Brain mapping in multiple sclerosis: Lessons learned about the human brain

Kynurenine pathway dysregulation as a mechanistic link between cognitive impairment and brain damage: Implications for multiple sclerosis

Cognitive impairment is a core symptom of multiple sclerosis (MS), resulting from inflammation-related brain damage and brain network dysfunction. Inflammation also causes dysregulation of the kynurenine pathway which is the primary route of tryptophan catabolism. Kynurenine pathway dysregulation is characterised by a shift in concentrations of tryptophan catabolites, also referred to as kynurenines. Some kynurenines have neurotoxic effects that partly resemble the molecular mechanisms of MS pathophysiology underpinning brain damage and network dysfunction. The kynurenine pathway may therefore qualify as a mechanistic link between systemic inflammation, brain damage, and cognitive impairment in MS. This perspective article (1) provides an overview of inflammation-related KP dysregulation and MS-relevant neuroimmune properties of kynurenines and (2) summarises the current evidence on associations between systemic kynurenines, imaging metrics of brain structure or related markers, and cognitive performance in populations that present with kynurenine pathway dysregulation and are prone to cognitive impairment. These findings (3) are used to set a research agenda for future studies aimed at clarifying the role of the kynurenine pathway in brain damage and cognitive impairment in MS.

Resting state functional connectivity modifications in monoaminergic circuits underpin fatigue development in patients with multiple sclerosis

Dysregulation of monoaminergic networks might have a role in the pathogenesis of fatigue in multiple sclerosis (MS). We investigated longitudinal changes of resting state (RS) functional connectivity (FC) in monoaminergic networks and their association with the development of fatigue in MS. Eighty-nine MS patients and 49 age- and sex-matched healthy controls (HC) underwent neurological, fatigue, and RS functional MRI assessment at baseline and after a median follow-up of 1.3 years (interquartile range = 1.01-2.01 years). Monoaminergic-related RS FC was estimated with an independent component analysis constrained to PET atlases for dopamine (DA), noradrenaline (NA), and serotonin (5-HT) transporters. At baseline, 24 (27%) MS patients were fatigued (F) and 65 were not fatigued (NF). Of these, 22 (34%) developed fatigue (DEV-FAT) at follow-up and 43 remained not fatigued (NO-FAT). At baseline, F-MS patients showed increased monoaminergic-related RS FC in the caudate nucleus vs NF-MS and in the hippocampal, postcentral, temporal, and occipital cortices vs NF-MS and HC. Moreover, F-MS patients exhibited decreased RS FC in the frontal cortex vs NF-MS and HC, and in the thalamus vs NF-MS. During the follow-up, no RS FC changes were observed in HC. NO-FAT patients showed limited DA-related RS FC modifications, whereas DEV-FAT MS patients showed increased DA-related RS FC in the left hippocampus, significant at time-by-group interaction analysis. In the NA-related network, NO-FAT patients showed decreased RS FC over time in the left superior frontal gyrus. This region showed increased RS FC in both DEV-FAT and F-MS patients; this divergent behavior was significant at time-by-group interaction analysis. Finally, DEV-FAT MS patients presented increased 5-HT-related RS FC in the angular and middle occipital gyri, while this latter region showed decreased 5-HT-related RS FC during the follow-up in F-MS patients. In MS patients, distinct patterns of alterations were observed in monoaminergic networks based on their fatigue status. Fatigue was closely linked to specific changes in the basal ganglia and hippocampal, superior frontal, and middle occipital cortices.

Motor function in multiple sclerosis assessed by navigated transcranial magnetic stimulation mapping

INTRODUCTION

Impaired motor function is a major cause of disability in multiple sclerosis (MS), involving various neuroplasticity processes typically assessed by neuroimaging. This study aimed to determine whether navigated transcranial magnetic stimulation (nTMS) could also provide biomarkers of motor cortex plasticity in patients with MS (pwMS).

METHODS

nTMS motor mapping was performed for hand and leg muscles bilaterally. nTMS variables included the amplitude and latency of motor evoked potentials (MEPs), corticospinal excitability measures, and the size of cortical motor maps (CMMs). Clinical assessment included disability (Expanded Disability Status Scale, EDSS), strength (MRC scale, pinch and grip), and dexterity (9-hole Pegboard Test).

RESULTS

nTMS motor mapping was performed in 68 pwMS. PwMS with high disability (EDSS ≥ 3) had enlarged CMMs with less dense distribution of MEPs and various MEP parameter changes compared to pwMS with low disability (EDSS < 3). Patients with progressive MS had also various MEP parameter changes compared to pwMS with relapsing remitting form. MRC score correlated positively with MEP amplitude and negatively with MEP latency, pinch strength correlated negatively with CMM volume and dexterity with MEP latency.

CONCLUSIONS

This is the first study to perform 4-limb cortical motor mapping in pwMS using a dedicated nTMS procedure. By quantifying the cortical surface representation of a given muscle and the variability of MEP within this representation, nTMS can provide new biomarkers of motor function impairment in pwMS. Our study opens perspectives for the use of nTMS as an objective method for assessing pwMS disability in clinical practice.

Advanced neuroimaging techniques to explore the effects of motor and cognitive rehabilitation in multiple sclerosis

INTRODUCTION

Progress in magnetic resonance imaging (MRI) technology and analyses is improving our comprehension of multiple sclerosis (MS) pathophysiology. These advancements, which enable the evaluation of atrophy, microstructural tissue abnormalities, and functional plasticity, are broadening our insights into the effectiveness and working mechanisms of motor and cognitive rehabilitative treatments.

AREAS COVERED

This narrative review with selected studies discusses findings derived from the application of advanced MRI techniques to evaluate structural and functional neuroplasticity modifications underlying the effects of motor and cognitive rehabilitative treatments in people with MS (PwMS). Current applications as outcome measure in longitudinal trials and observational studies, their interpretation and possible pitfalls and limitations in their use are covered. Finally, we examine how the use of these techniques could evolve in the future to improve monitoring of motor and cognitive rehabilitative treatments.

EXPERT COMMENTARY

Despite substantial variability in study design and participant characteristics in rehabilitative studies for PwMS, improvements in motor and cognitive functions accompanied by structural and functional brain modifications induced by rehabilitation can be observed. However, significant enhancements to refine rehabilitation strategies are needed. Future studies in this field should strive to implement standardized methodologies regarding MRI acquisition and processing, possibly integrating multimodal measures. This will help identifying relevant markers of treatment response in PwMS, thus improving the use of rehabilitative interventions at individual level. The combination of motor and cognitive strategies, longer periods of treatment, as well as adequate follow-up assessments will contribute to enhance the quality of evidence in support of their routine use.

Brain neuroplasticity in multiple sclerosis patients in functional magnetic resonance imaging. Part 1: Comparison with healthy volunteers

Purpose

The aim of this study was to assess the activity of motor cortical areas and the resting brain activity in a group of multiple sclerosis (MS) patients compared to a group of healthy individuals according to task-based functional magnetic resonance imaging (t-fMRI), resting state functional MRI (rs-fMRI), and volumetric MRI studies.

Material and methods

The study enrolled 28 MS patients and 20 healthy volunteers who underwent MRI examinations. Primary motor cortex (M1), premotor area (PMA), supplementary motor area, as well as resting state networks (RSN's) and volumes of selected brain structures were subjected to a detailed analysis.

