Trans-Synaptic Degeneration in the Visual Pathway in Patients With Myelin Oligodendrocyte Glycoprotein Antibody-Associated Disease.

BACKGROUND AND OBJECTIVES: We aimed to assess the presence of retinal neurodegeneration independent of optic neuritis (ON) in myelin oligodendrocyte glycoprotein antibody-associated disease (MOGAD) and to investigate the development of trans-synaptic anterograde degeneration in these patients after ON. METHODS: Cross-sectional, retrospective study of 34 adult patients with MOGAD and 23 healthy controls (HC). Clinical, optical coherence tomography (OCT), and MRI data were collected. Peripapillary retinal nerve fiber layer (pRNFL) and ganglion cell inner plexiform layer (GCIPL) were obtained using Heidelberg Spectralis. FreeSurfer7 was used to obtain the lateral geniculate nucleus (LGN), occipital volume fractions (to total estimated intracranial volume), and occipital cortical thickness. For the anterior visual pathway, the analysis was conducted using eyes, classified based on the history of ON (Eye-ON and Eye-NON) and compared with Eye-HC. The analysis of OCT and brain volumetric measures was conducted comparing MOGAD-ON, MOGAD-NON, and HC groups. The analysis of covariance with a Bonferroni-adjusted post hoc test was used to test differences between groups and linear regression analysis to evaluate OCT/MRI associations; age and sex were considered as covariates. RESULTS: 24 (70.5%) patients had a prior ON. Median pRNFL and GCIPL thickness (um) was significantly reduced in Eye-ON vs EyeNON and HC (pRNFL: 69.4 (17.3), 89.6 (13.7), 98.2 (11.7), p < 0.001; GCIPL: 55.8 (8.7), 67.39 (8.7), 72.6 (4.5), p < 0.001). pRNFL and GCIPL thickness had a negative correlation with the number of ON episodes (p = 0.025 and p = 0.031, respectively). LGN volume fraction was significantly lower in patients with MOGAD-ON than in HC (0.33 (0.05) vs 0.39 (0.04), p = 0.002). The occipital cortical thickness was lower in MOGAD-ON compared with MOGAD-NON and HC (p = 0.010). In patients with MOGAD-ON, pRNFL correlated with LGN volume (p = 0.006), occipital thickness (p = 0.002), and the medial occipital cortex (p = 0.002), but not the lateral occipital lobe. DISCUSSION: Compared with HC, MOGAD-ON exhibits reduced retinal thickness, primarily influenced by the presence and the number of prior ON episodes. Moreover, MOGAD-ON demonstrates significant atrophy in the retinal, subcortical, and cortical regions of the visual pathway, distinguishing them from MOGAD-NON and HC. These findings suggest that in patients with MOGAD neurodegeneration is tightly correlated with damage to the involved pathway.

T1/T2-weighted ratio: A feasible MRI biomarker in multiple sclerosis.

T1/T2-weighted ratio is a novel magnetic resonance imaging (MRI) biomarker based on conventional sequences, related to microstructural integrity and with increasing use in multiple sclerosis (MS) research. Different from other advanced MRI techniques, this method has the advantage of being based on routinely acquired MRI sequences, a feature that enables analysis of retrospective cohorts with considerable clinical value. This article provides an overview of this method, describing the previous cross-sectional and longitudinal findings in the main MS clinical phenotypes and in different brain tissues: focal white matter (WM) lesions, normal-appearing white matter (NAWM), cortical gray matter (GM), and deep normal-appearing gray matter (NAGM). We also discuss the clinical associations, possible reasons for conflicting results, correlations with other MRI-based measures, and histopathological associations. We highlight the limitations of the biomarker itself and the methodology of each study. Finally, we update the reader on its potential use as an imaging biomarker in research.

Optic chiasm manual and automated measurements in sub-acute optic neuritis with OCT and MRI correlations.

