Python CCEP Analyzer Accelerates Cortical Connectivity Mapping: iEEG Tool for Epilepsy Surgical Planning
Abstract number :
3.245
Submission category :
3. Neurophysiology / 3G. Computational Analysis & Modeling of EEG
Year :
2025
Submission ID :
1173
Source :
www.aesnet.org
Presentation date :
12/8/2025 12:00:00 AM
Published date :
Authors :
Presenting Author: Tyrell Pruitt, PhD – The University of Texas Southwestern Medical Center
Sevda Ardabili, BS – Southern Methodist University
Clyde Knox, BS – The University of Texas Southwestern Medical Center
Bradley Lega, MD – UT Southwestern Medical Center
Carlos Davila, PhD – Southern Methodist University
Rationale: Drug-resistant epilepsy often necessitates precise mapping of the brain’s seizure-generating networks to guide surgical or neuromodulatory interventions. Cortico-Cortical Evoked Potentials (CCEPs) recorded via intracranial electroencephalography (iEEG) provide direct measurements of inter-regional connectivity by applying brief electrical stimuli and capturing resultant cortical responses. This project addresses the critical need for a user-friendly, Python-based CCEP analysis tool that can accelerate data processing, enhance localization accuracy, and ultimately improve patient care.
Methods: The analyzer performs filtering of iEEG data (including a notch filter at 50/60 Hz and an optional bandpass FIR filter in the 30–50 Hz gamma range) to remove noise and isolate relevant signals. Stimulation pulses are detected via peak-finding algorithms, enabling automatic epoching of the data around each stimulus. Analyses include ERP-like averaging to visualize consistent CCEP responses, RMS gamma calculations to quantify power changes, and optional dimensionality reduction (PCA) to highlight prominent spatial or event-related patterns. A region-of-interest (ROI) feature allows users to focus on specific brain areas, while a 3D visualization module projects CCEP magnitudes onto a standard brain mesh (pial or white matter view). Results, including waveforms and 3D maps, are exportable in standard formats (CSV, PNG, MNE-FIF).
Results: Illustrative outputs show clear CCEP waveforms across multiple electrodes, with stronger or earlier responses potentially pinpointing epileptogenic pathways. Three-dimensional reconstructions overlay high-intensity signals in bright colors to indicate regions strongly connected to the stimulation site, guiding clinicians toward areas that may require resection or stimulation implants. The tool can fit into a broader epilepsy surgery workflow by automatically generating connectivity maps that inform pre-surgical planning, optimize electrode placement, and support post-hoc evaluations of neuromodulation efficacy.
Conclusions: By streamlining CCEP processing and delivering intuitive connectivity visualizations, this Python-based analyzer helps localize epileptic circuits with improved efficiency. Its modular design facilitates integration with clinical protocols, potentially reducing hospital stays and advancing personalized epilepsy treatment. Ongoing funding is sought to support broader clinical trials, enhance automated classification features, and expand real-time analysis capabilities. Such advancements promise better outcomes for patients worldwide and foster innovation in neurosurgical approaches to epilepsy care.
Funding: Wilson Foundation
Neurophysiology