Deep brain stimulation for movement disorder treatment: Exploring frequency-dependent efficacy in a computational network model (2010.07162v2)

Abstract: A large scale computational model of the basal ganglia (BG) network is proposed to describes movement disorder including deep brain stimulation (DBS). The model of this complex network considers four areas of the basal ganglia network: the subthalamic nucleus (STN) as target area of DBS, globus pallidus, both pars externa and pars interna (GPe-GPi), and the thalamus (THA). Parkinsonian conditions are simulated by assuming reduced dopaminergic input and corresponding pronounced inhibitory or disinhibited projections to GPe and GPi. Macroscopic quantities can be derived which correlate closely to thalamic responses and hence motor programme fidelity. It can be demonstrated that depending on different levels of striatal projections to the GPe and GPi, the dynamics of these macroscopic quantities switch from normal conditions to parkinsonian. Simulating DBS on the STN affects the dynamics of the entire network, increasing the thalamic activity to levels close to normal, while differing from both normal and parkinsonian dynamics. Using the mentioned macroscopic quantities, the model proposes optimal DBS frequency ranges above 130 Hz.

• The paper demonstrates that high-frequency DBS above 130 Hz normalizes thalamic output in a simulated Parkinsonian state.
• The methodology employs Hodgkin-Huxley equations and a Watts-Strogatz topology to accurately model neural dynamics in the basal ganglia.
• Key results identify optimal stimulation frequencies at 184 Hz and 210 Hz, effectively reducing pathological synchrony and guiding treatment strategies.

Deep Brain Stimulation for Movement Disorder Treatment: Exploring Frequency-Dependent Efficacy in a Computational Network Model

Introduction

The paper presents a comprehensive computational model of the basal ganglia (BG) network, focusing on treating movement disorders through deep brain stimulation (DBS). It provides insight into the frequency-dependent efficacy of DBS, specifically applied to the subthalamic nucleus (STN), and investigates how different stimulation frequencies impact network dynamics and motor symptoms.

Basal Ganglia Network Model

The model simulates the BG network, incorporating critical structures such as the STN, globus pallidus pars externa (GPe), globus pallidus pars interna (GPi), and thalamus (THA). The Parkinsonian state is modeled by reduced dopaminergic input, altering neural dynamics between normal and pathological states. Macroscopic quantities are derived from the model, correlating closely to thalamic responses and motor program fidelity.

Computational Approach

Neural Dynamics

The STN, GPe, and GPi neurons are modeled using a Hodgkin-Huxley formalism, which captures their biophysical properties. Equations governing the membrane potentials include currents such as leak, sodium, potassium, calcium, and others pertinent to the STN's unique characteristics under stimulation.

Network Topology

The model employs a Watts-Strogatz small-world topology for GPe and GPi nuclei to reflect the mixture of local and long-range connections observed in the brain. The STN's connectivity is sparser, based on current anatomical findings.

Simulation Scenarios

1. Normal State: The model replicates the typical firing patterns and interactions within the BG, demonstrating faithful thalamic response to cortical input.
2. Parkinsonian State: Characterized by aberrant firing due to altered striatal input, the model highlights increased synchrony and beta-band activity resulting in impaired thalamic responses.
3. DBS Application: High-frequency stimulation (HFS) above 130 Hz on the STN shows a significant modulation of network activity, reducing pathological synchrony and normalizing thalamic function.

Results and Analysis

The model indicates that optimal DBS frequencies exist above 130 Hz, with clear peaks at 184 Hz and 210 Hz where treatment efficacy in thalamic activation is highest. The findings align with experimental and clinical observations, emphasizing the role of DBS in disrupting pathological beta-band oscillations, which are a haLLMark of Parkinsonian state.

Macroscopic Measures

• Synchronisation Index: Indicates the degree of neural activity coherence within the GPi. It was useful in distinguishing between normal and pathological states and guiding DBS frequency optimization.
• Mean Synaptic Activity: Captures the average inhibition the GPi exerts over the thalamus, essential for understanding BG output in different scenarios.

Implications

The results underscore the importance of HFS in clinical DBS for movement disorders, where specific frequencies can effectively ameliorate symptoms by reorganizing pathological network dynamics. The model provides a predictive framework for optimizing DBS parameters in clinical settings.

Conclusion

This computational paper offers critical insights into the scaling and adaptive control of DBS strategies for Parkinson's disease and potentially other movement disorders. By bridging detailed biophysical modeling with macroscopic network analysis, it sets a foundation for developing optimized, frequency-specific DBS protocols to enhance therapeutic outcomes. The work suggests avenues for future investigation into DBS efficacy across varying neural disorder spectrums and a detailed examination of emergent network properties under modulation.