Towards automated phenotyping in epilepsy

美国有超过500万儿童和成人被诊断患有癫痫或癫痫发作 disorder.然而，癫痫的治疗选择仍然不足，因为许多患者患有 不受控制的癫痫发作和治疗的负面副作用。更快的主要障碍 新的抗惊厥治疗的发展是一个事实，即严格的临床前癫痫研究通常 需要劳动密集型和昂贵的24/7视频脑电图监测癫痫发作，依赖于主观 由人类观察者对癫痫发作表型进行评分（例如广泛使用的Racine量表， 行为性癫痫发作）。我们建议测试是否有可能执行客观，廉价和自动化 获得性和遗传性癫痫的各种小鼠模型中的小鼠表型。该方法依赖于 最近认识到，老鼠的行为是在亚秒的时间尺度上以刻板的模块结构化的， 是按照特定的规则来安排的。这些典型的行为模块， 它们可以通过组合3D成像和机器学习（ML）辅助在没有观察者偏见的情况下识别 分析方法我们建议采用这种新的ML辅助3D视频分析技术来治疗癫痫 研究，以测试它是否可用于识别慢性颞叶癫痫（TLE）小鼠， 在两种不同的实验性TLE模型中，以及在不同的实验性TLE模型中， 条件此外，我们还将测试该方法是否能够自动检测不仅 在严重儿童癫痫的遗传模型中的显性癫痫小鼠（纯合子电压门控钠离子通道）， 通道β亚基SCN 1B-/-敲除小鼠），而且还区分了看似正常的，非癫痫的， 来自野生型对照的SCN 1B +/-杂合小鼠。我们预计，这些结果将有一个 通过展示自动化，客观， 获得性和遗传性癫痫表型的用户独立，廉价的分析。

Over 5 million children and adults in the United States have had a diagnosis of epilepsy or a seizure disorder. However, treatment options for the epilepsies remain inadequate, because many patients suffer from uncontrolled seizures and from the negative side effects of treatment. A major obstacle to the faster development of new anti-convulsant therapies is the fact that rigorous preclinical epilepsy research typically requires labor-intensive and expensive 24/7 video-EEG monitoring of seizures that rests on the subjective scoring of seizure phenotypes by human observers (as exemplified by the widely used Racine scale of behavioral seizures). We propose to test if it is possible to perform objective, inexpensive and automated phenotyping of mice in various mouse models of acquired and genetic epilepsies. The approach rests on the recent recognition that mouse behaviors are structured in stereotyped modules at sub-second timescales that are arranged according to specific rules. These characteristic behavioral modules, and the transitions between them, can be identified without observer bias by combined 3D imaging and machine learning (ML) -assisted analytic methods. We propose to adopt this novel ML-assisted 3D video analysis technology to epilepsy research, in order to test if it can be used to identify mice with chronic temporal lobe epilepsy (TLE) during inter-ictal and ictal periods in two distinct experimental TLE models, and under various experimental conditions. In addition, we will also test whether the approach is able to automatically detect not only the overtly epileptic mice in a genetic model of severe childhood epilepsy (homozygous voltage-gated sodium channel β-subunit SCN1B-/- knock-out mice), but also distinguish the seemingly normal, non-epileptic, SCN1B+/- heterozygous mice from the wild-type controls. We anticipate that these results will have a potentially transformative effect on the field by demonstrating the feasibility and power of automated, objective, user-independent, inexpensive analysis of acquired and genetic epilepsy phenotypes.