RT Journal Article
SR Electronic
T1 Predicting brain-age from multimodal imaging data captures cognitive impairment
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 085506
DO 10.1101/085506
A1 Franziskus Liem
A1 Gaël Varoquaux
A1 Jana Kynast
A1 Frauke Beyer
A1 Shahrzad Kharabian Masouleh
A1 Julia M. Huntenburg
A1 Leonie Lampe
A1 Mehdi Rahim
A1 Alexandre Abraham
A1 R. Cameron Craddock
A1 Steffi Riedel-Heller
A1 Tobias Luck
A1 Markus Loeffler
A1 Matthias L. Schroeter
A1 Anja Veronica Witte
A1 Arno Villringer
A1 Daniel S. Margulies
YR 2016
UL http://biorxiv.org/content/early/2016/11/07/085506.abstract
AB The disparity between the chronological age of an individual and their brain-age measured based on biological information has the potential to offer clinically-relevant biomarkers of neurological syndromes that emerge late in the lifespan. While prior brain-age prediction studies have relied exclusively on either structural or functional brain data, here we investigate how multimodal brainimaging data improves age prediction. Using cortical anatomy and whole-brain functional connectivity on a large adult lifespan sample (N = 2354, age 19-82), we found that multimodal data improves brain-based age prediction, resulting in a mean absolute prediction error of 4.29 years. Furthermore, we found that the discrepancy between predicted age and chronological age captures cognitive impairment. Importantly, the brain-age measure was robust to confounding effects: head motion did not drive brain-based age prediction and our models generalized reasonably to an independent dataset acquired at a different site (N = 475). Generalization performance was increased by training models on a larger and more heterogeneous dataset. The robustness of multimodal brain-age prediction to confounds, generalizability across sites, and sensitivity to clinically-relevant impairments, suggests promising future application to the early prediction of neurocognitive disorders.Brain-based age prediction is improved with multimodal neuroimaging data.Participants with cognitive impairment show increased brain aging.Age prediction models are robust to motion and generalize to independent datasets from other sites.