Utilising activity patterns of a complex biophysical network model to optimise intra-striatal deep brain stimulation

Area of Science:

• Computational neuroscience
• Biophysics
• Neuromodulation

Background:

• Deep brain stimulation (DBS) is a therapeutic intervention for neurological disorders.
• Optimizing DBS parameters is crucial for maximizing efficacy and minimizing side effects.
• The striatal network's role in conditions like obsessive-compulsive disorder necessitates detailed modeling.

Purpose of the Study:

• To develop a large-scale biophysical network model of the isolated striatal body.
• To optimize intrastriatal deep brain stimulation (DBS) parameters (frequency, amplitude, location).
• To identify optimal stimulation strategies for conditions such as obsessive-compulsive disorder.

Main Methods:

• Utilized modified Hodgkin-Huxley equations with small-world connectivity.
• Incorporated spatial neuron positioning from a detailed human brain atlas.
• Employed three biomarkers (network activity, rhythmicity, combined) to guide optimization.

Main Results:

• The model successfully generated spatiotemporal activity patterns differentiating healthy and pathological states.
• Optimal DBS parameters were computed by minimizing biomarker deviation from normal states.
• A trade-off between frequency synchronization and overall network activity was identified during optimization.

Conclusions:

• The developed model provides a framework for optimizing DBS protocols.
• Findings highlight a critical trade-off in DBS parameter optimization, consistent with in vivo observations.
• This approach can aid in tailoring DBS for neurological and psychiatric disorders.