Localised grey matter atrophy in multiple sclerosis is network-based: a coordinate-based meta-analysis

Cortical lesions impact cognitive decline in multiple sclerosis via volume loss of nonlesional cortex

OBJECTIVE

To assess the interrelationship between cortical lesions and cortical thinning and volume loss in people with multiple sclerosis within cortical networks, and how this relates to future cognition.

METHODS

In this longitudinal study, 230 people with multiple sclerosis and 60 healthy controls underwent 3 Tesla MRI at baseline and neuropsychological assessment at baseline and 5-year follow-up. Cortical regions (N = 212) were divided into seven functional networks. Regions were defined as either lesional or normal-appearing cortex based on presence of a cortical lesion on artificial intelligence-generated double inversion-recovery scans. Cortical volume and thickness were determined within lesional or normal-appearing cortex.

RESULTS

Prevalence of at least one cortical lesion was highest in the limbic (73%) followed by the default mode network (70.9%). Multiple sclerosis-related cortical thinning was more pronounced in lesional (mean Z-score = 0.70 ± 0.84) compared to normal-appearing cortex (-0.45 ± 0.60; P < 0.001) in all, except sensorimotor, networks. Cognitive dysfunction, particularly of verbal memory, visuospatial memory, and inhibition, at follow-up was best predicted by baseline network volume of normal-appearing cortex of the default mode network [B (95% CI) = 0.31 (0.18; 0.43), P < 0.001]. Mediation analysis showed that the effect of cortical lesions on future cognition was mediated by volume loss of the normal-appearing instead of lesional cortex, independent of white matter lesion volume.

INTERPRETATION

Multiple sclerosis-related cortical thinning was worse in lesional compared to normal-appearing cortex, while volume loss of normal-appearing cortex was most predictive of subsequent cognitive decline, particularly in the default mode network. Mediation analyses indicate that cortical lesions impact cognitive decline plausibly by inducing atrophy, rather than through a direct effect.

Sleep quality in multiple sclerosis: A systematic review and meta-analysis based on Pittsburgh Sleep Quality Index

BACKGROUND

Sleep quality is individual satisfaction with the sleep experience and the Pittsburgh Sleep Quality Index (PSQI), one of the most reliable subjective measurements of sleep quality, assesses the quality of sleep over the previous month. This study aimed to explore the sleep quality in multiple sclerosis (MS) patients in comparison to healthy controls (HCs).

METHODS

Following the Joanna Briggs Institute (JBI) methods and PRISMA statement, a systematic search was conducted through PubMed, Web of Science, Scopus, and Embase online databases and studies that assessed the sleep quality based on the PSQI, in MS patients and HCs were included. The risk of bias in the included studies was assessed using the JBI critical appraisal tools and meta-analysis was conducted by the third version of Comprehensive Meta-Analysis (CMA3) software.

RESULTS

Out of 1574 identified records, 13 studies were included. Regarding the PSQI scores, the difference was statistically significant between patients with MS and HCs (10 studies; I2:94.59%; Standard difference in means: 1.056; 95%CI: 0.758-1.372; p-value < 0.001). MS patients were found to have more prevalence of poor sleep quality (PSQI > 5); however, the difference was not statistically significant (4 studies; I2: 87.08%; odds ratio: 2.31;95% CIs: 0.82-6.35; p-value: 0.113).

CONCLUSIONS

The limited available evidence suggests that subjective sleep quality is affected by MS and it should be considered by the clinicians for prevention of sleep-related symptoms such as depression and anxiety. Future well-designed prospective studies are needed to reach a comprehensive conclusion in this regard.

FUNDING

This study was supported by the Student Research Committee, Tabriz University of Medical Sciences (Registration Code: 70999).

Neuroimaging Meta-Analyses Reveal Convergence of Interoception, Emotion, and Social Cognition Across Neurodegenerative Diseases

BACKGROUND

Simultaneous interoceptive, emotional, and social cognition deficits are observed across neurodegenerative diseases. Indirect evidence suggests shared neurobiological bases underlying these impairments, termed the allostatic-interoceptive network (AIN). However, no study has yet explored the convergence of these deficits in neurodegenerative diseases or examined how structural and functional changes contribute to cross-domain impairments.

