Automated Phenotyping in Epilepsy

癫痫的自动表型分析

• 批准号: 10621942
• 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/10621942
• 关键词: 3-Dimensional; Address; Animal Behavior; Animal Experiments; Animal Model; Anticonvulsants; 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; Genetic Transcription; Head; Human; Image; Imaging Techniques; Intervention; Intuition; Methods; Modeling; Monitor; Motion; Mus; Observer Variation; Patients; Persons; 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; behavior test; behavioral phenotyping; comorbidity; drug use screening; improved; innovation; kainate; 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6500万癫痫患者，每年有15万新发癫痫病例