PURPOSE: The optic chiasm (OC) is a central structure in the visual pathway and can be visualized in conventional MRI, but no consensus regarding its measurement has been defined. We aim to investigate the most reproducible manual approach to OC measurement and to explore associations of OC with optical coherence tomography (OCT) parameters, and automatic brain segmentation (FreeSurfer) in subacute optic neuritis (sON), multiple sclerosis without optic neuritis (MSwoON), and healthy subjects (HS). MATERIALS AND METHODS: We reproduced two previously reported methodologies and implemented a new proposed simplified approach, entitled optic chiasm mean area (OCMA). The intra and inter-rater reliability and reproducibility were assessed through the intraclass correlation (ICC) and Dice similarity (DSC) coefficients. Partial correlations were calculated to gauge the associations between OCMA fraction (OCMA divided by total intracranial volume), brain regional segmentations derived from FreeSurfer, and OCT parameters. RESULTS: We have analysed 43 sON, 20 MSwoON, and 20 HS. OCMA presented better results for reliability in both intra- and inter-rater analysis (excellent ICC and DSC with over 80% overlap between masks), as compared to the other two approaches. OCMA fraction was associated with OC volume fraction obtained with Freesurfer in all groups, brain parenchymal fraction, and OCT parameters in MSwoON. CONCLUSIONS: The OCMA is a simplified approach to measure OC atrophy, has a higher reliability than the current approaches and shows association with an automated method. OC-derived measures seem to reflect diffuse neurodegenerative damage, whereas, in patients with subacute ON, it may be associated with local damage.

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.

Global and Regional Deep Learning Models for Multiple Sclerosis Stratification From MRI.

BACKGROUND: The combination of anatomical MRI and deep learning-based methods such as convolutional neural networks (CNNs) is a promising strategy to build predictive models of multiple sclerosis (MS) prognosis. However, studies assessing the effect of different input strategies on model's performance are lacking. PURPOSE: To compare whole-brain input sampling strategies and regional/specific-tissue strategies, which focus on a priori known relevant areas for disability accrual, to stratify MS patients based on their disability level. STUDY TYPE: Retrospective. SUBJECTS: Three hundred nineteen MS patients (382 brain MRI scans) with clinical assessment of disability level performed within the following 6 months (~70% training/~15% validation/~15% inference in-house dataset) and 440 MS patients from multiple centers (independent external validation cohort). FIELD STRENGTH/SEQUENCE: Single vendor 1.5 T or 3.0 T. Magnetization-Prepared Rapid Gradient-Echo and Fluid-Attenuated Inversion Recovery sequences. ASSESSMENT: A 7-fold patient cross validation strategy was used to train a 3D-CNN to classify patients into two groups, Expanded Disability Status Scale score (EDSS) ≥ 3.0 or EDSS < 3.0. Two strategies were investigated: 1) a global approach, taking the whole brain volume as input and 2) regional approaches using five different regions-of-interest: white matter, gray matter, subcortical gray matter, ventricles, and brainstem structures. The performance of the models was assessed in the in-house and the independent external cohorts. STATISTICAL TESTS: Balanced accuracy, sensitivity, specificity, area under receiver operating characteristic (ROC) curve (AUC). RESULTS: With the in-house dataset, the gray matter regional model showed the highest stratification accuracy (81%), followed by the global approach (79%). In the external dataset, without any further retraining, an accuracy of 72% was achieved for the white matter model and 71% for the global approach. DATA CONCLUSION: The global approach offered the best trade-off between internal performance and external validation to stratify MS patients based on accumulated disability. EVIDENCE LEVEL: 4 TECHNICAL EFFICACY: Stage 2.

Diffusion-based structural connectivity patterns of multiple sclerosis phenotypes.

BACKGROUND: We aimed to describe the severity of the changes in brain diffusion-based connectivity as multiple sclerosis (MS) progresses and the microstructural characteristics of these networks that are associated with distinct MS phenotypes. METHODS: Clinical information and brain MRIs were collected from 221 healthy individuals and 823 people with MS at 8 MAGNIMS centres. The patients were divided into four clinical phenotypes: clinically isolated syndrome, relapsing-remitting, secondary progressive and primary progressive. Advanced tractography methods were used to obtain connectivity matrices. Then, differences in whole-brain and nodal graph-derived measures, and in the fractional anisotropy of connections between groups were analysed. Support vector machine algorithms were used to classify groups. RESULTS: Clinically isolated syndrome and relapsing-remitting patients shared similar network changes relative to controls. However, most global and local network properties differed in secondary progressive patients compared with the other groups, with lower fractional anisotropy in most connections. Primary progressive participants had fewer differences in global and local graph measures compared with clinically isolated syndrome and relapsing-remitting patients, and reductions in fractional anisotropy were only evident for a few connections. The accuracy of support vector machine to discriminate patients from healthy controls based on connection was 81%, and ranged between 64% and 74% in distinguishing among the clinical phenotypes. CONCLUSIONS: In conclusion, brain connectivity is disrupted in MS and has differential patterns according to the phenotype. Secondary progressive is associated with more widespread changes in connectivity. Additionally, classification tasks can distinguish between MS types, with subcortical connections being the most important factor.

