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  Combining magnetoencephalography with magnetic resonance imaging enhances learning of surrogate-biomarkers

Engemann, D. A., Kozynets, O., Sabbagh, D., Lemaître, G., Varoquaux, G., Liem, F., et al. (2020). Combining magnetoencephalography with magnetic resonance imaging enhances learning of surrogate-biomarkers. eLife, 9: e54055. doi:10.7554/eLife.54055.

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 Creators:
Engemann, Denis A.1, 2, Author              
Kozynets, Oleh1, Author
Sabbagh, David1, 3, 4, Author
Lemaître, Guillaume1, Author
Varoquaux, Gael1, Author
Liem, Franz5, Author              
Gramfort, Alexandre1, Author
Affiliations:
1Université Paris-Saclay, Palaiseau, France, ou_persistent22              
2Department Neurology, MPI for Human Cognitive and Brain Sciences, Max Planck Society, Leipzig, DE, ou_634549              
3Université Paris Diderot, France, ou_persistent22              
4Department of Anaesthesiology and Critical Care, Hôpital Lariboisière, Paris, France, ou_persistent22              
5University Research Priority Program “Dynamics of Healthy Aging”, University of Zurich, Switzerland, ou_persistent22              

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Free keywords: Aging; Biomarker; Human; Human biology; Machine learning; Magnetic resonance imaging; Magnetoencephalogrphy; Medicine; Neuroscience; Oscillations
 Abstract: Electrophysiological methods, that is M/EEG, provide unique views into brain health. Yet, when building predictive models from brain data, it is often unclear how electrophysiology should be combined with other neuroimaging methods. Information can be redundant, useful common representations of multimodal data may not be obvious and multimodal data collection can be medically contraindicated, which reduces applicability. Here, we propose a multimodal model to robustly combine MEG, MRI and fMRI for prediction. We focus on age prediction as a surrogate biomarker in 674 subjects from the Cam-CAN dataset. Strikingly, MEG, fMRI and MRI showed additive effects supporting distinct brain-behavior associations. Moreover, the contribution of MEG was best explained by cortical power spectra between 8 and 30 Hz. Finally, we demonstrate that the model preserves benefits of stacking when some data is missing. The proposed framework, hence, enables multimodal learning for a wide range of biomarkers from diverse types of brain signals.

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Language(s): eng - English
 Dates: 2020-05-19
 Publication Status: Published online
 Pages: -
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 Table of Contents: -
 Rev. Type: -
 Identifiers: DOI: 10.7554/eLife.54055
PMID: 32423528
PMC: PMC7308092
 Degree: -

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Title: eLife
Source Genre: Journal
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Publ. Info: Cambridge : eLife Sciences Publications
Pages: - Volume / Issue: 9 Sequence Number: e54055 Start / End Page: - Identifier: ISSN: 2050-084X
CoNE: https://pure.mpg.de/cone/journals/resource/2050-084X