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

MRI; Machine learning; Multiple sclerosisRessonància magnètica; Aprenentatge automàtic; Esclerosi múltipleResonancia magnética; Aprendizaje automático; Esclerosis múltipleIntroduction: 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.This project has received funding under the European Union's Horizon 2020 Research and Innovation Programme under grant agreement No. 634541. FG received the support of a fellowship from “la Caixa” Foundation (ID 100010434). The fellowship code is “LCF/BQ/PR22/11920010”. FG has also received support from the Beatriu de Pinós (2020 BP 00117) programme, funded by the Secretary of Universities and Research (Government of Catalonia). BK, FP, and OC are supported by the National Institute of Health Research Biomedical Research Centre at UCL and UCLH. EPSRC grants EP/M020533/1 and EP/J020990/01, MRC MR/T046422/1 and MR/T046473/1, Wellcome Trust award 221915/Z/20/Z, and the NIHR UCLH BRC support DCA's work in this area. CGWK also receives funding from Horizon 2020 [Research and Innovation Action Grants Human Brain Project 945539 (SGA3)], BRC (#BRC704/CAP/CGW), MRC (#MR/S026088/1), and Ataxia UK

Comparison of Neurite Orientation Dispersion and Density Imaging and Two-Compartment Spherical Mean Technique Parameter Maps in Multiple Sclerosis

Microestructura; Esclerosi múltiple; Tècnica de mitjana esfèricaMicroestructura; Esclerosis múltiple; Técnica de la media esféricaMicrostructure; Multiple sclerosis; Spherical mean techniqueBackground: Neurite orientation dispersion and density imaging (NODDI) and the spherical mean technique (SMT) are diffusion MRI methods providing metrics with sensitivity to similar characteristics of white matter microstructure. There has been limited comparison of changes in NODDI and SMT parameters due to multiple sclerosis (MS) pathology in clinical settings. Purpose: To compare group-wise differences between healthy controls and MS patients in NODDI and SMT metrics, investigating associations with disability and correlations with diffusion tensor imaging (DTI) metrics. Methods: Sixty three relapsing-remitting MS patients were compared to 28 healthy controls. NODDI and SMT metrics corresponding to intracellular volume fraction (vin), orientation dispersion (ODI and ODE), diffusivity (D) (SMT only) and isotropic volume fraction (viso) (NODDI only) were calculated from diffusion MRI data, alongside DTI metrics (fractional anisotropy, FA; axial/mean/radial diffusivity, AD/MD/RD). Correlations between all pairs of MRI metrics were calculated in normal-appearing white matter (NAWM). Associations with expanded disability status scale (EDSS), controlling for age and gender, were evaluated. Patient-control differences were assessed voxel-by-voxel in MNI space controlling for age and gender at the 5% significance level, correcting for multiple comparisons. Spatial overlap of areas showing significant differences were compared using Dice coefficients. Results: NODDI and SMT show significant associations with EDSS (standardised beta coefficient −0.34 in NAWM and −0.37 in lesions for NODDI vin; 0.38 and −0.31 for SMT ODE and vin in lesions; p < 0.05). Significant correlations in NAWM are observed between DTI and NODDI/SMT metrics. NODDI vin and SMT vin strongly correlated (r = 0.72, p < 0.05), likewise NODDI ODI and SMT ODE (r = −0.80, p < 0.05). All DTI, NODDI and SMT metrics detect widespread differences between patients and controls in NAWM (12.57% and 11.90% of MNI brain mask for SMT and NODDI vin, Dice overlap of 0.42). Data Conclusion: SMT and NODDI detect significant differences in white matter microstructure between MS patients and controls, concurring on the direction of these changes, providing consistent descriptors of tissue microstructure that correlate with disability and show alterations beyond focal damage. Our study suggests that NODDI and SMT may play a role in monitoring MS in clinical trials and practice.This study has received funding under the European Union's Horizon 2020 research and innovation programme under grant agreement No. 634541 (CDS-QuaMRI) and 666992. This study has also received support from the Engineering and Physical Sciences Research Council (EPSRC R006032/1, M020533/1, G007748, I027084, N018702), Spinal Research (UK), Wings for Life (Austria), Craig H. Neilsen Foundation (USA) for INSPIRED and UK Multiple Sclerosis Society (grants 892/08 and 77/2017). This study was supported by the National Institute for Health Research University College London Hospitals Biomedical Research Centre. FP was supported by a Guarantors of Brain post-doctoral non-clinical fellowship. AT was supported by an MRC grant (MR/S026088/1). EK was supported from the NIHR Great Ormond Street Hospital Biomedical Research Centre. FG was currently supported by the investigator-initiated PREdICT study at the Vall d'Hebron Institute of Oncology (Barcelona), funded by AstraZeneca and CRIS Cancer Foundation. AstraZeneca was not involved in the study design, collection, analysis, interpretation of data, the writing of this article or the decision to submit it for publication

