A computational network dynamical modeling for abnormal oscillation and deep brain stimulation control of...

Area of Science:

• Computational neuroscience
• Neuropsychiatry
• Systems neuroscience

Background:

• Obsessive-compulsive disorder (OCD) involves brain network abnormalities, particularly in the cortical-striatal-thalamic-cortical (CSTC) loop.
• Dysregulation of neurotransmitter systems (serotonin, dopamine, glutamate) and pathway imbalances are implicated in OCD pathophysiology.
• Pathological low-frequency oscillations in the frontal electroencephalogram (EEG) and subthalamic nucleus (STN) firing patterns are observed in OCD.

Purpose of the Study:

• To extend a biophysical computational model to investigate the impact of orbitofronto-subcortical loop abnormalities on network oscillations in OCD.
• To simulate OCD-related neural changes, including altered connectivity and neurotransmitter levels.
• To explore the potential of abnormal brain rhythms as a biomarker for OCD.

Main Methods:

• Utilized a biophysical computational model of the orbitofronto-subcortical circuitry.
• Simulated OCD pathophysiology by modeling loss of striatal parvalbumin interneuron (PV) to medium spiny neuron (MSN) connectivity, excessive hyperdirect pathway activation, and elevated dopamine.
• Quantified low-frequency oscillation power in the STN, STN burst index, and average firing rates in cortical and thalamic regions.

Main Results:

• The model successfully replicated key aspects of OCD pathology, including glutamatergic and dopamine system dysregulation.
• Demonstrated the model's ability to explain effects of pathway imbalance and simulate neuropsychiatric treatment outcomes in OCD.
• Identified abnormal brain rhythms resulting from orbitofronto-subcortical loop dysregulation as potential biomarkers for OCD.

Conclusions:

• The computational model provides insights into the neural mechanisms underlying OCD, linking network abnormalities to pathological brain rhythms.
• Abnormal brain rhythms identified through modeling may serve as valuable biomarkers for OCD diagnosis.
• This research contributes to understanding OCD's etiology, potentially guiding the development of novel diagnostic tools and therapeutics.