Background: The clinical–radiological paradox in multiple sclerosis (MS) underscores the need for biomarkers that better reflect neurodegenerative pathology. Serum neurofilament light chain (sNfL) is a dynamic marker of neuroaxonal injury, while brain volumetry provides structural assessment of disease impact. However, the precise link between sNfL and regional atrophy patterns, as well as their combined utility for patient stratification and prediction, remains underexplored. Objective: This study aimed to establish a multimodal biomarker framework by integrating sNfL with comprehensive volumetric MRI to define neurodegenerative endophenotypes and predict neuroaxonal injury using Bayesian inference and machine learning. Methods: In a cohort of 57 MS patients, sNfL levels were measured using single-molecule array (Simoa) technology. Brain volumes for 42 regions were quantified via automated deep learning segmentation (mdbrain software). We employed (1) Bayesian correlation to quantify evidence for sNfL–volumetric associations; (2) mediation analysis to test whether grey matter atrophy mediates the EDSS–sNfL (Expanded Disability Status Scale) relationship; (3) unsupervised K-means clustering to identify patient subtypes based on combined sNfL–volumetric profiles; and (4) supervised machine learning (Elastic Net and Random Forest regression) to predict sNfL from volumetric features. Results: Bayesian analysis revealed strong evidence linking sNfL to total grey matter volume (r = −0.449, BF₁₀ = 0.022) and lateral ventricular volume (r = 0.349, BF₁₀ = 0.285). Mediation confirmed that grey matter atrophy significantly mediates the relationship between EDSS and sNfL (indirect effect = 0.45, 95% CI [0.20, 0.75]). Unsupervised clustering identified three distinct endophenotypes: “High Neurodegeneration” (elevated sNfL, severe atrophy, high disability), “Moderate Injury,” and “Benign Volumetry” (low sNfL, preserved volumes, mild disability). Supervised models predicted sNfL with high accuracy (R² = 0.65), identifying total grey matter volume, ventricular volume, and age as top predictors. Conclusions: This integrative multi-method analysis demonstrates that sNfL is robustly associated with global grey matter and ventricular volumes, and that these measures define clinically meaningful neurodegenerative subtypes in MS. Machine learning confirms that a concise set of volumetric features can effectively predict neuroaxonal injury. These findings advance a pathobiology-driven subtyping framework and provide a validated model for using routine MRI volumetry to assess neuroaxonal health, with implications for prognosis and personalised therapeutic strategies.

Background: The clinical-radiological paradox in multiple sclerosis (MS) underscores the need for biomarkers that better reflect neurodegenerative pathology. Serum neurofilament light chain (sNfL) is a dynamic marker of neuroaxonal injury, while brain volumetry provides structural assessment of disease impact. However, the precise link between sNfL and regional atrophy patterns, and their combined utility for patient stratification and prediction, remains underexplored. Objective: To establish a multimodal biomarker framework by integrating sNfL with comprehensive volumetric MRI to define neurodegenerative endophenotypes and predict neuroaxonal injury using Bayesian inference and machine learning. Methods: In a cohort of 57 MS patients, sNfL levels were measured using single-molecule array (Simoa) technology. Brain volumes for 42 regions were quantified via automated deep-learning segmentation (mdbrain software). We employed: 1) Bayesian correlation to quantify evidence for sNfL-volumetric associations; 2) mediation analysis to test whether gray matter atrophy mediates the EDSS–sNfL relationship; 3) unsupervised K-means clustering to identify patient subtypes based on combined sNfL-volumetric profiles; and 4) supervised machine learning (Elastic Net and Random Forest regression) to predict sNfL from volumetric features. Results: Bayesian analysis revealed strong evidence linking sNfL to total gray matter volume (r = -0.449, BF₁₀ = 0.022) and lateral ventricular volume (r = 0.349, BF₁₀ = 0.285). Mediation confirmed that gray matter atrophy significantly mediates the relationship between EDSS and sNfL (indirect effect = 0.45, 95% CI [0.20, 0.75]). Unsupervised clustering identified three distinct endophenotypes: "High Neurodegeneration" (elevated sNfL, severe atrophy, high disability), "Moderate Injury," and "Benign Volumetry" (low sNfL, preserved volumes, mild disability). Supervised models predicted sNfL with high accuracy (R² = 0.65), identifying total gray matter volume, ventricular volume, and age as top predictors. Conclusions: This integrative multi-method analysis demonstrates that sNfL is robustly associated with global gray matter and ventricular volumes, and that these measures define clinically meaningful neurodegenerative subtypes in MS. Machine learning confirms that a concise set of volumetric features can effectively predict neuroaxonal injury. These findings advance a pathobiology-driven subtyping framework and provide a validated model for using routine MRI volumetry to assess neuroaxonal health, with implications for prognosis and personalized therapeutic strategies.

