Background: The antisense oligonucleotide Nusinersen recently became the first approved drug against spinal muscular atrophy (SMA). It was approved for all ages, albeit the clinical trials were conducted exclusively on children. Hence, clinical data on adults being treated with Nusinersen is scarce. In this case series, we report on drug application, organizational demands, and preliminary effects during the first 10 months of treatment with Nusinersen in seven adult patients. Methods: All patients received intrathecal injections with Nusinersen. In cases with severe spinal deformities, we performed computed tomography (CT)-guided applications. We conducted a total of 40 administrations of Nusinersen. We evaluated the patients with motor, pulmonary, and laboratory assessments, and tracked patient-reported outcome. Results: Intrathecal administration of Nusinersen was successful in most patients, even though access to the lumbar intrathecal space in adults with SMA is often challenging. No severe adverse events occurred. Six of the seven patients reported stabilization of motor function or reduction in symptom severity. The changes in the assessed scores did not reach a significant level within this short time period. Conclusions: Treating adult SMA patients with Nusinersen is feasible and most patients consider it beneficial. It demands a complex organizational and interdisciplinary effort. Due to the slowly decreasing motor functions in adult SMA patients, long observation phases for this recently approved treatment are needed to allow conclusions about effectiveness of Nusinersen in adults.

Deep learning is increasingly being proposed for detecting neurological and psychiatric diseases from electroencephalogram (EEG) data but the method is prone to inadvertently incorporate biases from training data and exploit illegitimate patterns. The recent demonstration that deep learning can detect the sex from EEG implies potential sex‐related biases in deep learning‐based disease detectors for the many diseases with unequal prevalence between males and females. In this work, we present the male‐ and female‐typical patterns used by a convolutional neural network that detects the sex from clinical EEG (81% accuracy in a separate test set with 142 patients). We considered neural sources, anatomical differences, and non‐neural artifacts as sources of differences in the EEG curves. Using EEGs from 1140 patients, we found electrocardiac artifacts to be leaking into the supposedly brain activity‐based classifiers. Nevertheless, the sex remained detectable after rejecting heart‐related and other artifacts. In the cleaned data, EEG topographies were critical to detect the sex, but waveforms and frequencies were not. None of the traditional frequency bands was particularly important for sex detection. We were able to determine the sex even from EEGs with shuffled time points and therewith completely destroyed waveforms. Researchers should consider neural and non‐neural sources as potential origins of sex differences in their data, they should maintain best practices of artifact rejection, even when datasets are large, and they should test their classifiers for sex biases.

Pain in arthritis may be experienced in regions outside the affected joint, and hyperalgesia may even be widespread. The spread of pain is usually attributed to mechanisms in the central nervous system. We investigated whether rats with antigen-induced arthritis (AIA) exhibit peripheral changes in skin innervation remote from the inflamed joint. After immunization, unilateral AIA in the knee joint was induced in rats. Intraepidermal nerve fibre density was determined by immunohistochemical staining for protein gene product 9.5 (PGP 9.5) and for nerve fibres expressing calcitonin gene–related peptide (CGRP), substance P (SP), transient receptor potential vanilloid 1 (TRPV1; the heat and capsaicin receptor), β-tubulin, and growth-associated protein 43 (GAP-43; a marker of regenerating nerve fibres) in paw pad skin and back skin. Cluster of differentiation 11b (CD11b)-positive macrophages and CD3-positive T cells were quantified in skin, and macrophages were quantified in the lumbar dorsal root ganglia. In addition, pain-related behaviour was assessed. Intraepidermal nerve fibre density (PGP 9.5) and the numbers of fibres expressing CGRP, SP, TRPV1, or β-tubulin did not show a significant change in the acute (3 days) or chronic phase (21 days) of AIA compared with control rats that were only immunized. However, paw skin and back skin revealed a significantly higher number of nerve fibres expressing GAP-43 at both the acute and chronic stages of AIA. The skin of arthritic rats in these regions did not contain a greater density of CD11b and CD3 immune cells than the skin of control rats. Enhanced expression of GAP-43 in nerve fibres of the skin was not related to hyperalgesia in the joint, but it accompanied persistent secondary cutaneous hyperalgesia in the skin remote from the inflamed joint. Although the innervation of the skin remote from the joint did not show significant abnormalities of the other nerve fibre markers, the rapid and persistent increase of GAP-43 expression is conspicuous. The data suggest that immune-mediated arthritis is associated with changes in skin innervation remote from the inflamed joint, although the skin is not inflamed, which may contribute to symptoms in nonarticular tissue remote from the affected joint.

