Imaging of brain electric field networks with spatially resolved EEG

We present a method for spatially resolving the electric field potential throughout the entire volume of the human brain from electroencephalography (EEG) data. The method is not a variation of the well-known ‘source reconstruction’ methods, but rather a direct solution to the EEG inverse problem based on our recently developed model for brain waves that demonstrates the inadequacy of the standard ‘quasi-static approximation’ that has fostered the belief that such a reconstruction is not physically possible. The method retains the high temporal/frequency resolution of EEG, yet has spatial resolution comparable to (or better than) functional MRI (fMRI), without its significant inherent limitations. The method is validated using simultaneous EEG/fMRI data in healthy subjects, intracranial EEG data in epilepsy patients, comparison with numerical simulations, and a direct comparison with standard state-of-the-art EEG analysis in a well-established attention paradigm. The method is then demonstrated on a very large cohort of subjects performing a standard gambling task designed to activate the brain’s ‘reward circuit’. The technique uses the output from standard extant EEG systems and thus has potential for immediate benefit to a broad range of important basic scientific and clinical questions concerning brain electrical activity. By offering an inexpensive and portable alternative to fMRI, it provides a realistic methodology to efficiently promote the democratization of medicine.

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Publication Details

Journal
eLife
Published
2025-05-23
DOI
https://doi.org/10.7554/elife.100123.3
Primary Topic
Neural dynamics and brain function
Type
article
Field-Weighted Citation Impact
0.00

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article

Imaging of brain electric field networks with spatially resolved EEG

Stephan Bickel, Antı́gona Martı́nez, Olave E. Krigolson, Susan Frances Tapert et al.
eLife
Neural dynamics and brain function
article

Imaging of brain electric field networks with spatially resolved EEG

Stephan Bickel, Antı́gona Martı́nez, Olave E. Krigolson, Susan Frances Tapert, V. L. Galinsky, Lawrence R. Frank
article en

Abstract

We present a method for spatially resolving the electric field potential throughout the entire volume of the human brain from electroencephalography (EEG) data. The method is not a variation of the well-known ‘source reconstruction’ methods, but rather a direct solution to the EEG inverse problem based on our recently developed model for brain waves that demonstrates the inadequacy of the standard ‘quasi-static approximation’ that has fostered the belief that such a reconstruction is not physically possible. The method retains the high temporal/frequency resolution of EEG, yet has spatial resolution comparable to (or better than) functional MRI (fMRI), without its significant inherent limitations. The method is validated using simultaneous EEG/fMRI data in healthy subjects, intracranial EEG data in epilepsy patients, comparison with numerical simulations, and a direct comparison with standard state-of-the-art EEG analysis in a well-established attention paradigm. The method is then demonstrated on a very large cohort of subjects performing a standard gambling task designed to activate the brain’s ‘reward circuit’. The technique uses the output from standard extant EEG systems and thus has potential for immediate benefit to a broad range of important basic scientific and clinical questions concerning brain electrical activity. By offering an inexpensive and portable alternative to fMRI, it provides a realistic methodology to efficiently promote the democratization of medicine.

eLifeVol. 13
Nathan Kline Institute for Psychiatric Research (US), Feinstein Institute for Medical Research (US), University of Victoria (CA), University of California San Diego (US)
Simons Foundation Autism Research Initiative, National Institutes of Health, Natural Sciences and Engineering Research Council of Canada, National Institute on Deafness and Other Communication Disorders
Openalex Percentile: Top 88%
Neural dynamics and brain function
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