English
 
Help Privacy Policy Disclaimer
  Advanced SearchBrowse

Item

ITEM ACTIONSEXPORT

Released

Journal Article

Predicting brain-age from multimodal imaging data captures cognitive impairment

MPS-Authors
/persons/resource/persons188671

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

/persons/resource/persons183334

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

/persons/resource/persons225526

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;

/persons/resource/persons98578

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

/persons/resource/persons195482

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;

/persons/resource/persons188905

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;

/persons/resource/persons19981

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;

/persons/resource/persons128137

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;

/persons/resource/persons20065

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;

/persons/resource/persons19840

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

External Resource
No external resources are shared
Fulltext (public)
There are no public fulltexts stored in PuRe
Supplementary Material (public)
There is no public supplementary material available
Citation

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.


Cite as: http://hdl.handle.net/11858/00-001M-0000-002C-5630-9
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.