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• W2552208519 abstract "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." @default.
• W2552208519 created "2016-11-30" @default.
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• W2552208519 date "2017-03-01" @default.
• W2552208519 modified "2023-10-14" @default.
• W2552208519 title "Predicting brain-age from multimodal imaging data captures cognitive impairment" @default.
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• W2552208519 doi "https://doi.org/10.1016/j.neuroimage.2016.11.005" @default.
• W2552208519 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/27890805" @default.
• W2552208519 hasPublicationYear "2017" @default.
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