Nemzer Louis R, Cravens Gary D, Worth Robert M, Motta Francis, Placzek Andon, Castro Victor, Lou Jennie Q
Department of Chemistry and Physics, Halmos College of Arts and Sciences, Nova Southeastern University, Fort Lauderdale, FL, United States.
Department of Health Informatics, Dr. Kiran C. Patel College of Osteopathic Medicine, Nova Southeastern University, Fort Lauderdale, FL, United States.
Front Comput Neurosci. 2021 Jan 8;14:583350. doi: 10.3389/fncom.2020.583350. eCollection 2020.
Healthy brain function is marked by neuronal network dynamics at or near the critical phase, which separates regimes of instability and stasis. A failure to remain at this critical point can lead to neurological disorders such as epilepsy, which is associated with pathological synchronization of neuronal oscillations. Using full Hodgkin-Huxley (HH) simulations on a Small-World Network, we are able to generate synthetic electroencephalogram (EEG) signals with intervals corresponding to seizure (ictal) or non-seizure (interictal) states that can occur based on the hyperexcitability of the artificial neurons and the strength and topology of the synaptic connections between them. These interictal simulations can be further classified into scale-free critical phases and disjoint subcritical exponential phases. By changing the HH parameters, we can model seizures due to a variety of causes, including traumatic brain injury (TBI), congenital channelopathies, and idiopathic etiologies, as well as the effects of anticonvulsant drugs. The results of this work may be used to help identify parameters from actual patient EEG or electrocorticographic (ECoG) data associated with ictogenesis, as well as generating simulated data for training machine-learning seizure prediction algorithms.
健康的大脑功能以临界期或接近临界期的神经网络动力学为特征，临界期将不稳定状态和静止状态区分开来。未能维持在这个临界点可能会导致癫痫等神经系统疾病，癫痫与神经元振荡的病理性同步有关。通过在小世界网络上进行完整的霍奇金-赫胥黎（HH）模拟，我们能够生成合成脑电图（EEG）信号，其时间间隔对应于基于人工神经元的过度兴奋性以及它们之间突触连接的强度和拓扑结构可能出现的癫痫发作（发作期）或非癫痫发作（发作间期）状态。这些发作间期模拟可以进一步分为无标度临界期和不相交的亚临界指数期。通过改变HH参数，我们可以模拟由多种原因引起的癫痫发作，包括创伤性脑损伤（TBI）、先天性离子通道病和特发性病因，以及抗惊厥药物的作用。这项工作的结果可用于帮助从实际患者的脑电图或皮质脑电图（ECoG）数据中识别与癫痫发作产生相关的参数，以及生成用于训练机器学习癫痫发作预测算法的模拟数据。
