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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, Joshua T Vogelstein, Steffi Riedel-Heller, Tobias Luck, Markus Loeffler, Matthias L. Schroeter, Anja Veronica Witte, Arno Villringer, William Gray-Roncal

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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