Brain age as a surrogate marker for cognitive performance in multiple sclerosis. (11th July 2022)

• Record Type: Journal Article
• Authors: Denissen, Stijn; Engemann, Denis Alexander; De Cock, Alexander; Costers, Lars; Baijot, Johan; Laton, Jorne; Penner, Iris‐Katharina; Grothe, Matthias; Kirsch, Michael; D'hooghe, Marie Beatrice; D'Haeseleer, Miguel; Dive, Dominique; De Mey, Johan; Van Schependom, Jeroen; Sima, Diana Maria; Nagels, Guy
• Is Part Of: European journal of neurology. Volume 29:Number 10(2022)
• Page Start: 3039
• Page End: 3049
• Publication Date: 2022-07-11
• Subjects: multiple sclerosis -- cognition -- biomarkers -- machine learning -- magnetic resonance imaging -- brain age; Neurology -- Periodicals; Nervous system -- Diseases -- Periodicals; 616.8
• DOI: 10.1111/ene.15473
• Languages: English
• ISSNs: 1351-5101

Abstract

Abstract: Background and purpose: 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 magnetic resonance imaging 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 < 0.001) and contributed uniquely to variance in SDMT beyond chronological age, reflected by a significant correlation between BPAD and SDMT ( r = −0.24, p < 0.001) 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. Abstract : Brains of people with multiple sclerosis (MS) tend to look older than they are in reality. This brain age, as well as the overestimation compared to chronological age, correlates with cognitive performance (Symbol Digit Modalities Test) in MS (respectively r = −0.46 and r = −0.24). Given the simplicity of the linear brain age model presented in this paper, along with the interpretation of brain age as 'how old the brain looks', brain age could be a useful tool in clinical practice.