Progression of regional grey matter atrophy in multiple sclerosis

Vulnerability of Thalamic Nuclei at CSF Interface During the Entire Course of Multiple Sclerosis

BACKGROUND AND OBJECTIVES

Thalamic atrophy can be used as a proxy for neurodegeneration in multiple sclerosis (MS). Some data point toward thalamic nuclei that could be affected more than others. However, the dynamic of their changes during MS evolution and the mechanisms driving their differential alterations are still uncertain.

METHODS

We paired a large cohort of 1,123 patients with MS with the same number of healthy controls, all scanned with conventional 3D-T1 MRI. To highlight the main atrophic regions at the thalamic nuclei level, we validated a segmentation strategy consisting of deep learning-based synthesis of sequences, which were used for automatic multiatlas segmentation. Then, through a lifespan-based approach, we could model the dynamics of the 4 main thalamic nuclei groups.

RESULTS

All analyses converged toward a higher rate of atrophy for the posterior and medial groups compared with the anterior and lateral groups. We also demonstrated that focal MS white matter lesions were associated with atrophy of groups of nuclei when specifically located within the associated thalamocortical projections. The volumes of the most affected posterior group, but also of the anterior group, were better associated with clinical disability than the volume of the whole thalamus.

DISCUSSION

These findings point toward the thalamic nuclei adjacent to the third ventricle as more susceptible to neurodegeneration during the entire course of MS through potentiation of disconnection effects by regional factors. Because this information can be obtained even from standard T1-weighted MRI, this paves the way toward such an approach for future monitoring of patients with MS.

Enlarged choroid plexus in relapsing-remitting multiple sclerosis may lead to brain structural changes through the glymphatic impairment

OBJECTIVES

To investigate the potential link among choroid plexus (CP) volume, glymphatic clearance and brain structural change in relapsing-remitting multiple sclerosis (RRMS) patients.

MATERIALS AND METHODS

Sixty-five RRMS patients and 48 healthy controls (HC) underwent MRI examination. The diffusion tensor image analysis along the perivascular space (DTI-ALPS) was calculated to reflect glymphatic system function. The brain structure volume and DTI-ALPS index were compared between RRMS and HC. The mediating effect of the DTI-ALPS index between CP volume and brain structural changes was further investigated. The longitudinal changes of brain structure and DTI-ALPS index were compared in 20 RRMS patients.

RESULTS

Compared to HC, CP volume in RRMS was significantly increased (P < 0.001), and DTI-ALPS index was significantly decreased (P = 0.001). The volumes of white matter, thalamus, putamen and pallidum were significantly decreased in RRMS, and the volumes of lateral ventricle and third ventricle were increased. Mediation analysis showed DTI-ALPS index partially mediated the association between CP enlargement and deep gray matter (DGM) atrophy in RRMS, and between CP enlargement and ventricle enlargement. CP volume and DTI-ALPS index were also significantly correlated with Expanded Disability Status Scale (EDSS) (P = 0.006, P = 0.043). Notably, the variation of DTI_ALPS index during the follow-up period were significantly and negatively correlated with the variation of EDSS (P = 0.045).

CONCLUSION

Enlarged CP volume and decreased DTI_ALPS index may be closely related to DGM atrophy and ventricular enlargement in RRMS, and may be potential imaging markers of clinical disability.

Low-frequency MR elastography reveals altered deep gray matter viscoelasticity in multiple sclerosis

INTRODUCTION

Brain viscoelasticity as assessed by magnetic resonance elastography (MRE) has been discussed as a promising surrogate of microstructural alterations due to neurodegenerative processes. Existing studies indicate that multiple sclerosis (MS) is associated with a global reduction in brain stiffness. However, no study to date systematically investigated the MS-related characteristics of brain viscoelasticity separately in normal-appearing white matter (NAWM), deep gray matter (DGM) and T2-hyperintense white matter (WM) lesions.

METHODS

70 MS patients and 42 healthy volunteers underwent whole-cerebral MRE using a stimulated echo sequence (DENSE) with a low-frequency mechanical excitation at 20 Hertz. The magnitude |G∗| (Pa) and phase angle φ (rad) of the complex shear modulus G∗ were reconstructed by multifrequency dual elasto-visco (MDEV) inversion and related to structural imaging and clinical parameters.

RESULTS

We observed φ in the thalamus to be higher by 4.3 % in patients relative to healthy controls (1.11 ± 0.07 vs. 1.06 ± 0.07, p < 0.0001). Higher Expanded Disability Status Scale (EDSS) scores were negatively associated with φ in the basal ganglia (p = 0.01). We measured φ to be lower in MS lesions compared to surrounding NAWM (p = 0.001), which was most prominent for lesions in the temporal lobe (1.01 ± 0.22 vs. 1.06 ± 0.19, p = 0.003). Age was associated with lower values of |G∗| (p = 0.04) and φ (p = 0.004) in the thalamus of patients. No alteration in NAWM stiffness relative to WM in healthy controls was observed.

CONCLUSION

Low-frequency elastography in MS patients reveals age-independent alterations in the viscoelasticity of deep gray matter at early stages of disease.

Mapping brain volume change across time in primary-progressive multiple sclerosis

PURPOSE

Detection and prediction of the rate of brain volume loss with age is a significant unmet need in patients with primary progressive multiple sclerosis (PPMS). In this study we construct detailed brain volume maps for PPMS patients. These maps compare age-related changes in both cortical and sub-cortical regions with those in healthy individuals.

METHODS

We conducted retrospective analyses of brain volume using T1-weighted Magnetic Resonance Imaging (MRI) scans of a large cohort of PPMS patients and healthy subjects. The volume of brain parenchyma (BP), cortex, white matter (WM), deep gray matter, thalamus, and cerebellum were measured using the robust SynthSeg segmentation tool. Age- and gender-related regression curves were constructed based on data from healthy subjects, with the 95% prediction interval adopted as the normality threshold for each brain region.

RESULTS

We analyzed 495 MRI scans from 169 PPMS patients, aged 20-79 years, alongside 563 exams from healthy subjects aged 20-86. Compared to healthy subjects, a higher proportion of PPMS patients showed lower than expected brain volumes in all regions except the cerebellum. The most affected areas were BP, WM, and thalamus. Lower brain volumes correlated with longer disease duration for BP and WM, and higher disability for BP, WM, cortex, and thalamus.

CONCLUSIONS

Constructing age- and gender-related brain volume maps enabled identifying PPMS patients at a higher risk of brain volume loss. Monitoring these high-risk patients may lead to better treatment decisions and improve patient outcomes.

Cognitive phenotypes in multiple sclerosis: mapping the spectrum of impairment

BACKGROUND

Available criteria for cognitive phenotypes in multiple sclerosis (MS) do not consider the severity of impairment.

OBJECTIVES

To identify cognitive phenotypes with varying degrees of impairment in MS patients and describe their demographic, clinical and MRI characteristics.

METHODS

Two hundred and forty-three MS patients and 158 healthy controls underwent neuropsychological tests to assess memory, attention, and executive function. For each domain, mild impairment was defined as performing 1.5 standard deviations below the normative mean on two tests, while the threshold for significant impairment was 2 standard deviations. Patients were classified into cognitive phenotypes based on severity of the impairment (mild/significant) and number of domains affected (one/more).

RESULTS

Five cognitive phenotypes emerged: Preserved cognition (PC; 56%), Mild Single-Domain Impairment (MSD; 15%), Mild Multi-Domain Impairment (MMD; 9%), Significant Single-Domain Impairment (SSD; 12%), Significant Multi-Domain Impairment (SMD; 8%). Compared with PC, MSD patients were older, had longer disease duration (DD) and higher T2-hyperintense lesion volume (LV; all p ≤ 0.02); MMD patients were older, had longer DD, higher disability, higher T2 LV and lower thalamic volume (all p ≤ 0.01); SSD patients had longer DD and lower gray matter cortical volume, thalamic, caudate, putamen and accumbens volumes (all p ≤ 0.04); and SMD patients were older, had longer DD, higher disability and more extensive structural damage in all brain regions explored (all p ≤ 0.03), except white matter and amygdala volumes.

CONCLUSIONS

We identified five cognitive phenotypes with graded levels of impairment. These phenotypes were characterized by distinct demographic, clinical and MRI features, indicating potential variations in the neural substrates of dysfunction throughout disease stages.

B cell depletion with anti-CD20 promotes neuroprotection in a BAFF-dependent manner in mice and humans

Anti-CD20 therapy to deplete B cells is highly efficacious in preventing new white matter lesions in patients with relapsing-remitting multiple sclerosis (RRMS), but its protective capacity against gray matter injury and axonal damage is unclear. In a passive experimental autoimmune encephalomyelitis (EAE) model whereby TH17 cells promote brain leptomeningeal immune cell aggregates, we found that anti-CD20 treatment effectively spared myelin content and prevented myeloid cell activation, oxidative damage, and mitochondrial stress in the subpial gray matter. Anti-CD20 treatment increased B cell survival factor (BAFF) in the serum, cerebrospinal fluid, and leptomeninges of mice with EAE. Although anti-CD20 prevented gray matter demyelination, axonal loss, and neuronal atrophy, co-treatment with anti-BAFF abrogated these benefits. Consistent with the murine studies, we observed that elevated BAFF concentrations after anti-CD20 treatment in patients with RRMS were associated with better clinical outcomes. Moreover, BAFF promoted survival of human neurons in vitro. Together, our data demonstrate that BAFF exerts beneficial functions in MS and EAE in the context of anti-CD20 treatment.

Clinical trials for progressive multiple sclerosis: progress, new lessons learned, and remaining challenges

Despite the success of disease-modifying treatments in relapsing multiple sclerosis, for many individuals living with multiple sclerosis, progressive disability continues to accrue. How to interrupt the complex pathological processes underlying progression remains a daunting and ongoing challenge. Since 2014, several immunomodulatory approaches that have modest but clinically meaningful effects have been approved for the management of progressive multiple sclerosis, primarily for people who have active inflammatory disease. The approval of these drugs required large phase 3 trials that were sufficiently powered to detect meaningful effects on disability. New classes of drug, such as Bruton tyrosine-kinase inhibitors, are coming to the end of their trial stages, several candidate neuroprotective compounds have been successful in phase 2 trials, and innovative approaches to remyelination are now also being explored in clinical trials. Work continues to define intermediate outcomes that can provide results in phase 2 trials more quickly than disability measures, and more efficient trial designs, such as multi-arm multi-stage and futility approaches, are increasingly being used. Collaborations between patient organisations, pharmaceutical companies, and academic researchers will be crucial to ensure that future trials maintain this momentum and generate results that are relevant for people living with progressive multiple sclerosis.

