Dataset Information

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

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.

SUBMITTER: Hahn T

PROVIDER: S-EPMC8730629 | biostudies-literature | 2022 Jan

REPOSITORIES: biostudies-literature

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

Hahn Tim T Ernsting Jan J Winter Nils R NR Holstein Vincent V Leenings Ramona R Beisemann Marie M Fisch Lukas L Sarink Kelvin K Emden Daniel D Opel Nils N Redlich Ronny R Repple Jonathan J Grotegerd Dominik D Meinert Susanne S Hirsch Jochen G JG Niendorf Thoralf T Endemann Beate B Bamberg Fabian F Kröncke Thomas T Bülow Robin R Völzke Henry H von Stackelberg Oyunbileg O Sowade Ramona Felizitas RF Umutlu Lale L Schmidt Börge B Caspers Svenja S Kugel Harald H Kircher Tilo T Risse Benjamin B Gaser Christian C Cole James H JH Dannlowski Udo U Berger Klaus K

Science advances 20220105 1

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 be ...[more]

PMID: 34985964

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Dataset Information

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

Publications

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

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