Stimulus-related modulation in the 1/f spectral slope suggests an impaired inhibition during a working memory task in people with multiple sclerosis

BACKGROUND

An imbalance of excitatory and inhibitory synaptic transmission in multiple sclerosis (MS) may lead to cognitive impairment, such as impaired working memory. The 1/f slope of electroencephalography/magnetoencephalography (EEG/MEG) power spectra is shown to be a non-invasive proxy of excitation/inhibition balance. A flatter slope is associated with higher excitation/lower inhibition.

OBJECTIVES

To assess the 1/f slope modulation induced by stimulus and its association with behavioral and cognitive measures.

METHODS

We analyzed MEG recordings of 38 healthy controls (HCs) and 79 people with multiple sclerosis (pwMS) while performing an n-back task including target and distractor stimuli. Target trials require an answer, while distractor trials do not. We computed the 1/f spectral slope through the fitting oscillations and one over f (FOOOF) algorithm within the time windows 1 second before and after each stimulus presentation.

RESULTS

We observed a flatter 1/f slope after distractor stimuli in pwMS compared to HCs. The 1/f slope was significantly steeper after stimulus for both HCs and pwMS and was significantly correlated with reaction times. This modulation in 1/f slope was significantly correlated with visuospatial memory assessed by the BVMT-R test.

CONCLUSION

Our results suggest possible inhibitory mechanism deficits in pwMS during a working memory task.

Clinical effectiveness of coronavirus disease 2019 vaccination in patients with multiple sclerosis stratified by disease-modifying treatment

BACKGROUND AND PURPOSE

Coronavirus disease 2019 (COVID-19) vaccination has been associated with a dampened humoral and/or cellular immune response in patients with multiple sclerosis (MS) who were concurrently on disease-modifying treatment (DMT) with B-cell depleting agents or sphingosine-1-phosphate receptor modulators (S1PRMs). Our main goal was to investigate the impact of these DMT classes on the clinical effectiveness of COVID-19 vaccination.

METHODS

Since March 2020, demographics and clinical data of patients with MS who developed COVID-19 have been collected at the Belgian National MS Centre in Melsbroek. Patients were considered to be 'protected by vaccination' if they were (i) fully vaccinated and (ii) tested positive for COVID-19 in the period ranging from 14 days to 6 months after the last administered vaccine.

RESULTS

On 19 December 2022, 418 COVID-19 cases were retrospectively identified in 389 individual patients. Hospitalization and mortality rates resulting from the infection were 10.8% and 2.4%, respectively. Being 'unprotected by vaccination' was significantly associated with a worse COVID-19 outcome (i.e., hospitalization and/or death) in the total cohort (N = 418, odds ratio [OR] 3.96), in patients on ongoing DMT other than anti-CD20 agents or S1PRMs (N = 123, OR 31.75) and in patients without DMT (N = 182, OR 5.60), but not in those receiving anti-CD20 agents (N = 91, OR 0.39); the S1PRMs subgroup was considered too small (22 infections) for any meaningful analysis.

CONCLUSIONS

Coronavirus disease 2019 vaccination protects against severe infection in patients with MS but it was not possible to confirm this effect in those on DMT with B-cell depleting agents.

The Finger Dexterity Test: Validation study of a smartphone-based manual dexterity assessment

BACKGROUND

The Nine-Hole Peg Test (9HPT) is the golden standard to measure manual dexterity in people with multiple sclerosis (MS). However, administration requires trained personnel and dedicated time during a clinical visit.

OBJECTIVES

The objective of this study is to validate a smartphone-based test for remote manual dexterity assessment, the icompanion Finger Dexterity Test (FDT), to be included into the icompanion application.

METHODS

A total of 65 MS and 81 healthy subjects were tested, and 20 healthy subjects were retested 2 weeks later.

RESULTS

The FDT significantly correlated with the 9HPT (dominant: ρ = 0.62, p < 0.001; non-dominant: ρ = 0.52, p < 0.001). MS subjects had significantly higher FDT scores than healthy subjects (dominant: p = 0.015; non-dominant: p = 0.013), which was not the case for the 9HPT. A significant correlation with age (dominant: ρ = 0.46, p < 0.001; non-dominant: ρ = 0.40, p = 0.002), Expanded Disability Status Scale (EDSS, dominant: ρ = 0.36, p = 0.005; non-dominant: ρ = 0.31, p = 0.024), and disease duration for the non-dominant hand (ρ = 0.31, p = 0.016) was observed. There was a good test-retest reliability in healthy subjects (dominant: r = 0.69, p = 0.001; non-dominant: r = 0.87, p < 0.001).

CONCLUSIONS

The icompanion FDT shows a moderate-to-good concurrent validity and test-retest reliability, differentiates between the MS subjects and healthy controls, and correlates with clinical parameters. This test can be implemented into routine MS care for remote follow-up of manual dexterity.

The spectral slope as a marker of excitation/inhibition ratio and cognitive functioning in multiple sclerosis

Multiple sclerosis (MS) is a neurodegenerative disease characterized by neuronal and synaptic loss, resulting in an imbalance of excitatory and inhibitory synaptic transmission and potentially cognitive impairment. Current methods for measuring the excitation/inhibition (E/I) ratio are mostly invasive, but recent research combining neurocomputational modeling with measurements of local field potentials has indicated that the slope with which the power spectrum of neuronal activity captured by electro- and/or magnetoencephalography rolls off, is a non-invasive biomarker of the E/I ratio. A steeper roll-off is associated with a stronger inhibition. This novel method can be applied to assess the E/I ratio in people with multiple sclerosis (pwMS), detect the effect of medication such as benzodiazepines, and explore its utility as a biomarker for cognition. We recruited 44 healthy control subjects and 95 pwMS who underwent resting-state magnetoencephalographic recordings. The 1/f spectral slope of the neural power spectra was calculated for each subject and for each brain region. As expected, the spectral slope was significantly steeper in pwMS treated with benzodiazepines (BZDs) compared to pwMS not receiving BZDs (p = .01). In the sub-cohort of pwMS not treated with BZDs, we observed a steeper slope in cognitively impaired pwMS compared to cognitively preserved pwMS (p = .01) and healthy subjects (p = .02). Furthermore, we observed a significant correlation between 1/f spectral slope and verbal and spatial working memory functioning in the brain regions located in the prefrontal and parietal cortex. In this study, we highlighted the value of the spectral slope in MS by quantifying the effect of benzodiazepines and by putting it forward as a potential biomarker of cognitive deficits in pwMS.

