Resting state networks change in clinically isolated syndrome

The multidimensional assessment of body representation and interoception in multiple sclerosis

BACKGROUND

The mental representation of the body (or body representation, BR) derives from the processing of multiple sensory and motor inputs and plays a crucial role in guiding our actions and in how we perceive our body. Fundamental inputs for BR construction come also from the interoceptive systems which refer to the whole bidirectional processes between the brain and the body. People with Multiple sclerosis (MS) show an abnormal multisensory integration which may compromise BR and interoception integrity. However, no study has evaluated possible deficits on distinct and dissociable dimensions of body representation (i.e., action-oriented, aBR; and a nonaction-oriented body representation, NaBR) and interoception (i.e., interoceptive accuracy, interoceptive sensibility, and interoceptive awareness) in MS.

OBJECTIVE

In the present study, we aimed to determine whether participants with MS present changes in BR and interoceptive dimensions.

METHODS

We performed comparison analyses on tasks and questionnaires tapping all BR and interoceptive dimensions between 36 people with relapsing-remitting MS (RRMS) and 42 healthy controls, and between 23 people with progressive MS (PMS) and 33 healthy controls.

RESULTS

Overall, patients with MS exhibited lower interoceptive accuracy than matched controls. The RRMS group also showed higher visceral interoceptive sensibility levels. No differences were found in BR accuracy measures, but the PMS reported longer response times when performing the aBR task.

CONCLUSION

These findings open a new issue on the role of inner-signal monitoring in the body symptomatology of MS and highlight the need for an accurate BR and interoceptive assessment in a clinical setting.

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.

Resting-state functional magnetic resonance imaging reveals functional connectivity alteration in the experimental autoimmune encephalomyelitis model of multiple sclerosis

Multiple sclerosis (MS) is an autoimmune degenerative disease targeting white matter in the central nervous system. The most common animal model that mimics MS is experimental autoimmune encephalomyelitis (EAE) and it plays a crucial role in pharmacological research, from the identification of a therapeutic target to the in vivo validation of efficacy. Magnetic resonance imaging (MRI) is largely used to detect MS lesions, and resting-state functional MRI (rsfMRI) to investigate alterations in the brain functional connectivity (FC). MRI was mainly used in EAE studies to detect lesions in the spinal cord and brain. The current longitudinal MRI study aims to validate rsfMRI as a biomarker of the disease progression in the myelin oligodendrocyte glycoprotein 35-55 induced EAE animal model of MS. MR images were acquired 14, 25, and 50 days postimmunization. Seed-based analysis was used to investigate the whole-brain FC with some predefined areas, such as the thalamic regions, cerebellum, motor and somatosensory cortex. When compared with the control group, the EAE group exhibited a slightly altered FC and a decreasing trend in the total number of activated voxels along the disease progression. The most interesting result regards the whole-brain FC with the cerebellum. A hyperconnectivity behavior was found at an early phase and a significant reduced connectivity at a late phase. Moreover, we found a negative correlation between the total number of activated voxels during the late phase and the cumulative disease index. The results obtained provide a clinically relevant experimental platform that may be pivotal for the elucidation of the key mechanisms of accumulation of irreversible disability, as well as the development of innovative therapies for MS. Moreover, the negative correlation between the disease severity and the size of the activated area suggests a possible research pathway to follow for the resolution of the clinico-radiological paradox.

Flexibility of brain dynamics is increased and predicts clinical impairment in relapsing-remitting but not in secondary progressive multiple sclerosis

Large-scale brain activity has long been investigated under the erroneous assumption of stationarity. Nowadays, we know that resting-state functional connectivity is characterized by aperiodic, scale-free bursts of activity (i.e. neuronal avalanches) that intermittently recruit different brain regions. These different patterns of activity represent a measure of brain flexibility, whose reduction has been found to predict clinical impairment in multiple neurodegenerative diseases such as Parkinson's disease, amyotrophic lateral sclerosis and Alzheimer's disease. Brain flexibility has been recently found increased in multiple sclerosis, but its relationship with clinical disability remains elusive. Also, potential differences in brain dynamics according to the multiple sclerosis clinical phenotypes remain unexplored so far. We performed a brain dynamics study quantifying brain flexibility utilizing the 'functional repertoire' (i.e. the number of configurations of active brain areas) through source reconstruction of magnetoencephalography signals in a cohort of 25 multiple sclerosis patients (10 relapsing-remitting multiple sclerosis and 15 secondary progressive multiple sclerosis) and 25 healthy controls. Multiple sclerosis patients showed a greater number of unique reconfigurations at fast time scales as compared with healthy controls. This difference was mainly driven by the relapsing-remitting multiple sclerosis phenotype, whereas no significant differences in brain dynamics were found between secondary progressive multiple sclerosis and healthy controls. Brain flexibility also showed a different predictive power on clinical disability according to the multiple sclerosis type. For the first time, we investigated brain dynamics in multiple sclerosis patients through high temporal resolution techniques, unveiling differences in brain flexibility according to the multiple sclerosis phenotype and its relationship with clinical disability.

Altered functional connectivity in patients with post-stroke fatigue: A resting-state fMRI study

BACKGROUND

Post-stroke fatigue (PSF) was a common complication after stroke. This study aimed to explore the neuroimaging mechanism of PSF, which was rarely studied.

METHODS

Patients with the first episode of ischemic stroke were recruited from the First Affiliated Hospital of Wenzhou Medical University between March 2021 and December 2022. The fatigue severity scale (FSS) was used to assess fatigue symptoms. PSF was diagnosed by a neurologist based on the FSS score and PSF diagnostic criteria. All the patients were scanned by resting-state functional MRI (rs-fMRI). Precuneus, the posterior node of default-mode network (pDMN), was related to fatigue. Therefore, imaging data were further analyzed by the seed-based resting-state functional connectivity (FC) approach, with the left (PCUN.L) and right precuneus (PCUN.R) being the seeds.

RESULTS

A total of 70 patients with acute ischemic stroke were finally recruited, comprising 40 patients with PSF and 30 patients without PSF. Both the PCUN.L and PCUN.R seeds (pDMN) exhibited decreased FC with the prefrontal lobes located at the anterior part of DMN (aDMN), and the FC values were negatively correlated with FSS scores (both p < 0.001). These two seeds also exhibited increased FC with the right insula, and the FC values were positively correlated with FSS scores (both p < 0.05).

CONCLUSION

The abnormal FC between the aDMN and pDMN was associated with PSF. Besides, the insula, related to interoception, might also play an important role in PSF.

Severity of white matter microstructural damage in a Brazilian relapsing-remitting multiple sclerosis cohort: A possible window to optimize treatment

BACKGROUND

Multiple sclerosis (MS) is an important cause of acquired neurological disability in young adults, characterized by multicentric inflammation, demyelination, and axonal damage.

OBJECTIVE

The objective is to investigate white matter (WM) damage progression in a Brazilian MS patient cohort, using diffusion tensor imaging (DTI) post-processed by tract-based spatial statistics (TBSS).

METHODS

DTI scans were acquired from 76 MS patients and 37 sex-and-age matched controls. Patients were divided into three groups based on disease duration. DTI was performed along 30 non-collinear directions by using a 1.5T imager. For TBSS analysis, the WM skeleton was created, and a 5000 permutation-based inference with a threshold of p < .05 was used, to enable the identification of abnormalities in fractional anisotropy (FA), mean diffusivity (MD), radial diffusivity (RD), and axial diffusivity (AD).

RESULTS

Decreased FA and increased RD, MD, and AD were seen in patients compared to controls and a decreased FA and increased MD and RD were seen, predominantly after the first 5 years of disease, when compared between groups.

CONCLUSION

Progressive WM deterioration is seen over time with a more prominent pattern after 5 years of disease onset, providing evidence that the early years might be a window to optimize treatment and prevent disability.

An update on the role of magnetic resonance imaging in predicting and monitoring multiple sclerosis progression

INTRODUCTION

While magnetic resonance imaging (MRI) is established in diagnosing and monitoring disease activity in multiple sclerosis (MS), its utility in predicting and monitoring disease progression is less clear.

AREAS COVERED

The authors consider changing concepts in the phenotypic classification of MS, including progression independent of relapses; pathological processes underpinning progression; advances in MRI measures to assess them; how well MRI features explain and predict clinical outcomes, including models that assess disease effects on neural networks, and the potential role for machine learning.

