Altered static and dynamic functional network connectivity in Parkinson's disease: A multisite functional magnetic...

Area of Science:

• Neuroscience
• Radiology
• Systems Biology

Background:

• Aberrant functional network connectivity (FNC) in Parkinson's disease (PD) is inconsistently reported, likely due to limitations of small, single-site studies.
• Resting-state functional magnetic resonance imaging (rs-fMRI) is crucial for understanding brain network alterations in neurological disorders.

Purpose of the Study:

• To investigate static functional network connectivity (sFNC) and dynamic functional network connectivity (dFNC) differences in Parkinson's disease (PD) using a large, multisite rs-fMRI dataset.
• To identify potential neuroimaging biomarkers for PD by comparing FNC patterns between PD patients and healthy controls (HCs).

Main Methods:

• Pooled rs-fMRI data from 160 PD patients and 75 HCs across four repositories were analyzed using independent component analysis.
• Sliding-window and k-means clustering identified seven resting-state networks and examined static and dynamic FNC measures, including fraction time, mean dwell time, and transition number.
• Group comparisons were performed for sFNC and dFNC metrics across identified network states.

Main Results:

• PD patients showed decreased sFNC between visual, auditory, sensorimotor, and cognitive control networks, and increased connectivity involving auditory and subcortical/cerebellar networks.
• Four recurrent dynamic states were identified, with significant group differences in states 1, 3, and 4, exhibiting widespread intra- and inter-network connectivity abnormalities.
• PD patients displayed altered temporal dynamics, including reduced mean dwell time and fractional time in state 4, and increased mean dwell time in state 1, indicating network inflexibility.

Conclusions:

• Multisite rs-fMRI data reveal complex connectivity disruptions and inflexible temporal network reconfigurations in PD.
• Dynamic functional network connectivity (dFNC) metrics are more sensitive than static analysis, detecting extensive connectivity reductions and offering potential as biomarkers for PD progression and therapeutic response.