NeuroIntegrative Connectivity (NIC) Informatics Tool for Brain Functional Connectivity Network Analysis in Cohort Studies.

Objective: 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. Method: 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. Result and Conclusion: 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.

Demographic and Clinical Correlates of Seizure Frequency: Findings from the Managing Epilepsy Well Network Database

BACKGROUND AND PURPOSE: Epilepsy is a chronic neurological disease that represents a tremendous burden on both patients and society in general. Studies have addressed how demographic variables, socioeconomic variables, and psychological comorbidity are related to the quality of life (QOL) of people with epilepsy (PWE). However, there has been less focus on how these factors may differ between patients who exhibit varying degrees of seizure control. This study utilized data from the Managing Epilepsy Well (MEW) Network of the Centers for Disease Control and Prevention with the aim of elucidating differences in demographic variables, depression, and QOL between adult PWE. METHODS: Demographic variables, depression, and QOL were compared between PWE who experience clinically relevant differences in seizure occurrence. RESULTS: Gender, ethnicity, race, education, income, and relationship status did not differ significantly between the seizure-frequency categories (p>0.05). People with worse seizure control were significantly younger (p=0.039), more depressed (as assessed using the Patient Health Questionnaire) (p=0.036), and had lower QOL (as determined using the 10-item Quality of Life in Epilepsy for Adults scale) (p < 0.001). CONCLUSIONS: The present results underscore the importance of early screening, detection, and treatment of depression, since these factors relate to both seizure occurrence and QOL in PWE.