AI and Neurological Rare & Neuroimmune Diseases ── 2026-10-08
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（0） · 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
BACKGROUND: Parkinson's disease motor subtypes, tremor-dominant (TD) and postural instability/gait difficulty (PIGD), differ in disease trajectory and prognosis, yet their classification depends on MDS-UPDRS-based clinical ratings that are susceptible to inter-rater variability and medication state. PURPOSE: This exploratory study investigated whether T1-weighted and FLAIR MRI radiomics show differential associations with TD and PIGD motor subtypes in Parkinson's disease, and whether the two sequences contribute complementary subtype-relevant information when combined in a multimodal framework. METHODS: Baseline 3 T MRI data from 568 patients with Parkinson's disease in the PPMI cohort were analyzed (448 TD; 120 PIGD). Radiomics features were extracted from predefined basal ganglia and brainstem regions on T1-weighted and FLAIR images using PyRadiomics. Participants with only T1-weighted or only FLAIR MRI were used as sequence-specific development cohorts; those with both sequences formed a common evaluation cohort. ComBat harmonization was applied within each modality before cohort separation, followed by development-cohort-based feature selection and model fitting. L1-regularized logistic regression models were trained independently for each sequence and evaluated in the common cohort. A stacking model combined T1- and FLAIR-derived probability scores. Model performance was assessed using the area under the precision-recall curve (PR-AUC), balanced accuracy, and F1-score, and Shapley Additive Explanations (SHAP) analysis was used for interpretation. RESULTS: In the common evaluation cohort ( n = 148; 118 TD, 30 PIGD), the T1-weighted model achieved a PR-AUC of 0.814, balanced accuracy of 0.829, and F1-score of 0.750. The FLAIR model achieved a PR-AUC of 0.858, balanced accuracy of 0.832, and F1-score of 0.650. The stacked model showed higher performance, with a PR-AUC of 0.928, balanced accuracy of 0.912, and F1-score of 0.818. The T1-weighted model showed relatively better TD classification, whereas the FLAIR model showed higher PIGD sensitivity. SHAP analysis showed that T1-weighted contributions were mainly concentrated in the putamen, while FLAIR contributions were more broadly distributed across basal ganglia and brainstem regions. CONCLUSION: T1-weighted and FLAIR MRI radiomics showed distinct subtype-associated error profiles and preliminary evidence of cross-sequence complementarity in this PPMI-based exploratory analysis. These findings require external validation before clinical interpretation, but provide a hypothesis-generating framework for multimodal structural radiomics research in Parkinson's disease motor subtype characterization.
Journal IF-equivalent: 3.1 (OpenAlex 2-year mean citedness, value as of 2026-10-07, retrieved 2026-10-08; not the official Clarivate IF)Reference: Lim H, Lee J, Park S, Gi Y, Baek SH, Kim BJ, et al. Exploratory radiomics analysis of T1-weighted and FLAIR MRI in relation to Parkinson’s disease motor subtypes. Front Neurol. 2026 Sep 22;17:1920363. doi:10.3389/fneur.2026.1920363; PMCID: PMC13638411.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
- Abstract
Background: Cognitive impairment is a frequent manifestation of multiple sclerosis (MS). Although the Brief International Cognitive Assessment for Multiple Sclerosis (BICAMS) is the recommended screening battery, the Montreal Cognitive Assessment (MoCA) is more commonly used in routine clinical practice. The ability of published MoCA cutoffs to detect MS-specific cognitive impairment remains unclear.
Objective: This study aimed to evaluate the diagnostic performance of published Italian MoCA cutoffs for MS-specific cognitive impairment and develop a machine learning (ML) pipeline to improve screening accuracy.
Methods: We prospectively enrolled 222 people with MS who underwent both MoCA and BICAMS assessment. Cognitive impairment was defined as performance below the 5th percentile on at least one BICAMS test. The sensitivity, specificity, and balanced accuracy of four published Italian MoCA cutoffs were calculated. An elastic-net penalized logistic regression model incorporating age, education, and raw MoCA score was trained using nested five-fold cross-validation to predict cognitive impairment. We additionally evaluated the impact of disability and fatigue on cognitive impairment and model performance.
Results: Cognitive impairment was identified in 62 (27.9%) participants. Raw MoCA scores were significantly lower in cognitively impaired participants than in cognitively preserved participants (22.68 ± 4.29 vs. 25.77 ± 3.67; p < 0.0001). Published MoCA cutoffs, however, showed extreme specificity (0.92–0.99) but below-chance sensitivity (0.10–0.44), yielding low balanced accuracy (0.54–0.68). The ML model achieved good discrimination (ROC-AUC = 0.787, 95% CI 0.717–0.852), with sensitivity = 0.69, specificity = 0.72, and balanced accuracy = 0.71.
Conclusions: Published Italian MoCA cutoffs have insufficient sensitivity for screening MS-specific cognitive impairment. An ML-based scoring approach substantially improves sensitivity while maintaining acceptable specificity, offering a practical tool to enhance cognitive screening in MS.
Journal IF-equivalent: 5.2 (OpenAlex 2-year mean citedness, value as of 2026-10-07, retrieved 2026-10-08; not the official Clarivate IF)Reference: Dini M, Turchi L, Gamberini G, Caporali A, Lucchini G, Tacchini M, et al. A Feasible Machine Learning Approach to Improve Cognitive Screening in Multiple Sclerosis. Biomedicines. 2026 Oct 6;14(10):2262. doi:10.3390/biomedicines14102262.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.