A data-driven network decomposition of the temporal, spatial, and spectral dynamics underpinning visual-verbal working memory processes

The brain dynamics underlying working memory (WM) unroll via transient frequency-specific large-scale brain networks. This multidimensionality (time, space, and frequency) challenges traditional analyses. Through an unsupervised technique, the time delay embedded-hidden Markov model (TDE-HMM), we pursue a functional network analysis of magnetoencephalographic data from 38 healthy subjects acquired during an n-back task. Here we show that this model inferred task-specific networks with unique temporal (activation), spectral (phase-coupling connections), and spatial (power spectral density distribution) profiles. A theta frontoparietal network exerts attentional control and encodes the stimulus, an alpha temporo-occipital network rehearses the verbal information, and a broad-band frontoparietal network with a P300-like temporal profile leads the retrieval process and motor response. Therefore, this work provides a unified and integrated description of the multidimensional working memory dynamics that can be interpreted within the neuropsychological multi-component model of WM, improving the overall neurophysiological and neuropsychological comprehension of WM functioning.

A cross-sectional case-control study on the structural connectome in recovered hospitalized COVID-19 patients

COVID-19 can induce neurological sequelae, negatively affecting the quality of life. Unravelling this illness's impact on structural brain connectivity, white-matter microstructure (WMM), and cognitive performance may help elucidate its implications. This cross-sectional study aimed to investigate differences in these factors between former hospitalised COVID-19 patients (COV) and healthy controls. Group differences in structural brain connectivity were explored using Welch-two sample t-tests and two-sample Mann-Whitney U tests. Multivariate linear models were constructed (one per region) to examine fixel-based group differences. Differences in cognitive performance between groups were investigated using Wilcoxon Rank Sum tests. Possible effects of bundle-specific FD measures on cognitive performance were explored using a two-group path model. No differences in whole-brain structural organisation were found. Bundle-specific metrics showed reduced fiber density (p = 0.012, Hedges' g = 0.884) and fiber density cross-section (p = 0.007, Hedges' g = 0.945) in the motor segment of the corpus callosum in COV compared to healthy controls. Cognitive performance on the motor praxis and digit symbol substitution tests was worse in COV than healthy controls (p < 0.001, r = 0.688; p = 0.013, r = 422, respectively). Associations between the cognitive performance and bundle-specific FD measures differed significantly between groups. WMM and cognitive performance differences were observed between COV and healthy controls.

Reduced alpha2 power is associated with slowed information processing speed in multiple sclerosis

OBJECTIVE

Cognitive impairment is common in multiple sclerosis (MS), significantly impacts daily functioning, is time-consuming to assess, and is prone to practice effects. We examined whether the alpha band power measured with magnetoencephalography (MEG) is associated with the different cognitive domains affected by MS.

METHODS

Sixty-eight MS patients and 47 healthy controls underwent MEG, T1- and FLAIR-weighted magnetic resonance imaging (MRI), and neuropsychological testing. Alpha power in the occipital cortex was quantified in the alpha1 (8-10 Hz) and alpha2 (10-12 Hz) bands. Next, we performed best subset regression to assess the added value of neurophysiological measures to commonly available MRI measures.

RESULTS

Alpha2 power significantly correlated with information processing speed (p < 0.001) and was always retained in all multilinear models, whereas thalamic volume was retained in 80% of all models. Alpha1 power was correlated with visual memory (p < 0.001) but only retained in 38% of all models.

CONCLUSIONS

Alpha2 (10-12 Hz) power in rest is associated with IPS, independent of standard MRI parameters. This study stresses that a multimodal assessment, including structural and functional biomarkers, is likely required to characterize cognitive impairment in MS. Resting-state neurophysiology is thus a promising tool to understand and follow up changes in IPS.

Neuronal activity and NIBS in developmental myelination and remyelination - current state of knowledge

Oligodendrocytes are responsible for myelinating central nervous system (CNS) axons. and rapid electrical transmission through saltatory conduction of action potentials. Myelination and myelin repair rely partially on oligodendrogenesis, which comprises. oligodendrocyte precursor cell (OPC) migration, maturation, and differentiation into. oligodendrocytes (OL). In multiple sclerosis (MS), demyelination occurs due to an. inflammatory cascade with auto-reactive T-cells. When oligodendrogenesis fails, remyelination becomes aberrant and conduction impairments are no longer restored. Although current disease modifying therapies have achieved results in modulating the. faulty immune response, disease progression continues because of chronic. inflammation, neurodegeneration, and failure of remyelination. Therapies have been. tried to promote remyelination. Modulation of neuronal activity seems to be a very. promising strategy in preclinical studies. Additionally, studies in people with MS. (pwMS) have shown symptom improvement following non-invasive brain stimulation. (NIBS) techniques. The aforementioned mechanisms are yet unknown and probably. involve both the activation of neurons and glial cells. Noting neuronal activity. contributes to myelin plasticity and that NIBS modulates neuronal activity; we argue. that NIBS is a promising research horizon for demyelinating diseases. We review the. hypothesized pathways through which NIBS may affect both neuronal activity in the. CNS and how the resulting activity can affect oligodendrogenesis and myelination.