Genetic influences on disease course and severity, 30 years after a clinically isolated syndrome

Genetics; Multiple sclerosis; PhenotypeGenética; Esclerosis múltiple; FenotipoGenètica; Esclerosi múltiple; FenotipMultiple sclerosis risk has a well-established polygenic component, yet the genetic contribution to disease course and severity remains unclear and difficult to examine. Accurately measuring disease progression requires long-term study of clinical and radiological outcomes with sufficient follow-up duration to confidently confirm disability accrual and multiple sclerosis phenotypes. In this retrospective study, we explore genetic influences on long-term disease course and severity; in a unique cohort of clinically isolated syndrome patients with homogenous 30-year disease duration, deep clinical phenotyping and advanced MRI metrics. Sixty-one clinically isolated syndrome patients [41 female (67%): 20 male (33%)] underwent clinical and MRI assessment at baseline, 1-, 5-, 10-, 14-, 20- and 30-year follow-up (mean age ± standard deviation: 60.9 ± 6.5 years). After 30 years, 29 patients developed relapsing-remitting multiple sclerosis, 15 developed secondary progressive multiple sclerosis and 17 still had a clinically isolated syndrome. Twenty-seven genes were investigated for associations with clinical outcomes [including disease course and Expanded Disability Status Scale (EDSS)] and brain MRI (including white matter lesions, cortical lesions, and brain tissue volumes) at the 30-year follow-up. Genetic associations with changes in EDSS, relapses, white matter lesions and brain atrophy (third ventricular and medullary measurements) over 30 years were assessed using mixed-effects models. HLA-DRB1*1501-positive (n = 26) patients showed faster white matter lesion accrual [+1.96 lesions/year (0.64–3.29), P = 3.8 × 10−3], greater 30-year white matter lesion volumes [+11.60 ml, (5.49–18.29), P = 1.27 × 10−3] and higher annualized relapse rates [+0.06 relapses/year (0.005–0.11), P = 0.031] compared with HLA-DRB1*1501-negative patients (n = 35). PVRL2-positive patients (n = 41) had more cortical lesions (+0.83 [0.08–1.66], P = 0.042), faster EDSS worsening [+0.06 points/year (0.02–0.11), P = 0.010], greater 30-year EDSS [+1.72 (0.49–2.93), P = 0.013; multiple sclerosis cases: +2.60 (1.30–3.87), P = 2.02 × 10−3], and greater risk of secondary progressive multiple sclerosis [odds ratio (OR) = 12.25 (1.15–23.10), P = 0.031] than PVRL2-negative patients (n = 18). In contrast, IRX1-positive (n = 30) patients had preserved 30-year grey matter fraction [+0.76% (0.28–1.29), P = 8.4 × 10−3], lower risk of cortical lesions [OR = 0.22 (0.05–0.99), P = 0.049] and lower 30-year EDSS [−1.35 (−0.87,−3.44), P = 0.026; multiple sclerosis cases: −2.12 (−0.87, −3.44), P = 5.02 × 10−3] than IRX1-negative patients (n = 30). In multiple sclerosis cases, IRX1-positive patients also had slower EDSS worsening [−0.07 points/year (−0.01,−0.13), P = 0.015] and lower risk of secondary progressive multiple sclerosis [OR = 0.19 (0.04–0.92), P = 0.042]. These exploratory findings support diverse genetic influences on pathological mechanisms associated with multiple sclerosis disease course. HLA-DRB1*1501 influenced white matter inflammation and relapses, while IRX1 (protective) and PVRL2 (adverse) were associated with grey matter pathology (cortical lesions and atrophy), long-term disability worsening and the risk of developing secondary progressive multiple sclerosis.This study was funded by the Multiple Sclerosis Society of Great Britain and Northern Ireland (20; 984) and supported by the National Institute for Health and Care Research University College London Hospitals (UCLH) Biomedical Research Centre. Funding for extended SNP analysis was supported by a Small Acorns Fund from The National Brain Appeal (NBA/QSQ/SAF/R17)

