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Optimized temporal pattern of brain stimulation designed by computational evolution.

David T Brocker1, Brandon D Swan1, Rosa Q So1

  • 1Department of Biomedical Engineering, Duke University, Durham, NC 27708, USA.

Science Translational Medicine
|January 6, 2017
PubMed
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This summary is machine-generated.

Optimizing deep brain stimulation patterns using computational evolution improved Parkinson's disease symptom relief in rats and humans. This novel approach enhances treatment efficiency and reduces energy needs compared to standard high-frequency stimulation.

Area of Science:

  • Neuroscience
  • Neurological Disorders
  • Biomedical Engineering

Background:

  • Deep brain stimulation (DBS) is a key therapy for neurological disorders like Parkinson's disease (PD).
  • Current DBS parameter selection relies on empirical methods, primarily focusing on stimulation frequency and intensity.
  • Limitations exist in optimizing temporal stimulation patterns for enhanced therapeutic efficacy.

Purpose of the Study:

  • To investigate the efficacy of optimizing temporal patterns in deep brain stimulation for Parkinson's disease.
  • To compare a novel, computationally evolved stimulation pattern against standard high-frequency stimulation.
  • To assess the impact of optimized stimulation on motor symptoms and basal ganglia activity in PD models.

Main Methods:

  • Employed model-based computational evolution to optimize DBS temporal patterns.

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  • Tested optimized and standard high-frequency stimulation (130 or 185 Hz) in a parkinsonian rat model.
  • Evaluated the optimized pattern's effectiveness in human patients with Parkinson's disease.

    • Main Results:

    • The optimized stimulation pattern provided symptom relief comparable to standard high-frequency DBS.
    • Optimized patterns outperformed frequency-matched standard stimulation in both parkinsonian rats and human patients.
    • Both stimulation types effectively suppressed abnormal oscillatory activity in the basal ganglia of rats and humans.

    Conclusions:

    • Model-based computational evolution is a powerful tool for optimizing DBS temporal patterns.
    • Optimized temporal patterns can enhance the efficiency of brain stimulation for Parkinson's disease treatment.
    • This approach offers potential for reduced energy consumption compared to current DBS paradigms.