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: 10569081
• 负责人: Jorge Alvaro Gonzalez-Martinez
• 金额: $ 42.38万
• 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/10569081
• 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; 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; Periodicals; Pharmaceutical Preparations; Physiologic pulse; Positioning Attribute; Positron-Emission Tomography; Process; Property; Rest; Risk; Scalp structure; Seizures; Site; Source; Stimulus; System; Testing; Time; Universities; Validation; Visual; 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