Multiple Sclerosis Journal

BackgroundAccelerated long-term forgetting (ALF), defined as an increased rate of memory loss over extended intervals, has so far been detected in a pilot study of patients with mild multiple sclerosis (MS). This study aimed to (I) confirm the presence of ALF in a larger, heterogeneous MS sample, (II) explore associations with patient-reported outcomes, and (III) assess the diagnostic performance of ALF tests for subjective memory impairment. MethodsThis study compared 62 MS patients and 65 age-, sex-, and education-matched healthy controls using standardized memory tests (RAVLT, WMS-IV Logical Memory subtest). Recall was assessed immediately, after 30 minutes, and after 7 days. Seven-day/30-minute recall ratios (QRAVLT, QWMS) served as primary outcomes. Self-report measures included memory complaints, fatigue, depression, and sleep disturbances. Linear regression and Receiver operating characteristic (ROC) analyses assessed predictors and diagnostic accuracy. ResultsALF was observed in multiple sclerosis since QRAVLT was lower in patients than in controls (0.64 [95% CI 0.59-0.69] vs. 0.78 [0.73-0.82], p < 0.001), as was QWMS (0.79 [95% CI 0.74-0.84] vs. 0.95 [0.90-1.00], p < 0.001), despite comparable initial learning. Greater fatigue, higher memory complaints, longer disease duration, older age, and greater disability were associated with lower ALF scores. The combined ALF score moderately discriminated subjective memory impairment (AUC 0.74; sensitivity 0.73; specificity 0.73). ConclusionMS patients showed ALF despite normal initial learning, indicating a specific memory deficit undetected by standard tests. Long-delay recall using RAVLT and WMS-IV Logical Memory subtest may improve cognitive impairment detection in MS.

Background / ObjectivesWe investigated whether the interthalamic adhesion (IA), a midline structure connecting the thalami, is altered in MS and associated with thalamic damages and cognition. MethodsWe prospectively included 32 clinically isolated syndrome/early MS, 31 RRMS, 31 PPMS patients, and 103 matched controls. All underwent anatomical 3T MRI and completed a comprehensive cognitive battery. IA presence, subtype, and volume were assessed by two blinded readers. Thalamic nuclei and other brain structures were segmented automatically. We compared IA subtypes/volumes across groups, analyzed their predictors and explored cognitive associations with multivariate regressions. ResultsIA prevalence did not differ between MS and controls (81.9% vs 74.7%). MS patients showed a shift toward a short IA subtype and reduced IA volume (mean [SD], 146.8 [117.9] vs 230.2 [138.2] mm3; p<0.0001), worsening across phenotypes. Reduced IA volume was independently associated with medial and posterior thalamic nuclei volumes, but not with white matter lesion load or global atrophy. Among cognitive domains, smaller IA volume was independently associated only with executive dysfunction (OR = 0.89 [0.77-0.99], p = 0.021). ConclusionIA volume reduction in MS reflects vulnerability of adjacent thalamic nuclei and is associated with executive dysfunction, supporting IA as a marker of thalamic neurodegeneration. Trial RegistrationMICROSEP: NCT03692975; AUBACOG: NCT03768648; PROCOG: NCT03455582.

IntroductionThe 2024 McDonald criteria incorporate the central vein sign (CVS) and paramagnetic rim lesions (PRL) as supportive imaging biomarkers for MS diagnosis. While susceptibility-weighted-imaging (SWI) and T2*-weighted echo-planar-imaging (EPI) are generally used to assess CVS/PRL, their relative performance remains unclear. This study compared high-resolution isotropic-T2*-EPI with non-isotropic SWI for CVS/PRL detection. Materials and MethodsIn this multi-centre study, 21 patients with MS underwent harmonized 3T-MRI including EPI and SWI. CVS and PRL were evaluated according to NAIMS criteria. Whole-brain and controlled lesion analyses on 120 pre-selected lesions were performed independently for each contrast, with EPI serving as reference standard. ResultsIn whole-brain analyses, SWI showed good sensitivity for CVS eligibility and positivity (AC1=0.68-0.78) but significant directional disagreement with EPI (p<0.0001). Discrepancies were primarily attributed to limited lesion-parenchyma contrast and venous visibility on SWI, which improved using low-flip-angle SWI. Controlled lesion analyses supported these observations. For PRL, SWI demonstrated high sensitivity (88%) and precision (97%) compared to EPI, though systematic bias persisted (p<0.001). Controlled lesion analyses showed more balanced, albeit moderate performance. ConclusionSWI diverged systematically from EPI for CVS and PRL detection. When available, EPI should be preferred, while optimised low-flip-angle SWI may serve as an alternative to conventional SWI.

