Subcortical volumes as early predictors of fatigue in multiple sclerosis

Hub overload and failure as a final common pathway in neurological brain network disorders

Understanding the concept of network hubs and their role in brain disease is now rapidly becoming important for clinical neurology. Hub nodes in brain networks are areas highly connected to the rest of the brain, which handle a large part of all the network traffic. They also show high levels of neural activity and metabolism, which makes them vulnerable to many different types of pathology. The present review examines recent evidence for the prevalence and nature of hub involvement in a variety of neurological disorders, emphasizing common themes across different types of pathology. In focal epilepsy, pathological hubs may play a role in spreading of seizure activity, and removal of such hub nodes is associated with improved outcome. In stroke, damage to hubs is associated with impaired cognitive recovery. Breakdown of optimal brain network organization in multiple sclerosis is accompanied by cognitive dysfunction. In Alzheimer's disease, hyperactive hub nodes are directly associated with amyloid-beta and tau pathology. Early and reliable detection of hub pathology and disturbed connectivity in Alzheimer's disease with imaging and neurophysiological techniques opens up opportunities to detect patients with a network hyperexcitability profile, who could benefit from treatment with anti-epileptic drugs.

Prognostic value of single-subject grey matter networks in early multiple sclerosis

The identification of prognostic markers in early multiple sclerosis (MS) is challenging and requires reliable measures that robustly predict future disease trajectories. Ideally, such measures should make inferences at the individual level to inform clinical decisions. This study investigated the prognostic value of longitudinal structural networks to predict 5-year Expanded Disability Status Scale (EDSS) progression in patients with relapsing-remitting MS (RRMS). We hypothesized that network measures, derived from MRI, outperform conventional MRI measurements at identifying patients at risk of developing disability progression. This longitudinal, multicentre study within the Magnetic Resonance Imaging in MS (MAGNIMS) network included 406 patients with RRMS (mean age = 35.7 ± 9.1 years) followed up for 5 years (mean follow-up = 5.0 ± 0.6 years). EDSS was determined to track disability accumulation. A group of 153 healthy subjects (mean age = 35.0 ± 10.1 years) with longitudinal MRI served as controls. All subjects underwent MRI at baseline and again 1 year after baseline. Grey matter atrophy over 1 year and white matter lesion load were determined. A single-subject brain network was reconstructed from T1-weighted scans based on grey matter atrophy measures derived from a statistical parameter mapping-based segmentation pipeline. Key topological measures, including network degree, global efficiency and transitivity, were calculated at single-subject level to quantify network properties related to EDSS progression. Areas under receiver operator characteristic (ROC) curves were constructed for grey matter atrophy and white matter lesion load, and the network measures and comparisons between ROC curves were conducted. The applied network analyses differentiated patients with RRMS who experience EDSS progression over 5 years through lower values for network degree [H(2) = 30.0, P < 0.001] and global efficiency [H(2) = 31.3, P < 0.001] from healthy controls but also from patients without progression. For transitivity, the comparisons showed no difference between the groups [H(2) = 1.5, P = 0.474]. Most notably, changes in network degree and global efficiency were detected independent of disease activity in the first year. The described network reorganization in patients experiencing EDSS progression was evident in the absence of grey matter atrophy. Network degree and global efficiency measurements demonstrated superiority of network measures in the ROC analyses over grey matter atrophy and white matter lesion load in predicting EDSS worsening (all P-values < 0.05). Our findings provide evidence that grey matter network reorganization over 1 year discloses relevant information about subsequent clinical worsening in RRMS. Early grey matter restructuring towards lower network efficiency predicts disability accumulation and outperforms conventional MRI predictors.