Occurrence and Severity of Coronavirus Disease 2019 Are Associated With Clinical Disability Worsening in Patients With Multiple Sclerosis

BACKGROUND AND OBJECTIVES

Large-scale observational studies have shown that, in patients with multiple sclerosis (MS), the risk of becoming more severely ill from coronavirus disease 2019 (COVID-19) is determined by older age, male sex, cardiovascular comorbidities, African American ethnicity, progressive disease, recent use of corticosteroids, and B cell-depleting disease-modifying treatment. In contrast, the effect of COVID-19 on the disease course of MS has been studied much less extensively. Our main goal was to explore whether COVID-19 is associated with accelerated clinical disability worsening in patients with MS.

METHODS

Since March 2020, demographics and infectious outcome (categorized as ambulatory, hospitalized, and/or death) of patients with MS who developed COVID-19 have been collected at the Belgian National MS Center in Melsbroek. On February 28, 2022, this database was locked and complemented with clinical disability measures-Expanded Disability Status Scale (EDSS), Timed 25-Foot Walk Test (T25FWT), 9-Hole Peg Test (9HPT), and Symbol Digit Modalities Test (SDMT)-that were available from a larger local database, obtained during routine medical follow-up. For each parameter, the first 2 assessments before COVID-19 diagnosis (T0 and T1; T1 is the closest to COVID-19 diagnosis), and the first thereafter (T2), were retrieved.

RESULTS

We identified 234 unique cases of COVID-19. Thirty-one patients were hospitalized (13.2%), and 5 died (2.1%) as a result of their infection. Among survivors with complete EDSS results (N = 138), mean annualized T1-to-T2 EDSS worsening was more pronounced, compared with the respective change between T0 and T1 (0.3 ± 0.9 vs 0.1 ± 0.9, p = 0.012). No such differences were found for the T25FWT, 9HPT, and SDMT scores. Severe COVID-19 (hospitalization) was associated with clinically relevant T1-to-T2 EDSS worsening (OR 2.65, p = 0.042). Vaccination coverage in the total cohort was 53.8%. Being unprotected by vaccination at the time of infection was associated with a worse COVID-19 outcome (hospitalization and/or death; OR 3.52, p = 0.002) but not with clinically relevant T1-to-T2 EDSS worsening.

DISCUSSION

The occurrence and severity of COVID-19 are both associated with clinical disability worsening in patients with MS. Vaccination protects against a more severe course of COVID-19 in this specific population.

TRIAL REGISTRATION INFORMATION

The study has been registered at ClinicalTrials.gov (study registration number: NCT05403463).

Improved Alzheimer's Disease versus Frontotemporal Lobar Degeneration Differential Diagnosis Combining EEG and Neurochemical Biomarkers: A Pilot Study

BACKGROUND

Distinguishing between Alzheimer's disease (AD) and frontotemporal lobar degeneration (FTLD) results in poor diagnostic accuracy.

OBJECTIVE

To investigate the utility of electroencephalography (EEG)-based biomarkers in comparison and in addition to established cerebrospinal fluid (CSF) biomarkers in the AD versus FTLD differential diagnosis.

METHODS

The study cohort comprised 37 AD and 30 FTLD patients, of which 17 AD and 9 FTLD patients had definite diagnoses. All participants had CSF neurochemical (NCM) biomarker analyses (Aβ1-42, T-tau, P-tau181, and Nf-L) and underwent 19-channel resting-state EEG. From the EEG spectra, dominant frequency peaks were extracted in four regions resulting in four dominant frequencies. This produced eight features (4 NCM + 4 EEG).

RESULTS

When NCM and EEG markers were combined, the diagnostic accuracy increased significantly. In the whole group, the accuracy went up from 79% (NCM) to almost 82%, while in the definite group only, it went up from around 85% to almost 95%. Two differences in the occurrence of the dominant EEG frequency were discovered: people lacking a clear dominant peak almost all had definite AD, while people with two peaks more often had FTLD.

CONCLUSION

Combining EEG with NCM biomarkers resulted in differential diagnostic accuracies of 82% in clinically diagnosed AD and FTD patients and of 95% in patients having a definite diagnosis, which was significantly better than with EEG or NCM biomarkers alone. This suggests that NCM and EEG markers are complementary, revealing different aspects of the disease and therefore confirms again their relevance in developing additional diagnosis tools.

Cognitive-motor telerehabilitation in multiple sclerosis (CoMoTeMS): study protocol for a randomised controlled trial

Background

The management of cognitive impairment is an important goal in the treatment of multiple sclerosis (MS). While cognitive rehabilitation has been proven to be effective in improving cognitive performance in MS, research in the elderly indicates a higher effectiveness of combined cognitive-motor rehabilitation. Here, we present the protocol of a randomised controlled clinical trial to assess whether a combined cognitive-motor telerehabilitation programme is more effective in improving working memory than only cognitive or motor training.

Methods/design

The CoMoTeMS-trial is a two-centre, randomised, controlled and blinded clinical trial. A total of 90 patients with MS will receive 12 weeks of either a combined cognitive-motor telerehabilitation programme or only cognitive or motor training. The primary outcome is a change in the digit span backwards. Secondary outcomes are other cognitive changes (Brief International Cognitive Assessment for Multiple Sclerosis and Backward Corsi), Expanded Disability Status Scale (EDSS), 6-Min Walk Test, 25-Foot Walk Test, 9-Hole Peg Test, anxiety and depression, fatigue, quality of life, cognitive and physical activity level, electroencephalography and magnetic resonance imaging of the brain.

