Predicting brain-age from multimodal imaging data captures cognitive impairment

Franziskus Liem (1) , Gaël Varoquaux (2) , Jana Kynast (1) , Frauke Beyer (3) , Shahrzad Kharabian Masouleh (1) , Julia M. Huntenburg (1) , Leonie Lampe (4) , Mehdi Rahim (2, 5) , Alexandre Abraham (2, 5) , R. Cameron Craddock (6) , Steffi Riedel-Heller (7) , Tobias Luck (7) , Markus Loeffler (8) , Matthias L. Schroeter (7) , Anja Veronica Witte (7) , Daniel S. Margulies (1)

1 MPI-MiS - Max-Planck-Institut für Mathematik in den Naturwissenschaften
2 PARIETAL - Modelling brain structure, function and variability based on high-field MRI data
3 LJLL - Laboratoire Jacques-Louis Lions
4 UBC - University of British Columbia
5 NEUROSPIN - Service NEUROSPIN
6 NKI - Nathan S. Kline Institute for Psychiatric Research
7 Leipzig University / Universität Leipzig
8 IMISE - Institute for Medical Informatics, Statistics and Epidemiology [Leipzig]

Domaines: Neurosciences; Machine Learning [stat.ML]

Franziskus Liem, Gaël Varoquaux, Jana Kynast, Frauke Beyer, Shahrzad Kharabian Masouleh, et al.. Predicting brain-age from multimodal imaging data captures cognitive impairment. NeuroImage, 2016, ⟨10.1016/j.neuroimage.2016.11.005⟩. ⟨hal-01403005⟩