Associations Between Cognitive Impairment and Neuroimaging in Patients with Multiple Sclerosis

OBJECTIVE

Multiple sclerosis (MS) is a debilitating inflammatory and neurodegenerative disease which commonly involves cognitive dysfunction. Magnetic resonance imaging (MRI) studies have shown that patients with MS (pwMS) have diffuse patterns of brain atrophy, however, the relationship between the presentation of cognitive dysfunction and brain tissue loss remains understudied. Given the integral function of thalamus as a central nervous system relay center and its involvement in various brain circuits, thalamic atrophy may play a key role in the development and progression of cognitive dysfunction. The purpose of this study is to examine the relationship between cognitive impairment in pwMS and thalamic atrophy.

METHODS

A total of 121 pwMS who had neuropsychological testing and quantitative MRI within 1 year of each were retrospectively identified. Grouped LASSO linear regression with 10-fold cross validation was used to estimate each neuropsychological test score with thalamic volume as the focal predictor and all other demographic and MRI metrics as covariates.

RESULTS

Rates of impairment ranged from 19% to 44%. Results showed notable associations between thalamic volume and Symbol Digit Modalities Test (β = 0.11), Brief Visuospatial Memory Test, delayed (β = 0.12), California Verbal Learning Test, delayed and total (β = 0.24 and β = 0.15 respectively), and Trail Making Test Part A (β = -0.01), after adjusting for covariates.

CONCLUSIONS

These findings demonstrate an independent association between thalamic volumes and processing speed and memory performance, after accounting for demographic, clinical, and other MRI variables, among pwMS.

Early-stage volume losses in the corpus callosum and thalamus predict the progression of brain atrophy in patients with multiple sclerosis

BACKGROUND

A method that can be used in the early stage of multiple sclerosis (MS) to predict the progression of brain volume loss (BVL) has not been fully established.

METHODS

To develop a method of predicting progressive BVL in patients with MS (pwMS), eighty-two consecutive Japanese pwMS-with either relapsing-remitting MS (86%) or secondary progressive MS (14%)-and 41 healthy controls were included in this longitudinal retrospective analysis over an observational period of approximately 3.5 years. Using a hierarchical cluster analysis with multivariate imaging data obtained by FreeSurfer analysis, we classified the pwMS into clusters.

RESULTS

At baseline and follow-up, pwMS were cross-sectionally classified into three major clusters (Clusters 1, 2, and 3) in ascending order by disability and BVL. Among the patients included in Cluster 1 at baseline, approximately one-third of patients (12/52) transitioned into Cluster 2 at follow-up. The volumes of the corpus callosum, the thalamus, and the whole brain excluding the ventricles were significantly decreased in the transition group compared with the nontransition group and were found to be the most important predictors of transition.

CONCLUSION

Decreased volumes of the corpus callosum and thalamus in the relatively early stage of MS may predict the development of BVL.

Spatial correspondence among regional gene expressions and gray matter volume loss in multiple sclerosis

In multiple sclerosis (MS), a non-random and clinically relevant pattern of gray matter (GM) volume loss has been described. Whether differences in regional gene expression might underlay distinctive pathological processes contributing to this regional variability has not been explored yet. Two hundred eighty-six MS patients and 172 healthy controls (HC) underwent a brain 3T MRI, a complete neurological evaluation and a neuropsychological assessment. Using Allen Human Brain Atlas, voxel-based morphometry and MENGA platform, we integrated brain transcriptome and neuroimaging data to explore the spatial cross-correlations between regional GM volume loss and expressions of 2710 genes involved in MS (p < 0.05, family-wise error-corrected). Enrichment analyses were performed to evaluate overrepresented molecular functions, biological processes and cellular components involving genes significantly associated with voxel-based morphometry-derived GM maps (p < 0.05, Bonferroni-corrected). A diffuse GM volume loss was found in MS patients compared to HC and it was spatially correlated with 74 genes involved in GABA neurotransmission and mitochondrial oxidoreductase activity mainly expressed in neurons and astrocytes. A more severe GM volume loss was spatially associated, in more disabled MS patients, with 44 genes involved in mitochondrial integrity of all resident cells of the central nervous system (CNS) and, in cognitively impaired MS patients, with 64 genes involved in mitochondrial protein heterodimerization and oxidoreductase activities expressed also in microglia and endothelial cells. Specific differences in the expressions of genes involved in synaptic GABA receptor activities and mitochondrial functions in resident CNS cells may influence regional susceptibility to MS-related excitatory/inhibitory imbalance and oxidative stress, and subsequently, to GM volume loss.

MRI features and disability in multiple sclerosis: A systematic review and meta-analysis

BACKGROUND

In this systematic review and meta-analysis, we aimed to investigate the correlation between disability in patients with Multiple sclerosis (MS) measured by the Expanded Disability Status Scale (EDSS) and brain Magnetic Resonance Imaging (MRI) features to provide reliable results on which characteristics in the MRI can predict disability and prognosis of the disease.

METHODS

A systematic literature search was performed using three databases including PubMed, Scopus, and Web of Science. The selected peer-reviewed studies must report a correlation between EDSS scores and MRI features. The correlation coefficients of included studies were converted to the Fisher's z scale, and the results were pooled.

RESULTS

Overall, 105 studies A total of 16,613 patients with MS entered our study. We found no significant correlation between total brain volume and EDSS assessment (95 % CI: -0.37 to 0.08; z-score: -0.15). We examined the potential correlation between the volume of T1 and T2 lesions and the level of disability. A positive significant correlation was found (95 % CI: 0.19 to 0.43; z-score: 0.31), (95 % CI: 0.17 to 0.33; z-score: 0.25). We observed a significant correlation between white matter volume and EDSS score in patients with MS (95 % CI: -0.37 to -0.03; z-score: -0.21). Moreover, there was a significant negative correlation between gray matter volume and disability (95 % CI: -0.025 to -0.07; z-score: -0.16).

CONCLUSION

In conclusion, this systematic review and meta-analysis revealed that disability in patients with MS is linked to extensive changes in different brain regions, encompassing gray and white matter, as well as T1 and T2 weighted MRI lesions.

Data-driven modelling of neurodegenerative disease progression: thinking outside the black box

Data-driven disease progression models are an emerging set of computational tools that reconstruct disease timelines for long-term chronic diseases, providing unique insights into disease processes and their underlying mechanisms. Such methods combine a priori human knowledge and assumptions with large-scale data processing and parameter estimation to infer long-term disease trajectories from short-term data. In contrast to 'black box' machine learning tools, data-driven disease progression models typically require fewer data and are inherently interpretable, thereby aiding disease understanding in addition to enabling classification, prediction and stratification. In this Review, we place the current landscape of data-driven disease progression models in a general framework and discuss their enhanced utility for constructing a disease timeline compared with wider machine learning tools that construct static disease profiles. We review the insights they have enabled across multiple neurodegenerative diseases, notably Alzheimer disease, for applications such as determining temporal trajectories of disease biomarkers, testing hypotheses about disease mechanisms and uncovering disease subtypes. We outline key areas for technological development and translation to a broader range of neuroscience and non-neuroscience applications. Finally, we discuss potential pathways and barriers to integrating disease progression models into clinical practice and trial settings.

Enlarged choroid plexus in multiple sclerosis is associated with increased lesion load and atrophy in white matter but not gray matter atrophy

BACKGROUND

Enlargement of the choroid plexus (CP) is reported to associate with inflammatory activity and contribute to brain atrophy in patients with multiple sclerosis (pwMS). However, a recent study in healthy volunteers (HVTs) has suggested that CP enlargement can be attributed to ventriculomegaly.

OBJECTIVES

To clarify the pathological significance of the enlargement of CP in multiple sclerosis (MS).

METHODS

A total of 102 pwMS (89 with relapsing-remitting MS and 13 with secondary progressive MS) and 41 HVTs were cross-sectionally evaluated using brain volumetry. The CP volume was compared between disease groups and investigated for the relationships with other brain regional volumes.

RESULTS

CP volume was significantly larger in pwMS than in HVTs in the univariate analysis, but not in multivariable analysis. Meanwhile, the CP and lateral ventricle (LV) volumes were significantly correlated. CP enlargement was significantly associated with increased lesion load and cerebral white matter (WM) atrophy, even after adjusting for LV volume. In contrast, multivariable analyses revealed that LV enlargement, but not CP enlargement, was associated with total gray matter (GM) atrophy.

CONCLUSION

CP enlargement was closely associated with LV enlargement. After adjusting for LV volume, CP enlargement in pwMS was associated with increased lesion load and WM atrophy but not GM atrophy.

