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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.
期刊论文(35)
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会议论文
DOI: 10.2217/bmm.11.74
发表时间: 2011-10
期刊: Biomarkers in medicine
影响因子: 2.2
作者: [Worrell G, Gotman J]
通讯作者: Gotman J
DOI: 10.1001/jamaneurol.2013.209
发表时间: 2013-08
期刊: JAMA NEUROLOGY
影响因子: 29
作者: [Noe, Katherine, Sulc, Vlastimil, Wong-Kisiel, Lily, Wirrell, Elaine, Van Gompel, Jamie J., Wetjen, Nicholas, Britton, Jeffrey, So, Elson, Cascino, Gregory D., Marsh, W. Richard, Meyer, Fredric, Horinek, Daniel, Giannini, Caterina, Watson, Robert, Brinkmann, Benjamin H., Stead, Matt, Worrell, Gregory A.]
通讯作者: Worrell, Gregory A.
DOI: 10.3171/2010.12.jns10846
发表时间: 2011-04
期刊: Journal of neurosurgery
影响因子: 4.1
作者: [Van Gompel JJ, Koeller KK, Meyer FB, Marsh WR, Burger PC, Roncaroli F, Worrell GA, Giannini C]
通讯作者: Giannini C
DOI: 10.1016/j.cobme.2017.09.006
发表时间: 2017-12
期刊: Current opinion in biomedical engineering
影响因子: 3.9
作者: [Khadjevand F, Cimbalnik J, Worrell GA]
通讯作者: Worrell GA
15
    Reliable Seizure Prediction Using Physiological Signals and Machine Learning
    • 批准号:
      10518240
    • 项目类别:
    • 资助金额:
      $56.46万
    • 财政年份:
      2022
    • 负责人:
      Gregory A Worrell
    • 依托单位:
    Reliable Seizure Prediction Using Physiological Signals and Machine Learning
    • 批准号:
      10629373
    • 项目类别:
    • 资助金额:
      $58.68万
    • 财政年份:
      2022
    • 负责人:
      Gregory A Worrell
    • 依托单位:
    Reliable Seizure Prediction Using Physiological Signals and Machine Learning
    • 批准号:
      9445497
    • 项目类别:
    • 资助金额:
      $60.76万
    • 财政年份:
      2015
    • 负责人:
      Gregory A Worrell
    • 依托单位:
    Neurophysiologically Based Brain State Tracking & Modulation in Focal Epilepsy
    • 批准号:
      9921573
    • 项目类别:
    • 资助金额:
      $143.77万
    • 财政年份:
      2015
    • 负责人:
      Gregory A Worrell
    • 依托单位:
    海外基金