Single Unit Based Seizure Prediction.

基于单个单元的癫痫发作预测。

基本信息

批准号：
7826961
负责人：
JING-YU CHANG
金额：
$ 20.63万
依托单位：
NEUROSCIENCE RESEARCH INSTITUTE OF NC
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：
2009-05-15 至 2011-04-30
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/7826961
关键词：
Adverse effects Affect Algorithms American Amygdaloid structure Animals Area Biological Neural Networks Biomedical Engineering Brain Brain region Cells Cerebral cortex Cessation of life Clinic Collaborations Complex Computing Methodologies Data Data Analyses Data Set Deep Brain Stimulation Development Diffuse Drug Delivery Systems Electroencephalography Epilepsy Foundations Frequencies Future Goals Hippocampus (Brain)Implant Individual Injection of therapeutic agent Injury Intervention Intractable Epilepsy Joints Knowledge Lead Life Light Measurement Measures Medial Methodology Methods Microelectrodes Modeling Neurons Pathway interactions Patients Pharmaceutical Preparations Pilocarpine Population Principal Investigator Procedures Rattus Recurrence Relative (related person)Research Research Project Grants Resolution Sampling Seizures Signal Transduction Societies Specificity Substantia nigra structure Techniques Technology Temporal Lobe Epilepsy Thalamic structure Therapeutic Time Toxic effect Translating Translational Research analytical method base cranium effective therapy follow-up improved nervous system disorder novel novel strategies postsynaptic prevent programs prospective public health relevance relating to nervous system success tool

项目摘要

DESCRIPTION (provided by applicant): Epilepsy is one of the most prevalent neurological disorders, affecting 50 million individuals worldwide, a quarter of which have medically intractable cases. The unpredictability of seizure onset can lead to severe bodily injury or even death. Thus a reliable seizure prediction method is greatly needed to protect epileptic patients from life threading incidents. Furthermore, accurate seizure prediction would allow clinicians to provide well-timed drug delivery or therapeutic brain stimulation. This ability to optimally time interventions could substantially reduce the side effects and toxicity of long term medications for medically responsive patients and provide a novel treatment for patients with intractable epilepsy. In light of this need, the American Epilepsy Society has identified seizure predication as a primary priority for epilepsy research. Neuroscientists, clinicians, mathematicians and bioengineers have developed collaborations devoted to this research field. In spite of great progress in seizure prediction research, reliable seizure prediction remains a challenging task. EEG has been employed for the last 3 decades of seizure prediction research, whilst novel alternative research tools are lacking. EEG data reflect the summation of postsynaptic potentials across a large population of cells in diffuse brain areas and thus lack temporal and spatial specificity. In this proposed study, we will for the first time employ a multiple channel, single unit recording technique to record up to 80 single cells in 5 different brain areas that have been shown to be critically involved in temporal lobe epilepsy (TLE) in rat model of TLE. The major benefits of recording single units are that it provides high temporal and spatial resolution and multiple dimensional data, which allow us to detect subtle changes in brain areas before seizure onset. We will apply this information with a battery of single unit and EEG data analytic tools to try to detect the neural activity changes that portend seizure onset. In this initial study, we will analyze the data off-line to establish a reliable seizure prediction algorithm. Once this algorithm has been established, we will apply real time, prospective seizure perdiction methods to detect seizures in real time and we will combine this prediction tool with deep brain stimulation to prevent seizure development and progression. The long term goal of this research is to translate animal experimental results to the clinic to treat epilepsy patients with this novel approach. PUBLIC HEALTH RELEVANCE: Accurate seizure prediction can prevent bodily injury for epileptic patient and enable clinicians to treat epilepsy with timely drug administration or brain stimulation. This research project will develop a novel multiple channel, single unit recording method to predict spontaneous seizures in rat model of epilepsy. The success of this project will be instrumental in improving seizure prediction in the clinic and benefit millions of patients with epilepsy. PHS 398/2590 (Rev. 09/04) Page Continuation Format Page

描述（由申请人提供）：癫痫是最普遍的神经系统疾病之一，影响了全球5000万个人，其中四分之一患有医学上棘手的病例。癫痫发作的不可预测性会导致严重的人身伤害甚至死亡。因此，非常需要一种可靠的癫痫发作预测方法来保护癫痫患者免受生活束缚事件的侵害。此外，准确的癫痫发作预测将使临床医生能够提供及时的药物输送或治疗性脑刺激。这种最佳时间干预措施的能力可以大大降低医学反应敏感患者的长期药物的副作用和毒性，并为顽固性癫痫患者提供新的治疗方法。鉴于这种需求，美国癫痫学会已将癫痫发作的预测视为癫痫研究的主要优先事项。神经科学家，临床医生，数学家和生物工程师已经开发了专门针对该研究领域的合作。尽管癫痫发作预测研究取得了长足的进步，但可靠的癫痫发作预测仍然是一项艰巨的任务。在过去的3年的癫痫发作预测研究中，脑电图已被使用，而缺乏新颖的替代研究工具。脑电图数据反映了弥漫性大脑区域中大量细胞中突触后潜能的总和，因此缺乏时间和空间特异性。在这项拟议的研究中，我们将首次采用多个通道单位记录技术在5个不同的大脑区域中记录多达80个单个单元，这些细胞已被证明与TLE大鼠模型中的颞叶癫痫（TLE）相关。记录单个单元的主要好处是，它提供了高时空和空间分辨率和多维数据，这使我们能够在发作开始前检测大脑区域的细微变化。我们将使用单个单元和脑电图数据分析工具的电池应用此信息，以试图检测预示发作发作的神经活动变化。在这项最初的研究中，我们将离线分析数据以建立可靠的癫痫发作预测算法。一旦建立了该算法，我们将应用实时的，前瞻性癫痫发作方法来实时检测癫痫发作，我们将将该预测工具与深脑刺激结合起来，以防止癫痫发育和进展。这项研究的长期目标是将动物实验结果转化为诊所，以这种新方法治疗癫痫患者。公共卫生相关性：准确的癫痫发作预测可以防止癫痫患者的人身伤害，并使临床医生能够及时的药物给药或脑刺激治疗癫痫病。该研究项目将开发一种新型的多通道单位记录方法，以预测癫痫大鼠模型中的自发癫痫发作。该项目的成功将有助于改善诊所的癫痫发作预测，并使数百万癫痫患者受益。 PHS 398/2590（修订版09/04）页面延续格式页面