Annals of Clinical and Translational Neurology最新文献

Objective: To elucidate the features of plexin D1-immunoglobulin (Ig)G-associated neuropathic pain and its relationship to atopic myelitis (AM) in a nationwide Japanese survey.

Methods: A preliminary survey questionnaire was sent to 1574 selected departments (neurology and pediatrics/pediatric neurology) to explore the numbers of AM and plexin D1-IgG-positive patients between 2018 and 2022. A secondary survey collected detailed patient data via a questionnaire.

Results: In the preliminary survey, 987 (62.7%) institutions responded, reporting 87 AM patients (49 women) and 11 plexin D1-IgG-positive non-AM patients (8 women). The secondary survey collected 71 AM (plexin D1-IgG-positive: 6/31) and 11 plexin D1-IgG-positive non-AM patients (83.7% recovery rate). In AM, paresthesia/dysesthesia was most frequently experienced (> 90%), followed by pain (> 70%). The underlying diseases in 17 plexin D1-IgG-positive patients, all of whom had neuropathic pain, were AM and small fiber neuropathy in 6 each, neuromyelitis optica spectrum disorder with aquaporin-4-IgG in 2, and painful trigeminal neuropathy, erythromelalgia, and multiple sclerosis in 1 each. When 14 plexin D1-IgG-positive patients (excluding 3 patients with established demyelinating diseases) were compared with 25 plexin D1-IgG-negative AM patients, onset ≥ 50 years old, pain at onset, and allodynia/erythromelalgia/facial pain during the entire disease course were significantly more common in the plexin D1-IgG-positive group. Conversely, atopic disorders and hyperIgEemia were associated with plexin D1-IgG-negative AM but not plexin D1-IgG-positive patients.

Interpretation: Both AM and plexin D1-IgG-positive patients present long-standing neuropathic pain, whereas plexin D1-IgG is particularly associated with aged-onset neuropathic pain, allodynia, erythromelalgia, and facial pain, but not atopy.

Background: Inflammation is a critical risk factor for poor outcomes in cerebral infarction. Prior studies focused primarily on baseline inflammation status, neglecting dynamic longitudinal changes. We try to investigate the association between immune-inflammation status alterations and stroke prognosis, and evaluated three systemic biomarkers' predictive efficacy.

Methods: In this prospective cohort study of ischemic stroke patients, C-reactive protein-albumin-lymphocyte (CALLY), Systemic Immune-Inflammation Index (SII), and Systemic Inflammation Response Index (SIRI) were assessed. Immune-inflammation changes were measured between baseline and a second survey. The primary outcome was death or major disability (modified Rankin Scale score ≥ 3) after stroke onset. Prognostic performance for poor outcomes was evaluated at baseline. The association was calculated by Cox proportional hazard models.

Results: Elevated baseline CALLY correlated with reduced poor outcome risk (OR 0.33, 95% CI 0.20-0.53), while higher SII and SIRI indicated increased risk (SII OR 2.54, 95% CI 1.61-4.06; SIRI OR 2.37, 95% CI 1.47-3.87). Adding these markers to conventional risk factors significantly improved prediction, with CALLY showing the greatest enhancement (C-statistic 0.882 vs. 0.862; p = 0.004; NRI = 29.68%; IDI = 0.39%). Compared to stable mild status, progression to severe status increased poor outcome risk, while recovery to mild status reduced risk (CALLY OR 14.2, 95% CI 2.31-64.27; SII OR 1.27, 95% CI 1.01-7.10; SIRI OR 4.10, 95% CI 1.61-10.71). High inflammatory indices and significant changes predicted poor outcomes.

Conclusions: CALLY demonstrated superior predictive ability for ischemic stroke outcomes versus SII/SIRI. Both baseline levels and dynamic changes in immune-inflammation status significantly correlated with prognosis, suggesting these biomarkers could valuably predict outcomes and guide intervention.

Objective: Peripheral neuropathies contribute to patient disability but may be diagnosed late or missed altogether due to late referral, limitation of current diagnostic methods and lack of specialized testing facilities. To address this clinical gap, we developed NeuropathAI, an interpretable deep learning-based multiclass classification system for rapid, automated diagnosis and differentiation of 88 patients with diabetic peripheral neuropathy (DPN), chemotherapy-induced peripheral neuropathy (CIPN), chronic inflammatory demyelinating polyneuropathy (CIDP), and human immunodeficiency virus-associated sensory neuropathy (HIV-SN).

Methods: A deep learning-based multiclass system was developed to analyze corneal nerve images. These images were preprocessed to train and validate the proposed model and the diagnostic utility was evaluated from the accuracy, F1-score and area under the curve to derive sensitivity, specificity and precision.

Results: NeuropathAI achieved excellent results: AUC-96.75%, sensitivity-83.87%, specificity-95.07%, and demonstrated excellent discrimination for CIDP, CIPN, HIV-SN and DPN with one-vs-all AUC scores of 97%, 93.1%, 99.7% and 96.9%, respectively. Explainability visualization through heatmaps demonstrated that regions of decision making by the model localized to areas with nerve fiber loss, enhancing interpretability.

