AI and Neurological Rare & Neuroimmune Diseases ── 2026-10-22
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（0） · ALS（0） · Parkinson's（0） · Hereditary neurodegeneration（0） · Neuromuscular（1） · Multiple sclerosis（1） · NMOSD/MOGAD（0） · Myasthenia gravis（0） · Autoimmune encephalitis/CIDP/GBS（0）
Neurodegeneration and neuroimmunology research and AI
No new qualifying paper for this issue.
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
- Abstract
Background: Nusinersen has substantially increased survival and improved disease progression in Spinal Muscular Atrophy (SMA) patients. However, treatment response is heterogeneous, with some patients gaining the ability to sit and walk whilst others display less obvious changes. Method From the SMA Reach UK and the Italian Telethon Network, 124 non-sitter SMA patients treated with nusinersen under 4 years were included and randomly allocated to training and testing (80/20%). Tree and regression-based machine learning for survival outcomes were compared, and oblique random forests were selected, with a testing C-Index of 0.74. The features were selected from items of the CHOP-INTEND and HINE-2 motor function assessments, respiratory and swallowing status using mutual information.
Findings: Sixty-two patients (50%) achieved sitting, at a median age of 2.4 years. The predicted median time-to-sitting in those requiring tube feeding at treatment initiation was 4-months later than those without, and this was the most influential factor. Specific motor function features, including the ability to kick in supine, were strongly associated with a higher likelihood of sitting.
Interpretation: This work provides a framework for predicting nusinersen-response and represents the first stage in personalised counselling on treatment plans for SMA.
Funding: Support for SMA Reach is provided by Biogen, Roche and Novartis, and historically the NIHR BRC, SMA Trust, the MRC Translational Research Centre and MD UK. Support for the Italian network is provided by Famiglie SMA, Telethon (GSP 13002), and ASAMSI.
Journal IF-equivalent: 3.2 (OpenAlex 2-year mean citedness, value as of 2026-10-21, retrieved 2026-10-22; not the official Clarivate IF)Reference: Stimpson G, O’Reilly E, Coratti G, Ridout D, Chakraborti T, Mitra R, et al. Machine learning predicts treatment response to nusinersen in non-sitter spinal muscular atrophy (SMA). J Neuromuscul Dis. 2026 Oct 20 [Epub ahead of print]. doi:10.1177/22143602261496327. PMID: 42900957.Checked: Abstract only
See all 1 Neuromuscular papers →
Multiple sclerosis: treatment, trials and AI
- Abstract
Introduction: Magnetic resonance imaging (MRI) reveals macroscopic lesion burden in multiple sclerosis (MS) but correlates poorly with clinical phenotype, a phenomenon known as the clinico-radiological paradox. While diffusion tensor imaging (DTI) metrics assess microstructural damage, standard whole-lesion averaging may obscure microstructurally spared white matter within heterogeneous plaques.
Methods: We propose a fully automated, multimodal pipeline to identify and quantify reference-like intralesional segments (RLIS) within MS lesions (i.e., the cluster exhibiting maximum feature-space similarity to subject-specific, lesion-spared reference tissue). A modified 2.5D U-Net, developed and evaluated on a cohort of 75 MS patients (115 series), performed automated FLAIR lesion segmentation. In an independent cohort of 100 MS subjects, intralesional DTI metrics (FA and MD) were extracted along streamlines reconstructed via Constrained Spherical Deconvolution tractography. Unsupervised clustering algorithms, validated against a subject-specific, lesion-spared reference CST streamlines (internal capsule), identified operationally defined reference-like segments.
Results: The 2.5D U-Net achieved robust segmentation performance (DSC: 0.732, VC: 0.954), exhibited higher nominal metrics in a contextual benchmark comparison (DSC: 0.682, VC: 0.864). Across the clinical cohort, lesion count (11–131) and volume (0.54–57.3 mL) varied widely. Among the evaluated clustering methods, K-Means and Gaussian Mixture Models proved to be the most conservative, identifying median reference-like segment proportions of 26% and 22%, respectively. Crucially, the proportion of reference-like intralesional segments showed no significant monotonic association with macroscopic lesion volume or count.
Conclusion: This automated framework successfully integrates robust deep learning-based lesion segmentation with tract-specific analysis to quantify the proportion of reference-like intralesional segments. The observed dissociation between overall lesion burden and RLIS suggests that this index serves as a promising candidate metric for exploring microstructural features that may contribute to the clinico-radiological paradox.
Journal IF-equivalent: 3.6 (OpenAlex 2-year mean citedness, value as of 2026-10-21, retrieved 2026-10-22; not the official Clarivate IF)Reference: Toscano-Flores EA, Mireles-Ramírez MA, Guerrero-García JdJ, Alvarez-Padilla FJ. Revisiting the MS paradox: automated 2.5D FLAIR segmentation and DTI microstructural profiling reveals dissociation between lesion burden and reference-like intralesional tract profiles. Front Hum Neurosci. 2026 Oct 20;20:1901702. doi:10.3389/fnhum.2026.1901702.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.