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.
Human Diseases: No Human Disease specified
Citation: Neuroimage. 2017 Mar 1;148:179-188. doi: 10.1016/j.neuroimage.2016.11.005. Epub 2016 Nov 23.
Date Published: 1st Mar 2017
Created: 13th May 2019 at 08:15
Last updated: 13th May 2019 at 08:16