Automated Phenotyping in Epilepsy

全球有6500万癫痫患者，#年新增癫痫病例15万例 美国每年一次。然而，癫痫的治疗选择仍然不足，许多患者患有 治疗难治性癫痫、认知共病和治疗的负面副作用。一位少校 阻碍新疗法开发的障碍是临床前癫痫研究 通常需要劳动密集型和昂贵的全天候视频-EEG监测癫痫发作 人类观察者对癫痫发作表型的主观评分(以广泛使用的瑞辛评分为例 行为发作)。最近，达塔实验室表明，复杂的动物行为是由 亚秒级的刻板印象模块(音节)，并按照特定的规则排列 (“语法”)。使用一种称为运动排序的方法，可以在没有观察者偏见的情况下检测到这些音节 (MoSeq)，它使用3D摄像头进行视频成像，并结合人工智能(AI)辅助视频 对行为进行定性的分析。通过Soltesz和Datta实验室之间的合作，令人兴奋的数据 证明了MoSeq可适用于癫痫研究以实现目的， 在慢性颞叶癫痫小鼠模型中对小鼠进行廉价和自动化的表型分析。在这里我们 建议进一步测试和改进MoSeq，以解决癫痫长期存在的根本挑战 研究。这包括开发一种客观的替代拉辛量表，测试MoSeq AS 一种自动抗癫痫药物(AED)筛选方法，以及人类观察者的发展- 癫痫发作、癫痫发生和认知共病的独立行为生物标记物。此外，我们 计划大幅扩展MoSeq的癫痫相关功能，以包括自动跟踪 更精细的身体部位(如前肢和面部阵挛)，这是目前的方法无法实现的。最后， 我们建议将MoSeq的分析管道开发成一种直观、廉价、用户友好的形式 因此很容易与研究界共享。我们预计，这些结果将产生潜在的 通过展示自动化、客观、用户- 对获得性和遗传性癫痫表型进行独立、廉价的分析。

There are 65 million people worldwide with epilepsy and 150,000 new cases of epilepsy are diagnosed in the US annually. However, treatment options for epilepsy remain inadequate, with many patients suffering from treatment-resistant seizures, cognitive comorbidities and the negative side effects of treatment. A major obstacle to progress towards the development of new therapies is the fact that 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). Recently, the Datta lab showed that complex animal behaviors are structured in stereotyped modules (“syllables”) at sub-second timescales and arranged according to specific rules (“grammar”). These syllables can be detected without observer bias using a method called motion sequencing (MoSeq) that employs video imaging with a 3D camera combined with artificial intelligence (AI)-assisted video analysis to characterize behavior. Through collaboration between the Soltesz and Datta labs, exciting data were obtained that demonstrated that MoSeq can be adapted for epilepsy research to perform objective, inexpensive and automated phenotyping of mice in a mouse model of chronic temporal lobe epilepsy. Here we propose to test and improve MoSeq further to address long-standing, fundamental challenges in epilepsy research. This includes the development of an objective alternative to the Racine scale, testing of MoSeq as an automated anti-epileptic drug (AED) screening method, and the development of human observer- independent behavioral biomarkers for seizures, epileptogenesis, and cognitive comorbidities. In addition, we plan to dramatically extend the epilepsy-related capabilities of MoSeq to include the automated tracking of finer-scale body parts (e.g., forelimb and facial clonus) that are not possible with the current approach. Finally, we propose to develop the analysis pipeline for MoSeq into a form that is intuitive, inexpensive, user-friendly and thus easily sharable with the research community. 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 both acquired and genetic epilepsy phenotypes.