ObjectiveGray matter (GM) atrophy occurs in all multiple sclerosis (MS) phenotypes. We investigated whether there is a spatiotemporal pattern of GM atrophy that is associated with faster disability accumulation in MS.MethodsWe analyzed 3,604 brain high‐resolution T1‐weighted magnetic resonance imaging scans from 1,417 participants: 1,214 MS patients (253 clinically isolated syndrome [CIS], 708 relapsing‐remitting [RRMS], 128 secondary‐progressive [SPMS], and 125 primary‐progressive [PPMS]), over an average follow‐up of 2.41 years (standard deviation [SD] = 1.97), and 203 healthy controls (HCs; average follow‐up = 1.83 year; SD = 1.77), attending seven European centers. Disability was assessed with the Expanded Disability Status Scale (EDSS). We obtained volumes of the deep GM (DGM), temporal, frontal, parietal, occipital and cerebellar GM, brainstem, and cerebral white matter. Hierarchical mixed models assessed annual percentage rate of regional tissue loss and identified regional volumes associated with time‐to‐EDSS progression.ResultsSPMS showed the lowest baseline volumes of cortical GM and DGM. Of all baseline regional volumes, only that of the DGM predicted time‐to‐EDSS progression (hazard ratio = 0.73; 95% confidence interval, 0.65, 0.82; p < 0.001): for every standard deviation decrease in baseline DGM volume, the risk of presenting a shorter time to EDSS worsening during follow‐up increased by 27%. Of all longitudinal measures, DGM showed the fastest annual rate of atrophy, which was faster in SPMS (–1.45%), PPMS (–1.66%), and RRMS (–1.34%) than CIS (–0.88%) and HCs (–0.94%; p < 0.01). The rate of temporal GM atrophy in SPMS (–1.21%) was significantly faster than RRMS (–0.76%), CIS (–0.75%), and HCs (–0.51%). Similarly, the rate of parietal GM atrophy in SPMS (–1.24‐%) was faster than CIS (–0.63%) and HCs (–0.23%; all p values <0.05). Only the atrophy rate in DGM in patients was significantly associated with disability accumulation (beta = 0.04; p < 0.001).InterpretationThis large, multicenter and longitudinal study shows that DGM volume loss drives disability accumulation in MS, and that temporal cortical GM shows accelerated atrophy in SPMS than RRMS. The difference in regional GM atrophy development between phenotypes needs to be taken into account when evaluating treatment effect of therapeutic interventions. Ann Neurol 2018;83:210–222

See Stankoff and Louapre (doi:) for a scientific commentary on this article.Grey matter atrophy in multiple sclerosis affects certain areas preferentially. Eshaghi et al. use a data-driven computational model to predict the order in which regions atrophy, and use this sequence to stage patients. Atrophy begins in deep grey matter nuclei and posterior cortical regions, before spreading to other cortical areas.

The clinical course of relapse-onset multiple sclerosis is highly variable. Demographic factors, clinical features and global brain T2 lesion load have limited value in counselling individual patients. We investigated early MRI predictors of key long-term outcomes including secondary progressive multiple sclerosis, physical disability and cognitive performance, 15 years after a clinically isolated syndrome. A cohort of patients with clinically isolated syndrome (n = 178) was prospectively recruited within 3 months of clinical disease onset and studied with MRI scans of the brain and spinal cord at study entry (baseline) and after 1 and 3 years. MRI measures at each time point included: supratentorial, infratentorial, spinal cord and gadolinium-enhancing lesion number, brain and spinal cord volumetric measures. The patients were followed-up clinically after ∼15 years to determine disease course, and disability was assessed using the Expanded Disability Status Scale, Paced Auditory Serial Addition Test and Symbol Digit Modalities Test. Multivariable logistic regression and multivariable linear regression models identified independent MRI predictors of secondary progressive multiple sclerosis and Expanded Disability Status Scale, Paced Auditory Serial Addition Test and Symbol Digit Modalities Test, respectively. After 15 years, 166 (93%) patients were assessed clinically: 119 (72%) had multiple sclerosis [94 (57%) relapsing-remitting, 25 (15%) secondary progressive], 45 (27%) remained clinically isolated syndrome and two (1%) developed other disorders. Physical disability was overall low in the multiple sclerosis patients (median Expanded Disability Status Scale 2, range 0–10); 71% were untreated. Baseline gadolinium-enhancing (odds ratio 3.16, P < 0.01) and spinal cord lesions (odds ratio 4.71, P < 0.01) were independently associated with secondary progressive multiple sclerosis at 15 years. When considering 1- and 3-year MRI variables, baseline gadolinium-enhancing lesions remained significant and new spinal cord lesions over time were associated with secondary progressive multiple sclerosis. Baseline gadolinium-enhancing (β = 1.32, P < 0.01) and spinal cord lesions (β = 1.53, P < 0.01) showed a consistent association with Expanded Disability Status Scale at 15 years. Baseline gadolinium-enhancing lesions was also associated with performance on the Paced Auditory Serial Addition Test (β = − 0.79, P < 0.01) and Symbol Digit Modalities Test (β = −0.70, P = 0.02) at 15 years. Our findings suggest that early focal inflammatory disease activity and spinal cord lesions are predictors of very long-term disease outcomes in relapse-onset multiple sclerosis. Established MRI measures, available in routine clinical practice, may be useful in counselling patients with early multiple sclerosis about long-term prognosis, and personalizing treatment plans.

