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

RRMS patients were recruited consecutively from three MS units located in the South of Egypt (Assiut, Sout Valley and Luxor) during the period from the 1st of January to the end of September 2023. Patients were diagnosed with RRMS according to the 2017 McDonald diagnostic criteria [12]. Inclusion criteria: RRMS patients of all ages and either sex, both de novo and old cases attending the MS units during the study period. Exclusion criteria: (a) any patient with evidence of a relapse or steroid administration in the past 30 days; (b) any associated medical conditions that may affect cognition, and those on psychoactive pharmacotherapy; (c) patients who were unable to complete the test as due to visual impairment, upper limb weakness or tremors were also excluded; (d) patients with incomplete clinical data or could not complete all cognitive assessments; and (e) patients without MRI image DICOM files (see flowchart Fig. 1).

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This was a cross-sectional, each patient was submitted to the following.

The volumetric data were acquired from MRI data that included T1 and FLAIR sequences. These sequences were originally converted from DICOM files to two compressed, anonymized Neuroimaging Informatics Technology Initiative (NIfTI) files: one for T1 and one for FLAIR. These NIfTI files formed the foundation for further analysis.

LesionBrain 1.0 is a web-based program for segmenting white matter lesions [16] that was successfully incorporated into the platform “volBrain” (https://volbrain.upv.es/) [17], offering a complete tool for precise and efficient lesion detection. Image normalization and registration, structural segmentation to identify particular brain areas such as the intracranial cavity, brainstem, cerebellum, and lateral ventricles, and candidate mapping to locate probable lesion sites are all part of the processing pipeline. Following that, lesions are segmented voxel-wise using a three-step technique that includes patch-based multimodal segmentation, patch-based regularization of the resultant lesion probability map, and an ensemble of shallow neural networks to correct any erroneous patches, decreasing false positives. This pipeline has been rigorously tested using the MSSEG MICCAI Challenge 2016 dataset, displaying good performance with a mean Dice coefficient of 0.66 [18]. This thorough technique enabled the absolute volume of all lesions, whether total or localized, to be measured (in cubic centimeters).

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) of EDSS was 3.2 ± 1.73, the mean (± SD) of cognitive rating scores were; SDMT, 28.07 ± 11.78; CVLT-II, 42.32 ± 12.46; BVMRT, 16.13 ± 8.17. According to the BICAMS criteria, 29 cases (32.2%) had CI. Regarding the spatial distribution of lesions, the periventricular region had the highest lesion volume. Most of our patients (92.2%) were taking DMTs: Interferon was the commonest (55.6%) followed by Fingolimod (28.9%). Other demographic, clinical, psychometric, and volumetric data are shown in Table 1.

Comparisons between patients with and without CI are illustrated in Table 2. Patients with CI were significantly older, had fewer years of education, and had a longer duration of illness with a higher number of relapses. They also had higher EDSS scores, total WM lesion volumes, and Table 3 illustrates the results of the correlation analyses. Age, years of education, disease duration, number of relapses, EDSS, DMT duration, and time to start DMT were significantly correlated with all subtests of the BICAMS, while no correlation was found with age at onset of illness. Total WM-LL and periventricular lesion volume showed significant negative correlations with all BICAMS subtests.

The results of multivariate stepwise logistic regression analysis, with adjustment for age, sex, and current DMT are illustrated in Table 4. The key variables were years of education (inverse relationship with cognitive impairment, P = 0.008), disease duration (positive association, P = 0.004), and WM-LL (positive association, P = 0.042). These findings highlight the significance of education years, disease duration, and lesion volume as predictors of CI.

Table 5(A) and Fig. 2 show the ROC curve and an area under the curve (AUC) of 0.700 for total white matter lesion load (Total WM-LL) in diagnosing cognitive impairment (CI). The AUC, with a standard error of 0.057, is statistically significant (P = 0.002), suggesting moderate predictive accuracy.

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Table 5(B) provides a cut-off value of 12.85 cc for total WM-LL, yielding a sensitivity of 75.9% and specificity of 60.7%.

The odds of cognitive impairment decrease for each additional year of education. This is consistent with Elshebawy and colleagues, and Khedr and colleagues, who found that a low educational level was a predictor of CI in MS patients [15, 22]. Education is one of the factors responsible for the formation of cognitive reserve. A higher educational level is thought to increase the brain’s resilience to disease burden, at least up to a certain limit [32]. Several studies have linked better cognitive performance with higher education levels and cognitive reserve [15, 22, 33, 34]. However, Russo and colleagues [35] and Patti and colleagues [36] found no significant differences between cognitively preserved or impaired patients regarding their educational level.

We found that for each additional year of disease duration, the odds of CI increase. This aligns with the studies above, which identified longer disease duration as a predictor of CI [22].

