Dynamics of long range network interactions in focal epilepsy
局灶性癫痫中远程网络相互作用的动态
基本信息
- 批准号:10198042
- 负责人:
- 金额:$ 61.24万
- 依托单位:
- 依托单位国家:美国
- 项目类别:
- 财政年份:2013
- 资助国家:美国
- 起止时间:2013-09-01 至 2023-06-30
- 项目状态:已结题
- 来源:
- 关键词:AddressAffectAnimal ModelAreaAutomobile DrivingAwardBehaviorBiological MarkersBiophysicsBrainBrain DiseasesCell physiologyClinicalCollectionComplementComplexComputer ModelsCoupledDataData SetDecision MakingDevelopmentDistantElectroencephalographyElectrophysiology (science)EngineeringEpilepsyEventExcisionFailureFocal SeizureFoundationsFrequenciesGenerationsGoalsHealthHigh Frequency OscillationHumanImpact SeizuresIn VitroIncidenceInterdisciplinary StudyInterventionLinkLobeLocationMachine LearningMasksMathematicsMedicalMethodsMicroelectrodesMonitorNeuronsOperative Surgical ProceduresPartial EpilepsiesPathologicPathway AnalysisPatientsPredictive ValueProceduresProcessQuality of lifeResearch PersonnelRoleSamplingSeizuresShapesSignal TransductionSiteSourceSpecificityStudy modelsTechniquesTerminologyTestingTherapeuticTimeTravelUniversitiesWeightbaseimprovedinnovationmillimeterminimally invasivemulti-scale modelingneurophysiologyparallel computerrelating to nervous systemsupport vector machinesurgery outcomevoltage
项目摘要
PROJECT SUMMARY
Epilepsy is the world’s most prominent serious brain disorder, affecting nearly 50 million people worldwide. For
about 30% of these patients, seizures remain poorly controlled despite optimal medical management, with
attendant effects on health and quality of life. In order to enable advances in the therapeutic management of
epilepsy, a thorough understanding of how cellular processes that drive seizures are linked to large-scale
network effects is needed. While seizures impact large brain areas and often multiple lobes, the driving
processes span regions on the scale of millimeters. These have been well characterized in animal models,
but the relevance to human seizures, i.e. how seizures are driven by brain signals from small-scale processes
remains unclear. Instead, the view that naturally-occurring seizures may be attributable instead to large-scale
neural mass effects (i.e., the epileptic network) is a subject of ongoing debate. Previously, we defined a key
role for surround inhibition in shaping EEG recordings of seizures at the onset site and on small spatial scales.
We now propose that surround inhibition has a dual role. On a millimeter scale, its abrupt failure permits the
advance of a seizure. At long distances from the seizure focus, strong local inhibition serves to mask the
excitatory effects of seizures and may help to hasten seizure termination, while weakened inhibition may
permit emergence of ictal activity at a distant, noncontiguous seizure site. Multiple seizure foci may go
unrecognized with standard EEG interpretation methods, and are likely a critical factor in epilepsy surgery
failures. We hypothesize that once established, multiple ictal generators behave as delay-coupled oscillators,
demonstrating activity that is synchronized or even temporally reversed. This results in complex and at times
counterintuitive network behavior that can be challenging to reverse engineer from EEG recordings. Typically,
however, even intracranial EEG recordings provide only a limited view of neural activity. In this project, an
interdisciplinary research group with combined expertise in epilepsy, clinical neurophysiology, computational
modeling, and mathematics will conduct a comprehensive study of the neuronal contributors to epileptic
networks utilizing a unique combined dataset of simultaneous microelectrode and macroelectrode recordings
of human seizures. Using a machine learning approach, we will apply this information to develop a
multivariate EEG biomarker based on the inferred source of EEG discharges, high frequency oscillations, and
very low frequency (DC) shifts and assess its predictive value for post-resection surgical outcome. We
anticipate that the project will lead to a theoretical framework for rational development of innovative strategies
for developing interventions to control seizures.
项目总结
项目成果
期刊论文数量(0)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
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Catherine A Schevon其他文献
Catherine A Schevon的其他文献
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{{ truncateString('Catherine A Schevon', 18)}}的其他基金
CRCNS: Neural Populations, High Frequency Oscillations and EEG seizures
CRCNS:神经群体、高频振荡和脑电图癫痫发作
- 批准号:
9047876 - 财政年份:2015
- 资助金额:
$ 61.24万 - 项目类别:
CRCNS: Neural Populations, High Frequency Oscillations and EEG seizures
CRCNS:神经群体、高频振荡和脑电图癫痫发作
- 批准号:
9150334 - 财政年份:2015
- 资助金额:
$ 61.24万 - 项目类别:
Integrated Multiscale Data Acquisition System for Human Intracranial Neurophysiol
人类颅内神经生理学集成多尺度数据采集系统
- 批准号:
8640680 - 财政年份:2014
- 资助金额:
$ 61.24万 - 项目类别:
Dynamics of long range network interactions in focal epilepsy
局灶性癫痫中远程网络相互作用的动态
- 批准号:
9792276 - 财政年份:2013
- 资助金额:
$ 61.24万 - 项目类别:
Seizure localization in humans: the effect of inhibitory surround on the EEG
人类癫痫定位:抑制性周围环境对脑电图的影响
- 批准号:
8879228 - 财政年份:2013
- 资助金额:
$ 61.24万 - 项目类别:
Seizure localization in humans: the effect of inhibitory surround on the EEG
人类癫痫定位:抑制性周围环境对脑电图的影响
- 批准号:
8714087 - 财政年份:2013
- 资助金额:
$ 61.24万 - 项目类别:
Dynamics of long range network interactions in focal epilepsy
局灶性癫痫中远程网络相互作用的动态
- 批准号:
10456050 - 财政年份:2013
- 资助金额:
$ 61.24万 - 项目类别:
Seizure localization in humans: the effect of inhibitory surround on the EEG
人类癫痫定位:抑制性周围环境对脑电图的影响
- 批准号:
8563126 - 财政年份:2013
- 资助金额:
$ 61.24万 - 项目类别:
Seizure Location Using Signal Processing Techniques
使用信号处理技术进行癫痫发作定位
- 批准号:
7263942 - 财政年份:2005
- 资助金额:
$ 61.24万 - 项目类别:
Seizure Location Using Signal Processing Techniques
使用信号处理技术进行癫痫发作定位
- 批准号:
7099427 - 财政年份:2005
- 资助金额:
$ 61.24万 - 项目类别:
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