Results

In MS patients, a motor task more often resulted in the activation of ipsilateral M1 cortex (observed in 39% of the studied group) as well as the PMA cortex (observed in 32% of MS patients). No differences in resting brain activity were found between the studied groups. Significant differences were observed in volumetric parameters of the total brain volume (healthy volunteers vs. MS patients, respectively): (1197 cm³ vs. 1150 cm³) and volumes of the grey matter (517 cm³ vs. 481 cm³), cerebellum (150 cm³ vs. 136 cm³), thalamus (16.3 cm³ vs. 12.6 cm³), putamen (8.9 cm³ vs. 7.7 cm³), and globus pallidus (4.57 cm³ vs. 3.57 cm³).

Conclusions

In the MS patients, the motor task required significantly more frequent activation of the primary and secondary ipsilateral motor cortex compared to the group of healthy volunteers. The rs-fMRI study showed no differences in activity patterns within the RSN's. Differences in the total cerebral volume and the volume of the grey matter, cerebellum, thalamus, putamen, and globus pallidus were observed.

A Radiomic "Warning Sign" of Progression on Brain MRI in Individuals with MS

BACKGROUND AND PURPOSE

MS is a chronic progressive, idiopathic, demyelinating disorder whose diagnosis is contingent on the interpretation of MR imaging. New MR imaging lesions are an early biomarker of disease progression. We aimed to evaluate a machine learning model based on radiomics features in predicting progression on MR imaging of the brain in individuals with MS.

MATERIALS AND METHODS

This retrospective cohort study with external validation on open-access data obtained full ethics approval. Longitudinal MR imaging data for patients with MS were collected and processed for machine learning. Radiomics features were extracted at the future location of a new lesion in the patients' prior MR imaging ("prelesion"). Additionally, "control" samples were obtained from the normal-appearing white matter for each participant. Machine learning models for binary classification were trained and tested and then evaluated the external data of the model.

RESULTS

The total number of participants was 167. Of the 147 in the training/test set, 102 were women and 45 were men. The average age was 42 (range, 21-74 years). The best-performing radiomics-based model was XGBoost, with accuracy, precision, recall, and F1-score of 0.91, 0.91, 0.91, and 0.91 on the test set, and 0.74, 0.74, 0.74, and 0.70 on the external validation set. The 5 most important radiomics features to the XGBoost model were associated with the overall heterogeneity and low gray-level emphasis of the segmented regions. Probability maps were produced to illustrate potential future clinical applications.

CONCLUSIONS

Our machine learning model based on radiomics features successfully differentiated prelesions from normal-appearing white matter. This outcome suggests that radiomics features from normal-appearing white matter could serve as an imaging biomarker for progression of MS on MR imaging.

Diffusion tensor imaging changes of the cortico-thalamic-striatal tracts correlate with fatigue and disability in people with relapsing-remitting MS

PURPOSE

To investigate how the microstructural neural integrity of cortico-thalamic-striatal (CTS) tracts correlate with fatigue and disability over time. The primary outcome was diffusion tensor imaging (DTI) metrics change over time, and the secondary outcome was correlations with fatigue and disability in people with RRMS (pw-RRMS).

METHODS

76 clinically stable pw-RRMS and 43 matched healthy controls (HCs). The pw-RRMS cohort consisted of three different treatment subgroups. All participants underwent disability, cognitive, fatigue and mental health assessments. Structural and diffusion scans were performed at baseline (BL) and 2-year follow-up (2-YFU) for all participants. Fractional anisotropy (FA), mean, radial and axial diffusivities (MD, RD, AD) of normal-appearing white matter (NAWM) and white matter lesion (WML) in nine tracts-of-interests (TOIs) were estimated using our MRtrix3 in-house pipeline.

RESULTS

We found significant BL and 2-YFU differences in most diffusion metrics in TOIs in pw-RRMS compared to HCs (pFDR ≤ 0.001; false-detection-rate (FDR)-corrected). There was a significant decrease in WML diffusivities and an increase in FA over the follow-up period in most TOIs (pFDR ≤ 0.001). Additionally, there were no differences in DTI parameters across treatment groups. AD and MD were positively correlated with fatigue scores (r ≤ 0.33, p ≤ 0.01) in NAWM-TOIs, while disability (EDSS) was negatively correlated with FA in most NAWM-TOIs (|r|≤0.31, p ≤ 0.01) at both time points. Disability scores correlated with all diffusivity parameters (r ≤ 0.29, p ≤ 0.01) in most WML-TOIs at both time points.

CONCLUSION

Statistically significant changes in diffusion metrics in WML might be indicative of integrity improvement over two years in CTS tracts in clinically stable pw-RRMS. This finding represents structural changes within lesioned tracts. Measuring diffusivity in pw-RRMS affected tracts might be a relevant measure for future remyelination clinical trials.

Networks of microstructural damage predict disability in multiple sclerosis

BACKGROUND

Network-based measures are emerging MRI markers in multiple sclerosis (MS). We aimed to identify networks of white (WM) and grey matter (GM) damage that predict disability progression and cognitive worsening using data-driven methods.

METHODS

We analysed data from 1836 participants with different MS phenotypes (843 in a discovery cohort and 842 in a replication cohort). We calculated standardised T1-weighted/T2-weighted (sT1w/T2w) ratio maps in brain GM and WM, and applied spatial independent component analysis to identify networks of covarying microstructural damage. Clinical outcomes were Expanded Disability Status Scale worsening confirmed at 24 weeks (24-week confirmed disability progression (CDP)) and time to cognitive worsening assessed by the Symbol Digit Modalities Test (SDMT). We used Cox proportional hazard models to calculate predictive value of network measures.

RESULTS

We identified 8 WM and 7 GM sT1w/T2w networks (of regional covariation in sT1w/T2w measures) in both cohorts. Network loading represents the degree of covariation in regional T1/T2 ratio within a given network. The loading factor in the anterior corona radiata and temporo-parieto-frontal components were associated with higher risks of developing CDP both in the discovery (HR=0.85, p<0.05 and HR=0.83, p<0.05, respectively) and replication cohorts (HR=0.84, p<0.05 and HR=0.80, p<0.005, respectively). The decreasing or increasing loading factor in the arcuate fasciculus, corpus callosum, deep GM, cortico-cerebellar patterns and lesion load were associated with a higher risk of developing SDMT worsening both in the discovery (HR=0.82, p<0.01; HR=0.87, p<0.05; HR=0.75, p<0.001; HR=0.86, p<0.05 and HR=1.27, p<0.0001) and replication cohorts (HR=0.82, p<0.005; HR=0.73, p<0.0001; HR=0.80, p<0.005; HR=0.85, p<0.01 and HR=1.26, p<0.0001).

CONCLUSIONS

GM and WM networks of microstructural changes predict disability and cognitive worsening in MS. Our approach may be used to identify patients at greater risk of disability worsening and stratify cohorts in treatment trials.

Multiple sclerosis lesion segmentation: revisiting weighting mechanisms for federated learning

Background and introduction

Federated learning (FL) has been widely employed for medical image analysis to facilitate multi-client collaborative learning without sharing raw data. Despite great success, FL's applications remain suboptimal in neuroimage analysis tasks such as lesion segmentation in multiple sclerosis (MS), due to variance in lesion characteristics imparted by different scanners and acquisition parameters.

Methods

In this work, we propose the first FL MS lesion segmentation framework via two effective re-weighting mechanisms. Specifically, a learnable weight is assigned to each local node during the aggregation process, based on its segmentation performance. In addition, the segmentation loss function in each client is also re-weighted according to the lesion volume for the data during training.

