A dynamic brain network decomposition method discovers effective brain hemodynamic sub-networks for Parkinson's...

Area of Science:

• Neuroscience
• Biomedical Engineering
• Neurology

Background:

• Dopaminergic treatment is standard for Parkinson's disease (PD).
• Current PD treatment assessment relies on behavioral observation, overlooking functional brain changes.
• Understanding brain functional variations post-treatment is crucial for PD management.

Purpose of the Study:

• To investigate brain functional variations in PD patients following dopaminergic therapy.
• To develop and validate a method for objectively evaluating dopaminergic treatment efficacy in PD.
• To explore the relationship between brain network dynamics and treatment-induced gait improvements.

Main Methods:

• Developed a dynamic brain network decomposition method using functional near-infrared spectroscopy (fNIRS).
• Captured brain activations in 50 PD patients during a walking task in OFF and ON medication states.
• Constructed dynamic brain networks using sliding-window phase lag index analysis and an aggregated network decomposition algorithm.

Main Results:

• Dopaminergic therapy significantly enhanced flexibility in a specific brain sub-network linked to motor function improvement in PD patients.
• Other identified sub-networks correlated with axial symptoms but showed no significant dynamic changes with treatment.
• Dynamic sub-network features correlated with treatment-induced gait alterations.

Conclusions:

• The proposed method offers a quantitative and objective approach for evaluating dopaminergic treatment in PD.
• PD gait involves distinct motor domains with neural controls selectively responsive to dopaminergic medication.
• Findings highlight the potential for personalized treatment strategies based on individual brain network responses.