Bidirectional two-sample Mendelian randomization analyses support causal relationships between structural and diffusion imaging-derived phenotypes and the risk of major neurodegenerative diseases.

Previous observational investigations suggest that structural and diffusion imaging-derived phenotypes (IDPs) are associated with major neurodegenerative diseases; however, whether these associations are causal remains largely uncertain. Herein we conducted bidirectional two-sample Mendelian randomization analyses to infer the causal relationships between structural and diffusion IDPs and major neurodegenerative diseases using common genetic variants-single nucleotide polymorphism (SNPs) as instrumental variables. Summary statistics of genome-wide association study (GWAS) for structural and diffusion IDPs were obtained from 33,224 individuals in the UK Biobank cohort. Summary statistics of GWAS for seven major neurodegenerative diseases were obtained from the largest GWAS for each disease to date. The forward MR analyses identified significant or suggestively statistical causal effects of genetically predicted three structural IDPs on Alzheimer's disease (AD), frontotemporal dementia (FTD), and multiple sclerosis. For example, the reduction in the surface area of the left superior temporal gyrus was associated with a higher risk of AD. The reverse MR analyses identified significantly or suggestively statistical causal effects of genetically predicted AD, Lewy body dementia (LBD), and FTD on nine structural and diffusion IDPs. For example, LBD was associated with increased mean diffusivity in the right superior longitudinal fasciculus and AD was associated with decreased gray matter volume in the right ventral striatum. Our findings might contribute to shedding light on the prediction and therapeutic intervention for the major neurodegenerative diseases at the neuroimaging level.

Improving multiple sclerosis lesion segmentation across clinical sites: A federated learning approach with noise-resilient training.

Accurately measuring the evolution of Multiple Sclerosis (MS) with magnetic resonance imaging (MRI) critically informs understanding of disease progression and helps to direct therapeutic strategy. Deep learning models have shown promise for automatically segmenting MS lesions, but the scarcity of accurately annotated data hinders progress in this area. Obtaining sufficient data from a single clinical site is challenging and does not address the heterogeneous need for model robustness. Conversely, the collection of data from multiple sites introduces data privacy concerns and potential label noise due to varying annotation standards. To address this dilemma, we explore the use of the federated learning framework while considering label noise. Our approach enables collaboration among multiple clinical sites without compromising data privacy under a federated learning paradigm that incorporates a noise-robust training strategy based on label correction. Specifically, we introduce a Decoupled Hard Label Correction (DHLC) strategy that considers the imbalanced distribution and fuzzy boundaries of MS lesions, enabling the correction of false annotations based on prediction confidence. We also introduce a Centrally Enhanced Label Correction (CELC) strategy, which leverages the aggregated central model as a correction teacher for all sites, enhancing the reliability of the correction process. Extensive experiments conducted on two multi-site datasets demonstrate the effectiveness and robustness of our proposed methods, indicating their potential for clinical applications in multi-site collaborations to train better deep learning models with lower cost in data collection and annotation.

Diagnostic effectiveness of deep learning-based MRI in predicting multiple sclerosis: A meta-analysis.

OBJECTIVES: The brain and spinal cord, constituting the central nervous system (CNS), could be impacted by an inflammatory disease known as multiple sclerosis (MS). The convolutional neural networks (CNN), a machine learning method, can detect lesions early by learning patterns on brain magnetic resonance image (MRI). We performed this study to investigate the diagnostic performance of CNN based MRI in the identification, classification, and segmentation of MS lesions. METHODS: PubMed, Web of Science, Embase, the Cochrane Library, CINAHL, and Google Scholar were used to retrieve papers reporting the use of CNN based MRI in MS diagnosis. The accuracy, the specificity, the sensitivity, and the Dice Similarity Coefficient (DSC) were evaluated in this study. RESULTS: In total, 2174 studies were identified and only 15 articles met the inclusion criteria. The 2D-3D CNN presented a high accuracy (98.81, 95% CI: 98.50-99.13), sensitivity (98.76, 95% CI: 98.42-99.10), and specificity (98.67, 95% CI: 98.22-99.12) in the identification of MS lesions. Regarding classification, the overall accuracy rate was significantly high (91.38, 95% CI: 83.23-99.54). A DSC rate of 63.78 (95% CI: 58.29-69.27) showed that 2D-3D CNN-based MRI performed highly in the segmentation of MS lesions. Sensitivity analysis showed that the results are consistent, indicating that this study is robust. CONCLUSION: This metanalysis revealed that 2D-3D CNN based MRI is an automated system that has high diagnostic performance and can promptly and effectively predict the disease.