Interpretation: NeuropathAI achieved rapid and accurate diagnosis of four of the most prevalent peripheral neuropathies globally, underscoring the potential of artificial intelligence-driven corneal image analysis for the rapid diagnosis and differentiation of peripheral neuropathies.

Objective: To describe the use of central stimulants and amantadine for fatigue in MS and evaluate a potential association with reduced work loss in people with MS.

Methods: We conducted a nationwide, matched, register-based cohort study in Sweden (2006 to 2023) using national registers with prospective data collection. We included individuals with MS, identified via the Swedish MS register or ≥ 3 MS diagnoses in the National Patient Register, who initiated treatment with modafinil, amantadine, or central stimulants for ADHD (ADHD-Drugs), along with untreated individuals matched on modafinil start date, age, sex, time since MS diagnosis, and prior-year work loss. The main outcome was monthly work loss, defined as the sum of net days on sick leave, disability pension, and activity compensation.

Results: We identified 2162 new modafinil users, 462 amantadine, 424 ADHD drugs, and 9762 untreated. All cohorts showed increasing work loss before index, followed by no change in work loss over the subsequent 24 months. Modafinil users had a significantly greater attenuation, but with low effect size, of the increasing trajectory of average monthly work loss than untreated (-0.17 days; 95% CI: -0.22, -0.12), with no significant differences between modafinil and other treated groups. Modafinil was the most prescribed treatment, with 1-year prevalence of 8.5% in 2006 and 7% in 2023.

Interpretation: A potential minor treatment benefit is suggested by the statistically significant, small attenuation of worsening work loss following fatigue treatment initiation compared with untreated people with MS. No differences in treatment benefit were observed across fatigue treatments.

We present the case of a 28-year-old right-handed woman with medically refractory focal epilepsy. Her seizure semiology and electroencephalography (EEG) indicated a seizure onset zone in the right central-parietal area. However, both MRI and PET scans were unremarkable, showing no focal lesions or areas of altered metabolism. Intracranial monitoring, with extensive evaluation of both hemispheres, confirmed the seizure onset zone in the right central-parietal region. This area not only served as the site of seizure onset but also encompassed the primary sensory cortex.

Background: Myasthenia gravis (MG) is a rare disorder characterized by fluctuating muscle weakness with potential life-threatening crises. Timely interventions may be delayed by limited access to care and fragmented documentation. Our objective was to develop predictive algorithms for MG deterioration using multimodal telemedicine data.

Methods: In this 12-week randomized controlled study, 30 MG patients recorded symptoms using patient-reported outcome measures (PROMs) and patient-performed measures via a mobile app, alongside data from wearables. MG deterioration was defined as a ≥ 3-point worsening in the Quantitative Myasthenia Gravis score, occurrence of MG-related hospitalization or exacerbation. A machine learning linear classifier was trained to predict deterioration and cross-validated. The area under the receiver operator characteristic curve (AUROC) was calculated, accepting 1-2 false alarms (FAs) per week.

Results: The model achieved the best predictive performance when using all input signals (AUROC 0.85 (95% confidence interval 0.77-0.91)) and remained stable across look-back windows of 4-10 days. Model sensitivity was 0.65 (0.48-0.83) to 0.82 (0.60-1.00) (1 and 2 FAs per week, respectively). PROMs reflected worsening symptoms before deterioration; wearables alone showed higher AUROCs.

Interpretation: Multimodal self-monitoring via MyaLink predicted MG deterioration with good performance at acceptable FA rates. This approach may enable earlier clinical interventions of MG worsening.

Trial registration: The study was registered under the German Clinical Trial Registry (DRKS00029907).

Objective: Rett syndrome (RTT) is a devastating neurodevelopmental disorder with developmental regression affecting motor, sensory, and cognitive functions. Sensory disruptions contribute to the complex behavioral and cognitive difficulties and represent an important target for therapeutic interventions. Although genetic medicine-based therapies targeting MeCP2 have successfully restored motor and respiratory functions in animal models, their ability to reverse sensory deficits across levels of the visual pathway remains largely unexplored.

Methods: Using genetically reversible mouse models of MeCP2 deficiency (Mecp2^stop/y and Mecp2^stop/x), we applied advanced electrophysiological, anatomical, and behavioral techniques to evaluate visual function, a critical sensory domain impaired in both animal models and RTT patients.

Results: In Mecp2^stop/y mice, initiating MeCP2 expression after postnatal day 35 (P35) reversed progressive cortical dysfunction, prevented thalamic circuit disorganization, and restored visual function, despite some remaining cortical anatomical abnormalities. Even in fully regressed adult Mecp2^stop/x heterozygous female mice, MeCP2 reactivation was sufficient to reduce the symptoms.