Basal Ganglia Atrophy and Impaired Cognitive Processing Speed in Multiple Sclerosis

Impaired cognitive processing speed is among the important higher brain dysfunctions in multiple sclerosis (MS). However, the exact structural mechanisms of the dysfunction remain uncertain. This study aimed to identify the brain regions associated with the impaired cognitive processing speed in MS by comparing the cognitive processing speed, measured using the Cognitive Processing Speed Test (CogEval) z-score, and brain regional volumetric data. Altogether, 80 patients with MS (64 with relapsing-remitting MS [RRMS] and 16 with secondary progressive MS [SPMS]) were enrolled. Consequently, CogEval z-scores were worse in patients with SPMS than in those with RRMS (p=0.001). In the univariate correlation analyses, significant correlations with CogEval z-score were suggested in the MS lesion volume (p<0.001; Spearman's rank correlation test) and atrophies in the cerebral cortex (p=0.031), cerebral white matter (p=0.013), corpus callosum (p=0.001), thalamus (p=0.001), and putamen (p<0.001). Multiple linear regression analysis revealed that putamen atrophy was significantly associated with CogEval z-score (p=0.038) independent of volume in other brain regions, while thalamic atrophy was not (p=0.79). Univariate correlation analyses were further performed in each of RRMS and SPMS. None of the evaluated volumetric data indicated a significant correlation with the CogEval z-score in RRMS. Meanwhile, atrophies in the cerebral white matter (p=0.008), corpus callosum (p=0.002), putamen (p=0.011), and pallidum (p=0.017) demonstrated significant correlations with CogEval z-score in SPMS. In summary, the putamen could be an important region of atrophy contributing to the impaired cognitive speed in MS, especially in the later disease stages after a transition to SPMS.

The Cortical Silent Period and Its Association with Fatigue in Multiple Sclerosis: The Need for Standardized Data Collection

The cortical silent period (CSP), assessed with transcranial magnetic stimulation (TMS), provides insights into motor cortex excitability. Alterations in the CSP have been observed in multiple sclerosis (MS), although a comparison of the sometimes contradictory results is difficult due to methodological differences. The aim of this study is to provide a more profound neurophysiological understanding of fatigue's pathophysiology and its relationship to the CSP. Twenty-three patients with MS, along with a matched control group, underwent comprehensive CSP measurements at four intensities (125, 150, 175, and 200% resting motor threshold), while their fatigue levels were assessed using the Fatigue Scale for Motor and Cognitive Functions (FSMC) and its motor and cognitive subscore. MS patients exhibited a significantly increased CSP duration compared to controls (p = 0.02), but CSP duration was not associated with the total FSMC, or the motor or cognitive subscore. Our data suggest a systematic difference in MS patients compared to healthy controls in the CSP but no association with fatigue when measured with the FSMC. Based on these results, and considering the heterogeneous literature in the field, our study highlights the need for a more standardized approach to neurophysiological data collection and validation. This standardization is crucial for exploring the link between TMS and clinical impairments in diseases like MS.

Psychological factors and brain magnetic resonance imaging metrics associated with fatigue in persons with multiple sclerosis

BACKGROUND

Besides demographics and clinical factors, psychological variables and brain-tissue changes have been associated with fatigue in persons with multiple sclerosis (pwMS). Identifying predictors of fatigue could help to improve therapeutic approaches for pwMS. Therefore, we investigated predictors of fatigue using a multifactorial approach.

METHODS

136 pwMS and 49 normal controls (NC) underwent clinical, neuropsychological, and magnetic resonance imaging examinations. We assessed fatigue using the "Fatigue Scale for Motor and Cognitive Functions", yielding a total, motor, and cognitive fatigue score. We further analyzed global and subcortical brain volumes, white matter lesions and microstructural changes (examining fractional anisotropy; FA) along the cortico striatal thalamo cortical (CSTC) loop. Potential demographic, clinical, psychological, and magnetic resonance imaging predictors of total, motor, and cognitive fatigue were explored using multifactorial linear regression models.

RESULTS

53% of pwMS and 20% of NC demonstrated fatigue. Besides demographics and clinical data, total fatigue in pwMS was predicted by higher levels of depression and reduced microstructural tissue integrity in the CSTC loop (adjusted R2 = 0.52, p < 0.001). More specifically, motor fatigue was predicted by lower education, female sex, higher physical disability, higher levels of depression, and self-efficacy (adjusted R2 = 0.54, p < 0.001). Cognitive fatigue was also predicted by higher levels of depression and lower self-efficacy, but in addition by FA reductions in the CSTC loop (adjusted R2 = 0.45, p < 0.001).

CONCLUSIONS

Our results indicate that depression and self-efficacy strongly predict fatigue in MS. Incremental variance in total and cognitive fatigue was explained by microstructural changes along the CSTC loop, beyond demographics, clinical, and psychological variables.