BackgroundIn multiple sclerosis (MS), demyelination and degeneration of transcallosal pathways impair interhemispheric communication. While white matter damage is well documented, the impact of cortical lesions on transcallosal conduction remains unclear. ObjectiveTo determine whether cortical lesions in the sensorimotor hand area (SM1{square}HAND) contribute to impaired transcallosal motor interaction using ultra{square}high{square}field MRI and transcranial magnetic stimulation (TMS). MethodsTwenty healthy controls (HCs) and 38 MS patients underwent 7T structural and diffusion{square}weighted MRI. Structural scans were used to identify cortical lesions in SM1{square}HAND, while diffusion tensor imaging (DTI) quantified microstructural properties in the transcallosal tract connecting left and right SM1{square}HAND. Single{square}pulse TMS was delivered to each SM1{square}HAND during tonic first dorsal interosseous contraction to measure the ipsilateral silent period (iSP). Corticospinal conduction was measured with contralateral motor{square}evoked potentials (MEPs), while the iSP was used to compute transcallosal conduction time (TCT). ResultsAmong MS patients, 41 of 76 hemispheres contained an SM1{square}HAND lesion. TCT was significantly prolonged in MS relative to HCs (P<0.001). In patients, cortical lesions delayed transcallosal conduction from the non{square}lesion{square}bearing to the lesion{square}bearing hemisphere (P=0.026). This direction-specific delay was associated with an intracortical lesion type (P<0.001), but not with DTI{square}derived microstructural measures (P>0.05). ConclusionsThe presence of cortical lesions in the sensorimotor cortex affects transcallosal inhibition between homologous sensorimotor regions in MS, slowing the build-up of inhibitory influence on the corticospinal output in the lesioned cortex. This delayed inhibitory buildlup appears to be associated with an intracortical lesion type. HighlightsO_LIIpsilateral silent period reveals delayed transcallosal motor interaction in multiple sclerosis C_LIO_LICortical lesions in sensorimotor cortex delay the onset of transcallosal motor inhibition C_LIO_LIDelayed transcallosal inhibition is only present toward the lesioned cortex C_LIO_LIIntracortical lesions, not callosal microstructure, is linked to this directionlspecific delay C_LI

Disability worsening is the critical long-term outcome in multiple sclerosis, yet the Expanded Disability Status Scale incompletely captures neurological deterioration and has limited sensitivity in the short time windows of clinical trials. Composite endpoints incorporating functional measures have been proposed to address these limitations, but whether they reliably improve detection of treatment effects has not been established across trials. We conducted a post-hoc analysis of individual patient data from ten phase III randomised controlled trials (ASCEND, BRAVO, CONFIRM, DEFINE, EXPAND, INFORMS, OLYMPUS, OPERA I/II, and ORATORIO; n = 9,369), spanning relapsing-remitting and progressive multiple sclerosis. Confirmed disability worsening was defined using harmonised criteria with the msprog package and confirmed at 24 weeks. Treatment effects were estimated using Cox proportional hazards models and combined across trials in a one-stage individual patient data framework. Composite endpoints were constructed from the Expanded Disability Status Scale, the timed 25-foot walk test, and the nine-hole peg test using logical unions (OR-type), intersections (AND-type), and majority-vote structures. Sensitivity to treatment effect was quantified using Z-scores (the ratio of the pooled log-hazard ratio to its standard error) and compared to the Expanded Disability Status Scale reference using interaction tests. Event rates varied across components: the timed walk test generated the highest rates (up to 46.8%) while the nine-hole peg test generated the lowest (as low as 2.1%). OR-type composite endpoints showed weaker treatment effects than the Expanded Disability Status Scale alone, with the largest reductions in sensitivity observed for endpoints incorporating the timed walk test ({Delta}Z up to +2.26; interaction p = 0.004). These findings were confirmed across disease subtypes and were pronounced in relapsing-remitting trials, where no composite endpoint outperformed the Expanded Disability Status Scale. In progressive multiple sclerosis, the combination of the Expanded Disability Status Scale and the nine-hole peg test showed numerically stronger treatment effects ({Delta}Z = -1.65), though interaction tests did not reach statistical significance (p = 0.051). Composite endpoints do not systematically improve treatment effect detection in multiple sclerosis trials. Increased event capture driven by the timed walk test introduces noise that dilutes the treatment signal rather than amplifying it, highlighting that event rate and endpoint quality are not interchangeable. Upper limb function assessed by the nine-hole peg test provides complementary and specific information, particularly in progressive disease. The combination of global disability and upper limb measures represents a promising direction for future endpoint development in progressive multiple sclerosis trials, warranting validation.