Results

The proposed method has been validated on two FL MS segmentation scenarios using public and clinical datasets. Specifically, the case-wise and voxel-wise Dice score of the proposed method under the first public dataset is 65.20 and 74.30, respectively. On the second in-house dataset, the case-wise and voxel-wise Dice score is 53.66, and 62.31, respectively.

Discussions and conclusions

The Comparison experiments on two FL MS segmentation scenarios using public and clinical datasets have demonstrated the effectiveness of the proposed method by significantly outperforming other FL methods. Furthermore, the segmentation performance of FL incorporating our proposed aggregation mechanism can achieve comparable performance to that from centralized training with all the raw data.

Structural and functional magnetic resonance imaging correlates of fatigue and dual-task performance in progressive multiple sclerosis

BACKGROUND

Frontal cortico-subcortical dysfunction may contribute to fatigue and dual-task impairment of walking and cognition in progressive multiple sclerosis (PMS).

PURPOSE

To explore the associations among fatigue, dual-task performance and structural and functional abnormalities of frontal cortico-subcortical network in PMS.

METHODS

Brain 3 T structural and functional MRI sequences, Modified Fatigue Impact Scale (MFIS), dual-task motor and cognitive performances were obtained from 57 PMS patients and 10 healthy controls (HC). The associations of thalamic, caudate nucleus and dorsolateral prefrontal cortex (DLPFC) atrophy, microstructural abnormalities of their connections and their resting state effective connectivity (RS-EC) with fatigue and dual-task performance were investigated using random forest.

RESULTS

Thirty-seven PMS patients were fatigued (F) (MFIS ≥ 38). Compared to HC, non-fatigued (nF) and F-PMS patients had significantly worse dual-task performance (p ≤ 0.002). Predictors of fatigue (out-of-bag [OOB]-accuracy = 0.754) and its severity (OOB-R² = 0.247) were higher Expanded Disability Status scale (EDSS) score, lower RS-EC from left-caudate nucleus to left-DLPFC, lower fractional anisotropy between left-caudate nucleus and left-thalamus, higher mean diffusivity between right-caudate nucleus and right-thalamus, and longer disease duration. Microstructural abnormalities in connections among thalami, caudate nuclei and DLPFC, mainly left-lateralized in nF-PMS and more bilateral in F-PMS, higher RS-EC from left-DLPFC to right-DLPFC in nF-PMS and lower RS-EC from left-caudate nucleus to left-DLPFC in F-PMS, higher EDSS score, higher WM lesion volume, and lower cortical volume predicted worse dual-task performances (OOB-R² from 0.426 to 0.530).

CONCLUSIONS

In PMS, structural and functional frontal cortico-subcortical abnormalities contribute to fatigue and worse dual-task performance, with different patterns according to the presence of fatigue.

Correspondence among gray matter atrophy and atlas-based neurotransmitter maps is clinically relevant in multiple sclerosis

In multiple sclerosis (MS), gray matter (GM) atrophy progresses in a non-random manner, possibly in regions with a high distribution of specific neurotransmitters involved in several relevant central nervous system functions. We investigated the associations among regional GM atrophy, atlas-based neurotransmitter distributions and clinical manifestations in a large MS patients' group. Brain 3 T MRI scans, neurological examinations and neuropsychological evaluations were obtained from 286 MS patients and 172 healthy controls (HC). Spatial correlations among regional GM volume differences and atlas-based nuclear imaging-derived neurotransmitter maps, and their associations with MS clinical features were investigated using voxel-based morphometry and JuSpace toolbox. Compared to HC, MS patients showed widespread GM atrophy being spatially correlated with the majority of neurotransmitter maps (false discovery rate [FDR]-p ≤ 0.004). Patients with a disease duration ≥ 5 vs < 5 years had significant cortical, subcortical and cerebellar atrophy, being spatially correlated with a higher distribution of serotoninergic and dopaminergic receptors (FDR-p ≤ 0.03). Compared to mildly-disabled patients, those with Expanded Disability Status Scale ≥ 3.0 or ≥ 4.0 had significant cortical, subcortical and cerebellar atrophy being associated with serotonergic, dopaminergic, opioid and cholinergic maps (FDR-p ≤ 0.04). Cognitively impaired vs cognitively preserved patients had widespread GM atrophy being spatially associated with serotonergic, dopaminergic, noradrenergic, cholinergic and glutamatergic maps (FDR-p ≤ 0.04). Fatigued vs non-fatigued MS patients had significant cortical, subcortical and cerebellar atrophy, not associated with neurotransmitter maps. No significant association between GM atrophy and neurotransmitter maps was found for depression. Regional GM atrophy with specific neurotransmitter systems may explain part of MS clinical manifestations, including locomotor disability, cognitive impairment and fatigue.

Recent Developments of Silk-Based Scaffolds for Tissue Engineering and Regenerative Medicine Applications: A Special Focus on the Advancement of 3D Printing

Regenerative medicine has received potential attention around the globe, with improving cell performances, one of the necessary ideas for the advancements of regenerative medicine. It is crucial to enhance cell performances in the physiological system for drug release studies because the variation in cell environments between in vitro and in vivo develops a loop in drug estimation. On the other hand, tissue engineering is a potential path to integrate cells with scaffold biomaterials and produce growth factors to regenerate organs. Scaffold biomaterials are a prototype for tissue production and perform vital functions in tissue engineering. Silk fibroin is a natural fibrous polymer with significant usage in regenerative medicine because of the growing interest in leftovers for silk biomaterials in tissue engineering. Among various natural biopolymer-based biomaterials, silk fibroin-based biomaterials have attracted significant attention due to their outstanding mechanical properties, biocompatibility, hemocompatibility, and biodegradability for regenerative medicine and scaffold applications. This review article focused on highlighting the recent advancements of 3D printing in silk fibroin scaffold technologies for regenerative medicine and tissue engineering.

Advances in 3D bioprinting of tissues/organs for regenerative medicine and in-vitro models

Tissue/organ shortage is a major medical challenge due to donor scarcity and patient immune rejections. Furthermore, it is difficult to predict or mimic the human disease condition in animal models during preclinical studies because disease phenotype differs between humans and animals. Three-dimensional bioprinting (3DBP) is evolving into an unparalleled multidisciplinary technology for engineering three-dimensional (3D) biological tissue with complex architecture and composition. The technology has emerged as a key driver by precise deposition and assembly of biomaterials with patient's/donor cells. This advancement has aided in the successful fabrication of in vitro models, preclinical implants, and tissue/organs-like structures. Here, we critically reviewed the current state of 3D-bioprinting strategies for regenerative therapy in eight organ systems, including nervous, cardiovascular, skeletal, integumentary, endocrine and exocrine, gastrointestinal, respiratory, and urinary systems. We also focus on the application of 3D bioprinting to fabricated in vitro models to study cancer, infection, drug testing, and safety assessment. The concept of in situ 3D bioprinting is discussed, which is the direct printing of tissues at the injury or defect site for reparative and regenerative therapy. Finally, issues such as scalability, immune response, and regulatory approval are discussed, as well as recently developed tools and technologies such as four-dimensional and convergence bioprinting. In addition, information about clinical trials using 3D printing has been included.

Current perspectives on the diagnosis and management of fatigue in multiple sclerosis

INTRODUCTION

Fatigue is a common and debilitating symptom among multiple sclerosis (MS) patients with a prevalence up to 81% and with a considerable impact on quality of life. However, its subjective nature makes it difficult to define and quantify in clinical practice. Research aimed at a more precise definition and knowledge of this construct is thus continuously growing.