Spatial patterns of brain lesions assessed through covariance estimations of lesional voxels in multiple Sclerosis: The SPACE-MS technique

Anisotropy; Lesion spatial distribution; Multiple sclerosisAnisotropia; Distribució espacial de la lesió; Esclerosi múltipleAnisotropía; Distribución espacial de la lesión; Esclerosis múltiplePredicting disability in progressive multiple sclerosis (MS) is extremely challenging. Although there is some evidence that the spatial distribution of white matter (WM) lesions may play a role in disability accumulation, the lack of well-established quantitative metrics that characterise these aspects of MS pathology makes it difficult to assess their relevance for clinical progression. This study introduces a novel approach, called SPACE-MS, to quantitatively characterise spatial distributional features of brain MS lesions, so that these can be assessed as predictors of disability accumulation. In SPACE-MS, the covariance matrix of the spatial positions of each patient’s lesional voxels is computed and its eigenvalues extracted. These are combined to derive rotationally-invariant metrics known to be common and robust descriptors of ellipsoid shape such as anisotropy, planarity and sphericity. Additionally, SPACE-MS metrics include a neuraxis caudality index, which we defined for the whole-brain lesion mask as well as for the most caudal brain lesion. These indicate how distant from the supplementary motor cortex (along the neuraxis) the whole-brain mask or the most caudal brain lesions are. We applied SPACE-MS to data from 515 patients involved in three studies: the MS-SMART (NCT01910259) and MS-STAT1 (NCT00647348) secondary progressive MS trials, and an observational study of primary and secondary progressive MS. Patients were assessed on motor and cognitive disability scales and underwent structural brain MRI (1.5/3.0 T), at baseline and after 2 years. The MRI protocol included 3DT1-weighted (1x1x1mm3) and 2DT2-weighted (1x1x3mm3) anatomical imaging. WM lesions were semiautomatically segmented on the T2-weighted scans, deriving whole-brain lesion masks. After co-registering the masks to the T1 images, SPACE-MS metrics were calculated and analysed through a series of multiple linear regression models, which were built to assess the ability of spatial distributional metrics to explain concurrent and future disability after adjusting for confounders. Patients whose WM lesions laid more caudally along the neuraxis or were more isotropically distributed in the brain (i.e. with whole-brain lesion masks displaying a high sphericity index) at baseline had greater motor and/or cognitive disability at baseline and over time, independently of brain lesion load and atrophy measures. In conclusion, here we introduced the SPACE-MS approach, which we showed is able to capture clinically relevant spatial distributional features of MS lesions independently of the sheer amount of lesions and brain tissue loss. Location of lesions in lower parts of the brain, where neurite density is particularly high, such as in the cerebellum and brainstem, and greater spatial spreading of lesions (i.e. more isotropic whole-brain lesion masks), possibly reflecting a higher number of WM tracts involved, are associated with clinical deterioration in progressive MS. The usefulness of the SPACE-MS approach, here demonstrated in MS, may be explored in other conditions also characterised by the presence of brain WM lesions.Carmen Tur is currently being funded by a Junior Leader La Caixa Fellowship. The project that gave rise to these results received the support of a fellowship from ”la Caixa” Foundation (ID 100010434). The fellowship code is LCF/BQ/PI20/11760008. She has also received the 2021 Merck’s Award for the Investigation in MS, awarded by the Merck Foundation. In 2015, she received an ECTRIMS Post-doctoral Research Fellowship and has received funding from the UK MS Society. She has also received honoraria from Roche and Novartis. Francesco Grussu has received funding under the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 634541, from the Engineering and Physical Sciences Research Council (EPSRC EP/R006032/1, M020533/1) and Rosetrees Trust (UK), and is now supported by PREdICT (a study funded by AstraZeneca in Spain). Ferran Prados was supported by the Guarantors of Brain and the National Institute for Health Research, University College London Hospitals Biomedical Research Centre. Rosa Cortese is supported by the ECTRIMS-MAGNIMS fellowships programme. Alberto Calvi is supported by ECTRIMS-MAGNIMS fellowship (2018), Guarantors of Brain “Entry” clinical fellowship (2019) and the UK MS Society PhD studentship (2020). Declan Chard is a consultant for Biogen and Hoffmann-La Roche. In the last 3 years, he has received research funding from the International Progressive MS Alliance, the UK MS Society, and the National Institute for Health Research (NIHR) University College London Hospitals (UCLH) Biomedical Research Centre. Jeremy Chataway, in the last three years, has received support from the Efficacy and Evaluation (EME) Programme, a Medical Research Council (MRC) and National Institute for Health Research (NIHR) partnership and the Health Technology Assessment (HTA) Programme (NIHR), the UK MS Society, the US National MS Society and the Rosetrees Trust. He is supported in part by the National Institute for Health Research, University College London Hospitals, Biomedical Research Centre, London, UK. He has been a local principal investigator for a trial in MS funded by the Canadian MS society. A local principal investigator for commercial trials funded by: Actelion, Biogen, Novartis and Roche; has received an investigator grant from Novartis; and has taken part in advisory boards/consultancy for Azadyne, Biogen, Celgene, Janssen, MedDay, Merck, Novartis and Roche. Alan J Thompson acknowledges grant support from the National Institute for Health Research HTA and BRC, and has received honoraria for consultancy from Eisai and Abbvie (paid to Institution), support for travel for consultancy from the International Progressive MS Alliance and National MS Society (USA), and receives an honorarium from SAGE Publishers as Editor-in-Chief of Multiple Sclerosis Journal. Olga Ciccarelli is supported by the National Institute for Health Research, University College London Hospitals Biomedical Research Centre. OC also receives research grant support from the MS Society of Great Britain and Northern Ireland, and the NIHR UCLH Biomedical Research Centre. She is an Associate Editor for Neurology, for which he receives an honorarium. Claudia A.M. Gandini Wheeler-Kingshott has received research grants (principal investigator and co-applicant) from the UK MS Society (#77), Wings for Life (#169111), BRC (#BRC704/CAP/CGW), UCL Global Challenges Research Fund (GCRF), MRC (#MR/S026088/1), Ataxia UK. CGWK is a shareholder in Queen Square Analytics Ltd