Clinically silent MRI lesions occur frequently in people with relapsing-remitting multiple sclerosis (RRMS) despite disease modifying therapy (DMT). Guidelines only recommend DMT escalation after multiple silent lesions, and adherence is variable. We explored outcomes and the effect of treatment escalation following single and multiple on-treatment silent lesions. This cohort study and emulated target trial used MSBase registry data from 99 clinics in 26 countries between 2007 and 2025. Clinically stable participants receiving any DMT for RRMS with silent lesions versus without silent lesions were compared. Among participants with silent lesions while taking platform or moderate-efficacy DMTs, outcomes following treatment escalation within 6 months versus no treatment escalation (unless a post-MRI clinical event occurred) were compared. The primary outcome was an MS relapse, and the secondary outcome was 6-month confirmed disability worsening. A total of 10,232 participants met inclusion criteria (71.7% female, mean age 41 [SD 11]). The 2-year cumulative incidence of relapse was 27.8% (95% CI 25.7%-29.9%) in participants with silent lesions versus 14.3% (95% CI 13.5%-15.2%) without (adjusted hazard ratio [aHR] 1.76 [95% CI 1.57-1.97]). The 2-year cumulative incidence of disability worsening was 13.8% (95% CI 12.2%-15.5%) in participants with silent lesions versus 11.4% (95% CI 10.7%-12.2%) without (aHR 1.38 [95% CI 1.18-1.62]). Rates of relapse and disability worsening were higher following single and multiple silent lesions versus no silent lesions. The emulated trial included 2,264 participants with [&ge;]1 silent lesion on platform or moderate efficacy DMTs, 286 of whom escalated DMT within 6 months following silent lesions. The 4-year cumulative incidence of relapse was lower following treatment escalation (16.8% [95% CI 12.4%-23.4%]) versus continuation (38.9% [95% CI 35.8%-42.1%]), aHR 0.34 (95% CI 0.23-0.47), with similar aHRs following single and multiple silent lesions. The 4-year cumulative incidence of disability worsening was similar following treatment escalation (16.0% [95% CI 10.8%-22.2%]) versus continuation (17.7% [95% CI 15.3%-20.1%]), aHR 0.89 (95% CI 0.56-1.33). People with RRMS with single or multiple on-treatment silent MRI lesions have higher subsequent risks of relapse and disability worsening than people without silent lesions. DMT escalation mitigates the relapse risk, though disability worsening continues at a similar rate over 4 years. Contrary to guidelines, DMT escalation should be considered after single or multiple silent lesions.

Despite important advances in understanding the etiopathology of multiple sclerosis, factors determining disease progression remain partially understood and often difficult to predict. Specific diagnostic and prognostic biomarkers are needed to optimize the risk-benefit ratio of treatment for each patient. The aim of our study was to identify a cerebrospinal fluid proteomic signature associated with diagnosis and short- to mid-term prognosis across the multiple sclerosis continuum. Our multicentric cohort study analyzed CSF samples from 120 patients using a proteomics data-independent acquisition strategy. Differentially expressed proteins were identified across diagnostic groups: 62 patients with multiple sclerosis, 15 patients with clinically isolated syndrome, and 43 healthy controls. We also compared the CSF of patients with no evidence of disease activity with those with disease activity at 2 and 5 years of follow-up. A diagnostic and prognostic classification model was built using iterative cross-validated logistic regression models on shared differentially expressed proteins across these two comparisons. A total of 1,257 proteins were quantified, and 162 differentially expressed proteins were identified across comparisons. We identified a set of ten proteins associated with the diagnosis and prognosis of multiple sclerosis, including previously identified potential biomarkers (CH3L2, IGHG1, IGKC, LAMP2, ADA2), proteins known to be involved in the pathophysiology of multiple sclerosis (A0A8J8YUT9, AT2A2, CO3A1) and two yet unreported proteins (DSC2 and MMRN2). Multivariate models based on these proteins achieved good accuracy for the diagnosis of MS compared with CIS (area under the receiver operating characteristics curve [AUROC] up to 80% using 3 proteins) and prognosis (NEDA vs. EDA; AUROC up to 96% at 2 and 5 years; using 5 proteins). These results, which will require further investigation to validate the new biomarkers, open new perspectives on multiple sclerosis pathophysiology and therapeutic targets.

Multiple sclerosis (MS) is a chronic autoimmune demyelinating disease of the central nervous system with a complex etiology. Recent genomic studies highlight the contribution of expression quantitative trait loci (eQTLs) in modulating gene expression and disease susceptibility. Given the emerging role of circular RNAs (circRNAs) in MS, we hypothesized that genetic variants may regulate circRNA expression through circRNA-specific eQTLs (circ-eQTLs). We performed a cis-circ-eQTL analysis integrating circRNA expression and whole-genome genotyping data from 30 MS patients and 18 healthy controls using a linear regression model adjusted for disease status and sex. Candidate circ-eQTLs were prioritized based on MS-associated regions and known splicing QTLs (sQTLs) from GTEx and validated in an independent cohort (67 MS, 64 controls). Association analysis in a larger cohort (2831 MS, 3191 controls) evaluated two candidate variants for MS risk. We identified 42,077 significant cis-circ-eQTLs and validated three. Two SNPs, rs7214410 and rs11079784, modulated hsa_circ_0106983 expression, and rs7214410 also acted as an sQTL affecting EFCAB13 splicing. rs7214410 showed stronger association with MS than rs11079784. Our findings reveal extensive genetic regulation of circRNA expression and highlight rs7214410 as a dual-function variant refining the MS susceptibility locus on chromosome 17.