Radiologic Lag and Brain MRI Lesion Dynamics During Attacks in MOG Antibody-Associated Disease.

BACKGROUND AND OBJECTIVES: Knowledge of the evolution of CNS demyelinating lesions within attacks could assist diagnosis. We evaluated intra-attack lesion dynamics in patients with myelin oligodendrocyte glycoprotein antibody-associated disease (MOGAD) vs multiple sclerosis (MS) and aquaporin-4 antibody seropositive neuromyelitis optica spectrum disorder (AQP4+NMOSD). METHODS: This retrospective observational multicenter study included consecutive patients from Mayo Clinic (USA) and Great Ormond Street Hospital for Children (UK). Inclusion criteria were as follows: (1) MOGAD, MS, or AQP4+NMOSD diagnosis; (2) availability of ≥2 brain MRIs (within 30 days of attack onset); and (3) brain involvement (i.e., ≥1 T2 lesion) on ≥1 brain MRI. The initial and subsequent brain MRIs within a single attack were evaluated for the following: new T2 lesions(s); resolved T2 lesion(s); both; or no change. This was compared between MOGAD, MS, and AQP4+NMOSD attacks. We used the Mann-Whitney U test and χ2/Fisher exact test for statistical analysis. RESULTS: Our cohort included 55 patients with MOGAD (median age, 14 years; interquartile range [IQR] 5-34; female sex, 29 [53%]) for a total of 58 attacks. The comparison groups included 38 patients with MS, and 19 with AQP4+NMOSD. In MOGAD, the initial brain MRI (median of 5 days from onset [IQR 3-9]) was normal in 6/58 (10%) attacks despite cerebral symptoms (i.e., radiologic lag). The commonest reason for repeat MRI was clinical worsening or no improvement (33/56 [59%] attacks with details available). When compared with the first MRI, the second intra-attack MRI (median of 8 days from initial scan [IQR 5-13]) showed the following: new T2 lesion(s) 27/58 (47%); stability 24/58 (41%); resolution of T2 lesion(s) 4/58 (7%); or both new and resolved T2 lesions 3/58 (5%). Findings were similar between children and adults. Steroid treatment was associated with resolution of ≥1 T2 lesion (6/28 [21%] vs 1/30 [3%], p = 0.048) and reduced the likelihood of new T2 lesions (9/28 vs 18/30, p = 0.03). Intra-attack MRI changes favored MOGAD (34/58 [59%]) over MS (10/38 [26%], p = 0.002) and AQP4+NMOSD (4/19 [21%], p = 0.007). Resolution of ≥1 T2 lesions was exclusive to MOGAD (7/58 [12%]). DISCUSSION: Radiologic lag is common within MOGAD attacks. Dynamic imaging with frequent appearance and occasional disappearance of lesions within a single attack suggest MOGAD diagnosis over MS and AQP4+NMOSD. These findings have implications for clinical practice, clinical trial attack adjudication, and understanding of MOGAD pathogenesis.

Central Vein Sign, Cortical Lesions, and Paramagnetic Rim Lesions for the Diagnostic and Prognostic Workup of Multiple Sclerosis.

