AI and Neurological Rare & Neuroimmune Diseases ── 2026-10-12
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（1） · Parkinson's（1） · Hereditary neurodegeneration（0） · Neuromuscular（0） · Multiple sclerosis（0） · 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
- Abstract
Amyotrophic lateral sclerosis (ALS) is a progressive neurodegenerative disorder characterized by the progressive loss of motor neurons. It is caused by multiple factors, and understanding its molecular pathogenesis remains challenging, particularly in the early stages for identifying diagnostic biomarkers and effective treatments. In this study, we aimed to identify novel biomarkers in ALS and elucidate their molecular mechanisms using transcriptomics and artificial intelligence approaches. We considered two RNA-seq datasets of ALS, namely GSE277709 and GSE234297, obtained from the NCBI GEO database. Initially, the data were preprocessed, which included quality control and filtering. Common genes (12,281 genes) between the datasets were identified and merged into a unified dataset comprising 216 samples. Batch effects were corrected using ComBat-seq and validated through principal component analysis and ANOVA-based $$R^2$$ analysis. We applied machine learning (ML) models including logistic regression (LR), support vector machine (SVM), random forest (RF), and eXtreme Gradient Boosting (XGBoost) to identify differentially expressed genes (DEGs). The identified biomarkers were further validated using DESeq2 log $$_2$$ fold change with a threshold of 0.6 and p value $$< 0.05$$ . Finally, functional enrichment analysis was performed on the identified DEGs using Enrichr tools. Our results demonstrated that batch correction significantly reduced batch-driven separation, with principal component variance decreasing from 70.2 to 19.9% before and after correction, respectively. Furthermore, ANOVA-based $$R^2$$ analysis confirmed an 86.65% reduction in batch-associated variance. We identified a total of 29 coding genes and 38 non-coding genes (67 genes) between DESeq2 and ML models. However, few genes are common between ML models and we received a total of 41 genes (18 coding genes and 22 non-coding genes, and gene symbols were not available for one Ensembl IDs). Our ML models such as LR, SVM, RF, and XGBoost, achieved balanced accuracies of 77.46%, 80%, 77%, and 75.82%, respectively. The enrichment analysis revealed that 10 DEGs were associated with 34 ALS-related biological pathways. Notably, CCND1 and MYL9 was found to be involved in GO, KEGG and Reactome pathways. Overall, our results demonstrate that computational models combining transcriptomics and artificial intelligence can effectively support biomarker identification and pathway discovery in ALS.
Journal IF-equivalent: 3.1 (OpenAlex 2-year mean citedness, value as of 2026-10-11, retrieved 2026-10-12; not the official Clarivate IF)Reference: Yadav R, Sriram Kumar P, Pragya P, Ronickom JFA. Computational models for biomarker identification in amyotrophic lateral sclerosis using transcriptomics and artificial intelligence. Discov Artif Intell. 2026 Oct 10;6(1):1421. doi:10.1007/s44163-026-02415-5.Checked: Abstract only
Parkinson's disease and parkinsonism: treatment, trials and AI
- Abstract
INTRODUCTION: Early differentiation between Parkinson's disease (PD) and multiple system atrophy-parkinsonian type (MSA-P) remains clinically challenging because of overlapping motor symptoms and subtle imaging abnormalities. METHODS: We developed a fully automated multimodal PET/MRI framework for early PD-MSA-P differentiation that integrates atlas-guided subject-specific label generation, nnU-Net segmentation of the caudate nuclei and putamina, multimodal image fusion, and Vision Transformer classification with multi-slice voting (ViT+MSV). A total of 155 early-stage patients, including 121 with PD and 34 with MSA-P, were evaluated using patient-level stratified 4-fold cross-validation. RESULTS: The segmentation model achieved an overall Dice score of 0.853 (95% CI, 0.830-0.877) and an IoU of 0.747 (95% CI, 0.725-0.770). ViT+MSV achieved a cross-validated AUC of 0.947 (95% CI, 0.905-0.981), an accuracy of 0.929 (95% CI, 0.890-0.968), a sensitivity of 0.942 (95% CI, 0.900-0.982), and a specificity of 0.882 (95% CI, 0.765-0.974). The combination of [ 18 F]fluorodeoxyglucose (FDG) PET, [ 11 C]CFT dopamine-transporter PET, and T2-weighted imaging (T2WI) showed the best overall classification performance. Grad-CAM visualizations showed that class-discriminative gradient-based saliency was mainly concentrated on the bilateral putamen. DISCUSSION: These findings suggest that anatomically guided multimodal PET/MRI analysis with transformer-based classification may support early PD-MSA-P differentiation while providing spatially interpretable evidence for model decisions.
Journal IF-equivalent: 4.8 (OpenAlex 2-year mean citedness, value as of 2026-10-11, retrieved 2026-10-12; not the official Clarivate IF)Reference: Cong C, Zhang X, Rifat ST, Tan H, Sun J, Peng W, et al. Differentiating early Parkinson’s disease from multiple system atrophy on multimodal PET/MRI using atlas-guided segmentation and vision transformer classification. Front Aging Neurosci. 2026 Sep 25;18:1928959. doi:10.3389/fnagi.2026.1928959; PMCID: PMC13649701.Checked: Full text checked
See all 1 Parkinson's papers →
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
No new qualifying paper for this issue.
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.