Longitudinal fibre-specific white matter damage predicts cognitive decline in multiple sclerosis

During the course of multiple sclerosis, many patients experience cognitive deficits which are not simply driven by lesion number or location. By considering the full complexity of white matter structure at macro- and microstructural levels, our understanding of cognitive impairment in multiple sclerosis may increase substantially. Accordingly, this study aimed to investigate specific patterns of white matter degeneration, the evolution over time, the manifestation across different stages of the disease and their role in cognitive impairment using a novel fixel-based approach. Neuropsychological test scores and MRI scans including 30-direction diffusion-weighted images were collected from 327 multiple sclerosis patients (mean age = 48.34 years, 221 female) and 95 healthy controls (mean age = 45.70 years, 55 female). Of those, 233 patients and 61 healthy controls had similar follow-up assessments 5 years after. Patients scoring 1.5 or 2 standard deviations below healthy controls on at least two out of seven cognitive domains (from the Brief Repeatable Battery of Neuropsychological Tests, BRB-N) were classified as mildly cognitively impaired or cognitively impaired, respectively, or otherwise cognitively preserved. Fixel-based analysis of diffusion data was used to calculate fibre-specific measures (fibre density, reflecting microstructural diffuse axonal damage; fibre cross-section, reflecting macrostructural tract atrophy) within atlas-based white matter tracts at each visit. At baseline, all fixel-based measures were significantly worse in multiple sclerosis compared with healthy controls (P < 0.05). For both fibre density and fibre cross-section, a similar pattern was observed, with secondary progressive multiple sclerosis patients having the most severe damage, followed by primary progressive and relapsing-remitting multiple sclerosis. Similarly, damage was least severe in cognitively preserved (n = 177), more severe in mildly cognitively impaired (n = 63) and worst in cognitively impaired (n = 87; P < 0.05). Microstructural damage was most pronounced in the cingulum, while macrostructural alterations were most pronounced in the corticospinal tract, cingulum and superior longitudinal fasciculus. Over time, white matter alterations worsened most severely in progressive multiple sclerosis (P < 0.05), with white matter atrophy progression mainly seen in the corticospinal tract and microstructural axonal damage worsening in cingulum and superior longitudinal fasciculus. Cognitive decline at follow-up could be predicted by baseline fixel-based measures (R2 = 0.45, P < 0.001). Fixel-based approaches are sensitive to white matter degeneration patterns in multiple sclerosis and can have strong predictive value for cognitive impairment. Longitudinal deterioration was most marked in progressive multiple sclerosis, indicating that degeneration in white matter remains important to characterize further in this phenotype.

Causal relationship between multiple sclerosis and cortical structure: a Mendelian randomization study

BACKGROUND

Observational studies have suggested an association between multiple sclerosis (MS) and cortical structure, but the results have been inconsistent.

OBJECTIVE

We used two-sample Mendelian randomization (MR) to assess the causal relationship between MS and cortical structure.

METHODS

MS data as the exposure trait, including 14,498 cases and 24,091 controls, were obtained from the International Multiple Sclerosis Genetics Consortium. Genome-wide association study (GWAS) data for cortical surface area (SAw/nw) and thickness (THw/nw) in 51,665 individuals of European ancestry were obtained from the ENIGMA Consortium. The inverse-variance weighted (IVW) method was used as the primary analysis for MR. Sensitivity analyses were conducted to evaluate heterogeneity and pleiotropy. Enrichment analysis was performed on MR analyses filtered by sensitivity analysis.

RESULTS

After IVW and sensitivity analysis filtering, only six surviving MR results provided suggestive evidence supporting a causal relationship between MS and cortical structure, including lingual SAw (p = .0342, beta (se) = 5.7127 (2.6969)), parahippocampal SAw (p = .0224, beta (se) = 1.5577 (0.6822)), rostral middle frontal SAw (p = .0154, beta (se) = - 9.0301 (3.7281)), cuneus THw (p = .0418, beta (se) =  - 0.0020 (0.0010)), lateral orbitofrontal THw (p = .0281, beta (se) = 0.0025 (0.0010)), and lateral orbitofrontal THnw (p = .0417, beta (se) = 0.0029 (0.0014)). Enrichment analysis suggested that leukocyte cell-related pathways, JAK-STAT signaling pathway, NF-kappa B signaling pathway, cytokine-cytokine receptor interaction, and prolactin signaling pathway may be involved in the effect of MS on cortical morphology.

CONCLUSION

Our results provide evidence supporting a causal relationship between MS and cortical structure. Enrichment analysis suggests that the pathways mediating brain morphology abnormalities in MS patients are mainly related to immune and inflammation-driven pathways.

Multiple sclerosis clinical forms classification with graph convolutional networks based on brain morphological connectivity

Multiple Sclerosis (MS) is an autoimmune disease that combines chronic inflammatory and neurodegenerative processes underlying different clinical forms of evolution, such as relapsing-remitting, secondary progressive, or primary progressive MS. This identification is usually performed by clinical evaluation at the diagnosis or during the course of the disease for the secondary progressive phase. In parallel, magnetic resonance imaging (MRI) analysis is a mandatory diagnostic complement. Identifying the clinical form from MR images is therefore a helpful and challenging task. Here, we propose a new approach for the automatic classification of MS forms based on conventional MRI (i.e., T1-weighted images) that are commonly used in clinical context. For this purpose, we investigated the morphological connectome features using graph based convolutional neural network. Our results obtained from the longitudinal study of 91 MS patients highlight the performance (F1-score) of this approach that is better than state-of-the-art as 3D convolutional neural networks. These results open the way for clinical applications such as disability correlation only using T1-weighted images.

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

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

Novel data-driven subtypes and stages of brain atrophy in the ALS-FTD spectrum

BACKGROUND

TDP-43 proteinopathies represent a spectrum of neurological disorders, anchored clinically on either end by amyotrophic lateral sclerosis (ALS) and frontotemporal degeneration (FTD). The ALS-FTD spectrum exhibits a diverse range of clinical presentations with overlapping phenotypes, highlighting its heterogeneity. This study was aimed to use disease progression modeling to identify novel data-driven spatial and temporal subtypes of brain atrophy and its progression in the ALS-FTD spectrum.

METHODS

We used a data-driven procedure to identify 13 anatomic clusters of brain volume for 57 behavioral variant FTD (bvFTD; with either autopsy-confirmed TDP-43 or TDP-43 proteinopathy-associated genetic variants), 103 ALS, and 47 ALS-FTD patients with likely TDP-43. A Subtype and Stage Inference (SuStaIn) model was trained to identify subtypes of individuals along the ALS-FTD spectrum with distinct brain atrophy patterns, and we related subtypes and stages to clinical, genetic, and neuropathological features of disease.

RESULTS

SuStaIn identified three novel subtypes: two disease subtypes with predominant brain atrophy in either prefrontal/somatomotor regions or limbic-related regions, and a normal-appearing group without obvious brain atrophy. The limbic-predominant subtype tended to present with more impaired cognition, higher frequencies of pathogenic variants in TBK1 and TARDBP genes, and a higher proportion of TDP-43 types B, E and C. In contrast, the prefrontal/somatomotor-predominant subtype had higher frequencies of pathogenic variants in C9orf72 and GRN genes and higher proportion of TDP-43 type A. The normal-appearing brain group showed higher frequency of ALS relative to ALS-FTD and bvFTD patients, higher cognitive capacity, higher proportion of lower motor neuron onset, milder motor symptoms, and lower frequencies of genetic pathogenic variants. The overall SuStaIn stages also correlated with evidence for clinical progression including longer disease duration, higher King's stage, and cognitive decline. Additionally, SuStaIn stages differed across clinical phenotypes, genotypes and types of TDP-43 pathology.

CONCLUSIONS

Our findings suggest distinct neurodegenerative subtypes of disease along the ALS-FTD spectrum that can be identified in vivo, each with distinct brain atrophy, clinical, genetic and pathological patterns.

The sequence of regional structural disconnectivity due to multiple sclerosis lesions

Prediction of disease progression is challenging in multiple sclerosis as the sequence of lesion development and retention of inflammation within a subset of chronic lesions is heterogeneous among patients. We investigated the sequence of lesion-related regional structural disconnectivity across the spectrum of disability and cognitive impairment in multiple sclerosis. In a full cohort of 482 multiple sclerosis patients (age: 41.83 ± 11.63 years, 71.57% females), the Expanded Disability Status Scale was used to classify patients into (i) no or mild (Expanded Disability Status Scale <3) versus (ii) moderate or severe disability groups (Expanded Disability Status Scale ≥3). In 363 out of 482 patients, quantitative susceptibility mapping was used to identify paramagnetic rim lesions, which are maintained by a rim of iron-laden innate immune cells. In 171 out of 482 patients, Brief International Cognitive Assessment was used to identify subjects as being cognitively preserved or impaired. Network Modification Tool was used to estimate the regional structural disconnectivity due to multiple sclerosis lesions. Discriminative event-based modelling was applied to investigate the sequence of regional structural disconnectivity due to (i) all representative T2 fluid-attenuated inversion recovery lesions, (ii) paramagnetic rim lesions versus non-paramagnetic rim lesions separately across disability groups ('no to mild disability' to 'moderate to severe disability'), (iii) all representative T2 fluid-attenuated inversion recovery lesions and (iv) paramagnetic rim lesions versus non-paramagnetic rim lesions separately across cognitive status ('cognitively preserved' to 'cognitively impaired'). In the full cohort, structural disconnection in the ventral attention and subcortical networks, particularly in the supramarginal and putamen regions, was an early biomarker of moderate or severe disability. The earliest biomarkers of disability progression were structural disconnections due to paramagnetic rim lesions in the motor-related regions. Subcortical structural disconnection, particularly in the ventral diencephalon and thalamus regions, was an early biomarker of cognitive impairment. Our data-driven model revealed that the structural disconnection in the subcortical regions, particularly in the thalamus, is an early biomarker for both disability and cognitive impairment in multiple sclerosis. Paramagnetic rim lesions-related structural disconnection in the motor cortex may identify the patients at risk for moderate or severe disability in multiple sclerosis. Such information might be used to identify people with multiple sclerosis who have an increased risk of disability progression or cognitive decline in order to provide personalized treatment plans.

Lifespan neurodegeneration of the human brain in multiple sclerosis

Atrophy related to multiple sclerosis (MS) has been found at the early stages of the disease. However, the archetype dynamic trajectories of the neurodegenerative process, even prior to clinical diagnosis, remain unknown. We modeled the volumetric trajectories of brain structures across the entire lifespan using 40,944 subjects (38,295 healthy controls and 2649 MS patients). Then, we estimated the chronological progression of MS by assessing the divergence of lifespan trajectories between normal brain charts and MS brain charts. Chronologically, the first affected structure was the thalamus, then the putamen and the pallidum (around 4 years later), followed by the ventral diencephalon (around 7 years after thalamus) and finally the brainstem (around 9 years after thalamus). To a lesser extent, the anterior cingulate gyrus, insular cortex, occipital pole, caudate and hippocampus were impacted. Finally, the precuneus and accumbens nuclei exhibited a limited atrophy pattern. Subcortical atrophy was more pronounced than cortical atrophy. The thalamus was the most impacted structure with a very early divergence in life. Our experiments showed that lifespan models of most impacted structures could be an important tool for future preclinical/prodromal prognosis and monitoring of MS.