Discussion

We hypothesise that the improvement in digit span backwards after 12 weeks of treatment will be significantly higher in the group treated with the combined cognitive-motor telerehabilitation programme, compared to the groups receiving only cognitive and only motor training.

Trial registration

ClinicalTrials.gov NCT05355389. Registered on 2 May 2022.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13063-022-06697-9.

Plasma glial fibrillary acidic protein and neurofilament light chain in relation to disability worsening in multiple sclerosis

Background:

Predicting disability worsening in multiple sclerosis (MS) remains an important challenge. Glial fibrillary acidic protein (GFAP) and neurofilament light chain (NfL) seem promising biomarkers. Studies investigating blood GFAP in relation to longitudinal outcome measures in MS are scarce.

Objective:

To compare plasma-GFAP (p-GFAP) and plasma-NfL (p-NfL) levels in relation to sustained disability worsening.

Methods:

We measured baseline p-GFAP and p-NfL in a prospective cohort of 115 individuals with MS and 30 matched controls, using Single Molecule Array (Simoa). Disability worsening was defined as an increase in at least one of three measures (Expanded Disability Status Scale, Timed 25-foot walk, 9-Hole Peg test), confirmed after 6 months and persistent upon data closure.

Results:

In a multivariable Cox proportional-hazards model, p-GFAP was not significantly associated with sustained disability worsening after 4.40 ± 0.82 years, while p-NfL (HR = 1.046, p = 0.001), EDSS (HR = 1.24, p = 0.039), and disease duration (HR = 1.048, p = 0.017) were. Area under the curve of ROC curves in relation to worsening was 0.61 for p-GFAP ( p = 0.031) and 0.63 for p-NfL ( p = 0.015). Kaplan–Meier curves showed similar patterns for both proteins.

Conclusion:

p-NfL emerged as a significant explanatory variable for worsening in Cox regression analysis, and p-GFAP did not. Both p-GFAP and p-NfL were related to worsening based on ROC curves.

Brain Volume Loss Can Occur at the Rate of Normal Aging in Patients with Multiple Sclerosis Who Are Free from Disease Activity

Multiple sclerosis (MS) is a chronic inflammatory demyelinating and degenerative disorder of the central nervous system. Accelerated brain volume loss (BVL) has emerged as a promising magnetic resonance imaging marker (MRI) of neurodegeneration, correlating with present and future clinical disability. We have systematically selected MS patients fulfilling ‘no evidence of disease activity-3′ (NEDA-3) criteria under high-efficacy disease-modifying treatment (DMT) from the database of two Belgian MS centers. BVL between both MRI scans demarcating the NEDA-3 period was assessed and compared with a group of prospectively recruited healthy volunteers who were matched for age and gender. Annualized whole brain volume percentage change was similar between 29 MS patients achieving NEDA-3 and 24 healthy controls (−0.25 ± 0.49 versus −0.24 ± 0.20, p = 0.9992; median follow-up 21 versus 33 months; respectively). In contrast, we found a mean BVL increase of 72%, as compared with the former, in a second control group of MS patients (n = 21) whom had been excluded from the NEDA-3 group due to disease activity (p = 0.1371). Our results suggest that neurodegeneration in MS can slow down to the rate of normal aging once inflammatory disease activity has been extinguished and advocate for an early introduction of high-efficacy DMT to reduce the risk of future clinical disability.

Towards Multimodal Machine Learning Prediction of Individual Cognitive Evolution in Multiple Sclerosis

Multiple sclerosis (MS) manifests heterogeneously among persons suffering from it, making its disease course highly challenging to predict. At present, prognosis mostly relies on biomarkers that are unable to predict disease course on an individual level. Machine learning is a promising technique, both in terms of its ability to combine multimodal data and through the capability of making personalized predictions. However, most investigations on machine learning for prognosis in MS were geared towards predicting physical deterioration, while cognitive deterioration, although prevalent and burdensome, remained largely overlooked. This review aims to boost the field of machine learning for cognitive prognosis in MS by means of an introduction to machine learning and its pitfalls, an overview of important elements for study design, and an overview of the current literature on cognitive prognosis in MS using machine learning. Furthermore, the review discusses new trends in the field of machine learning that might be adopted for future studies in the field.

Signal quality as Achilles' heel of graph theory in functional magnetic resonance imaging in multiple sclerosis

Graph-theoretical analysis is a novel tool to understand the organisation of the brain.

We assessed whether altered graph theoretical parameters, as observed in multiple sclerosis (MS), reflect pathology-induced restructuring of the brain's functioning or result from a reduced signal quality in functional MRI (fMRI). In a cohort of 49 people with MS and a matched group of 25 healthy subjects (HS), we performed a cognitive evaluation and acquired fMRI. From the fMRI measurement, Pearson correlation-based networks were calculated and graph theoretical parameters reflecting global and local brain organisation were obtained. Additionally, we assessed metrics of scanning quality (signal to noise ratio (SNR)) and fMRI signal quality (temporal SNR and contrast to noise ratio (CNR)). In accordance with the literature, we found that the network parameters were altered in MS compared to HS. However, no significant link was found with cognition. Scanning quality (SNR) did not differ between both cohorts. In contrast, measures of fMRI signal quality were significantly different and explained the observed differences in GTA parameters. Our results suggest that differences in network parameters between MS and HS in fMRI do not reflect a functional reorganisation of the brain, but rather occur due to reduced fMRI signal quality.