AREAS COVERED

This review summarizes the most relevant updates available on PubMed up to July 1^st 2022 regarding: the assessment methods that aim to measure the concept of fatigue (as opposed to fatigability), the possible treatment pathways currently available to clinicians, interconnection with the pathophysiological substrates and with the common comorbidities of MS, such as depression and mood disorders.

EXPERT OPINION

The in-depth study of fatigue can help to better understand its actual impact on MS patients and can stimulate clinicians towards a more valid approach, through a targeted analysis of this symptom. Considering fatigue from a multidimensional perspective allows the use of patient-tailored methods for its identification and subsequent treatment by different professional figures. Better identification of methods and treatment pathways would reduce the extremely negative impact of fatigue on MS patients' quality of life.

Quantitative mapping of the brain's structural connectivity using diffusion MRI tractography: A review

Diffusion magnetic resonance imaging (dMRI) tractography is an advanced imaging technique that enables in vivo reconstruction of the brain's white matter connections at macro scale. It provides an important tool for quantitative mapping of the brain's structural connectivity using measures of connectivity or tissue microstructure. Over the last two decades, the study of brain connectivity using dMRI tractography has played a prominent role in the neuroimaging research landscape. In this paper, we provide a high-level overview of how tractography is used to enable quantitative analysis of the brain's structural connectivity in health and disease. We focus on two types of quantitative analyses of tractography, including: 1) tract-specific analysis that refers to research that is typically hypothesis-driven and studies particular anatomical fiber tracts, and 2) connectome-based analysis that refers to research that is more data-driven and generally studies the structural connectivity of the entire brain. We first provide a review of methodology involved in three main processing steps that are common across most approaches for quantitative analysis of tractography, including methods for tractography correction, segmentation and quantification. For each step, we aim to describe methodological choices, their popularity, and potential pros and cons. We then review studies that have used quantitative tractography approaches to study the brain's white matter, focusing on applications in neurodevelopment, aging, neurological disorders, mental disorders, and neurosurgery. We conclude that, while there have been considerable advancements in methodological technologies and breadth of applications, there nevertheless remains no consensus about the "best" methodology in quantitative analysis of tractography, and researchers should remain cautious when interpreting results in research and clinical applications.

Efficacy of transcranial direct current stimulation in people with multiple sclerosis: a review

BACKGROUND AND PURPOSE

Multiple sclerosis (MS) is a chronic inflammatory disease causing a wide range of symptoms including motor and cognitive impairment, fatigue and pain. Over the last two decades, non-invasive brain stimulation, especially transcranial direct current stimulation (tDCS), has increasingly been used to modulate brain function in various physiological and pathological conditions. However, its experimental applications for people with MS were noted only as recently as 2010 and have been growing since then. The efficacy for use in people with MS remains questionable with the results of existing studies being largely conflicting. Hence, the aim of this review is to paint a picture of the current state of tDCS in MS research grounded on studies applying tDCS that have been done to date.

METHODS

A keyword search was performed to retrieve articles from the earliest article identified until 14 February 2021 using a combination of the groups (1) 'multiple sclerosis', 'MS' and 'encephalomyelitis' and (2) 'tDCS' and 'transcranial direct current stimulation'.

RESULTS

The analysis of the 30 articles included in this review underlined inconsistent effects of tDCS on the motor symptoms of MS based on small sample sizes. However, tDCS showed promising benefits in ameliorating fatigue, pain and cognitive symptoms.

CONCLUSION

Transcranial direct current stimulation is attractive as a non-drug approach in ameliorating MS symptoms, where other treatment options remain limited. The development of protocols tailored to the individual's own neuroanatomy using high definition tDCS and the introduction of network mapping in the experimental designs might help to overcome the variability between studies.

Structural and Functional Connectivity Substrates of Cognitive Impairment in Multiple Sclerosis

Cognitive impairment (CI) occurs in 43 to 70% of multiple sclerosis (MS) patients at both early and later disease stages. Cognitive domains typically involved in MS include attention, information processing speed, memory, and executive control. The growing use of advanced magnetic resonance imaging (MRI) techniques is furthering our understanding on the altered structural connectivity (SC) and functional connectivity (FC) substrates of CI in MS. Regarding SC, different diffusion tensor imaging (DTI) measures (e.g., fractional anisotropy, diffusivities) along tractography-derived white matter (WM) tracts showed relevance toward CI. Novel diffusion MRI techniques, including diffusion kurtosis imaging, diffusion spectrum imaging, high angular resolution diffusion imaging, and neurite orientation dispersion and density imaging, showed more pathological specificity compared to the traditional DTI but require longer scan time and mathematical complexities for their interpretation. As for FC, task-based functional MRI (fMRI) has been traditionally used in MS to brain mapping the neural activity during various cognitive tasks. Analysis methods of resting fMRI (seed-based, independent component analysis, graph analysis) have been applied to uncover the functional substrates of CI in MS by revealing adaptive or maladaptive mechanisms of functional reorganization. The relevance for CI in MS of SC–FC relationships, reflecting common pathogenic mechanisms in WM and gray matter, has been recently explored by novel MRI analysis methods. This review summarizes recent advances on MRI techniques of SC and FC and their potential to provide a deeper understanding of the pathological substrates of CI in MS.

Disability in multiple sclerosis is related to thalamic connectivity and cortical network atrophy

BACKGROUND

Thalamic atrophy is proposed to be a major predictor of disability progression in multiple sclerosis (MS), while thalamic function remains understudied.

OBJECTIVES

To study how thalamic functional connectivity (FC) is related to disability and thalamic or cortical network atrophy in two large MS cohorts.

METHODS

Structural and resting-state functional magnetic resonance imaging (fMRI) was obtained in 673 subjects from Amsterdam (MS: N = 332, healthy controls (HC): N = 96) and Graz (MS: N = 180, HC: N = 65) with comparable protocols, including disability measurements in MS (Expanded Disability Status Scale, EDSS). Atrophy was measured for the thalamus and seven well-recognized resting-state networks. Static and dynamic thalamic FC with these networks was correlated with disability. Significant correlates were included in a backward multivariate regression model.

RESULTS

Disability was most strongly related (adjusted R² = 0.57, p < 0.001) to higher age, a progressive phenotype, thalamic atrophy and increased static thalamic FC with the sensorimotor network (SMN). Static thalamus-SMN FC was significantly higher in patients with high disability (EDSS ⩾ 4) and related to network atrophy but not thalamic atrophy or lesion volumes.

CONCLUSION

The severity of disability in MS was related to increased static thalamic FC with the SMN. Thalamic FC changes were only related to cortical network atrophy, but not to thalamic atrophy.

Neurocognitive impairment and social cognition in multiple sclerosis

PURPOSE/AIM OF THE STUDY

The impairment of neurocognitive functions occurs in all subtypes of multiple sclerosis, even from the earliest stages of the disease. Commonly reported manifestations of cognitive impairment include deficits in attention, conceptual reasoning, processing efficiency, information processing speed, memory (episodic and working), verbal fluency (language), and executive functions. Multiple sclerosis patients also suffer from social cognition impairment, which affects their social functioning. The objective of the current paper is to assess the effect of neurocognitive impairment and its potential correlation with social cognition performance and impairment in multiple sclerosis patients.

MATERIALS AND METHODS

An overview of the available-to-date literature on neurocognitive impairment and social cognition performance in multiple sclerosis patients by disease subtype was performed.