We found that for each unit increase in lesion volume, the odds of cognitive impairment increase. The area under the curve of total WM-LL predicting CI was 0.700 (95% confidence interval 0.589–0.810). The optimal cut-off value of the total WM-LL predicting CI was equal to or greater than 12.85 cc with a sensitivity of 75.9% and specificity of 60.7%. Calculation of the cut-off value of WM-LL could be utilized for early detection of CI even in asymptomatic patients. However, it is important to note that cognitive impairment in MS is a complex issue and can be influenced by many factors such as frequent relapses, progressive form, higher clinical disability, and immunosuppressive treatment [22]. It might be beneficial to consider these additional factors in future analyses.

Although most previous studies agree that there is a relationship between WM-LL and CI, as reflected in the statements of the National MS Society [37]. Fulton and colleagues discovered that out of 12 neurocognitive indicators assessed, only SDMT and Rey Auditory Verbal Learning test were associated substantially with lesion burden [38]. Patti and colleagues (2015) observed that aberrant white matter (AWM) percentage, a marker of lesion burden, predicted poor performance in SDMT Test 9 years in this study [39]. According to Giorgio et al., the connection between CI and lesion burden is modest, suggesting that CI in MS has a complicated and multifaceted etiology that is insufficiently described by pathological markers detected by standard MRI [40]. Nocentini et al. demonstrated that CI was linked with global brain atrophy and T2-lesion volumes as determined by voxel-based morphometry (VBM) [41]. Another research employing 3.0 T MRI with enhanced identification of tiny lesions undetected by conventional MRI found a significant connection between CI and WM-LL [42]. A recent Egyptian study that compared WM-LL in double inversion recovery (DIR) with SDMT cognitive scores discovered that the total WM-LL was adversely connected with SDMT cognitive scores [43].

Overall, the connection between cognitive performance and lesion burden is modest, suggesting that cognitive impairment in MS has a complex and multifaceted etiology that is not fully described by pathological markers detected by standard MRI [40]. While some studies have found a significant correlation between regional lesion load and CI in MS patients, others suggest that this relationship is moderate or complex. More research is needed to fully understand this relationship.

Disruption of cortico-cortical and cortico-subcortical connections involved in cognitive processing may be the mechanism through which WM-LL causes deficiencies in specific domains of cognition[39]. According to Reuter and colleagues, CI is present in approximately one-quarter of MS patients in the early stages, and the location of macroscopic lesions altered performance in verbal and spatial learning but had no effect on attention and executive functioning [44]. Despite the comparable anatomical distribution of WM-LL, Rossi and colleagues discovered that lesion volume was larger in cognitively impaired individuals than in cognitively preserved patients [45]. Khedr and colleagues, 2023 found that GM atrophy particularly thalamic atrophy was the best predictor of CI as they measure only the gray matter in their study [15]. Kutzelnigg and colleagues showed that, with disease progression, WM abnormalities become more diffuse with increased demyelination in the GM [46]. Since neither WM nor GM abnormalities alone could fully explain CI in MS, WM lesion location may give a stronger correlation with the severity of cognitive impairment [47]. It has been shown that lesions in WM tracts connecting associative areas are correlated with CI in RRMS patients [48, 49]. The effect of WM-LL in strategic tracts in predicting CI may be far more important than the proposed diffuse abnormalities in the normal-appearing WM [50].

Confirming our result Papadopoulou et al. found that WM lesion volume significantly predicted SDMT and by trend PASAT performance [51]. However, cortical lesion volume did not predict CI. Others [52, 53] found the cortical lesions and atrophy associated with cognitive impairment in RRMS while Akaishi et al., and Naghavi et al., and Khedr et al. found that deep gray matter atrophy is highly correlated with overall cognitive impairment in RRMS. This controversy in results may be related to methodological differenced in measuring MRI [15, 54, 55].

Integration of the relatively simple measures of automated brain volumetry and WM-LL and location in routine monitoring of RRMS patients may be able to detect patients’ early patients with CI and consequently could be detecting transiting to secondary progression and allow early intervention. Recently Kania et al. concluded that baseline volumetric measures are stronger predictors of cognitive performance than relapse activity, which yet again highlights the importance of atrophy in MS prognosis [56].

The present study was cross-sectional and did not allow us to shed light on the longitudinal changes in the relationship between the quantitative MRI measures of WM-LL with CI over the course of the disease. Also, the use of a 1.5 T MRI scanner did not allow more advanced MRI measurements (WM tractography). We recommend further studies on larger populations and on other phenotypes like PPMS for a better understanding of the underlying correlates.

Conclusion: In the current study, total WM-LL can be used as a predictor of CI and this finding means that the CI in RRMS is a subcortical type of impairment due to periventricular WM-LL. It may be related to affection of anterior commissural fibers of corpus callosum while years of education and duration of disease are the best demographic predictors for CI.