BackgroundPeople with multiple sclerosis (pwMS) often experience impaired quality of life (QoL) despite receiving standard care. Digital therapeutics (DTx) may offer support, but prior trials yielded mixed results, possibly due to active controls and high baseline QoL. We therefore evaluated a DTx (levidex) as an adjunct to treatment as usual (TAU) in pwMS with impaired QoL. MethodsIn this pragmatic, online randomised controlled trial (LAMONT; NCT06090305), n = 470 pwMS with a score [&ge;]2 on the Hamburg Quality of Life Questionnaire in Multiple Sclerosis (HAQUAMS) were randomised to levidex + TAU or TAU alone. The primary endpoint was HAQUAMS total score at 6 months, analysed by intention-to-treat ANCOVA. ResultsCompared with TAU, levidex + TAU improved MS-specific QoL at 6 months (baseline-adjusted mean difference -0.10; 95% CI -0.18 to -0.03; p = 0.008; Cohens d = 0.26). Clinically relevant HAQUAMS improvement ([&ge;]0.22) occurred more often with levidex (39.5% vs 27.8%; number needed to treat = 9). Benefits also emerged for depressive symptoms and social/work functioning but not for anxiety. No serious adverse events occurred and user satisfaction was high. ConclusionsIn pwMS with impaired QoL, adding the scalable DTx levidex to TAU yields meaningful improvements in QoL and functioning.

BackgroundMultiple sclerosis (MS) is a chronic neurodegenerative disease charac-terised by progressive neurological disability and heterogeneous symptom trajectories. Cur-rent clinical monitoring methods, including magnetic resonance imaging (MRI) and episodic neurological assessments, provide limited insight into subtle disease progression and real-world functional changes. Digital health technologies integrating multimodal biosignals and behavioural assessments may enable continuous monitoring and personalised rehabilitation for patients with MS. ObjectiveThis study aims to evaluate the clinical utility of the BodyMirror Clinical MS platform, a multimodal software-as-a-medical-device (SaMD) that combines wearable biosensors, neuroscience-based games, and machine learning algorithms to remotely monitor disease progression and deliver personalised neurorehabilitation for individuals with multiple sclerosis. MethodsThis study is a prospective, randomised, double-blind, controlled, multisite clinical trial enrolling 400 participants, including 300 individuals with multiple sclerosis and 100 healthy controls. MS participants will be randomly assigned (1:1) to either an adaptive neurorehabilitation intervention group or a control group receiving non-therapeutic digital activities matched for engagement and exposure. Participants will perform three 30-minute sessions per week over a 24-month period using the BodyMirror platform. The system integrates multiple biosignals, including electroencephalography (EEG), electromyography (EMG), inertial measurement unit (IMU) motion data, speech analysis, and behavioural performance metrics, to generate digital biomarkers of neurological function. The primary endpoint is change in Expanded Disability Status Scale (EDSS) score from baseline to 24 months. Secondary outcomes include changes in Multiple Sclerosis Functional Composite (MSFC), MRI brain volume, cognitive performance, patient-reported outcomes, adherence to digital rehabilitation, and health-economic outcomes. ConclusionsThis trial will provide the first large-scale clinical evaluation of a mul-timodal digital neurotechnology platform combining wearable biosensors and game-based neurorehabilitation for remote management of multiple sclerosis. If successful, BodyMirror Clinical MS may enable scalable remote monitoring, earlier detection of disease progres-sion, and personalised digital rehabilitation for individuals living with MS.

