The DOI matches its Crossref record; 29 of the 29 references it cites were found in a registry or an index.
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]
Citations
Cites
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Multiple dipole modeling and localization from spatio-temporal MEG data
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Syntactically Based Sentence Processing Classes: Evidence from Event-Related Brain Potentials
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Scherg, M. (1990) · Max Planck Institute for Plasma Physics · Cited by 530
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Research works printed but not linked: 3
- Barth D. S., 1990, Journal of N I ?
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Items 101–102 of 102
Forward and inverse problems of MEG/EEG,
Baillet, S. (2014) · Encyclopedia of Computational Neuroscience · doi:10.1007/978-1-4614-7320-6_529-1 · Cited by 10
Three-Dimensional Imaging of Complex Neural Activation in Humans From EEG
Ding, L., Zhang, N., Chen, W., & He, B. (2009) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2009.2020438 · Cited by 4
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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