition to spatial structure. Overall, DMD offers a promising tool for spatio-temporal analysis of brain functional dynamics.
Institutions: Shenzhen University, University of British Columbia
Language: English
Subfield: Cognitive Neuroscience
Field: Neuroscience
Domain: Life Sciences
Open Access status: hybrid
OpenAlex ID: W7202359738