Background and Objectives: Normal appearing white matter (NAWM) may already harbor subtle microstructural alterations not yet visible on conventional MRI. Quantitative Multi-Parametric Mapping (qMPM) such as Magnetization Transfer saturation (MTsat), longitudinal relaxation rate (R1), and Proton Density (PD) offer new possibilities for analyzing NAWM which are sensitive to demyelination, axonal loss, and edema. We aimed to characterize these alterations within white matter hyperintensities (WMH) and the perilesional NAWM (pNAWM), to gain insights into the underlying process of lesion progression. We also investigated their association with cerebrovascular risk factors (CVRF) and long-term cognitive performance. Methods: This investigation included the cerebral MRI data of 245 participants from the prospective Berlin Longterm Observation of Vascular Events (BeLOVE) study. Furthermore, 121 participants cognitive performance was evaluated at baseline and longitudinally at 2 years follow-up using Montreal Cognitive Assessment (MoCA). Regions of interest (ROIs) of WMH, pNAWM at 1, 2, 3 mm were assessed in comparison to the mirrored contralesional white matter (cWM). Linear mixed effects models were employed to demonstrate the pairwise comparisons between each region using estimated marginal means and the association of MPM metrics with CVRFs. Linear regression was used to assess the association with cognitive performance. Results: In 245 participants, (mean age 62 years, SD: 12 years; 29.8% females), MPM metrics demonstrated a clear spatial gradient of microstructural injury. MTsat and R1 values were lower in WMH compared to cWM (lower case Greek beta = -0.48 (-0.52 - -0.44) and lower case Greek beta = -0.07 (-0.08 - -0.06), p<0.001, respectively) and showed gradual recovery with increasing distance indicating a microstructural gradient in pNAWM. Conversely, PD values were higher in WMH and decreased peripherally (lower case Greek beta = 2.32 (2.05 - 2.61, p<0.001). No substantial associations were found between MPM parameters and CVRFs in our cohort. At baseline and 2-year follow-up, cognitive performance was associated with higher pNAWM R1 values, whereas MTsat were only moderately associated. Discussion: Quantitative MPM reliably detects microstructural alterations not only within WMH, but also in pNAWM, confirming the high sensitivity of qMPM to subtle tissue pathology and support its utility as a promising biomarker for longitudinal studies and monitoring therapeutic effects.

BackgroundMultiple sclerosis (MS) is a demyelinating disease of the central nervous system (CNS) that affects both the brain and spinal cord, although the brain has historically received greater attention. In the inducible, oligodendrocyte-specific knockout model of Myrf, which results in white matter damage to both the brain and spinal cord, our laboratory previously demonstrated that the brain undergoes partial remyelination following white matter damage, whereas the spinal cord fails to do so. We also observed that brain microglia display a much stronger activation than spinal cord microglia in this model. Microglia regulate remyelination by clearing myelin debris, processing resulting lipids, and modulating an inflammation response. ResultsHere, to test our hypothesis, we characterized microglial phenotypes during demyelination in both tissues. The brain exhibited greater early microglial recruitment and higher baseline expression of activation and phagocytic markers, suggesting a primed state for responding to damage. In contrast, spinal cord microglia showed delayed phagocytic marker expression, sustained inflammation, and a predominately amoeboid morphology during demyelination. ConclusionsTogether, these findings indicate that brain microglia mount a timely and coordinated response to demyelination that supports remyelination, whereas spinal cord microglia adopt a dysfunctional phenotype that may contribute to impaired myelin repair.

BackgroundChronic relapsing inflammatory optic neuropathy (CRION) is a steroid-dependent form of optic neuritis with incompletely understood pathophysiology. The identification of myelin oligodendrocyte glycoprotein antibodies (MOG-IgG) in a substantial patient subset has challenged the diagnostic and therapeutic management. The aim of this study was to investigate clinical profiles and treatment outcomes of patients with CRION, comparing MOG-IgG-positive (MOG+) and seronegative (MOG-) subgroups. MethodsPatients from six European tertiary centers fulfilling diagnostic criteria for CRION were included. All underwent cell-based autoantibody testing. Clinical outcomes (visual acuity, annualized relapse rate), laboratory and imaging findings (MRI, OCT), and treatment responses were retrospectively analyzed. ResultsSixty patients were included (median age 33 years; 70% female); 27 (45%) were MOG+. MOG+ CRION was associated with later onset, higher ARR before treatment (median [IQR] 2 [1-3] vs. 1 [1-2], p = 0.023), and a trend toward shorter inter-relapse intervals. Additional distinguishing features included higher frequencies of antinuclear antibody positivity, elevated CSF interleukin-6, and extensive optic neuritis on MRI. Relapse burden correlated with visual acuity decline and retinal thinning. In MOG+ patients, monoclonal antibody therapy reduced the ARR (n = 21; 2 [1-3] vs. 0 [0-2], p = 0.024), primarily driven by tocilizumab (n = 11; 2 [1-3] vs. 0 [0-1], p = 0.023). In MOG-patients, rituximab and azathioprine showed a trend toward ARR reduction. ConclusionCRION represents a heterogeneous syndrome encompassing distinct subgroups. MOG+ patients demonstrate higher disease activity but respond favorably to tocilizumab. Serological testing is critical for treatment stratification and preventing relapses.

