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Predicting brain-age from multimodal imaging data captures cognitive impairment

By Franziskus Liem, Gaƫl 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

Posted 07 Nov 2016
bioRxiv DOI: 10.1101/085506 (published DOI: 10.1016/j.neuroimage.2016.11.005)

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 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.

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