Microseizures, Ultra-slow & High Frequency Oscillations: Biomarkers of epilepsy

微惊厥,超慢

基本信息

  • 批准号:
    8234974
  • 负责人:
  • 金额:
    $ 30.03万
  • 依托单位:
  • 依托单位国家:
    美国
  • 项目类别:
  • 财政年份:
    2009
  • 资助国家:
    美国
  • 起止时间:
    2009-04-15 至 2014-03-31
  • 项目状态:
    已结题

项目摘要

DESCRIPTION (provided by applicant): The goal of this proposal is to localize human epileptic networks by characterizing their electrophysiological activity over a wide range of spatiotemporal scales. Decades of clinical intracranial EEG (IEEG) using restricted spatial (centimeter scale) and temporal (~0.5-100 Hz) bandwidth, based more on tradition than modern neuroscience, have frustrated epileptologists looking for discrete, resectable "electrographic lesions" during evaluation for epilepsy surgery. Similarly, recent efforts to apply direct brain stimulation to abort seizures after they are sufficiently established to be detected on standard clinical macroelectrodes have, so far, met with only partial success. We hypothesize that enhancing the spatial and temporal resolution of clinical intracranial EEG can improve the efficacy of epilepsy surgery and responsive brain stimulation to control seizures. Human epileptic networks produce pathological activity that ranges from seizures and spikes, generated by cubic centimeters of brain tissue, to high frequency oscillations that occur on sub-millimeter dimensions. Recent evidence suggests that important components of these signals are found at frequencies not detected by standard clinical IEEG. Using simultaneous IEEG recordings from microwire arrays and clinical macroelectrodes, our group has begun to characterize two potential signatures of epileptogenic brain, high frequency oscillations and "micro-seizures," that are outside the resolution of conventional clinical IEEG. In this application, we propose analysis of continuous, high-resolution, wide- bandwidth IEEG recorded simultaneously from microwire arrays and clinical macroelectrodes in order to localize human epileptic networks. We will correlate our findings with surgical outcome, prospectively, in a cohort of patients undergoing evaluation for epilepsy surgery. This work builds upon our established effort in Translational Neuroengineering melding state of the art epilepsy care with cutting-edge research. PUBLIC HEALTH RELEVANCE The neuronal networks of human epileptic brain are multiscale; extending from cellular assemblies organized on the scale of cortical columns (~300 - 600 <m) to large-scale networks organized over lobar structures. These pathological networks generate oscillations over a wide range of frequency (0.01 - 1000 Hz) and spatial scales not probed by clinical EEG. Our laboratory and others have identified pathological network oscillations occurring outside the range of clinical IEEG that appear to be signatures of the epileptogenic zone.
描述(由申请人提供):这项建议的目标是通过表征人类癫痫网络在广泛的时空尺度上的电生理活动来定位人类癫痫网络。几十年来,临床颅内脑电(IEEG)使用有限的空间(厘米尺度)和时间(~0.5-100赫兹)带宽,更多地基于传统,而不是现代神经科学,在癫痫手术评估期间,令寻找离散的、可切除的“电信号病变”的癫痫专家受挫。同样,最近在充分确定癫痫发作可以在标准临床大电极上检测到之后,应用直接脑刺激来终止癫痫发作的努力,到目前为止只取得了部分成功。我们假设,提高临床颅内脑电的空间和时间分辨率可以提高癫痫手术和反应性脑刺激控制癫痫发作的效果。人类癫痫网络产生的病理活动范围从立方厘米脑组织产生的癫痫发作和尖峰,到发生在亚毫米尺度上的高频振荡。最近的证据表明,这些信号的重要成分是在标准临床iEEG检测不到的频率上发现的。使用微线阵列和临床大电极的同时iEEG记录,我们的团队已经开始表征致痫大脑的两个潜在特征:高频振荡和“微癫痫”,这两个特征超出了传统临床iEEG的分辨率。在这一应用中,我们建议对从微线阵列和临床大电极同时记录的连续、高分辨率、宽带iEEG进行分析,以定位人类癫痫网络。我们将在一组接受癫痫手术评估的患者中,前瞻性地将我们的发现与手术结果相关联。这项工作建立在我们在翻译神经工程学方面的既定努力基础上,融合了最先进的癫痫护理和尖端研究。人类癫痫脑的神经元网络是多尺度的;从皮质柱(~300-600&lt;m)尺度上组织的细胞集合延伸到叶结构上组织的大规模网络。这些病理网络在广泛的频率范围(0.01-1000赫兹)和空间尺度上产生振荡,这是临床脑电没有探测到的。我们的实验室和其他人已经发现了发生在临床iEEG范围之外的病理性网络振荡,似乎是致痫区域的特征。

项目成果

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Gregory A Worrell其他文献

Spatiotemporal Rhythmic Seizure Sources Can be Imaged by means of Biophysically Constrained Deep Neural Networks
时空节律性癫痫发作源可以通过生物物理约束的深度神经网络进行成像

Gregory A Worrell的其他文献

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{{ truncateString('Gregory A Worrell', 18)}}的其他基金

Reliable Seizure Prediction Using Physiological Signals and Machine Learning
使用生理信号和机器学习进行可靠的癫痫发作预测
  • 批准号:
    10518240
  • 财政年份:
    2022
  • 资助金额:
    $ 30.03万
  • 项目类别:
Reliable Seizure Prediction Using Physiological Signals and Machine Learning
使用生理信号和机器学习进行可靠的癫痫发作预测
  • 批准号:
    10629373
  • 财政年份:
    2022
  • 资助金额:
    $ 30.03万
  • 项目类别:
Reliable Seizure Prediction Using Physiological Signals and Machine Learning
使用生理信号和机器学习进行可靠的癫痫发作预测
  • 批准号:
    9445497
  • 财政年份:
    2015
  • 资助金额:
    $ 30.03万
  • 项目类别:
Neurophysiologically Based Brain State Tracking & Modulation in Focal Epilepsy
基于神经生理学的大脑状态跟踪
  • 批准号:
    9921573
  • 财政年份:
    2015
  • 资助金额:
    $ 30.03万
  • 项目类别:
Reliable Seizure Prediction Using Physiological Signals and Machine Learning
使用生理信号和机器学习进行可靠的癫痫发作预测
  • 批准号:
    9238808
  • 财政年份:
    2015
  • 资助金额:
    $ 30.03万
  • 项目类别:
Neurophysiologically Based Brain State Tracking & Modulation in Focal Epilepsy
基于神经生理学的大脑状态跟踪
  • 批准号:
    9972970
  • 财政年份:
    2015
  • 资助金额:
    $ 30.03万
  • 项目类别:
Microseizures, Ultra-slow & High Frequency Oscillations: Biomarkers of epilepsy
微惊厥,超慢
  • 批准号:
    8448247
  • 财政年份:
    2009
  • 资助金额:
    $ 30.03万
  • 项目类别:
Microseizures, Ultra-slow & High Frequency Oscillations: Biomarkers of epilepsy
微惊厥,超慢
  • 批准号:
    7653568
  • 财政年份:
    2009
  • 资助金额:
    $ 30.03万
  • 项目类别:
Microseizures, Ultra-slow & High Frequency Oscillations: Biomarkers of epilepsy
微惊厥,超慢
  • 批准号:
    8053265
  • 财政年份:
    2009
  • 资助金额:
    $ 30.03万
  • 项目类别:
Epileptiform oscillations, EEG & seizure prediction
癫痫样振荡,脑电图
  • 批准号:
    6832791
  • 财政年份:
    2004
  • 资助金额:
    $ 30.03万
  • 项目类别:

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