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Towards the Interpretability of Deep Learning Models for Human Neuroimaging

View ORCID ProfileSimon M. Hofmann, View ORCID ProfileFrauke Beyer, View ORCID ProfileSebastian Lapuschkin, 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
1Department of Neurology, Max Planck Institute for Human Cognitive and Brain Sciences, 04103 Leipzig, Germany
2Department of Artificial Intelligence, Fraunhofer Institute Heinrich Hertz, 10587 Berlin, Germany
3Clinic for Cognitive Neurology, University of Leipzig Medical Center, 04103 Leipzig, Germany
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  • For correspondence: simon.hofmann@cbs.mpg.de
Frauke Beyer
1Department of Neurology, Max Planck Institute for Human Cognitive and Brain Sciences, 04103 Leipzig, Germany
3Clinic for Cognitive Neurology, University of Leipzig Medical Center, 04103 Leipzig, Germany
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Sebastian Lapuschkin
2Department of Artificial Intelligence, Fraunhofer Institute Heinrich Hertz, 10587 Berlin, Germany
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Markus Loeffler
4IMISE, University of Leipzig, 04103 Leipzig, Germany
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Klaus-Robert Müller
5Machine Learning Group, Technical University Berlin, 10623 Berlin, Germany
6Department of Artificial Intelligence, Korea University, 02841 Seoul, South Korea
7Brain Team, Google Research, 10117 Berlin, Germany
8Max Planck Institute for Informatics, 66123 Saarbrücken, Germany
9BIFOLD - Berlin Institute for the Foundations of Learning and Data, 10587 Berlin, Germany
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Arno Villringer
1Department of Neurology, Max Planck Institute for Human Cognitive and Brain Sciences, 04103 Leipzig, Germany
3Clinic for Cognitive Neurology, University of Leipzig Medical Center, 04103 Leipzig, Germany
10MindBrainBody Institute, Berlin School of Mind and Brain, Humboldt-Universität zu Berlin, 10099 Berlin, Germany
11Center for Stroke Research, Charité – Universitätsmedizin Berlin, 10117 Berlin, Germany
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Wojciech Samek
2Department of Artificial Intelligence, Fraunhofer Institute Heinrich Hertz, 10587 Berlin, Germany
9BIFOLD - Berlin Institute for the Foundations of Learning and Data, 10587 Berlin, Germany
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A. Veronica Witte
1Department of Neurology, Max Planck Institute for Human Cognitive and Brain Sciences, 04103 Leipzig, Germany
3Clinic for Cognitive Neurology, University of Leipzig Medical Center, 04103 Leipzig, Germany
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  • For correspondence: simon.hofmann@cbs.mpg.de
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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 lesions, iron accumulations 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

  • Via a 10-fold cross validation approach, the initial models were trained such that all subjects lie once in the test set. This provided age-estimates for each subject, in contrast to the initial small (test-set) subset. Consequently, DBA-correlation analyses could be expended and are more robust due to the greater number of subject in these analyses. Result and Discussion sections were adapted accordingly.

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

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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 August 26, 2021.
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Towards the Interpretability of Deep Learning Models for Human Neuroimaging
Simon M. Hofmann, Frauke Beyer, Sebastian Lapuschkin, 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 Human Neuroimaging
Simon M. Hofmann, Frauke Beyer, Sebastian Lapuschkin, 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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