Spatially sparse source cluster modeling by compressive neuromagnetic tomography
Open accessAbstract
Magnetoencephalography enables non-invasive detection of weak cerebral magnetic fields by utilizing super-conducting quantum interference devices (SQUIDs). Solving the MEG inverse problem requires reconstructing the locations and orientations of the underlying neuronal current sources based on the extracranial measurements. Most inverse problem solvers explicitly favor either spatially more focal or diffuse current source patterns. Naturally, in a situation where both focal and spatially extended sources are present, such reconstruction methods may yield inaccurate estimates. To address this problem, we propose a novel ComprEssive Neuromagnetic Tomography (CENT) method based on the assumption that the current sources are compressible. The compressibility is quantified by the joint sparsity of the source representation in the standard source space and in a transformed domain. The purpose of the transformation sparsity constraint is to incorporate local spatial structure adaptively by exploiting the natural redundancy of the source configurations in the transform domain. By combining these complementary constraints of standard and transformed domain sparsity we obtain source estimates, which are not only locally smooth and regular but also form globally separable clusters. In this work, we use the l(1)-norm as a measure of sparsity and convex optimization to yield compressive estimates in a computationally tractable manner. We study the Laplacian matrix (CENT(L)) and spherical wavelets (CENT(W)) as alternatives for the transformation in the compression constraint. In addition to the two prior constraints on the sources, we control the discrepancy between the modeled and measured data by restricting the power of residual error below a specified value. The results show that both CENT(L) and CENT(W) are capable of producing robust spatially regular source estimates with high computational efficiency. For simulated sources of focal, diffuse, or combined types, the CENT method shows better accuracy on estimating the source locations and spatial extents than the minimum l(1)-norm or minimum l(2)-norm constrained inverse solutions. Different transformations yield different benefits: By utilizing CENT with the Laplacian matrix it is possible to suppress physiologically atypical activations extending across two opposite banks of a deep sulcus. With the spherical wavelet transform CENT can improve the detection of two nearby yet not directly connected sources. As demonstrated by simulations, CENT is capable of reflecting the spatial extent for both focal and spatially extended current sources. The analysis of in vivo MEG data by CENT produces less physiologically inconsistent "clutter" current sources in somatosensory and auditory MEG measurements. Overall, the CENT method is demonstrated to be a promising tool for adaptive modeling of distributed neuronal currents associated with cognitive tasks.
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- Authors
- Chang, W.-T., Nummenmaa, A., Hsieh, J.-C., & Lin, F.-H.
- Affiliations
- National Taiwan University (TW)
- Massachusetts General Hospital (US)
- National Yang Ming University (TW)
- Corresponding author
- Lin, Fa-Hsuan
- Venue
- NeuroImage
- Publisher
- Elsevier BV
- Published
- 2010-10
- Volume
- 53
- Issue
- 1
- Pages
- 146-160
- ISSN
- 1053-8119 (ISSN-L)
- PMC
- PMC2914202
- OpenAlex
- W2146157652
- Cited by
- 52
- References
- 46
- Citations by year
- 2026: 3 · 2025: 3 · 2024: 6 · 2023: 4 · 2022: 2 · 2021: 3 · 2020: 3 · 2019: 2 · 2018: 2 · 2017: 6 · 2016: 4 · 2015: 4 · 2014: 2 · 2013: 3 · 2012: 5
- FWCI
- 3.02
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- NeuroImage (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Institutes of Health (R01EB006847, R01HD040712, R01DA14178, P41 RR14075, RO1EB000790, R21EB007298, R01NS037462)
- National Science Council (97-2221-E-002-005, NHRI-EX98-9715EC, 97-2320-B-002-058-MY3)
- Tekniikan Edistämissäätiö
- Suomen Kulttuurirahasto
- Topics
- Physical Sciences › Mathematics › Mathematical Physics › Numerical methods in inverse problems
- Physical Sciences › Engineering › Computational Mechanics › Sparse and Compressive Sensing Techniques
- Life Sciences › Neuroscience › Cognitive Neuroscience › EEG and Brain-Computer Interfaces
- Keywords
- MEG inverse problem, source localization, joint sparsity, transform-domain sparsity, Laplacian matrix, spherical wavelet transform, L1 regularization, convex optimization, squid
- MeSH terms
- Adult, Algorithms, Brain, Brain Mapping, Computer Simulation, Evoked Potentials, Female, Humans, Image Enhancement, Image Interpretation, Computer-Assisted, Male, Models, Neurological, Pattern Recognition, Automated, Sensitivity and Specificity, Reproducibility of Results, Magnetoencephalography
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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