METHODS

A Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) activated likelihood estimate meta-analysis encompassed studies that met the following inclusion criteria: interoception, emotion, or social cognition tasks; neurodegenerative diseases (behavioral variant frontotemporal dementia, primary progressive aphasias, Alzheimer's disease, Parkinson's disease, multiple sclerosis); and neuroimaging (structural: magnetic resonance imaging voxel-based morphometry; functional: magnetic resonance imaging and fluorodeoxyglucose-positron emission tomography).

RESULTS

Of 20,593 studies, 170 met inclusion criteria (58 interoception, 65 emotion, and 47 social cognition) involving 7032 participants (4963 patients and 2069 healthy control participants). In all participants combined, conjunction analyses revealed AIN involvement of the insula, amygdala, orbitofrontal cortex, anterior cingulate, striatum, thalamus, and hippocampus across domains. In behavioral variant frontotemporal dementia, this conjunction was replicated across domains, with further involvement of the temporal pole, temporal fusiform cortex, and angular gyrus. A convergence of interoception and emotion in the striatum, thalamus, and hippocampus in Parkinson's disease and the posterior insula in primary progressive aphasias was also observed. In Alzheimer's disease and multiple sclerosis, disruptions in the AIN were observed during interoception, but no convergence with emotion was identified.

CONCLUSIONS

Neurodegeneration induces dysfunctional AIN across atrophy, connectivity, and metabolism, more accentuated in behavioral variant frontotemporal dementia. Findings bolster the predictive coding theories of large-scale AIN, calling for more synergistic approaches to understanding interoception, emotion, and social cognition impairments in neurodegeneration.

Causal relationships of grey matter structures in multiple sclerosis and neuromyelitis optica spectrum disorder: insights from Mendelian randomization

Multiple sclerosis and neuromyelitis optica spectrum disorder are two debilitating inflammatory demyelinating diseases of the CNS. Although grey matter alterations have been linked to both multiple sclerosis and neuromyelitis optica spectrum disorder in observational studies, it is unclear whether these associations indicate causal relationships between these diseases and grey matter changes. Therefore, we conducted a bidirectional two-sample Mendelian randomization analysis to investigate the causal relationships between 202 grey matter imaging-derived phenotypes (33 224 individuals) and multiple sclerosis (47 429 cases and 68 374 controls) as well as neuromyelitis optica spectrum disorder (215 cases and 1244 controls). Our results suggested that genetically predicted multiple sclerosis was positively associated with the surface area of the left parahippocampal gyrus (β = 0.018, P = 2.383 × 10-4) and negatively associated with the volumes of the bilateral caudate (left: β = -0.020, P = 7.203 × 10-5; right: β = -0.021, P = 3.274 × 10-5) and putamen nuclei (left: β = -0.030, P = 2.175 × 10-8; right: β = -0.024, P = 1.047 × 10-5). In addition, increased neuromyelitis optica spectrum disorder risk was associated with an increased surface area of the left paracentral gyrus (β = 0.023, P = 1.025 × 10-4). Conversely, no evidence was found for the causal impact of grey matter imaging-derived phenotypes on disease risk in the opposite direction. We provide suggestive evidence that genetically predicted multiple sclerosis and neuromyelitis optica spectrum disorder are associated with increased cortical surface area and decreased subcortical volume in specific regions. Our findings shed light on the associations of grey matter alterations with the risk of multiple sclerosis and neuromyelitis optica spectrum disorder.