Benefits of a mosaic approach for assessing cortical atrophy in individual multiple sclerosis patients

OBJECTIVE

Cortical gray matter (GM) atrophy plays a central role in multiple sclerosis (MS) pathology. However, it is not commonly assessed in clinical routine partly because a number of methodological problems hamper the development of a robust biomarker to quantify GM atrophy. In previous work, we have demonstrated the clinical utility of the "mosaic approach" (MAP) to assess individual GM atrophy in the motor neuron disease spectrum and frontotemporal dementia. In this study, we investigated the clinical utility of MAP in MS, comparing this novel biomarker to existing methods for computing GM atrophy in single patients. We contrasted the strategies based on correlations with established biomarkers reflecting MS disease burden.

METHODS

We analyzed T1-weighted MPRAGE magnetic resonance imaging data from 465 relapsing-remitting MS patients and 89 healthy controls. We inspected how variations of existing strategies to estimate individual GM atrophy ("standard approaches") as well as variations of MAP (i.e., different parcellation schemes) impact downstream analysis results, both on a group and an individual level. We interpreted individual cortical disease burden as single metric reflecting the fraction of significantly atrophic data points with respect to the control group. In addition, we evaluated the correlations to lesion volume (LV) and Expanded Disability Status Scale (EDSS).

RESULTS

We found that the MAP method yielded highest correlations with both LV and EDSS as compared to all other strategies. Although the parcellation resolution played a minor role in terms of absolute correlations with clinical variables, higher resolutions provided more clearly defined statistical brain maps which may facilitate clinical interpretability.

CONCLUSION

This study provides evidence that MAP yields high potential for a clinically relevant biomarker in MS, outperforming existing methods to compute cortical disease burden in single patients. Of note, MAP outputs brain maps illustrating individual cortical disease burden which can be directly interpreted in daily clinical routine.

Mapping the Relationship of White Matter Lesions to Depression in Multiple Sclerosis

BACKGROUND

Multiple sclerosis (MS) is an immune-mediated neurological disorder, and up to 50% of patients experience depression. We investigated how white matter network disruption is related to depression in MS.

METHODS

Using electronic health records, 380 participants with MS were identified. Depressed individuals (MS+Depression group; n = 232) included persons who had an ICD-10 depression diagnosis, had a prescription for antidepressant medication, or screened positive via Patient Health Questionnaire (PHQ)-2 or PHQ-9. Age- and sex-matched nondepressed individuals with MS (MS-Depression group; n = 148) included persons who had no prior depression diagnosis, had no psychiatric medication prescriptions, and were asymptomatic on PHQ-2 or PHQ-9. Research-quality 3T structural magnetic resonance imaging was obtained as part of routine care. We first evaluated whether lesions were preferentially located within the depression network compared with other brain regions. Next, we examined if MS+Depression patients had greater lesion burden and if this was driven by lesions in the depression network. Primary outcome measures were the burden of lesions (e.g., impacted fascicles) within a network and across the brain.

RESULTS

MS lesions preferentially affected fascicles within versus outside the depression network (β = 0.09, 95% CI = 0.08 to 0.10, p < .001). MS+Depression patients had more lesion burden (β = 0.06, 95% CI = 0.01 to 0.10, p = .015); this was driven by lesions within the depression network (β = 0.02, 95% CI = 0.003 to 0.040, p = .020).

CONCLUSIONS

We demonstrated that lesion location and burden may contribute to depression comorbidity in MS. MS lesions disproportionately impacted fascicles in the depression network. MS+Depression patients had more disease than MS-Depression patients, which was driven by disease within the depression network. Future studies relating lesion location to personalized depression interventions are warranted.

Optical coherence tomography (OCT) measurements and disability in multiple sclerosis (MS): A systematic review and meta-analysis

BACKGROUND

Studies have demonstrated that people with multiple sclerosis (pwMS) experience visual impairments and neurodegenerative retinal processes. The disability progression in pwMS may be associated with retinal changes assessed with optical coherence tomography (OCT). This meta-analysis aims at synthesizing the correlations between OCT measurements of disability in pwMS.

METHODS

We systematically searched four databases (PubMed/MEDLINE, Embase, Scopus, and Web of Science) from inception to November 2022, then conducted a meta-analysis using a random effects model to determine the pooled correlation coefficient(r) between OCT measurements and disability scales by R version 4.2.3 with the meta version 6.2-1 package.

RESULTS

From 3129 studies, 100 studies were included. Among 9051 pwMS, the female-to-male ratio was 3.15:1, with a mean age of 39.57 ± 6.07 years. The mean disease duration and Expanded Disability Status Scale (EDSS) were 8.5 ± 3.7 and 2.7 ± 1.1, respectively. Among the pooled subgroup analyses, macular ganglion cell inner plexiform layer (mGCIPL) in patients with relapsing-remitting (pwRRMS) and peripapillary retinal nerve fiber layer (pRNFL) in patients with progressive MS (pwPMS) had strong correlations with EDSS, r = -0.33 (95% CI: -0.45 to -0.20, I2 = 45%, z-score = -4.86, p < 0.001) and r = -0.20 (95% CI:-0.58 to 0.26, I2 = 76%, z-score = -0.85, p = 0.395), respectively. According to subgroup analysis on pwMS without optic neuritis (ON) history, the largest correlation was seen between EDSS and macular ganglion cell complex (mGCC): r = -0.39 (95% CI: -0.70 to 0.04, I2 = 79%, z-score = -1.79, p = 0.073).

CONCLUSION

OCT measurements are correlated with disability in pwMS, and they can complement the comprehensive neurological visit as an additional paraclinical test.

Sex differences in the neuronal transcriptome and synaptic mitochondrial function in the cerebral cortex of a multiple sclerosis model

Introduction

Multiple sclerosis (MS) affects the cerebral cortex, inducing cortical atrophy and neuronal and synaptic pathology. Despite the fact that women are more susceptible to getting MS, men with MS have worse disability progression. Here, sex differences in neurodegenerative mechanisms are determined in the cerebral cortex using the MS model, chronic experimental autoimmune encephalomyelitis (EAE).

Methods

Neurons from cerebral cortex tissues of chronic EAE, as well as age-matched healthy control, male and female mice underwent RNA sequencing and gene expression analyses using RiboTag technology. The morphology of mitochondria in neurons of cerebral cortex was assessed using Thy1-CFP-MitoS mice. Oxygen consumption rates were determined using mitochondrial respirometry assays from intact as well as permeabilized synaptosomes.

Results

RNA sequencing of neurons in cerebral cortex during chronic EAE in C57BL/6 mice showed robust differential gene expression in male EAE compared to male healthy controls. In contrast, there were few differences in female EAE compared to female healthy controls. The most enriched differential gene expression pathways in male mice during EAE were mitochondrial dysfunction and oxidative phosphorylation. Mitochondrial morphology in neurons showed significant abnormalities in the cerebral cortex of EAE males, but not EAE females. Regarding function, synaptosomes isolated from cerebral cortex of male, but not female, EAE mice demonstrated significantly decreased oxygen consumption rates during respirometry assays.

Discussion

Cortical neuronal transcriptomics, mitochondrial morphology, and functional respirometry assays in synaptosomes revealed worse neurodegeneration in male EAE mice. This is consistent with worse neurodegeneration in MS men and reveals a model and a target to develop treatments to prevent cortical neurodegeneration and mitigate disability progression in MS men.

Feasibility of detecting atrophy relevant for disability and cognition in multiple sclerosis using 3D-FLAIR

BACKGROUND AND OBJECTIVES

Disability and cognitive impairment are known to be related to brain atrophy in multiple sclerosis (MS), but 3D-T1 imaging required for brain volumetrics is often unavailable in clinical protocols, unlike 3D-FLAIR. Here our aim was to investigate whether brain volumes derived from 3D-FLAIR images result in similar associations with disability and cognition in MS as do those derived from 3D-T1 images.

METHODS

3T-MRI scans of 329 MS patients and 76 healthy controls were included in this cross-sectional study. Brain volumes were derived using FreeSurfer on 3D-T1 and compared with brain volumes derived with SynthSeg and SAMSEG on 3D-FLAIR. Relative agreement was evaluated by calculating the intraclass correlation coefficient (ICC) of the 3D-T1 and 3D-FLAIR volumes. Consistency of relations with disability and average cognition was assessed using linear regression, while correcting for age and sex. The findings were corroborated in an independent validation cohort of 125 MS patients.

RESULTS

The ICC between volume measured with FreeSurfer and those measured on 3D-FLAIR for brain, ventricle, cortex, total deep gray matter and thalamus was above 0.74 for SAMSEG and above 0.91 for SynthSeg. Worse disability and lower average cognition were similarly associated with brain (adj. R2 = 0.24-0.27, p < 0.01; adj. R2 = 0.26-0.29, p < 0.001) ventricle (adj. R2 = 0.27-0.28, p < 0.001; adj. R2 = 0.19-0.20, p < 0.001) and deep gray matter volumes (adj. R2 = 0.24-0.28, p < 0.001; adj. R2 = 0.27-0.28, p < 0.001) determined with all methods, except for cortical volumes derived from 3D-FLAIR.

DISCUSSION

In this cross-sectional study, brain volumes derived from 3D-FLAIR and 3D-T1 show similar relationships to disability and cognitive dysfunction in MS, highlighting the potential of these techniques in clinical datasets.

Multimodal Investigation of Deep Gray Matter Nucleus in Patients with Multiple Sclerosis and Their Clinical Correlations: A Multivariate Pattern Analysis Study

Deep gray matter (DGM) nucleus are involved in patients with multiple sclerosis (MS) and are strongly associated with clinical symptoms. We used machine learning approach to further explore microstructural alterations in DGM of MS patients. One hundred and fifteen MS patients and seventy-one healthy controls (HC) underwent brain MRI. The fractional anisotropy (FA), mean diffusivity (MD), quantitative susceptibility value (QSV) and volumes of the caudate nucleus (CN), putamen (PT), globus pallidus (GP), and thalamus (TH) were measured. Multivariate pattern analysis, based on a machine-learning algorithm, was applied to investigate the most damaged regions. Partial correlation analysis was used to investigate the correlation between MRI quantitative metrics and clinical neurological scores. The area under the curve of FA-based classification model was 0.83, while they were 0.93 for MD and 0.81 for QSV. The Montreal cognitive assessment scores were correlated with the volume of the DGM and the expanded disability status scale scores were correlated with the MD of the GP and PT. The study results indicated that MS patients had involvement of DGM with the CN being the most affected. The atrophy of DGM in MS patients mainly affected cognitive function and the microstructural damage of DGM was mainly correlated with clinical disability.

