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Correlation of brain segmental volume changes with clinical parameters: a longitudinal study in multiple sclerosis patients

Correlação das alterações do volume segmentar cerebral com parâmetros clínicos: um estudo longitudinal em pacientes com esclerose múltipla

Abstract

Objective

To measure the cranial volume differences from 15 different parts in the follow-up of relapsing-remitting multiple sclerosis (RRMS) patients and correlate them with clinical parameters.

Methods

Forty-seven patients with RRMS were included in the study. Patients were grouped into two categories; low Expanded Disability Status Scale (EDSS) (< 3; group 1), and moderate-high EDSS (≥ 3; group 2). Patients were evaluated with Beck Depression Inventory (BDI), Montreal Cognitive Assessment (MOCA), Symbol Digit Modalities Test(SDMT), Fatigue Severity Scale (FSS), and calculated Annualized Relapse Rate (ARR) scores. Magnetic resonance imaging (MRI) was performed with a 1.5T MRI device (Magnetom AERA, Siemens, Erlangen, Germany) twice in a 1-year period. Volumetric analysis was performed by a free, automated, online MRI brain volumetry software. The differences in volumetric values between the two MRI scans were calculated and correlated with the demographic and clinical parameters of the patients.

Results

The number of attacks, disease duration, BDI, and FSS scores were higher in group 2; SDMT was higher in group 1. As expected, volumetric analyses have shown volume loss in total cerebral white matter in follow-up patients (p < 0.001). In addition, putaminal volume loss was related to a higher number of attacks. Besides, a negative relation between FSS with total amygdala volumes, a link between atrophy of globus pallidus and ARR, and BDI scores was found with the aid of network analysis.

Conclusions

Apart from a visual demonstration of volume loss, cranial MRI with volumetric analysis has a great potential for revealing covert links between segmental volume changes and clinical parameters.

Keywords:
Multiple Sclerosis; Magnetic Resonance Imaging; Social Network Analysis; Atrophy

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