Personalized estimates of morphometric similarity in multiple sclerosis and neuromyelitis optica spectrum disorders

Brain morphometric alterations involve multiple brain regions on progression of the disease in multiple sclerosis (MS) and neuromyelitis optica spectrum disorder (NMOSD) and exhibit age-related degenerative changes during the pathological aging. Recent advance in brain morphometry as measured using MRI have leveraged Person-Based Similarity Index (PBSI) approach to assess the extent of within-diagnosis similarity or heterogeneity of brain neuroanatomical profiles between individuals of healthy populations and validate in neuropsychiatric disorders. Brain morphometric changes throughout the lifespan would be invaluable for understanding regional variability of age-related structural degeneration and the substrate of inflammatory demyelinating disease. Here, we aimed to quantify the neuroanatomical profiles with PBSI measures of cortical thickness (CT) and subcortical volumes (SV) in 263 MS, 207 NMOSD, and 338 healthy controls (HC) from six separate central datasets (aged 11-80). We explored the between-group comparisons of PBSI measures, as well as the advancing age and sex effects on PBSI measures. Compared to NMOSD, MS showed a lower extent of within-diagnosis similarity. Significant differences in regional contributions to PBSI score were observed in 29 brain regions between MS and NMOSD (P < 0.05/164, Bonferroni corrected), of which bilateral cerebellum in MS and bilateral parahippocampal gyrus in NMOSD represented the highest divergence between the two patient groups, with a high similarity effect within each group. The PBSI scores were generally lower with advancing age, but their associations showed different patterns depending on the age range. For MS, CT profiles were significantly negatively correlated with age until the early 30 s (ρ = -0.265, P = 0.030), while for NMOSD, SV profiles were significantly negatively correlated with age with 51 year-old and older (ρ = -0.365, P = 0.008). The current study suggests that PBSI approach could be used to quantify the variation in brain morphometric changes in CNS inflammatory demyelinating disease, and exhibited a greater neuroanatomical heterogeneity pattern in MS compared with NMOSD. Our results reveal that, as an MR marker, PBSI may be sensitive to distribute the disease-associated grey matter diversity and complexity. Disease-driven production of regionally selective and age stage-dependency changes in the neuroanatomical profile of MS and NMOSD should be considered to facilitate the prediction of clinical outcomes and assessment of treatment responses.

Sleep in multiple sclerosis: a systematic review and meta-analysis of polysomnographic findings

STUDY OBJECTIVES

This study aims to explore the polysomnographically measured sleep differences between patients with multiple sclerosis (MS) and healthy control patients.

METHODS

An electronic literature search was conducted in EMBASE, MEDLINE, all EBM databases, CINAHL, and PsycINFO from inception to March 2022. A random-effects model was applied to explore the pooled effect sizes of polysomnographic differences between patients with MS and control patients.

RESULTS

Thirteen studies were identified for meta-analysis. The meta-analyses revealed significant reductions in stage N2 sleep and sleep efficiency and increases in wake time after sleep onset, the periodic limb movement index, and the periodic limb movement arousal index in patients with MS compared with control patients. Meta-regression analyses showed that some of the heterogeneity was explained by age and daytime sleepiness of patients with MS.

CONCLUSIONS

Our study showed that polysomnographic abnormalities are present in MS. Our findings also underscore the need for a comprehensive polysomnographic assessment of sleep changes in patients with MS. Furthermore, the effects of age and daytime sleepiness in patients with MS on sleep changes should also be carefully considered and closely monitored in the management of MS.

CITATION

Zhang Y, Ren R, Yang L, et al. Sleep in multiple sclerosis: a systematic review and meta-analysis of polysomnographic findings. J Clin Sleep Med. 2023;19(2):253-265.

