Parkinson's disease speech production network as determined by graph-theoretical network analysis

Area of Science:

• Neuroscience
• Cognitive Science
• Medical Imaging

Background:

• Parkinson's disease (PD) is known to affect speech and emotion processing.
• Understanding the neural network alterations in PD is crucial for developing targeted interventions.

Purpose of the Study:

• To investigate changes in the speech-processing network (SPN) in Parkinson's disease using graph-theoretical network analysis.
• To assess the impact of emotional distraction on the SPN in individuals with PD.

Main Methods:

• Functional magnetic resonance imaging (fMRI) was used to study 14 PD patients and 23 healthy controls during a picture-naming task.
• The task involved supraliminal priming with neutral or emotional facial expressions.
• Whole-brain graph-theoretical network analysis was applied to fMRI data.

Main Results:

• Patients with PD exhibited significantly decreased network metrics, including mean nodal degree, mean nodal strength, global network efficiency, and mean clustering coefficient.
• The SPN in PD showed an absence of connector hubs and reduced network integration and segregation.
• Emotional distraction caused key network hubs in PD patients to become disorganized and shift towards auditory, sensory, and motor cortices, unlike in controls.

Conclusions:

• The whole-brain SPN in Parkinson's disease is characterized by decreased integration and segregation, and modularized information flow.
• Emotional stimuli significantly disrupt the SPN in PD, leading to disorganized network hubs and altered functional connectivity.
• These findings highlight the vulnerability of the PD speech network to emotional processing demands.