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  Predicting brain-age from multimodal imaging data captures cognitive impairment

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.

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Item Permalink: http://hdl.handle.net/11858/00-001M-0000-002C-5630-9 Version Permalink: http://hdl.handle.net/21.11116/0000-0003-C4B5-3
Genre: Journal Article

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 Creators:
Liem, Franz1, Author              
Varoquaux, Gaël2, 3, Author
Kynast, Jana4, Author              
Beyer, Frauke4, 5, Author              
Kharabian, Shahrzad4, Author              
Huntenburg, Julia M.1, 6, Author              
Lampe, Leonie4, 7, Author              
Rahim, Mehdi2, 3, Author
Abraham, Alexandre2, 3, Author
Craddock, R. Cameron8, 9, Author
Riedel-Heller, Steffi7, 10, Author
Luck, Tobias7, 10, Author
Loeffler, Markus7, 11, Author
Schroeter, Matthias L.4, 7, 12, Author              
Witte, Veronica4, 5, 7, Author              
Villringer, Arno4, 5, 7, 12, Author              
Margulies, Daniel S.1, Author              
Affiliations:
1Max Planck Research Group Neuroanatomy and Connectivity, MPI for Human Cognitive and Brain Sciences, Max Planck Society, ou_1356546              
2INRIA, Saclay, France, ou_persistent22              
3Neurospin, French Alternative Energies and Atomic Energy Commission (CEA), Gif-sur-Yvette, France, ou_persistent22              
4Department Neurology, MPI for Human Cognitive and Brain Sciences, Max Planck Society, ou_634549              
5Collaborative Research Center Obesity Mechanisms, Institute of Biochemistry, University of Leipzig, Germany, ou_persistent22              
6Neurocomputation and Neuroimaging Unit, FU Berlin, Germany, ou_persistent22              
7Leipzig Research Center for Civilization Diseases (LIFE), University of Leipzig, Germany, ou_persistent22              
8Center for Biomedical Imaging and Neuromodulation, Nathan S. Kline Institute for Psychiatric Research, Orangeburg, NY, USA, ou_persistent22              
9Center for the Developing Brain, Child Mind Institute, New York, NY, USA, ou_persistent22              
10Institute of Social Medicine, Occupational Health and Public Health (ISAP), University of Leipzig, Germany, ou_persistent22              
11Institute for Medical Informatics, Statistics and Epidemiology (IMISE), University of Leipzig, Germany, ou_persistent22              
12Clinic for Cognitive Neurology, University of Leipzig, Germany, ou_persistent22              

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Free keywords: Machine learning; Head motion; Cognition; Biomarker
 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.

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Language(s): eng - English
 Dates: 2016-07-062016-11-012016-11-232017-03-01
 Publication Status: Published in print
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Method: Peer
 Identifiers: DOI: 10.1016/j.neuroimage.2016.11.005
PMID: 27890805
Other: Epub 2016
 Degree: -

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Project name : -
Grant ID : P2ZHP1_155200
Funding program : -
Funding organization : Swiss National Science Foundation
Project name : NiConnect project
Grant ID : ANR-11-BINF-0004
Funding program : -
Funding organization : Inria - Institut national de recherche en informatique et en automatique
Project name : -
Grant ID : -
Funding program : -
Funding organization : Max-Planck International Research Network on Aging (MaxNetAging)

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Title: NeuroImage
Source Genre: Journal
 Creator(s):
Affiliations:
Publ. Info: Orlando, FL : Academic Press
Pages: - Volume / Issue: 148 Sequence Number: - Start / End Page: 179 - 188 Identifier: ISSN: 1053-8119
CoNE: /journals/resource/954922650166