Vrije Universiteit Brussel
AIMS lab, Center for Neurosciences

Author Of 1 Presentation

Machine Learning/Network Science Poster Presentation

P0015 - Predicted brain age as a cognitive biomarker in Multiple Sclerosis (ID 1388)

Presentation Number
Presentation Topic
Machine Learning/Network Science



Brain-predicted age difference (BPAD) is the difference between chronological and brain age, the latter being decoded from brain MR images by a machine learning model. BPAD has been established as a biomarker for physical deterioration in multiple sclerosis (MS).


The objective was to examine the value of brain-predicted age difference as a biomarker for cognitive deterioration in MS.


In the first phase we used supervised machine learning (ridge regression, 7-fold cross-validation) to predict chronological age from brain volumes. This was achieved on a sample of 1743 healthy controls (HC) with T1-weighted MR images from a range of public collections, using the icobrain software to compute normalized brain volumes (whole brain, white matter, (cortical) grey matter and lateral ventricles). The age range of this sample was 8 to 94 years. In the second phase we applied this algorithm to decode brain age in 231 T1-weighted MR images from MS patients from two clinical centers. The age range of this sample was 14 to 77 years. BPAD computed for HC (BPADHC) and MS (BPADMS) were compared with an independent student t-test. Cognitive functioning was assessed through the symbol digit modalities test (SDMT), available for all 231 included MS patients, the brief visuospatial memory test revised (BVMT-R) and California verbal learning test II (CVLT-II), the latter two being available for a subset of 97 patients from one center. Pearson correlation was computed between the BPAD values and scores on each cognitive test. P-values were corrected for multiple comparisons by the Benjamini/Hochberg method.


After training, the mean (SD) BPADHC in the best-performing of seven folds was -0.5 (9.2) years, opposed to 9.7 (12.0) years for BPADMS. This difference was statistically significant (p < 0.001). Correlations between BPADMS and SDMT, BVMT-R and CVLT-II were -0.19 (p = 0.005), -0.14 (p = 0.157) and -0.30 (p = 0.005) respectively.


Our results showed a significant association between BPADMS and two cognitive measures in MS, information processing speed and verbal memory. These findings support a potential role for BPADMS as a cognitive biomarker in MS.