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

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.

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 Creators:
Hofmann, Simon1, 2, 3, Author                 
Beyer, Frauke1, 3, Author           
Lapuschkin, Sebastian2, Author
Goltermann, Ole1, 4, 5, Author
Loeffler, Markus6, Author
Müller, Klaus-Robert7, 8, 9, 10, 11, Author
Villringer, Arno1, 3, 12, 13, Author           
Samek, Wojciech2, 11, Author
Witte, A. Veronica1, 3, Author           
Affiliations:
1Department Neurology, MPI for Human Cognitive and Brain Sciences, Max Planck Society, ou_634549              
2Department of Artificial Intelligence, Fraunhofer Heinrich Hertz Institute, Berlin, Germany, ou_persistent22              
3Clinic for Cognitive Neurology, University of Leipzig, Germany, ou_persistent22              
4Max Planck School of Cognition, Leipzig, Germany, ou_persistent22              
5Department of Systems Neuroscience, University Medical Center Hamburg-Eppendorf, Germany, ou_persistent22              
6Institute for Medical Informatics, Statistics and Epidemiology (IMISE), University of Leipzig, Germany, ou_persistent22              
7Department of Machine Learning, TU Berlin, Germany, ou_persistent22              
8Center for Artificial Intelligence, Korea University, Seoul, Republic of Korea, ou_persistent22              
9Brain Team, Google Research, Berlin, Germany, ou_persistent22              
10Max Planck Institute for Informatics, Saarbrücken, Germany, ou_persistent22              
11BIFOLD Berlin Institute for the Foundations of Learning and Data, Germany, ou_persistent22              
12MindBrainBody Institute, Berlin School of Mind and Brain, Germany, ou_persistent22              
13Center for Stroke Research, Charité University Medicine Berlin, Germany, ou_persistent22              

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Free keywords: Aging; Brain-age; Cardiovascular risk factors; Deep learning; Explainable A.I.; Structural MRI
 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.

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Language(s): eng - English
 Dates: 2022-07-152022-07-212022-07-232022-11-01
 Publication Status: Published in print
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: -
 Identifiers: DOI: 10.1016/j.neuroimage.2022.119504
Other: epub 2022
PMID: 35882272
 Degree: -

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Project name : -
Grant ID : 965221, 01GQ1115 and 01GQ0850
Funding program : Horizon 2020
Funding organization : European Union
Project name : -
Grant ID : Grant Math+, EXC 2046/1
Funding program : -
Funding organization : Deutsche Forschungsgemeinschaft (DFG)
Project name : -
Grant ID : 2019-0-00079
Funding program : -
Funding organization : Korea Government

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Title: NeuroImage
Source Genre: Journal
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Affiliations:
Publ. Info: Orlando, FL : Academic Press
Pages: - Volume / Issue: 261 Sequence Number: 119504 Start / End Page: - Identifier: ISSN: 1053-8119
CoNE: https://pure.mpg.de/cone/journals/resource/954922650166