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Predicting brain-age from multimodal imaging data captures cognitive impairment

MPG-Autoren
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Liem,  Franz
Max Planck Research Group Neuroanatomy and Connectivity, MPI for Human Cognitive and Brain Sciences, Max Planck Society;

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Kynast,  Jana
Department Neurology, MPI for Human Cognitive and Brain Sciences, Max Planck Society;

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Beyer,  Frauke
Department Neurology, MPI for Human Cognitive and Brain Sciences, Max Planck Society;
Collaborative Research Center Obesity Mechanisms, Institute of Biochemistry, University of Leipzig, Germany;

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Kharabian,  Shahrzad
Department Neurology, MPI for Human Cognitive and Brain Sciences, Max Planck Society;

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Huntenburg,  Julia M.
Max Planck Research Group Neuroanatomy and Connectivity, MPI for Human Cognitive and Brain Sciences, Max Planck Society;
Neurocomputation and Neuroimaging Unit, FU Berlin, Germany;

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Lampe,  Leonie
Department Neurology, MPI for Human Cognitive and Brain Sciences, Max Planck Society;
Leipzig Research Center for Civilization Diseases (LIFE), University of Leipzig, Germany;

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Schroeter,  Matthias L.
Department Neurology, MPI for Human Cognitive and Brain Sciences, Max Planck Society;
Clinic for Cognitive Neurology, University of Leipzig, Germany;
Leipzig Research Center for Civilization Diseases (LIFE), University of Leipzig, Germany;

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Witte,  Veronica
Department Neurology, MPI for Human Cognitive and Brain Sciences, Max Planck Society;
Collaborative Research Center Obesity Mechanisms, Institute of Biochemistry, University of Leipzig, Germany;
Leipzig Research Center for Civilization Diseases (LIFE), University of Leipzig, Germany;

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Villringer,  Arno
Department Neurology, MPI for Human Cognitive and Brain Sciences, Max Planck Society;
Collaborative Research Center Obesity Mechanisms, Institute of Biochemistry, University of Leipzig, Germany;
Clinic for Cognitive Neurology, University of Leipzig, Germany;
Leipzig Research Center for Civilization Diseases (LIFE), University of Leipzig, Germany;

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Margulies,  Daniel S.
Max Planck Research Group Neuroanatomy and Connectivity, MPI for Human Cognitive and Brain Sciences, Max Planck Society;

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Zitation

Liem, F., Varoquaux, G., Kynast, J., Beyer, F., Kharabian, S., Huntenburg, J. M., et al. (2017). Predicting brain-age from multimodal imaging data captures cognitive impairment. NeuroImage, 148, 179-188. doi:10.1016/j.neuroimage.2016.11.005.


Zitierlink: https://hdl.handle.net/11858/00-001M-0000-002C-5630-9
Zusammenfassung
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.