Co-alteration Network Architecture of Major Depressive Disorder: A Multi-modal Neuroimaging Assessment of Large-scale Disease Effects

Major depressive disorder (MDD) exhibits diverse symptomology and neuroimaging studies report widespread disruption of key brain areas. Numerous theories underpinning the network degeneration hypothesis (NDH) posit that neuropsychiatric diseases selectively target brain areas via meaningful network mechanisms rather than as indistinct disease effects. The present study tests the hypothesis that MDD is a network-based disorder, both structurally and functionally. Coordinate-based meta-analysis and Activation Likelihood Estimation (CBMA-ALE) were used to assess the convergence of findings from 92 previously published studies in depression. An extension of CBMA-ALE was then used to generate a node-and-edge network model representing the co-alteration of brain areas impacted by MDD. Standardized measures of graph theoretical network architecture were assessed. Co-alteration patterns among the meta-analytic MDD nodes were then tested in independent, clinical T1-weighted structural magnetic resonance imaging (MRI) and resting-state functional (rs-fMRI) data. Differences in co-alteration profiles between MDD patients and healthy controls, as well as between controls and clinical subgroups of MDD patients, were assessed. A 65-node 144-edge co-alteration network model was derived for MDD. Testing of co-alteration profiles in replication data using the MDD nodes provided distinction between MDD and healthy controls in structural data. However, co-alteration profiles were not distinguished between patients and controls in rs-fMRI data. Improved distinction between patients and healthy controls was observed in clinically homogenous MDD subgroups in T1 data. MDD abnormalities demonstrated both structural and functional network architecture, though only structural networks exhibited between-groups differences. Our findings suggest improved utility of structural co-alteration networks for ongoing biomarker development.

Individual differences in visual evoked potential latency are associated with variance in brain tissue volume in people with multiple sclerosis: An analysis of brain function-structure correlates

Visual evoked potentials (VEP) index visual pathway functioning, and are often used for clinical assessment and as outcome measures in people with multiple sclerosis (PwMS). VEPs may also reflect broader neural disturbances that extend beyond the visual system, but this possibility requires further investigation. In the present study, we examined the hypothesis that delayed latency of the P100 component of the VEP would be associated with broader structural changes in the brain in PwMS. We obtained VEP latency for a standard pattern-reversal checkerboard stimulus paradigm, in addition to Magnetic Resonance Imaging (MRI) measures of whole brain volume (WBV), gray matter volume (GMV), white matter volume (WMV), and T2-weighted fluid attenuated inversion recovery (FLAIR) white matter lesion volume (FLV). Correlation analyses indicated that prolonged VEP latency was significantly associated with lower WBV, GMV, and WMV, and greater FLV. VEP latency remained significantly associated with WBV, GMV, and WMV even after controlling for the variance associated with inter-ocular latency, age, time between VEP and MRI assessments, and other MRI variables. VEP latency delays were most pronounced in PwMS that exhibited low volume in both white and gray matter simultaneously. Furthermore, PwMS that had delayed VEP latency based on a clinically relevant cutoff (VEP latency ≥ 113 ms) in both eyes had lower WBV, GMV, and WMV and greater FLV in comparison to PwMS that had normal VEP latency in one or both eyes. The findings suggest that PwMS that have delayed latency in both eyes may be particularly at risk for exhibiting greater brain atrophy and lesion volume. These analyses also indicate that VEP latency may index combined gray matter and white matter disturbances, and therefore broader network connectivity and efficiency. VEP latency may therefore provide a surrogate marker of broader structural disturbances in the brain in MS.

Structural and Functional Characterization of Gray Matter Alterations in Female Patients With Neuropsychiatric Systemic Lupus

Objective

To investigate morphological and functional alterations within gray matter (GM) in female patients with neuropsychiatric systemic lupus (NPSLE) and to explore their clinical significance.

Methods

54 female patients with SLE (30 NPSLE and 24 non-NPSLE) and 32 matched healthy controls were recruited. All subjects received a quantitative MRI scan (FLAIR, 3DT1, resting-state functional MRI). GM volume (GMV), fractional amplitude of low-frequency fluctuation (fALFF), regional homogeneity (ReHo), and degree of centrality (DC) were obtained. Between-group comparison, clinical correlation, and discrimination of NPSLE from non-NPSLE were achieved by voxel-based analysis, cerebellar seed-based functional connectivity analysis, regression analysis, and support vector machine (SVM), respectively.