The Background: monoclonal antibody therapies represent high-efficacy treatment options for relapsing forms of multiple sclerosis (MS). However, the absence of direct head-to-head randomized trials and the use of heterogeneous comparators across pivotal studies complicate comparative effectiveness assessments. While network meta-analysis (NMA) offers a framework to integrate evidence, the fragmented structure of the available evidence base precludes a conventional NMA with global indirect comparisons and treatment ranking. Methods: A systematic review with qualitative assessment of treatment effects of randomized controlled trials evaluating monoclonal antibody therapies in relapsing forms of multiple sclerosis was conducted. Annualized relapse rate (ARR) was analyzed as the primary outcome, and six-month confirmed disability progression (CDP) as the key secondary outcome. Network geometry and connectivity were explicitly assessed for each outcome prior to quantitative synthesis. Analyses were restricted to comparator-defined connected components of the evidence base, and indirect comparisons across disconnected components were not performed. Sensitivity analyses, including descriptive analyses in progressive multiple sclerosis, were conducted where appropriate. Results: nine randomized controlled trials involving 6762 patients were included. For ARR, the evidence network was fragmented into three disconnected components defined by placebo-, interferon beta-1a-, and teriflunomide-controlled trials. Within connected sub-networks, monoclonal antibody therapies consistently demonstrated substantial reductions in ARR relative to their respective comparators, with overlapping confidence intervals suggesting broadly comparable relapse suppression among high-efficacy agents. For CDP, network connectivity was more limited, and treatment effects were more heterogeneous. Significant reductions in disability progression were observed for some agents within comparator-specific networks, while uncertainty remained for others. Due to network disconnection, no global treatment ranking was performed. Conclusions: this study provides a transparent synthesis of randomized evidence on monoclonal antibody therapies in relapsing MS. By explicitly accounting for network connectivity and comparator heterogeneity, the analysis avoids unsupported indirect comparisons and global treatment hierarchies. The findings support robust relapse suppression across monoclonal antibody therapies within comparable trial frameworks, while highlighting heterogeneity in disability outcomes. These results illustrate the importance of contextual interpretation in comparative effectiveness research in MS.

Background: Disability progression in multiple sclerosis (MS) is increasingly recognized as a consequence of large-scale brain network disruption rather than isolated regional damage. Although diffusion tensor imaging (DTI) is the reference method for assessing structural connectivity, its limited availability restricts widespread clinical application. There is therefore a critical need for alternative approaches capable of capturing network-level alterations using routinely acquired MRI data. Objective: This study aimed to determine whether synthetic structural connectivity matrices derived from standard regional volumetric MRI can capture clinically meaningful network alterations in MS and predict subsequent functional progression, particularly upper limb decline. Methods: Regional brain volumetry was obtained from routine T1-weighted MRI using an automated, clinically approved volumetric pipeline. Synthetic structural connectivity matrices were generated by integrating principles of structural covariance, distance-dependent connectivity, and disease-specific vulnerability patterns. Graph-theoretical network metrics were extracted to characterize global and regional topology. Machine learning models including logistic regression, support vector machines, random forests, and gradient boosting were trained to predict clinical progression defined by worsening on the 9-Hole Peg Test. Dimensionality reduction was performed using principal component analysis, and model performance was evaluated using balanced accuracy, AUC-ROC, and resampling-based validation. Feature importance analyses were conducted to identify network vulnerability patterns. Results: Synthetic connectivity networks exhibited biologically plausible properties, including preserved but attenuated small-world organization. Global efficiency showed a strong inverse correlation with disability severity (EDSS). Patients with clinical progression demonstrated marked reductions in network integration and segregation, alongside increased characteristic path length. Machine learning models achieved robust prediction of upper limb functional decline, with ensemble-based methods performing best (balanced accuracy > 80%, AUC-ROC up to 0.85). A limited subset of connections accounted for a disproportionate share of predictive power, predominantly involving frontoparietal associative networks, thalamocortical pathways, and inter-hemispheric connections. In a longitudinal subset, network-level alterations preceded measurable clinical deterioration by several months. Conclusions: Synthetic structural connectivity derived from routine volumetric MRI captures clinically relevant network-level disruption in multiple sclerosis and enables accurate prediction of functional progression. By bridging network neuroscience with widely accessible imaging data, this framework provides a pragmatic alternative for connectomic analysis when diffusion imaging is unavailable and supports a network-based understanding of disease evolution in MS.