The role of hippocampal theta oscillations in working memory impairment in multiple sclerosis

Working memory (WM) problems are frequently present in people with multiple sclerosis (MS). Even though hippocampal damage has been repeatedly shown to play an important role, the underlying neurophysiological mechanisms remain unclear. This study aimed to investigate the neurophysiological underpinnings of WM impairment in MS using magnetoencephalography (MEG) data from a visual-verbal 2-back task. We analysed MEG recordings of 79 MS patients and 38 healthy subjects through event-related fields and theta (4-8 Hz) and alpha (8-13 Hz) oscillatory processes. Data was source reconstructed and parcellated based on previous findings in the healthy subject sample. MS patients showed a smaller maximum theta power increase in the right hippocampus between 0 and 400 ms than healthy subjects (p = .014). This theta power increase value correlated negatively with reaction time on the task in MS (r = -.32, p = .029). Evidence was provided that this relationship could not be explained by a 'common cause' confounding relationship with MS-related neuronal damage. This study provides the first neurophysiological evidence of the influence of hippocampal dysfunction on WM performance in MS.

Brain dysconnectivity relates to disability and cognitive impairment in multiple sclerosis

The pathophysiology of cognitive dysfunction in multiple sclerosis (MS) is still unclear. This magnetoencephalography (MEG) study investigates the impact of MS on brain resting-state functional connectivity (rsFC) and its relationship to disability and cognitive impairment. We investigated rsFC based on power envelope correlation within and between different frequency bands, in a large cohort of participants consisting of 99 MS patients and 47 healthy subjects. Correlations were investigated between rsFC and outcomes on disability, disease duration and 7 neuropsychological scores within each group, while stringently correcting for multiple comparisons and possible confounding factors. Specific dysconnections correlating with MS-induced physical disability and disease duration were found within the sensorimotor and language networks, respectively. Global network-level reductions in within- and cross-network rsFC were observed in the default-mode network. Healthy subjects and patients significantly differed in their scores on cognitive fatigue and verbal fluency. Healthy subjects and patients showed different correlation patterns between rsFC and cognitive fatigue or verbal fluency, both of which involved a shift in patients from the posterior default-mode network to the language network. Introducing electrophysiological rsFC in a regression model of verbal fluency and cognitive fatigue in MS patients significantly increased the explained variance compared to a regression limited to structural MRI markers (relative thalamic volume and lesion load). This MEG study demonstrates that MS induces distinct changes in the resting-state functional brain architecture that relate to disability, disease duration and specific cognitive functioning alterations. It highlights the potential value of electrophysiological intrinsic rsFC for monitoring the cognitive impairment in patients with MS.

Feasibility of Real Time Internet-Based Teleconsultation in Patients With Multiple Sclerosis: Interventional Pilot Study

BACKGROUND

Telemedicine (TM) is currently flourishing in rural and emergency settings, but its implementation in the routine management of chronic neurological disorders has developed with more hesitation. Limited access to specialized care facilities and expanding patient populations, combined with unprecedented mobility restrictions imposed by the coronavirus disease pandemic, are currently stressing the need for remote solutions in this field. Studies in patients with multiple sclerosis (MS) have been heterogeneous in objectives and methodology but generally support the concept that TM interventions produce clinical benefits, cost-effectiveness, and user satisfaction. Nonetheless, data on live interaction between patients and health care providers for MS teleconsultation purposes remain scarce.

OBJECTIVE

The aim of this study is to demonstrate the feasibility of planned real time audiovisual teleconsultation over the internet for patients with MS.

METHODS

A total of 20 patients with MS presenting at a specialized MS center in Belgium were recruited for this study. One teleconsultation was scheduled for each participant. Patients were provided a unique hyperlink by mail in advance, leading them automatically and directly to the virtual waiting room, where they could accept or decline our incoming call. All teleconsultations were performed by a trained medical student with the intention to keep the conversation similar to what is usually discussed during a classic face-to-face MS consultation; no remote physical exams were performed. The approach was considered feasible if at least 80% of the planned TM visits could be successfully completed at the foreseen moment. Patient satisfaction (technical quality, convenience, and overall quality of care) was evaluated at the end of each teleconsultation by means of 5-point Likert scales containing the categories very unsatisfied, unsatisfied, neutral, satisfied, and highly satisfied.

RESULTS

Out of 20 consultations, 17 were successfully completed (85%). Failures were due to patients not responding (n=2) and technical issues (n=1). Out of the 17 consultations, 17 patients declared themselves satisfied or highly satisfied for technical quality, 15 patients for convenience, and 16 patients for overall quality of care.

CONCLUSIONS

Planned real time audiovisual teleconsultation over the internet is feasible and highly appreciated in patients with MS. Incorporation of such services in routine clinical MS practice is expected to improve access to specialized care facilities for affected patients.

Spatiotemporal and spectral dynamics of multi-item working memory as revealed by the n-back task using MEG

Multi‐item working memory (WM) is a complex cognitive function thought to arise from specific frequency band oscillations and their interactions. While some theories and consistent findings have been established, there is still a lot of unclarity about the sources, temporal dynamics, and roles of event‐related fields (ERFs) and theta, alpha, and beta oscillations during WM activity. In this study, we performed an extensive whole‐brain ERF and time‐frequency analysis on n‐back magnetoencephalography data from 38 healthy controls. We identified the previously unknown sources of the n‐back M300, the right inferior temporal and parahippocampal gyrus and left inferior temporal gyrus, and frontal theta power increase, the orbitofrontal cortex. We shed new light on the role of the precuneus during n‐back activity, based on an early ERF and theta power increase, and suggest it to be a crucial link between lower‐level and higher‐level information processing. In addition, we provide strong evidence for the central role of the hippocampus in multi‐item WM behavior through the dynamics of theta and alpha oscillatory changes. Almost simultaneous alpha power decreases observed in the hippocampus and occipital fusiform gyri, regions known to be involved in letter processing, suggest that these regions together enable letter recognition, encoding and storage in WM. In summary, this study offers an extensive investigation into the spatial, temporal, and spectral characteristics of n‐back multi‐item WM activity.

