Department of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA 30332, United States of America.
J Neural Eng. 2020 Jul 16;17(4):046009. doi: 10.1088/1741-2552/ab9909.
Developing a new neuromodulation method for epilepsy treatment requires a large amount of time and resources to find effective stimulation parameters and often fails due to inter-subject variability in stimulation effect. As an alternative, we present a novel data-driven surrogate approach which can optimize the neuromodulation efficiently by investigating the stimulation effect on surrogate neural states.
Medial septum (MS) optogenetic stimulation was applied for modulating electrophysiological activities of the hippocampus in a rat temporal lobe epilepsy model. For the new approach, we implemented machine learning techniques to describe the pathological neural states and to optimize the stimulation parameters. Specifically, first, we found neural state surrogates to estimate a seizure susceptibility based on hippocampal local field potentials. Second, we modulated the neural state surrogates in a desired way with the subject-specific optimal stimulation parameters found by in vivo Bayesian optimization. Finally, we tested whether modulating the neural state surrogates affected seizure frequency.
We found two neural state surrogates: The first was hippocampal theta power by considering its well-known relationship with epilepsy, and the second was the output of pre-ictal state model (PriSM) which was built by characterizing the hippocampal activity during the pre-ictal period. The optimal stimulation parameters found by Bayesian optimization outperformed the other parameters in terms of modulating the surrogates toward anti-seizure neural state. When treatment efficacy was tested, the subject-specific optimal parameters for increasing theta power were more effective to suppress seizures than fixed stimulation parameter (7 Hz). However, modulation of the other neural state surrogate, PriSM, did not suppress seizures.
The surrogate approach can save enormous time and resources to find subject-specific optimal stimulation parameters which can effectively modulate neural states and further improve therapeutic effectiveness. This approach can also be used for improving neuromodulation treatment of other neurological or psychiatric diseases.
开发新的癫痫治疗神经调控方法需要大量的时间和资源来寻找有效的刺激参数，并且由于刺激效果的个体间变异性，往往会失败。作为一种替代方法，我们提出了一种新的数据驱动替代方法，通过研究对替代神经状态的刺激效果，可以有效地优化神经调控。
内侧隔（MS）光遗传学刺激用于调节大鼠颞叶癫痫模型中海马的电生理活动。对于新方法，我们实施了机器学习技术来描述病理神经状态并优化刺激参数。具体来说，首先，我们找到了神经状态的替代物，根据海马局部场电位来估计癫痫易感性。其次，我们使用通过体内贝叶斯优化找到的个体特异性最优刺激参数以期望的方式调制神经状态的替代物。最后，我们测试了调制神经状态替代物是否会影响癫痫发作频率。
我们找到了两个神经状态的替代物：第一个是考虑到其与癫痫的已知关系的海马θ功率，第二个是通过描述前癫痫期海马活动而建立的预癫痫状态模型（PriSM）的输出。贝叶斯优化找到的最优刺激参数在将替代物调制为抗癫痫神经状态方面优于其他参数。当测试治疗效果时，增加θ功率的个体特异性最优参数比固定刺激参数（7 Hz）更有效地抑制癫痫发作。然而，对其他神经状态替代物 PriSM 的调制并不能抑制癫痫发作。
替代方法可以节省大量的时间和资源来寻找个体特异性的最优刺激参数，这些参数可以有效地调制神经状态并进一步提高治疗效果。这种方法也可以用于改善其他神经或精神疾病的神经调控治疗。
