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An uncertainty-aware, shareable, and transparent neural network architecture for brain-age modeling

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Item Type:Article
Title:An uncertainty-aware, shareable, and transparent neural network architecture for brain-age modeling
Creators Name:Hahn, T. and Ernsting, J. and Winter, N.R. and Holstein, V. and Leenings, R. and Beisemann, M. and Fisch, L. and Sarink, K. and Emden, D. and Opel, N. and Redlich, R. and Repple, J. and Grotegerd, D. and Meinert, S. and Hirsch, J.G. and Niendorf, T. and Endemann, B. and Bamberg, F. and Kröncke, T. and Bülow, R. and Völzke, H. and von Stackelberg, O. and Sowade, R.F. and Umutlu, L. and Schmidt, B. and Caspers, S. and Kugel, H. and Kircher, T. and Risse, B. and Gaser, C. and Cole, J.H. and Dannlowski, U. and Berger, K.
Abstract:The deviation between chronological age and age predicted from neuroimaging data has been identified as a sensitive risk marker of cross-disorder brain changes, growing into a cornerstone of biological age research. However, machine learning models underlying the field do not consider uncertainty, thereby confounding results with training data density and variability. Also, existing models are commonly based on homogeneous training sets, often not independently validated, and cannot be shared because of data protection issues. Here, we introduce an uncertainty-aware, shareable, and transparent Monte Carlo dropout composite quantile regression (MCCQR) Neural Network trained on N = 10,691 datasets from the German National Cohort. The MCCQR model provides robust, distribution-free uncertainty quantification in high-dimensional neuroimaging data, achieving lower error rates compared with existing models. In two examples, we demonstrate that it prevents spurious associations and increases power to detect deviant brain aging. We make the pretrained model and code publicly available.
Source:Science Advances
ISSN:2375-2548
Publisher:American Association for the Advancement of Science
Volume:8
Number:1
Page Range:eabg9471
Date:7 January 2022
Official Publication:https://doi.org/10.1126/sciadv.abg9471
PubMed:View item in PubMed

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