CounterAct Administrative Supplement to NS114020 Automated Phenotyping in Epilepsy
CounterAct Administrative Supplement to NS114020 Automated Phenotyping in Epilepsy
批准号:
10227611
负责人:
Sandeep R Datta
金额:
$12.38万
依托单位:
依托单位国家:
美国
项目类别:
财政年份:
2020
资助国家:
美国
项目状态:
已结题
起止时间:
2020-10-01 至 2021-06-30
关键词:
3-DimensionalAcuteAddressAdministrative SupplementAnimal BehaviorAnimalsAntiepileptic AgentsArtificial IntelligenceBehaviorBehavioralCannabidiolChronicCognitiveCommunitiesComplexDataDetectionDevelopmentDrug ScreeningElectroencephalographyEpilepsyExposure toGoalsHumanIntoxicationIsoflurophateLong-Term EffectsLongitudinal StudiesMethodsModelingMonitorMotionMusNeurologic EffectObserver VariationPesticidesPharmaceutical PreparationsPhenotypePlayProbabilityPublic HealthReproducibilityResearchRestSeizuresStatus EpilepticusStereotypingStructureTechniquesTechnologyTestingThree-dimensional analysisTimeacquired epilepsyautomated algorithmcomorbidityimprovedinnovationmedical countermeasurenerve agentneuroinflammationneuronal circuitrynovel therapeuticsparent grantpre-clinical researchscreeningtoolvalproate
中文摘要
有机磷农药急性中毒是一个重大的公共健康问题。
担忧和长期的神经影响还没有被很好地理解。一个主要障碍是
OP-2长期疗效的可重复性、严谨临床前研究进展
诱发性癫痫持续状态是目前的实验方法通常需要令人望而却步的
时间和劳动密集型全天候视频脑电监控和固有的主观评分
人类观察者的癫痫发作(如广泛使用的拉辛量表)。而算法则是
EEG中的自动癫痫检测正在改进,这是至关重要的行为
获得性癫痫的表现及其认知合并症的评估仍然很差
量化的。我们的父母基金专注于开发一种客观、高通量的技术来
使用一种名为运动序列(MoSeq)的新方法来表征癫痫表型
将其应用于抗癫痫药物(AED)的自动筛选。MoSeq的核心思想基于
发现复杂的动物行为是以刻板印象的模块(“音节”)构成的
在根据特定规则(语法)排列的亚秒级时间尺度上,可以
通过人工智能(AI)辅助的3D视频分析在没有观察者偏见的情况下进行检测。在这
行政补充项目,我们建议聘用和细化MoSeq来解决关键问题
研究开发新的医学对策(MCM)面临的挑战
神经毒剂和有机磷杀虫剂。这包括测试是否可能客观地研究
有机磷农药中毒的长期效应及癫痫特异性量表评价
急性有机磷农药暴露后小鼠的行为模块和相关转移概率。在……里面
此外,鉴于神经炎症可能在OP诱导的持久性中发挥关键作用
神经元电路障碍,我们将测试小胶质细胞耗竭是否可以挽救OP诱导的
行为音节和转换概率的慢性变化。总而言之,这次会议的目的是
行政补助将受益于我们的母公司赠款目标,并为其做出贡献
为研究界开发一个可靠、可共享的工具来研究癫痫发作和认知
癫痫的共病。
英文摘要
Acute intoxication with organophosphorus (OP) pesticides is a significant public health
concern and long-term neurological effects are not well understood. A major obstacle to
progress towards reproducible, rigorous preclinical research in the long-term effects of OP-
induced status epilepticus is that current experimental approaches often require prohibitively
time and labor-intensive 24/7 video-EEG monitoring and inherently subjective scoring of
seizures by human observers (like the widely used Racine scale). While algorithms for
automated seizure detection in EEG are improving, the critically important behavioral
manifestations of acquired epilepsy and assessment of its cognitive comorbidities remain poorly
quantified. Our parent grant focuses on developing an objective, high-throughput technique to
characterize epileptic phenotypes using a new method called motion sequencing (MoSeq) and
apply it to automated anti-epileptic drugs (AED) screening. The central idea of MoSeq rests on
the discovery that complex animal behaviors are structured in stereotyped modules (“syllables”)
at sub-second timescales that are arranged according to specific rules (“grammar”) that can be
detected without observer bias by artificial intelligence (AI)-assisted 3D video analysis. In this
administrative supplement project, we propose to employ and refine MoSeq to address key
challenges in research into the development of new medical countermeasures (MCM) against
nerve agents and OP pesticides. This includes testing if it is possible to objectively study the
long-term effects of OP intoxication and evaluate MCMs at scale by determine epilepsy-specific
behavioral modules and associated transition probabilities in mice after acute OP exposure. In
addition, given that neuroinflammation is likely to play a key role in OP-induced persistent
neuronal circuit disturbance, we will test if microglial depletion can rescue the OP-induced
chronic changes in behavioral syllables and transition probabilities. Together, the aims in this
administrative supplement will both benefit from and contribute to our parent grant’s goal to
develop a reliable, sharable tool for the research community to study seizures and cognitive
comorbidities of epilepsy.
期刊论文(0)
专著(0)
科研奖励(0)
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