Gershon Arthur, Devulapalli Pramith, Zonjy Bilal, Ghosh Kaushik, Tatsuoka Curtis, Sahoo Satya S
Department of Population and Quantitative Health Sciences, School of Medicine, Case Western Reserve University, Cleveland, OH.
Department of Neurology, School of Medicine, Case Western Reserve University, Cleveland, OH.
AMIA Jt Summits Transl Sci Proc. 2019 May 6;2019:107-116. eCollection 2019.
Brain functional network connectivity is an important measure for characterizing changes in a variety of neurological disorders, for example Alzheimer's Disease, Parkinson Disease, and Epilepsy. Epilepsy is a serious neurological disorder affecting more than 50 million persons worldwide with severe impact on the quality of life of patients and their family members due to recurrent seizures. More than 30% of epilepsy patients are refractive to pharmacotherapy and are considered for resection to disrupt epilepsy seizure networks. However, 20-50% of these patients continue to have seizures after surgery. Therefore, there is a critical need to gain new insights into the characteristics of epilepsy seizure networks involving one of more brain regions and accurately delineate epileptogenic zone as a target for surgery. Although there is growing availability of large volume of high resolution stereotactic electroencephalogram (SEEG) data recorded from intracranial electrodes during presurgical evaluation of patients, there are significant informatics challenges associated with processing and analyzing this large signal dataset for characterizing epilepsy seizure networks. In this paper, we describe the development and application of a high-performance indexing structure for efficient retrieval of large-scale SEEG signal data to compute seizure network patterns corresponding to brain functional connectivity networks. This novel Neuro-Integrative Connectivity (NIC) search and retrieval method has been developed by extending the red-black tree index model together with an efficient lookup algorithm. We systematically perform a comparative evaluation of the proposed NIC index using de-identified SEEG data from a patient with temporal lobe epilepsy to retrieve segments of signal data corresponding to multiple seizure events and demonstrate the significant advantages of the NIC index as compared to existing methods. This new NIC Index enables faster computation of brain functional connectivity measures in epilepsy patients for large-scale network analysis and potentially provide new insights into the organization as well as evolution of seizure networks in epilepsy patients.
脑功能网络连通性是表征多种神经系统疾病（如阿尔茨海默病、帕金森病和癫痫）变化的重要指标。癫痫是一种严重的神经系统疾病，全球有超过5000万人受其影响，由于反复发作的癫痫发作，对患者及其家庭成员的生活质量产生严重影响。超过30%的癫痫患者对药物治疗无效，因此考虑进行切除手术以破坏癫痫发作网络。然而，这些患者中有20%-50%在手术后仍继续发作。因此，迫切需要深入了解涉及一个或多个脑区的癫痫发作网络的特征，并准确划定致痫区作为手术靶点。尽管在患者术前评估期间，从颅内电极记录的大量高分辨率立体定向脑电图（SEEG）数据越来越容易获得，但在处理和分析这个大信号数据集以表征癫痫发作网络方面，存在重大的信息学挑战。在本文中，我们描述了一种高性能索引结构的开发和应用，用于高效检索大规模SEEG信号数据，以计算与脑功能连通网络相对应的癫痫发作网络模式。这种新颖的神经整合连通性（NIC）搜索和检索方法是通过扩展红黑树索引模型并结合高效查找算法而开发的。我们使用来自一名颞叶癫痫患者的去识别SEEG数据，系统地对所提出的NIC索引进行了比较评估，以检索与多个癫痫发作事件相对应的信号数据段，并证明了NIC索引与现有方法相比的显著优势。这种新的NIC索引能够更快地计算癫痫患者脑功能连通性指标，用于大规模网络分析，并有可能为癫痫患者发作网络的组织和演变提供新的见解。
