An uncertainty-aware, shareable, and transparent neural network architecture for brain-age modeling Jan 1, 2022· 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 Kröncke , Robin Bülow , Henry Völzke , Oyunbileg Von Stackelberg , Ramona Felizitas Sowade , Lale Umutlu , Börge Schmidt , Svenja Caspers , Harald Kugel , Tilo Kircher , Benjamin Risse , Christian Gaser , James H. Cole , Udo Dannlowski , Klaus Berger · 0 min read Cite DOI 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 biolog… Type Journal article Publication Science Advances Last updated on Jan 1, 2022 Deep Learning Machine Learning Brain Age Neural Networks Uncertainty ← Towards a network control theory of electroconvulsive therapy response Jan 1, 2023 Association Between Genetic Risk for Type 2 Diabetes and Structural Brain Connectivity in Major Depressive Disorder Jan 1, 2022 →