EXPERT OPINION

Relapsing-remitting and progressive MS have evolved from being viewed as mutually exclusive to having considerable overlap. Progression is likely the consequence of several pathological elements, each important in building more holistic prognostic models beyond conventional phenotypes. MRI is well placed to assess pathogenic processes underpinning progression, but we need to bridge the gap between MRI measures and clinical outcomes. Mapping pathological effects on specific neural networks may help and machine learning methods may be able to optimize predictive markers while identifying new, or previously overlooked, clinically relevant features. The ever-increasing ability to measure features on MRI raises the dilemma of what to measure and when, and the challenge of translating research methods into clinically useable tools.

Abnormal functional connectivity in radiologically isolated syndrome: A resting-state fMRI study

BACKGROUND

Radiologically isolated syndrome (RIS) patients might have psychiatric and cognitive deficits, which suggests an involvement of major resting-state functional networks. Notwithstanding, very little is known about the neural networks involved in RIS.

OBJECTIVE

To examine functional connectivity differences between RIS and healthy controls using resting-state functional magnetic resonance imaging (fMRI).

METHODS

Resting-state fMRI data in 25 RIS patients and 28 healthy controls were analyzed using an independent component analysis; in addition, seed-based correlation analysis was used to obtain more information about specific differences in the functional connectivity of resting-state networks. Participants also underwent neuropsychological testing.

RESULTS

RIS patients did not differ from the healthy controls regarding age, sex, and years of education. However, in memory (verbal and visuospatial) and executive functions, RIS patients' cognitive performance was significantly worse than the healthy controls. In addition, fluid intelligence was also affected. Twelve out of 25 (48%) RIS patients failed at least one cognitive test, and six (24.0%) had cognitive impairment. Compared to healthy controls, RIS patients showed higher functional connectivity between the default mode network and the right middle and superior frontal gyri and between the central executive network and the right thalamus (pFDR < 0.05; corrected). In addition, the seed-based correlation analysis revealed that RIS patients presented higher functional connectivity between the posterior cingulate cortex, an important hub in neural networks, and the right precuneus.

CONCLUSION

RIS patients had abnormal brain connectivity in major resting-state neural networks and worse performance in neurocognitive tests. This entity should be considered not an "incidental finding" but an exclusively non-motor (neurocognitive) variant of multiple sclerosis.

Altered neural intrinsic oscillations in patients with multiple sclerosis: effects of cortical thickness

Objective

To investigate the effects of cortical thickness on the identification accuracy of fractional amplitude of low-frequency fluctuation (fALFF) in patients with multiple sclerosis (MS).

Methods

Resting-state functional magnetic resonance imaging data were collected from 31 remitting MS, 20 acute MS, and 42 healthy controls (HCs). After preprocessing, we first calculated two-dimensional fALFF (2d-fALFF) maps using the DPABISurf toolkit, and 2d-fALFF per unit thickness was obtained by dividing 2d-fALFF by cortical thickness. Then, between-group comparison, clinical correlation, and classification analyses were performed in 2d-fALFF and 2d-fALFF per unit thickness maps. Finally, we also examined whether the effect of cortical thickness on 2d-fALFF maps was affected by the subfrequency band.

Results

In contrast with 2d-fALFF, more changed regions in 2d-fALFF per unit thickness maps were detected in MS patients, such as increased region of the right inferior frontal cortex and faded regions of the right paracentral lobule, middle cingulate cortex, and right medial temporal cortex. There was a significant positive correlation between the disease duration and the 2d-fALFF values in the left early visual cortex in remitting MS patients (r = 0.517, Bonferroni-corrected, p = 0.008 × 4 < 0.05). In contrast with 2d-fALFF, we detected a positive correlation between the 2d-fALFF per unit thickness of the right ventral stream visual cortex and the modified Fatigue Impact Scale (MFIS) scores (r = 0.555, Bonferroni-corrected, p = 0.017 × 4 > 0.05). For detecting MS patients, 2d-fALFF and 2d- fALFF per unit thickness both performed remarkably well in support vector machine (SVM) analysis, especially in the remitting phase (AUC = 86, 83%). Compared with 2d-fALFF, the SVM model of 2d-fALFF per unit thickness had significantly higher classification performance in distinguishing between remitting and acute MS. More changed regions and more clinically relevant 2d-fALFF per unit thickness maps in the subfrequency band were also detected in MS patients.

Conclusion

By dividing the functional value by the cortical thickness, the identification accuracy of fALFF in MS patients was detected to be potentially influenced by cortical thickness. Additionally, 2d-fALFF per unit thickness is a potential diagnostic marker that can be utilized to distinguish between acute and remitting MS patients. Notably, we observed similar variations in the subfrequency band.

Discovering functional connectivity features characterizing multiple sclerosis phenotypes using explainable artificial intelligence

Multiple sclerosis (MS) is a neurological condition characterized by severe structural brain damage and by functional reorganization of the main brain networks that try to limit the clinical consequences of structural burden. Resting-state (RS) functional connectivity (FC) abnormalities found in this condition were shown to be variable across different MS phases, according to the severity of clinical manifestations. The article describes a system exploiting machine learning on RS FC matrices to discriminate different MS phenotypes and to identify relevant functional connections for MS stage characterization. To this end, the system exploits some mathematical properties of covariance-based RS FC representation, which can be described by a Riemannian manifold. The classification performance of the proposed framework was significantly above the chance level for all MS phenotypes. Moreover, the proposed system was successful in identifying relevant RS FC alterations contributing to an accurate phenotype classification.

Resting-state functional connectivity in multiple sclerosis patients receiving nabiximols for spasticity

BACKGROUND

Nabiximols (Sativex®) is a cannabinoid approved for multiple sclerosis (MS)-related spasticity. Its mechanism of action is partially understood, and efficacy is variable.

OBJECTIVE

To conduct an exploratory analysis of brain networks connectivity changes on resting state (RS) functional MRI (fMRI) of MS patients treated with nabiximols.

METHODS

We identified a group of MS patients treated with Sativex® at Verona University Hospital, who underwent RS brain fMRI in the 4 weeks before (T0) and 4-8 weeks after (T1) treatment start. Sativex® response was defined as ≥ 20% spasticity Numerical Rating Scale score reduction at T1 vs. T0. Connectivity changes on fMRI were compared between T0 and T1 in the whole group and according to response status. ROI-to-ROI and seed-to-voxel connectivity were evaluated.

RESULTS

Twelve MS patients (7 males) were eligible for the study. Seven patients (58.3%) resulted Sativex® responders at T1. On fMRI analysis, Sativex® exposure was associated with global brain connectivity increase (particularly in responders), decreased connectivity of motor areas, and bidirectional connectivity changes of the left cerebellum with a number of cortical areas.

CONCLUSIONS

Nabiximols administration is associated with brain connectivity increase of MS patients with spasticity. Modulation of sensorimotor cortical areas and cerebellum connectivity could play a role in nabiximols effect.

Resting-State Functional Connectivity in Relapsing-Remitting Multiple Sclerosis with Mild Disability: A Data-Driven, Whole-Brain Multivoxel Pattern Analysis Study

Background: Multivoxel pattern analysis (MVPA) has emerged as a powerful unbiased approach for generating seed regions of interest (ROIs) in resting-state functional connectivity (RSFC) analysis in a data-driven manner. Studies exploring RSFC in multiple sclerosis have produced diverse and often incongruent results. Objectives: The aim of the present study was to investigate RSFC differences between people with relapsing-remitting multiple sclerosis (RRMS) and healthy controls (HC). Methods: We performed a whole-brain connectome-wide MVPA in 50 RRMS patients with expanded disability status scale ≤4 and 50 age and gender-matched HCs. Results: Significant group differences were noted in RSFC in three clusters distributed in the following regions: anterior cingulate gyrus, right middle frontal gyrus, and frontal medial cortex. Whole-brain seed-to-voxel RSFC characterization of these clusters as seed ROIs revealed network-specific abnormalities, specifically in the anterior cingulate cortex and the default mode network. Conclusions: The network-wide RSFC abnormalities we report agree with the previous findings in RRMS, the cognitive and clinical implications of which are discussed herein. Impact statement This study investigated resting-state functional connectivity (RSFC) in relapsing-remitting multiple sclerosis (RRMS) people with mild disability (expanded disability status scale ≤4). Whole-brain connectome-wide multivoxel pattern analysis was used for assessing RSFC. Compared with healthy controls, we were able to identify three regions of interest for significant differences in connectivity patterns, which were then extracted as a mask for whole-brain seed-to-voxel analysis. A reduced connectivity was noted in the RRMS group, particularly in the anterior cingulate cortex and the default mode network regions, providing insights into the RSFC abnormalities in RRMS.