RESULTS

It is not clear if social cognition impairment occurs independently or secondarily to neurocognitive impairment. There are associations of variable strengths between neurocognitive and social cognition deficits and their neural basis is increasingly investigated.

CONCLUSIONS

The prompt detection of neurocognitive predictors of social cognition impairment that may be applicable to all multiple sclerosis subtypes and intervention are crucial to prevent further neural and social cognition decline in multiple sclerosis patients.

Biochemical Correlations with Fatigue in Multiple Sclerosis Detected by MR 2D Localized Correlated Spectroscopy

BACKGROUND AND PURPOSE

Fatigue is the common symptom in patients with multiple sclerosis (MS), yet its pathophysiological mechanism is poorly understood. We investigated the metabolic changes in fatigue in a group of relapsing-remitting MS (RRMS) patients using MR two-dimensional localized correlated spectroscopy (2D L-COSY).

METHODS

Sixteen RRMS and 16 healthy controls were included in the study. Fatigue impact was assessed with the Modified Fatigue Impact Scale (MFIS). MR 2D L-COSY data were collected from the posterior cingulate cortex. Nonparametric statistical analysis was used to calculate the changes in creatine scaled metabolic ratios and their correlations with fatigue scores.

RESULTS

Compared to healthy controls, the RRMS group showed significantly higher fatigue and lower metabolic ratios for tyrosine, glutathione, homocarnosine (GSH+Hca), fucose-3, glutamine+glutamate (Glx), glycerophosphocholine (GPC), total choline, and N-acetylaspartate (NAA-2), while increased levels for isoleucine and glucose (P ≤ .05). Only GPC showed positive correlation with all fatigue domains (r = .537, P ≤ .05). On the other hand, Glx-upper, NAA-2, GSH+Hca, and fucose-3 showed negative correlations with all fatigue domains (r = -.345 to -.580, P ≤ .05). While tyrosine showed positive correlation with MFIS (r = .499, P ≤ .05), cognitive fatigue was negatively correlated with total GSH (r = -.530, P ≤ .05). No correlations were found between lesion load or brain volumes with fatigue score.

CONCLUSIONS

Our results suggest that fatigue in MS is strongly correlated with an imbalance in neurometabolites but not structural brain measurements.

Risk of Psychiatric Disorders in Multiple Sclerosis: A Nationwide Cohort Study in an Asian Population

BACKGROUND

Multiple sclerosis (MS) is a demyelinating disease that can damage neurons in the brain and spinal cord and is associated with several psychiatric disorders. However, few studies have evaluated the risk of psychiatric disorders in patients with MS by using a nationwide database. This study investigated the association between MS and the risk of psychiatric disorders.

METHODS

Using data from the Taiwan National Health Insurance Research Database from 2000 to 2015, we identified 1066 patients with MS. After adjustment for confounding factors, Fine and Gray's competing risk model was used to compare the risk of psychiatric disorders during 15 years of follow-up.

RESULTS

Of the patients with MS, 531 (4622.86 per 10⁵ person years) developed psychiatric disorders; by contrast, 891 of the 3198 controls (2485.31 per 10⁵ person years) developed psychiatric disorders. Fine and Gray's competing risk model revealed an adjusted hazard ratio (HR) of 5.044 (95% confidence interval = 4.448-5.870, p < 0.001) after adjustment for all the covariates. MS was associated with depression, anxiety, bipolar disorder, sleep disorders, schizophrenia, schizophreniform disorder, and other psychotic disorders (adjusted HR: 12.464, 4.650, 6.987, 9.103, 2.552, 2.600, 2.441, and 2.574, respectively; all p < 0.001). Some disease-modifying drugs were associated with a lower risk of anxiety or depression.

CONCLUSION

Patients with MS were determined to have a higher risk of developing a wide range of psychiatric disorders.

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.

RimNet: A deep 3D multimodal MRI architecture for paramagnetic rim lesion assessment in multiple sclerosis

OBJECTIVES

In multiple sclerosis (MS), the presence of a paramagnetic rim at the edge of non-gadolinium-enhancing lesions indicates perilesional chronic inflammation. Patients featuring a higher paramagnetic rim lesion burden tend to have more aggressive disease. The objective of this study was to develop and evaluate a convolutional neural network (CNN) architecture (RimNet) for automated detection of paramagnetic rim lesions in MS employing multiple magnetic resonance (MR) imaging contrasts.

MATERIALS AND METHODS

Imaging data were acquired at 3 Tesla on three different scanners from two different centers, totaling 124 MS patients, and studied retrospectively. Paramagnetic rim lesion detection was independently assessed by two expert raters on T2*-phase images, yielding 462 rim-positive (rim+) and 4857 rim-negative (rim-) lesions. RimNet was designed using 3D patches centered on candidate lesions in 3D-EPI phase and 3D FLAIR as input to two network branches. The interconnection of branches at both the first network blocks and the last fully connected layers favors the extraction of low and high-level multimodal features, respectively. RimNet's performance was quantitatively evaluated against experts' evaluation from both lesion-wise and patient-wise perspectives. For the latter, patients were categorized based on a clinically relevant threshold of 4 rim+ lesions per patient. The individual prediction capabilities of the images were also explored and compared (DeLong test) by testing a CNN trained with one image as input (unimodal).

RESULTS

The unimodal exploration showed the superior performance of 3D-EPI phase and 3D-EPI magnitude images in the rim+/- classification task (AUC = 0.913 and 0.901), compared to the 3D FLAIR (AUC = 0.855, Ps < 0.0001). The proposed multimodal RimNet prototype clearly outperformed the best unimodal approach (AUC = 0.943, P < 0.0001). The sensitivity and specificity achieved by RimNet (70.6% and 94.9%, respectively) are comparable to those of experts at the lesion level. In the patient-wise analysis, RimNet performed with an accuracy of 89.5% and a Dice coefficient (or F1 score) of 83.5%.

CONCLUSIONS

The proposed prototype showed promising performance, supporting the usage of RimNet for speeding up and standardizing the paramagnetic rim lesions analysis in MS.

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

BACKGROUND

The Symbol Digit Modalities Test (SDMT) is essential in the screening of cognitive impairments in multiple sclerosis (MS). Methodological adaptions of the SDMT on functional MRI exist, but without specific investigation of more cognitive components of information processing speed (IPS). Additionally, there is only limited data on functional differences between MS-patients and healthy controls (HC).

METHODS

20 MS-patients and 20 HC were investigated executing the original version of the SDMT on fMRI. We analyzed (1) neural networks as indicated in the methodological adaptions (i.e. frontal (Brodman area BA6, BA9), parietal (BA7), occipital (BA17) and cerebellar), (2) functional activations of cognitive components of IPS and (3) functional differences between MS and HC during SDMT.

RESULTS

MS patients performed worse during the SDMT. Both groups demonstrated activation on each region of interest. Cognitive component of IPS was driven by superior parietal and posterior cerebellar activation. MS-patients showed decreased cingulate activation during SDMT as compared to HC.

CONCLUSION

The original SDMT task revealed comparable fMRI-activation sites as reported for previous adaptions. Cognitive components of IPS depend on superior parietal and medial posterior cerebellar regions known to process visuo-spatial integration and anticipation. Attention related areas in the cingulate cortex were decreased in MS-patients.