Results

Patients with NPSLE showed overt subcortical GM atrophy without significantly abnormal brain functions in the same region compared with controls. The dysfunction within the left superior temporal gyri (L-STG) was found precede the GM volumetric loss. The function of the nodes in default mode network (DMN) and salience network (SN) were weakened in NPSLE patients compared to controls. The function of the cerebellar posterior lobes was significantly activated in non-NPSLE patients but attenuated along with GM atrophy and presented higher connectivity with L-STG and DMN in NPSLE patients, while the variation of the functional activities in the sensorimotor network (SMN) was the opposite. These structural and functional alterations were mainly correlated with disease burden and anti-phospholipid antibodies (aPLs) (r ranges from -1.53 to 1.29). The ReHos in the bilateral cerebellar posterior lobes showed high discriminative power in identifying patients with NPSLE with accuracy of 87%.

Conclusion

Patients with NPSLE exhibit both structural and functional alterations in the GM of the brain, which especially involved the deep GM, the cognitive, and sensorimotor regions, reflecting a reorganization to compensate for the disease damage to the brain which was attenuated along with pathologic burden and cerebral vascular risk factors. The GM within the left temporal lobe may be one of the direct targets of lupus-related inflammatory attack. The function of the cerebellar posterior lobes might play an essential role in compensating for cortical functional disturbances and may contribute to identifying patients with suspected NPSLE in clinical practice.

Advanced brain ageing in Parkinson's disease is related to disease duration and individual impairment

Machine learning can reliably predict individual age from MRI data, revealing that patients with neurodegenerative disorders show an elevated biological age. A surprising gap in the literature, however, pertains to Parkinson's disease. Here, we evaluate brain age in two cohorts of Parkinson's patients and investigated the relationship between individual brain age and clinical characteristics. We assessed 372 patients with idiopathic Parkinson's disease, newly diagnosed cases from the Parkinson's Progression Marker Initiative database and a more chronic local sample, as well as age- and sex-matched healthy controls. Following morphometric preprocessing and atlas-based compression, individual brain age was predicted using a multivariate machine learning model trained on an independent, multi-site reference sample. Across cohorts, healthy controls were well predicted with a mean error of 4.4 years. In turn, Parkinson's patients showed a significant (controlling for age, gender and site) increase in brain age of ∼3 years. While this effect was already present in the newly diagnosed sample, advanced biological age was significantly related to disease duration as well as worse cognitive and motor impairment. While biological age is increased in patients with Parkinson's disease, the effect is at the lower end of what is found for other neurological and psychiatric disorders. We argue that this may reflect a heterochronicity between forebrain atrophy and small but behaviourally salient midbrain pathology. Finally, we point to the need to disentangle physiological ageing trajectories, lifestyle effects and core pathological changes.

Brain pathology recapitulates physiology: A network meta-analysis

Network architecture is a brain-organizational motif present across spatial scales from cell assemblies to distributed systems. Structural pathology in some neurodegenerative disorders selectively afflicts a subset of functional networks, motivating the network degeneration hypothesis (NDH). Recent evidence suggests that structural pathology recapitulating physiology may be a general property of neuropsychiatric disorders. To test this possibility, we compared functional and structural network meta-analyses drawing upon the BrainMap database. The functional meta-analysis included results from >7,000 experiments of subjects performing >100 task paradigms; the structural meta-analysis included >2,000 experiments of patients with >40 brain disorders. Structure-function network concordance was high: 68% of networks matched (pFWE < 0.01), confirming the broader scope of NDH. This correspondence persisted across higher model orders. A positive linear association between disease and behavioral entropy (p = 0.0006;R2 = 0.53) suggests nodal stress as a common mechanism. Corroborating this interpretation with independent data, we show that metabolic 'cost' significantly differs along this transdiagnostic/multimodal gradient.