Altered connectivity of default mode and executive control networks among female patients with persistent post-concussion symptoms

OBJECTIVE

To examine the roles of the default mode network (DMN) and executive control network (ECN) in prolonged recovery after mild traumatic brain injury (mTBI), and relationships with indices of white matter microstructural injury.

METHODS

Seventeen mTBI patients with persistent symptoms were imaged an average of 21.5 months post-injury, along with 23 healthy controls. Resting-state functional magnetic resonance imaging (rs-fMRI) was used to evaluate functional connectivity (FC) of the DMN and ECN. Diffusion tensor imaging (DTI) quantified fractional anisotropy, along with mean, axial and radial diffusivity of white matter tracts.

RESULTS

Compared to controls, patients with mTBI had increased functional connectivity of the DMN and ECN to brain regions implicated in salience and frontoparietal networks, and increased white matter diffusivity within the cerebrum and brainstem. Among the patients, FC was correlated with better neurocognitive test scores, while diffusivity was correlated with more severe self-reported symptoms. The FC and diffusivity values within abnormal brain regions were not significantly correlated.

CONCLUSION

For female mTBI patients with prolonged symptoms, hyper-connectivity may represent a compensatory response that helps to mitigate the effects of mTBI on cognition. These effects are unrelated to indices of microstructural injury, which are correlated with symptom severity, suggesting that rs-fMRI and DTI may capture distinct aspects of pathophysiology.

Task- and resting-state fMRI studies in multiple sclerosis: From regions to systems and time-varying analysis. Current status and future perspective

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Functional MRI is able to detect adaptive and maladaptive abnormalities at different MS stages.

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Increased fMRI activity is a feature of early MS, while progressive exhaustion of adaptive mechanisms is detected later on in the disease.

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Collapse of long-range connections and impaired hub integration characterize MS network reorganization.

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Time-varying connectivity analysis provides useful and complementary pieces of information to static functional connectivity.

•

New perspectives might be the use of multimodal MRI and artificial intelligence.

Multiple sclerosis (MS) is a neurological disorder affecting the central nervous system and features extensive functional brain changes that are poorly understood but relate strongly to clinical impairments. Functional magnetic resonance imaging (fMRI) is a non-invasive, powerful technique able to map activity of brain regions and to assess how such regions interact for an efficient brain network. FMRI has been widely applied to study functional brain changes in MS, allowing to investigate functional plasticity consequent to disease-related structural injury. The first studies in MS using active fMRI tasks mainly aimed to study such plastic changes by identifying abnormal activity in salient brain regions (or systems) involved by the task. In later studies the focus shifted towards resting state (RS) functional connectivity (FC) studies, which aimed to map large-scale functional networks of the brain and to establish how MS pathology impairs functional integration, eventually leading to the hypothesized network collapse as patients clinically progress. This review provides a summary of the main findings from studies using task-based and RS fMRI and illustrates how functional brain alterations relate to clinical disability and cognitive deficits in this condition. We also give an overview of longitudinal studies that used task-based and RS fMRI to monitor disease evolution and effects of motor and cognitive rehabilitation. In addition, we discuss the results of studies using newer technologies involving time-varying FC to investigate abnormal dynamism and flexibility of network configurations in MS. Finally, we show some preliminary results from two recent topics (i.e., multimodal MRI analysis and artificial intelligence) that are receiving increasing attention. Together, these functional studies could provide new (conceptual) insights into disease stage-specific mechanisms underlying progression in MS, with recommendations for future research.

Anatomical and functional visual network patterns in progressive multiple sclerosis

The gradual accrual of disability over time in progressive multiple sclerosis is believed to be driven by widespread degeneration. Yet another facet of the problem may reside in the loss of the brain's ability to adapt to the damage incurred as the disease progresses. In this study, we attempted to examine whether changes associated with optic neuritis in the structural and functional visual networks can still be discerned in progressive patients even years after the acute insult. Forty-eight progressive multiple sclerosis patients, 21 with and 27 without prior optic neuritis, underwent structural and functional MRI, including DTI and resting state fMRI. Anatomical and functional visual networks were analyzed using graph theory-based methods. While no functional metrics were significantly different between the two groups, anatomical global efficiency and density were significantly lower in the optic neuritis group, despite no significant difference in lesion load between the groups. We conclude that long-standing distal damage to the optic nerve causes trans-synaptic effects and the early ability of the cortex to adapt may be altered, or possibly nullified. We suggest that this limited ability of the brain to compensate should be considered when attempting to explain the accumulation of disability in progressive multiple sclerosis patients.

Dynamic Functional Connectivity Better Predicts Disability Than Structural and Static Functional Connectivity in People With Multiple Sclerosis

Background: Advanced imaging techniques such as diffusion and functional MRI can be used to identify pathology-related changes to the brain's structural and functional connectivity (SC and FC) networks and mapping of these changes to disability and compensatory mechanisms in people with multiple sclerosis (pwMS). No study to date performed a comparison study to investigate which connectivity type (SC, static or dynamic FC) better distinguishes healthy controls (HC) from pwMS and/or classifies pwMS by disability status.

Aims: We aim to compare the performance of SC, static FC, and dynamic FC (dFC) in classifying (a) HC vs. pwMS and (b) pwMS who have no disability vs. with disability. The secondary objective of the study is to identify which brain regions' connectome measures contribute most to the classification tasks.

Materials and Methods: One hundred pwMS and 19 HC were included. Expanded Disability Status Scale (EDSS) was used to assess disability, where 67 pwMS who had EDSS<2 were considered as not having disability. Diffusion and resting-state functional MRI were used to compute the SC and FC matrices, respectively. Logistic regression with ridge regularization was performed, where the models included demographics/clinical information and either pairwise entries or regional summaries from one of the following matrices: SC, FC, and dFC. The performance of the models was assessed using the area under the receiver operating curve (AUC).

Results: In classifying HC vs. pwMS, the regional SC model significantly outperformed others with a median AUC of 0.89 (p <0.05). In classifying pwMS by disability status, the regional dFC and dFC metrics models significantly outperformed others with a median AUC of 0.65 and 0.61 (p < 0.05). Regional SC in the dorsal attention, subcortical and cerebellar networks were the most important variables in the HC vs. pwMS classification task. Increased regional dFC in dorsal attention and visual networks and decreased regional dFC in frontoparietal and cerebellar networks in certain dFC states was associated with being in the group of pwMS with evidence of disability.

Discussion: Damage to SCs is a hallmark of MS and, unsurprisingly, the most accurate connectomic measure in classifying patients and controls. On the other hand, dynamic FC metrics were most important for determining disability level in pwMS, and could represent functional compensation in response to white matter pathology in pwMS.

Concussion Risk and Resilience: Relationships with Pre-Injury Salience Network Connectivity

Concussion is a major health concern, making it critical to identify factors that influence risk and resilience. The salience network (SN) likely plays a key role in concussion risk, given its roles in orienting attention, functional adaptability, and interoceptive awareness. The SN's functions are thought to be mediated through causal control of other networks, including the default mode network (DMN) and executive control network (ECN). It was therefore hypothesized that the SN of at-risk individuals would have altered functional and structural connectivity with the DMN and ECN. For this prospective study, 167 university athletes had baseline clinical assessments and magnetic resonance imaging scans and were monitored for the rest of their varsity career, with any concussions recorded. Athletes concussed in the same season as imaging (CSS; n = 17) and those concussed in later seasons (CLS; n = 15) were matched to controls that were not concussed after imaging. Functional connectivity and white matter fractional anisotropy (FA) were compared between concussed and control groups. Prior to injury, CSS athletes had significantly elevated total symptom severity scores, elevated SN-DMN functional connectivity and reduced FA of connecting white matter tracts, whereas CLS athletes showed no significant clinical or imaging effects. These findings provide new insights into the neurobiology of concussion risk and resilience, as indices of SN-DMN network connectivity are associated with short-term but not long-term concussion risk.

