Towards the interpretability of deep learning models for multi-modal 
neuroimaging: Finding structural changes of the ageing brain

Hofmann, Simon; Beyer, Frauke; Lapuschkin, Sebastian; Goltermann, Ole; Loeffler, Markus; Müller, Klaus-Robert; Villringer, Arno; Samek, Wojciech; Witte, A. Veronica

doi:10.1016/j.neuroimage.2022.119504

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Towards the interpretability of deep learning models for multi-modal neuroimaging: Finding structural changes of the ageing brain

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Hofmann, Simon
Department Neurology, MPI for Human Cognitive and Brain Sciences, Max Planck Society;
Department of Artificial Intelligence, Fraunhofer Heinrich Hertz Institute, Berlin, Germany;
Clinic for Cognitive Neurology, University of Leipzig, Germany;

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Beyer, Frauke
Department Neurology, MPI for Human Cognitive and Brain Sciences, Max Planck Society;
Clinic for Cognitive Neurology, University of Leipzig, Germany;

Goltermann, Ole
Department Neurology, MPI for Human Cognitive and Brain Sciences, Max Planck Society;
Max Planck School of Cognition, Leipzig, Germany;
Department of Systems Neuroscience, University Medical Center Hamburg-Eppendorf, Germany;

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Villringer, Arno
Department Neurology, MPI for Human Cognitive and Brain Sciences, Max Planck Society;
Clinic for Cognitive Neurology, University of Leipzig, Germany;
MindBrainBody Institute, Berlin School of Mind and Brain, Germany;
Center for Stroke Research, Charité University Medicine Berlin, Germany;

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Witte, A. Veronica
Department Neurology, MPI for Human Cognitive and Brain Sciences, Max Planck Society;
Clinic for Cognitive Neurology, University of Leipzig, Germany;

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Citation

Hofmann, S., Beyer, F., Lapuschkin, S., Goltermann, O., Loeffler, M., Müller, K.-R., et al. (2022). Towards the interpretability of deep learning models for multi-modal neuroimaging: Finding structural changes of the ageing brain. NeuroImage, 261: 119504. doi:10.1016/j.neuroimage.2022.119504.

Cite as: https://hdl.handle.net/21.11116/0000-000A-C9F6-E

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.