AI and Neurological Rare & Neuroimmune Diseases ── 2026-10-25
New papers on neurological rare and neuroimmune diseases and AI, collected from journal feeds, PubMed (searched across all journals) and OpenAlex (a public index used instead of Google Scholar), judged by Jev (an AI that makes language judgements): original research, AI central, which of 9 disease groups, treatment or trials, and then ranked by importance up to a daily limit. Journals are not filtered by impact factor; each paper shows its journal's IF-equivalent (OpenAlex two-year mean citedness) and the date of that value. Each entry shows the full original abstract.
Read by disease: Basic research（1） · ALS（0） · Parkinson's（0） · Hereditary neurodegeneration（0） · Neuromuscular（0） · Multiple sclerosis（1） · NMOSD/MOGAD（0） · Myasthenia gravis（0） · Autoimmune encephalitis/CIDP/GBS（0）
Neurodegeneration and neuroimmunology research and AI
- Abstract
This study aimed to develop and validate a fully automated pipeline for vertebral level identification (C1–C3) of the upper cervical spinal cord (SC) in T1-weighted brain MRI with a quality control module to assess the generated masks. MRI data from 998 controls and 879 patients with hereditary ataxias (32 sites; age 5–85 years; 46% male) were used to train deep learning models (nnU-Net, U-Net with deep supervision, SwinUNETR) for automatic vertebral labeling of C1–C3 levels. A secondary model based on VGG, ResNet, and EfficientNet architectures was trained to identify vertebral labeling errors. The pipeline also generates image quality metrics including signal-to-noise ratio, contrast-to-noise ratio, coefficient of joint variation, and entropy focus criterion. External validation was performed using 2737 MRIs from four public datasets, and clinical utility was demonstrated in a longitudinal cohort of 51 patients with multiple sclerosis. The nnU-Net achieved the highest performance (Dice: C1 = 0.85, C2 = 0.89, C3 = 0.89), labeling 95.8% of mislabeled cases from the Spinal Cord Toolbox. The VGG-based classifier achieved 97.9% accuracy and 98.7% sensitivity. External validation showed labeling accuracies exceeding 92% in four of five datasets and sensitivity to longitudinal SC atrophy in multiple sclerosis. This pipeline enables scalable, reliable vertebral labeling and morphometric extraction of the upper cervical SC for multicenter studies and supports biomarker development in neurodegenerative diseases.
Journal IF-equivalent: 13.9 (OpenAlex 2-year mean citedness, value as of 2026-10-18, retrieved 2026-10-19; not the official Clarivate IF)Reference: Jarola GM, Shiraishi DH, Adanyeguh IM, Ashizawa T, Barsottini OGP, Bender B, et al. A deep learning framework for automated cervical spinal cord vertebral labeling and morphometric extraction from brain MRI with large-scale multicenter validation. npj Digit Med. 2026 Oct 23 [Epub ahead of print]. doi:10.1038/s41746-026-03403-w.Checked: Abstract only
See all 1 Basic research papers →
ALS: treatment, trials and AI
No new qualifying paper for this issue.
Parkinson's disease and parkinsonism: treatment, trials and AI
No new qualifying paper for this issue.
Hereditary neurodegenerative diseases: treatment, trials and AI
No new qualifying paper for this issue.
Neuromuscular diseases (muscular dystrophy, SMA): treatment, trials and AI
No new qualifying paper for this issue.
Multiple sclerosis: treatment, trials and AI
- Abstract
Understanding balance in virtual reality (VR) is important not only for measuring whether a user is stable or unstable but also for explaining how stability is maintained during immersive interaction. This is especially important for people with Multiple Sclerosis (MS), because similar levels of overall balance may be accompanied by different motion patterns across individuals. Existing VR-based approaches typically focus on raw sway signals, concurrent validity comparisons, or task-specific classification, and rarely capture balance as a single continuous measure or explain the motion patterns underlying stability maintenance. Using a publicly available multimodal dataset of head-mounted display (HMD), controller, center-of-pressure (CoP), and motion-capture recordings from 37 participants (15 MS, 22 non-MS), we present a three-stage framework for predicting and interpreting balance ability from HMD and controller motion sensors. First, we develop a continuous CoP-derived balance score from standard CoP features, representing balance as a single communicable measure, and examine its consistency across MS and non-MS participants. Second, we predict this score from HMD and controller sensors using machine learning. Third, we analyze VR motion to identify exploratory patterns beyond overall balance-score severity and test whether their participant-level prevalence differs between MS and non-MS groups. The balance score showed moderate between-trial reliability and similar feature-to-score correlation patterns across the MS and non-MS groups. Predictions were evaluated at two levels: the window level, which uses short segments of VR motion-sensor data from the HMD and controllers, and the participant level, which aggregates all segments for a participant into a single participant-level prediction. CatBoost showed the strongest overall predictive performance. At the window level, it achieved R2 = 0.388, MAE = 0.597, and RMSE = 0.782. At the participant level, it achieved R2 = 0.715, Pearson, r = 0.867, and Spearman, ρ = 0.838. The balance score also aligned with motion-capture-derived center-of-mass (CoM) mean velocity, providing additional biomechanical grounding within the available dataset. Motion-pattern analysis identified two distinct exploratory patterns: the Lower head-dominance pattern, characterized by relatively greater hand contribution, and the Head-dominant pattern, characterized by relatively greater head contribution. Although the Head-dominant pattern occurred more frequently at the descriptive window level among participants with MS, the participant-level comparison did not provide statistically significant evidence of a group difference. These findings indicate that VR headset and controller motion alone can support participant-level prediction of balance ability while also providing exploratory insight into the relative organization of head and hand motion during immersive interaction.
Journal IF: unknown (could not be matched)Reference: Pavel MI, Anjum N, Cordova A, Quarles J, Mahmud MR. Predicting Balance Ability and Explaining Control Strategies from VR Motion Sensors: An Interpretable CoP-Derived Balance Score Approach for Multiple Sclerosis. Proceedings of the 2026 28th International ACM SIGACCESS Conference on Computers and Accessibility. 2026 Oct 24 [Epub ahead of print]. doi:10.1145/3797867.3828977.Checked: Abstract only
See all 1 Multiple sclerosis papers →
NMOSD and MOGAD: treatment, trials and AI
No new qualifying paper for this issue.
Myasthenia gravis: treatment, trials and AI
No new qualifying paper for this issue.
Autoimmune encephalitis, CIDP and Guillain-Barre syndrome: treatment, trials and AI
No new qualifying paper for this issue.