BACKGROUND AND OBJECTIVES: The diagnosis of multiple sclerosis (MS) can be challenging in clinical practice because MS presentation can be atypical and mimicked by other diseases. We evaluated the diagnostic performance, alone or in combination, of the central vein sign (CVS), paramagnetic rim lesion (PRL), and cortical lesion (CL), as well as their association with clinical outcomes. METHODS: In this multicenter observational study, we first conducted a cross-sectional analysis of the CVS (proportion of CVS-positive lesions or simplified determination of CVS in 3/6 lesions-Select3*/Select6*), PRL, and CL in MS and non-MS cases on 3T-MRI brain images, including 3D T2-FLAIR, T2*-echo-planar imaging magnitude and phase, double inversion recovery, and magnetization prepared rapid gradient echo image sequences. Then, we longitudinally analyzed the progression independent of relapse and MRI activity (PIRA) in MS cases over the 2 years after study entry. Receiver operating characteristic curves were used to test diagnostic performance and regression models to predict diagnosis and clinical outcomes. RESULTS: The presence of ≥41% CVS-positive lesions/≥1 CL/≥1 PRL (optimal cutoffs) had 96%/90%/93% specificity, 97%/84%/60% sensitivity, and 0.99/0.90/0.77 area under the curve (AUC), respectively, to distinguish MS (n = 185) from non-MS (n = 100) cases. The Select3*/Select6* algorithms showed 93%/95% specificity, 97%/89% sensitivity, and 0.95/0.92 AUC. The combination of CVS, CL, and PRL improved the diagnostic performance, especially when Select3*/Select6* were used (93%/94% specificity, 98%/96% sensitivity, 0.99/0.98 AUC; p = 0.002/p < 0.001). In MS cases (n = 185), both CL and PRL were associated with higher MS disability and severity. Longitudinal analysis (n = 61) showed that MS cases with >4 PRL at baseline were more likely to experience PIRA at 2-year follow-up (odds ratio 17.0, 95% confidence interval: 2.1-138.5; p = 0.008), whereas no association was observed between other baseline MRI measures and PIRA, including the number of CL. DISCUSSION: The combination of CVS, CL, and PRL can improve MS differential diagnosis. CL and PRL also correlated with clinical measures of poor prognosis, with PRL being a predictor of disability accrual independent of clinical/MRI activity.

Artificial intelligence applied to MRI data to tackle key challenges in multiple sclerosis.

Artificial intelligence (AI) is the branch of science aiming at creating algorithms able to carry out tasks that typically require human intelligence. In medicine, there has been a tremendous increase in AI applications thanks to increasingly powerful computers and the emergence of big data repositories. Multiple sclerosis (MS) is a chronic autoimmune condition affecting the central nervous system with a complex pathogenesis, a challenging diagnostic process strongly relying on magnetic resonance imaging (MRI) and a high and largely unexplained variability across patients. Therefore, AI applications in MS have the great potential of helping us better support the diagnosis, find markers for prognosis to eventually design more powerful randomised clinical trials and improve patient management in clinical practice and eventually understand the mechanisms of the disease. This topical review aims to summarise the recent advances in AI applied to MRI data in MS to illustrate its achievements, limitations and future directions.

Synaptic Protein Loss in Extracellular Vesicles Reflects Brain and Retinal Atrophy in People With Multiple Sclerosis.

OBJECTIVES: To assess whether the rate of change in synaptic proteins isolated from neuronally enriched extracellular vesicles (NEVs) is associated with brain and retinal atrophy in people with multiple sclerosis (MS). METHODS: People with MS were followed with serial blood draws, MRI (MRI), and optical coherence tomography (OCT) scans. NEVs were immunocaptured from plasma, and synaptopodin and synaptophysin proteins were measured using ELISA. Subject-specific rates of change in synaptic proteins, as well as brain and retinal atrophy, were determined and correlated. RESULTS: A total of 50 people with MS were included, 46 of whom had MRI and 45 had OCT serially. The rate of change in NEV synaptopodin was associated with whole brain (rho = 0.31; p = 0.04), cortical gray matter (rho = 0.34; p = 0.03), peripapillary retinal nerve fiber layer (rho = 0.37; p = 0.01), and ganglion cell/inner plexiform layer (rho = 0.41; p = 0.006) atrophy. The rate of change in NEV synaptophysin was also correlated with whole brain (rho = 0.31; p = 0.04) and cortical gray matter (rho = 0.31; p = 0.049) atrophy. DISCUSSION: NEV-derived synaptic proteins likely reflect neurodegeneration and may provide additional circulating biomarkers for disease progression in MS.

AI-based model for automatic identification of multiple sclerosis based on enhanced sea-horse optimizer and MRI scans.