A generalized deep learning network for fractional anisotropy reconstruction: Application to epilepsy and multiple sclerosis

Fractional anisotropy (FA) is a quantitative map sensitive to microstructural properties of tissues in vivo and it is extensively used to study the healthy and pathological brain. This map is classically calculated by model fitting (standard method) and requires many diffusion weighted (DW) images for data quality and unbiased readings, hence needing the acquisition time of several minutes. Here, we adapted the U-net architecture to be generalized and to obtain good quality FA from DW volumes acquired in 1 minute. Our network requires 10 input DW volumes (hence fast acquisition), is robust to the direction of application of the diffusion gradients (hence generalized), and preserves/improves map quality (hence good quality maps). We trained the network on the human connectome project (HCP) data using the standard model-fitting method on the entire set of DW directions to extract FA (ground truth). We addressed the generalization problem, i.e., we trained the network to be applicable, without retraining, to clinical datasets acquired on different scanners with different DW imaging protocols. The network was applied to two different clinical datasets to assess FA quality and sensitivity to pathology in temporal lobe epilepsy and multiple sclerosis, respectively. For HCP data, when compared to the ground truth FA, the FA obtained from 10 DW volumes using the network was significantly better (p <10-4) than the FA obtained using the standard pipeline. For the clinical datasets, the network FA retained the same microstructural characteristics as the FA calculated with all DW volumes using the standard method. At the subject level, the comparison between white matter (WM) ground truth FA values and network FA showed the same distribution; at the group level, statistical differences of WM values detected in the clinical datasets with the ground truth FA were reproduced when using values from the network FA, i.e., the network retained sensitivity to pathology. In conclusion, the proposed network provides a clinically available method to obtain FA from a generic set of 10 DW volumes acquirable in 1 minute, augmenting data quality compared to direct model fitting, reducing the possibility of bias from sub-sampled data, and retaining FA pathological sensitivity, which is very attractive for clinical applications