PurposeTo characterize microstructural alterations across distinct white matter tissue classes in MS using Standard Model Imaging (SMI), and to place its performance in context relative to conventional diffusion tensor imaging (DTI). MethodsDTI and SMI were applied to treatment-naive individuals at early stages of MS, including patients with MS and healthy controls. Over 3,602 manually delineated regions of interest were classified into normal white matter (NWM), normal-appearing white matter (NAWM), T2-hyperintense lesions, and chronic black holes (cBHs) differences were assessed using linear mixed-effects models with false discovery rate correction. Discriminative performance was evaluated using receiver operating characteristic (ROC) analysis within a generalized linear mixed modeling framework for individual parameters and multivariate DTI, SMI, and combined DTI+SMI models. ResultsBoth DTI and SMI metrics demonstrated widespread and significant differences across tissue classes. Robust discriminative performance was observed for lesion-NWM and lesion-NAWM comparisons (AUC > 0.8), whereas discrimination between NAWM and NWM and between cBHs and T2-lesions was limited (AUC [&le;] 0.66). In terms of model performance, SMI achieved slightly higher AUC values than DTI across most contrasts, while the combined DTI+SMI model consistently provided the highest diagnostic performance. ROI-based analyses revealed additional SMI alterations, including changes in extra-axonal parallel diffusivity, not consistently reported in prior studies. ConclusionDTI and SMI metrics are sensitive to microstructural abnormalities across a broad spectrum of white matter tissue classes in MS, capturing both lesion-related damage and more subtle alterations extending into NAWM. While discriminative performance varies by tissue contrast, integrating DTI and SMI provides complementary information and modestly improves diagnostic performance, supporting a multi-model diffusion MRI approach for comprehensive characterization of MS-related white matter pathology

Background: Neurite orientation dispersion and density imaging (NODDI) shows promise in providing specific insights into the neurite morphology underlying white matter (WM) damage in neurodegenerative diseases. This study aimed to advance the currently limited knowledge by characterizing NODDI-derived microstructural WM alterations in Wilson disease (WD) and examining their relationships with clinical symptoms. Methods: 30 WD patients, including 19 with predominant neurological involvement (neuro-WD) and 11 with hepatic manifestation (hep-WD), and 30 matched healthy controls underwent multi-shell diffusion-weighted magnetic resonance imaging. NODDI metrics, including neurite density index (NDI), orientation dispersion index (ODI), and isotropic volume fraction (ISOVF), and diffusion tensor imaging-based fractional anisotropy (FA) were estimated. Group differences in diffusion parameters across the WM skeleton were determined using tract-based spatial statistics. Additionally, voxel-wise correlations with neurological and cognitive scores were investigated. Results: We observed widespread NDI and ODI reductions in neuro-WD patients and ISOVF increases in hep-WD patients compared with healthy controls, particularly involving the corpus callosum, corona radiata, superior longitudinal fasciculus, external and internal capsule, and superior fronto-occipital fasciculus. A comparable yet more subtle pattern was found when comparing phenotypes. Distinct NDI and ODI constellations were identified as the microstructural determinants of FA alterations. Decreased NDI in the aforementioned fibers were correlated with neurological impairment, processing speed, and visual attention. Conclusions: Phenotype-specific microstructural WM alterations were identified, characterized by globally reduced axonal density and fiber organization in neuro-WD and excess free water in hep-WD. NODDI could be useful as an imaging biomarker for forecasting conversion to neurological WD manifestations and monitoring of disease progression.

BackgroundMRI plays an essential role in diagnosing and monitoring neurological diseases. Conventional protocols rely on multiple sequences to obtain complementary contrasts, increasing scan time, cost, and tolerability. Generating multiple contrasts from a single acquisition may streamline workflow while maintaining clinical utility. PurposeTrain attention-based convolutional neural networks (ACNNs) to generate clinical-quality FLAIR, MPRAGE, R2*, and derived contrasts from a single Gradient Echo Plural Contrast Imaging (GEPCI) acquisition, enabling multi-contrast imaging from one scan. Study TypeRetrospective. Population43 MRI scans from individuals with multiple sclerosis (25/18 F/M, 49{+/-}11 years old). Field Strength/Sequence3T MRI was used to obtain 3D GEPCI, MPRAGE, and FLAIR sequences. AssessmentTechnical quality of the AI-generated contrasts was evaluated against directly acquired MRI images using structural similarity index (SSIM). Quantitative accuracy for R2* maps was evaluated using normalized root-mean-square error (NRMSE). Clinical image quality was assessed by expert physicians. Lesion volumes and counts were obtained using automated segmentation. ResultsAI-generated FLAIR and MPRAGE images achieved mean SSIM values of 0.923{+/-}0.028 and 0.935{+/-}0.022, respectively. The generated R2* maps achieved a mean SSIM of 0.996{+/-}0.006, with quantitative accuracy reflected by an NRMSE of 0.031{+/-}0.020. Physicians rated GEPCI-FLAIR images at 4.2 and GEPCI-MPRAGE images at 4.5 (on a 1-to-5 scale), both exceeding the clinically routine standard of 4.0. Lesion volume and count comparisons from automated segmentation showed strong agreement between AI-generated and ground-truth measurements (R{superscript 2}=0.988 and R{superscript 2}=0.933, respectively). ConclusionAI-GEPCI generated multiple clinically relevant MRI contrasts from a single GEPCI acquisition with high similarity to corresponding acquired images. Radiological reviews and quantitative analyses supported the feasibility of producing high-quality, intrinsically co-registered multi-contrasts for comprehensive brain evaluation.