Deciphering multiple sclerosis disability with deep learning attention maps on clinical MRI.

The application of convolutional neural networks (CNNs) to MRI data has emerged as a promising approach to achieving unprecedented levels of accuracy when predicting the course of neurological conditions, including multiple sclerosis, by means of extracting image features not detectable through conventional methods. Additionally, the study of CNN-derived attention maps, which indicate the most relevant anatomical features for CNN-based decisions, has the potential to uncover key disease mechanisms leading to disability accumulation. From a cohort of patients prospectively followed up after a first demyelinating attack, we selected those with T1-weighted and T2-FLAIR brain MRI sequences available for image analysis and a clinical assessment performed within the following six months (N = 319). Patients were divided into two groups according to expanded disability status scale (EDSS) score: ≥3.0 and < 3.0. A 3D-CNN model predicted the class using whole-brain MRI scans as input. A comparison with a logistic regression (LR) model using volumetric measurements as explanatory variables and a validation of the CNN model on an independent dataset with similar characteristics (N = 440) were also performed. The layer-wise relevance propagation method was used to obtain individual attention maps. The CNN model achieved a mean accuracy of 79% and proved to be superior to the equivalent LR-model (77%). Additionally, the model was successfully validated in the independent external cohort without any re-training (accuracy = 71%). Attention-map analyses revealed the predominant role of frontotemporal cortex and cerebellum for CNN decisions, suggesting that the mechanisms leading to disability accrual exceed the mere presence of brain lesions or atrophy and probably involve how damage is distributed in the central nervous system.

Using The Virtual Brain to study the relationship between structural and functional connectivity in patients with multiple sclerosis: a multicenter study.

The relationship between structural connectivity (SC) and functional connectivity (FC) captured from magnetic resonance imaging, as well as its interaction with disability and cognitive impairment, is not well understood in people with multiple sclerosis (pwMS). The Virtual Brain (TVB) is an open-source brain simulator for creating personalized brain models using SC and FC. The aim of this study was to explore SC-FC relationship in MS using TVB. Two different model regimes have been studied: stable and oscillatory, with the latter including conduction delays in the brain. The models were applied to 513 pwMS and 208 healthy controls (HC) from 7 different centers. Models were analyzed using structural damage, global diffusion properties, clinical disability, cognitive scores, and graph-derived metrics from both simulated and empirical FC. For the stable model, higher SC-FC coupling was associated with pwMS with low Single Digit Modalities Test (SDMT) score (F=3.48, P$\lt$0.05), suggesting that cognitive impairment in pwMS is associated with a higher SC-FC coupling. Differences in entropy of the simulated FC between HC, high and low SDMT groups (F=31.57, P$\lt$1e-5), show that the model captures subtle differences not detected in the empirical FC, suggesting the existence of compensatory and maladaptive mechanisms between SC and FC in MS.

Spinal cord reserve in multiple sclerosis.

BACKGROUND: The spinal cord (SC) is a preferential target of multiple sclerosis (MS) damage highly relevant towards disability. Differential impact of such damage could be due to the initial amount of SC tissue, as described for the brain parenchyma (brain reserve concept). We aimed to test the existence of SC reserve by using spinal canal area (SCaA) as a proxy. METHODS: Brain sagittal three-dimensional T1-weighted scans covering down to C5 level were acquired in 2930 people with MS and 43 healthy controls (HCs) in a cross-sectional, multicentre study. SC area (SCA) and SCaA were obtained with the Spinal Cord Toolbox. Demographical data and patient-derived disability scores were obtained. SC parameters were compared between groups with age-adjusted and sex-adjusted linear regression models. The main outcome of the study, the existence of an association between SCaA and Patient Determined Disease Steps, was tested with scaled linear models. RESULTS: 1747 persons with MS (mean age: 46.35 years; 73.2% female) and 42 HCs (mean age: 45.56 years; 78.6% female) were analysed after exclusion of post-processing errors and application of quality criteria. SCA (60.41 mm2 vs 65.02 mm2, p<0.001) was lower in people with MS compared with HC; no differences in SCaA were observed (213.24 mm2 vs 212.61 mm2, p=0.125). Adjusted scaled linear models showed that a larger SCaA was significantly associated with lower scores on Patient Determined Disease Steps (beta coefficient: -0.12, p=0.0124) independently of spinal cord atrophy, brain T2 lesion volume, age and sex. CONCLUSIONS: A larger SCaA may be protective against disability in MS, possibly supporting the existence of SC reserve.

