DESIGN AND IMPLEMENTATION OF A SUPPORT TOOL FOR THE AUTOMATIZATION OF SOURCE LOCALIZATION PIPELINES IN EPILEPSY

Abstract

Epilepsy is a neurological disorder characterized by abnormal neuronal activity that can lead to recurrent seizures. During the presurgical evaluation of patients with drug-resistant epilepsy, electrical source imaging (ESI) can be used to estimate the location of epileptiform activity by combining electroencephalography (EEG) and magnetic resonance imaging (MRI). However, ESI workflows are often complex, require multiple software tools, and involve a considerable amount of manual intervention.

The aim of this project was to develop EPIESI, a desktop application that automates the main stages of the ESI workflow and facilitates source localization analyses using EEG and structural MRI data. The application was implemented using Python and MATLAB and integrates several neuroimaging tools within a single environment. MRI segmentation, EEG preprocessing, head model generation, source localization, and automatic report generation are coordinated through a graphical user interface, reducing the complexity of the overall process. Both Standardized Low-Resolution Electromagnetic Tomography (sLORETA) and Linear Constraint Minimal Variance (LCMV) inverse methods were incorporated into the pipeline.

The developed software successfully automated the ESI workflow and produced source localization results generally compatible with those obtained using clinician-defined parameters. In addition, the incorporation of literature-based recommendations improved the stability of the analyses and reduced the spatial dispersion of the estimated sources.

Overall, EPIESI provides a more standardized and reproducible approach to ESI analysis. By reducing technical barriers and simplifying the execution of complex neuroimaging workflows, the application may facilitate the adoption of ESI in clinical environments. As a result, a larger number of patients could potentially benefit from source localization analyses by making these techniques more accessible to clinicians regardless of their previous experience with ESI methodologies.