BackgroundCurrent diagnostic criteria for multiple sclerosis (MS) rely on white matter lesions (WMLs), which are not specific and often occur in other disorders. Microstructural abnormalities in normal-appearing white matter (NAWM) may provide complementary information beyond focal lesions. However, the diagnostic use of NAWM in MS remains limited because a reproducible, diagnostically specific NAWM signature has not been established, and NAWM abnormalities detection typically requires quantitative MRI methods beyond routine clinical MRI protocols. MethodsIn this retrospective study, we proposed DeepMS, a deep learning model trained with both quantitative diffusion MRI (dMRI) and structural MRI (sMRI) to diagnose MS by integrating WML and NAWM features captured from routine MRI alone. Development utilized 8,450 scans from 7,703 patients (NYU Langone/ADNI). Evaluation included an internal test set (n=837) and two independent external cohorts: the Krakow cohort (Poland, n=293) and a public multi-site cohort curated from 15 datasets (n=1,756). We compared DeepMS against 2024 McDonald criteria biomarkers (Dissemination in Time [DIT], Dissemination in Space [DIS], Central Vein Sign [CVS], and Paramagnetic Rim Lesion [PRL]) in a multireader study (n=308). To validate the models use of NAWM, we performed lesion-masking experiments (n=550), comparing performance after removal of focal lesions. FindingsDeepMS achieved robust AUCs in the internal (0{middle dot}968 [95% CI 0{middle dot}946-0{middle dot}987]), Krakow (0{middle dot}940 [0{middle dot}898-0{middle dot}974]), and public external (0{middle dot}974 [0{middle dot}966-0{middle dot}982]) cohorts. In the multireader study, DeepMS outperformed established biomarkers: at matched sensitivity (92{middle dot}9%), DeepMS achieved higher specificity than DIS (89{middle dot}0% vs 78{middle dot}5%; p=0{middle dot}0061); at matched specificity (92{middle dot}8%), DeepMS achieved higher sensitivity than CVS (88{middle dot}2% vs 52{middle dot}0%; p<0{middle dot}0001). Furthermore, DeepMS retained diagnostic capability after WML masking (AUC 0{middle dot}959 to 0{middle dot}881) compared to the model trained with only sMRI (0{middle dot}895 to 0{middle dot}764). InterpretationOur findings suggest it is feasible for deep learning models to leverage NAWM-related information directly from routine sMRI. Integrating these features could help MS diagnosis in patients with ambiguous white matter abnormalities. FundingNational Institute of Neurological Disorders and Stroke, the National Institute of Biomedical Imaging and Bioengineering, and the Irma T. Hirschl Trust.

BackgroundDiffusion MRI (dMRI) is widely used to assess microstructural abnormalities in multiple sclerosis (MS), yet conventional diffusion tensor imaging (DTI) is limited by single b-shell acquisitions and reduced pathological specificity. Higher-order diffusion models enabled by multi-b-shell data may provide complementary information, but their relative performance across tissue classes remains unclear. PurposeTo evaluate lesion-resolved microstructural alterations across MS tissue classes using multiple diffusion models and to assess the impact of diffusion acquisition strategy on discriminative performance. MethodsMulti-shell dMRI was acquired in 57 treatment-naive patients with early MS and 17 healthy controls. Five diffusion models were evaluated (DTI, DKI, NODDI, SMT, and SMI). 3602 manually delineated ROIs, including chronic black holes, T2 lesions, lesion-matched normal-appearing white matter (NAWM), and normal white matter (NWM), were analyzed. Microstructural differences were assessed using linear mixed-effects models, and discriminative performance was evaluated using ROC analysis across single-shell, multi-shell, and joint modeling strategies. Feature selection was performed using LASSO regression. ResultsAcross all models, lesions exhibited coherent microstructural abnormalities relative to normal white matter, while NAWM showed concordant but more subtle alterations. Lesion-normal tissue contrasts demonstrated strong discriminative performance, whereas classification of NAWM versus NWM and lesion subtypes remained limited, reflecting substantial biological overlap. Two b-shell and joint modeling approaches consistently outperformed single-shell analyses, yielding the highest AUCs. LASSO identified a small set of biologically meaningful diffusion features driving tissue discrimination. ConclusionMulti-b-shell diffusion MRI enables more robust and informative characterization of MS-related white matter pathology than single-shell acquisitions alone, supporting multi-model, multi-b-shell strategies for lesion-resolved assessment in MS.