Association of Early Progression Independent of Relapse Activity With Long-term Disability After a First Demyelinating Event in Multiple Sclerosis.

Importance: Progression independent of relapse activity (PIRA) is the main event responsible for irreversible disability accumulation in relapsing multiple sclerosis (MS). Objective: To investigate clinical and neuroimaging predictors of PIRA at the time of the first demyelinating attack and factors associated with long-term clinical outcomes of people who present with PIRA. Design, Setting, and Participants: This cohort study, conducted from January 1, 1994, to July 31, 2021, included patients with a first demyelinating attack from multiple sclerosis; patients were recruited from 1 study center in Spain. Patients were excluded if they refused to participate, had alternative diagnoses, did not meet protocol requirements, had inconsistent demographic information, or had less than 3 clinical assessments. Exposures: Exposures included (1) clinical and neuroimaging features at the first demyelinating attack and (2) presenting PIRA, ie, confirmed disability accumulation (CDA) in a free-relapse period at any time after symptom onset, within (vs after) the first 5 years of the disease (ie, early/late PIRA), and in the presence (vs absence) of new T2 lesions in the previous 2 years (ie, active/nonactive PIRA). Main Outcomes and Measures: Expanded Disability Status Scale (EDSS) yearly increase rates since the first attack and adjusted hazard ratios (HRs) for predictors of time to PIRA and time to EDSS 6.0. Results: Of the 1128 patients (mean [SD] age, 32.1 [8.3] years; 781 female individuals [69.2%]) included in the study, 277 (25%) developed 1 or more PIRA events at a median (IQR) follow-up time of 7.2 (4.6-12.4) years (for first PIRA). Of all patients with PIRA, 86 of 277 (31%) developed early PIRA, and 73 of 144 (51%) developed active PIRA. Patients with PIRA were slightly older, had more brain lesions, and were more likely to have oligoclonal bands than those without PIRA. Older age at the first attack was the only predictor of PIRA (HR, 1.43; 95% CI, 1.23-1.65; P < .001 for each older decade). Patients with PIRA had steeper EDSS yearly increase rates (0.18; 95% CI, 0.16-0.20 vs 0.04; 95% CI, 0.02-0.05; P < .001) and an 8-fold greater risk of reaching EDSS 6.0 (HR, 7.93; 95% CI, 2.25-27.96; P = .001) than those without PIRA. Early PIRA had steeper EDSS yearly increase rates than late PIRA (0.31; 95% CI, 0.26-0.35 vs 0.13; 95% CI, 0.10-0.16; P < .001) and a 26-fold greater risk of reaching EDSS 6.0 from the first attack (HR, 26.21; 95% CI, 2.26-303.95; P = .009). Conclusions and Relevance: Results of this cohort study suggest that for patients with multiple sclerosis, presenting with PIRA after a first demyelinating event was not uncommon and suggests an unfavorable long-term prognosis, especially if it occurs early in the disease course.

Relationship between paramagnetic rim lesions and slowly expanding lesions in multiple sclerosis.

BACKGROUND: Magnetic resonance imaging (MRI) markers for chronic active lesions in MS include slowly expanding lesions (SELs) and paramagnetic rim lesions (PRLs). OBJECTIVES: To identify the relationship between SELs and PRLs in MS, and their association with disability. METHODS: 61 people with MS (pwMS) followed retrospectively with MRI including baseline susceptibility-weighted imaging, and longitudinal T1 and T2-weighted scans. SELs were computed using deformation field maps; PRLs were visually identified. Mixed-effects models assessed differences in Expanded Disability Status Scale (EDSS) score changes between the group defined by the presence of SELs and or PRLs. RESULTS: The median follow-up time was 3.2 years. At baseline, out of 1492 lesions, 616 were classified as SELs, and 80 as PRLs. 92% of patients had ⩾ 1 SEL, 56% had ⩾ 1 PRL, while both were found in 51%. SELs compared to non-SELs were more likely to also be PRLs (7% vs. 4%, p = 0.027). PRL counts positively correlated with SEL counts (ρ= 0.28, p = 0.03). SEL + PRL + patients had greater increases in EDSS over time (beta = 0.15/year, 95% confidence interval (0.04, 0.27), p = 0.009) than SEL+PRL-patients. CONCLUSION: SELs are more numerous than PRLs in pwMS. Compared with either SELs or PRLs found in isolation, their joint occurrence was associated with greater clinical progression.

