Nonlinear Functional Connectivity and ICA Reveal Default Mode Network Hyperconnectivity in Parkinson's Disease: A Resting-State fMRI Study.
Nonlinear Functional Connectivity and ICA Reveal Default Mode Network Hyperconnectivity in Parkinson's Disease: A Resting-State fMRI Study.
Where did the research take place?
The study site has not been established. Author addresses may differ from where the research occurred.
Tianjin, CN · Author affiliation
School of Microelectronics, Tianjin University, Tianjin, China. ncharlz@tju.edu.cn.Location evidence
Mbeya, TZ · Author affiliation
Department of Electronics and Telecommunication Engineering, Mbeya University of Science and Technology, Mbeya, Tanzania. ncharlz@tju.edu.cn.Location evidence
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Original abstract
Parkinson's disease (PD) disrupts intrinsic brain networks that support motor and cognitive functions. Using resting-state fMRI from 138 PD patients and 54 controls, we combined independent component analysis (ICA) with nonlinear functional connectivity (FC) based on distance correlation. Compared with conventional Pearson-based measures, nonlinear FC revealed stronger and spatially distinct connectivity patterns, especially in parietal and sensorimotor regions. Group comparisons showed robust differences (p < 1×10⁻⁷), with four ICA networks (Components 4, 10, 19, and 20) displaying consistent alterations. Importantly, hyperconnectivity within a posterior midline network (Component 10), overlapping with regions commonly associated with the default mode network, correlated positively with Hoehn & Yahr stage (r = 0.51, p = 0.022), linking network reorganization to clinical severity. These findings demonstrate that nonlinear FC enhances sensitivity to PD-related network alterations, while spatial features highlight clinically relevant biomarkers. By integrating advanced connectivity metrics with data-driven network analysis, our study contributes to the methodological development of resting-state fMRI and its translational application to PD.