Factors associated with depressive mood at the onset of multiple sclerosis - an analysis of 781 patients of the German NationMS cohort

Background

Depression has a major impact on the disease burden of multiple sclerosis (MS). Analyses of overlapping MS and depression risk factors [smoking, vitamin D (25-OH-VD) and Epstein-Barr virus (EBV) infection] and sex, age, disease characteristics and neuroimaging features associated with depressive symptoms in early MS are scarce.

Objectives

To assess an association of MS risk factors with depressive symptoms within the German NationMS cohort.

Design

Cross-sectional analysis within a multicenter observational study.

Methods

Baseline data of n = 781 adults with newly diagnosed clinically isolated syndrome or relapsing-remitting MS qualified for analysis. Global and region-specific magnetic resonance imaging (MRI)-volumetry parameters were available for n = 327 patients. Association of demographic factors, MS characteristics and risk factors [sex, age, smoking, disease course, presence of current relapse, expanded disability status scale (EDSS) score, fatigue (fatigue scale motor cognition), 25-OH-VD serum concentration, EBV nuclear antigen-1 IgG (EBNA1-IgG) serum levels] and depressive symptoms (Beck Depression Inventory-II, BDI-II) was tested as a primary outcome by multivariable linear regression. Non-parametric correlation and group comparison were performed for associations of MRI parameters and depressive symptoms.

Results

Mean age was 34.3 years (95% confidence interval: 33.6-35.0). The female-to-male ratio was 2.3:1. At least minimal depressive symptoms (BDI-II > 8) were present in n = 256 (32.8%), 25-OH-VD deficiency (<20 ng/ml) in n = 398 (51.0%), n = 246 (31.5%) participants were smokers. Presence of current relapse [coefficient (c) = 1.48, p = 0.016], more severe fatigue (c = 0.26, p < 0.0001), lower 25-OH-VD (c = -0.03, p = 0.034) and smoking (c = 0.35, p = 0.008) were associated with higher BDI-II scores. Sex, age, disease course, EDSS, month of visit, EBNA1-IgG levels and brain volumes at baseline were not.

Conclusion

Depressive symptoms need to be assessed in early MS. Patients during relapse seem especially vulnerable to depressive symptoms. Contributing factors such as fatigue, vitamin D deficiency and smoking, could specifically be targeted in future interventions and should be investigated in prospective studies.

Microstructural changes precede depression in patients with relapsing-remitting Multiple Sclerosis

BACKGROUND

Multiple Sclerosis lesions in the brain and spinal cord can lead to different symptoms, including cognitive and mood changes. In this study we explore the temporal relationship between early microstructural changes in subcortical volumes and cognitive and emotional function in a longitudinal cohort study of patients with relapsing-remitting Multiple Sclerosis.

METHODS

In vivo imaging in forty-six patients with relapsing-remitting Multiple Sclerosis was performed annually over 3 years magnetic resonance imaging. Microstructural changes were estimated in subcortical structures using the free water fraction, a diffusion-based MRI metric. In parallel, patients were assessed with the Hospital Anxiety and Depression Scale amongst other tests. Predictive structural equation modeling was set up to further explore the relationship between imaging and the assessment scores. In a general linear model analysis, the cohort was split into patients with higher and lower depression scores.

RESULTS

Nearly all subcortical diffusion microstructure estimates at the baseline visit correlate with the depression score at the 2 years follow-up. The predictive nature of baseline free water estimates and depression subscores after 2 years are confirmed in the predictive structural equation modeling analysis with the thalamus showing the greatest effect size. The general linear model analysis shows patterns of MRI free water differences in the thalamus and amygdala/hippocampus area between participants with high and low depression score.

CONCLUSIONS

Our data suggests a relationship between higher levels of free-water in the subcortical structures in an early stage of Multiple Sclerosis and depression symptoms at a later stage of the disease.

Structural brain changes in patients with post-COVID fatigue: a prospective observational study

BACKGROUND

Post-COVID syndrome is a severe long-term complication of COVID-19. Although fatigue and cognitive complaints are the most prominent symptoms, it is unclear whether they have structural correlates in the brain. We therefore explored the clinical characteristics of post-COVID fatigue, describe associated structural imaging changes, and determine what influences fatigue severity.