A systematic review of resting state functional MRI connectivity changes and cognitive impairment in multiple sclerosis

INTRODUCTION

Cognitive impairment in multiple sclerosis (MS) is increasingly being investigated with resting state functional MRI (rs-fMRI) functional connectivity (FC) . However, results remain difficult to interpret, showing both high and low FC associated with cognitive impairment. We conducted a systematic review of rs-fMRI studies in MS to understand whether the direction of FC change relates to cognitive dysfunction, and how this may be influenced by the choice of methodology.

METHODS

Embase, Medline and PsycINFO were searched for studies assessing cognitive function and rs-fMRI FC in adults with MS.

RESULTS

Fifty-seven studies were included in a narrative synthesis. Of these, 50 found an association between cognitive impairment and FC abnormalities. Worse cognition was linked to high FC in 18 studies, and to low FC in 17 studies. Nine studies found patterns of both high and low FC related to poor cognitive performance, in different regions or for different MR metrics. There was no clear link to increased FC during early stages of MS and reduced FC in later stages, as predicted by common models of MS pathology. Throughout, we found substantial heterogeneity in study methodology, and carefully consider how this may impact on the observed findings.

DISCUSSION

These results indicate an urgent need for greater standardisation in the field - in terms of the choice of MRI analysis and the definition of cognitive impairment. This will allow us to use rs-fMRI FC as a biomarker in future clinical studies, and as a tool to understand mechanisms underpinning cognitive symptoms in MS.

Cognitive Profiles of Amyotrophic Lateral Sclerosis Differ in Resting-State Functional Connectivity: An fMRI Study

Background

Half of all amyotrophic lateral sclerosis-frontotemporal spectrum disorder (ALS-FTSD) patients are classified as cognitively impaired, of which 10% have frontotemporal dementia (FTD), and an additional 40% suffer from a frontotemporal syndrome not severe enough to be described as dementia (cognitively impaired/ALSci). As changes in cerebral function measured by resting-state magnet resonance imaging (rs-fMRI) are known in ALS, we investigated whether group differences in resting-state functional connectivity (RSFC) networks could be observed between ALS patients with different cognitive profiles against healthy controls (HC). Furthermore, we correlated cognition and motor functioning with network connectivity.

Methods

Healthy controls, 69, and 97 ALS patients underwent functional MRI scanning and cognitive assessment. The ALS patients were categorized as non-impaired (ALSni; n = 68), cognitively impaired (ALSci; n = 21), and ALS-FTD (n = 8). Group differences in connectivity of the default mode network (DMN), motor network (MN), and ventral attention network (VAN) were investigated using a full-factorial model; correlations between global cognitive performance, shifting, and motor symptom severity were established using Pearson’s correlation.

Results

At a liberal alpha level of uncorrected p < 0.005 and a cluster size exceeding 20 voxels, we found widespread decreases in functional connectivity in all three networks when comparing ALS patients to HC. Similar patterns of hypoconnectivity in the bilateral motor cortices and frontotemporal emerged when comparing the ALSci and ALS-FTD patients to those not cognitively impaired. Hyperconnectivity in the DMN temporal gyrus correlated with worse global cognition; moreover, hyperconnectivity in the VAN thalamus, insula, and putamen correlated with worse shifting ability. Better-preserved motor function correlated with higher MN connectivity. Only the motor-related effects prevailed at a more conservative significance level of p_FDR< 0.001.

Conclusion

Resting-state functional connectivity differs between cognitive profiles of ALS and is directly associated with clinical presentation, specifically with motor function, and cognitive shifting.

Clinical predictivity of thalamic sub-regional connectivity in clinically isolated syndrome: a 7-year study

Here, we explored trajectories of sub-regional thalamic resting state (RS) functional connectivity (FC) modifications occurring in clinically isolated syndrome (CIS) patients early after their first clinical episode, and assessed their relationship with disability over 7 years. RS fMRI and clinical data were prospectively acquired from 59 CIS patients and 13 healthy controls (HC) over 2 years. A clinical re-assessment was performed in 53 (89%) patients after 7 years. Using a structural connectivity-based atlas, five thalamic sub-regions (frontal, motor, postcentral, occipital, and temporal) were used for seed-based RS FC. Thalamic RS FC abnormalities and their longitudinal changes were correlated with disability. Thirty-nine (66.1%) patients suffered a second clinical relapse, but the median EDSS remained stable over time. At baseline, CIS patients vs HC showed reduced RS FC (p < 0.001, uncorrected) with: (1) frontal cortices, for the whole thalamus, occipital, postcentral, and temporal thalamic sub-regions, (2) occipital cortices, for the occipital thalamic sub-region. In CIS, the longitudinal analysis revealed at year 2 vs baseline: (1) no significant whole-thalamic RS FC changes; (2) reduction of motor, postcentral, and temporal sub-regional RS FC with occipital cortices (p < 0.05, corrected); (3) an increase (p < 0.001, uncorrected) of postcentral and occipital sub-regional thalamic RS FC with frontal cortices, left putamen, and ipsi- and contralateral thalamus, this latter correlating with less severe clinical disability at year 7. Thalamo-cortical disconnections were present in CIS mainly in thalamic sub-regions closer to the third ventricle early after the demyelinating event, evolved in the subsequent 2 years, and were associated with long-term clinical disability.

Alterations in functional connectivity are associated with white matter lesions and information processing efficiency in multiple sclerosis

Functional connectivity (FC) is typically altered in individuals with Multiple Sclerosis (MS). However, in relapsing-remitting multiple sclerosis (RRMS) patients, the relationship between brain FC, tissue integrity and cognitive impairment is still unclear as contradictory findings have been documented. In this exploratory study we compared both the whole brain connectome and resting state networks (RSNs) FC of twenty-one RRMS and seventeen healthy controls (HCs), using combined network based statistics and independent component analyses. The total white matter (WM) lesion volume and information processing efficiency were also correlated with FC in the RRMS group. Both whole brain connectome and individual RSNs FC were diminished in patients with RRMS compared to HC. Additionally, the reduction in FC was found to be a function of the total WM lesion volume, with greatest impact in those harboring the largest lesion volume. Finally, a positive correlation between FC and information processing efficiency was observed in RRMS. This complimentary whole brain and RSNs FC approach can contribute to clarify literature inconsistencies regarding FC alterations and provide new insights on the white matter structural damage in explaining functional abnormalities in RRMS.

Deciphering Multiple Sclerosis Progression

Multiple sclerosis (MS) is primarily an inflammatory and degenerative disease of the central nervous system, triggered by unknown environmental factors in patients with predisposing genetic risk profiles. The prevention of neurological disability is one of the essential goals to be achieved in a patient with MS. However, the pathogenic mechanisms driving the progressive phase of the disease remain unknown. It was described that the pathophysiological mechanisms associated with disease progression are present from disease onset. In daily practice, there is a lack of clinical, radiological, or biological markers that favor an early detection of the disease's progression. Different definitions of disability progression were used in clinical trials. According to the most descriptive, progression was defined as a minimum increase in the Expanded Disability Status Scale (EDSS) of 1.5, 1.0, or 0.5 from a baseline level of 0, 1.0–5.0, and 5.5, respectively. Nevertheless, the EDSS is not the most sensitive scale to assess progression, and there is no consensus regarding any specific diagnostic criteria for disability progression. This review document discusses the current pathophysiological concepts associated with MS progression, the different measurement strategies, the biomarkers associated with disability progression, and the available pharmacologic therapeutic approaches.

Cognitive Issues in Pediatric Multiple Sclerosis

Multiple sclerosis (MS) is one of the leading causes of disability in young adults. The onset of MS during developmental age makes pediatric patients particularly susceptible to cognitive impairment, resulting from both disease-related damage and failure of age-expected brain growth. Despite different test batteries and definitions, cognitive impairment has been consistently reported in approximately one-third of pediatric patients with MS. However, the lack of a uniform definition of cognitive impairment and the adoption of different test batteries have led to divergent results in terms of cognitive domains more frequently affected across the cohorts explored. This heterogeneity has hampered large international collaborative studies. Moreover, research aimed at the identification of risk factors (e.g., demographic, clinical, and radiological features) or protective factors (e.g., cognitive reserve, leisure activities) for cognitive decline is still scanty. Mood disorders, such as depression and anxiety, can be detected in these patients alongside cognitive decline or in isolation, and can negatively affect quality of life scores as well as academic performances. By using MRI, cognitive impairment was attributed to damage to specific brain compartments as well as to abnormal network activation patterns. However, multimodal MRI studies are still needed in order to assess the contribution of each MRI metric to cognitive impairment. Importantly, longitudinal studies have recently demonstrated failure of age-expected brain growth and of white matter (WM) and gray matter (GM) maturation plays a relevant role in determining cognitive dysfunction, in addition to MS-related direct damage. Whether these growth retardations might result in specific cognitive profiles according to the age at disease onset has not been studied, yet. A better characterization of cognitive profiles in pediatric MS patients, as well as the definition of neuroanatomical substrates of cognitive impairment and their longitudinal evolution are needed to develop efficient therapeutic strategies against cognitive impairment in this patient population.

