AI and Neurological Rare & Neuroimmune Diseases ── 2026-10-26
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（2） · 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: Parkinson's disease (PD) is characterized by progressive motor dysfunction, in which gait disturbances often emerge early yet remain underutilized in diagnostic frameworks. Advances in wearable sensor technology enable high-resolution quantification of gait dynamics, but the clinical translation of such high-dimensional data is limited by redundancy and model overfitting. OBJECTIVE: To determine the exploratory diagnostic efficacy of synthesizing wearable sensor-derived gait parameters, cognitive scores, and blood biomarkers utilizing a dual-stage dimensionality reduction framework coupled with logistic regression machine learning for the precise identification of PD. METHODS: In this cross-sectional study, demographic characteristics, blood biomarkers, cognitive assessments, and wearable sensor-derived gait parameters were collected from patients with PD and matched controls. Following initial group comparisons, least absolute shrinkage and selection operator (LASSO) regression was applied to identify candidate predictors from multimodal variables. Principal component analysis (PCA) was subsequently performed to reduce dimensionality and extract latent structures. Logistic regression models were constructed using both the original LASSO-selected variables and PCA-derived components. Model performance was assessed using 10-fold cross-validation, with discrimination quantified by the area under the receiver operating characteristic curve (AUC) and accuracy. RESULTS: Baseline characteristics were comparable between groups except for age. LASSO identified 22 potential predictors spanning demographic, biochemical, cognitive, and gait domains. PCA further condensed these variables into six principal components, explaining 62.983% of the total variance. Using a rigorous nested cross-validation framework on the primary cohort, the PCA-based model achieved an internal mean AUC of 0.949 with an accuracy of 0.827. Furthermore, when evaluated on a completely independent external cohort ( N = 36) to ensure generalizability, the finalized PCA-based model demonstrated robust diagnostic performance, yielding an AUC of 0.891 and an accuracy of 0.750. Both approaches showed robust discriminative capacity, with dimensionality reduction enhancing overall efficiency and stability. CONCLUSION: Integrating wearable gait signatures with LASSO-PCA dimensionality reduction and logistic regression yields reliable, interpretable PD classification, with PCA-condensed features demonstrating enhanced diagnostic efficiency. These findings warrant external validation in multicentre, longitudinal cohorts to advance non-invasive, sensor-based PD screening.
Journal IF-equivalent: 2.6 (OpenAlex 2-year mean citedness, value as of 2026-10-08, retrieved 2026-10-09; not the official Clarivate IF)Reference: Hong G, Wen X, Gao J, Lin J, Zumulaiti S, Yang Y, et al. Wearable sensor–derived gait signatures for Parkinson's disease: an exploratory diagnostic study integrating dimensionality reduction and machine learning. Front Med (Lausanne). 2026 Oct 8;13:1939598. doi:10.3389/fmed.2026.1939598; PMCID: PMC13686899.Checked: Full text checked - Abstract
BACKGROUND: Vocal impairment constitutes a prominent prodromal biomarker for Parkinson's disease (PD). However, existing deep learning solutions often rely on computationally intensive architectures or biased validation protocols. This study proposes the lightweight spectrotemporal hybrid network (LST-Net), a resource-efficient framework for robust PD screening. METHODS: The model employs a dual-stream architecture that concurrently processes raw one-dimensional waveforms and two-dimensional Mel-spectrograms to capture complementary cues, specifically transient temporal jitters and spectral energy patterns. The proposed architecture was evaluated on the Italian Parkinson's Voice and Speech dataset using a strict subject-independent 5-fold cross-validation protocol to prevent data leakage. RESULTS: The proposed LST-Net achieved a robust segment-level accuracy of 96.69%. By implementing a subject-level majority voting strategy, mimicking clinical diagnosis where patients are assessed over time, the model successfully classified all subjects in the held-out test sets (100% subject-level accuracy). Quantitative Gradient-weighted Class Activation Mapping analysis confirmed the model's focus on clinically relevant low-frequency tremors (mean energy: 61.56% ±27.90%). CONCLUSIONS: This framework demonstrates that explicit spectrotemporal fusion yields superior diagnostic performance without heavy computational burdens, offering a verifiable and scalable solution for real-time deployment on edge devices.
