AI and Neurological Rare & Neuroimmune Diseases ── 2026-11-10
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
Background and ObjectiveParkinson’s disease (PD) affects both motor and cognitive functions, resulting in hypokinetic dysarthria and language deficits. Acoustic and linguistic speech analysis offer a non-invasive and intuitive way to assess and characterize these impairments. In this study, we classified PD individuals and differentiated cognitive phenotypes using an automated framework integrating acoustic and linguistic features captured from a connected speech task.MethodsWe recruited a cohort of 34 healthy controls (HCs) and 36 PD patients, who underwent a complete neurological and neuropsychological assessment in “ON” phase. PD patients were further subdivided into 16 PD without mild cognitive impairment (PD-nMCI) and 20 PD with MCI (PD-MCI). Connected speech was elicited using a picture description task. From the audio recordings, we extracted 42 speech features and 19 linguistic features, spanning multiple levels of speech and language. These features were used both individually and in combination to train a machine learning model. A nested 10-fold cross-validation pipeline was implemented, incorporating Recursive Feature Elimination for feature selection and grid-search for hyperparameter tuning, to train a Support Vector Machine for the following classifications: PD vs. HCs, PD-nMCI vs. HCs, PD-MCI vs. HCs, and PD-nMCI vs. PD-MCI. Model explainability was assessed using Shapley values. Finally, partial correlations were computed between speech features and clinical scales.ResultsClassification analysis using a 10-fold cross-validation strategy revealed high discrimination performance, with average accuracies of 94.29% for PD vs. HCs, 91.67% for PD-nMCI vs. HCs, and 87.50% for PD-MCI vs. HCs when combining both feature domains. Interestingly, the linguistic domain alone provided better discrimination between PD-MCI and PD-nMCI, achieving an average accuracy of 87.50%. Model explainability analysis identified key predictive features, including articulation entropy, fundamental frequency variability, vowel space metrics, and morphological error ratios.ConclusionsDespite the small sample size, these results highlight the potential of automated speech and language analysis as an accurate and interpretable tool for detecting PD and its cognitive impairments, offering scalable and clinically meaningful approaches for early diagnosis, patient stratification, and personalized monitoring.
Journal IF-equivalent: 7.1 (OpenAlex 2-year mean citedness, value as of 2026-11-09, retrieved 2026-11-10; not the official Clarivate IF)Reference: Pierotti F, Aresta S, Benzini A, Santi GC, Palmirotta C, Tagliente S, et al. Automated multimodal acoustic and linguistic analysis for detection and cognitive characterization in Parkinson’s disease. BioMed Eng OnLine. 2026 Nov 7 [Epub ahead of print]. doi:10.1186/s12938-026-01653-z.Checked: Abstract only
See all 1 Parkinson's papers →
Hereditary neurodegenerative diseases: treatment, trials and AI
- Abstract
Huntington’s disease is a hereditary neurodegenerative disorder requiring early diagnosis for effective clinical intervention. Traditional diagnostic methods are often invasive and inadequate for early-stage detection. This study presents a Chaotic Lyrebird Optimization–based Cascaded Convolutional Neural Network (ChLyO_CCNN) for accurate Huntington’s disease detection using MRI images. To address data scarcity and bias, Generative Adversarial Network (GAN)–based data augmentation is applied, followed by Gaussian filtering for noise reduction. The cascaded CNN architecture integrates P-Net, R-Net, and O-Net to progressively refine feature extraction and classification. Hyperparameters of the network are optimized using the ChLyO algorithm to enhance convergence and generalization. Experimental results demonstrate that the proposed method achieves high diagnostic performance with an accuracy of 99.76%, precision of 99.65%, and sensitivity of 99.81%, outperforming existing approaches. The proposed framework offers a reliable and efficient AI-assisted solution for Huntington’s disease detection in clinical neuroimaging applications.
Journal IF-equivalent: 1.1 (OpenAlex 2-year mean citedness, value as of 2026-11-09, retrieved 2026-11-10; not the official Clarivate IF)Reference: Amudaria S, Joseph Jawhar S. Huntington’s disease detection using the cascaded convolutional neural network with generative adversarial network based augmentation. Communications in Statistics - Simulation and Computation. 2026 Nov 8 [Epub ahead of print]. doi:10.1080/03610918.2026.2757043.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
Multiple sclerosis (MS) is an autoimmune demyelinating disease of the central nervous system that affects many adults and causes substantial neurological disability. Its clinical presentations are highly heterogeneous, and distinguishing clinically isolated syndrome (CIS), relapsing-remitting MS and progressive subtypes remains difficult, even though subtype determines treatment. Machine learning offers a way to address this difficulty: it identifies subtle patterns in high-dimensional biomedical data and has shown promise for MS classification from neuroimaging, whereas its performance on transcriptomic data has received less attention. To examine this question, we benchmarked three ensemble classifiers, AdaBoost, Random Forest and XGBoost, on 594 peripheral-blood mononuclear-cell transcriptomes from three public datasets, using a shared 6,033-gene matrix for four binary tasks: MS versus non-MS, CIS versus non-CIS, primary progressive versus relapsing-remitting MS, and secondary progressive versus relapsing-remitting MS. Models were evaluated on a 3:1 training/held-out split after standardization and two-component principal component analysis. All three classifiers achieved high held-out performance across the four tasks. These results indicate that ensemble machine learning can effectively classify MS and its clinical subtypes from blood transcriptomic profiles.
Journal IF-equivalent: 6.3 (OpenAlex 2-year mean citedness, value as of 2026-11-09, retrieved 2026-11-10; not the official Clarivate IF)Reference: Liu Y, Liu H, Li W. Clinical classification of multiple sclerosis by machine learning. Sci Rep. 2026 Nov 7 [Epub ahead of print]. doi:10.1038/s41598-026-79792-5.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.