Interpretation: These findings highlight the remarkable sensitivity of cortical circuits to MeCP2 expression in both developing and mature brain. Importantly, restoring just 60%-70% of MeCP2 protein levels was sufficient to rescue sensory functions, even after the onset of regression. This underscores the transformative potential of genetic medicine-based therapies in RTT, suggesting that even partial restoration of MeCP2 can meaningfully improve sensory processing and quality of life for patients.

Objective: To explore the feasibility of classical (CH50) and alternative (AH50) complement pathway activity as potential biomarkers for treatment guidance and monitoring during therapy with ravulizumab in patients with generalized myasthenia gravis (gMG) and compare these to therapeutic drug monitoring under eculizumab.

Methods: In this prospective, exploratory real-world study CH50 and AH50, eculizumab and ravulizumab blood levels were assessed in patients with acetylcholine-receptor-antibody (AChR-ab) positive gMG. Patients were either pretreated with eculizumab or C5-inhibitor naïve. Samples were collected before the next infusion (end-of-dose). Laboratory data were correlated with patient-reported subjective duration of the ravulizumab effect.

Results: Overall, 61 patients were enrolled. At the end of their respective dosing interval, median AH50 levels were more strongly suppressed with eculizumab than with ravulizumab (1% [0%-2%] versus 5% [3%-9%]; Cohen's d 2.2 [95% CI 1.5-2.8]). Patients who discontinued ravulizumab due to insufficient effects (n = 19; 33%) had higher median AH50 levels than those who continued (7% [4%-13%] versus 5% [3%-8%]; Cohen's d -0.9 [95% CI -1.3 to -0.4]). In 81% (n = 46) of patients, the therapeutic effect of ravulizumab diminished before the subsequent infusion after the standard 8-week interval. Higher AH50 levels were correlated with earlier symptom recurrence.

Interpretation: Our results indicate potential differences in the ability of eculizumab and ravulizumab to suppress complement pathway activity through their respective dosing intervals. Additionally, higher AH50 levels might be a potential biomarker to predict poor therapy response and faster wearing off of ravulizumab's effect. However, these findings need to be validated in large multicenter studies.

Objectives: Variants in the FHL1 gene cause FHL1-related myopathies (FHL1-RMs), a group of neuromuscular disorders with diverse clinical presentations. This study aimed to comprehensively characterize the spatial and temporal patterns of skeletal muscle fat replacement throughout the whole body in FHL1-RMs, to examine disease progression over time, and to evaluate the relationship between imaging findings and clinical symptoms.

Methods: We retrospectively analyzed 21 whole-body imaging studies from 10 patients with genetically confirmed FHL1-RMs. Fatty replacement was scored in 47 muscles using the modified Mercuri score (mMS). Longitudinal data were used to stratify patients into slow, moderate, and rapid progression groups. K-means clustering was applied to classify muscles based on their chronological patterns of fatty infiltration. Hierarchical clustering and violin plots were used to explore inter-muscle and inter-patient variations.

Results: Despite notable variability in the rate of disease progression, a consistent pattern of muscle involvement was observed across patients. Muscles were classified into three progression clusters: early-onset and early attainment of the maximal mMS (e.g., paraspinal and posterior thigh muscles), steadily progressive (e.g., trunk and lower leg muscles), and late-onset with slow changes (e.g., shoulder and anterior thigh muscles). These patterns paralleled the clinical symptom progression. In early-stage patients, STIR imaging revealed muscle signal abnormalities preceding fat replacement detectable on T1-weighted images.

Interpretation: The rate of fat replacement in FHL1-RMs varies individually, but spatial patterns are conserved and reflect clinical evolution. Serial imaging is a valuable tool to monitor disease progression and may serve as a sensitive biomarker in clinical trials.

Background: Fatigue is among the most common symptoms and one of the main factors determining the quality of life in multiple sclerosis (MS). However, the neurobiological mechanisms underlying fatigue are not fully understood. Here we studied lesion locations and their connections in individuals with MS, aiming to identify brain networks associated with fatigue.

Methods: 38 MS patients with and 21 without fatigue were included in the study. Association between fatigue and lesion locations was investigated using voxel-lesion symptom mapping and lesion connectivity using lesion network mapping. The findings were tested in two independent datasets, including (1) MS patients scanned using resting-state functional connectivity MRI (rs-fcMRI) (n = 199) and (2) individuals with stroke lesions (n = 85).

Results: There were no specific anatomical MS lesion locations significantly associated with fatigue, but lesions associated with fatigue were connected to a common network with peak positive connectivity to the right premotor cortex and negative connectivity to the left temporal pole (p_FWE < 0.05). Of the two identified network nodes, connectivity from the premotor cortex to multiple other brain regions was significantly associated with MS fatigue severity in the independent dataset of MS patients (p < 0.05). The MS fatigue network was also reproducible in poststroke fatigue (spatial correlation r = 0.57, permutation test p = 0.02), again showing that lesion connectivity to the premotor cortex, but not the temporal pole, was associated with fatigue (p = 0.04).

Conclusions: Our results show that fatigue in MS localizes to a brain network, lending insight into the neural substrates of fatigue.