Visual Snow Syndrome Improves With Modulation of Resting-State Functional MRI Connectivity After Mindfulness-Based Cognitive Therapy: An Open-Label Feasibility Study

BACKGROUND: Visual snow syndrome (VSS) is associated with functional connectivity (FC) dysregulation of visual networks (VNs). We hypothesized that mindfulness-based cognitive therapy, customized for visual symptoms (MBCT-vision), can treat VSS and modulate dysfunctional VNs. METHODS: An open-label feasibility study for an 8-week MBCT-vision treatment program was conducted. Primary (symptom severity; impact on daily life) and secondary (WHO-5; CORE-10) outcomes at Week 9 and Week 20 were compared with baseline. Secondary MRI outcomes in a subcohort compared resting-state functional and diffusion MRI between baseline and Week 20. RESULTS: Twenty-one participants (14 male participants, median 30 years, range 22-56 years) recruited from January 2020 to October 2021. Two (9.5%) dropped out. Self-rated symptom severity (0-10) improved: baseline (median [interquartile range (IQR)] 7 [6-8]) vs Week 9 (5.5 [3-7], P = 0.015) and Week 20 (4 [3-6], P < 0.001), respectively. Self-rated impact of symptoms on daily life (0-10) improved: baseline (6 [5-8]) vs Week 9 (4 [2-5], P = 0.003) and Week 20 (2 [1-3], P < 0.001), respectively. WHO-5 Wellbeing (0-100) improved: baseline (median [IQR] 52 [36-56]) vs Week 9 (median 64 [47-80], P = 0.001) and Week 20 (68 [48-76], P < 0.001), respectively. CORE-10 Distress (0-40) improved: baseline (15 [12-20]) vs Week 9 (12.5 [11-16.5], P = 0.003) and Week 20 (11 [10-14], P = 0.003), respectively. Within-subject fMRI analysis found reductions between baseline and Week 20, within VN-related FC in the i) left lateral occipital cortex (size = 82 mL, familywise error [FWE]-corrected P value = 0.006) and ii) left cerebellar lobules VIIb/VIII (size = 65 mL, FWE-corrected P value = 0.02), and increases within VN-related FC in the precuneus/posterior cingulate cortex (size = 69 mL, cluster-level FWE-corrected P value = 0.02). CONCLUSIONS: MBCT-vision was a feasible treatment for VSS, improved symptoms and modulated FC of VNs. This study also showed proof-of-concept for intensive mindfulness interventions in the treatment of neurological conditions

Treatment reduces the incidence of newly appearing multiple sclerosis lesions evolving into chronic active, slowly expanding lesions: A retrospective analysis

Background and purpose: Newly appearing lesions in multiple sclerosis (MS) may evolve into chronically active, slowly expanding lesions (SELs), leading to sustained disability progression. The aim of this study was to evaluate the incidence of newly appearing lesions developing into SELs, and their correlation to clinical evolution and treatment. // Methods: A retrospective analysis of a fingolimod trial in primary progressive MS (PPMS; INFORMS, NCT 00731692) was undertaken. Data were available from 324 patients with magnetic resonance imaging scans up to 3 years after screening. New lesions at year 1 were identified with convolutional neural networks, and SELs obtained through a deformation-based method. Clinical disability was assessed annually by Expanded Disability Status Scale (EDSS), Nine-Hole Peg Test, Timed 25-Foot Walk, and Paced Auditory Serial Addition Test. Linear, logistic, and mixed-effect models were used to assess the relationship between the Jacobian expansion in new lesions and SELs, disability scores, and treatment status. // Results: One hundred seventy patients had ≥1 new lesions at year 1 and had a higher lesion count at screening compared to patients with no new lesions (median = 27 vs. 22, p = 0.007). Among the new lesions (median = 2 per patient), 37% evolved into definite or possible SELs. Higher SEL volume and count were associated with EDSS worsening and confirmed disability progression. Treated patients had lower volume and count of definite SELs (β = −0.04, 95% confidence interval [CI] = −0.07 to −0.01, p = 0.015; β = −0.36, 95% CI = −0.67 to −0.06, p = 0.019, respectively). // Conclusions: Incident chronic active lesions are common in PPMS, and fingolimod treatment can reduce their number

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