An uncertainty-aware, shareable, and transparent neural network architecture for brain-age modeling
Year: 2022
Type: article
Abstract: = 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
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 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
Institutions: University of Münster, TU Dortmund University, Martin Luther University Halle-Wittenberg, Fraunhofer Institute for Digital Medicine, Max Delbrück Center, University of Freiburg, University Medical Center Freiburg, University Hospital Augsburg, Universitätsmedizin Greifswald, Universität Greifswald, Heidelberg University, University Hospital Heidelberg, University of Duisburg-Essen, Forschungszentrum Jülich, Düsseldorf University Hospital, Heinrich Heine University Düsseldorf, Philipps University of Marburg, Jena University Hospital, King's College London
Language: English
Subfield: Cognitive Neuroscience
Field: Neuroscience
Domain: Life Sciences
Open Access status: gold
Estimated APC paid: $4,500
Awards: G1039077240, G1091985369, G1105553380, G1116979012, G1140567864, G1237346504, G127306367, G1472190865, G1633307283, G1650405572, G170627215, G1890467901, G191257129, G2057016418, G2175120754, G2180930666, G2242696881, G235093469, G238323396, G257003283, G2751758070, 静息状态功能磁共振扫描参数优化与计算方法学研究, G329794889, G3315718513, G335221335, G3371384612, G3607711934, G3645360039, G3813944750, Die bidirektionale Analyse der Beziehung zwischen subklinischer Arteriosklerose und der Depression (BiDirect), G4210348610, G4307976252, G4445738529, G4512443360, G4588023527, G4892460169, G5004922667, G5269937202, G5508578559, G5838968043, G595280944, G6201964673, G6335375501, G6488228673, G648958512, G6733821374, G6930459766, Analyse der Beziehung zwischen subklinischer Arteriosklerose und der Depression (BiDirect) (Anschlussvorhaben), NAKO Gesundheitsstudie - 1. Förderphase, G727061632, G7445694801, G752819877, G7578079922, G829097553, G8640365972, G8819810260
OpenAlex ID: W3184635293