通过计算进化设计的大脑刺激的优化时间模式。
Optimized temporal pattern of brain stimulation designed by computational evolution.
机构信息
Department of Biomedical Engineering, Duke University, Durham, NC 27708, USA.
Department of Neurobiology, Duke University Medical Center, Durham, NC 27710, USA.
出版信息
Sci Transl Med. 2017 Jan 4;9(371). doi: 10.1126/scitranslmed.aah3532.
Abstract
Brain stimulation is a promising therapy for several neurological disorders, including Parkinson's disease. Stimulation parameters are selected empirically and are limited to the frequency and intensity of stimulation. We varied the temporal pattern of deep brain stimulation to ameliorate symptoms in a parkinsonian animal model and in humans with Parkinson's disease. We used model-based computational evolution to optimize the stimulation pattern. The optimized pattern produced symptom relief comparable to that from standard high-frequency stimulation (a constant rate of 130 or 185 Hz) and outperformed frequency-matched standard stimulation in a parkinsonian rat model and in patients. Both optimized and standard high-frequency stimulation suppressed abnormal oscillatory activity in the basal ganglia of rats and humans. The results illustrate the utility of model-based computational evolution of temporal patterns to increase the efficiency of brain stimulation in treating Parkinson's disease and thereby reduce the energy required for successful treatment below that of current brain stimulation paradigms.
摘要
脑刺激是治疗几种神经疾病的一种很有前途的疗法，包括帕金森病。刺激参数是通过经验选择的，并且仅限于刺激的频率和强度。我们改变了深部脑刺激的时间模式，以改善帕金森病动物模型和帕金森病患者的症状。我们使用基于模型的计算进化来优化刺激模式。优化后的模式产生的症状缓解与标准高频刺激（恒定速率为 130 或 185 Hz）相当，并且在帕金森病大鼠模型和患者中优于频率匹配的标准刺激。优化和标准高频刺激均抑制了大鼠和人类基底神经节中异常的振荡活动。结果表明，基于模型的计算进化时间模式在提高脑刺激治疗帕金森病的效率方面具有实用性，从而降低了成功治疗所需的能量，低于当前的脑刺激范式。
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