NeuroIntegrative Connectivity (NIC) Informatics Tool for Brain Functional Connectivity Network Analysis in Cohort
Satya S Sahoo1,2, Arthur Gershon1, Shafiabadi Nassim1,2
1epartment of Population and Quantitative Health Sciences, Case Western Reserve University School of Medicine, Cleveland, OH, USA.
Summary
A new tool, NeuroIntegrative Connectivity (NIC), streamlines the analysis of brain functional connectivity in epilepsy by managing complex electrophysiological signal data. NIC enables efficient characterization of epileptic networks, aiding in understanding neurological disorders.
Area of Science:
- Neuroscience
- Computational Biology
- Medical Informatics
Background:
- Brain functional connectivity analysis is crucial for studying neurological disorders like epilepsy.
- Existing data formats and computational methods hinder complex analysis of electrophysiological signals across multiple seizures.
- Effective data management is essential for advancing the study of epileptic networks.
Purpose of the Study:
- To develop a novel workflow-based tool, NeuroIntegrative Connectivity (NIC), for managing and analyzing brain functional connectivity data in epilepsy.
- To address limitations in current signal data formats and computational methods for complex electrophysiological signal processing.
- To facilitate the characterization of epileptic networks using a common data abstraction model.
Main Methods:
- Developed NIC, a compositional workflow-based tool utilizing the Cloudwave Signal Format (CSF) as a common data model.
- Implemented automated signal data pre-processing and semantic annotation using an epilepsy domain ontology.
- Integrated a functional network computation component for deriving connectivity metrics and utilized NIC-Index for efficient data retrieval.
Main Results:
- NIC processed and analyzed signal data from 28 seizure events in two epilepsy patients.
- Identified brain regions with high local connectivity (e.g., total degree) during ictal events.
- Observed increases in global connectivity measures (transitivity, efficiency) during early seizure phases, followed by decreases.
Conclusions:
- The NIC tool effectively streamlines signal data management and analysis for epilepsy research.
- NIC enables efficient application of network analysis metrics to study global and local changes in epileptic networks.
- Facilitates patient cohort studies by providing efficient tools for analyzing complex electrophysiological data.