This study aims to develop an AI-enhanced methodology for the expedited and accurate diagnosis of Multiple Sclerosis (MS), a chronic disease affecting the central nervous system leading to progressive impairment. Traditional diagnostic methods are slow and require substantial expertise, underscoring the need for innovative solutions. Our approach involves two phases: initially, extracting features from brain MRI images using first-order histograms, the gray level co-occurrence matrix, and local binary patterns. A unique feature selection technique combining the Sine Cosine Algorithm with the Sea-horse Optimizer is then employed to identify the most significant features. Utilizing the eHealth lab dataset, which includes images from 38 MS patients (mean age 34.1 ± 10.5 years; 17 males, 21 females) and matched healthy controls, our model achieved a remarkable 97.97% detection accuracy using the k-nearest neighbors classifier. Further validation on a larger dataset containing 262 MS cases (199 females, 63 males; mean age 31.26 ± 10.34 years) and 163 healthy individuals (109 females, 54 males; mean age 32.35 ± 10.30 years) demonstrated a 92.94% accuracy for FLAIR images and 91.25% for T2-weighted images with the Random Forest classifier, outperforming existing MS detection methods. These results highlight the potential of the proposed technique as a clinical decision-making tool for the early identification and management of MS.

Inflammatory and neurodegenerative serum protein biomarkers increase sensitivity to detect clinical and radiographic disease activity in multiple sclerosis.

The multifaceted nature of multiple sclerosis requires quantitative biomarkers that can provide insights related to diverse physiological pathways. To this end, proteomic analysis of deeply-phenotyped serum samples, biological pathway modeling, and network analysis were performed to elucidate inflammatory and neurodegenerative processes, identifying sensitive biomarkers of multiple sclerosis disease activity. Here, we evaluated the concentrations of > 1400 serum proteins in 630 samples from three multiple sclerosis cohorts for association with clinical and radiographic new disease activity. Twenty proteins were associated with increased clinical and radiographic multiple sclerosis disease activity for inclusion in a custom assay panel. Serum neurofilament light chain showed the strongest univariate correlation with gadolinium lesion activity, clinical relapse status, and annualized relapse rate. Multivariate modeling outperformed univariate for all endpoints. A comprehensive biomarker panel including the twenty proteins identified in this study could serve to characterize disease activity for a patient with multiple sclerosis.

Improving explanation of motor disability with diffusion-based graph metrics at onset of the first demyelinating event.

BACKGROUND: Conventional magnetic resonance imaging (MRI) does not account for all disability in multiple sclerosis. OBJECTIVE: The objective was to assess the ability of graph metrics from diffusion-based structural connectomes to explain motor function beyond conventional MRI in early demyelinating clinically isolated syndrome (CIS). METHODS: A total of 73 people with CIS underwent conventional MRI, diffusion-weighted imaging and clinical assessment within 3 months from onset. A total of 28 healthy controls underwent MRI. Structural connectomes were produced. Differences between patients and controls were explored; clinical associations were assessed in patients. Linear regression models were compared to establish relevance of graph metrics over conventional MRI. RESULTS: Local efficiency (p = 0.045), clustering (p = 0.034) and transitivity (p = 0.036) were reduced in patients. Higher assortativity was associated with higher Expanded Disability Status Scale (EDSS) (ß = 74.9, p = 0.026) scores. Faster timed 25-foot walk (T25FW) was associated with higher assortativity (ß = 5.39, p = 0.026), local efficiency (ß = 27.1, p = 0.041) and clustering (ß = 36.1, p = 0.032) and lower small-worldness (ß = -3.27, p = 0.015). Adding graph metrics to conventional MRI improved EDSS (p = 0.045, ΔR2 = 4) and T25FW (p < 0.001, ΔR2 = 13.6) prediction. CONCLUSION: Graph metrics are relevant early in demyelination. They show differences between patients and controls and have relationships with clinical outcomes. Segregation (local efficiency, clustering, transitivity) was particularly relevant. Combining graph metrics with conventional MRI better explained disability.

[Optical coherence tomography angiography in the diagnosis of multiple sclerosis]. / Opticheskaya kogerentnaya tomografiya-angiografiya v diagnostike rasseyannogo skleroza.