Relationship between visuoperceptual functions and parietal structural abnormalities in temporal lobe epilepsy.

Progressive gray matter volume reductions beyond the epileptogenic area has been described in temporal lobe epilepsy. There is less evidence regarding correlations between gray and white matter volume changepres and multi-domain cognitive performance in this setting. We aimed to investigate correlations between volume changes in parietal structures and visuospatial performance in temporal lobe epilepsy patients. we performed a cross-sectional study comparing global and regional brain volume data from 34 temporal lobe epilepsy patients and 30 healthy controls. 3D T1-weighted sequences were obtained on a 3.0 T magnet, and data were analyzed using age and sex-adjusted linear regression models. Global and regional brain volumes and cortical thickness in patients were correlated with standardized visual memory, visuoperceptual, visuospatial, and visuoconstructive parameters obtained in a per-protocol neuropsychological assessment. temporal lobe epilepsy patients had smaller volume fractions of the deep gray matter structures, putamen and nucleus accumbens, and larger cerebrospinal fluid volume fraction than controls. Correlations were found between: 1) visual memory and precuneus and inferior parietal cortical thickness; 2) visuoperceptual performance and precuneus and supramarginal white matter volumes; 3) visuospatial skills and precuneus, postcentral, and inferior and superior parietal white matter volumes; 4) visuoconstructive performance and inferior parietal white matter volume. Brain volume loss is widespread in temporal lobe epilepsy. Volumetric reductions in parietal lobe structures were associated with visuoperceptual cognitive performance.

Detection of lesions in the optic nerve with magnetic resonance imaging using a 3D convolutional neural network.

BACKGROUND: Optic neuritis (ON) is one of the first manifestations of multiple sclerosis, a disabling disease with rising prevalence. Detecting optic nerve lesions could be a relevant diagnostic marker in patients with multiple sclerosis. OBJECTIVES: We aim to create an automated, interpretable method for optic nerve lesion detection from MRI scans. MATERIALS AND METHODS: We present a 3D convolutional neural network (CNN) model that learns to detect optic nerve lesions based on T2-weighted fat-saturated MRI scans. We validated our system on two different datasets (N = 107 and 62) and interpreted the behaviour of the model using saliency maps. RESULTS: The model showed good performance (68.11% balanced accuracy) that generalizes to unseen data (64.11%). The developed network focuses its attention to the areas that correspond to lesions in the optic nerve. CONCLUSIONS: The method shows robustness and, when using only a single imaging sequence, its performance is not far from diagnosis by trained radiologists with the same constraint. Given its speed and performance, the developed methodology could serve as a first step to develop methods that could be translated into a clinical setting.

Neuroimaging analyses from a randomized, controlled study to evaluate plasma exchange with albumin replacement in mild-to-moderate Alzheimer's disease: additional results from the AMBAR study.