Disruption of the Atrophy-based Functional Network in Multiple Sclerosis Is Associated with Clinical Disability: Validation of a Meta-Analytic Model in Resting-State Functional MRI

Background In multiple sclerosis (MS), gray matter (GM) atrophy exhibits a specific pattern, which correlates strongly with clinical disability. However, the mechanism of regional specificity in GM atrophy remains largely unknown. Recently, the network degeneration hypothesis (NDH) was quantitatively defined (using coordinate-based meta-analysis) as the atrophy-based functional network (AFN) model, which posits that localized GM atrophy in MS is mediated by functional networks. Purpose To test the NDH in MS in a data-driven manner using the AFN model to direct analyses in an independent test sample. Materials and Methods Model fit testing was conducted with structural equation modeling, which is based on the computation of semipartial correlations. Model verification was performed in coordinate-based data of healthy control participants from the BrainMap database (https://www.brainmap.org). Model validation was conducted in prospectively acquired resting-state functional MRI in participants with relapsing-remitting MS who were recruited between September 2018 and January 2019. Correlation analyses of model fit indices and volumetric measures with Expanded Disability Status Scale (EDSS) scores and disease duration were performed. Results Model verification of healthy control participants included 80 194 coordinates from 9035 experiments. Model verification in healthy control data resulted in excellent model fit (root mean square error of approximation, 0.037; 90% CI: 0.036, 0.039). Twenty participants (mean age, 36 years ± 9 [standard deviation]; 12 women) with relapsing-remitting MS were evaluated. Model validation in resting-state functional MRI in participants with MS resulted in deviation from optimal model fit (root mean square error of approximation, 0.071; 90% CI: 0.070, 0.072), which correlated with EDSS scores (r = 0.68; P = .002). Conclusion The atrophy-based functional network model predicts functional network disruption in multiple sclerosis (MS), thereby supporting the network degeneration hypothesis. On resting-state functional MRI scans, reduced functional network integrity in participants with MS had a strong positive correlation with clinical disability. © RSNA, 2021 Online supplemental material is available for this article.

Localised Grey Matter Atrophy in Multiple Sclerosis and Clinically Isolated Syndrome-A Coordinate-Based Meta-Analysis, Meta-Analysis of Networks, and Meta-Regression of Voxel-Based Morphometry Studies

Background: Atrophy of grey matter (GM) is observed in the earliest stages of multiple sclerosis (MS) and is associated with cognitive decline and physical disability. Localised GM atrophy in MS can be explored and better understood using magnetic resonance imaging and voxel-based morphometry (VBM). However, results are difficult to interpret due to methodological differences between studies. Methods: Coordinate-based analysis is a way to find the reliably observable results across multiple independent VBM studies. This work uses coordinate-based meta-analysis, meta-analysis of networks, and meta-regression to summarise the evidence from voxel-based morphometry of regional GM hanges in patients with MS and clinically isolated syndrome (CIS), and whether these measured changes are relatable to clinical features. Results: Thirty-four published articles reporting forty-four independent experiments using VBM for the assessment of GM atrophy between MS or CIS patients and healthy controls were identified. Analysis identified eight clusters of consistent cross-study reporting of localised GM atrophy involving both cortical and subcortical regions. Meta-network analysis identified a network-like pattern indicating that GM loss occurs with some symmetry between hemispheres. Meta-regression analysis indicates a relationship between disease duration or age and the magnitude of reported statistical effect in some deep GM structures. Conclusions: These results suggest consistency in MRI-detectible regional GM loss across multiple MS studies, and the estimated effect sizes and symmetries can help design prospective studies to test specific hypotheses.

Impaired Verb-Related Morphosyntactic Production in Multiple Sclerosis: Evidence From Greek

Background

A recent systematic review found that language deficits are not very common in individuals with multiple sclerosis (MS). However, there are significant gaps in our knowledge about language abilities in MS. For instance, morphosyntactic production has not been explored adequately thus far. This study investigated verb-related morphosyntactic production in MS focusing on Greek, a morphologically rich language.

