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

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, t...

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Detalles Bibliográficos
Autores principales: Hahn, Tim, Ernsting, Jan, Winter, Nils R., Holstein, Vincent, Leenings, Ramona, Beisemann, Marie, Fisch, Lukas, Sarink, Kelvin, Emden, Daniel, Opel, Nils, Redlich, Ronny, Repple, Jonathan, Grotegerd, Dominik, Meinert, Susanne, Hirsch, Jochen G., Niendorf, Thoralf, Endemann, Beate, Bamberg, Fabian, Kröncke, Thomas, Bülow, Robin, Völzke, Henry, von Stackelberg, Oyunbileg, Sowade, Ramona Felizitas, Umutlu, Lale, Schmidt, Börge, Caspers, Svenja, Kugel, Harald, Kircher, Tilo, Risse, Benjamin, Gaser, Christian, Cole, James H., Dannlowski, Udo, Berger, Klaus
Formato: Online Artículo Texto
Lenguaje:English
Publicado: American Association for the Advancement of Science 2022
Materias:
Neuroscience
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8730629/
https://www.ncbi.nlm.nih.gov/pubmed/34985964
http://dx.doi.org/10.1126/sciadv.abg9471

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8730629/
https://www.ncbi.nlm.nih.gov/pubmed/34985964
http://dx.doi.org/10.1126/sciadv.abg9471

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