Objective. 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. Approach. 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...

中文翻译：

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基于替代神经状态优化神经调节以抑制大鼠颞叶癫痫模型中的癫痫发作。

目的。开发用于癫痫治疗的新的神经调节方法需要大量的时间和资源来找到有效的刺激参数，并且常常由于受试者之间刺激效果的差异而失败。作为替代方案，我们提出了一种新颖的数据驱动的替代方法，该方法可以通过研究替代神经状态的刺激作用来有效地优化神经调节。方法。在大鼠颞叶癫痫模型中，应用内侧隔（MS）光遗传学刺激调节海马的电生理活动。对于新方法，我们实施了机器学习技术来描述病理性神经状态并优化刺激参数。具体来说，首先，我们发现神经状态替代物可根据海马局部场电位来估计癫痫发作的易感性。其次，我们以特定于受试者的最佳方式以期望的方式调制了神经状态替代物。