Neuroimaging Correlates of Cognitive Dysfunction in Adults with Multiple Sclerosis

Cognitive impairment is a frequent and meaningful symptom in multiple sclerosis (MS), caused by the accrual of brain structural damage only partially counteracted by effective functional reorganization. As both these aspects can be successfully investigated through the application of advanced neuroimaging, here, we offer an up-to-date overview of the latest findings on structural, functional and metabolic correlates of cognitive impairment in adults with MS, focusing on the mechanisms sustaining damage accrual and on the identification of useful imaging markers of cognitive decline.

Imaging Mechanisms of Disease Progression in Multiple Sclerosis: Beyond Brain Atrophy

Clinicians involved with different aspects of the care of persons with multiple sclerosis (MS) and scientists with expertise on clinical and imaging techniques convened in Dallas, TX, USA on February 27, 2019 at a North American Imaging in Multiple Sclerosis Cooperative workshop meeting. The aim of the workshop was to discuss cardinal pathobiological mechanisms implicated in the progression of MS and novel imaging techniques, beyond brain atrophy, to unravel these pathologies. Indeed, although brain volume assessment demonstrates changes linked to disease progression, identifying the biological mechanisms leading up to that volume loss are key for understanding disease mechanisms. To this end, the workshop focused on the application of advanced magnetic resonance imaging (MRI) and positron emission tomography (PET) imaging techniques to assess and measure disease progression in both the brain and the spinal cord. Clinical translation of quantitative MRI was recognized as of vital importance, although the need to maintain a relatively short acquisition time mandated by most radiology departments remains the major obstacle toward this effort. Regarding PET, the panel agreed upon its utility to identify ongoing pathological processes. However, due to costs, required expertise, and the use of ionizing radiation, PET was not considered to be a viable option for ongoing care of persons with MS. Collaborative efforts fostering robust study designs and imaging technique standardization across scanners and centers are needed to unravel disease mechanisms leading to progression and discovering medications halting neurodegeneration and/or promoting repair.

Mind the gap: from neurons to networks to outcomes in multiple sclerosis

MRI studies have provided valuable insights into the structure and function of neural networks, particularly in health and in classical neurodegenerative conditions such as Alzheimer disease. However, such work is also highly relevant in other diseases of the CNS, including multiple sclerosis (MS). In this Review, we consider the effects of MS pathology on brain networks, as assessed using MRI, and how these changes to brain networks translate into clinical impairments. We also discuss how this knowledge can inform the targeting of MS treatments and the potential future directions for research in this area. Studying MS is challenging as its pathology involves neurodegenerative and focal inflammatory elements, both of which could disrupt neural networks. The disruption of white matter tracts in MS is reflected in changes in network efficiency, an increasingly random grey matter network topology, relative cortical disconnection, and both increases and decreases in connectivity centred around hubs such as the thalamus and the default mode network. The results of initial longitudinal studies suggest that these changes evolve rather than simply increase over time and are linked with clinical features. Studies have also identified a potential role for treatments that functionally modify neural networks as opposed to altering their structure.

Altered Functional Connectivity in White and Gray Matter in Patients With Multiple Sclerosis

Background: Functional magnetic resonance imaging (fMRI) has been widely used to assess neural activity changes in gray matter (GM) in patients with multiple sclerosis (MS); however, brain function alterations in white matter (WM) relatively remain under-explored. Purpose: This work aims to identify the functional connectivity in both the WM and the GM of patients with MS using fMRI and the correlations between these functional changes and cumulative disability as well as the lesion ratio. Materials and Methods: For this retrospective study, 37 patients with clinically definite MS and 43 age-matched healthy controls were included between 2010 and 2014. Resting-state fMRI was performed. The WFU Pick and JHU Eve atlases were used to define 82 GM and 48 WM regions in common spaces, respectively. The time courses of blood oxygen level-dependent (BOLD) signals were averaged over each GM or WM region. The averaged time courses for each pair of GM and WM regions were correlated. All 82 × 48 correlations for each subject formed a functional correlation matrix. Results: Compared with the healthy controls, the MS patients had a decreased temporal correlation between the WM and the GM regions. Five WM bundles and four GM regions had significantly decreased mean correlation coefficients (CCs). More specifically, the WM functional alterations were negatively correlated with the lesion volume in the bilateral fornix, and the mean GM-averaged CCs of the WM bundles were inversely correlated with the lesion ratio (r = -0.36, P = 0.012). No significant correlation was found between WM functional alterations and the paced auditory serial addition test score, Expanded Disease Severity Scale score, and Multiple Sclerosis Severity Score (MSSS) in MS. Conclusions: These findings highlight current gaps in our knowledge of the WM functional alterations in patients with MS and may link WM function with pathological mechanisms.

Altered regional homogeneity and functional brain networks in Type 2 diabetes with and without mild cognitive impairment

Patients with Type-2 Diabetes Mellitus (T2DM) have a considerably higher risk of developing mild cognitive impairment (MCI) and dementia. The initial symptoms are very insidious at onset. We investigated the alterations in spontaneous brain activity and network connectivity through regional homogeneity (ReHo) and graph theoretical network analyses, respectively, of resting-state functional Magnetic Resonance Imaging (rs-fMRI) in T2DM patients with and without MCI, so as to facilitate early diagnose. Twenty-five T2DM patients with MCI (DM-MCI), 25 T2DM patients with normal cognition (DM-NC), 27 healthy controls were enrolled. Whole-brain ReHo values were calculated and topological properties of functional networks were analyzed. The DM-MCI group exhibited decreased ReHo in the left inferior/middle occipital gyrus and right inferior temporal gyrus, and increased ReHo in frontal gyrus compared to the DM-NCs. Significant correlations were found between ReHo values and clinical measurements. The DM-MCI group illustrated greater clustering coefficient/local efficiency and altered nodal characteristics (efficiency, degree and betweenness), which increased in certain occipital, temporal and parietal regions but decreased in the right inferior temporal gyrus, compared to the DM-NCs. The altered ReHo and impaired network organization may underlie the impaired cognitive functions in T2DM and suggesting a compensation mechanism. These rs-fMRI measures have the potential as biomarkers of disease progression in diabetic encephalopathy.

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.

Increased functional sensorimotor network efficiency relates to disability in multiple sclerosis

BACKGROUND

Network abnormalities could help explain physical disability in multiple sclerosis (MS), which remains poorly understood.

OBJECTIVE

This study investigates functional network efficiency changes in the sensorimotor system.

METHODS

We included 222 MS patients, divided into low disability (LD, Expanded Disability Status Scale (EDSS) ⩽3.5, n = 185) and high disability (HD, EDSS ⩾6, n = 37), and 82 healthy controls (HC). Functional connectivity was assessed between 23 sensorimotor regions. Measures of efficiency were computed and compared between groups using general linear models corrected for age and sex. Binary logistic regression models related disability status to local functional network efficiency (LE), brain volumes and demographics. Functional connectivity patterns of regions important for disability were explored.

RESULTS

HD patients demonstrated significantly higher LE of the left primary somatosensory cortex (S1) and right pallidum compared to LD and HC, and left premotor cortex compared to HC only. The logistic regression model for disability (R² = 0.38) included age, deep grey matter volume and left S1 LE. S1 functional connectivity was increased with prefrontal and secondary sensory areas in HD patients, compared to LD and HC.

CONCLUSION

Clinical disability in MS associates with functional sensorimotor increases in efficiency and connectivity, centred around S1, independent of structural damage.

Changes in structural and functional connectivity during two years of fingolimod therapy for multiple sclerosis

BACKGROUND

Fingolimod, an oral drug, has been reported to reduce relapse rate in multiple sclerosis (MS). However disease progression may still occur in spite of control of inflammation. Functional imbalances within and between cerebral networks associated with disruption of structural and functional network integrity, have been reported in MS. An effective therapy is expected to stabilize such functional network integrity.