PURPOSE: This study analyzes the main changes in retinal microcirculation in patients with multiple sclerosis (MS) and their relationship with the type of disease course. MATERIAL AND METHODS: 159 patients (318 eyes) were examined. The groups were formed according to the type of course and duration of MS: group 1 - 37 patients (74 eyes; 23.27%) with relapsing-remitting MS (RRMS) less than 1 year; group 2 - 47 patients (94 eyes; 29.56%) with RRMS from 1 year to 10 years; group 3 - 44 patients (86 eyes; 27.05%) with RRMS >10 years; group 4 - 32 patients (64 eyes; 20.12%) with secondary progressive MS (SPMS). Subgroups A and B were allocated within each group depending on the absence or presence of optic neuritis (ON). Patients underwent standard ophthalmological examination, including optical coherence tomography angiography (OCTA). RESULTS: A decrease in the vessel density (wiVD) and perfusion density (wiPD) in the macular and peripapillary regions was revealed, progressing with the duration of the disease and with its transition to the progressive type. The minimum values were observed in patients with SPMS (group 4), with the most pronounced in the subgroup with ON (wiVD = 16.06±3.65 mm/mm2, wiPD = 39.38±9.46%, ppwiPD = 44.06±3.09%, ppwiF = 0.41±0.05). CONCLUSION: OCTA provides the ability to detect subclinical vascular changes and can be considered a comprehensive, reliable method for early diagnosis and monitoring of MS progression.

Predictors of multiple sclerosis progression: A systematic review of conventional magnetic resonance imaging studies.

INTRODUCTION: Multiple Sclerosis (MS) is a chronic neurodegenerative disorder that affects the central nervous system (CNS) and results in progressive clinical disability and cognitive decline. Currently, there are no specific imaging parameters available for the prediction of longitudinal disability in MS patients. Magnetic resonance imaging (MRI) has linked imaging anomalies to clinical and cognitive deficits in MS. In this study, we aimed to evaluate the effectiveness of MRI in predicting disability, clinical progression, and cognitive decline in MS. METHODS: In this study, according to PRISMA guidelines, we comprehensively searched the Web of Science, PubMed, and Embase databases to identify pertinent articles that employed conventional MRI in the context of Relapsing-Remitting and progressive forms of MS. Following a rigorous screening process, studies that met the predefined inclusion criteria were selected for data extraction and evaluated for potential sources of bias. RESULTS: A total of 3028 records were retrieved from database searching. After a rigorous screening, 53 records met the criteria and were included in this study. Lesions and alterations in CNS structures like white matter, gray matter, corpus callosum, thalamus, and spinal cord, may be used to anticipate disability progression. Several prognostic factors associated with the progression of MS, including presence of cortical lesions, changes in gray matter volume, whole brain atrophy, the corpus callosum index, alterations in thalamic volume, and lesions or alterations in cross-sectional area of the spinal cord. For cognitive impairment in MS patients, reliable predictors include cortical gray matter volume, brain atrophy, lesion characteristics (T2-lesion load, temporal, frontal, and cerebellar lesions), white matter lesion volume, thalamic volume, and corpus callosum density. CONCLUSION: This study indicates that MRI can be used to predict the cognitive decline, disability progression, and disease progression in MS patients over time.

Hippocampal subfields and thalamic nuclei associations with clinical outcomes in multiple sclerosis: An ultrahigh field MRI study.

