Establishing novel properties of dynamic systems models to identify epileptogenic networks in patients with drug resistant epilepsy

建立动态系统模型的新特性来识别耐药性癫痫患者的致癫痫网络

• 批准号: 10445867
• 负责人: Jorge Alvaro Gonzalez-Martinez
• 金额: $ 44.7万
• 依托单位: JOHNS HOPKINS UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-02-15 至 2027-01-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10445867
• 关键词: Algorithms; Antiepileptic Agents; Biological Markers; Biological Models; Brain; Brain region; Clinical; Consumption; Data; Data Set; Diagnosis; Electric Stimulation; Electroencephalography; Electrophysiology (science); Epilepsy; Evaluation; Evoked Potentials; Excision; Eye; Floor; Freedom; Frequencies; Gold; Hemorrhage; Hospitals; Image; Individual; Infection; Intractable Epilepsy; Magnetic Resonance Imaging; Measures; Medical; Medical center; Methods; Monitor; Motor; Network-based; Neurologic Deficit; Operative Surgical Procedures; Outcome; Pathologic; Patients; Periodicity; Pharmaceutical Preparations; Physiologic pulse; Positioning Attribute; Positron-Emission Tomography; Process; Property; Rest; Risk; Scalp structure; Seizures; Site; Source; Stimulus; System; Testing; Time; Universities; Validation; base; cost; dynamic system; efficacy testing; experimental study; in vivo; network models; noninvasive diagnosis; novel; randomized controlled study; response; signal processing; success; tool; voltage

PROJECT SUMMARY Over 15 million epilepsy patients worldwide have medically refractory epilepsy (MRE), i.e., they do not respond to drugs [1]. Successful surgery is a hopeful alternative for seizure freedom but can only be achieved through complete resection or disconnection of the epileptogenic zone (EZ), the brain region(s) where seizures originate. Unfortunately, surgical success rates vary between 30%-70% because no clinically validated biological markers of the EZ exist. Localizing the EZ has thus become a costly and time-consuming process during which a team of clinicians obtain imaging data (e.g. MRI, PET) and scalp EEG recordings, which is often followed by invasive monitoring involving days-to-weeks of EEG recordings captured intracranially (iEEG). Clinicians visually inspect iEEG data, looking for abnormal activity (e.g. low-voltage high frequency activity) on individual channels occurring immediately before seizures. They also look for abnormal iEEG spikes that last a few seconds occurring in between seizures. In the end, clinicians use <1% of the iEEG data captured to assist in EZ localization (minutes of seizure data versus days of recordings), which begs the question-“are we missing significant opportunities to leverage these largely ignored data sets to better diagnose and treat patients?” Intracranial EEG offers a unique opportunity to observe rich epileptic cortical network dynamics, which are only visible by the naked eye during seizures. But, waiting for seizures to occur is risky for the patient as invasive monitoring is associated with complications including bleedings, infections, and neurological deficits. Further, the costs of monitoring are very high, with one estimate quoting that the cost is at least $5,000 per day. In the proposed study, we aim to leverage iEEG data in between seizures by (ii) testing a new networked-based inter- ictal (between seizure) iEEG marker of the EZ, and by (i) modulating seizure networks with single-pulse electrical stimulation (SPES) and analyzing the associated cortico-cortical evoked potentials (CCEPs). We hypothesize that patient-specific dynamical network models (DNMs), built from each patient’s inter-ictal iEEG and CCEPs data, can characterize brain network dynamics and reveal pathological nodes, i.e., the EZ. The DNM characterizes how each iEEG node (channel) dynamically influences the rest of the network and how the network responds to exogenous stimuli. Our team has expertise in dynamical systems modeling, signal processing of iEEG data, electrophysiology, and surgical treatment of epilepsy, and is uniquely positioned to test our main hypothesis through the following aims: (i) to investigate source-sink properties of DNMs derived from interictal iEEG data to localize the EZ, (ii) to investigate resonance properties of DNMs derived from SPES evoked responses to localize the EZ, and (iii) to test whether stimulating suspected EZ nodes with resonant periodic pulse inputs triggers seizures. If successful, the proposed computational approaches and SPES experiments have the potential to significantly reduce invasive monitoring times, avoiding further risks to patients and reducing costs to hospitals by leveraging access to patient iEEG networks during passive monitoring.

项目总结 全世界有超过1500万癫痫患者患有药物难治性癫痫(MRE)，即他们没有反应 到毒品[1]。成功的手术是缓解癫痫发作的一种有希望的选择，但只能通过 完全切除或切断致痫区域(EZ)，即癫痫发作起源的脑区(S)。 不幸的是，手术成功率在30%-70%之间，因为没有经过临床验证的生物标志物。 EZ的存在。因此，将EZ本地化成为一个昂贵且耗时的过程，在此过程中，团队 的临床医生获得成像数据(例如MRI、PET)和头皮脑电记录，这通常是侵入性的 监测涉及几天到几周的脑电记录，从脑内捕捉(IEEG)。临床医生目测检查 IEEG数据，寻找各个通道上的异常活动(例如，低电压高频活动) 在癫痫发作之前发生的。他们还寻找持续几秒钟的异常iEEG尖峰 在两次发作之间发生的。最后，临床医生使用捕获的1%的iEEG数据来辅助EZ 本地化(几分钟的扣押数据与几天的记录)，这回避了一个问题--“我们错过了吗？ 利用这些基本上被忽视的数据集来更好地诊断和治疗患者的重大机会？ 颅内脑电提供了一个独特的机会来观察丰富的癫痫皮质网络动力学，只有 癫痫发作时肉眼可见。但是，等待癫痫发作对患者来说是有风险的，因为它是侵入性的。 监测与并发症有关，包括出血、感染和神经缺陷。此外， 监测的成本非常高，据估计，每天的成本至少为5,000美元。在 建议的研究，我们的目标是利用发作之间的iEEG数据：(Ii)测试一种新的基于网络的 发作期(发作间期)EZ的iEEG标记，并通过(I)用单脉冲电调制发作网络 刺激(SPES)和分析相关的皮质诱发电位(CCEP)。我们假设 根据每个患者发作间歇期的iEEG和CCEP建立的特定于患者的动态网络模型(DNM) 数据，可以表征大脑网络的动力学，并揭示病理节点，即大脑半球。DNM 描述了每个iEEG节点(通道)如何动态影响网络的其余部分，以及网络如何 对外界刺激做出反应。我们的团队在动态系统建模、信号处理方面拥有专业知识 IEEG数据，电生理学和癫痫的外科治疗，并处于独特的地位，以测试我们的主要 通过以下目的提出假说：(I)研究发作间期DNM的源-汇特性 IEEG数据用于定位EZ，(Ii)研究由SPES诱发的DNM的共振特性 响应以定位EZ，以及(Iii)测试是否以共振周期刺激可疑的EZ结节 脉冲输入会触发癫痫发作。如果成功，建议的计算方法和SPES实验 有可能显著减少侵入性监测时间，避免对患者的进一步风险，并减少 在被动监测期间利用对患者iEEG网络的访问，为医院带来了成本。