OBJECTIVE

The purpose of this study was to investigate changes in structural and resting-state functional connectivity of motor and cognitive networks, and associated changes in neurologic scores in MS, during 2 years of fingolimod therapy.

METHODS

Twenty five subjects with MS were recruited for this study. Subjects were scanned with diffusion tensor imaging (DTI) and resting-state functional connectivity MRI (fcMRI) scan protocol at 3 T with 6-month interval over a period of 2 years. Neurologic performance scores of motor and cognitive performances were also obtained.

RESULTS

DTI measures worsened during the 1st year and then stabilized; any trend of stabilization of fcMRI was delayed until the 2nd year. While motor performance did not change, cognitive performance showed improvement. Several baseline DTI measures correlated with relevant neurologic scores.

CONCLUSION

Initial worsening of motor and cognitive network was reported after 1 year of treatment, but seems DTI and fcMRI measures seem to stabilize after around one year fingolimod therapy.

Impaired connectivity within neuromodulatory networks in multiple sclerosis and clinical implications

There is mounting evidence regarding the role of impairment in neuromodulatory networks for neurodegenerative diseases, such as Parkinson's and Alzheimer's disease. However, the role of neuromodulatory networks in multiple sclerosis (MS) has not been assessed. We applied resting-state functional connectivity and graph theory to investigate the changes in the functional connectivity within neuromodulatory networks including the serotonergic, noradrenergic, cholinergic, and dopaminergic systems in MS. Twenty-nine MS patients and twenty-four age- and gender-matched healthy controls performed clinical and cognitive assessments including the expanded disability status score, symbol digit modalities test, and Hamilton Depression rating scale. We demonstrated a diffuse reorganization of network topography (P < 0.01) in serotonergic, cholinergic, noradrenergic, and dopaminergic networks in patients with MS. Serotonergic, noradrenergic, and cholinergic network functional connectivity derangement was associated with disease duration, EDSS, and depressive symptoms (P < 0.01). Derangements in serotonergic, noradrenergic, cholinergic, and dopaminergic network impairment were associated with cognitive abilities (P < 0.01). Our results indicate that functional connectivity changes within neuromodulatory networks might be a useful tool in predicting disability burden over time, and could serve as a surrogate endpoint to assess efficacy for symptomatic treatments.

Multi-scale resting state functional reorganization in response to multiple sclerosis damage

PURPOSE

In multiple sclerosis (MS), how brain functional changes relate to clinical conditions is still a matter of debate. The aim of this study was to investigate how functional connectivity (FC) reorganization at three different scales, ranging from local to whole brain, is related to tissue damage and disability.

METHODS

One-hundred-nineteen patients with MS were clinically evaluated with the Expanded Disability Status Scale and the Multiple Sclerosis Functional Composite. Patients and 42 healthy controls underwent a multimodal 3 T MRI, including resting-state functional MRI.

RESULTS

We identified 16 resting-state networks via independent component analysis and measured within-network, between-network, and whole-brain (global efficiency and degree centrality) FC. Within-network FC was higher in patients than in controls in default mode, frontoparietal, and executive-control networks, and corresponded to low clinical impairment (default mode network versus Expanded Disability Status Scale r = - 0.31, p < 0.01; right frontoparietal network versus Paced Auditory Serial Addition Test r = 0.33, p < 0.01). All measures of between-network and whole-brain FC, except default mode network global efficiency, were lower in patients than in controls, and corresponded to high disability (i.e., basal ganglia global efficiency versus Timed 25-Foot Walk r = - 0.25, p < 0.03; default mode global efficiency versus Expanded Disability Status Scale r = - 0.44, p < 0.001). Altered measures of within-network, between-network, and whole-brain FC were combined in functional indices that were linearly related to disease duration, Paced Auditory Serial Addition Test and lesion load and non-linearly related to Expanded Disability Status Scale.

CONCLUSION

We suggest that the combined evaluation of functional alterations occurring at different levels, from local to whole brain, could exhaustively describe neuroplastic changes in MS, while increased within-network FC likely represents adaptive compensatory processes, decreased between-network and whole-brain FC likely represent loss of functional network integration consequent to structural disruption.

The effect of chronic neuroglycopenia on resting state networks in GLUT1 syndrome across the lifespan

Glucose transporter type I deficiency syndrome (GLUT1DS) is an encephalopathic disorder due to a chronic insufficient transport of glucose into the brain. PET studies in GLUT1DS documented a widespread cortico‐thalamic hypometabolism and a signal increase in the basal ganglia, regardless of age and clinical phenotype. Herein, we captured the pattern of functional connectivity of distinct striatal, cortical, and cerebellar regions in GLUT1DS (10 children, eight adults) and in healthy controls (HC, 19 children, 17 adults) during rest. Additionally, we explored for regional connectivity differences in GLUT1 children versus adults and according to the clinical presentation. Compared to HC, GLUT1DS exhibited increase connectivity within the basal ganglia circuitries and between the striatal regions with the frontal cortex and cerebellum. The excessive connectivity was predominant in patients with movement disorders and in children compared to adults, suggesting a correlation with the clinical phenotype and age at fMRI study. Our findings highlight the primary role of the striatum in the GLUT1DS pathophysiology and confirm the dependency of symptoms to the patients' chronological age. Despite the reduced chronic glucose uptake, GLUT1DS exhibit increased connectivity changes in regions highly sensible to glycopenia. Our results may portrait the effect of neuroprotective brain strategy to overcome the chronic poor energy supply during vulnerable ages.

Continuous reorganization of cortical information flow in multiple sclerosis: A longitudinal fMRI effective connectivity study

Effective connectivity (EC) is able to explore causal effects between brain areas and can depict mechanisms that underlie repair and adaptation in chronic brain diseases. Thus, the application of EC techniques in multiple sclerosis (MS) has the potential to determine directionality of neuronal interactions and may provide an imaging biomarker for disease progression. Here, serial longitudinal structural and resting-state fMRI was performed at 12-week intervals over one year in twelve MS patients. Twelve healthy subjects served as controls (HC). Two approaches for EC quantification were used: Causal Bayesian Network (CBN) and Time-resolved Partial Directed Coherence (TPDC). The EC strength was correlated with the Expanded Disability Status Scale (EDSS) and Fatigue Scale for Motor and Cognitive functions (FSMC). Our findings demonstrated a longitudinal increase in EC between specific brain regions, detected in both the CBN and TPDC analysis in MS patients. In particular, EC from the deep grey matter, frontal, prefrontal and temporal regions showed a continuous increase over the study period. No longitudinal changes in EC were attested in HC during the study. Furthermore, we observed an association between clinical performance and EC strength. In particular, the EC increase in fronto-cerebellar connections showed an inverse correlation with the EDSS and FSMC. Our data depict continuous functional reorganization between specific brain regions indicated by increasing EC over time in MS, which is not detectable in HC. In particular, fronto-cerebellar connections, which were closely related to clinical performance, may provide a marker of brain plasticity and functional reserve in MS.

Differential Resting State Connectivity Responses to Glycemic State in Type 1 Diabetes

CONTEXT

Individuals with type 1 diabetes mellitus (T1DM) have alterations in brain activity that have been postulated to contribute to the adverse neurocognitive consequences of T1DM; however, the impact of T1DM and hypoglycemic unawareness on the brain's resting state activity remains unclear.

OBJECTIVE

To determine whether individuals with T1DM and hypoglycemia unawareness (T1DM-Unaware) had changes in the brain resting state functional connectivity compared to healthy controls (HC) and those with T1DM and hypoglycemia awareness (T1DM-Aware).

DESIGN

Observational study.

SETTING

Academic medical center.

PARTICIPANTS

27 individuals with T1DM and 12 HC volunteers participated in the study.

INTERVENTION

All participants underwent blood oxygenation level dependent (BOLD) resting state functional magnetic brain imaging during a 2-step hyperinsulinemic euglycemic (90 mg/dL)-hypoglycemic (60 mg/dL) clamp.

OUTCOME

Changes in resting state functional connectivity.

RESULTS

Using 2 separate methods of functional connectivity analysis, we identified distinct differences in the resting state brain responses to mild hypoglycemia between HC, T1DM-Aware, and T1DM-Unaware participants, particularly in the angular gyrus, an integral component of the default mode network (DMN). Furthermore, changes in angular gyrus connectivity also correlated with greater symptoms of hypoglycemia (r = 0.461, P = 0.003) as well as higher scores of perceived stress (r = 0.531, P = 0.016).