Graph theoretical quantification of white matter reorganization after cortical stroke in mice

Stroke is a devastating disease leading to cell death and disconnection between neurons both locally and remote, often resulting in severe long-term disability. Spontaneous reorganization of areas and pathways not primarily affected by ischemia is, however, associated with albeit limited recovery of function. Quantitative mapping of whole-brain changes of structural connectivity concerning the ischemia-induced sensorimotor deficit and recovery thereof would help to target structural plasticity in order to improve rehabilitation. Currently, only in vivo diffusion MRI can extract the structural whole-brain connectome noninvasively. This approach is, however, used primarily in human studies. Here, we applied atlas-based MRI analysis and graph theory to DTI in wild-type mice with cortical stroke lesions. Using a DTI network approach and graph theory, we aimed at gaining insights into the dynamics of the spontaneous reorganization after stroke related to the recovery of function. We found evidence for altered structural integrity of connections of specific brain regions, including the breakdown of connections between brain regions directly affected by stroke as well as long-range rerouting of intra- and transhemispheric connections related to improved sensorimotor behavior.

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.

Gray matter atrophy cannot be fully explained by white matter damage in patients with MS

BACKGROUND

Source-based morphometry (SBM) was recently used for non-random "patterns" of gray matter (GM) atrophy or white matter (WM) microstructural damage.

OBJECTIVE

To assess whether and to what extent such patterns may be inter-related in MS.

METHODS

SBM was applied to images of GM concentration and fractional anisotropy (FA) in MS patients (n = 41, median EDSS = 1) and normal controls (NC, n = 28). The same procedure was repeated on an independent and similar data set (39 MS patients and 13 NC).

RESULTS

We found in MS patterns of GM atrophy and reduced FA (p < 0.05, corrected). Deep GM atrophy was mostly (70%) explained by lesion load in projection tracts and lower FA in posterior corona radiata and thalamic radiation. By contrast, sensorimotor and posterior cortex atrophy was less (50%) dependent from WM damage. All patterns correlated with EDSS (r from -0.33 to -0.56, p < 0.03) while the only cognition-related correlation was between posterior GM atrophy pattern and processing speed (r = 0.45, p = 0.014). Reliability analysis showed similar results.

CONCLUSION

In relatively early MS, we found a close link between deep GM atrophy pattern and WM damage while sensorimotor and posterior cortex patterns were partially independent from WM damage and perhaps related to primary mechanisms. Patterns were clinically relevant.

Analysis of the Influence of Different Settings of Scan Sequence Parameters on Vibration and Noise Generated in the Open-Air MRI Scanning Area

A system of gradient coils of the magnetic resonance imaging (MRI) device produces significant vibration and noise. Energetic relations of these phenomena are analyzed depending on MRI scan parameters (sequence type, repetition time (TR), echo time (TE), slice orientation, body weight). This issue should be investigated because of negative physiological and psychological effects on a person exposed to vibration and acoustic noise. We also measured the sound pressure level in the MRI scanning area and its vicinity in order to minimize these negative impacts, depending on intensity and time duration of exposition. From the recorded vibration and noise signals, the energy parameters were determined and statistically analyzed, and the obtained results were visually and numerically compared. Finally, subjective evaluation by a listening test method was used to analyze the influence of the generated MRI noise on the human psyche.

Brain anatomical correlates of fatigue in multiple sclerosis

Fatigue is one of the most debilitating symptoms in patients with multiple sclerosis (MS). Despite its clinical significance, the aetiology and pathophysiology of MS-related fatigue are not well understood. Current evidence and understanding of the neuroanatomical underpinnings of MS-related fatigue are reviewed in this article. The aims of this paper are to (1) review the findings of previous structural neuroimaging studies on MS-related fatigue and summarize consistent findings regarding brain circuitry associated with fatigue in MS, (2) contextualize these findings with the neurochemistry of the relevant circuits and (3) discuss future perspectives with regard to impact on fatigue management of MS patients and methodological challenges towards improved understanding of fatigue pathogenesis. The detailed understanding of the neuroanatomical underpinnings of fatigue might contribute to the identification of novel treatment targets and factors determining treatment resistance to drugs used in current clinical practice.

Structural and functional brain connectomes in patients with systemic lupus erythematosus

BACKGROUND AND PURPOSE

Systemic lupus erythematosus (SLE) is an immune-mediated disease that may affect the nervous system. We explored the topographical organization of structural and functional brain connectivity in patients with SLE and its correlation with neuropsychiatric (NP) involvement and autoantibody profiles.

METHODS

Graph theoretical analysis was applied to diffusion tensor magnetic resonance imaging (MRI) and resting-state functional MRI data from 32 patients with SLE and 32 age- and sex-matched healthy controls. Structural and functional connectivity matrices between 116 cortical/subcortical brain regions were estimated using a bivariate correlation analysis, and global and nodal network metrics were calculated.

RESULTS

Structural, but not functional, global network properties (strength, transitivity, global efficiency and path length) were abnormal in patients with SLE versus controls (P < 0.0001), especially in patients with anti-double-stranded DNA (ADNA) autoantibodies (P = 0.03). No difference was found according to NP involvement or anti-phospholipid autoantibody status. Patients with SLE and controls shared identical structural hubs and the majority of functional hubs. In patients with SLE, all structural hubs showed reduced strength and clustering coefficient compared with controls (P from 0.001 to <0.0001), especially in patients with ADNA autoantibodies. Only a few differences in functional hub properties were found between patients with SLE and controls. Structural and functional hub measures did not differ according to NP involvement or anti-phospholipid autoantibody status. Significant correlations were found between clinical, MRI and network measures (r from -0.56 to 0.60, P from 0.0003 to 0.05).

CONCLUSIONS

Abnormalities of global and nodal structural connectivity occur in patients with SLE, especially with ADNA autoantibodies, with a diffuse disruption of structural integrity. Functional network integrity may contribute to preserve clinical functions.

Characterizing Rapid Fluctuations of Resting State Functional Connectivity in Demyelinating, Neurodegenerative, and Psychiatric Conditions: From Static to Time-Varying Analysis

Functional magnetic resonance imaging (fMRI) at resting state (RS) has been widely used to characterize the main brain networks. Functional connectivity (FC) has been mostly assessed assuming that FC is static across the whole fMRI examination. However, FC is highly variable at a very fast time-scale, as demonstrated by neurophysiological techniques. Time-varying functional connectivity (TVC) is a novel approach that allows capturing reoccurring patterns of interaction among functional brain networks. Aim of this review is to provide a description of the methods currently used to assess TVC on RS fMRI data, and to summarize the main results of studies applying TVC in healthy controls and patients with multiple sclerosis (MS). An overview of the main results obtained in neurodegenerative and psychiatric conditions is also provided. The most popular TVC approach is based on the so-called “sliding windows,” in which the RS fMRI acquisition is divided in small temporal segments (windows). A window of fixed length is shifted over RS fMRI time courses, and data within each window are used to calculate FC and its variability over time. Sliding windows can be combined with clustering techniques to identify recurring FC states or used to assess global TVC properties of large-scale functional networks or specific brain regions. TVC studies have used heterogeneous methodologies so far. Despite this, similar results have been obtained across investigations. In healthy subjects, the default-mode network (DMN) exhibited the highest degree of connectivity dynamism. In MS patients, abnormal global TVC properties and TVC strengths were found mainly in sensorimotor, DMN and salience networks, and were associated with more severe structural MRI damage and with more severe physical and cognitive disability. Conversely, abnormal TVC measures of the temporal network were correlated with better cognitive performances and less severe fatigue. In patients with neurodegenerative and psychiatric conditions, TVC abnormalities of the DMN, attention and executive networks were associated to more severe clinical manifestations. TVC helps to provide novel insights into fundamental properties of functional networks, and improves the understanding of brain reorganization mechanisms. Future technical advances might help to clarify TVC association with disease prognosis and response to treatment.