Reliability of brain atrophy measurements in multiple sclerosis using MRI: an assessment of six freely available software packages for cross-sectional analyses

PURPOSE

Volume measurement using MRI is important to assess brain atrophy in multiple sclerosis (MS). However, differences between scanners, acquisition protocols, and analysis software introduce unwanted variability of volumes. To quantify theses effects, we compared within-scanner repeatability and between-scanner reproducibility of three different MR scanners for six brain segmentation methods.

METHODS

Twenty-one people with MS underwent scanning and rescanning on three 3 T MR scanners (GE MR750, Philips Ingenuity, Toshiba Vantage Titan) to obtain 3D T1-weighted images. FreeSurfer, FSL, SAMSEG, FastSurfer, CAT-12, and SynthSeg were used to quantify brain, white matter and (deep) gray matter volumes both from lesion-filled and non-lesion-filled 3D T1-weighted images. We used intra-class correlation coefficient (ICC) to quantify agreement; repeated-measures ANOVA to analyze systematic differences; and variance component analysis to quantify the standard error of measurement (SEM) and smallest detectable change (SDC).

RESULTS

For all six software, both between-scanner agreement (ICCs ranging 0.4-1) and within-scanner agreement (ICC range: 0.6-1) were typically good, and good to excellent (ICC > 0.7) for large structures. No clear differences were found between filled and non-filled images. However, gray and white matter volumes did differ systematically between scanners for all software (p < 0.05). Variance component analysis yielded within-scanner SDC ranging from 1.02% (SAMSEG, whole-brain) to 14.55% (FreeSurfer, CSF); and between-scanner SDC ranging from 4.83% (SynthSeg, thalamus) to 29.25% (CAT12, thalamus).

CONCLUSION

Volume measurements of brain, GM and WM showed high repeatability, and high reproducibility despite substantial differences between scanners. Smallest detectable change was high, especially between different scanners, which hampers the clinical implementation of atrophy measurements.

Multiparametric magnetic resonance imaging for detection of pathological changes in the central nervous system of a mouse model of multiple sclerosis in vivo

Multiple sclerosis (MS) is an autoimmune disease involving demyelination and axonal damage in the central nervous system (CNS). In this study, we investigated pathological changes in the lumbar spinal cord of C57BL/6 mice induced with progressive experimental autoimmune encephalomyelitis (EAE) disease using 9.4-T magnetic resonance imaging (MRI). Multiparametric MRI measurements including MR spectroscopy, diffusion tensor imaging (DTI) and volumetric analyses were applied to detect metabolic changes in the CNS of EAE mice. Compared with healthy mice, EAE mice showed a significant reduction in N-acetyl aspartate and increases in choline, glycine, taurine and lactate. DTI revealed a significant reduction in fractional anisotropy and axial diffusivity and an increase in radial diffusivity in the lumbar spinal cord white matter (WM), while in the grey matter (GM), fractional anisotropy increased. High-resolution structural imaging also revealed lumbar spinal cord WM hypertrophy and GM atrophy. Importantly, these MRI changes were strongly correlated with EAE disease scoring and pathological changes in the lumbar (L2-L6), particularly WM demyelination lesions and aggregation of immune cells (microglia/macrophages and astrocytes) in this region. This study identified changes in MRI biomarker signatures that can be useful for evaluating the efficacy of novel drugs using EAE models in vivo.

White matter disease derived from vascular and demyelinating origins

Damage or microstructural alterations of the white matter can cause dysfunction of the intrinsic neural networks in a condition termed as white matter disease (WMD). Frequently detected on brain computed tomography and magnetic resonance imaging scans, WMD is commonly presented in inflammatory demyelinating diseases like multiple sclerosis (MS) and vascular diseases such as cerebral small vessel disease (CSVD). Prevention of MS and CSVD progression requires early treatments with drastically different medications and approaches, as such, early and accurate diagnosis of WMD, derived from vascular or demyelinating etiologies, is of paramount importance. However, the clinical and imaging similarities between MS, especially during the early stage, and CSVD, pose a significant dilemma in differentiating these two conditions. In this review, we attempt to summarize and contrast the distinguishing features of MS and CSVD for aiding accurate diagnosis to ensure timely corresponding management in the early stages of MS and CSVD.

Caudate volume and symptoms of apathy in older adults with multiple sclerosis

BACKGROUND

Apathy is common in multiple sclerosis (MS) and neurological disease, but its presence and underlying brain mechanisms in older adults with MS (OAMS) have not been evaluated.

OBJECTIVE

Examine apathy and its association with caudate nuclei volume in OAMS and controls. We hypothesized that compared to controls, OAMS would demonstrate: a) greater apathy; b) stronger associations between apathy and caudate nuclei volumes.

METHODS

OAMS (n = 67, mean age = 64.55 ± 3.89) and controls (n = 74, mean age = 69.04 ± 6.32) underwent brain MRI, cognitive assessment, psychological, and motoric testing. Apathy was assessed through the apathy subscale of the 30-item Geriatric Depression Scale.

RESULTS

OAMS reported greater apathy compared to controls (β = 0.281, p = 0.004). Adjusted moderation analyses revealed a significantly stronger association between caudate volume and apathy (left: B = -1.156, p = 0.039, right: B = -1.163, p = 0.040) among OAMS compared to controls. Conditional effects revealed that in adjusted models, lower volume of both the left (b = -0.882, p = 0.037) and right (b = -0.891, p = 0.038) caudate nuclei was significantly associated with greater apathy only among OAMS.

CONCLUSION

Caudate nuclei, which are susceptible to adverse MS effects and implicated in mediating cognitive and motor function, may influence the presence and severity of apathy in OAMS.

Functional and structural brain MRI changes associated with cognitive worsening in multiple sclerosis: a 3-year longitudinal study

BACKGROUND

Heterogeneous processes may contribute to cognitive impairment in multiple sclerosis (MS).

OBJECTIVE

To apply a longitudinal multiparametric MRI approach to identify mechanisms associated with cognitive worsening in MS patients.

METHODS

3 T brain functional and structural MRI scans were acquired at baseline and after a median follow-up of 3.4 years in 35 MS patients and 22 healthy controls (HC). Associations between cognitive worsening (reliable change index score < - 1.25 at the Rao's battery) and longitudinal changes in regional T2-hyperintense white matter (WM) lesions, diffusion tensor microstructural WM damage, gray matter (GM) atrophy and resting state (RS) functional connectivity (FC) were explored.

RESULTS

At follow-up, HC showed no clusters of significant microstructural WM damage progression, GM atrophy or changes in RS FC. At follow-up, 10 MS patients (29%) showed cognitive worsening. Compared to cognitively stable, cognitively worsened MS patients showed more severe GM atrophy of the right anterior cingulate cortex and bilateral supplementary motor area (p < 0.001). Cognitively worsened vs cognitively stable MS patients showed also decreased RS FC in the right hippocampus of the right working memory network and in the right insula of the default mode network. Increased RS FC in the left insula of the executive control network was found in the opposite comparison (p < 0.001). No significant regional accumulation of focal WM lesions nor microstructural WM abnormalities occurred in both patients' groups.

CONCLUSIONS

GM atrophy progression in cognitively relevant brain regions combined with functional impoverishment in networks involved in cognitive functions may represent the substrates underlying cognitive worsening in MS.

Thalamic asymmetry in Multiple Sclerosis

BACKGROUND

Multiple Sclerosis (MS) is a chronic neuroinflammatory disease that affects the central nervous system. Asymmetry is one of the finding in brain MRI of these patients, which is related to the debilitating symptoms of the disease. This study aimed to investigate and compare the thalamic asymmetry in MS patients and its relationship with other MRI and clinical findings of these patients.

METHODS

This cross-sectional study conducted on 83 patients with relapse-remitting MS (RRMS), 43 patients with secondary progressive MS (SPMS), and 89 healthy controls. The volumes of total intracranial, total gray matter, total white matter, lesions, thalamus, and also the thalamic asymmetry indices were calculated. The 9-hole peg test (9-HPT) and Expanded Disability Status Scale (EDSS) were assessed as clinical findings.

RESULTS

We showed that the normalized whole thalamic volume in healthy subjects was higher than MS patients (both RRMS and SPMS). Thalamic asymmetry index (TAI) was significantly different between RRMS patients and SPMS patients (p = 0.011). The absolute value of TAI was significantly lower in healthy subjects than in RRMS (p < 0.001) and SPMS patients (p < 0.001), and SPMS patients had a higher absolute TAI compared to RRMS patients (p = 0.037).

CONCLUSIONS

In this cross-sectional study we showed a relationship between normalized whole thalamic volume and MS subtype. Also, we showed that the asymmetric indices of the thalamus can be related to the progression of the disease. Eventually, we showed that thalamic asymmetry can be related to the disease progression and subtype changes in MS.

Novel data-driven subtypes and stages of brain atrophy in the ALS-FTD spectrum

Background

TDP-43 proteinopathies represents a spectrum of neurological disorders, anchored clinically on either end by amyotrophic lateral sclerosis (ALS) and frontotemporal degeneration (FTD). The ALS-FTD spectrum exhibits a diverse range of clinical presentations with overlapping phenotypes, highlighting its heterogeneity. This study aimed to use disease progression modeling to identify novel data-driven spatial and temporal subtypes of brain atrophy and its progression in the ALS-FTD spectrum.

Methods

We used a data-driven procedure to identify 13 anatomic clusters of brain volumes for 57 behavioral variant FTD (bvFTD; with either autopsy-confirmed TDP-43 or TDP-43 proteinopathy-associated genetic variants), 103 ALS, and 47 ALS-FTD patients with likely TDP-43. A Subtype and Stage Inference (SuStaIn) model was trained to identify subtypes of individuals along the ALS-FTD spectrum with distinct brain atrophy patterns, and we related subtypes and stages to clinical, genetic, and neuropathological features of disease.