Journal IF-equivalent: 3.0 (OpenAlex 2-year mean citedness, value as of 2026-10-24, retrieved 2026-10-26; not the official Clarivate IF)Reference: [No authors listed]. Subject-independent Parkinson's Disease Detection through A Lightweight Spectrotemporal Hybrid Network. J Med Signals Sens. [Epub ahead of print]. doi:10.4103/jmss.jmss_137_25. PMID: 42924316.Checked: Abstract only
See all 2 Parkinson's papers →
Hereditary neurodegenerative diseases: treatment, trials and AI
- Abstract
BackgroundCerebellar ataxia is characterized by abnormalities in gait, speech, ocular movement, and coordination, making accurate detection challenging.PurposeThis study develops a lightweight multimodal Deep Learning (DL)-based framework for cerebellar ataxia detection.Research DesignThe proposed approach integrates voice, gaze, and body pose features from independent public datasets and performs class-level multimodal feature fusion to capture complementary motor and behavioral cues. Fine-grained features such as Mel Frequency Cepstral Coefficients (MFCCs) and vocal perturbation measures, gaze fixation dynamics, and spatiotemporal body pose features are extracted using Python-compatible tools. Each modality is processed with a specific Multilayer Perceptron (MLP)-based encoder to produce small embeddings, which are fused through feature concatenation and processed by a fully connected MLP classifier. To ensure stable convergence and robust generalization capability on limited and noisy clinical data, a novel hybrid optimizer, Adaptive Sharp-Lookahead Lite, is used.ResultsExperimental results demonstrate that the proposed framework achieves an accuracy of 99.56% in distinguishing ataxia from normal cases under the adopted experimental setting.ConclusionsThe findings demonstrate the potential of the framework as a lightweight AI-based screening approach for resource-constrained settings.
Journal IF-equivalent: 2.5 (OpenAlex 2-year mean citedness, value as of 2026-10-25, retrieved 2026-10-26; not the official Clarivate IF)Reference: A N, V S. Lightweight Multimodal Deep Learning Framework for Cerebellar Ataxia Detection Using an MLP Encoder and Hybrid Optimizer. Percept Mot Skills. 2026 Oct 24 [Epub ahead of print]. doi:10.1177/00315125261501241. PMID: 42925486.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
Brain’s structural and functional connectomes (SC and FC) offer insights into the relationship between brain circuits and cognitive functioning in people with multiple sclerosis (MS). However, diffusion and functional MRI, which are used to extract SC and FC, is expensive and time consuming, therefore are not currently used in the clinical setting. Our prior work demonstrated that estimated SC and FC (eSC and eFC) generated using MS lesion masks and artificial intelligence (AI) models can predict disability as effectively as SC and FC derived from diffusion and functional MRI in MS. In this study, we identify the eSC and eFC brain circuits that are associated with baseline and 4-year follow-up cognition and assess the ability of eSC and eFC in predicting cognition in MS. In a cohort of 171 with MS, the Network Modification tool was performed to estimate SC from the MS lesion mask, which is extracted from structural MRI scans. The eSC was then used as an input to Krakencoder, an encoder-decoder model, to estimate FC. The highest accuracy was obtained when predicting follow-up Symbol Digit Modalities Test (SDMT) using regional eSC or eFC, with a median Spearman’s correlation coefficient of 0.58 for eSC and 0.56 for eFC. Decreased eSC and eFC in the cerebellum and increased eFC in the default mode network were associated with lower follow-up SDMT scores. Our findings demonstrate that eSC and eFC derived from clinically acquired MRI and AI models can effectively predict cognition. The use of lesion-based estimates of connectomes may potentially inform clinical trials by identifying the specific brain circuits that could be targeted for the non-invasive treatments aimed at improving cognitive function in MS. Researchers used AI and clinically acquired MRI scans to estimate brain connectivity networks in people with multiple sclerosis, finding these estimates can moderately predict cognitive performance four years later and highlighting key brain circuits involved in cognition.
Journal IF-equivalent: 18.6 (OpenAlex 2-year mean citedness, value as of 2026-10-25, retrieved 2026-10-26; not the official Clarivate IF)Reference: Tozlu C, Ong D, Piccirillo C, Schwartz H, Jaywant A, Nguyen T, et al. Predicting follow-up cognition using estimated structural and functional connectivity networks and artificial intelligence in multiple sclerosis. Nat Commun. 2026 Oct 23 [Epub ahead of print]. doi:10.1038/s41467-026-75827-7.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.