Changes in cortical motor outputs after a motor relapse of multiple sclerosis

BACKGROUND

Motor recovery following a multiple sclerosis (MS) relapse depends on mechanisms of tissue repair but also on the capacity of the central nervous system for compensating of permanent damage.

OBJECTIVES

We aimed to investigate changes in corticospinal plasticity and interhemispheric connections after a relapse of MS using transcranial magnetic stimulation (TMS).

METHODS

Twenty healthy and 13 relapsing-remitting MS subjects with a first motor relapse were included. TMS mapping and ipsilateral silent period (iSP) were performed after relapse and at 6-month follow-up.

RESULTS

Strength and dexterity of the paretic hand were impaired at baseline and improved over time. After relapse, mapamplitude and mapdensity were decreased for the ipsilesional-corticospinal tract (IL-CST) while expanded for the contralesional-CST (CL-CST). At follow-up, map parameters normalized for the CL-CST independently from recovery while the increase of outputs from the IL-CST was associated with straight and dexterity improvement. iSP measurements were impaired in MS irrespective of the phase of the disease. Prolonged iSPduration at baseline was associated with less dexterity recovery.

CONCLUSIONS

After a motor relapse, TMS mapping shows acute changes in corticospinal excitability and rearrangements of motor outputs. iSP is less influenced by the phase of disease but may better predict recovery, possibly reflecting the integrity of interhemispheric motor networks.

Pathophysiological and cognitive mechanisms of fatigue in multiple sclerosis

Fatigue is one of the most common symptoms in multiple sclerosis (MS), with a major impact on patients' quality of life. Currently, treatment proceeds by trial and error with limited success, probably due to the presence of multiple different underlying mechanisms. Recent neuroscientific advances offer the potential to develop tools for differentiating these mechanisms in individual patients and ultimately provide a principled basis for treatment selection. However, development of these tools for differential diagnosis will require guidance by pathophysiological and cognitive theories that propose mechanisms which can be assessed in individual patients. This article provides an overview of contemporary pathophysiological theories of fatigue in MS and discusses how the mechanisms they propose may become measurable with emerging technologies and thus lay a foundation for future personalised treatments.

Cross-Sectional and Longitudinal MRI Brain Scans Reveal Accelerated Brain Aging in Multiple Sclerosis

Multiple sclerosis (MS) is an inflammatory disorder of the central nervous system. By combining longitudinal MRI-based brain morphometry and brain age estimation using machine learning, we tested the hypothesis that MS patients have higher brain age relative to chronological age than healthy controls (HC) and that longitudinal rate of brain aging in MS patients is associated with clinical course and severity. Seventy-six MS patients [71% females, mean age 34.8 years (range 21-49) at inclusion] were examined with brain MRI at three time points with a mean total follow up period of 4.4 years (±0.4 years). We used additional cross-sectional MRI data from 235 HC for case-control comparison. We applied a machine learning model trained on an independent set of 3,208 HC to estimate individual brain age and to calculate the difference between estimated and chronological age, termed brain age gap (BAG). We also assessed the longitudinal change rate in BAG in individuals with MS. MS patients showed significantly higher BAG (4.4 ± 6.6 years) compared to HC (Cohen's D = 0.69, p = 4.0 × 10-6). Longitudinal estimates of BAG in MS patients showed high reliability and suggested an accelerated rate of brain aging corresponding to an annual increase of 0.41 (SE = 0.15) years compared to chronological aging (p = 0.008). Multiple regression analyses revealed higher rate of brain aging in patients with more brain atrophy (Cohen's D = 0.86, p = 4.3 × 10-15) and increased white matter lesion load (WMLL) (Cohen's D = 0.55, p = 0.015). On average, patients with MS had significantly higher BAG compared to HC. Progressive brain aging in patients with MS was related to brain atrophy and increased WMLL. No significant clinical associations were found in our sample, future studies are warranted on this matter. Brain age estimation is a promising method for evaluation of subtle brain changes in MS, which is important for predicting clinical outcome and guide choice of intervention.

Symptoms of fatigue and depression is reflected in altered default mode network connectivity in multiple sclerosis

Background

Fatigue and depression are frequent and often co-occurring symptoms in multiple sclerosis (MS). Resting-state functional magnetic resonance imaging (rs-fMRI) represents a promising tool for disentangling differential associations between depression and fatigue and brain network function and connectivity. In this study we tested for associations between symptoms of fatigue and depression and DMN connectivity in patients with MS.

Materials and methods

Seventy-four MS patients were included on average 14 months after diagnosis. They underwent MRI scanning of the brain including rs-fMRI, and symptoms of fatigue and depression were assessed with Fatigue Severity Scale (FSS) and Beck Depression Inventory II (BDI). A principal component analysis (PCA) on FSS and BDI scores was performed, and the component scores were analysed using linear regression models to test for associations with default mode network (DMN) connectivity.

Results

We observed higher DMN connectivity with higher scores on the primary principal component reflecting common symptom burden for fatigue and depression (Cohen’s f² = 0.075, t = 2.17, p = 0.03). The secondary principal component reflecting a pattern of low fatigue scores with high scores of depression was associated with lower DMN connectivity (Cohen’s f² = 0.067, t = -2.1, p = 0.04). Using continuous mean scores of FSS we also observed higher DMN connectivity with higher symptom burden (t = 3.1, p = 0.003), but no significant associations between continuous sum scores of BDI and DMN connectivity (t = 0.8, p = 0.4).

Conclusion

Multivariate decomposition of FSS and BDI data supported both overlapping and unique manifestation of fatigue and depression in MS patients. Rs-fMRI analyses showed that symptoms of fatigue and depression were reflected in altered DMN connectivity, and that higher DMN activity was seen in MS patients with fatigue even with low depression scores.

Functional Connectivity Changes After Initial Treatment With Fingolimod in Multiple Sclerosis

On the basis of recent functional MRI studies, Multiple Sclerosis (MS) has been interpreted as a multisystem disconnection syndrome. Compared to normal subjects, MS patients show alterations in functional connectivity (FC). However, the mechanisms underlying these alterations are still debated. The aim of the study is to investigate resting state (RS) FC changes after initial treatment with fingolimod, a proven anti-inflammatory and immunomodulating agent for MS. We studied 32 right-handed relapsing-remitting MS patients (median Expanded Disability Status Scale: 2.0, mean disease duration: 8.8 years) who underwent both functional and conventional MRI with a 3 Tesla magnet. All assessments were performed 3 weeks before starting fingolimod, then, at therapy-initiation stage and at month 6. Each imaging session included scans at baseline (run1) and after (run2) a 25-min, within-session, motor-practice task, consisting of a paced right-thumb flexion. FC was assessed using a seed on the left primary motor cortex to obtain parametric maps at run1 and task-induced FC change (run2-run1). Comparison between 3-week before- and fingolimod start sessions accounted for a test-retest effect. The main outcome was the changes in both baseline and task-induced changes in FC, between initiation and 6 months. MRI contrast enhancement was detected in 14 patients at initiation and only in 3 at month 6. There was a significant improvement (p < 0.05) in cognitive function, as measured by the Paced Auditory Serial Addition Task, at month 6 compared to initiation. After accounting for test-retest effect, baseline FC significantly decreased at month 6, with respect to initiation (p < 0.05, family-wise error corrected) in bilateral occipito-parietal areas and cerebellum. A task-induced change in FC at month 6 showed a significant increment in all examined sessions, involving not only areas of the sensorimotor network, but also posterior cortical areas (cuneus and precuneus) and areas of the prefrontal and temporal cortices (p < 0.05, family-wise error corrected). Cognitive improvement at month 6 was significantly (p < 0.05) related to baseline FC reduction in posterior cortical areas. This study shows significant changes in functional connectivity, both at baseline and after the execution of a simple motor task following 6 months of fingolimod therapy.

