Ashmaig Omer, Connolly Mark, Gross Robert E, Mahmoudi Babak
Annu Int Conf IEEE Eng Med Biol Soc. 2018 Jul;2018:2683-2686. doi: 10.1109/EMBC.2018.8512801.
There is a great need for an electrical stimulation therapy to treat medication-resistant, surgically ineligible epileptic patients that successfully reduces seizure incidence with minimal side effects. Critical to advancing such therapies will be identifying the trade-offs between therapeutic efficacy and side effects. One novel treatment developed in the tetanus toxin rat model of mesial temporal lobe epilepsy, asynchronous distributed microelectrode stimulation (ADMETS) in the hippocampus has been shown to significantly reduce seizure frequency. However, our results have demonstrated that ADMETS has a negative effect on spatial memory that scales with the amplitude of stimulation. Given the high dimensional space of possible stimulation parameters, it is difficult to construct a mapping from variations in stimulation to behavioral effect. In this project, we present a novel, principled approach using closed-loop Bayesian optimization to tune stimulation that successfully maximize a desired objective - performance on a spatial memory assay.
对于治疗耐药且不适合手术的癫痫患者，迫切需要一种电刺激疗法，该疗法能以最小的副作用成功降低癫痫发作率。推进此类疗法的关键在于确定治疗效果与副作用之间的权衡。在颞叶内侧癫痫的破伤风毒素大鼠模型中开发的一种新型治疗方法——海马体异步分布式微电极刺激（ADMETS），已被证明能显著降低癫痫发作频率。然而，我们的结果表明，ADMETS对空间记忆有负面影响，且这种影响与刺激幅度成正比。鉴于可能的刺激参数的高维空间，很难构建从刺激变化到行为效应的映射。在本项目中，我们提出了一种新颖的、基于原理的方法，即使用闭环贝叶斯优化来调整刺激，从而在空间记忆测试中成功地最大化期望目标——表现。
