Predicting brain-age from multimodal imaging data captures cognitive impairment.

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.

PubMed ID: 27890805

Projects: LIFE Adult

Publication type: Journal article

Journal: Neuroimage

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

Registered Mode: by PubMed ID

Authors: F. Liem, G. Varoquaux, J. Kynast, F. Beyer, S. Kharabian Masouleh, J. M. Huntenburg, L. Lampe, M. Rahim, A. Abraham, R. C. Craddock, S. Riedel-Heller, T. Luck, M. Loeffler, M. L. Schroeter, A. V. Witte, A. Villringer, D. S. Margulies

Help
help Submitter
Activity

Views: 1923

Created: 13th May 2019 at 08:15

Last updated: 7th Dec 2021 at 17:58

help Attributions

None

Related items

Powered by
(v.1.13.0-master)
Copyright © 2008 - 2021 The University of Manchester and HITS gGmbH
Institute for Medical Informatics, Statistics and Epidemiology, University of Leipzig

By continuing to use this site you agree to the use of cookies