Multivariate source prelocalization (MSP): Use of functionally informed basis functions for better conditioning the MEG inverse problem
Abstract
Spatially characterizing and quantifying the brain electromagnetic response using MEG/EEG data still remains a critical issue since it requires solving an ill-posed inverse problem that does not admit a unique solution. To overcome this lack of uniqueness, inverse methods have to introduce prior information about the solution. Most existing approaches are directly based upon extrinsic anatomical and functional priors and usually attempt at simultaneously localizing and quantifying brain activity. By contrast, this paper deals with a preprocessing tool which aims at better conditioning the source reconstruction process, by relying only upon intrinsic knowledge (a forward model and the MEG/EEG data itself) and focusing on the key issue of localization. Based on a discrete and realistic anatomical description of the cortex, we first define functionally Informed Basis Functions (fIBF) that are subject specific. We then propose a multivariate method which exploits these fIBF to calculate a probability-like coefficient of activation associated with each dipolar source of the model. This estimated distribution of activation coefficients may then be used as an intrinsic functional prior, either by taking these quantities into account in a subsequent inverse method, or by thresholding the set of probabilities in order to reduce the dimension of the solution space. These two ways of constraining the source reconstruction process may naturally be coupled. We successively describe the proposed Multivariate Source Prelocalization (MSP) approach and illustrate its performance on both simulated and real MEG data. Finally, the better conditioning induced by the MSP process in a classical regularization scheme is extensively and quantitatively evaluated.
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Parcellation improves the condition of the EEG inverse problem and reduces computation time, making it attractive for real-time applications.
“Parcellation is particularly attractive for real-time applications as it improves the condition of the EEG inverse problem and reduces computation time (48).”
From Noninvasive neuroimaging enhances continuous neural tracking for robotic device control (2019) · cites this paper as [48]
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Noninvasive neuroimaging enhances continuous neural tracking for robotic device control
Edelman, B. J., Meng, J., Suma, D., Zurn, C., Nagarajan, E., Baxter, B. S., Cline, C. C., & He, B. (2019) · Science Robotics · doi:10.1126/scirobotics.aaw6844 · Cited by 380 · see citing works
- Authors
- MATTOUT, J., PELEGRINIISSAC, M., GARNERO, L., & BENALI, H.
- Affiliations
- Centre National de la Recherche Scientifique (FR)
- Inserm (FR)
- Corresponding author
- MATTOUT, J
- ORCID
- Venue
- NeuroImage
- Publisher
- Elsevier BV
- Published
- 2005-06
- Volume
- 26
- Issue
- 2
- Pages
- 356-373
- ISSN
- 1053-8119 (ISSN-L)
- OpenAlex
- W2062962405
- Cited by
- 52
- References
- 35
- Citations by year
- 2026: 3 · 2025: 2 · 2024: 2 · 2023: 3 · 2022: 4 · 2021: 2 · 2020: 6 · 2019: 1 · 2018: 3 · 2017: 4 · 2016: 6 · 2015: 3 · 2014: 1 · 2013: 3 · 2012: 1
- FWCI
- 1.32
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- NeuroImage (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › EEG and Brain-Computer Interfaces
- Physical Sciences › Mathematics › Mathematical Physics › Numerical methods in inverse problems
- Physical Sciences › Computer Science › Signal Processing › Direction-of-Arrival Estimation Techniques
- Keywords
- MEG inverse problem, ill-posed inverse problems, source localization, regularization, dimensionality reduction
- MeSH terms
- Algorithms, Computer Simulation, Data Interpretation, Statistical, Evoked Potentials, Somatosensory, Humans, Functional Laterality, Models, Neurological, Neural Pathways, Neurons, Magnetoencephalography, Models, Statistical, Multivariate Analysis, Pyramidal Cells, Principal Component Analysis
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