Imaging correlates of hand motor performance in multiple sclerosis: A multiparametric structural and functional MRI study

BACKGROUND

Hand motor impairment has considerable effects on daily-life activities of patients with multiple sclerosis (pwMS). Understanding its anatomo-functional substrates is relevant to provide more specific therapeutic interventions.

OBJECTIVES

To investigate the association between hand motor performance and anatomo-functional magnetic resonance imaging (MRI) abnormalities in pwMS.

METHODS

A total of 134 healthy controls (HC) and 366 pwMS underwent the Nine-Hole-Peg-Test (9HPT), structural and resting state (RS) functional MRI. Multivariate analyses identified the independent predictors of hand motor performance.

RESULTS

PwMS versus HC showed widespread gray matter atrophy, microstructural white matter abnormalities, and decreased RS functional connectivity in motor and cognitive networks. Predictors of worse right-9HPT (R² = 0.52) were decreased right superior cerebellar peduncle and right lemniscus fractional anisotropy (FA) (p ⩽ 0.02), left angular gyrus atrophy (p < 0.003), decreased RS connectivity in left superior frontal gyrus, and left posterior cerebellum (p < 0.001). Worse left 9HPT (R² = 0.56) was predicted by decreased right corticospinal FA (p = 0.003), atrophy of left anterior cingulum and left cerebellum (p ⩽ 0.02), decreased RS connectivity of left lingual gyrus and right posterior cerebellum in cerebellar and executive networks (p ⩽ 0.02).

CONCLUSION

Structural and functional abnormalities of regions involved in motor functions contribute to explain motor disability in pwMS. The integration of clinical and advanced MRI measures contributes to improve our understanding of multiple sclerosis clinical manifestations.

Application of advanced MRI techniques to monitor pharmacologic and rehabilitative treatment in multiple sclerosis: current status and future perspectives

Introduction: Advances in magnetic resonance imaging (MRI) technology and analyses are improving our understanding of the pathophysiology of multiple sclerosis (MS). Due to their ability to grade the presence of irreversible tissue loss, microstructural tissue abnormalities, metabolic changes and functional plasticity, the application of these techniques is also expanding our knowledge on the efficacy and mechanisms of action of different pharmacological and rehabilitative treatments. Areas covered: This review discusses recent findings derived from the application of advanced MRI techniques to evaluate the structural and functional substrates underlying the effects of pharmacologic and rehabilitative treatments in patients with MS. Current applications as outcome in clinical trials and observational studies, their interpretation and possible pitfalls in their use are discussed. Finally, how these techniques could evolve in the future to improve monitoring of disease progression and treatment response is examined. Expert commentary: The number of treatments currently available for MS is increasing. The application of advanced MRI techniques is providing reliable and specific measures to better understand the targets of different treatments, including neuroprotection, tissue repair, and brain plasticity. This is a fundamental progress to move toward personalized medicine and individual treatment selection.

Multiple sclerosis

Multiple sclerosis (MS) is the most common chronic inflammatory, demyelinating and neurodegenerative disease of the central nervous system in young adults. This disorder is a heterogeneous, multifactorial, immune-mediated disease that is influenced by both genetic and environmental factors. In most patients, reversible episodes of neurological dysfunction lasting several days or weeks characterize the initial stages of the disease (that is, clinically isolated syndrome and relapsing-remitting MS). Over time, irreversible clinical and cognitive deficits develop. A minority of patients have a progressive disease course from the onset. The pathological hallmark of MS is the formation of demyelinating lesions in the brain and spinal cord, which can be associated with neuro-axonal damage. Focal lesions are thought to be caused by the infiltration of immune cells, including T cells, B cells and myeloid cells, into the central nervous system parenchyma, with associated injury. MS is associated with a substantial burden on society owing to the high cost of the available treatments and poorer employment prospects and job retention for patients and their caregivers.

Reproducibility of structural brain connectivity and network metrics using probabilistic diffusion tractography

The structural connectivity network constructed using probabilistic diffusion tractography can be characterized by the network metrics. In this study, short-term test-retest reproducibility of structural networks and network metrics were evaluated on 30 subjects in terms of within- and between-subject coefficient of variance (CV_ws, CV_bs), and intra class coefficient (ICC) using various connectivity thresholds. The short-term reproducibility under various connectivity thresholds were also investigated when subject groups have same or different sparsity. In summary, connectivity threshold of 0.01 can exclude around 80% of the edges with CV_ws = 73.2 ± 37.7%, CV_bs = 119.3 ± 44.0% and ICC = 0.62 ± 0.19. The rest 20% edges have CV_ws < 45%, CV_bs < 90%, ICC = 0.75 ± 0.12. The presence of 1% difference in the sparsity can cause additional within-subject variations on network metrics. In conclusion, applying connectivity thresholds on structural network to exclude spurious connections for the network analysis should be considered as necessities. Our findings suggest that a connectivity threshold over 0.01 can be applied without significant effect on the short-term when network metrics are evaluated at the same sparsity in subject group. When the sparsity is not the same, the procedure of integration over various connectivity thresholds can provide reliable estimation of network metrics.

Progressive brain rich-club network disruption from clinically isolated syndrome towards multiple sclerosis

Objective

To investigate the rich-club organization in clinically isolated syndrome (CIS) and multiple sclerosis (MS), and to characterize its relationships with physical disabilities and cognitive impairments.

Methods

We constructed high-resolution white matter (WM) structural networks in 41 CIS, 32 MS and 35 healthy controls (HCs) using diffusion MRI and deterministic tractography. Group differences in rich-club organization, global and local network metrics were investigated. The relationship between the altered network metrics, brain lesions and clinical variables including EDSS, MMSE, PASAT, disease duration were calculated. Additionally, reproducibility analysis was performed using different parcellation schemes.

Results

Compared with HCs, MS patients exhibited a decreased strength in all types of connections (rich-club: p < 0.0001; feeder: p = 0.0004; and local: p = 0.0026). CIS patients showed intermediate values between MS patients and HCs and exhibited a decreased strength in feeder and local connections (feeder: p = 0.019; and local: p = 0.031) but not in rich-club connections. Compared with CIS patients, MS patients showed significant reductions in rich-club connections (p = 0.0004). The reduced strength of rich-club and feeder connections was correlated with cognitive impairments in the MS group. These results were independent of lesion distribution and reproducible across different brain parcellation schemes.

Conclusion

The rich-club organization was disrupted in MS patients and relatively preserved in CIS. The disrupted rich-club connectivity was correlated with cognitive impairment in MS. These findings suggest that impaired rich-club connectivity is an essential feature of progressive structural network disruption, heralding the development of clinical disability in MS.

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The rich-club organization was disrupted in MS patients and preserved in CIS.

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The disrupted rich-club connectivity correlated with cognitive impairment in MS.

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The rich-club results are reproducible across data analysis methods.