Macroscale Gradient-Informed Neural Oscillation Topography in Parkinson's Disease.

Area of Science:

• Neuroscience
• Computational Neuroscience
• Biomarkers

Background:

• Parkinson's disease (PD) involves widespread disruptions in brain oscillations.
• Current biomarkers like subcortical beta power may not fully represent the cortical impact of PD.
• Understanding cortical hierarchy changes is crucial for PD research.

Purpose of the Study:

• To characterize frequency-specific cortical hierarchy reorganization in Parkinson's disease.
• To identify topographic biomarkers across different behavioral states.
• To determine if these hierarchical features predict motor severity in PD.

Main Methods:

• Utilized high-density electroencephalography and MRI-based source reconstruction in PD patients and controls.
• Applied manifold learning to derive frequency-specific functional gradients from cortical connectivity.
• Quantified diagnostic and predictive utility of hierarchical features and performed transcriptomic enrichment.

Main Results:

• Parkinson's disease causes a macroscale, frequency-specific, and state-dependent reorganization of the cortical hierarchy.
• Gradient-based biomarkers effectively distinguished PD patients and predicted motor severity (UPDRS Part III).
• Identified distinct topographical signatures indicating signal redistribution across cortical regions.

Conclusions:

• Parkinson's disease induces a significant macroscale reorganization of the cortical hierarchy.
• State-dependent topographical biomarkers accurately predict clinical severity and align with PD pathology.
• Findings offer a framework for developing next-generation, cortical-guided adaptive deep brain stimulation (aDBS).