Predicting brain-age from multimodal imaging data captures cognitive impairment

By Franziskus Liem, Gaël Varoquaux, J. Kynast, F. Beyer, S. K. Masouleh, Julia M. Huntenburg, L. Lampe, Mehdi Rahim, Alexandre Abraham, Cameron R. Craddock and 7 more. bioRxiv, 2016

505 citations recorded by the source corpus.

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 brainimaging 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. Highlights Brain-based age prediction is improved with multimodal neuroimaging data. Participants with cognitive impairment show increased brain aging. Age prediction models are robust to motion and generalize to independent datasets from other sites.

Subfields: Cognitive Neuroscience, Pediatrics, Perinatology and Child Health, Psychiatry and Mental health

View Paper: https://www.semanticscholar.org/paper/822a75d7da2e61f5c1ae18a4f0c60033ce648886

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Citations in the corpus

Benchmarking functional connectome‐based predictive models for resting‐state fMRI (2019, unknown): “…and mental health (Craddock et al., 2009; Richiardi et al., 2010; Milazzo et al., 2014; Smith et al., 2015; Miller et al., 2016; Colclough et al., 2017; Dubois et al., 2018), aging (Liem et al., 2017) as well as brain pathologies (Drysdale et al., 2016; Abraham et al., 2017; Ng et al., 2017).”
Predicting Age Using Neuroimaging: Innovative Brain Ageing Biomarkers. (2017, unknown): “These include the combination of multiple neuroimaging modalities; for example, combining structural and functional MRI data or multiple structural MRI modalities (T2*, diffusion MRI) resulting in improved prediction performance [10,53].”
Brain age prediction using deep learning uncovers associated sequence variants (2019, background): “As mentioned above, previous studies have linked high PAD to cognitive impairment 5,8,13,14 .”