Towards automated phenotyping in epilepsy

癫痫的自动化表型分析

• 批准号: 9369284
• 负责人: IVAN SOLTESZ
• 金额: $ 19.75万
• 依托单位: STANFORD UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-07-01 至 2019-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9369284
• 关键词: Adopted; Adult; Adverse effects; Animal Behavior; Animal Model; Animals; Anticonvulsants; Behavior; Behavioral; Characteristics; Child; Childhood; Chronic; Complex; Data; Development; Diagnosis; Electroencephalography; Epilepsy; Exhibits; Frequencies; Genetic; Genetic Models; Hippocampus (Brain); Human; Human immunodeficiency virus test; Image; Knockout Mice; Machine Learning; Modeling; Monitor; Mus; Neurons; Observer Variation; Patients; Phenotype; Pilocarpine; Probability; Recurrence; Research; Rest; Seizures; Sodium Channel; Stereotyping; Structure; Syndrome; Technology; Temporal Lobe Epilepsy; Testing; Three-Dimensional Imaging; Three-dimensional analysis; Time; Translational Research; United States; Wild Type Mouse; analytical method; base; cost; evidence base; high throughput analysis; innovation; kainate; learning strategy; mouse model; novel; novel therapeutics; pre-clinical; voltage

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.

美国超过500万儿童和成人已诊断出癫痫或癫痫发作 紊乱。但是，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影响。更快的主要障碍 新的抗惊厥疗法的开发是严格的临床前癫痫研究的事实 需要劳动密集型且昂贵的24/7视频 - EEG对癫痫发作的监测，这是基于主观的 人类观察者对癫痫发作表型的评分（如广泛使用的Racine量表所举例说明 行为癫痫发作）。我们建议测试是否可以执行客观，廉价和自动化 在获得和遗传性癫痫的各种小鼠模型中，小鼠的表型。该方法基于 最近认识到小鼠行为是在刻板印象模块中构造的， 根据特定规则安排。这些特征性的行为模块以及 通过组合3D成像和机器学习（ML）辅助，可以在没有观察者偏见的情况下确定它们 分析方法。我们建议采用这种新颖的ML辅助3D视频分析技术来癫痫 研究，以测试是否可以使用慢性临时叶癫痫（TLE）来识别小鼠 在两个不同的实验tle模型中，在各种实验中的间隔和发作时期 状况。此外，我们还将测试该方法是否能够自动检测 在严重儿童癫痫的遗传模型中公开的癫痫小鼠（纯合电压钠 通道β-亚基SCN1B - / - 敲除小鼠），但也区分看似正常的，非癫痫 来自野生型对照的SCN1B +/-杂合小鼠。我们预计这些结果将有一个 通过证明自动化，客观的可行性和力量，对现场产生可能的变革影响 对获得和遗传癫痫表型的用户无关，廉价的分析。