Detecting neurodegenerative pathology in multiple sclerosis before irreversible brain tissue loss sets in

Background

Multiple sclerosis (MS) is a complex chronic inflammatory and degenerative disorder of the central nervous system. Accelerated brain volume loss, or also termed atrophy, is currently emerging as a popular imaging marker of neurodegeneration in affected patients, but, unfortunately, can only be reliably interpreted at the time when irreversible tissue damage likely has already occurred. Timing of treatment decisions based on brain atrophy may therefore be viewed as suboptimal.

Main body

This Narrative Review focuses on alternative techniques with the potential of detecting neurodegenerative events in the brain of subjects with MS prior to the atrophic stage. First, metabolic and molecular imaging provide the opportunity to identify early subcellular changes associated with energy dysfunction, which is an assumed core mechanism of axonal degeneration in MS. Second, cerebral hypoperfusion has been observed throughout the entire clinical spectrum of the disorder but it remains an open question whether this serves as an alternative marker of reduced metabolic activity, or exists as an independent contributing process, mediated by endothelin-1 hyperexpression. Third, both metabolic and perfusion alterations may lead to repercussions at the level of network performance and structural connectivity, respectively assessable by functional and diffusion tensor imaging. Fourth and finally, elevated body fluid levels of neurofilaments are gaining interest as a biochemical mirror of axonal damage in a wide range of neurological conditions, with early rises in patients with MS appearing to be predictive of future brain atrophy.

Conclusions

Recent findings from the fields of advanced neuroradiology and neurochemistry provide the promising prospect of demonstrating degenerative brain pathology in patients with MS before atrophy has installed. Although the overall level of evidence on the presented topic is still preliminary, this Review may pave the way for further longitudinal and multimodal studies exploring the relationships between the abovementioned measures, possibly leading to novel insights in early disease mechanisms and therapeutic intervention strategies.

The Impact of Cognitive Dysfunction on Locomotor Rehabilitation Potential in Multiple Sclerosis

Background:

Cognitive dysfunction is a frequent manifestation of multiple sclerosis (MS) but its effect on locomotor rehabilitation is unknown.

Objective:

To study the impact of cognitive impairment on locomotor rehabilitation outcome in people with MS.

Methods:

We performed a retrospective analysis involving ambulatory patients with MS who were admitted for intensive, inpatient, multidisciplinary rehabilitation at the National Multiple Sclerosis Center of Melsbroek between the years 2012 and 2017. The Brief Repeatable Battery of Neuropsychological Tests (BRB-N) was used to determine the cognitive status of subjects as either impaired (COG–) or preserved (COG+). Locomotor outcome was compared between groups with the difference in 6-minute walk test (6MWT) measured at admission and discharge (Δ6MWT). In addition, individual test scores of the BRB-N for attention (Paced Auditory Serial Addition Test 2” and 3”), visuospatial learning/memory (7/24 Spatial Recall Test), verbal learning/memory (Selective Reminding Test) and verbal fluency (Controlled Oral Word Association Test) were correlated to the Δ6MWT.

Results:

A total of 318 complete and unique records were identified. Both groups showed a significant within-group Δ6MWT during hospitalization (COG+: 47.51 m; COG–: 40.97 m; P < .01). In contrast, Δ6MWT values were comparable between groups. The odds of achieving a minimal clinical important difference on the 6MWT did not differ significantly between both groups. Only attention/concentration was significantly correlated with Δ6MWT (r = 0.16, P = .013).

Conclusion:

Cognitive impairment based on BRB-N results appears not to impede locomotor rehabilitation in ambulatory patients with MS. Attentional deficits are correlated to the extent of locomotor rehabilitation, suggesting the presence of a subtle effect of cognition.

Altered transient brain dynamics in multiple sclerosis: Treatment or pathology?

Multiple sclerosis (MS) is a demyelinating, neuroinflammatory, and -degenerative disease that affects the brain's neurophysiological functioning through brain atrophy, a reduced conduction velocity and decreased connectivity. Currently, little is known on how MS affects the fast temporal dynamics of activation and deactivation of the different large-scale, ongoing brain networks. In this study, we investigated whether these temporal dynamics are affected in MS patients and whether these changes are induced by the pathology or by the use of benzodiazepines (BZDs), an important symptomatic treatment that aims at reducing insomnia, spasticity and anxiety and reinforces the inhibitory effect of GABA. To this aim, we employed a novel method capable of detecting these fast dynamics in 90 MS patients and 46 healthy controls. We demonstrated a less dynamic frontal default mode network in male MS patients and a reduced activation of the same network in female MS patients, regardless of BZD usage. Additionally, BZDs strongly altered the brain's dynamics by increasing the time spent in the deactivating sensorimotor network and the activating occipital network. Furthermore, BZDs induced a decreased power in the theta band and an increased power in the beta band. The latter was strongly expressed in those states without activation of the sensorimotor network. In summary, we demonstrate gender-dependent changes to the brain dynamics in the frontal DMN and strong effects from BZDs. This study is the first to characterise the effect of multiple sclerosis and BZDs in vivo in a spatially, temporally and spectrally defined way.

Callosal circularity as an early marker for Alzheimer's disease

Background

Although brain atrophy is considered to be a downstream marker of Alzheimer's disease (AD), subtle changes may allow to identify healthy subjects at risk of developing AD. As the ability to select at-risk persons is considered to be important to assess the efficacy of drugs and as MRI is a widely available imaging technique we have recently developed a reliable segmentation algorithm for the corpus callosum (CC). Callosal atrophy within AD has been hypothesized to reflect both myelin breakdown and Wallerian degeneration.

