AI and Neurological Rare & Neuroimmune Diseases ── 2026-10-07
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（1） · ALS（0） · Parkinson's（2） · 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
- Abstract
Early differentiation of neurodegenerative movement disorders remains challenging because of overlapping cognitive-behavioral profiles. The Montreal Cognitive Assessment (MoCA) is a widely used 30-point screening instrument; however, the extent to which MoCA subdomain scores alone can support automated multi-class differential diagnosis or score prediction requires rigorous methodological evaluation. In this study, we investigated supervised machine learning algorithms across two distinct tasks using clinical data from the National Institute of Neurological Disorders and Stroke (NINDS) Parkinson’s Disease Biomarkers Program (N=64): (1) multi-class classification across movement disorder diagnoses, including Parkinson’s disease (PD, n=27), No Neurological Diagnosis (Controls, n=14), Progressive Supranuclear Palsy (PSP, n=9), Essential Tremor (ET, n=8), Corticobasal Degeneration (CBD, n=1), Multiple System Atrophy (MSA, n=1), and Other (n=4); and (2) numerical prediction/reconstruction of the MoCA total score from its constitutive domain items, with external validation on 1499 records from the Parkinson’s Progression Markers Initiative (PPMI). Under stratified 5-fold cross-validation, multi-class diagnostic classification was severely limited: the highest macro-averaged F1-score achieved by any trained model was 0.362 (95% CI: 0.284–0.440; Logistic Regression), trailing a naive Constant majority-class baseline in classification accuracy (0.463 vs. 0.366). Bootstrap resampling yielded apparent increases in performance (Neural Network macro-F1: 0.558, 95% CI: 0.512–0.604; AdaBoost: 0.555); however, this reflects optimistic bias from pseudo-replicate sampling rather than genuine clinical generalizability. For continuous score modeling, Linear Regression and Stochastic Gradient Descent achieved near-perfect internal fit (R2=1.000 and 0.996, respectively), and retained near-perfect performance in the external PPMI cohort (R2=0.992 and 0.991; MSE ≤0.077). We demonstrate that this regression success reflects the mathematical recovery of an additive scoring definition, i.e., target leakage, rather than independent clinical prediction. MoCA domain scores alone possess insufficient disease-specific variance to differentiate complex parkinsonian syndromes, underscoring that automated clinical classification requires the integration of multimodal neuroimaging, fluid, and digital motor biomarkers.
Journal IF-equivalent: 5.3 (OpenAlex 2-year mean citedness, value as of 2026-10-06, retrieved 2026-10-07; not the official Clarivate IF)Reference: Olanrewaju KB, Mbue ND. Machine Learning Applied to Montreal Cognitive Assessment Subdomains for Differential Diagnosis of Neurodegenerative Movement Disorders: Classification Limits and Deterministic Score Reconstruction. Bioengineering. 2026 Oct 4;13(10):1161. doi:10.3390/bioengineering13101161.Checked: Abstract only
See all 1 Basic research papers →
ALS: treatment, trials and AI
No new qualifying paper for this issue.
Parkinson's disease and parkinsonism: treatment, trials and AI
- Abstract
Purpose: Diagnosis of Alzheimer's disease (AD) and Parkinson's disease (PD) remains challenging, and existing AI-based neuroimaging tools are typically applied in isolation for diagnosis, multimodal analysis, or progression tracking. This study aims to develop an integrated, three-stage deep learning framework that unifies these tasks into a single diagnostic and prognostic pipeline. Design/Methodology/Approach: The framework was developed and validated using the OASIS, ADNI, PPMI, and TCIA databases and comprises three sequential stages. The first stage, MHNFSP, applies fuzzy c-means segmentation to MRI/fMRI scans, followed by extraction of Fourier transform, entropy, and convolutional features; the Whale Optimisation Algorithm is then used for feature selection, and a hybrid neural-statistical classifier performs final prediction. The second stage conducts multimodal assessment by fusing MRI, CT, and X-ray data through a multi-head self-attention mechanism, using UNet++ for region-of-interest segmentation and Grad-CAM++ for visual explainability. The third stage, LMST-ADNet, focuses on longitudinal disease modelling by combining MRI, PET, and fMRI connectivity data with clinical-genetic information, employing modality-specific encoders, temporal modelling, and self-supervised pre-training. Research Limitation: The study relies on retrospective, publicly available datasets that may not fully capture the demographic and clinical diversity of real-world patient populations. External, prospective, multi-site validation and testing on unseen clinical cohorts would be required to confirm generalisability before clinical deployment.
