An Uncertainty-Aware, Shareable and Transparent Neural Network Architecture for Brain-Age Modeling

• URL: http://arxiv.org/abs/2107.07977v1
• Date: Fri, 16 Jul 2021 15:48:08 GMT
• Title: An Uncertainty-Aware, Shareable and Transparent Neural Network Architecture for Brain-Age Modeling
• Authors: Tim Hahn, Jan Ernsting, Nils R. Winter, Vincent Holstein, Ramona Leenings, Marie Beisemann, Lukas Fisch, Kelvin Sarink, Daniel Emden, Nils Opel, Ronny Redlich, Jonathan Repple, Dominik Grotegerd, Susanne Meinert, Jochen G. Hirsch, Thoralf Niendorf, Beate Endemann, Fabian Bamberg, Thomas Kr\"oncke, Robin B\"ulow, Henry V\"olzke, Oyunbileg von Stackelberg, Ramona Felizitas Sowade, Lale Umutlu, B\"orge Schmidt, Svenja Caspers, German National Cohort Study Center Consortium, Harald Kugel, Tilo Kircher, Benjamin Risse, Christian Gaser, James H. Cole, Udo Dannlowski, Klaus Berger
• Abstract summary: We introduce an uncertainty-aware, shareable, and transparent Monte-Carlo Dropout Composite-Quantile-Regression Neural Network. The MCCQR model provides robust, distribution-free uncertainty quantification in high-dimensional neuroimaging data. It achieves lower error rates compared to existing models across ten recruitment centers and in three independent validation samples.
• Score: 0.45763926712997843
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 due to 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 to existing models across ten recruitment centers and in three independent validation samples (N=4,004). In two examples, we demonstrate that it prevents spurious associations and increases power to detect accelerated brain-aging. We make the pre-trained model publicly available.

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