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Improved Localizadon of Cortical Activity by Combining EEG and MEG with MRI Cortical Surface Reconstruction: A Linear Approach
Abstract
We describe a comprehensive linear approach to the problem of imaging brain activity with high temporal as well as spatial resolution based on combining EEG and MEG data with anatomical constraints derived from MRI images. The "inverse problem" of estimating the distribution of dipole strengths over the cortical surface is highly underdetermined, even given closely spaced EEG and MEG recordings. We have obtained much better solutions to this problem by explicitly incorporating both local cortical orientation as well as spatial covariance of sources and sensors into our formulation. An explicit polygonal model of the cortical manifold is first constructed as follows: (1) slice data in three orthogonal planes of section (needle-shaped voxels) are combined with a linear deblurring technique to make a single high-resolution 3-D image (cubic voxels), (2) the image is recursively flood-filled to determine the topology of the gray-white matter border, and (3) the resulting continuous surface is refined by relaxing it against the original 3-D gray-scale image using a deformable template method, which is also used to computationally flatten the cortex for easier viewing. The explicit solution to an error minimization formulation of an optimal inverse linear operator (for a particular cortical manifold, sensor placement, noise and prior source covariance) gives rise to a compact expression that is practically computable for hundreds of sensors and thousands of sources. The inverse solution can then be weighted for a particular (averaged) event using the sensor covariance for that event. Model studies suggest that we may be able to localize multiple cortical sources with spatial resolution as good as PET with this technique, while retaining a much finer grained picture of activity over time.
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“The latter is more preferable since the sources of EEG/MEG are primarily currents through pyramidal cells aligned perpendicular to the cortical surface, as proposed in the cortical current density (CCD) model (22).”
From Electrophysiological source imaging: A noninvasive window to brain dynamics (2018) · cites this paper as [22]
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Spectral and spatial shifts of post-ictal slow waves in temporal lobe seizures
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Does the mismatch negativity operate on a consciously accessible memory trace?
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“Source analysis Weused a combination of anatomically constrained, noise-normalized, minimum-norm estimates (73, 74) known as dynamic statistical parametric mapping (dSPM) (75) and sparse inverse estimates based on an L1/L2mixednorm (MxNE) (76).”
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Apolipoprotein E ε4-related thickening of the cerebral cortex modulates selective attention
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Dynamic imaging of seizure activity in pediatric epilepsy patients
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Electrophysiological Mapping and Neuroimaging
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Laminar specificity of the auditory perceptual awareness negativity: a biophysical modeling study
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Metadata
- Authors
- Dale, A. M., & Sereno, M. I.
- Affiliations
- University of California San Diego (US)
- Venue
- Journal of Cognitive Neuroscience
- Publisher
- MIT Press
- Published
- 1993
- Volume
- 5
- Issue
- 2
- Pages
- 162-176
- ISSN
- 0898-929X (ISSN-L)
- 1530-8898
- OpenAlex
- W2031425398
- Cited by
- 2,003
- References
- 29
- Citations by year
- 2026: 44 · 2025: 52 · 2024: 58 · 2023: 56 · 2022: 74 · 2021: 100 · 2020: 81 · 2019: 97 · 2018: 68 · 2017: 98 · 2016: 97 · 2015: 76 · 2014: 94 · 2013: 89 · 2012: 97
- FWCI
- 7.51
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Journal of Cognitive Neuroscience (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › EEG and Brain-Computer Interfaces
- Physical Sciences › Computer Science › Signal Processing › Direction-of-Arrival Estimation Techniques
- Physical Sciences › Mathematics › Mathematical Physics › Numerical methods in inverse problems
- Keywords
- cortical surface reconstruction, source localization, linear inverse problems, dipole modeling, deformable templates
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