Results

SuStaIn identified three novel subtypes: two disease subtypes with predominant brain atrophy either in prefrontal/somatomotor regions or limbic-related regions, and a normal-appearing group without obvious brain atrophy. The Limbic-predominant subtype tended to present with more impaired cognition, higher frequencies of pathogenic variants in TBK1 and TARDBP genes, and a higher proportion of TDP-43 type B, E and C. In contrast, the Prefrontal/Somatomotor-predominant subtype had higher frequencies of pathogenic variants in C9orf72 and GRN genes and higher proportion of TDP-43 type A. The normal-appearing brain group showed higher frequency of ALS relative to ALS-FTD and bvFTD patients, higher cognitive capacity, higher proportion of lower motor neuron onset, milder motor symptoms, and lower frequencies of genetic pathogenic variants. Overall SuStaIn stages also correlated with evidence for clinical progression including longer disease duration, higher King's stage, and cognitive decline. Additionally, SuStaIn stages differed across clinical phenotypes, genotypes and types of TDP-43 pathology.

Conclusions

Our findings suggest distinct neurodegenerative subtypes of disease along the ALS-FTD spectrum that can be identified in vivo, each with distinct brain atrophy, clinical, genetic and pathological patterns.

The temporal event-based model: Learning event timelines in progressive diseases

Timelines of events, such as symptom appearance or a change in biomarker value, provide powerful signatures that characterise progressive diseases. Understanding and predicting the timing of events is important for clinical trials targeting individuals early in the disease course when putative treatments are likely to have the strongest effect. However, previous models of disease progression cannot estimate the time between events and provide only an ordering in which they change. Here, we introduce the temporal event-based model (TEBM), a new probabilistic model for inferring timelines of biomarker events from sparse and irregularly sampled datasets. We demonstrate the power of the TEBM in two neurodegenerative conditions: Alzheimer's disease (AD) and Huntington's disease (HD). In both diseases, the TEBM not only recapitulates current understanding of event orderings but also provides unique new ranges of timescales between consecutive events. We reproduce and validate these findings using external datasets in both diseases. We also demonstrate that the TEBM improves over current models; provides unique stratification capabilities; and enriches simulated clinical trials to achieve a power of 80 % with less than half the cohort size compared with random selection. The application of the TEBM naturally extends to a wide range of progressive conditions.

Emerging imaging and liquid biomarkers in multiple sclerosis

The advent of highly effective disease modifying therapy has transformed the landscape of multiple sclerosis (MS) care over the last two decades. However, there remains a critical, unmet need for sensitive and specific biomarkers to aid in diagnosis, prognosis, treatment monitoring, and the development of new interventions, particularly for people with progressive disease. This review evaluates the current data for several emerging imaging and liquid biomarkers in people with MS. MRI findings such as the central vein sign and paramagnetic rim lesions may improve MS diagnostic accuracy and evaluation of therapy efficacy in progressive disease. Serum and cerebrospinal fluid levels of several neuroglial proteins, such as neurofilament light chain and glial fibrillary acidic protein, show potential to be sensitive biomarkers of pathologic processes such as neuro-axonal injury or glial-inflammation. Additional promising biomarkers, including optical coherence tomography, cytokines and chemokines, microRNAs, and extracellular vesicles/exosomes, are also reviewed, among others. Beyond their potential integration into MS clinical care and interventional trials, several of these biomarkers may be informative of MS pathogenesis and help elucidate novel targets for treatment strategies.

Pathophysiology of multiple sclerosis damage and repair: Linking cerebral hypoperfusion to the development of irreversible tissue loss in multiple sclerosis using magnetic resonance imaging

BACKGROUND AND PURPOSE

Reduced cerebral perfusion has been observed in multiple sclerosis (MS) and may contribute to tissue loss both acutely and chronically. Here, we test the hypothesis that hypoperfusion occurs in MS and relates to the presence of irreversible tissue damage.

METHODS

In 91 patients with relapsing MS and 26 healthy controls (HC), gray matter (GM) cerebral blood flow (CBF) was assessed using pulsed arterial spin labeling. GM volume, T1 hypointense and T2 hyperintense lesion volumes (T1LV and T2LV, respectively), and the proportion of T2-hyperintense lesion volume that appears hypointense on T1-weighted magnetic resonance imaging (T1LV/T2LV) were quantified. GM CBF and GM volume were evaluated globally, as well as regionally, using an atlas-based approach.

RESULTS

Global GM CBF was lower in patients (56.9 ± 12.3 mL/100 g/min) than in HC (67.7 ± 10.0 mL/100 g/min; p < 0.001), a difference that was widespread across brain regions. Although total GM volume was comparable between groups, significant reductions were observed in a subset of subcortical structures. GM CBF negatively correlated with T1LV (r = -0.43, p = 0.0002) and T1LV/T2LV (r = -0.37, p = 0.0004), but not with T2LV.

CONCLUSIONS

GM hypoperfusion occurs in MS and is associated with irreversible white matter damage, thus suggesting that cerebral hypoperfusion may actively contribute and possibly precede neurodegeneration by hampering tissue repair abilities in MS.

Patterns of brain atrophy in recently-diagnosed relapsing-remitting multiple sclerosis

Recurrent neuroinflammation in relapsing-remitting MS (RRMS) is thought to lead to neurodegeneration, resulting in progressive disability. Repeated magnetic resonance imaging (MRI) of the brain provides non-invasive measures of atrophy over time, a key marker of neurodegeneration. This study investigates regional neurodegeneration of the brain in recently-diagnosed RRMS using volumetry and voxel-based morphometry (VBM). RRMS patients (N = 354) underwent 3T structural MRI <6 months after diagnosis and 1-year follow-up, as part of the Scottish multicentre 'FutureMS' study. MRI data were processed using FreeSurfer to derive volumetrics, and FSL for VBM (grey matter (GM) only), to establish regional patterns of change in GM and normal-appearing white matter (NAWM) over time throughout the brain. Volumetric analyses showed a decrease over time (q<0.05) in bilateral cortical GM and NAWM, cerebellar GM, brainstem, amygdala, basal ganglia, hippocampus, accumbens, thalamus and ventral diencephalon. Additionally, NAWM and GM volume decreased respectively in the following cortical regions, frontal: 14 out of 26 regions and 16/26; temporal: 18/18 and 15/18; parietal: 14/14 and 11/14; occipital: 7/8 and 8/8. Left GM and NAWM asymmetry was observed in the frontal lobe. GM VBM analysis showed three major clusters of decrease over time: 1) temporal and subcortical areas, 2) cerebellum, 3) anterior cingulum and supplementary motor cortex; and four smaller clusters within the occipital lobe. Widespread GM and NAWM atrophy was observed in this large recently-diagnosed RRMS cohort, particularly in the brainstem, cerebellar GM, and subcortical and occipital-temporal regions; indicative of neurodegeneration across tissue types, and in accord with limited previous studies in early disease. Volumetric and VBM results emphasise different features of longitudinal lobar and loco-regional change, however identify consistent atrophy patterns across individuals. Atrophy measures targeted to specific brain regions may provide improved markers of neurodegeneration, and potential future imaging stratifiers and endpoints for clinical decision making and therapeutic trials.

Delineating COVID-19 subgroups using routine clinical data identifies distinct in-hospital outcomes

The COVID-19 pandemic has been a great challenge to healthcare systems worldwide. It highlighted the need for robust predictive models which can be readily deployed to uncover heterogeneities in disease course, aid decision-making and prioritise treatment. We adapted an unsupervised data-driven model-SuStaIn, to be utilised for short-term infectious disease like COVID-19, based on 11 commonly recorded clinical measures. We used 1344 patients from the National COVID-19 Chest Imaging Database (NCCID), hospitalised for RT-PCR confirmed COVID-19 disease, splitting them equally into a training and an independent validation cohort. We discovered three COVID-19 subtypes (General Haemodynamic, Renal and Immunological) and introduced disease severity stages, both of which were predictive of distinct risks of in-hospital mortality or escalation of treatment, when analysed using Cox Proportional Hazards models. A low-risk Normal-appearing subtype was also discovered. The model and our full pipeline are available online and can be adapted for future outbreaks of COVID-19 or other infectious disease.

Mapping the relationship of white matter lesions to depression in multiple sclerosis

Importance

Multiple sclerosis (MS) is an immune-mediated neurological disorder that affects nearly one million people in the United States. Up to 50% of patients with MS experience depression.

Objective

To investigate how white matter network disruption is related to depression in MS.

Design

Retrospective case-control study of participants who received research-quality 3-tesla neuroimaging as part of MS clinical care from 2010-2018. Analyses were performed from May 1 to September 30, 2022.

Setting

Single-center academic medical specialty MS clinic.

Participants

Participants with MS were identified via the electronic health record (EHR). All participants were diagnosed by an MS specialist and completed research-quality MRI at 3T. After excluding participants with poor image quality, 783 were included. Inclusion in the depression group (MS+Depression) required either: 1) ICD-10 depression diagnosis (F32-F34.*); 2) prescription of antidepressant medication; or 3) screening positive via Patient Health Questionnaire-2 (PHQ-2) or -9 (PHQ-9). Age- and sex-matched nondepressed comparators (MS-Depression) included persons with no depression diagnosis, no psychiatric medications, and were asymptomatic on PHQ-2/9.

Exposure

Depression diagnosis.

Main Outcomes and Measures

We first evaluated if lesions were preferentially located within the depression network compared to other brain regions. Next, we examined if MS+Depression patients had greater lesion burden, and if this was driven by lesions specifically in the depression network. Outcome measures were the burden of lesions (e.g., impacted fascicles) within a network and across the brain. Secondary measures included between-diagnosis lesion burden, stratified by brain network. Linear mixed-effects models were employed.