BACKGROUND: Previous studies have shown that thalamic and hippocampal neurodegeneration is associated with clinical decline in Multiple Sclerosis (MS). However, contributions of the specific thalamic nuclei and hippocampal subfields require further examination. OBJECTIVE: Using 7 Tesla (7T) magnetic resonance imaging (MRI), we investigated the cross-sectional associations between functionally grouped thalamic nuclei and hippocampal subfields volumes and T1 relaxation times (T1-RT) and subsequent clinical outcomes in MS. METHODS: High-resolution T1-weighted and T2-weighted images were acquired at 7T (n=31), preprocessed, and segmented using the Thalamus Optimized Multi Atlas Segmentation (THOMAS, for thalamic nuclei) and the Automatic Segmentation of Hippocampal Subfields (ASHS, for hippocampal subfields) packages. We calculated Pearson correlations between hippocampal subfields and thalamic nuclei volumes and T1-RT and subsequent multi-modal rater-determined and patient-reported clinical outcomes (∼2.5 years after imaging acquisition), correcting for confounders and multiple tests. RESULTS: Smaller volume bilaterally in the anterior thalamus region correlated with worse performance in gait function, as measured by the Patient Determined Disease Steps (PDDS). Additionally, larger volume in most functional groups of thalamic nuclei correlated with better visual information processing and cognitive function, as measured by the Symbol Digit Modalities Test (SDMT). In bilateral medial and left posterior thalamic regions, there was an inverse association between volumes and T1-RT, potentially indicating higher tissue degeneration in these regions. We also observed marginal associations between the right hippocampal subfields (both volumes and T1-RT) and subsequent clinical outcomes, though they did not survive correction for multiple testing. CONCLUSION: Ultrahigh field MRI identified markers of structural damage in the thalamic nuclei associated with subsequently worse clinical outcomes in individuals with MS. Longitudinal studies will enable better understanding of the role of microstructural integrity in these brain regions in influencing MS outcomes.

Functional gradients reveal cortical hierarchy changes in multiple sclerosis.

Functional gradient (FG) analysis represents an increasingly popular methodological perspective for investigating brain hierarchical organization but whether and how network hierarchy changes concomitant with functional connectivity alterations in multiple sclerosis (MS) has remained elusive. Here, we analyzed FG components to uncover possible alterations in cortical hierarchy using resting-state functional MRI (rs-fMRI) data acquired in 122 MS patients and 97 healthy control (HC) subjects. Cortical hierarchy was assessed by deriving regional FG scores from rs-fMRI connectivity matrices using a functional parcellation of the cerebral cortex. The FG analysis identified a primary (visual-to-sensorimotor) and a secondary (sensory-to-transmodal) component. Results showed a significant alteration in cortical hierarchy as indexed by regional changes in FG scores in MS patients within the sensorimotor network and a compression (i.e., a reduced standard deviation across all cortical parcels) of the sensory-transmodal gradient axis, suggesting disrupted segregation between sensory and cognitive processing. Moreover, FG scores within limbic and default mode networks were significantly correlated ( ρ = 0.30 $$ \rho =0.30 $$ , p < .005 after Bonferroni correction for both) with the symbol digit modality test (SDMT) score, a measure of information processing speed commonly used in MS neuropsychological assessments. Finally, leveraging supervised machine learning, we tested the predictive value of network-level FG features, highlighting the prominent role of the FG scores within the default mode network in the accurate prediction of SDMT scores in MS patients (average mean absolute error of 1.22 ± 0.07 points on a hold-out set of 24 patients). Our work provides a comprehensive evaluation of FG alterations in MS, shedding light on the hierarchical organization of the MS brain and suggesting that FG connectivity analysis can be regarded as a valuable approach in rs-fMRI studies across different MS populations.

Temporal Relationship Between Serum Neurofilament Light Chain and Radiologic Disease Activity in Patients With Multiple Sclerosis.

BACKGROUND AND OBJECTIVES: Serum neurofilament light chain (sNfL) levels correlate with multiple sclerosis (MS) disease activity, but the dynamics of this correlation are unknown. We evaluated the relationship between sNfL levels and radiologic MS disease activity through monthly assessments during the 24-week natalizumab treatment interruption period in RESTORE (NCT01071083). METHODS: In the RESTORE trial, participants with relapsing forms of MS who had received natalizumab for ≥12 months were randomized to either continue or stop natalizumab and followed with MRI and blood draws every 4 weeks to week 28 and again at week 52 The sNfL was measured, and its dynamics were correlated with the development of gadolinium-enhancing (Gd+) lesions. Log-linear trend in sNfL levels were modeled longitudinally using generalized estimating equations with robust variance estimator from baseline to week 28. RESULTS: Of 175 patients enrolled in RESTORE, 166 had serum samples for analysis. Participants with Gd+ lesions were younger (37.7 vs 43.1, p = 0.001) and had lower Expanded Disability Status Scale scores at baseline (2.7 vs 3.4, p = 0.017) than participants without Gd+ lesions. sNfL levels increased in participants with Gd+ lesions (n = 65) compared with those without (n = 101, mean change from baseline to maximum sNfL value, 12.1 vs 3.2 pg/mL, respectively; p = 0.003). As the number of Gd+ lesions increased, peak median sNfL change also increased by 1.4, 3.0, 4.3, and 19.6 pg/mL in the Gd+ lesion groups of 1 (n = 12), 2-3 (n = 18), 4-9 (n = 21), and ≥10 (n = 14) lesions, respectively. However, 46 of 65 (71%) participants with Gd+ lesions did not increase above the 95th percentile threshold of the group without Gd+ lesions. The initial increase of sNfL typically trailed the first observation of Gd+ lesions, and the peak increase in sNfL was a median [interquartile range] of 8 [0, 12] weeks after the first appearance of the Gd+ lesion. DISCUSSION: Although sNfL correlated with the presence of Gd+ lesions, most participants with Gd+ lesions did not have elevations in sNfL levels. These observations have implications for the use and interpretation of sNfL as a biomarker for monitoring MS disease activity in controlled trials and clinical practice.

