AI and Neurological Rare & Neuroimmune Diseases ── 2026-10-21
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（1） · Hereditary neurodegeneration（1） · Neuromuscular（0） · 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
- Abstract
Mitochondrial and lysosomal dysfunction are central features of Parkinson's disease (PD) across major genetic forms including PRKN, SNCA, and LRRK2. We applied cell morphomics, a machine-learning-based framework combining high-content imaging with quantitative feature extraction, to analyse mitochondrial and lysosomal morphology at single-cell resolution in iPS cell-derived cortical neurons from PD patients and healthy controls (13 lines total). Supervised machine-learning models distinguished PD neurons from controls with high accuracy (AUC = 0.87) and reliably separated individual genotypes. Feature importance and attribution analysis revealed genotype-specific organelle biases in the relative contribution of mitochondrial and lysosomal features, with mitochondrial features dominating classification in PRKN neurons, balanced mitochondrial and lysosomal contributions in SNCA neurons, and a greater lysosomal contribution in LRRK2 neurons. Multi-class models retained strong performance, and findings were reproduced across two independent laboratories using different dyes and imaging conditions. These results demonstrate that morphomics provides a robust and scalable framework to quantify genotype-specific organelle abnormalities in PD neurons and supports its application for cellular stratification and biomarker discovery.
Journal IF-equivalent: 8.0 (OpenAlex 2-year mean citedness, value as of 2026-10-20, retrieved 2026-10-21; not the official Clarivate IF)Reference: Li Y, Powell M, Chedid J, Sutharsan R, Labrador-Garrido A, Abu-Bonsrah D, et al. Machine learning image analysis predicts Parkinson disease genotype and mitochondrial lysosomal abnormalities in iPS neurons. npj Parkinsons Dis. 2026 Aug 1;12(1):255. doi:10.1038/s41531-026-01458-w.Checked: Abstract only
See all 1 Parkinson's papers →
Hereditary neurodegenerative diseases: treatment, trials and AI
- Abstract
Huntington's disease (HD) is a progressive neurodegenerative disorder characterized by motor dysfunction, cognitive decline, and psychiatric abnormalities. Magnetic resonance imaging (MRI) provides valuable structural information for identifying disease-associated brain changes, particularly in the striatum and associated subcortical regions. However, conventional image-analysis methods may inadequately capture complex spatial relationships and subtle anatomical variations. This paper presents a machine learning and convolutional transformer neural network framework for automated Huntington's disease detection from brain MRI. The proposed architecture integrates convolutional feature extraction, transformer-based global context modeling, and machine learning classification to identify discriminative neuroanatomical patterns. Image preprocessing, intensity normalization, anatomical region analysis, and class-imbalance management are incorporated into the processing pipeline. Model performance is assessed using accuracy, sensitivity, specificity, F1-score, receiver operating characteristic area under the curve, and explainability measures. The framework also incorporates model interpretation and patient-level validation to support transparent assessment. The proposed approach establishes a methodological foundation for automated MRI-based Huntington's disease screening, subject to independent clinical validation and rigorous evaluation across heterogeneous imaging datasets.
Journal IF-equivalent: 0.1 (OpenAlex 2-year mean citedness, value as of 2026-10-20, retrieved 2026-10-21; not the official Clarivate IF)Reference: [No authors listed]. MACHINE LEARNING AND CONVOLUTIONAL TRANSFORMER NEURAL NETWORK FRAMEWORK FOR AUTOMATED HUNTINGTON'S BRAIN DISEASE DETECTION FROM MRI. ACTA. 2026 Oct 18;63(2s). doi:10.62441/actainnovations.vi.1902.Checked: Abstract only
See all 1 Hereditary neurodegeneration papers →
Neuromuscular diseases (muscular dystrophy, SMA): treatment, trials and AI
No new qualifying paper for this issue.
Multiple sclerosis: treatment, trials and AI
- Abstract
Bipolar disorder (BD) is closely associated with multiple sclerosis (MS), but their shared molecular mechanisms and specific diagnostic biomarkers remain unclear. This study integrated microarray expression profiling and machine learning to identify shared core genes and underlying mechanisms between BD and MS. Gene expression datasets and peripheral-blood transcriptome datasets were obtained from the GEO database. After per-cohort quantile normalization, shared genes were identified by differential expression analysis and weighted gene co-expression network analysis. A protein–protein interaction network was constructed, and key genes were screened using the MCODE plugin. Three machine learning algorithms were combined to identify the final core gene. A total of 229 shared genes were initially obtained and further filtered to 55 key genes. BRD4 was identified as the final shared core gene. Discriminative performance (AUC, 95% CI) was: BD discovery GSE23848 (20 cases/15 controls) 0.713 (0.54–0.89); MS discovery GSE21942 (12/15) 0.998 (0.99–1.00); BD external GSE39653 (8/24) 0.775 (0.59–0.96); MS external GSE203241 (38/21) 0.828 (0.71–0.93). BRD4 shows moderate-to-good discriminative performance as a non-invasive peripheral biomarker and may aid further investigation of shared BD–MS mechanisms.
Journal IF-equivalent: 4.7 (OpenAlex 2-year mean citedness, value as of 2026-10-20, retrieved 2026-10-21; not the official Clarivate IF)Reference: Zhou X, Zhang Y, Xu Y, Huo C. Identification of BRD4 as a Shared Diagnostic Biomarker Between Bipolar Disorder and Multiple Sclerosis Using Machine Learning. Curr Issues Mol Biol. 2026 Oct 19;48(10):1077. doi:10.3390/cimb48101077.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.