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  Brain age as a surrogate marker for cognitive performance in multiple sclerosis

Denissen, S., Engemann, D. A., De Cock, A., Costers, L., Baijot, J., Laton, J., et al. (2022). Brain age as a surrogate marker for cognitive performance in multiple sclerosis. European Journal of Neurology. doi:10.1111/ene.15473.

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 Creators:
Denissen, S.1, 2, Author
Engemann, Denis A.3, 4, Author              
De Cock, A.1, Author
Costers, L.1, 2, Author
Baijot, J.1, Author
Laton, J.1, 5, Author
Penner, I. K.6, 7, Author
Grothe, M.8, Author
Kirsch, M.9, Author
D’hooghe, M. B.1, 10, Author
D’Haeseleer, M.10, Author
Dive, D.11, Author
De Mey, J.12, Author
Van Schependom, J.1, 13, Author
Sima, D. M.1, 2, Author
Nagels, G.1, 2, 14, Author
Affiliations:
1Center for Neurosciences, Vrije Universiteit Brussel, Belgium, ou_persistent22              
2Kolonel Begaultlaan, Belgium, ou_persistent22              
3Université Paris-Saclay, France, ou_persistent22              
4Department Neurology, MPI for Human Cognitive and Brain Sciences, Max Planck Society, ou_634549              
5Nuffield Department Clinical Neurosciences, FMRIB Centre, University of Oxford, United Kingdom, ou_persistent22              
6Cogito Center for Applied Neurocognition and Neuropsychological Research, Düsseldorf, Germany, ou_persistent22              
7Department of Neurology, Institute for Clinical Neuroscience and Medical Psychology, Heinrich Heine University Düsseldorf, Germany, ou_persistent22              
8Department of Neurology, Ernst Moritz Arndt University of Greifswald, Germany, ou_persistent22              
9Center for Diagnostic Radiology and Neuroradiology, Ernst Moritz Arndt University of Greifswald, Germany, ou_persistent22              
10National Multiple Sclerosis Center Melsbroek, Belgium, ou_persistent22              
11Department of Neurology, Centre Hospitalier Universitaire de Liège (CHU Liège), Belgium, ou_persistent22              
12Department of Radiology, Universitair Ziekenhuis Brussel, Belgium, ou_persistent22              
13Department of Electronics and Informatics, Vrije Universiteit Brussel, Belgium, ou_persistent22              
14St Edmund Hall, University of Oxford, United Kingdom, ou_persistent22              

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Free keywords: Biomarkers; Brain age; Cognition; Machine learning; Magnetic resonance imaging; Multiple sclerosis
 Abstract: Background: Data from neuro-imaging techniques allow us to estimate a brain's age. Brain age is easily interpretable as "how old the brain looks", and could therefore be an attractive communication tool for brain health in clinical practice. This study aimed to investigate its clinical utility by investigating the relationship between brain age and cognitive performance in multiple sclerosis (MS). Methods: A linear regression model was trained to predict age from brain MRI volumetric features and sex in a healthy control dataset (HC_train, n=1673). This model was used to predict brain age in two test sets: HC_test (n=50) and MS_test (n=201). Brain-Predicted Age Difference (BPAD) was calculated as BPAD=brain age minus chronological age. Cognitive performance was assessed by the Symbol Digit Modalities Test (SDMT). Results: Brain age was significantly related to SDMT scores in the MS_test dataset (r=-0.46, p<.001), and contributed uniquely to variance in SDMT beyond chronological age, reflected by a significant correlation between BPAD and SDMT (r=-0.24, p<.001) and a significant weight (-0.25, p=0.002) in a multivariate regression equation with age. Conclusions: Brain age is a candidate biomarker for cognitive dysfunction in MS and an easy to grasp metric for brain health.

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Language(s): eng - English
 Dates: 2022-06-23
 Publication Status: Published online
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: -
 Identifiers: DOI: 10.1111/ene.15473
Other: online ahead of print
PMID: 35737867
 Degree: -

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Title: European Journal of Neurology
  Other : Eur. J. Neurol.
Source Genre: Journal
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Affiliations:
Publ. Info: Oxford : Rapid Communications
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: - Identifier: ISSN: 1351-5101
CoNE: https://pure.mpg.de/cone/journals/resource/954925617087