Artificial intelligence (AI) is increasingly being integrated into healthcare, particularly in data-intensive chronic diseases that rely on longitudinal monitoring and shared decision-making. Multiple sclerosis is a prototypical example of such care, but real-world benefit will depend on whether people accept AI support in different clinical roles. We conducted a cross-sectional, web-based survey among 241 people with MS (pwMS) to assess comfort with AI across eight clinical domains and to identify predictors of acceptance. We derived an artificial-intelligence attitudes composite with high internal consistency (Cronbach alpha = 0.90). Overall acceptance was moderate (mean 3.39 {+/-} 0.78). Acceptance differed across domains, demonstrating a responsibility gradient: comfort was highest for supportive applications such as chronic management (54.4%) and symptom screening (50.2%), but lower for treatment selection (38.6%) and diagnosis (35.3%; P < 0.001). In multivariable models, frequent general AI use (at least weekly; 30.7%) was the strongest independent predictor of acceptance (P < 0.001). Acceptance also differed by region (Eastern vs Western Germany, P = 0.025), whereas clinical disability was not significantly associated. Older age was associated with lower acceptance of AI-supported management. Most participants viewed AI as a logistical support tool but, assuming equal diagnostic accuracy, 78.8% preferred joint artificial-intelligence-clinician decision-making with clinician final responsibility. These findings indicate that acceptance is context-dependent and aligns more closely with prior familiarity than with disease severity. Implementation should move beyond technical validation to transparent, clinician-ledhuman-in-the-loop workflows with explicit accountability and staged adoption beginning with low-risk use cases. Author SummaryWe use artificial intelligence more and more in everyday life, and similar tools are now being introduced into medical care. For long-term conditions such as multiple sclerosis, digital systems could help manage large amounts of clinical information and support monitoring between visits. At the same time, these tools will only be useful if the people receiving care are willing to use them and understand what role they play. In this study, we asked 241 people living with multiple sclerosis in Germany how comfortable they would feel with artificial intelligence in different parts of care. We found that comfort depended strongly on the task. Participants were most open to artificial intelligence when it supported practical, lower-risk functions such as ongoing monitoring or symptom screening, and they were more cautious when it was described as influencing diagnosis or treatment choices. Most participants wanted clinicians to remain responsible for final decisions. Acceptance was higher among people who already used artificial intelligence frequently in everyday life, and it differed by age and by region. Our findings suggest that successful implementation will require more than technical performance: it should be introduced transparently, with clinician oversight, and in a stepwise way that builds familiarity without shifting responsibility away from the clinical team.

Objective: To systematically evaluate the efficacy and safety of Deep Brain Stimulation (DBS) for the management of disabling tremor in patients with Multiple Sclerosis (MS) by synthesizing data from available clinical studies. Methods: This systematic review and meta analysis followed PRISMA 2020 guidelines and was registered with PROSPERO (CRD420261347426). A comprehensive search of PubMed, Scopus, Web of Science, and Embase was performed from database inception until December 2025 with no time or language limitation. A pre-post meta analysis design was used to estimate the pooled effect size using the Standardized Mean Change (SMC) between baseline and follow up tremor severity. Because most included studies were single arm cohorts and clinical heterogeneity was anticipated, a random effects model using the Restricted Maximum Likelihood (REML) estimator with the Hartung-Knapp adjustment was applied. Safety outcomes including hardware complications and postoperative infections were pooled using random effects meta analysis of proportions. Results: Thirteen studies including 131 patients met the eligibility criteria. Eight studies with adequate outcome data were included in the pooled efficacy analysis. DBS was associated with a significant reduction in tremor severity with an overall pooled SMC of 1.42 (95% CI 1.07 to 1.77). Statistical heterogeneity was minimal (I2 = 0.0%, p = 0.6300), although this finding should be interpreted cautiously given the limited number of studies and clinical variability in surgical targets, most commonly the ventral intermediate nucleus (VIM), and follow up duration ranging from months to more than 20 years. The pooled incidence of postoperative infection was approximately 7% with substantial heterogeneity across studies (I2 = 74.1%). The most frequently reported adverse events were stimulation related effects such as dysarthria and disequilibrium, which were generally reversible after adjustment of stimulation parameters. Overall methodological quality of included studies was predominantly moderate. Conclusion: Deep brain stimulation may provide meaningful tremor reduction in selected patients with disabling and medication refractory MS tremor, with a large pooled treatment effect (SMC = 1.42). Although complications such as postoperative infection (approximately 7%) and transient stimulation related adverse effects can occur, these events appear manageable in most cases. However, the current evidence base remains limited by small sample sizes, heterogeneous study designs, and variability in surgical targets and outcome reporting. Larger prospective studies with standardized tremor outcome measures and consistent reporting of safety outcomes are needed to better define the long term efficacy and optimal clinical role of DBS in patients with MS related tremor.