Author Of 2 Presentations
P0015 - Predicted brain age as a cognitive biomarker in Multiple Sclerosis (ID 1388)
Abstract
Background
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).
Objectives
The objective was to examine the value of brain-predicted age difference as a biomarker for cognitive deterioration in MS.
Methods
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.
Results
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.
Conclusions
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.
P0636 - Relationship of real-world brain atrophy to MS disability using icobrain: 4 centre pilot study (ID 716)
- A. Nguyen
- D. Horakova
- E. Havrdova
- M. Barnett
- M. Sormani
- E. Lui
- F. Gaillard
- P. Desmond
- H. Prime
- M. Datta
- A. Van Der Walt
- V. Jokubaitis
- B. Weinstock-Guttman
- M. D’hooghe
- G. Nagels
- V. Van Pesch
- G. Laureys
- L. Van Hijfte
- J. Lechner-Scott
- F. Patti
- E. Cristiano
- J. Rojas
- D. Sima
- W. Van Hecke
- T. Kalincik
- H. Butzkueven
Abstract
Background
To date, no studies have explored the relationship between brain atrophy and MS disability using differing MRI protocols and scanners at multiple sites.
Objectives
To assess the association between brain atrophy and MS disability, as measured by EDSS and 6-month confirmed disability progression (CDP).
Methods
In this retrospective study at 4 MS centres, a total of 1300 patients had brain MRI imaging assessed by icobrain. Relapse-onset MS patients were included if they had two clinical MRIs 12 (±3) months apart and ≥2 EDSS scores post MRI-2, the first ≤3 months from MRI-2, with ≥6 months between first and last EDSS. Volumetric data were analysed if the alignment similarity between two images was as good as that of same-scanner scan-rescan images (normalised mutual information ≥0.2). The percentage brain volume change (PBVC), percentage grey matter change (PGMC), FLAIR lesion volume change, whole brain volume, grey matter volume, FLAIR lesion volume and T1 hypointense lesion volume at MRI-2 were calculated. Ordinal mixed effect models were used to determine the association between these volumetric MRI measures and all EDSS scores post MRI-2. Cox proportional hazards models were used for the 6-month CDP outcome, using a subset of patients with ≥3 EDSS. Models were adjusted for proportion of time spent on disease-modifying therapy during MRIs ± whole brain/grey matter volume at baseline MRI.
Results
Of the 260 relapse-onset MS patients included, 204 (78%) MRI pairs were performed in the same scanner and 56 (22%) pairs were from different scanners. During the follow-up period (median 3.8 years, range 1.3-8.9), 29 of 244 (12%) patients experienced 6-month CDP. There was no evidence for association between annualised PBVC or PGMC and CDP or EDSS (p>0.05). Cross-sectional whole brain and grey matter volume (at MRI-2) tended to associate with CDP (HR 0.99, 95% CI 0.98-1.00, p=0.06). Every 1ml of whole brain or grey matter volume lost represented a 1% higher chance of reaching 6-month CDP. Only whole brain volume (at MRI-2) was associated with EDSS score (β -0.03, SE 0.01, p<0.001) and the slope of EDSS change over time (β -0.001, SE 0.0003, p=0.02). On average, every 33ml reduction of brain volume was associated with a 1 step increase in EDSS.
Conclusions
In this real-world clinical setting where a fifth of the brain atrophy analysis were performed on different scanners, we found no association between individual brain atrophy and MS disability. However, there was an association between cross-sectional whole brain volume with EDSS and slope of EDSS change.