Findings: The MHNFSP stage achieved 96.39% classification accuracy on MRI/fMRI data. The multimodal fusion stage achieved 95.3% accuracy, a mean Dice score of 0.92 and an Intersection over Union (IoU) of 88.0% agreement with expert-marked regions. The LMST-ADNet stage achieved 96.8% classification accuracy, an AUC of 0.982, and a C-index of 0.91 for progression risk prediction. Ablation studies confirmed that temporal modelling substantially contributes to performance, with additional gains obtained through self-supervised pre-training. Practical Implication: The proposed three-stage system offers clinicians and radiologists an integrated tool capable of supporting early diagnosis, multimodal cross-verification, and long-term monitoring of neurodegenerative disease progression, potentially reducing diagnostic delay and supporting more personalised treatment planning. Social Implication: Earlier and more reliable detection of AD and PD could improve patient quality of life, reduce caregiver burden, and lower long-term healthcare costs associated with delayed diagnosis and disease management, particularly benefiting ageing populations at higher risk. Originality/Value: Unlike prior approaches that address diagnosis, multimodal fusion, and progression prediction as separate problems, this study proposes a unified three-stage architecture that integrates segmentation-based classification, explainable multimodal fusion, and longitudinal transformer-based progression modelling within a single coherent framework, offering a more holistic assessment pathway for neurodegenerative disease research.
Journal IF-equivalent: 1.2 (OpenAlex 2-year mean citedness, value as of 2026-10-06, retrieved 2026-10-07; not the official Clarivate IF)Reference: Mohod SK, Thakare RD. Three-Stage Deep Learning Approach for Alzheimer’s and Parkinson’s Disease Detection. AJAR. 2026 Oct 6;12(6):279-88. doi:10.26437/2tqabv67.Checked: Abstract only - Abstract
Parkinson’s disease (PD) is a neurodegenerative disorder characterized by the degeneration of dopaminergic neurons, leading to a variety of motor and non-motor symptoms. Analyzing brain functional connectivity is crucial for understanding PD, with electroencephalography (EEG) serving as a valuable, non-invasive tool for this purpose. Integrating complex network analysis with explainable artificial intelligence (XAI) provides deeper insights into the brain’s neural mechanisms affected by PD. Personalized connectome analysis allows for more accurate diagnosis and treatment, as it considers individual variations in brain networks, enabling the identification of specific patterns associated with the disease. This study analyzed electroencephalographic signals from 31 subjects to create complex networks. In our study, we decomposed the spectral content of the EEG signal into different frequency sub-bands, creating a connectome for each sub-band. Key network features, such as degree and centrality measures, were extracted and fed into the XGBoost algorithm for classification and employed explainable artificial intelligence (XAI) models. These models offer a structured and organized framework for EEG data analysis, enabling full utilization of the information contained in different frequency bands. The overall model achieved an AUC of $$0.85\pm 0.03$$ , precision of $$0.84\pm 0.07$$ , and accuracy of $$0.80\pm 0.05$$ . Each connectome of the five EEG rhythms was analyzed separately, with the theta band connectome showing the best performance (AUC $$0.82\pm 0.03$$ , precision $$0.76\pm 0.04$$ , accuracy $$0.75\pm 0.03$$ ). The multi-connectome, considering all EEG rhythm connectome, was also analyzed obtaining $$0.72\pm 0.04$$ as AUC, $$0.67\pm 0.06$$ as accuracy and $$0.68\pm 0.07$$ as precision score. The SHAP algorithm was used to evaluate feature contributions. By leveraging this integration, the model seeks to capture both band-specific, multi-band and global patterns in brain activity, enhancing the accuracy and interpretability of the final predictions. This study offers new perspectives on brain connectivity and the neural mechanisms involved in Parkinson’s disease. It underscores the importance of EEG connectivity analysis across different frequency bands and the crucial role of XAI models in achieving a more comprehensive understanding of the neural connections, highlighting the mechanisms underlying Parkinson’s disease. Our contributions include the use of a frequency-specific connectome model, integration of multiple complex network features with an XGBoost classifier, and the application of SHAP-based XAI to enable interpretable, personalized analysis of PD-related brain connectivity changes.
Journal IF-equivalent: 5.2 (OpenAlex 2-year mean citedness, value as of 2026-10-06, retrieved 2026-10-07; not the official Clarivate IF)Reference: Romano D, Magarelli M, Novielli P, Cuna F, Diacono D, Di Bitonto P, et al. An explainable EEG connectome framework for frequency-resolved analysis of functional connectivity alterations in Parkinson’s disease. Sci Rep. 2026 Oct 5 [Epub ahead of print]. doi:10.1038/s41598-026-71386-5.Checked: Abstract only
See all 2 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.