PURPOSE: This study was designed to detect structural and functional brain changes in Alzheimer's disease (AD) patients treated with therapeutic plasma exchange (PE) with albumin replacement, as part of the recent AMBAR phase 2b/3 clinical trial. METHODS: Mild-to-moderate AD patients were randomized into four arms: three arms receiving PE with albumin (one with low-dose albumin, and two with low/high doses of albumin alternated with IVIG), and a placebo (sham PE) arm. All arms underwent 6 weeks of weekly conventional PE followed by 12 months of monthly low-volume PE. Magnetic resonance imaging (MRI) volumetric analyses and regional and statistical parametric mapping (SPM) analysis on 18F-fluorodeoxyglucose positron emission tomography (18FDG-PET) were performed. RESULTS: MRI analyses (n = 198 patients) of selected subcortical structures showed fewer volume changes from baseline to final visit in the high albumin + IVIG treatment group (p < 0.05 in 3 structures vs. 4 to 9 in other groups). The high albumin + IVIG group showed no statistically significant reduction of right hippocampus. SPM 18FDG-PET analyses (n = 213 patients) showed a worsening of metabolic activity in the specific areas affected in AD (posterior cingulate, precuneus, and parieto-temporal regions). The high-albumin + IVIG treatment group showed the greatest metabolic stability over the course of the study, i.e., the smallest percent decline in metabolism (MaskAD), and least progression of defect compared to placebo. CONCLUSIONS: PE with albumin replacement was associated with fewer deleterious changes in subcortical structures and less metabolic decline compared to the typical of the progression of AD. This effect was more marked in the group treated with high albumin + IVIG. TRIAL REGISTRATION: (AMBAR trial registration: EudraCT#: 2011-001,598-25; ClinicalTrials.gov ID: NCT01561053).

Riemannian Geometry of Functional Connectivity Matrices for Multi-Site Attention-Deficit/Hyperactivity Disorder Data Harmonization.

The use of multi-site datasets in neuroimaging provides neuroscientists with more statistical power to perform their analyses. However, it has been shown that the imaging-site introduces variability in the data that cannot be attributed to biological sources. In this work, we show that functional connectivity matrices derived from resting-state multi-site data contain a significant imaging-site bias. To this aim, we exploited the fact that functional connectivity matrices belong to the manifold of symmetric positive-definite (SPD) matrices, making it possible to operate on them with Riemannian geometry. We hereby propose a geometry-aware harmonization approach, Rigid Log-Euclidean Translation, that accounts for this site bias. Moreover, we adapted other Riemannian-geometric methods designed for other domain adaptation tasks and compared them to our proposal. Based on our results, Rigid Log-Euclidean Translation of multi-site functional connectivity matrices seems to be among the studied methods the most suitable in a clinical setting. This represents an advance with respect to previous functional connectivity data harmonization approaches, which do not respect the geometric constraints imposed by the underlying structure of the manifold. In particular, when applying our proposed method to data from the ADHD-200 dataset, a multi-site dataset built for the study of attention-deficit/hyperactivity disorder, we obtained results that display a remarkable correlation with established pathophysiological findings and, therefore, represent a substantial improvement when compared to the non-harmonization analysis. Thus, we present evidence supporting that harmonization should be extended to other functional neuroimaging datasets and provide a simple geometric method to address it.

Exploring in vivo multiple sclerosis brain microstructural damage through T1w/T2w ratio: a multicentre study.

OBJECTIVES: To evaluate white matter and grey matter T1-weighted (w)/T2w ratio (T1w/T2w ratio) in healthy controls and patients with multiple sclerosis, and its association with clinical disability. METHODS: In this cross-sectional study, 270 healthy controls and 434 patients with multiple sclerosis were retrospectively selected from 7 European sites. T1w/T2w ratio was obtained from brain T2w and T1w scans after intensity calibration using eyes and temporal muscle. RESULTS: In healthy controls, T1w/T2w ratio increased until 50-60 years both in white and grey matter. Compared with healthy controls, T1w/T2w ratio was significantly lower in white matter lesions of all multiple sclerosis phenotypes, and in normal-appearing white matter and cortex of patients with relapsing-remitting and secondary progressive multiple sclerosis (p≤0.026), but it was significantly higher in the striatum and pallidum of patients with relapsing-remitting, secondary progressive and primary progressive multiple sclerosis (p≤0.042). In relapse-onset multiple sclerosis, T1w/T2w ratio was significantly lower in white matter lesions and normal-appearing white matter already at Expanded Disability Status Scale (EDSS) <3.0 and in the cortex only for EDSS ≥3.0 (p≤0.023). Conversely, T1w/T2w ratio was significantly higher in the striatum and pallidum for EDSS ≥4.0 (p≤0.005). In primary progressive multiple sclerosis, striatum and pallidum showed significantly higher T1w/T2w ratio beyond EDSS=6.0 (p≤0.001). In multiple sclerosis, longer disease duration, higher EDSS, higher brain lesional volume and lower normalised brain volume were associated with lower lesional and cortical T1w/T2w ratio and a higher T1w/T2w ratio in the striatum and pallidum (ß from -1.168 to 0.286, p≤0.040). CONCLUSIONS: T1w/T2w ratio may represent a clinically relevant marker sensitive to demyelination, neurodegeneration and iron accumulation occurring at the different multiple sclerosis phases.

