Measurement and clinical effect of grey matter pathology in multiple sclerosis.
ABSTRACT During the past 10 years, the intense involvement of the grey matter of the CNS in the pathology of multiple sclerosis has become evident. On gross inspection, demyelination in the grey matter is rather inconspicuous, and lesions in the grey matter are mostly undetectable with traditional MRI sequences. However, the results of immunohistochemical studies have shown extensive involvement of grey matter, and researchers have developed and applied new MRI acquisition methods as a result. Imaging techniques specifically developed to visualise grey matter lesions indicate early involvement, and image analysis techniques designed to measure the volume of grey matter show progressive loss. Together, these techniques have shown that grey matter pathology is associated with neurological and neuropsychological disability, and the strength of this association exceeds that related to white matter lesions or whole brain atrophy. By focusing on the latest insights into the in-vivo measurement of grey matter lesions and atrophy, we can assess their clinical effects.
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ABSTRACT: In patients with multiple sclerosis (MS), grey matter damage is widespread and might underlie many of the clinical symptoms, especially cognitive impairment. This relation between grey matter damage and cognitive impairment has been lent support by findings from clinical and MRI studies. However, many aspects of cognitive impairment in patients with MS still need to be characterised. Standardised neuropsychological tests that are easy to administer and sensitive to disease-related abnormalities are needed to gain a better understanding of the factors affecting cognitive performance in patients with MS than exists at present. Imaging measures of the grey matter are necessary, but not sufficient to fully characterise cognitive decline in MS. Imaging measures of both lesioned and normal-appearing white matter lend support to the hypothesis of the existence of an underlying disconnection syndrome that causes clinical symptoms to trigger. Findings on cortical reorganisation support the contribution of brain plasticity and cognitive reserve in limiting cognitive deficits. The development of clinical and imaging biomarkers that can monitor disease development and treatment response is crucial to allow early identification of patients with MS who are at risk of cognitive impairment. Copyright © 2015 Elsevier Ltd. All rights reserved.The Lancet Neurology 02/2015; 14(3). DOI:10.1016/S1474-4422(14)70250-9 · 21.82 Impact Factor
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ABSTRACT: Cortical lesions (CLs) and atrophy are pivotal in multiple sclerosis (MS) pathology. This study determined the effect of disease modifying drugs (DMDs) on CL development and cortical atrophy progression in patients with relapsing-remitting MS (RRMS) over 48 months. Patients (n = 165) were randomized to sc IFN β-1a 44 μg, im IFN β-1a 30 μg, or glatiramer acetate 20 mg. The reference population comprised 50 DMD-untreated patients with RRMS. After 24 months, 43 of the untreated patients switched to DMDs. The four groups of patients were followed up for an additional 24 months. At 48 months the mean standard deviation number of new CLs was significantly lower in patients treated with sc IFN β-1a (1.4 ± 1.0, range 0-5) compared with im IFN β-1a (2.3 ± 1.3, range 0-6, P = 0.004) and glatiramer acetate (2.2 ± 1.5, range 0-7, P = 0.03). Significant reductions in CL accumulation and new white matter and gadolinium-enhancing lesions were also observed in the 43 patients who switched to DMDs after 24 months, compared with the 24 months of no treatment. Concluding, this study confirms that DMDs significantly reduce CL development and cortical atrophy progression compared with no treatment.01/2015; 2015:369348. DOI:10.1155/2015/369348
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ABSTRACT: Neuromyelitis optica (NMO) exhibits substantial similarities to multiple sclerosis (MS) in clinical manifestations and imaging results and has long been considered a variant of MS. With the advent of a specific biomarker in NMO known as anti-aquaporin 4, this assumption has changed; however, the differential diagnosis remains challenging and it is still not clear whether a combination of neuroimaging and clinical data could be used to aid clinical decision-making. Computer-aided diagnosis is a rapidly evolving process that holds great promise to facilitate objective differential diagnoses of disorders that show similar presentations. In this study, we aimed to use a powerful method for multi-modal data fusion, known as a multi-kernel learning and performed automatic diagnosis of subjects. We included 30 patients with NMO, 25 patients with MS and 35 healthy volunteers and performed multi-modal imaging with T1-weighted high resolution scans, diffusion tensor imaging (DTI) and resting-state functional MRI (fMRI). In addition, subjects underwent clinical examinations and cognitive assessments. We included 18 a priori predictors from neuroimaging, clinical and cognitive measures in the initial model. We used 10-fold cross-validation to learn the importance of each modality, train and finally test the model performance. The mean accuracy in differentiating between MS and NMO was 88%, where visible white matter lesion load, normal appearing white matter (DTI) and functional connectivity had the most important contributions to the final classification. In a multi-class classification problem we distinguished between all of 3 groups (MS, NMO and healthy controls) with an average accuracy of 84%. In this classification, visible white matter lesion load, functional connectivity, and cognitive scores were the 3 most important modalities. Our work provides preliminary evidence that computational tools can be used to help make an objective differential diagnosis of NMO and MS.01/2015; 16. DOI:10.1016/j.nicl.2015.01.001