AI and Neurological Rare & Neuroimmune Diseases ── 2026-10-10
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（1） · ALS（0） · 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
- Abstract
Neurodegenerative diseases (NDDs) often cause gait impairments with overlapping motor symptoms, complicating multi-class discrimination. This study proposes a lightweight dual-branch convolutional neural network integrating recurrence plots and spectrogram representations of vertical ground reaction force signals to classify Parkinson's disease, amyotrophic lateral sclerosis, Huntington's disease, and healthy controls. Evaluated on the Gait in NDDs Dataset comprising 64 subjects, the proposed framework achieved 96.15% accuracy, outperforming recurrence-based (94.19%) and spectrogram-based (95.75%) single-branch models. The findings demonstrate that combining complementary temporal recurrence and spectral information improves discrimination of neurodegenerative gait patterns while maintaining low computational complexity.
Journal IF-equivalent: 1.7 (OpenAlex 2-year mean citedness, value as of 2026-10-09, retrieved 2026-10-10; not the official Clarivate IF)Reference: Ozeloglu İG, Akman Aydin E. Gait-based neurodegenerative disease classification using multimodal temporal–spectral representations and a dual-branch CNN. Computer Methods in Biomechanics and Biomedical Engineering. 2026 Oct 8 [Epub ahead of print]. doi:10.1080/10255842.2026.2736243.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
INTRODUCTION: Parkinson's Disease (PD) is a neurological disorder that worsens over time and is marked by interhemispheric asymmetry, dopaminergic degeneration, and structural changes in the brain. METHODS: This paper presents a novel approach: AGMNet- Adaptive Gated Multimodel Network, which is an explainable gated multimodal deep learning framework that combines structural magnetic resonance imaging (MRI), dopamine transporter (DaT) imaging, asymmetry-aware representations, and biomarker features for robust PD classification. The Grad-CAM method is incorporated with the proposed AGMNet framework to improve the model interpretability by identifying image regions that influence classification decisions. The performance of AGMNet framework was evaluated using five-fold cross-validation and compared with established deep learning architectures, including VGG16, VGG19, InceptionV3, and Xception. RESULTS: The proposed AGMNet has achieved 0.8318 of accuracy, 0.8278 of balanced accuracy, sensitivity of 0.8556, specificity of 0.8000, AUC of 0.711, and Matthews correlation coefficient (MCC) of 0.6458. The proposed AGMNet model demonstrated competitive classification performance relative to the evaluated baseline architectures. The combination of DaT imaging, asymmetry-aware features, and biomarker representations in the proposed model, contributed more strongly to the fused representation than MRI features alone. DISCUSSION: The findings indicate that adaptive multimodal fusion in the AGMNet framework can improve the integration of complementary structural, functional, asymmetry-related, and biomarker information for PD classification. The learned gate weights further demonstrate the relevance of functional and asymmetry-aware information within the proposed framework, while Grad-CAM provides additional insight into model decision-making. Therefore, the AGMNet framework offers a potentially useful and interpretable framework for multimodal PD classification.
Journal IF-equivalent: 6.0 (OpenAlex 2-year mean citedness, value as of 2026-10-09, retrieved 2026-10-10; not the official Clarivate IF)Reference: Abinaya TG, Sivashankari R. AGMNet: an explainable gated multimodal framework integrating MRI heterogeneity and dopaminergic asymmetry for Parkinson's disease classification. Front Artif Intell. 2026 Sep 24;9:1849408. doi:10.3389/frai.2026.1849408; PMCID: PMC13645758.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.