Results

Three hundred-eighty participants met inclusion criteria, (232 MS+Depression: age[SD]=49[12], %females=86; 148 MS-Depression: age[SD]=47[13], %females=79). MS lesions preferentially affected fascicles within versus outside the depression network (β=0.09, 95% CI=0.08-0.10, P<0.001). MS+Depression had more white matter lesion burden (β=0.06, 95% CI=0.01-0.10, P=0.015); this was driven by lesions within the depression network (β=0.02, 95% CI 0.003-0.040, P=0.020).

Conclusions and Relevance

We provide new evidence supporting a relationship between white matter lesions and depression in MS. MS lesions disproportionately impacted fascicles in the depression network. MS+Depression had more disease than MS-Depression, which was driven by disease within the depression network. Future studies relating lesion location to personalized depression interventions are warranted.

Functional Disability and Brain MRI Volumetry Results among Multiple Sclerosis Patients during 5-Year Follow-Up

Background and Objectives: We aimed to determine the link between brain volumetry results and functional disability calculated using the Expanded Disability Status Scale (EDSS) among multiple sclerosis (MS) patients in relation to the provided treatment (disease-modifying therapies (DMTs)) during a 5-year follow-up period. Materials and Methods: A retrospective cohort study was performed enrolling 66 consecutive patients with a confirmed diagnosis of MS, predominantly females (62% (n = 41)). Relapsing-remitting (RR) MS was noted in 92% (n = 61) of patients, with the rest being patients with secondary progressive (SP) MS. The mean age was 43.3 years (SD 8.3 years). All patients were evaluated clinically using the EDSS and "FreeSurfer© 7.2.0" radiologically during a 5-year follow-up. Results: A significant increase in patient functional disability was noted, calculated using the EDSS during a 5-year follow-up. The baseline EDSS ranged between 1 and 6 with a median of 1.5 (IQR 1.5-2.0), and after 5 years, the EDSS was between 1 and 7, with a median EDSS of 3.0 (IQR 2.4-3.6). Compared with RRMS patients, SPMS patients demonstrated a significant increase in EDSS score during a 5-year period, with a median EDSS of 2.5 in RRMS patients (IQR 2.0-3.3) and 7.0 (IQR 5.0-7.0) among SPMS patients. Significantly lower brain volumetry results in different brain areas were found, including cortical, total grey and white matter, p < 0.05. Statistically significant differences were observed between baseline volumetry results of the hippocampus and the middle anterior part of the corpus callosum and their volumetry results after 5 years, p < 0.001. In this study population, the thalamus did not demonstrate significant changes in volumetry results during follow-up, p > 0.05. The provided treatment (DMTs) did not demonstrate a significant impact on the brain MRI volumetry results during a 5-year follow-up, p > 0.05. Conclusions: Brain MRI volumetry seriously impacts the early detection of brain atrophic changes. In this study, significant relationship between brain magnetic resonance volumetry results and disability progression among MS patients with no important impact of the provided treatment was described. Brain MRI volumetry may aid in the identification of early disease progression among MS patients, as well as enrich the clinical evaluation of MS patients in clinical patient care.

Accounting for uncertainty in training data to improve machine learning performance in predicting new disease activity in early multiple sclerosis

Introduction

Machine learning (ML) has great potential for using health data to predict clinical outcomes in individual patients. Missing data are a common challenge in training ML algorithms, such as when subjects withdraw from a clinical study, leaving some samples with missing outcome labels. In this study, we have compared three ML models to determine whether accounting for label uncertainty can improve a model's predictions.

Methods

We used a dataset from a completed phase-III clinical trial that evaluated the efficacy of minocycline for delaying the conversion from clinically isolated syndrome to multiple sclerosis (MS), using the McDonald 2005 diagnostic criteria. There were a total of 142 participants, and at the 2-year follow-up 81 had converted to MS, 29 remained stable, and 32 had uncertain outcomes. In a stratified 7-fold cross-validation, we trained three random forest (RF) ML models using MRI volumetric features and clinical variables to predict the conversion outcome, which represented new disease activity within 2 years of a first clinical demyelinating event. One RF was trained using subjects with the uncertain labels excluded (RF_exclude), another RF was trained using the entire dataset but with assumed labels for the uncertain group (RF_naive), and a third, a probabilistic RF (PRF, a type of RF that can model label uncertainty) was trained on the entire dataset, with probabilistic labels assigned to the uncertain group.

Results

Probabilistic random forest outperformed both the RF models with the highest AUC (0.76, compared to 0.69 for RF_exclude and 0.71 for RF_naive) and F1-score (86.6% compared to 82.6% for RF_exclude and 76.8% for RF_naive).

Conclusion

Machine learning algorithms capable of modeling label uncertainty can improve predictive performance in datasets in which a substantial number of subjects have unknown outcomes.

Detection of grey matter microstructural substrates of neurodegeneration in multiple sclerosis

Multiple sclerosis features complex pathological changes in grey matter that begin early and eventually lead to diffuse atrophy. Novel approaches to image grey-matter microstructural alterations in vivo are highly sought after and would enable more sensitive monitoring of disease activity and progression. This cross-sectional study aimed to assess the sensitivity of high-gradient diffusion MRI for microstructural tissue damage in cortical and deep grey matter in people with multiple sclerosis and test the hypothesis that reduced cortical cell body density is associated with cortical and deep grey-matter volume loss. Forty-one people with multiple sclerosis (age 24-72, 14 females) and 37 age- and sex-matched healthy controls were scanned on a 3 T Connectom MRI scanner equipped with 300 mT/m gradients using a multi-shell diffusion MRI protocol. The soma and neurite density imaging model was fitted to high-gradient diffusion MRI data to obtain estimates of intra-neurite, intra-cellular and extra-cellular signal fractions and apparent soma radius. Cortical and deep grey-matter microstructural imaging metrics were compared between multiple sclerosis and healthy controls and correlated with grey-matter volume, clinical disability and cognitive outcomes. People with multiple sclerosis showed significant cortical and deep grey-matter volume loss compared with healthy controls. People with multiple sclerosis showed trends towards lower cortical intra-cellular signal fraction and significantly lower intra-cellular and higher extra-cellular signal fractions in deep grey matter, especially the thalamus and caudate, compared with healthy controls. Changes were most pronounced in progressive disease and correlated with the Expanded Disability Status Scale, but not the Symbol Digit Modalities Test. In multiple sclerosis, normalized thalamic volume was associated with thalamic microstructural imaging metrics. Whereas thalamic volume loss did not correlate with cortical volume loss, cortical microstructural imaging metrics were significantly associated with thalamic volume, and not with cortical volume. Compared with the short diffusion time (Δ = 19 ms) achievable on the Connectom scanner, at the longer diffusion time of Δ = 49 ms attainable on clinical scanners, multiple sclerosis-related changes in imaging metrics were generally less apparent with lower effect sizes in cortical and deep grey matter. Soma and neurite density imaging metrics obtained from high-gradient diffusion MRI data provide detailed grey-matter characterization beyond cortical and thalamic volumes and distinguish multiple sclerosis-related microstructural pathology from healthy controls. Cortical cell body density correlates with thalamic volume, appears sensitive to the microstructural substrate of neurodegeneration and reflects disability status in people with multiple sclerosis, becoming more pronounced as disability worsens.

Unraveling the heterogeneous pathological substrates of relapse-onset multiple sclerosis: a multiparametric voxel-wise 3 T MRI study

BACKGROUND

In multiple sclerosis (MS), pathological processes affecting brain gray (GM) and white matter (WM) are heterogeneous.

OBJECTIVE

To apply a multimodal MRI approach to investigate the regional distribution of the different pathological processes occurring in the brain WM and GM of relapse-onset MS patients.

METHODS

Fifty-seven MS patients (forty-two relapsing remitting [RR], fifteen secondary progressive [SP]) and forty-seven age- and sex-matched healthy controls (HC) underwent a multimodal 3 T MRI acquisition. Between-group voxel-wise differences of brain WM and GM volumes, magnetization transfer ratio (MTR), T₁-weighted(w)/T₂w ratio, intracellular volume fraction (ICV_f), and quantitative susceptibility mapping (QSM) maps were investigated.

RESULTS

Compared to HC, RRMS showed significant WM, deep GM and cortical atrophy, significantly lower MTR and T₁w/T₂w ratio of periventricular and infratentorial WM, deep GM and several cortical areas, lower ICV_f in supratentorial and cerebellar WM and in some cortical areas, and lower QSM values in bilateral periventricular WM (p < 0.001). Compared to RRMS, SPMS patients showed significant deep GM and widespread cortical atrophy, significantly lower MTR of periventricular WM, deep GM and cerebellum, lower T₁w/T₂w ratio of fronto-temporal WM regions, lower ICV_f of some fronto-tempo-occipital WM and cortical areas. They also had increased QSM and T₁w/T₂w ratio in the pallidum, bilaterally (p < 0.001).

CONCLUSION

A periventricular pattern of demyelination and widespread GM and WM neuro-axonal loss are detectable in RRMS and are more severe in SPMS. Higher T₁w/T₂w ratio and QSM in the pallidum, possibly reflecting iron accumulation and neurodegeneration, may represent a relevant MRI marker to differentiate SPMS from RRMS.

The effects of cognition, quality of life, and fatigue on olfactory function in patients with multiple sclerosis

AIM

The frequency of olfactory dysfunction in patients with Multiple Sclerosis (MS) has revealed very different results in studies. Some studies have shown that olfactory dysfunction may be associated with cognitive impairment and poor quality of life. In these studies, different odor tests and cognitive tests were used and different results were obtained.

MATERIALS AND METHODS

Forty literate patients over the age of 18 and 24 healthy volunteers of similar age and education were included in the study. Sniffin' Sticks Odor Test, California Verbal Learning Test II, Symbol Digit Modalities Test, Revised Brief Visuospatial Memory Test, Trail-Making Test, Quality of Life Short Form-36, Fatigue Impact Scale, Beck Depression Inventory, and Beck Anxiety Inventory were applied to the individuals.

RESULTS

Olfactory dysfunction was detected in 50 % of the patients. High disability rate, low cognitive functions, low quality of life, and fatigue were identified as the factors affecting olfactory function negatively. Odor discrimination and identification abilities were associated with disability level and cognitive functions, whereas quality of life was linked to odor threshold scores. The olfactory function and cognitive abilities of patients with progressive MS (n = 5) were worse than those of patients with relapsing remitting MS (n = 35).