Multiple sclerosis (MS) can be divided into four phenotypes based on clinical evolution. The pathophysiological boundaries of these phenotypes are unclear, limiting treatment stratification. Machine learning can identify groups with similar features using multidimensional data. Here, to classify MS subtypes based on pathological features, we apply unsupervised machine learning to brain MRI scans acquired in previously published studies. We use a training dataset from 6322 MS patients to define MRI-based subtypes and an independent cohort of 3068 patients for validation. Based on the earliest abnormalities, we define MS subtypes as cortex-led, normal-appearing white matter-led, and lesion-led. People with the lesion-led subtype have the highest risk of confirmed disability progression (CDP) and the highest relapse rate. People with the lesion-led MS subtype show positive treatment response in selected clinical trials. Our findings suggest that MRI-based subtypes predict MS disability progression and response to treatment and may be used to define groups of patients in interventional trials.

Progressive multifocal leukoencephalopathy (PML) is an uncommon and often fatal demyelinating disease of human central nervous system, which is caused by reactivation of the polyomavirus JC (JCV). PML generally occurs in patients with profound immunosuppression such as AIDS patients. Recently, a number of PML cases have been associated with administration of natalizumab for treatment of multiple sclerosis (MS) patients. Diagnosis and management of PML became a major concern after its occurrence in multiple sclerosis patients treated with natalizumab. Diagnosis of PML usually rests on neuroimaging in the appropriate clinical context and is further confirmed by cerebrospinal fluid polymerase chain reaction (PCR) for JCV DNA. Treatment with antiretroviral therapies in HIV-seropositive patients or discontinuing natalizumab in MS patients with PML may lead to the development of immune reconstitution inflammatory syndrome (IRIS) which presents with deterioration of the previous symptoms and may lead to death. In patients under treatment with monoclonal antibodies in routine practice, or new ones in ongoing clinical trials, differentiating PML from new MS lesions on brain MRI is critical for both the neurologists and neuroradiologists. In this review, we discuss the clinical features, neuroimaging manifestations of PML, IRIS and neuroimaging clues to differentiate new MS lesions from PML. In addition, various neuroimaging features of PML on the non-conventional MR techniques such as diffusion-weighted imaging (DWI), diffusion tensor imaging (DTI), and MR spectroscopy (MRS) are discussed.

Objective During the natural course of multiple sclerosis (MS), the brain is exposed to aging as well as disease effects. Brain aging can be modeled statistically; the so‐called “brain‐age” paradigm. Here, we evaluated whether brain‐predicted age difference (brain‐PAD) was sensitive to the presence of MS, clinical progression, and future outcomes. Methods In a longitudinal, multicenter sample of 3,565 magnetic resonance imaging (MRI) scans, in 1,204 patients with MS and clinically isolated syndrome (CIS) and 150 healthy controls (mean follow‐up time: patients 3.41 years, healthy controls 1.97 years), we measured “brain‐predicted age” using T1‐weighted MRI. We compared brain‐PAD among patients with MS and patients with CIS and healthy controls, and between disease subtypes. Relationships between brain‐PAD and Expanded Disability Status Scale (EDSS) were explored. Results Patients with MS had markedly higher brain‐PAD than healthy controls (mean brain‐PAD +10.3 years; 95% confidence interval [CI] = 8.5–12.1] versus 4.3 years; 95% CI = 2.1 to 6.4; p < 0.001). The highest brain‐PADs were in secondary‐progressive MS (+13.3 years; 95% CI = 11.3–15.3). Brain‐PAD at study entry predicted time‐to‐disability progression (hazard ratio 1.02; 95% CI = 1.01–1.03; p < 0.001); although normalized brain volume was a stronger predictor. Greater annualized brain‐PAD increases were associated with greater annualized EDSS score (r = 0.26; p < 0.001). Interpretation The brain‐age paradigm is sensitive to MS‐related atrophy and clinical progression. A higher brain‐PAD at baseline was associated with more rapid disability progression and the rate of change in brain‐PAD related to worsening disability. Potentially, “brain‐age” could be used as a prognostic biomarker in early‐stage MS, to track disease progression or stratify patients for clinical trial enrollment. ANN NEUROL 2020 ANN NEUROL 2020;88:93–105

This study provides Class II evidence that GM imaging biomarkers can distinguish patients with NMO from those with MS.

Cognitive dysfunction is common in multiple sclerosis (MS) and validated batteries are limited in languages other than English. We aimed to translate, cross-culturally adapt, validate, and assess reliability of Minimal Assessment of Cognitive Function in MS (MACFIMS) in Persian. The MACFIMS is a well-constructed battery in the MS literature. The battery was adapted to Persian in accordance with available guidelines. A total of 158 MS patients and 90 controls underwent neuropsychological assessment. For reliability assessment the battery was re-administered in a subset of 41 patients after a short interval using alternate forms to mitigate practice effects (approximately 10 days). Patients performed significantly worse than controls in all cognitive tests, supporting discriminant validity of our adapted battery. Approximately half of patients (46.2%) showed cognitive impairment as defined by the impairment in two or more tests. The Symbol Digit Modalities Test was the most robust test by ROC analysis. All tests showed acceptable to good level of reliability. This is the first validation of gold-standard cognitive testing in Persian. The Persian MACFIMS shows nearly the same psychometrics as its English counterpart.