Characterizing resting-state networks in Parkinson's disease: A multi-aspect functional connectivity study

Area of Science:

• Neuroimaging
• Neurology
• Systems Neuroscience

Background:

• Parkinson's disease (PD) is a neurodegenerative disorder affecting motor and non-motor functions.
• Alterations in resting-state brain networks (RSNs) are increasingly recognized in PD.
• Resting-state functional magnetic resonance imaging (Rs-fMRI) is a key tool for investigating these RSN alterations.

Purpose of the Study:

• To identify differences in RSNs between Parkinson's disease (PD) patients and healthy controls (HCs).
• To determine the most significant discriminatory characteristics of RSNs between PD and HCs.
• To elucidate the neurobiological underpinnings of PD through RSN analysis.

Main Methods:

• Rs-fMRI data from 23 PD patients and 18 HCs were analyzed.
• Group independent component analysis (ICA) extracted 23 RSNs.
• Comparisons included RSN scores (dual regression), voxel-wise permutation tests, and hierarchical clustering.

Main Results:

• Significant decreases in connectivity were found in seven RSNs in PD patients compared to HCs.
• Connectivity deficits were prominent in the lateral and medial posterior occipital cortices.
• Hierarchical clustering showed the default mode network fragmented into three distinct clusters in PD patients.

Conclusions:

• Parkinson's disease is associated with widespread alterations in resting-state brain network connectivity.
• Specific RSN changes, particularly in occipital regions and the default mode network, characterize PD.
• These findings provide valuable insights into the biological mechanisms underlying Parkinson's disease.