CONCLUSION

Olfactory dysfunction is common in patients with MS and is associated with disability and quality of life. Olfactory function can be used in the follow-up of patients and olfactory dysfunction deserves further study as a metric that might emerge as a biomarker.

The association between brain volume loss and disability in multiple sclerosis: A systematic review

BACKGROUND

Multiple sclerosis (MS) is a chronic, inflammatory, demyelinating, degenerative disease of the central nervous system that affects approximately 2.8 million people worldwide. Compelling evidence from observational studies and clinical trials indicates a strong association between brain volume loss (BVL) and the accumulation of disability in MS. However, the considerable heterogeneity in study designs and methods of assessment of BVL invites questions concerning the generalizability of the reported findings. Therefore, we conducted this systematic review to characterize the relationship between BVL and physical disability in patients with MS.

METHODS

A systematic literature search of MEDLINE and EMBASE databases was performed supplemented by gray literature searches. The following study designs were included: prospective/retrospective cohort, cross-sectional and case-control. Only English language articles published from 2010 onwards were eligible for final inclusion. There were no restrictions on MS subtype, age, or ethnicity. Of the 1620 citations retrieved by the structured searches, 50 publications met our screening criteria and were included in the final data set.

RESULTS

Across all BVL measures, there was considerable heterogeneity in studies regarding the underlying study population, the definitions of BVL and image analysis methodologies, the physical disability measure used, the measures of association reported and whether the analysis conducted was univariable or multivariable. A total of 36 primary studies providing data on the association between whole BVL and physical disability in MS collectively suggest that whole brain atrophy is associated with greater physical disability progression in MS patients. Similarly, a total of 15 primary studies providing data on the association between ventricular atrophy and physical disability in MS suggest that ventricular atrophy is associated with greater physical disability progression in MS patients. Along similar lines, the existing evidence based on a total of 13 primary studies suggests that gray matter atrophy is associated with greater physical disability progression in MS patients. Four primary studies suggest that corpus callosum atrophy is associated with greater physical disability progression in MS patients. The majority of the existing evidence (6 primary studies) suggests no association between white matter atrophy and physical disability in MS. It is difficult to assign a relationship between basal ganglia volume loss and physical disability as well as medulla oblongata width and physical disability in MS due to very limited data.

CONCLUSION

The evidence gathered from this systematic review, although very heterogeneous, suggests that whole brain atrophy is associated with greater physical disability progression in MS patients. Our review can help define future imaging biomarkers for physical disability progression and treatment monitoring in MS.

Multiple sclerosis plasma IgG aggregates induce complement-dependent neuronal apoptosis

Grey matter pathology is central to the progression of multiple sclerosis (MS). We discovered that MS plasma immunoglobulin G (IgG) antibodies, mainly IgG1, form large aggregates (>100 nm) which are retained in the flow-through after binding to Protein A. Utilizing an annexin V live-cell apoptosis detection assay, we demonstrated six times higher levels of neuronal apoptosis induced by MS plasma IgG aggregates (n = 190, from two cohorts) compared to other neurological disorders (n = 116) and healthy donors (n = 44). MS IgG aggregate-mediated, complement-dependent neuronal apoptosis was evaluated in multiple model systems including primary human neurons, primary human astrocytes, neuroblastoma SH-SY5Y cells, and newborn mouse brain slices. Immunocytochemistry revealed the co-deposition of IgG, early and late complement activation products (C1q, C3b, and membrane attack complex C5b9), as well as active caspase 3 in treated neuronal cells. Furthermore, we found that MS plasma cytotoxic antibodies are not present in Protein G flow-through, nor in the paired plasma. The neuronal apoptosis can be inhibited by IgG depletion, disruption of IgG aggregates, pan-caspase inhibitor, and is completely abolished by digestion with IgG-cleaving enzyme IdeS. Transmission electron microscopy and nanoparticle tracking analysis revealed the sizes of MS IgG aggregates are greater than 100 nm. Our data support the pathological role of MS IgG antibodies and corroborate their connection to complement activation and axonal damage, suggesting that apoptosis may be a mechanism of neurodegeneration in MS.

Patterns of inflammation, microstructural alterations, and sodium accumulation define multiple sclerosis subtypes after 15 years from onset

Introduction

Conventional MRI is routinely used for the characterization of pathological changes in multiple sclerosis (MS), but due to its lack of specificity is unable to provide accurate prognoses, explain disease heterogeneity and reconcile the gap between observed clinical symptoms and radiological evidence. Quantitative MRI provides measures of physiological abnormalities, otherwise invisible to conventional MRI, that correlate with MS severity. Analyzing quantitative MRI measures through machine learning techniques has been shown to improve the understanding of the underlying disease by better delineating its alteration patterns.

Methods

In this retrospective study, a cohort of healthy controls (HC) and MS patients with different subtypes, followed up 15 years from clinically isolated syndrome (CIS), was analyzed to produce a multi-modal set of quantitative MRI features encompassing relaxometry, microstructure, sodium ion concentration, and tissue volumetry. Random forest classifiers were used to train a model able to discriminate between HC, CIS, relapsing remitting (RR) and secondary progressive (SP) MS patients based on these features and, for each classification task, to identify the relative contribution of each MRI-derived tissue property to the classification task itself.

Results and discussion

Average classification accuracy scores of 99 and 95% were obtained when discriminating HC and CIS vs. SP, respectively; 82 and 83% for HC and CIS vs. RR; 76% for RR vs. SP, and 79% for HC vs. CIS. Different patterns of alterations were observed for each classification task, offering key insights in the understanding of MS phenotypes pathophysiology: atrophy and relaxometry emerged particularly in the classification of HC and CIS vs. MS, relaxometry within lesions in RR vs. SP, sodium ion concentration in HC vs. CIS, and microstructural alterations were involved across all tasks.

Presence but not the timing of onset of REM sleep behavior disorder distinguishes evolution patterns in Parkinson's disease

BACKGROUND

Rapid eye movement (REM) sleep behavior disorder (RBD) could develop preceding or come after motor symptoms during Parkinson's disease (PD). It remains unknown that whether PD with different timing of RBD onset relative to motor symptoms suggests different spatiotemporal sequence of neurodegeneration. This study aimed to explore the sequence of disease progression in crucially involved brain regions in PD with different timing of RBD onset.

METHOD

We recruited 157 PD, 16 isolated RBD (iRBD), and 78 healthy controls. PD patients were identified as (1) PD with RBD preceding motor symptoms (PD-preRBD, n = 50), (2) PD with RBD posterior to motor symptoms (PD-postRBD, n = 31), (3) PD without RBD (PD-nonRBD, n = 75). The volumes of crucial brain regions, including the basal ganglia and limbic structures in T1-weighted imaging, and the contrast-noise-ratios of locus coeruleus (LC) and substantia nigra (SN) in neuromelanin-sensitive magnetic resonance imaging, were extracted. To simulate the sequence of disease progression for cross-sectional data, an event-based model was introduced to estimate the maximum likelihood sequence of regions' involvement for each group. Then, a statistical parameter, the Bhattacharya coefficient (BC), was used to evaluate the similarity of the sequence.

RESULTS

The model predicted that SN occupied the highest likelihood in the maximum likelihood sequence of disease progression in the all PD subgroups, while LC was specifically positioned earlier to SN in iRBD, a prodromal phase of PD. Subsequent early involvement of LC was observed in the both PD-preRBD and PD-postRBD. In contrast, atrophy in the para-hippocampal gyrus but relatively intact LC in the early stage was demonstrated in PD-nonRBD. Then, the similarity comparisons indicated higher BC between PD-postRBD and PD-preRBD (BC = 0.76) but lower BC between PD-postRBD and PD-nonRBD group (BC = 0.41). iRBD had higher BC against PD-preRBD (BC = 0.66) and PD-postRBD (BC = 0.63) but lower BC against PD- nonRBD (BC = 0.48).

CONCLUSION

The spatiotemporal sequence of neurodegeneration between PD-pre and PD-post were similar but distinct from PD-nonRBD. The presence of RBD may be the essential factor for differentiating the degeneration patterns of PD, but the timing of RBD onset has currently proved to be not.

Lifespan Neurodegeneration Of The Human Brain In Multiple Sclerosis

Background

Atrophy related to Multiple Sclerosis (MS) has been found at the early stages of the disease. However, the archetype dynamic trajectories of the neurodegenerative process, even prior to clinical diagnosis, remain unknown.

Methods

We modeled the volumetric trajectories of brain structures across the entire lifespan using 40944 subjects (38295 healthy controls and 2649 MS patients). Then, we estimated the chronological progression of MS by assessing the divergence of lifespan trajectories between normal brain charts and MS brain charts.

Results

Chronologically, the first affected structure was the thalamus, then the putamen and the pallidum (3 years later), followed by the ventral diencephalon (7 years after thalamus) and finally the brainstem (9 years after thalamus). To a lesser extent, the anterior cingulate gyrus, insular cortex, occipital pole, caudate and hippocampus were impacted. Finally, the precuneus and accumbens nuclei exhibited a limited atrophy pattern.

Conclusion

Subcortical atrophy was more pronounced than cortical atrophy. The thalamus was the most impacted structure with a very early divergence in life. It paves the way toward utilization of these lifespan models for future preclinical/prodromal prognosis and monitoring of MS.

An open-source tool for longitudinal whole-brain and white matter lesion segmentation

In this paper we describe and validate a longitudinal method for whole-brain segmentation of longitudinal MRI scans. It builds upon an existing whole-brain segmentation method that can handle multi-contrast data and robustly analyze images with white matter lesions. This method is here extended with subject-specific latent variables that encourage temporal consistency between its segmentation results, enabling it to better track subtle morphological changes in dozens of neuroanatomical structures and white matter lesions. We validate the proposed method on multiple datasets of control subjects and patients suffering from Alzheimer's disease and multiple sclerosis, and compare its results against those obtained with its original cross-sectional formulation and two benchmark longitudinal methods. The results indicate that the method attains a higher test-retest reliability, while being more sensitive to longitudinal disease effect differences between patient groups. An implementation is publicly available as part of the open-source neuroimaging package FreeSurfer.