Modeling and simulation for prediction of multiple sclerosis progression.

In light of extensive work that has created a wide range of techniques for predicting the course of multiple sclerosis (MS) disease, this paper attempts to provide an overview of these approaches and put forth an alternative way to predict the disease progression. For this purpose, the existing methods for estimating and predicting the course of the disease have been categorized into clinical, radiological, biological, and computational or artificial intelligence-based markers. Weighing the weaknesses and strengths of these prognostic groups is a profound method that is yet in need and works directly at the level of diseased connectivity. Therefore, we propose using the computational models in combination with established connectomes as a predictive tool for MS disease trajectories. The fundamental conduction-based Hodgkin-Huxley model emerged as promising from examining these studies. The advantage of the Hodgkin-Huxley model is that certain properties of connectomes, such as neuronal connection weights, spatial distances, and adjustments of signal transmission rates, can be taken into account. It is precisely these properties that are particularly altered in MS and that have strong implications for processing, transmission, and interactions of neuronal signaling patterns. The Hodgkin-Huxley (HH) equations as a point-neuron model are used for signal propagation inside a small network. The objective is to change the conduction parameter of the neuron model, replicate the changes in myelin properties in MS and observe the dynamics of the signal propagation across the network. The model is initially validated for different lengths, conduction values, and connection weights through three nodal connections. Later, these individual factors are incorporated into a small network and simulated to mimic the condition of MS. The signal propagation pattern is observed after inducing changes in conduction parameters at certain nodes in the network and compared against a control model pattern obtained before the changes are applied to the network. The signal propagation pattern varies as expected by adapting to the input conditions. Similarly, when the model is applied to a connectome, the pattern changes could give an insight into disease progression. This approach has opened up a new path to explore the progression of the disease in MS. The work is in its preliminary state, but with a future vision to apply this method in a connectome, providing a better clinical tool.

Structural and functional correlates of disability, motor and cognitive performances in multiple sclerosis: Focus on the globus pallidus.

OBJECTIVES: To explore structural and functional alterations of external (GPe) and internal (GPi) globus pallidus in people with multiple sclerosis (pwMS) compared to healthy controls (HC) and analyze their relationship with measures of clinical disability, motor and cognitive impairment. METHODS: Sixty pwMS and 30 HC comparable for age and sex underwent 3.0T MRI, including conventional, diffusion tensor MRI and resting state (RS) functional MRI. Expanded Disability Status Scale (EDSS) scores were rated and timed 25-foot walk (T25FW) test, nine-hole peg test (9HPT), and paced auditory serial addition test (PASAT) were administered. Two operators segmented the GP into GPe and GPi. Volumes, T1/T2 ratio, diffusivity indices and seed-based RS functional connectivity (FC) of the GP and its components were assessed. RESULTS: PwMS had no atrophy or altered diffusivity measures of the GP. Compared to HC, pwMS had higher T1/T2 ratio in both GP regions, which correlated with EDSS score (r = 0.26-0.39, p = 0.01-0.05). RS FC analysis highlighted component-specific functional alterations in pwMS: the GPe had decreased RS FC with fronto-parietal cortices, whereas the GPi had decreased intra-GP RS FC and increased RS FC with the thalamus. Worse EDSS, 9HPT, T25FW and PASAT scores were associated with GP RS FC modifications (r=-0.51‒0.51, p < 0.001). CONCLUSIONS: Structural GP involvement in MS was homogeneous across its portions. Increased T1/T2 ratio values, possibly representing iron accumulation, were related to more severe disability. RS FC alterations of the GPe and GPi were consistent with their roles within the basal ganglia network and correlated with worse functional status, suggesting less efficient communication between structures.

