Cortical information flow in Parkinson's disease: a composite network/field model

Area of Science:

• Neuroscience
• Computational Neuroscience

Background:

• The basal ganglia are critical for motor control, with dysfunction in Parkinson's disease (PD) leading to severe motor deficits.
• Understanding how basal ganglia influence cortical information flow is key to explaining PD pathophysiology.

Purpose of the Study:

• To investigate the impact of basal ganglia dysfunction in Parkinson's disease on cortical information processing.
• To model the pathological changes in neural activity and information flow within the thalamocortical system during PD.

Main Methods:

• Developed a composite model integrating a spiking neuronal network and a neural field model of the cortex, thalamus, and basal ganglia.
• Validated three field models: healthy, PD-specific basal ganglia parameters, and thalamocortical only.
• Used field model outputs to drive the network model, analyzing firing rates, spectral power, bursting, and Granger causality.

Main Results:

• The PD-driven network exhibited lower firing rates, a shift to lower frequencies, and increased bursting compared to the healthy model, consistent with empirical PD data.
• Granger causality between cortical layers, particularly from layer 4 to layer 5, was significantly reduced in the PD model.
• The brain's large-scale oscillatory environment strongly influences subnetwork information processing.

Conclusions:

• Basal ganglia dysfunction in PD profoundly alters cortical information flow, potentially explaining motor symptoms like bradykinesia.
• Physiologically realistic inputs derived from large-scale models are crucial for accurately simulating spiking network dynamics.