Background: Although thrombophilia represents a major risk factor for adverse maternal outcomes, particularly in the postpartum period, methods for its systematic screening remain costly and limited. This case–control study aimed to evaluate whether routinely available hematological inflammatory indices combined with postpartum uterine ultrasonographic assessment can predict the presence of thrombophilia in peripartum women. Methods: Eighty women with previously diagnosed and treated thrombophilia undergoing cesarean section at term were prospectively enrolled and matched by age and parity with 80 control patients without thrombophilia. Hematological inflammatory markers derived from complete blood counts obtained within 24 h before delivery and the postpartum uterine ultrasonographic score were analyzed. Multivariable logistic regression was performed to identify independent predictors of thrombophilia, and odds ratios (ORs) with 95% confidence intervals (CIs) were calculated. Results: Impaired postpartum uterine involution—defined as a postpartum uterine ultrasonographic score ic—was significantly more frequent in thrombophilia cases than in controls (OR > 1, 95% CI excluding 1; p < 0.05). Thrombophilia patients exhibited significantly higher Neutrophil-to-Lymphocyte and Platelet Ratio and Cumulative Inflammatory Index values when compared with the controls, with both emerging as independent predictors in the multivariable model (OR > 1, 95% CI excluding 1; p < 0.05). The final model demonstrated good discriminative performance, with an overall classification accuracy of 88.6% and excellent specificity for excluding thrombophilia when the postpartum uterine ultrasonographic score was 0. Conclusions: The integration of postpartum uterine ultrasonographic assessment with simple hematological inflammatory indices provides a non-invasive, cost-effective approach for identifying women at increased risk of underlying thrombophilia in the immediate postpartum period. This strategy may support targeted thromboprophylaxis and rationalize the use of specialized thrombophilia testing.

Background/Objectives: Regional atrophy of frontal, temporal, hippocampal and thalamic structures has been linked to depression in multiple sclerosis (MS), but it is unclear whether such regional loss exceeds what global atrophy predicts. This study characterized atrophy in these depression-relevant regions relative to global brain volume and examined its clinical correlates. Methods: In this single-center observational study, 83 patients with MS underwent automated MRI volumetry. Bilateral frontal and temporal percentiles were averaged into a relative frontotemporal atrophy, and hippocampal and thalamic percentiles into a hippocampal–thalamic domain. Relative atrophy was defined as the residual of each domain regressed on total brain volume percentile. Associations with baseline Expanded Disability Status Scale (EDSS), Symbol Digit Modalities Test (SDMT), and lesion count were tested with regression models adjusted for age, sex, and lesion count. Results: The median total brain volume percentile was 6.5 (IQR, 1.3–30.4). Total brain volume explained 48.8% and 32.7% of the variance in the frontotemporal and relative hippocampal–thalamic atrophy, respectively, and the two relative atrophy measures were weakly correlated (r = −0.130, p = 0.241). Greater relative frontotemporal atrophy was associated with higher EDSS (β = 0.024 per percentile point, 95% CI 0.006–0.042; p = 0.011), with a consistent result in ordinal regression; this association did not remain significant after false discovery rate correction across the multivariable models (adjusted p = 0.066). Neither measure was associated with SDMT or lesion count. Conclusions: Atrophy in depression-relevant regions partly departs from global brain atrophy and may relate to disability. Because depression was not assessed, these regional patterns are candidate substrates of affective vulnerability that require testing in prospective studies with standardized depression measures.