Background: Virtual reality (VR) technology offers immersive and interactive experiences and is increasingly being explored for rehabilitation therapies. However, concerns about side effects such as nausea and dizziness—collectively referred to as VR sickness—are holding back clinical translation. Sensorimotor mismatches, while potentially beneficial for motor learning, may exacerbate these effects. The age groups in VR applications differ, with younger users common in gaming and older adults prevalent in rehabilitation. This study investigated whether sensorimotor mismatches in a VR-based motor task make the experience more uncomfortable and whether older adults are more affected by these mismatches. Methods: We conducted a randomized controlled trial with 104 healthy right-handed adults, including elderly participants up to 84 years old, to cover the relevant demographics for rehabilitation. Participants were divided into three intervention groups and performed a VR ball-throwing task using an Oculus Rift S head-mounted display. The groups differed in task difficulty and exposure to deliberately induced sensorimotor mismatches. The design avoided visual-vestibular conflicts typically responsible for VR sickness and instead introduced proprioceptive mismatches during hand-object interaction. VR sickness was measured using the Simulator Sickness Questionnaire (SSQ), and user experience was assessed through a self-developed questionnaire. Statistical analysis was performed using rank-transformed ANOVA, ordinal logistic regression, and Spearman’s rho with FDR correction for multiple comparisons. Results: Results indicated no significant differences in SSQ scores among the three intervention groups, suggesting that sensorimotor mismatches do not increase VR sickness. However, the Mismatch group reported higher levels of exhaustion and frustration compared to the Error-based and Errorless groups, indicating the impact of cognitive strain and task difficulty on user experience. Interestingly, younger participants reported higher (worse) SSQ scores, while older participants experienced weaker symptoms. Conclusions: VR environments with sensorimotor mismatches during hand-object interaction tasks may be feasible for rehabilitation, as they did not lead to significant discomfort in this setting. Moreover, despite concerns about age-related susceptibility to dizziness, older adults showed high tolerance to VR, supporting its potential for broader applications in rehabilitation settings. This study was reported in accordance with the CONSORT guidelines. It was registered in the German Clinical Trials Register (DRKS00034901).

Background/Objectives: Early and accurate prediction of stroke severity is crucial for optimizing guided therapeutic decisions and improving outcomes. This study investigates the predictive value of lesion size and functional connectivity for neurological deficits, assessed by the National Institutes of Health Stroke Scale (NIHSS score), in patients with acute or subacute subcortical ischemic stroke. Methods: Forty-four patients (mean age: 68.11 years, 23 male, and admission NIHSS score 4.30 points) underwent high-resolution anatomical and resting-state functional Magnetic Resonance Imaging (rs-fMRI) within seven days of stroke onset. Lesion size was volumetrically quantified, while functional connectivity within the motor, default mode, and frontoparietal networks was analyzed using seed-based correlation methods. Multiple linear regression and cross-validation were applied to develop predictive models for stroke severity. Results: Our results showed that lesion size explained 48% of the variance in NIHSS scores (R² = 0.48, cross-validated R² = 0.49). Functional connectivity metrics alone were less predictive but enhanced model performance when combined with lesion size (achieving an R² = 0.71, cross-validated R² = 0.73). Additionally, left hemisphere connectivity features were particularly informative, as models based on left-hemispheric connectivity outperformed those using right-hemispheric or bilateral predictors. This suggests that the inclusion of contralateral hemisphere data did not enhance, and in some configurations, slightly reduced, model performance—potentially due to lateralized functional organization and lesion distribution in our cohort. Conclusions: The findings highlight lesion size as a reliable early marker of stroke severity and underscore the complementary value of functional connectivity analysis. Integrating rs-fMRI into clinical stroke imaging protocols offers a potential approach for refining prognostic models. Future research efforts should prioritize establishing this approach in larger cohorts and analyzing additional biomarkers to improve predictive models, advancing personalized therapeutic strategies for stroke management.