Functional representation of the symbol digit modalities test in relapsing remitting multiple sclerosis

Microstates in multiple sclerosis: an electrophysiological signature of altered large-scale networks functioning?

Multiple sclerosis has a highly variable course and disabling symptoms even in absence of associated imaging data. This clinical-radiological paradox has motivated functional studies with particular attention to the resting-state networks by functional MRI. The EEG microstates analysis might offer advantages to study the spontaneous fluctuations of brain activity. This analysis investigates configurations of voltage maps that remain stable for 80-120 ms, termed microstates. The aim of our study was to investigate the temporal dynamic of microstates in patients with multiple sclerosis, without reported cognitive difficulties, and their possible correlations with clinical and neuropsychological parameters. We enrolled fifty relapsing-remitting multiple sclerosis patients and 24 healthy subjects, matched for age and sex. Demographic and clinical data were collected. All participants underwent to high-density EEG in resting-state and analyzed 15 min free artefact segments. Microstates analysis consisted in two processes: segmentation, to identify specific templates, and back-fitting, to quantify their temporal dynamic. A neuropsychological assessment was performed by the Brief International Cognitive Assessment for Multiple Sclerosis. Repeated measures two-way ANOVA was run to compare microstates parameters of patients versus controls. To evaluate association between clinical, neuropsychological and microstates data, we performed Pearsons' correlation and stepwise multiple linear regression to estimate possible predictions. The alpha value was set to 0.05. We found six templates computed across all subjects. Significant differences were found in most of the parameters (global explained variance, time coverage, occurrence) for the microstate Class A (P < 0.001), B (P < 0.001), D (P < 0.001), E (P < 0.001) and F (P < 0.001). In particular, an increase of temporal dynamic of Class A, B and E and a decrease of Class D and F were observed. A significant positive association of disease duration with the mean duration of Class A was found. Eight percent of patients with multiple sclerosis were found cognitive impaired, and the multiple linear regression analysis showed a strong prediction of Symbol Digit Modalities Test score by global explained variance of Class A. The EEG microstate analysis in patients with multiple sclerosis, without overt cognitive impairment, showed an increased temporal dynamic of the sensory-related microstates (Class A and B), a reduced presence of the cognitive-related microstates (Class D and F), and a higher activation of a microstate (Class E) associated to the default mode network. These findings might represent an electrophysiological signature of brain reorganization in multiple sclerosis. Moreover, the association between Symbol Digit Modalities Test and Class A may suggest a possible marker of overt cognitive dysfunctions.

Performance in information processing speed is associated with parietal white matter tract integrity in multiple sclerosis

Background

The Symbol Digit Modalities Test (SDMT) is most frequently used to test processing speed in patients with multiple sclerosis (MS). Functional imaging studies emphasize the importance of frontal and parietal areas for task performance, but the influence of frontoparietal tracts has not been thoroughly studied. We were interested in tract-specific characteristics and their association with processing speed in MS patients.

Methods

Diffusion tensor imaging was obtained in 100 MS patients and 24 healthy matched controls to compare seed-based tract characteristics descending from the superior parietal lobule [Brodman area 7A (BA7A)], atlas-based tract characteristics from the superior longitudinal fasciculus (SLF), and control tract characteristics from the corticospinal tract (CST) and their respective association with ability on the SDMT.

Results

Patients had decreased performance on the SDMT and decreased white matter volume (each p &lt; 0.05). The mean fractional anisotropy (FA) for the BA7A tract and CST (p &lt; 0.05), but not the SLF, differed between MS patients and controls. Furthermore, only the FA of the SLF was positively associated with SDMT performance even after exclusion of the lesions within the tract (r = 0.25, p &lt; 0.05). However, only disease disability and total white matter volume were associated with information processing speed in a linear regression model.

Conclusions

Processing speed in MS is associated with the structural integrity of frontoparietal white matter tracts.

Interplay Between Cognitive and Bowel/Bladder Function in Multiple Sclerosis

Purpose

The aim of this study was to evaluate the prevalence of bowel/bladder dysfunction in multiple sclerosis (MS) and its associations with cognitive impairment.

Methods

We prospectively enrolled 150 MS patients. Patients were administered the Symbol Digit Modality Test (SDMT), the Neurogenic Bowel Dysfunction Score (NBDS), and the Actionable Bladder Symptom Screening Tool (ABSST). The associations between bowel/bladder dysfunction and cognitive function were assessed through hierarchical regression models using the SDMT and clinicodemographic features as independent variables and NBDS and ABSST scores as dependent variables.

Results

The prevalence of bowel/bladder deficits was 44.7%, with 26 patients (17.3%) suffering from bowel deficits and 60 patients (40%) from bladder deficits. The total NBDS and ABSST scores were correlated with the SDMT (β=-0.10, P<0.001 and β=-0.03, P=0.04, respectively) after correction for demographic features and physical disability.

Conclusions

Bowel/bladder disorders are common in MS and are associated with both physical and cognitive disability burdens. As SDMT is embedded into routine clinical assessments, a lower score may warrant investigating bowel/bladder dysfunction due to the strong interplay of these factors.

Classifying multiple sclerosis patients on the basis of SDMT performance using machine learning

Objective:

To build a model to predict cognitive status reflecting structural, functional, and white matter integrity changes in early multiple sclerosis (MS).

Methods:

Based on Symbol Digit Modalities Test (SDMT) performance, 183 early MS patients were assigned “lower” or “higher” performance groups. Three-dimensional (3D)-T2, T1, diffusion weighted, and resting-state magnetic resonance imaging (MRI) data were acquired in 3T. Using Random Forest, five models were trained to classify patients into two groups based on 1—demographic/clinical, 2—lesion volume/location, 3—local/global tissue volume, 4—local/global diffusion tensor imaging, and 5—whole-brain resting-state-functional-connectivity measures. In a final model, all important features from previous models were concatenated. Area under the receiver operating characteristic curve (AUC) values were calculated to evaluate classifier performance.

Results:

The highest AUC value (0.90) was achieved by concatenating all important features from neuroimaging models. The top 10 contributing variables included volumes of bilateral nucleus accumbens and right thalamus, mean diffusivity of left cingulum-angular bundle, and functional connectivity among hubs of seven large-scale networks.

Conclusion:

These results provide an indication of a non-random brain pattern mostly compromising areas involved in attentional processes specific to patients who perform worse in SDMT. High accuracy of the final model supports this pattern as a potential neuroimaging biomarker of subtle cognitive changes in early MS.