Artificial intelligence in multiple sclerosis management: Challenges in a new era.

Multiple sclerosis poses diagnostic and therapeutic challenges for healthcare professionals, with a high risk of misdiagnosis and difficulties in assessing therapeutic effectiveness. Artificial intelligence, particularly machine learning and deep neural networks, emerges as a promising tool to address these challenges. These technologies have the capability to analyze a wide range of data, from magnetic resonance imaging to genetic information, to provide more accurate diagnoses, classify multiple sclerosis subtypes, and predict disease progression and treatment response with extraordinary precision. However, their implementation raises ethical dilemmas, such as accountability in case of errors and the risk of excessive reliance on healthcare personnel. That said, this manuscript aims to urge healthcare professionals dedicated to the care and research of multiple sclerosis patients to recognize artificial intelligence as a valuable and complementary resource in their clinical practice. It also seeks to emphasize the importance of integrating this type of technology safely and responsibly, thereby ensuring the ethics and welfare of patients.

Event-related desynchronization and synchronization in multiple sclerosis.

BACKGROUND: Motor preparation and execution can be impaired in patients with multiple sclerosis (pwMS). These neural processes can be assessed using electroencephalography (EEG). During a self-paced movement, EEG signal amplitude decreases before movement (event-related desynchronization, ERD) and increases after movement (event-related synchronization, ERS). OBJECTIVE: To reappraise ERD/ERS changes in pwMS compared to healthy controls (HC). METHODS: This single-center study included 13 pwMS and 10 sex/age-matched HC. 60-channel EEG was recorded during two self-paced movements of the right hand: a simple index finger extension task and a more complex finger tapping task. Clinical variables included MS type, sex, age, disease duration, disability, grip strength, fatigue and attentional performance. EEG variables included ERD and ERS onset latency, duration, and amplitude determined using two methods of signal analyses (based on visual or automated determination) in the alpha and beta frequency bands in five cortical regions: right and left frontocentral and centroparietal regions and a midline region. Neuroimaging variables included the volumes of four deep brain structures (thalamus, putamen, pallidum and caudate nucleus) and the relative lesion load. RESULTS: ERD/ERS changes in pwMS compared to HC were observed only in the beta band. In pwMS, beta-ERD had a delayed onset in the midline and right parietocentral regions and a shortened duration or increased amplitude in the parietocentral region; beta-ERS had a shorter duration, delayed onset, or reduced amplitude in the left parieto/frontocentral region. In addition, pwMS with a more delayed beta-ERD in the midline region had less impaired executive functions but increased caudate nuclei volume, while pwMS with a more delayed beta-ERS in the parietocentral region contralateral to the movement had less fatigue but increased thalami volume. CONCLUSION: This study confirms an alteration of movement preparation and execution in pwMS, mainly characterized by a delayed cortical activation (ERD) and a delayed and reduced post-movement inhibition (ERS) in the beta band. Compensatory mechanisms could be involved in these changes, associating more preserved clinical performance and overactivation of deep brain structures.

Radiologically isolated syndrome in the spectrum of multiple sclerosis.

The radiologically isolated syndrome (RIS) currently represents the earliest detectable preclinical phase of multiple sclerosis (MS). Remarkable advancements have been recently made, including the identification of risk factors for disease evolution, revisions to the existing 2009 RIS criteria, and our understanding of the impact of early disease-modifying therapy use in the prevention/delay of symptomatic MS from two randomized clinical trials. Here, we discuss RIS in the context of the spectrum of MS, implications in the clinical management of individuals, and provide insights into future opportunities and challenges given the anticipated inclusion of asymptomatic MS in the formal definition of MS.