Towards Precision Functional Brain Network Mapping in Parkinson's Disease.

Area of Science:

• Neuroscience
• Medical Imaging
• Systems Biology

Background:

• Parkinson's disease (PD) involves complex neurodegeneration affecting brain networks with significant individual variability.
• Current resting-state functional connectivity (RSFC) studies struggle with reliability and group-averaged network definitions, hindering the study of interindividual differences.
• Precision RSFC, using extended data collection and individual network definition, offers a potential solution but needs validation in PD.

Purpose of the Study:

• To assess the feasibility and reliability of precision RSFC in individuals with Parkinson's disease.
• To compare the quality of precision RSFC measures against conventional methods in PD.
• To explore the potential of individualized brain network measures in PD.

Main Methods:

• Collected over 100 minutes of RSFC data from 20 PD patients and 6 healthy controls (HC).
• Evaluated motion, reliability, and stability of RSFC measures, comparing PD and HC groups, and contrasting with conventional 5-minute RSFC data.
• Developed individualized brain network maps for PD participants to test feasibility.

Main Results:

• Precision RSFC yielded reliable and stable brain network measures in PD participants, comparable to HC.
• Precision methods significantly outperformed conventional methods in data quality.
• Individualized network maps in PD showed distinct patterns compared to group averages and among individuals, including motor systems.

Conclusions:

• Precision RSFC is a feasible and reliable technique for use in individuals with Parkinson's disease.
• This advanced imaging approach shows promise for developing personalized diagnostic tools.
• Precision RSFC may help identify brain-based biomarkers that explain clinical variability in PD.