Serum neurofilament light chain levels predict long-term disability progression in patients with progressive multiple sclerosis.

ObjectiveThere is a lack of sensitive and specific biomarkers for use in progressive multiple sclerosis (MS). The study aimed to assess the potential of serum neurofilament light chain (sNfL) levels as biomarker of disability progression in patients with progressive MS. METHODS: We performed a prospective observational cohort study in 51 patients with progressive MS who participated in a 2-year phase II single-centre, randomised, double-blind, placebo-controlled trial of interferon-beta. Mean (SD) follow-up duration was 13.9 (6.2) years. Levels of sNfL were measured using a single molecule array immunoassay at baseline, 1, 2 and 6 years. Univariable and multivariable analyses were carried out to evaluate associations between sNfL levels and disability progression at short term (2 years), medium term (6 years) and long term (at the time of the last follow-up). RESULTS: A sNfL cut-off value of 10.2 pg/mL at baseline discriminated between long-term progressors and non-progressors with a 75% sensitivity and 67% specificity (adjusted OR 7.8; 95% CI 1.8 to 46.4; p=0.01). Similar performance to discriminate between long-term progressors and non-progressors was observed using age/body mass index-adjusted sNfL Z-scores derived from a normative database of healthy controls. A cut-off increase of 5.1 pg/mL in sNfL levels between baseline and 6 years also discriminated between long-term progressors and non-progressors with a 71% sensitivity and 86% specificity (adjusted OR 49.4; 95% CI 4.4 to 2×103; p=0.008). CONCLUSIONS: sNfL can be considered a prognostic biomarker of future long-term disability progression in patients with progressive MS. These data expand the little knowledge existing on the role of sNfL as long-term prognostic biomarker in patients with progressive MS.

Can Cognitive training Reignite Compensatory Mechanisms in Advanced Multiple Sclerosis Patients? An Explorative Morphological Network Approach.

Multiple Sclerosis (MS) has been shown to significantly impair brain connectivity, as alterations in functional and structural networks have been identified and associated with clinical status, particularly cognitive deficits. We aimed to identify structural connectivity changes in grey matter networks following cognitive rehabilitation (CR) in persons with MS (PwMS). Fifteen long-standing PwMS underwent a 5-week CR-program and five healthy controls (HC) were also investigated. T1-weighted MRI scans and neuropsychological tests were obtained before and after CR. T1-weighted scans were used to examine grey matter networks with graph analytic parameters [betweenness centrality (BC), degree (D), clustering (Cl), path length (PL) and small world properties: connectedness, gamma and lambda values]. Results were analysed at the whole brain level and for each brain lobe. Before CR, PwMS displayed lower values for D in the left parietal lobe (p = 0.009) compared to HC. After CR, significant increases in Cl located in frontal (p = 0.024) and temporal (p = 0.026) regions in PwMS were accompanied by significant decreases in PL located in the right parietal lobe (p = 0.025) and BC globally (p = 0.010). Overall, CR may prevent a network worsening in long-standing PwMS by increasing local efficiency of the brain and therefore facilitating compensation mechanisms.

MAGNIMS recommendations for harmonization of MRI data in MS multicenter studies.

There is an increasing need of sharing harmonized data from large, cooperative studies as this is essential to develop new diagnostic and prognostic biomarkers. In the field of multiple sclerosis (MS), the issue has become of paramount importance due to the need to translate into the clinical setting some of the most recent MRI achievements. However, differences in MRI acquisition parameters, image analysis and data storage across sites, with their potential bias, represent a substantial constraint. This review focuses on the state of the art, recent technical advances, and desirable future developments of the harmonization of acquisition, analysis and storage of large-scale multicentre MRI data of MS cohorts. Huge efforts are currently being made to achieve all the requirements needed to provide harmonized MRI datasets in the MS field, as proper management of large imaging datasets is one of our greatest opportunities and challenges in the coming years. Recommendations based on these achievements will be provided here. Despite the advances that have been made, the complexity of these tasks requires further research by specialized academical centres, with dedicated technical and human resources. Such collective efforts involving different professional figures are of crucial importance to offer to MS patients a personalised management while minimizing consumption of resources.