The electro-MICA toolbox for integrating electrophysiology within multimodal imaging and connectomics workflows
- 1Centre of Excellence in Epilepsy, Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada
- 2Duke University, Durham, NC, United States
Abstract
The integration of electrophysiological recordings with multimodal neuroimaging data holds great promise for advancing our understanding of brain function and neurological disorders. To facilitate this endeavor, we present electro-MICA, an open-access Python toolbox designed to project electrophysiological features from scalp and intracranial electroencephalography (EEG) onto cortical and hippocampal surfaces generated by validated multimodal imaging ecosystems. The toolbox comprises two pipelines: one for intracranial EEG (iEEG) recorded with stereo-EEG depth electrodes and one for scalp EEG source localization. Both pipelines are grounded in numerical solutions to the electromagnetic equations governing electric activity in the brain, solved using the Boundary Element Method. A key methodological contribution is the use of a current density double layer model for neural generators, which avoids the mathematical singularities introduced by conventional dipole-based models when electrodes are near the cortical surface, a situation that can arise in iEEG. Electrode contacts are additionally modeled with non-zero length, improving physical realism. Scalp EEG source localization is performed using eLORETA on a subject-specific three-layer head model derived from the anatomical input. Validation against empirical gamma-band iEEG data from 32 subjects demonstrates that the distributed generator model outperforms both distance-based and dipole-based alternatives. An illustrative clinical example demonstrates the toolbox’s capacity to reveal associations between intracranial spike rates, cortical thickness, and anatomical connectivity in an epilepsy patient. Electro-MICA requires no parameter selection from the user, facilitating straightforward multimodal analyses. The toolbox is available at http://github.com/MICA-MNI/electromica with extensive online documentation at http://electromica.readthedocs.io.
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BibTeX
@article{ellenrieder2026electromica,
title = {{The electro-MICA toolbox for integrating electrophysiology within multimodal imaging and connectomics workflows}},
author = {Nicolás von Ellenrieder and Zhengchen Cai and Thaera Arafat and Laura Vavassori and Chifaou Abdallah and Jordan De Kraker and Raul Rodriguez-Cruces and Jessica Royer and Ella Sahlas and Paul Bautin and Raluca Pana and Olivier Aron and Birgit Frauscher and Boris C. Bernhardt},
journal = {Imaging Neuroscience},
year = {2026},
month = oct,
doi = {10.1162/imag.a.1413},
url = {https://doi.org/10.1162/imag.a.1413},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - The electro-MICA toolbox for integrating electrophysiology within multimodal imaging and connectomics workflows
AU - Nicolás von Ellenrieder
AU - Zhengchen Cai
AU - Thaera Arafat
AU - Laura Vavassori
AU - Chifaou Abdallah
AU - Jordan De Kraker
AU - Raul Rodriguez-Cruces
AU - Jessica Royer
AU - Ella Sahlas
AU - Paul Bautin
AU - Raluca Pana
AU - Olivier Aron
AU - Birgit Frauscher
AU - Boris C. Bernhardt
PY - 2026
DA - 2026/10/22
JO - Imaging Neuroscience
DO - 10.1162/imag.a.1413
UR - https://doi.org/10.1162/imag.a.1413
AB - The integration of electrophysiological recordings with multimodal neuroimaging data holds great promise for advancing our understanding of brain function and neurological disorders. To facilitate this endeavor, we present electro-MICA, an open-access Python toolbox designed to project electrophysiological features from scalp and intracranial electroencephalography (EEG) onto cortical and hippocampal surfaces generated by validated multimodal imaging ecosystems. The toolbox comprises two pipelines: one for intracranial EEG (iEEG) recorded with stereo-EEG depth electrodes and one for scalp EEG source localization. Both pipelines are grounded in numerical solutions to the electromagnetic equations governing electric activity in the brain, solved using the Boundary Element Method. A key methodological contribution is the use of a current density double layer model for neural generators, which avoids the mathematical singularities introduced by conventional dipole-based models when electrodes are near the cortical surface, a situation that can arise in iEEG. Electrode contacts are additionally modeled with non-zero length, improving physical realism. Scalp EEG source localization is performed using eLORETA on a subject-specific three-layer head model derived from the anatomical input. Validation against empirical gamma-band iEEG data from 32 subjects demonstrates that the distributed generator model outperforms both distance-based and dipole-based alternatives. An illustrative clinical example demonstrates the toolbox’s capacity to reveal associations between intracranial spike rates, cortical thickness, and anatomical connectivity in an epilepsy patient. Electro-MICA requires no parameter selection from the user, facilitating straightforward multimodal analyses. The toolbox is available at http://github.com/MICA-MNI/electromica with extensive online documentation at http://electromica.readthedocs.io.
SN - 2837-6056
ER -