CONCLUSION

These findings provide evidence that individuals with T1DM have changes in the brain's resting state connectivity patterns, which may be further associated with differences in awareness to hypoglycemia. These changes in connectivity may be associated with alterations in functional outcomes among individuals with T1DM.

Modeling Resilience to Damage in Multiple Sclerosis: Plasticity Meets Connectivity

Multiple sclerosis (MS) is a chronic inflammatory disease of the central nervous system (CNS) characterized by demyelinating white matter lesions and neurodegeneration, with a variable clinical course. Brain network architecture provides efficient information processing and resilience to damage. The peculiar organization characterized by a low number of highly connected nodes (hubs) confers high resistance to random damage. Anti-homeostatic synaptic plasticity, in particular long-term potentiation (LTP), represents one of the main physiological mechanisms underlying clinical recovery after brain damage. Different types of synaptic plasticity, including both anti-homeostatic and homeostatic mechanisms (synaptic scaling), contribute to shape brain networks. In MS, altered synaptic functioning induced by inflammatory mediators may represent a further cause of brain network collapse in addition to demyelination and grey matter atrophy. We propose that impaired LTP expression and pathologically enhanced upscaling may contribute to disrupting brain network topology in MS, weakening resilience to damage and negatively influencing the disease course.

Fatigue in multiple sclerosis is associated with multimodal interoceptive abnormalities

BACKGROUND

Fatigue ranks among the most common and disabling symptoms in multiple sclerosis (MS). Recent theoretical works have surmised that this trait might be related to alterations across interoceptive mechanisms. However, this hypothesis has not been empirically evaluated.

OBJECTIVES

To determine whether fatigue in MS patients is associated with specific behavioral, structural, and functional disruptions of the interoceptive domain.

METHODS

Fatigue levels were established via the Modified Fatigue Impact Scale. Interoception was evaluated through a robust measure indexed by the heartbeat detection task. Structural and functional connectivity properties of key interoceptive hubs were tested by magnetic resonance imaging (MRI) and resting-state functional MRI. Machine learning analyses were employed to perform pairwise classifications.

RESULTS

Only patients with fatigue presented with decreased interoceptive accuracy alongside decreased gray matter volume and increased functional connectivity in core interoceptive regions, the insula, and the anterior cingulate cortex. Each of these alterations was positively associated with fatigue. Finally, machine-learning analysis with a combination of the above interoceptive indices (behavioral, structural, and functional) successfully discriminated (area under the curve > 90%) fatigued patients from both non-fatigued and healthy controls.

CONCLUSION

This study offers unprecedented evidence suggesting that disruptions of neurocognitive markers subserving interoception may constitute a signature of fatigue in MS.

Morphological MRI phenotypes of multiple sclerosis differ in resting-state brain function

We aimed to assess differences in resting-state functional connectivity (FC) between distinct morphological MRI-phenotypes in multiple sclerosis (MS). Out of 180 MS patients, we identified those with high T2-hyperintense lesion load (T2-LL) and high normalized brain volume (NBV; a predominately white matter damage group, WMD; N = 37) and patients with low T2-LL and low NBV (N = 37; a predominately grey matter damage group; GMD). Independent component analysis of resting-state fMRI was used to test for differences in the sensorimotor network (SMN) between MS MRI-phenotypes and compared to 37 age-matched healthy controls (HC). The two MS groups did not differ regarding EDSS scores, disease duration and distribution of clinical phenotypes. WMD compared to GMD patients showed increased FC in all sub-units of the SMN (sex- and age-corrected). WMD patients had increased FC compared to HC and GMD patients in the central SMN (leg area). Only in the WMD group, higher EDSS scores and T2-LL correlated with decreased connectivity in SMN sub-units. MS patients with distinct morphological MRI-phenotypes also differ in brain function. The amount of focal white matter pathology but not global brain atrophy affects connectivity in the central SMN (leg area) of the SMN, consistent with the notion of a disconnection syndrome.

Analysis of brain functional connectivity network in MS patients constructed by modular structure of sparse weights from cognitive task-related fMRI

Cognitive dysfunction in multiple sclerosis (MS) seems to be the result of neural disconnections, leading to a wide range of brain functional network alterations. It is assumed that the analysis of the topological structure of brain connectivity network can be used to assess cognitive impairments in MS disease. We aimed to identify these brain connectivity pattern alterations and detect the significant features for the distinction of MS patients from healthy controls (HC). In this regard, the importance of functional brain networks construction for better exhibition of changes, inducing the improved reflection of functional organization structure should be precisely considered. In this paper, we strove to introduce a framework for modeling the functional connectivity network by considering the two most important intrinsic sparse and modular structures of brain. For the proposed approach, we first derived group-wise sparse representation via learning a common over-complete dictionary matrix from the aggregated cognitive task-based functional magnetic resonance imaging (fMRI) data of all subjects of the two groups to be able to investigate between-group differences. We then applied the modularity concept on achieved sparse coefficients to compute the connectivity strength between the two brain regions. We examined the changes in network topological properties between relapsing-remitting MS (RRMS) and matched HC groups by considering the pairwise connections of regions of the resulted weighted networks and extracting graph-based measures. We found that the informative brain regions were related to their important connectivity weights, which could distinguish MS patients from the healthy controls. The experimental findings also proved the discrimination ability of the modularity measure among all the global features. In addition, we identified such local feature subsets as eigenvector centrality, eccentricity, node strength, and within-module degree, which significantly differed between the two groups. Moreover, these nodal graph measures have been served as the detectors of brain regions, affected by different cognitive deficits. In general, our findings illustrated that integration of sparse representation, modular structure, and pairwise connectivity strength in combination with the graph properties could help us with the early diagnosis of cognitive alterations in the case of MS.

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.

Multiple sclerosis lesions affect intrinsic functional connectivity of the spinal cord

Patients with multiple sclerosis present with focal lesions throughout the spinal cord. There is a clinical need for non-invasive measurements of spinal cord activity and functional organization in multiple sclerosis, given the cord's critical role in the disease. Recent reports of spontaneous blood oxygenation level-dependent fluctuations in the spinal cord using functional MRI suggest that, like the brain, cord activity at rest is organized into distinct, synchronized functional networks among grey matter regions, likely related to motor and sensory systems. Previous studies looking at stimulus-evoked activity in the spinal cord of patients with multiple sclerosis have demonstrated increased levels of activation as well as a more bilateral distribution of activity compared to controls. Functional connectivity studies of brain networks in multiple sclerosis have revealed widespread alterations, which may take on a dynamic trajectory over the course of the disease, with compensatory increases in connectivity followed by decreases associated with structural damage. We build upon this literature by examining functional connectivity in the spinal cord of patients with multiple sclerosis. Using ultra-high field 7 T imaging along with processing strategies for robust spinal cord functional MRI and lesion identification, the present study assessed functional connectivity within cervical cord grey matter of patients with relapsing-remitting multiple sclerosis (n = 22) compared to a large sample of healthy controls (n = 56). Patient anatomical images were rated for lesions by three independent raters, with consensus ratings revealing 19 of 22 patients presented with lesions somewhere in the imaged volume. Linear mixed models were used to assess effects of lesion location on functional connectivity. Analysis in control subjects demonstrated a robust pattern of connectivity among ventral grey matter regions as well as a distinct network among dorsal regions. A gender effect was also observed in controls whereby females demonstrated higher ventral network connectivity. Wilcoxon rank-sum tests detected no differences in average connectivity or power of low frequency fluctuations in patients compared to controls. The presence of lesions was, however, associated with local alterations in connectivity with differential effects depending on columnar location. The patient results suggest that spinal cord functional networks are generally intact in relapsing-remitting multiple sclerosis but that lesions are associated with focal abnormalities in intrinsic connectivity. These findings are discussed in light of the current literature on spinal cord functional MRI and the potential neurological underpinnings.

Two-year dynamic functional network connectivity in clinically isolated syndrome

Background:

The features of functional network connectivity reorganization at the earliest stages of MS have not been investigated yet.

Objective:

To combine static and dynamic analysis of resting state (RS) functional connectivity (FC) to identify mechanisms of clinical dysfunction and recovery occurring in clinically isolated syndrome (CIS) patients.

