Automated Phenotyping in Epilepsy

癫痫的自动表型分析

• 批准号: 10410427
• 负责人: Sandeep R Datta
• 金额: $ 42.27万
• 依托单位: STANFORD UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-09-30 至 2024-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10410427
• 关键词: 3-Dimensional; Address; Animal Behavior; Animal Model; Animals; Antiepileptic Agents; Artificial Intelligence; Behavior; Behavioral; Biological Markers; Body part; Cannabidiol; Carbamazepine; Cells; Chronic; Clonazepam; Clonus; Cognitive; Collaborations; Communities; Complex; Data; Development; Diagnosis; Drug Screening; Electroencephalography; Epilepsy; Epileptogenesis; Face; Forelimb; Future; Genetic; Genetic Models; Head; Hippocampus (Brain); Human; Image; Imaging Techniques; Intervention; Intuition; Methods; Modeling; Monitor; Motion; Mus; Observer Variation; Patients; Persons; Pharmaceutical Preparations; Phenotype; Phenytoin; Pilocarpine; Probability; Reproducibility; Research; Research Personnel; Resistance; Resolution; Rest; Seizures; Specificity; Stereotyping; Structure; Tail; Technology; Temporal Lobe Epilepsy; Testing; Three-dimensional analysis; Time; Treatment Side Effects; Valproic Acid; analysis pipeline; base; behavior test; behavioral phenotyping; comorbidity; drug use screening; improved; innovation; kainate; method development; mouse model; novel therapeutics; optogenetics; pre-clinical; pre-clinical research; selective expression; user-friendly

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.

全世界有6500万人患有癫痫，15万例新的癫痫病例被诊断出来， 美国每年。然而，癫痫的治疗选择仍然不足，许多患者患有 难治性癫痫发作、认知合并症和治疗的负面副作用。一个主要 临床前癫痫研究是开发新疗法的障碍， 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， 由人类观察者对癫痫发作表型进行主观评分（如广泛使用的Racine量表所示 行为癫痫发作）。最近，达塔实验室表明，复杂的动物行为是在 亚秒级时间尺度的定型模块（“音节”），并根据特定规则排列 （“grammar”）。这些音节可以检测到没有观察者偏见使用一种方法称为运动排序 （MoSeq）采用3D相机的视频成像与人工智能（AI）辅助视频相结合 分析以描述行为。通过Soltesz和Datta实验室之间的合作， 证明MoSeq可以适用于癫痫研究， 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。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 research.这包括开发Racine量表的客观替代品，测试MoSeq作为 一种自动化的抗癫痫药物（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志物。另外我们 计划大幅扩展MoSeq的癫痫相关功能，包括自动跟踪 更精细尺度的身体部分（例如，前肢和面部阵挛），这是目前的方法不可能实现的。最后， 我们建议将MoSeq的分析管道开发成直观、廉价、用户友好的形式 因此很容易与研究团体共享。我们预计，这些结果将有可能 通过展示自动化，客观，用户- 获得性和遗传性癫痫表型的独立、廉价分析。