White matter lesion load and location in relation to cognitive impairment in relapsing–remitting multiple sclerosis

Abstract

Abstract Background In relapsing–remitting multiple sclerosis (RRMS) the connection between cognitive impairment (CI) and white matter lesion load (WM-LL) and location is still unclear. This study aimed to identify the relationship between CI in RRMS patients and WM-LL and locations using a fully automated platform. CI and WM-LL were evaluated in 90 patients with RRMS using the Brief International Cognitive Assessment for Multiple Sclerosis (BICAMS) and Automated MRI volumetric measures of WM-LL and lesion distribution. Regression analysis of BICAMS as a dependent variable with different clinical and radiological parameters was performed. Results Data were obtained from 90 patients with RRMS who had a mean age of 32.74 ± 8.43 years and a female-to-male ratio of 3:1. The mean (± SD) cognitive rating scores for the BICAMS subtests were 28.07 ± 11.78 for the Symbol Digit Modalities Test (SDMT), 42.32 ± 12.46 for the California Verbal Learning Test-II (CVLT-II), and 16.13 ± 8.17 for the Brief Visuospatial Memory Test-Revised (BVMT-R). According to the BICAMS criteria, 29 cases (32.2%) had CI. BICAMS scores were significantly correlated with age, education level, relapse frequency, disease duration, and time to start disease-modifying therapies. Whole WM-LL and periventricular lesion load were significantly associated with CI. After controlling for age, sex, and education, logistic regression analysis revealed that total WM-LL was the best predictor for CI together with duration of illness and years of education. The cut-off value of 12.85 cc for total WM-LL predicted CI. Conclusions Whole WM-LL and periventricular lesion load are the best anatomical predictors for CI probably due to the effect on the anterior commissural fibers while years of education and duration of disease are the best demographic predictors for CI.

Keywords

• Multiple sclerosis
• Cognitive impairment
• BICAMS
• Volumetric assessment
• Lesion load
• Lesion location