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A reusable benchmark of brain-age prediction from M/EEG resting-state signals

View ORCID ProfileDenis A. Engemann, Apolline Mellot, View ORCID ProfileRichard Höchenberger, View ORCID ProfileHubert Banville, David Sabbagh, Lukas Gemein, View ORCID ProfileTonio Ball, View ORCID ProfileAlexandre Gramfort
doi: https://doi.org/10.1101/2021.12.14.472691
Denis A. Engemann
aRoche Pharma Research and Early Development, Neuroscience and Rare Diseases, Roche Innovation Center Basel, F. Hoffmann–La Roche Ltd., Basel, Switzerland
bUniversité Paris-Saclay, Inria, CEA, Palaiseau, France
cMax Planck Institute for Human Cognitive and Brain Sciences, Department of Neurology, D-04103, Leipzig, Germany
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  • For correspondence: denis.engemann@roche.com
Apolline Mellot
bUniversité Paris-Saclay, Inria, CEA, Palaiseau, France
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Richard Höchenberger
bUniversité Paris-Saclay, Inria, CEA, Palaiseau, France
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Hubert Banville
bUniversité Paris-Saclay, Inria, CEA, Palaiseau, France
hInteraXon Inc., Toronto, Canada
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David Sabbagh
bUniversité Paris-Saclay, Inria, CEA, Palaiseau, France
dInserm, UMRS-942, Paris Diderot University, Paris, France
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Lukas Gemein
eNeuromedical AI Lab, Department of Neurosurgery, Medical Center – University of Freiburg, Faculty of Medicine, University of Freiburg, Engelbergerstr. 21, 79106, Freiburg, Germany
fNeurorobotics Lab, Computer Science Department – University of Freiburg, Faculty of Engineering, University of Freiburg, Georges-Köhler-Allee 80, 79110, Freiburg, Germany
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Tonio Ball
eNeuromedical AI Lab, Department of Neurosurgery, Medical Center – University of Freiburg, Faculty of Medicine, University of Freiburg, Engelbergerstr. 21, 79106, Freiburg, Germany
gBrainLinks-BrainTools Cluster of Excellence, University of Freiburg, Freiburg, Germany
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Alexandre Gramfort
bUniversité Paris-Saclay, Inria, CEA, Palaiseau, France
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Abstract

Population-level modeling can define quantitative measures of individual aging by applying machine learning to large volumes of brain images. These measures of brain age, obtained from the general population, helped characterize disease severity in neurological populations, improving estimates of diagnosis or prognosis. Magnetoencephalography (MEG) and Electroencephalography (EEG) have the potential to further generalize this approach towards prevention and public health by enabling assessments of brain health at large scales in socioeconomically diverse environments. However, more research is needed to define methods that can handle the complexity and diversity of M/EEG signals across diverse real-world contexts. To catalyse this effort, here we propose reusable benchmarks of competing machine learning approaches for brain age modeling. We benchmarked popular classical machine learning pipelines and deep learning architectures previously used for pathology decoding or brain age estimation in 4 international M/EEG cohorts from diverse countries and cultural contexts, including recordings from more than 2500 participants. Our benchmarks were built on top of the M/EEG adaptations of the BIDS standard, providing tools that can be applied with minimal modification on any M/EEG dataset provided in the BIDS format. Our results suggest that, regardless of whether classical machine learning or deep learning was used, the highest performance was reached by pipelines and architectures involving spatially aware representations of the M/EEG signals, leading to R^2 scores between 0.60-0.71. Hand-crafted features paired with random forest regression provided robust benchmarks even in situations in which other approaches failed. Taken together, this set of benchmarks, accompanied by open-source software and high-level Python scripts, can serve as a starting point and quantitative reference for future efforts at developing M/EEG-based measures of brain aging. The generality of the approach renders this benchmark reusable for other related objectives such as modeling specific cognitive variables or clinical endpoints.

Highlights

  • - We provide systematic reusable benchmarks for brain age from M/EEG signals

  • - The benchmarks were carried out on M/EEG from four countries > 2500 recordings

  • - We compared machine learning pipelines capable of handling the non-linear regression task of relating biomedical outcomes to M/EEG dynamics, based on classical machine learning and deep learning

  • - Next to data-driven methods we benchmarked template-based source localization as a practical tool for generating features less affected by electromagnetic field spread

  • - The benchmarks are built on top of the MNE ecosystem and the braindecode package and can be applied on any M/EEG dataset presented in the BIDS format

Competing Interest Statement

D.E. is a full-time employee of F. Hoffmann-La Roche Ltd. H.B. receives graduate funding support from InteraXon Inc.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY 4.0 International license.
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Posted December 16, 2021.
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A reusable benchmark of brain-age prediction from M/EEG resting-state signals
Denis A. Engemann, Apolline Mellot, Richard Höchenberger, Hubert Banville, David Sabbagh, Lukas Gemein, Tonio Ball, Alexandre Gramfort
bioRxiv 2021.12.14.472691; doi: https://doi.org/10.1101/2021.12.14.472691
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A reusable benchmark of brain-age prediction from M/EEG resting-state signals
Denis A. Engemann, Apolline Mellot, Richard Höchenberger, Hubert Banville, David Sabbagh, Lukas Gemein, Tonio Ball, Alexandre Gramfort
bioRxiv 2021.12.14.472691; doi: https://doi.org/10.1101/2021.12.14.472691

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