Methods

We applied our fully automated segmentation and feature extraction algorithm to two datasets: the OASIS database consisting of 316 healthy controls (HC) and 100 patients affected by either mild cognitive impairment (MCI) or Alzheimer's disease dementia (ADD) and a second database that was collected at the Memory Clinic of Hospital Network Antwerp and consists of 181 subjects, including healthy controls, subjects with subjective cognitive decline (SCD), MCI, and ADD. All subjects underwent (among others) neuropsychological testing including the Mini-Mental State Examination (MMSE). The extracted features were the callosal area (CCA), the circularity (CIR), the corpus callosum index (CCI) and the thickness profile.

Results

CIR and CCI differed significantly between most groups. Furthermore, CIR allowed us to discriminate between SCD and HC with an accuracy of 77%. The more detailed callosal thickness profile provided little added value towards the discrimination of the different AD stages. The largest effect of normal ageing on callosal thickness was found in the frontal callosal midbody.

Conclusions

To the best of our knowledge, this is the first study investigating changes in corpus callosum morphometry in normal ageing and AD by exploring both summarizing features (CCA, CIR and CCI) and the complete CC thickness profile in two independent cohorts using a completely automated algorithm. We showed that callosal circularity allows to discriminate between an important subgroup of the early AD spectrum (SCD) and age and sex matched healthy controls.

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Callosal circularity allows to discriminate between subjects with subjective cognitive decline and matched healthy controls

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Callosal circularity is smaller in subjects with AD dementia as compared to matched subjects with mild cognitive impairment

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The callosal thickness profile differs between AD and HC, but not between the different clinical AD stages

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The AD thickness profile strongly correlates with age in HCs

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Callosal circularity correlates with CSF biomarkers (T-tau and P-tau) in MCI.

Targeting Cognitive Impairment in Multiple Sclerosis-The Road toward an Imaging-based Biomarker

Multiple Sclerosis (MS) is a neuro-degenerative and -inflammatory disease leading to physical and cognitive impairment, pathological fatigue and depression, and affecting patients' quality of life and employment status. The combination of inflammation, demyelination, and neurodegeneration leads to the emergence of MS lesions, reduced white and gray matter brain volumes, a reduced conduction velocity and microstructural changes in the so-called Normal Appearing White Matter (NAWM). Currently, there are very limited options to treat cognitive impairment and its origin is only poorly understood. Therefore, several studies have attempted to relate clinical scores with features calculated either using T1- and/or FLAIR weighted MR images or using neurophysiology. The aim of those studies is not only to provide an improved understanding of the processes that underlie the different symptoms, but also to develop a biomarker—sensitive to therapy induced change—that could be used to speed up therapeutic developments (e.g., cognitive training/drug discovery/…). Here, we provide an overview of studies that have established relationships between either neuro-anatomical or neurophysiological measures and cognitive outcome scores. We discuss different avenues that may help to improve the prediction of cognitive impairment, and how well we can expect them to predict cognitive scores.

Reliability of measuring regional callosal atrophy in neurodegenerative diseases

The Corpus Callosum (CC) is an important structure connecting the two brain hemispheres. As several neurodegenerative diseases are known to alter its shape, it is an interesting structure to assess as biomarker. Yet, currently, the CC-segmentation is often performed manually and is consequently an error prone and time-demanding procedure. In this paper, we present an accurate and automated method for corpus callosum segmentation based on T1-weighted MRI images.

After the initial construction of a CC atlas based on healthy controls, a new image is subjected to a mid-sagittal plane (MSP) detection algorithm and a 3D affine registration in order to initialise the CC within the extracted MSP. Next, an active shape model is run to extract the CC. We calculated the reliability of most popular CC features (area, circularity, corpus callosum index and thickness profile) in healthy controls, Alzheimer's Disease patients and Multiple Sclerosis patients. Importantly, we also provide inter-scanner reliability estimates.

We obtained an intra-class correlation coefficient (ICC) of over 0.95 for most features and most datasets. The inter-scanner reliability assessed on the MS patients was remarkably well and ranged from 0.77 to 0.97.

In summary, we have constructed an algorithm that reliably detects the CC in 3D T1 images in a fully automated way in healthy controls and different neurodegenerative diseases. Although the CC area and the circularity are the most reliable features (ICC > 0.97); the reliability of the thickness profile (ICC > 0.90; excluding the tip) is sufficient to warrant its inclusion in future clinical studies.

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A completely automated segmentation of the Corpus Callosum

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Both traditional features and the thickness profile using Laplace's equation are calculated.

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Excellent reproducibility and accuracy in healthy controls

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Excellent reproducibility and accuracy in Alzheimer's Dementia and Multiple Sclerosis patients

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Excellent inter-scanner reliability enabling the pooling of multi-center data

Do advanced statistical techniques really help in the diagnosis of the metabolic syndrome in patients treated with second-generation antipsychotics?

OBJECTIVE

Metabolic and cardiovascular diseases in patients with schizophrenia have gained a lot of interest in recent years. Developing an algorithm to detect the metabolic syndrome based on readily available variables would eliminate the need for blood sampling, which is considered expensive and inconvenient in this population.

METHOD

All patients fulfilled DSM-IV diagnosis of schizophrenia or schizoaffective disorder. We used the International Diabetes Federation criteria (European population) to diagnose the metabolic syndrome. We used logistic regression and optimized artificial neural networks and support vector machines to detect the metabolic syndrome in a cohort of schizophrenic patients of the University Psychiatric Center Kortenberg, KU Leuven, Belgium. Testing was done on one-third of the included cohort (202 patients); training was performed using a 10-fold stratified cross-validation scheme. The data were collected between 2000 and 2008.

