Automated Phenotyping in Epilepsy

癫痫的自动表型分析

• 批准号: 10178133
• 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/10178133
• 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; 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6500万人患有癫痫病，有15万例癫痫病诊断 美国每年。但是，癫痫的治疗选择仍然不足，许多患者患有 耐药性癫痫发作，认知合并症和治疗的负面影响。专业 促进新疗法的障碍是临床前癫痫研究的事实 通常需要劳动密集型且昂贵的24/7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（如广泛使用的Racine量表所示例 行为癫痫发作）。最近，达塔实验室表明，复杂的动物行为是在 刻板印象的模块（“音节”）在子秒尺度上，并根据特定规则进行安排 （“语法”）。可以使用一种称为运动测序的方法检测到这些sylables （Moseq）员工使用3D摄像头与人工智能（AI）辅助视频进行视频成像 分析以表征行为。通过Soltesz和Datta Labs之间的合作，令人兴奋的数据 得到的证明，可以将Moseq适应癫痫研究以执行客观， 在慢性临时叶癫痫的小鼠模型中，小鼠的廉价和自动表型。我们在这里 进一步测试和改进Moseq的建议，以应对癫痫的长期存在的基本挑战 研究。这包括开发Racine量表的客观替代方案，将Moseq的测试作为 一种自动抗癫痫药（AED）筛查方法，以及人类观察者的发展 独立的行为生物标志物，用于癫痫发作，癫痫发生和认知合并症。另外，我们 计划急剧扩展Moseq的癫痫相关功能，包括自动跟踪 最终尺度的身体部位（例如，前肢和面部克隆）是不可能的。最后， 我们建议将Moseq的分析管道开发为一种直观，廉价，用户友好的形式 因此很容易与研究社区共享。我们预计这些结果将有可能 通过证明自动化，客观，用户的可行性和功能，对现场的变革效应 对获得和遗传癫痫表型的独立，廉价的分析。