Targeting pathologic spike-ripples to isolate and disrupt epileptic dynamics

针对病理性尖峰波纹来隔离和破坏癫痫动力学

基本信息

批准号：
10322163
负责人：
Catherine J Chu
金额：
$ 68.31万
依托单位：
MASSACHUSETTS GENERAL HOSPITAL
依托单位国家：
美国
项目类别：
财政年份：
2021
资助国家：
美国
起止时间：
2021-01-01 至 2025-11-30
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10322163
关键词：
Action Potentials Address Affect Animal Experiments Animal Model Automobile Driving Biological Biological Markers Brain Brain Diseases Brain region Caring Clinical Clinical Data Computer Models Computer Simulation Consumption Diffuse Electric Stimulation Epilepsy Event Excision Failure Generations High Frequency Oscillation Human Image Imaging Techniques Individual Institution Intervention Intractable Epilepsy Machine Learning Medical Methods Modeling Modernization Monitor Neurons Operative Surgical Procedures Optics Pathologic Pathologic Processes Patient Care Patients Pattern Persons Pharmaceutical Preparations Physiological Physiological Processes Protocols documentation Recurrence Refractory Research Resected Seizures Signal Transduction Specificity Testing Time Tissues Treatment Efficacy clinical practice detector effective therapy excitatory neuron experimental study human data imaging approach improved in silico in vivo inhibitory neuron large datasets models and simulation mouse model neurophysiology neuroregulation neurosurgery novel prevent statistics successful intervention surgery outcome voltage

项目摘要

Project Summary Epilepsy is the world’s most common, serious brain disorder, affecting nearly 50 million people worldwide. For one-third of patients, seizures remain poorly controlled despite maximal medical management. In these patients, seizures often arise from a localized brain region, and neurosurgical interventions are the most effective treatment option. When successful, surgical interventions provide cure from seizures, and also prevent or reverse the disabling consequences of uncontrolled seizures. Critical to successful intervention is accurate identification of the core tissue responsible for generating seizures (i.e., the epileptogenic zone). Traditionally, this tissue would be surgically resected, but modern approaches aim to focally disrupt this tissue with targeted electrical stimulation (i.e. neuromodulation). Improvements in epilepsy care are now limited by (i) the inability to accurately identify the epileptogenic zone; (ii) a limited understanding of the mechanisms underlying epileptiform activity; (iii) a lack of understanding of how to target these mechanisms with neurostimulation. The most common approach to identify the epileptogenic zone is through continuous recording of a patient’s cortical electrical activity to capture seizures. However, because seizures are infrequent, this approach is expensive, time consuming, and unpleasant for patients. Moreover, this approach often fails to identify the epileptogenic zone, resulting in unsuccessful neurosurgical intervention in 20-70% of cases. To address this, interictal biomarkers of the epileptogenic zone that manifest between seizures are required. Two such biomarkers have been proposed: (a) interictal discharges or spikes, and (b) high frequency oscillations or ripples. While both signals have been extensively studied, neither accurately delimits the epileptogenic zone. Spikes are specific for epilepsy, but too spatially diffuse to identify the epileptogenic zone. Ripples are spatially focal, but represent both pathologic and physiologic processes. We address these limitations by focusing on the simultaneous occurrence of a spike and ripple, “spike-ripple” discharges, as an improved biomarker for the epileptogenic zone. Spike-ripples commonly occur in patients with epilepsy, improve the spatial specificity of spikes for the epileptogenic zone, and disentangle physiologic from pathologic ripples. Our interdisciplinary team will apply expertise in epilepsy, neurophysiology, neurosurgery, animal experiments, modeling, and statistics to: (i) develop a fully automated spike-ripple detector and compare its clinical utility to predict surgical outcome to spikes and ripples alone, (ii) identify the biological mechanisms that generate spike-ripple discharges using novel voltage imaging techniques in animal models combined with computational models; and (iii) develop principled neurostimulation protocols to disrupt the mechanisms that generate spike-ripples. Completion of these Aims will represent significant progress towards resolving fundamental questions in modern epilepsy research, an understanding of mechanisms in the core epileptogenic network that generate spike-ripples, and a principled approach to neurostimulation to focally disrupt these pathologic dynamics.

项目概要癫痫是世界上最常见、最严重的脑部疾病，影响着全世界近 5000 万人。尽管采取了最大程度的医疗管理，仍有三分之一的患者癫痫发作仍得不到控制。癫痫发作通常来自局部大脑区域，神经外科干预是最有效的治疗选择如果成功，手术干预可以治愈癫痫发作，也可以预防或预防癫痫发作。扭转失控癫痫发作的致残后果是成功干预的关键。识别导致癫痫发作的核心组织（即致癫痫区）。该组织将被手术切除，但现代方法旨在通过有针对性的方法来局部破坏该组织电刺激（即神经调节）的改善现在受到以下因素的限制：(i) 无法准确识别癫痫发生区；(ii) 对癫痫样机制的了解有限；活动；(iii) 缺乏对如何通过神经刺激来靶向这些机制的了解。识别致痫区的方法是通过连续记录患者的皮质电然而，由于癫痫发作并不频繁，这种方法既昂贵又耗时。而且，这种方法通常无法识别致癫痫区域，导致 20-70% 病例的神经外科干预失败。已经提出了两个这样的生物标志物： (a) 发作间期放电或尖峰，以及 (b) 高频振荡或纹波，同时两个信号都已被检测到。研究表明，两者都不能准确界定癫痫发生区，但尖峰也不是癫痫所特有的。空间扩散以识别致癫痫区域。波纹在空间上是焦点，但代表病理性和病理性。我们通过关注同时发生的尖峰和生理过程来解决这些局限性。波纹，“尖峰波纹”放电，通常作为尖峰波纹的改进生物标志物。发生在癫痫患者中，提高癫痫发生区尖峰的空间特异性，以及我们的跨学科团队将运用癫痫方面的专业知识，将生理波动与病理波动分开。神经生理学、神经外科、动物实验、建模和统计学，以：（i）开发完全自动化的尖峰波纹检测器并比较其临床实用性以单独预测尖峰和波纹的手术结果，(ii) 使用新型电压成像技术识别产生尖峰纹波放电的生物机制与计算模型相结合的动物模型；以及（iii）开发原则性的神经刺激方案破坏产生尖峰波动的机制。这些目标的完成将代表着重大进展。解决现代癫痫研究中的基本问题，了解癫痫的机制产生尖峰波纹的核心致癫痫网络，以及局部神经刺激的原则方法破坏这些病理动态。