METHODS

We prospectively recruited 50 patients from neurological post-COVID outpatient clinics (age 18-69 years, 39f/8m) and matched non-COVID healthy controls between April 15 and December 31, 2021. Assessments included diffusion and volumetric MR imaging, neuropsychiatric, and cognitive testing. At 7.5 months (median, IQR 6.5-9.2) after the acute SARS-CoV-2 infection, moderate or severe fatigue was identified in 47/50 patients with post-COVID syndrome who were included in the analyses. As a clinical control group, we included 47 matched multiple sclerosis patients with fatigue.

FINDINGS

Our diffusion imaging analyses revealed aberrant fractional anisotropy of the thalamus. Diffusion markers correlated with fatigue severity, such as physical fatigue, fatigue-related impairment in everyday life (Bell score) and daytime sleepiness. Moreover, we observed shape deformations and decreased volumes of the left thalamus, putamen, and pallidum. These overlapped with the more extensive subcortical changes in MS and were associated with impaired short-term memory. While fatigue severity was not related to COVID-19 disease courses (6/47 hospitalised, 2/47 with ICU treatment), post-acute sleep quality and depressiveness emerged as associated factors and were accompanied by increased levels of anxiety and daytime sleepiness.

INTERPRETATION

Characteristic structural imaging changes of the thalamus and basal ganglia underlie the persistent fatigue experienced by patients with post-COVID syndrome. Evidence for pathological changes to these subcortical motor and cognitive hubs provides a key to the understanding of post-COVID fatigue and related neuropsychiatric complications.

FUNDING

Deutsche Forschungsgemeinschaft (DFG) and German Ministry of Education and Research (BMBF).

Neural bases of motor fatigue in multiple sclerosis: A multimodal approach using neuromuscular assessment and TMS-EEG

Motor fatigue is one of the most common symptoms in multiple sclerosis (MS) patients. Previous studies suggested that increased motor fatigue in MS may arise at the central nervous system level. However, the mechanisms underlying central motor fatigue in MS are still unclear. This paper investigated whether central motor fatigue in MS reflects impaired corticospinal transmission or suboptimal primary motor cortex (M1) output (supraspinal fatigue). Furthermore, we sought to identify whether central motor fatigue is associated with abnormal M1 excitability and connectivity within the sensorimotor network. Twenty-two patients affected by relapsing-remitting MS and 15 healthy controls (HCs) performed repeated blocks of contraction at different percentages of maximal voluntary contraction with the right first dorsal interosseus muscle until exhaustion. Peripheral, central, and supraspinal components of motor fatigue were quantified by a neuromuscular assessment based on the superimposed twitch evoked by peripheral nerve and transcranial magnetic stimulation (TMS). Corticospinal transmission, excitability and inhibition during the task were tested by measurement of motor evoked potential (MEP) latency, amplitude, and cortical silent period (CSP). M1 excitability and connectivity was measured by TMS-evoked electroencephalography (EEG) potentials (TEPs) elicited by M1 stimulation before and after the task. Patients completed fewer blocks of contraction and showed higher values of central and supraspinal fatigue than HCs. We found no MEP or CSP differences between MS patients and HCs. Patients showed a post-fatigue increase in TEPs propagation from M1 to the rest of the cortex and in source-reconstructed activity within the sensorimotor network, in contrast to the reduction observed in HCs. Post-fatigue increase in source-reconstructed TEPs correlated with supraspinal fatigue values. To conclude, MS-related motor fatigue is caused by central mechanisms related explicitly to suboptimal M1 output rather than impaired corticospinal transmission. Furthermore, by adopting a TMS-EEG approach, we proved that suboptimal M1 output in MS patients is associated with abnormal task-related modulation of M1 connectivity within the sensorimotor network. Our findings shed new light on the central mechanisms of motor fatigue in MS by highlighting a possible role of abnormal sensorimotor network dynamics. These novel results may point to new therapeutical targets for fatigue in MS.

An investigation of the association between focal damage and global network properties in cognitively impaired and cognitively preserved patients with multiple sclerosis

Introduction

The presence of focal cortical and white matter damage in patients with multiple sclerosis (pwMS) might lead to specific alterations in brain networks that are associated with cognitive impairment. We applied microstructure-weighted connectomes to investigate (i) the relationship between global network metrics and information processing speed in pwMS, and (ii) whether the disruption provoked by focal lesions on global network metrics is associated to patients' information processing speed.