Methods:

RS functional magnetic resonance imaging (fMRI) and clinical data were prospectively acquired from 50 CIS patients and 13 healthy controls (HC) at baseline, month 12 and month 24. Between-group differences and longitudinal evolution of network FC were analysed across 41 functionally relevant networks.

Results:

At follow-up, 47 patients developed MS. Disability remained stable (and relatively low). CIS and HC exhibited two recurring RS FC states (states 1 and 2, showing low and high internetwork connectivity, respectively). At baseline, patients showed reduced state 2 connectivity strength in the default-mode and cerebellar networks, and no differences in global dynamism versus HC. A selective FC reduction in networks affected by the clinical attack was also detected. At follow-up, increased state 2 connectivity strength and global connectivity dynamism was observed in patients versus HC.

Conclusion:

Longitudinal FC modifications occurring relatively early in the course of multiple sclerosis may represent a protective mechanism contributing to preserve clinical function over time.

Reduced dynamics of functional connectivity and cognitive impairment in multiple sclerosis

BACKGROUND

In multiple sclerosis (MS), abnormalities of brain network dynamics and their relevance for cognitive impairment have never been investigated.

OBJECTIVES

The aim of this study was to assess the dynamic resting state (RS) functional connectivity (FC) on 62 relapsing-remitting MS patients and 65 sex-matched healthy controls enrolled at 7 European sites.

METHODS

MS patients underwent clinical and cognitive evaluation. Between-group network FC differences were evaluated using a dynamic approach (based on sliding-window correlation analysis) and grouping correlation matrices into recurrent FC states.

RESULTS

Dynamic FC analysis revealed, in healthy controls and MS patients, three recurrent FC states: two characterized by strong intra- and inter-network connectivity and one characterized by weak inter-network connectivity (State 3). A total of 23 MS patients were cognitively impaired (CI). Compared to cognitively preserved (CP), CI-MS patients had reduced RS-FC between subcortical and default-mode networks in the low-connectivity State 3 and lower dwell time (i.e. time spent in a given state) in the high-connectivity State 2. CI-MS patients also exhibited a lower number and a less frequent switching between meta-states, as well as a smaller distance traveled through connectivity states.

CONCLUSION

Time-varying RS-FC was markedly less dynamic in CI- versus CP-MS patients, suggesting that slow inter-network connectivity contributes to cognitive dysfunction in MS.

Disrupted resting-state spontaneous neural activity in stable COPD

Introduction and aim

Abnormal brain structure and function in COPD has been reported on MRI. However, the deficit in local synchronization of spontaneous activity in patients with stable COPD remains unknown. The main aim of the present study was to explore spontaneous brain activity in patients with COPD compared with normal controls using the regional homogeneity (ReHo) method based on resting-state functional MRI.

Methods

Nineteen patients with stable COPD and 20 well-matched (including age, sex, and number of years of education) normal controls who were recruited for the present study underwent resting-state functional MRI examinations and a series of neuropsychological and clinical assessments. The ReHo method was used to assess the strength of local brain signal synchrony. The mean ReHo values in brain areas with abnormal ReHo were evaluated with a receiver operating characteristic curve. The relationships between the brain regions with altered ReHo values and the clinical and neuropsychological parameters in COPD patients were assessed using Pearson’s correlation.

Results

Patients with COPD showed significantly lower ReHo values in the left occipital lobe and the right lingual, bilateral precuneus, and right precentral gyrus. The result of receiver operating characteristic curve analysis showed that the altered average ReHo values have high efficacy for distinguishing function. The mean lower ReHo values in the precuneus gyrus showed a significant positive correlation with FEV₁%, FEV₁/FVC, and orientation function but a significant negative correlation with arterial partial pressure of carbon dioxide.

Conclusion

The COPD patients demonstrated abnormal synchrony of regional spontaneous activity, and the regions with abnormal activity were all correlated with visual processing pathways, which might provide us with a new perspective to further understand the underlying pathophysiology of cognitive impairment in patients with COPD.

Default Mode Network Structural Integrity and Cerebellar Connectivity Predict Information Processing Speed Deficit in Multiple Sclerosis

Cognitive impairment affects about 50% of multiple sclerosis (MS) patients, but the mechanisms underlying this remain unclear. The default mode network (DMN) has been linked with cognition, but in MS its role is still poorly understood. Moreover, within an extended DMN network including the cerebellum (CBL-DMN), the contribution of cortico-cerebellar connectivity to MS cognitive performance remains unexplored. The present study investigated associations of DMN and CBL-DMN structural connectivity with cognitive processing speed in MS, in both cognitively impaired (CIMS) and cognitively preserved (CPMS) MS patients. 68 MS patients and 22 healthy controls (HCs) completed a symbol digit modalities test (SDMT) and had 3T brain magnetic resonance imaging (MRI) scans that included a diffusion weighted imaging protocol. DMN and CBL-DMN tracts were reconstructed with probabilistic tractography. These networks (DMN and CBL-DMN) and the cortico-cerebellar tracts alone were modeled using a graph theoretical approach with fractional anisotropy (FA) as the weighting factor. Brain parenchymal fraction (BPF) was also calculated. In CIMS SDMT scores strongly correlated with the FA-weighted global efficiency (GE) of the network [GE(CBL-DMN): ρ = 0.87, R² = 0.76, p < 0.001; GE(DMN): ρ = 0.82, R² = 0.67, p < 0.001; GE(CBL): ρ = 0.80, R² = 0.64, p < 0.001]. In CPMS the correlation between these measures was significantly lower [GE(CBL-DMN): ρ = 0.51, R² = 0.26, p < 0.001; GE(DMN): ρ = 0.48, R² = 0.23, p = 0.001; GE(CBL): ρ = 0.52, R² = 0.27, p < 0.001] and SDMT scores correlated most with BPF (ρ = 0.57, R² = 0.33, p < 0.001). In a multivariable regression model where SDMT was the independent variable, FA-weighted GE was the only significant explanatory variable in CIMS, while in CPMS BPF and expanded disability status scale were significant. No significant correlation was found in HC between SDMT scores, MRI or network measures. DMN structural GE is related to cognitive performance in MS, and results of CBL-DMN suggest that the cerebellum structural connectivity to the DMN plays an important role in information processing speed decline.

Upper Extremity Capability Tests in Multiple Sclerosis

Multiple sclerosis is a chronic demyelinating disease. Since attacks are accompanied by psychiatric and physical disabilities, additional symptoms such as withdrawal from the social environment and psychiatric disorders are also observed. It is very important to evaluate patients adequately and correctly, to determine the disability, and treat them with appropriate clinical approach. For this reason, Expanded Disability Status Scale score and upper extremity capacity measurement tests are used in many studies by investigators. These tests provide detailed examination on the patient's upper limbs, and indirectly provide information about their cognitive function. A multi-disciplinary approach for multiple sclerosis patients is the most crucial factor in clinical follow-up and treatment success.

The Role of fMRI in the Assessment of Neuroplasticity in MS: A Systematic Review

Neuroplasticity, which is the ability of the brain to adapt to internal and external environmental changes, physiologically occurs during growth and in response to damage. The brain's response to damage is of particular interest in multiple sclerosis, a chronic disease characterized by inflammatory and neurodegenerative damage to the central nervous system. Functional MRI (fMRI) is a tool that allows functional changes related to the disease and to its evolution to be studied in vivo. Several studies have shown that abnormal brain recruitment during the execution of a task starts in the early phases of multiple sclerosis. The increased functional activation during a specific task observed has been interpreted mainly as a mechanism of adaptive plasticity designed to contrast the increase in tissue damage. More recent fMRI studies, which have focused on the activity of brain regions at rest, have yielded nonunivocal results, suggesting that changes in functional brain connections represent mechanisms of either adaptive or maladaptive plasticity. The few longitudinal studies available to date on disease evolution have also yielded discrepant results that are likely to depend on the clinical features considered and the length of the follow-up. Lastly, fMRI has been used in interventional studies to investigate plastic changes induced by pharmacological therapy or rehabilitation, though whether such changes represent a surrogate of neuroplasticity remains unclear. The aim of this paper is to systematically review the existing literature in order to provide an overall description of both the neuroplastic process itself and the evolution in the use of fMRI techniques as a means of assessing neuroplasticity. The quantitative and qualitative approach adopted here ensures an objective analysis of published, peer-reviewed research and yields an overview of up-to-date knowledge.