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Tytuł pozycji:

Predictive regression modeling with MEG/EEG: from source power to signals and cognitive states

Tytuł:
Predictive regression modeling with MEG/EEG: from source power to signals and cognitive states
Autorzy:
David Sabbagh
Pierre Ablin
Gaël Varoquaux
Alexandre Gramfort
Denis A. Engemann
Temat:
MEG/EEG
Neuronal oscillations
Machine learning
Covariance
Spatial filters
Riemannian geometry
Neurosciences. Biological psychiatry. Neuropsychiatry
RC321-571
Źródło:
NeuroImage, Vol 222, Iss , Pp 116893- (2020)
Wydawca:
Elsevier, 2020.
Rok publikacji:
2020
Kolekcja:
LCC:Neurosciences. Biological psychiatry. Neuropsychiatry
Typ dokumentu:
article
Opis pliku:
electronic resource
Język:
English
ISSN:
1095-9572
Relacje:
http://www.sciencedirect.com/science/article/pii/S1053811920303797; https://doaj.org/toc/1095-9572
DOI:
10.1016/j.neuroimage.2020.116893
Dostęp URL:
https://doaj.org/article/6e264500378c423aa5666f73a6dd9152  Link otwiera się w nowym oknie
Numer akcesji:
edsdoj.6e264500378c423aa5666f73a6dd9152
Czasopismo naukowe
Predicting biomedical outcomes from Magnetoencephalography and Electroencephalography (M/EEG) is central to applications like decoding, brain-computer-interfaces (BCI) or biomarker development and is facilitated by supervised machine learning. Yet, most of the literature is concerned with classification of outcomes defined at the event-level. Here, we focus on predicting continuous outcomes from M/EEG signal defined at the subject-level, and analyze about 600 MEG recordings from Cam-CAN dataset and about 1000 EEG recordings from TUH dataset. Considering different generative mechanisms for M/EEG signals and the biomedical outcome, we propose statistically-consistent predictive models that avoid source-reconstruction based on the covariance as representation. Our mathematical analysis and ground-truth simulations demonstrated that consistent function approximation can be obtained with supervised spatial filtering or by embedding with Riemannian geometry. Additional simulations revealed that Riemannian methods were more robust to model violations, in particular geometric distortions induced by individual anatomy. To estimate the relative contribution of brain dynamics and anatomy to prediction performance, we propose a novel model inspection procedure based on biophysical forward modeling. Applied to prediction of outcomes at the subject-level, the analysis revealed that the Riemannian model better exploited anatomical information while sensitivity to brain dynamics was similar across methods. We then probed the robustness of the models across different data cleaning options. Environmental denoising was globally important but Riemannian models were strikingly robust and continued performing well even without preprocessing. Our results suggest each method has its niche: supervised spatial filtering is practical for event-level prediction while the Riemannian model may enable simple end-to-end learning.

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