epartment of Population and Quantitative Health Sciences, Case Western Reserve University School of Medicine, Cleveland, OH, USA.
Department of Neurology, University Hospitals Cleveland Medical Center, Cleveland, OH, USA.
AMIA Annu Symp Proc. 2021 Jan 25;2020:1090-1099. eCollection 2020.
: Brain functional connectivity measures are often used to study interactions between brain regions in various neurological disorders such as epilepsy. In particular, functional connectivity measures derived from high resolution electrophysiological signal data have been used to characterize epileptic networks in epilepsy patients. However, existing signal data formats as well as computational methods are not suitable for complex multi-step methods used for processing and analyzing signal data across multiple seizure events. To address the significant data management challenges associated with signal data, we have developed a new workflow-based tool called NeuroIntegrative Connectivity (NIC) using the Cloudwave Signal Format (CSF) as a common data abstraction model. : The NIC compositional workflow-based tool consists of: (1) Signal data processing component for automated pre- processing and generation of CSF files with semantic annotation using epilepsy domain ontology; and (2) Functional network computation component for deriving functional connectivity metrics from signal data analysis across multiple recording channels. The NIC tool streamlines signal data management using a modular software implementation architecture that supports easy extension with new libraries of signal coupling measures and fast data retrieval using a binary search tree indexing structure called NIC-Index. : We evaluated the NIC tool by processing and analyzing signal data for 28 seizure events in two patients with refractory epilepsy. The result shows that certain brain regions have high local measure of connectivity, such as total degree, as compared to other regions during ictal events in both patients. In addition, global connectivity measures, which characterize transitivity and efficiency, increase in value during the initial period of the seizure followed by decrease towards the end of seizure. The NIC tool allows users to efficiently apply several network analysis metrics to study global and local changes in epileptic networks in patient cohort studies.
脑功能连接测量常用于研究各种神经障碍（如癫痫）中脑区之间的相互作用。特别是，从高分辨率电生理信号数据中得出的功能连接测量被用于描述癫痫患者的癫痫网络。然而，现有的信号数据格式和计算方法并不适合用于处理和分析多个癫痫发作事件的信号数据的复杂多步骤方法。为了解决与信号数据相关的重大数据管理挑战，我们开发了一种新的基于工作流的工具，称为神经综合连接（NIC），并使用 Cloudwave 信号格式（CSF）作为通用数据抽象模型。
NIC 基于工作流的组合工具包括：（1）信号数据处理组件，用于使用癫痫领域本体对信号数据进行自动预处理，并生成具有语义注释的 CSF 文件；（2）功能网络计算组件，用于从多个记录通道的信号数据分析中得出功能连接度量。NIC 工具通过使用模块化软件实现架构来简化信号数据管理，该架构支持使用称为 NIC-Index 的二叉搜索树索引结构轻松扩展新的信号耦合度量库，并快速检索数据。
我们通过对两名耐药性癫痫患者的 28 次癫痫发作事件的信号数据进行处理和分析，评估了 NIC 工具。结果表明，与其他区域相比，两名患者在发作期间，某些大脑区域具有较高的局部连接度量，例如总度数。此外，全局连接度量，用于描述传递性和效率，在发作初期增加，然后在发作结束时降低。NIC 工具允许用户有效地应用几种网络分析度量来研究患者队列研究中癫痫网络的全局和局部变化。
