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Towards the Interpretability of Deep Learning Models for Multi-modal Neuroimaging: Finding Structural Changes of the Ageing Brain

View ORCID ProfileSimon M. Hofmann, View ORCID ProfileFrauke Beyer, View ORCID ProfileSebastian Lapuschkin, View ORCID ProfileOle Goltermann, Markus Loeffler, View ORCID ProfileKlaus-Robert Müller, View ORCID ProfileArno Villringer, View ORCID ProfileWojciech Samek, View ORCID ProfileA. Veronica Witte
doi: https://doi.org/10.1101/2021.06.25.449906
Simon M. Hofmann
aDepartment of Neurology, Max Planck Institute for Human Cognitive and Brain Sciences, 04103 Leipzig, Germany
bDepartment of Artificial Intelligence, Fraunhofer Institute Heinrich Hertz, 10587 Berlin, Germany
cClinic for Cognitive Neurology, University of Leipzig Medical Center, 04103 Leipzig, Germany
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  • For correspondence: simon.hofmann@cbs.mpg.de
Frauke Beyer
aDepartment of Neurology, Max Planck Institute for Human Cognitive and Brain Sciences, 04103 Leipzig, Germany
cClinic for Cognitive Neurology, University of Leipzig Medical Center, 04103 Leipzig, Germany
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Sebastian Lapuschkin
bDepartment of Artificial Intelligence, Fraunhofer Institute Heinrich Hertz, 10587 Berlin, Germany
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Ole Goltermann
aDepartment of Neurology, Max Planck Institute for Human Cognitive and Brain Sciences, 04103 Leipzig, Germany
dMax Planck School of Cognition, 04103 Leipzig, Germany
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Markus Loeffler
eIMISE, University of Leipzig, 04103 Leipzig, Germany
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Klaus-Robert Müller
fMachine Learning Group, Technical University Berlin, 10623 Berlin, Germany
gDepartment of Artificial Intelligence, Korea University, 02841 Seoul, South Korea
hBrain Team, Google Research, 10117 Berlin, Germany
iMax Planck Institute for Informatics, 66123 Saarbrücken, Germany
jBIFOLD - Berlin Institute for the Foundations of Learning and Data, 10587 Berlin, Germany
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Arno Villringer
aDepartment of Neurology, Max Planck Institute for Human Cognitive and Brain Sciences, 04103 Leipzig, Germany
cClinic for Cognitive Neurology, University of Leipzig Medical Center, 04103 Leipzig, Germany
kMindBrainBody Institute, Berlin School of Mind and Brain, Humboldt-Universität zu Berlin, 10099 Berlin, Germany
lCenter for Stroke Research, Charité – Universitätsmedizin Berlin, 10117 Berlin, Germany
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Wojciech Samek
bDepartment of Artificial Intelligence, Fraunhofer Institute Heinrich Hertz, 10587 Berlin, Germany
jBIFOLD - Berlin Institute for the Foundations of Learning and Data, 10587 Berlin, Germany
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A. Veronica Witte
aDepartment of Neurology, Max Planck Institute for Human Cognitive and Brain Sciences, 04103 Leipzig, Germany
cClinic for Cognitive Neurology, University of Leipzig Medical Center, 04103 Leipzig, Germany
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Abstract

Brain-age (BA) estimates based on deep learning are increasingly used as neuroimaging biomarker for brain health; however, the underlying neural features have remained unclear. We combined ensembles of convolutional neural networks with Layer-wise Relevance Propagation (LRP) to detect which brain features contribute to BA. Trained on magnetic resonance imaging (MRI) data of a population-based study (n=2637, 18-82 years), our models estimated age accurately based on single and multiple modalities, regionally restricted and whole-brain images (mean absolute errors 3.37-3.86 years). We find that BA estimates capture aging at both small and large-scale changes, revealing gross enlargements of ventricles and subarachnoid spaces, as well as white matter lesions, and atrophies that appear throughout the brain. Divergence from expected aging reflected cardiovascular risk factors and accelerated aging was more pronounced in the frontal lobe. Applying LRP, our study demonstrates how superior deep learning models detect brain-aging in healthy and at-risk individuals throughout adulthood.

Competing Interest Statement

The authors have declared no competing interest.

Footnotes

  • We added more information on the training regime, and the LRP algorithm and its implementation for our regression model. Moreover, we ran further simulation studies, and other supplementary analyses, which can be found in the appendices.

  • https://github.com/SHEscher/XDLreg

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-NC-ND 4.0 International license.
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Posted June 08, 2022.
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Towards the Interpretability of Deep Learning Models for Multi-modal Neuroimaging: Finding Structural Changes of the Ageing Brain
Simon M. Hofmann, Frauke Beyer, Sebastian Lapuschkin, Ole Goltermann, Markus Loeffler, Klaus-Robert Müller, Arno Villringer, Wojciech Samek, A. Veronica Witte
bioRxiv 2021.06.25.449906; doi: https://doi.org/10.1101/2021.06.25.449906
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Towards the Interpretability of Deep Learning Models for Multi-modal Neuroimaging: Finding Structural Changes of the Ageing Brain
Simon M. Hofmann, Frauke Beyer, Sebastian Lapuschkin, Ole Goltermann, Markus Loeffler, Klaus-Robert Müller, Arno Villringer, Wojciech Samek, A. Veronica Witte
bioRxiv 2021.06.25.449906; doi: https://doi.org/10.1101/2021.06.25.449906

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