Materials and methods

Sixty-eight pwMS and 92 healthy controls (HC) underwent neuropsychological examination and 3T brain MRI including multishell diffusion (dMRI), 3D FLAIR, and MP2RAGE. Whole-brain deterministic tractography and connectometry were performed on dMRI. Connectomes were obtained using the Spherical Mean Technique and were weighted for the intracellular fraction. We identified white matter lesions and cortical lesions on 3D FLAIR and MP2RAGE images, respectively. PwMS were subdivided into cognitively preserved (CPMS) and cognitively impaired (CIMS) using the Symbol Digit Modalities Test (SDMT) z-score at cut-off value of -1.5 standard deviations. Statistical analyses were performed using robust linear models with age, gender, and years of education as covariates, followed by correction for multiple testing.

Results

Out of 68 pwMS, 18 were CIMS and 50 were CPMS. We found significant changes in all global network metrics in pwMS vs HC (p < 0.05), except for modularity. All global network metrics were positively correlated with SDMT, except for modularity which showed an inverse correlation. Cortical, leukocortical, and periventricular lesion volumes significantly influenced the relationship between (i) network density and information processing speed and (ii) modularity and information processing speed in pwMS. Interestingly, this was not the case, when an exploratory analysis was performed in the subgroup of CIMS patients.

Discussion

Our study showed that cortical (especially leukocortical) and periventricular lesions affect the relationship between global network metrics and information processing speed in pwMS. Our data also suggest that in CIMS patients increased focal cortical and periventricular damage does not linearly affect the relationship between network properties and SDMT, suggesting that other mechanisms (e.g. disruption of local networks, loss of compensatory processes) might be responsible for the development of processing speed deficits.

Monoaminergic network abnormalities: a marker for multiple sclerosis-related fatigue and depression

OBJECTIVE

To investigate monoaminergic network abnormalities in patients with multiple sclerosis (MS) according to their fatigue and depressive status through a positron emission tomography (PET)-based constrained independent component analysis (ICA) on resting state (RS) functional MRI (fMRI).

METHODS

In this prospective study, 213 patients with MS (mean age=40.6±12.5 years; 94/119 men/women; 153 relapsing-remitting; 60 progressive) and 62 healthy controls (HCs, mean age=39.0±10.4 years; 30/32 men/women) underwent neurological, fatigue, depression and RS fMRI assessment. Patterns of dopamine, norepinephrine-related and serotonin-related RS functional connectivity (FC) were derived by ICA, constrained to PET atlases for dopamine, norepinephrine and serotonin transporters, obtained in HCs' brain.

RESULTS

Compared with HCs, patients with MS showed abnormalities in all three explored monoaminergic networks, mostly with decreased RS FC within PET-guided monoaminergic networks in frontal regions and subcortical areas including the cerebellum and thalamus, and increased RS FC in temporo-parieto-occipital cortical areas, including bilateral precunei.MS-related fatigue was associated with decreased RS FC within the PET-guided dopamine network in the left thalamus and left cerebellum, and with increased RS FC within the PET-guided serotonin network in the left middle occipital gyrus. MS-related depression was associated with more distributed abnormalities involving the three explored monoaminergic networks, resulting in overall reduced RS FC in the frontal lobe, limbic areas and the precuneus.

CONCLUSIONS

Patients with MS present diffuse dysregulation in the monoaminergic networks. Specific alterations in these networks were associated with fatigue and depression, providing a pathological marker for these bothersome symptoms and putative targets for their treatment.