RESULTS

All 3 methods yielded similar results, with satisfying accuracies of about 80%. However, none of the advanced statistical methods could improve on the results obtained using a very simple and naive model including only central obesity and information on blood pressure.

CONCLUSIONS

Although so-called pattern recognition techniques bear high promise in improving clinical decision making, the results should be presented with caution and preferably in comparison with a less complicated technique.

Single-subject classification of schizophrenia patients based on a combination of oddball and mismatch evoked potential paradigms

OBJECTIVE

The diagnostic process for schizophrenia is mainly clinical and has to be performed by an experienced psychiatrist, relying primarily on clinical signs and symptoms. Current neurophysiological measurements can distinguish groups of healthy controls and groups of schizophrenia patients. Individual classification based on neurophysiological measurements mostly shows moderate accuracy. We wanted to examine whether it is possible to distinguish controls and patients individually with a good accuracy. To this end we used a combination of features extracted from the auditory and visual P300 paradigms and the mismatch negativity paradigm.

METHODS

We selected 54 patients and 54 controls, matched for age and gender, from the data available at the UPC Kortenberg. The EEG-data were high- and low-pass filtered, epoched and averaged. Features (latencies and amplitudes of component peaks) were extracted from the averaged signals. The resulting dataset was used to train and test classification algorithms. First on separate paradigms and then on all combinations, we applied Naïve Bayes, Support Vector Machine and Decision Tree, with two of its improvements: Adaboost and Random Forest.

RESULTS

For at least two classifiers the performance increased significantly by combining paradigms compared to single paradigms. The classification accuracy increased from at best 79.8% when trained on features from single paradigms, to 84.7% when trained on features from all three paradigms.

CONCLUSION

A combination of features originating from three evoked potential paradigms allowed us to accurately classify individual subjects as either control or patient. Classification accuracy was mostly above 80% for the machine learners evaluated in this study and close to 85% at best.

Reduced information processing speed as primum movens for cognitive decline in MS

Background:

Cognitive impairment affects half of the multiple sclerosis (MS) patient population and is an important contributor to patients’ daily activities. Most cognitive impairment studies in MS are, however, cross-sectional or/and focused on the early disease stages.

Objective:

We aim to assess the time course of decline of different cognitive domains.

Methods:

We collected neuropsychological data on 514 MS patients to construct Kaplan-Meier survival curves of the tests included in the Neuropsychological Screening Battery for MS (NSBMS) and the Symbol Digit Modalities Test (SDMT). Cox-proportional hazard models were constructed to examine the influence of MS onset type, age at onset, gender, depression and level of education on the time course, expressed as age or disease.

Results:

Survival curves of tests focusing on information processing speed (IPS) declined significantly faster than tests with less specific demands of IPS. Median age for pathological decline was 56.2 years (95% CI: 54.4–58.2) on the SDMT and 63.9 years (95% CI: 60–66.9) on the CLTR, a memory task.

Conclusion:

In conclusion, IPS is the cognitive domain not only most widely affected by MS but it is also the first cognitive deficit to emerge in MS.

Graph theoretical analysis indicates cognitive impairment in MS stems from neural disconnection

BACKGROUND

The mechanisms underlying cognitive impairment in MS are still poorly understood. However, due to the specific pathology of MS, one can expect alterations in connectivity leading to physical and cognitive impairment.

AIM

In this study we aimed at assessing connectivity differences in EEG between cognitively impaired (CI) and cognitively preserved (CP) MS patients. We also investigated the influence of the measures used to construct networks.

METHODS

We included 308 MS patients and divided them into two groups based on their cognitive score. Graph theoretical network analyses were conducted based on networks constructed using different connectivity measures, i.e. correlation, correlation in the frequency domain, coherence, partial correlation, the phase lag index and the imaginary part of coherency. The most commonly encountered network parameters were calculated and compared between the two groups using Wilcoxon's rank test. Clustering coefficients and path lengths were normalized to a randomized mean clustering coefficient and path length for each patient. False discovery rate was used to correct for the multiple comparisons and Cohen's d effect sizes are reported.

RESULTS

Coherence analysis suggests that theta and delta connectivity is significantly smaller in cognitively impaired patients. Small-worldness differences are found in networks based on correlation, theta and delta coherence and correlation in the frequency domain. Modularity was related to age but not to cognition.

CONCLUSION

Cognitive deterioration in MS is a symptom that seems to be caused by neural disconnections, probably the white matter tracts connecting both hemispheres, and leads to a wide range in network differences which can be assessed by applying GTA to EEG data. In the future, these results may lead to cheaper and more objective assessments of cognitive impairment in MS.

Relative contribution of cognitive and physical disability components to quality of life in MS

BACKGROUND

Information on the relative influence of cognitive and physical impairment on the quality of life in multiple sclerosis is currently limited and no scientific consensus has been reached yet.

OBJECTIVE

For this reason, we wanted to examine the relative contribution of cognitive and physical impairment measures comprised in the MSFC test on quality of life in MS.

METHODS

In the National MS Center Melsbroek, patients regularly undergo MSFC and EQ5D measurements. We investigated the correlations between the EQ5D, EQVAS and the MSFC and EDSS scores by the use of ANOVA and multilinear models.

RESULTS

We found a significant correlation between the EQVAS score and cognition in a univariate model. When including EDSS score and MSFC outcomes into the model, cognition was, however, excluded based on the Akaike Information Criterion. Cognition was, on the other hand, a significant predictor for the "Usual Activities" question of the EQ5D.

CONCLUSIONS

Although cognitive performance as measured on the PASAT-3s does not correlate with a patient's perceived quality of life in a multivariate model, it remains an important predictor for the patient's usual activities.