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Background: Cortical lesions (CLs) have been reported to be a better predictor for cognitive impairment than white matter (WM) lesions in relapsing–remitting multiple sclerosis (RRMS). Objectives: The objectives of this article are to investigate the contribution of CLs and WM lesions to cognitive impairment in 91 patients with MS and clinically isolated syndrome, and to test potential associations of CLs and WM lesions with fatigue and depression. Methods: Lesions were scored and segmented on 3D double inversion recovery sequences, according to their location (cortical, WM). Normalised grey matter volume was also determined. Cognitive performance was assessed with the SDMT and PASAT-3, fatigue with the FSMC and depression with the German version of the CES-D. Results: CL volume did not correlate with fatigue or depression, but correlated significantly with both neuropsychological outcome measures: PASAT-3 (r = −0.275, p = 0.009) and SDMT (r = −0.377, p < 0.001). Multiple regression analyses with age, WM lesions, CLs and GM volume as independent variables, however, did not reveal CL volume as a significant predictor of neuropsychological outcomes, whereas WM lesion volume significantly predicted SDMT and by trend PASAT performance. Conclusions: These findings suggest a role of WM lesions in the development of cognitive deficits, especially information-processing speed, which may be higher than previously assumed. Abbreviations: CES-D: Center for Epidemiologic Studies Depression scale (ADS-L: Allgemeine Depressions Skala-L, German version of CES-D), CIS: clinically isolated syndrome, CL: cortical lesion, DIR: double inversion recovery, EDSS: Expanded Disability Status Scale, FSMC: fatigue scale for motor and cognitive functions, GM: grey matter, MRI: magnetic resonance imaging, MS: multiple sclerosis, PASAT-3: paced auditory serial addition test 3s, PPMS: primary progressive multiple sclerosis, RRMS: relapsing–remitting multiple sclerosis, SDMT: symbol digit modalities test, SPM: statistical parametric mapping, SPMS: secondary progressive multiple sclerosis, WM: white matter

Background Lesion location is a prognostic factor of disease progression and disability accrual. Objective To investigate lesion formation in 11 brain regions, assess correlation between lesion location and physical and cognitive disability measures and investigate treatment effects by region. Methods In 2355 relapsing–remitting multiple sclerosis patients from the FREEDOMS and FREEDOMS II studies, we extracted T2-weighted lesion number, volume and density for each brain region; we investigated the (Spearman) correlation in lesion formation between brain regions, studied association between location and disability (at baseline and change over 2 years) using linear/logistic regression and assessed the regional effects of fingolimod versus placebo in negative binomial models. Results At baseline, the majority of lesions were found in the supratentorial brain. New and enlarging lesions over 24 months developed mainly in the frontal and sublobar regions and were substantially correlated to pre-existing lesions at baseline in the supratentorial brain (p = 0.37–0.52), less so infratentorially (p = −0.04–0.23). High sublobar lesion density was consistently and significantly associated with most disability measures at baseline and worsening of physical disability over 24 months. The treatment effect of fingolimod 0.5 mg was consistent across the investigated areas and tracts. Conclusion These results highlight the role of sublobar lesions for the accrual of disability in relapsing–remitting multiple sclerosis.

Magnetic resonance imaging (MRI) plays a vital role in the scientific investigation and clinical management of multiple sclerosis. Analyses of binary multiple sclerosis lesion maps are typically \"mass univariate\" and conducted with standard linear models that are ill suited to the binary nature of the data and ignore the spatial dependence between nearby voxels (volume elements). Smoothing the lesion maps does not entirely eliminate the non-Gaussian nature of the data and requires an arbitrary choice of the smoothing parameter. Here we present a Bayesian spatial model to accurately model binary lesion maps and to determine if there is spatial dependence between lesion location and subject specific covariates such as MS subtype, age, gender, disease duration and disease severity measures. We apply our model to binary lesion maps derived from T2-weighted MRI images from 250 multiple sclerosis patients classified into five clinical subtypes, and demonstrate unique modeling and predictive capabilities over existing methods.

Magnetic resonance imaging (MRI) plays a vital role in the scientific investigation and clinical management of multiple sclerosis. Analyses of binary multiple sclerosis lesion maps are typically \"mass univariate\" and conducted with standard linear models that are ill suited to the binary nature of the data and ignore the spatial dependence between nearby voxels (volume elements). Smoothing the lesion maps does not entirely eliminate the non-Gaussian nature of the data and requires an arbitrary choice of the smoothing parameter. Here we present a Bayesian spatial model to accurately model binary lesion maps and to determine if there is spatial dependence between lesion location and subject specific covariates such as MS subtype, age, gender, disease duration and disease severity measures. We apply our model to binary lesion maps derived from \\(T_2\\)-weighted MRI images from 250 multiple sclerosis patients classified into five clinical subtypes, and demonstrate unique modeling and predictive capabilities over existing methods.