Transcranial direct current stimulation for post-COVID fatigue: a randomized, double-blind, controlled pilot study

Fatigue is one of the most frequent and disabling symptoms of the post-COVID syndrome. In this study, we aimed to assess the effects of transcranial direct current stimulation on fatigue severity in a group of patients with post-COVID syndrome and chronic fatigue. We conducted a double-blind, parallel-group, sham-controlled study to evaluate the short-term effects of anodal transcranial direct current stimulation (2 mA, 20 min/day) on the left dorsolateral prefrontal cortex. The modified fatigue impact scale score was used as the primary endpoint. Secondary endpoints included cognition (Stroop test), depressive symptoms (Beck depression inventory) and quality of life (EuroQol-5D). Patients received eight sessions of transcranial direct current stimulation and were evaluated at baseline, immediately after the last session, and one month later. Forty-seven patients were enrolled (23 in the active treatment group and 24 in the sham treatment group); the mean age was 45.66 ± 9.49 years, and 37 (78.72%) were women. The mean progression time since the acute infection was 20.68 ± 6.34 months. Active transcranial direct current stimulation was associated with a statistically significant improvement in physical fatigue at the end of treatment and 1 month as compared with sham stimulation. No significant effect was detected for cognitive fatigue. In terms of secondary outcomes, active transcranial direct current stimulation was associated with an improvement in depressive symptoms at the end of treatment. The treatment had no effects on the quality of life. All the adverse events reported were mild and transient, with no differences between the active stimulation and sham stimulation groups. In conclusion, our results suggest that transcranial direct current stimulation on the dorsolateral prefrontal cortex may improve physical fatigue. Further studies are needed to confirm these findings and optimize stimulation protocols.

Association of obesity with disease outcome in multiple sclerosis

BACKGROUND

Obesity reportedly increases the risk for developing multiple sclerosis (MS), but little is known about its association with disability accumulation.

METHODS

This nationwide longitudinal cohort study included 1066 individuals with newly diagnosed MS from the German National MS cohort. Expanded Disability Status Scale (EDSS) scores, relapse rates, MRI findings and choice of immunotherapy were compared at baseline and at years 2, 4 and 6 between obese (body mass index, BMI ≥30 kg/m²) and non-obese (BMI <30 kg/m²) patients and correlated with individual BMI values.

RESULTS

Presence of obesity at disease onset was associated with higher disability at baseline and at 2, 4 and 6 years of follow-up (p<0.001). Median time to reach EDSS 3 was 0.99 years for patients with BMI ≥30 kg/m² and 1.46 years for non-obese patients. Risk to reach EDSS 3 over 6 years was significantly increased in patients with BMI ≥30 kg/m² compared with patients with BMI <30 kg/m² after adjustment for sex, age, smoking (HR 1.87; 95% CI 1.3 to 2.6; log-rank test p<0.001) and independent of disease-modifying therapies. Obesity was not significantly associated with higher relapse rates, increased number of contrast-enhancing MRI lesions or higher MRI T2 lesion burden over 6 years of follow-up.

CONCLUSIONS

Obesity in newly diagnosed patients with MS is associated with higher disease severity and poorer outcome. Obesity management could improve clinical outcome of MS.

Making Visible the Invisible: Automatically Measured Global and Regional Brain Volume Is Associated with Cognitive Impairment and Fatigue in Multiple Sclerosis

In multiple sclerosis (MS), the transition from relapsing-remitting to the secondary-progressive phase is characterized by a progression independent of relapse activity (PIRA), resulting in physical disability accumulation and invisible symptoms, i.e., fatigue and cognitive impairment (CI). These symptoms are related to neurodegenerative processes and have been correlated with MRI measures of brain atrophy only at a group level; however, the application in clinical practice of atrophy-based measurements for single-patient evaluation is yet to be fully investigated. In the present study, we aimed to evaluate the association between brain atrophy, measured with easy-to-use automatic software, and the "invisible" MS symptoms of cognition and fatigue. A total of 69 MS patients were included in the study; cognitive impairment and fatigue (FSS) (in addition to neurological disability, EDSS) were assessed and correlated with brain volumes calculated using the automated software QyScore^® which is validated for single-patient use in the clinical setting. Results showed that the cognitive status was accurately reflected by measures of atrophy, with a sensitivity of up to 90%. CI patients showed a lower volume compared to cognitively normal patients in the whole brain (p = 0.017), gray matter (p = 0.042), insula (p = 0.035), cerebellum (p = 0.008), and limbic lobe (p = 0.049). FSS was associated with temporal lobe (r = -0.37, p = 0.013) and insular (r = -0.36, p = 0.019) volumes. The volumes of the same regions were also associated with EDSS. The global/regional atrophy results, assessed with automatic and easy-to-use software, correlated with cognitive and fatigue symptoms, thus supporting the clinical application in routine patient management.