Application of magnetic resonance morphometry in patients with multiple sclerosis to study disease progression

Background. Multiple sclerosis is a chronic inflammatory neurodegenerative disease that often leads to disability. Modern neuroimaging methods, including magnetic resonance morphometry, allow detection of brain structural changes associated with disease progression and clarification of their relationship with the clinical and functional status of patients.
Objective: To evaluate the capabilities of magnetic resonance morphometry in detecting cerebral atrophy in patients with verified multiple sclerosis and establish the relationship between volumetric parameters of brain structures and clinical-functional data in different types of disease course.
Materials and methods: A retrospective study of brain MRI data from 38 patients with verified diagnosis of multiple sclerosis aged 22 to 67 years was conducted. MR morphometry was performed on a high-field MR scanner with magnetic field induction strength of 3.0 Tesla. The study protocol consisted of T2-WI, T2 TIRM, T1-MPRAGE pulse sequences with slice thickness of 4.0 mm. Post-processing of MR data was performed using the online volBrain platform designed for automatic assessment of volumetric parameters of brain structures. Data from previously conducted neurological examination and testing using the Expanded Disability Status Scale, Fatigue Impact Scale, cognitive functions according to the Montreal Cognitive Assessment, and functional status using the 9-hole peg test and 25-foot walk test were also evaluated. Statistical analysis was performed using GraphPad Prism 8.00 and Statistica software.
Results. Significant intergroup differences were revealed in total brain volume, gray matter volume, cerebellum and occipital lobes volumes. Statistically significant correlations between morphometric parameters and functional scales were established.
Conclusions. Magnetic resonance morphometry allows objective detection of cerebral atrophy associated with multiple sclerosis progression and can be used to evaluate treatment efficacy and predict disease course.

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