Authors
Franziskus Liem, Gaël Varoquaux, Jana Kynast, Frauke Beyer, Shahrzad Kharabian Masouleh, Julia M Huntenburg, Leonie Lampe, Mehdi Rahim, Alexandre Abraham, R Cameron Craddock, Steffi Riedel-Heller, Tobias Luck, Markus Loeffler, Matthias L Schroeter, Anja Veronica Witte, Arno Villringer, Daniel S Margulies
Publication date
2017/3/1
Journal
Neuroimage
Volume
148
Pages
179-188
Publisher
Academic Press
Description
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 brain-imaging 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 …
Scholar articles
F Liem, G Varoquaux, J Kynast, F Beyer, SK Masouleh… - Neuroimage, 2017