Methods

A sentence completion task tapping into the production of subject-verb agreement, time reference/tense, and grammatical aspect was administered to 39 Greek-speaking individuals with MS [25 individuals with relapsing-remitting MS (RRMS group) and 14 individuals with secondary progressive MS (SPMS group)]. The task included only regular verbs. Generalized linear mixed-effects models were used to investigate the ability of individuals with MS to produce the above-mentioned morphosyntactic categories.

Results

Overall, the RRMS and SPMS groups performed significantly worse than their matched control groups. Moreover, all four groups performed significantly worse on grammatical aspect than on subject-verb agreement and time reference. The difference between subject-verb agreement and time reference was not significant in any of the four groups. The overall performances of the RRMS and SPMS groups did not differ significantly.

Conclusion

Individuals with MS are impaired in verb-related morphosyntactic production. Moreover, the pattern of performance of individuals with MS is identical to that exhibited by neurologically healthy individuals. Thus, the production performance of individuals with MS on verb inflection differs from that of healthy controls quantitatively but not qualitatively.

MRI biomarkers of disease progression and conversion to secondary-progressive multiple sclerosis

INTRODUCTION

Conventional imaging measures remain a key clinical tool for the diagnosis multiple sclerosis (MS) and monitoring of patients. However, most measures used in the clinic show unsatisfactory performance in predicting disease progression and conversion to secondary progressive MS.

AREAS COVERED

Sophisticated imaging techniques have facilitated the identification of imaging biomarkers associated with disease progression, such as global and regional brain volume measures, and with conversion to secondary progressive MS, such as leptomeningeal contrast enhancement and chronic inflammation. The relevance of emerging imaging approaches partially overcoming intrinsic limitations of traditional techniques is also discussed.

EXPERT OPINION

Imaging biomarkers capable of detecting tissue damage early on in the disease, with the potential to be applied in multicenter trials and at an individual level in clinical settings, are strongly needed. Several measures have been proposed, which exploit advanced imaging acquisitions and/or incorporate sophisticated post-processing, can quantify irreversible tissue damage. The progressively wider use of high-strength field MRI and the development of more advanced imaging techniques will help capture the missing pieces of the MS puzzle. The ability to more reliably identify those at risk for disability progression will allow for earlier intervention with the aim to favorably alter the disease course.

Cognitive Impairment and Brain Reorganization in MS: Underlying Mechanisms and the Role of Neurorehabilitation

Multiple sclerosis (MS) is a chronic, immune-mediated, inflammatory, and degenerative disease of the central nervous system (CNS) that affects both white and gray matter. Various mechanisms throughout its course, mainly regarding gray matter lesions and brain atrophy, result in cognitive network dysfunction and can cause clinically significant cognitive impairment in roughly half the persons living with MS. Altered cognition is responsible for many negative aspects of patients' lives, independently of physical disability, such as higher unemployment and divorce rates, reduced social activities, and an overall decrease in quality of life. Despite its devastating impact it is not included in clinical ratings and decision making in the way it should be. It is interesting that only half the persons with MS exhibit cognitive dysfunction, as this implies that the other half remain cognitively intact. It appears that a dynamic balance between brain destruction and brain reorganization is taking place. This balance acts in favor of keeping brain systems functioning effectively, but this is not so in all cases, and the effect does not last forever. When these systems collapse, functional brain reorganization is not effective anymore, and clinically apparent impairments are evident. It is therefore important to reveal which factors could make provision for the subpopulation of patients in whom cognitive impairment occurs. Even if we manage to detect this subpopulation earlier, effective pharmaceutical treatments will still be lacking. Nevertheless, recent evidence shows that cognitive rehabilitation and neuromodulation, using non-invasive techniques such as transcranial magnetic or direct current stimulation, could be effective in cognitively impaired patients with MS. In this Mini Review, we discuss the mechanisms underlying cognitive impairment in MS. We also focus on mechanisms of reorganization of cognitive networks, which occur throughout the disease course. Finally, we review theoretical and practical issues of neurorehabilitation and neuromodulation for cognition in MS as well as factors that influence them and prevent them from being widely applied in clinical settings.