AI and Neurological Rare & Neuroimmune Diseases ── 2026-10-17
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
Background : Normal aging involves gradual declines in brain function, whereas neurodegenerative disorders show accelerated deterioration. Electroencephalography (EEG) captures alterations in brain oscillatory activity, making it promising for detecting neurodegenerative disorders. However, existing approaches often emphasize classification performance over spectral biomarker identification, limiting clinical translation. Methods : We propose an EEG-based framework integrating explainable machine learning, analyses of inter-subject heterogeneity and temporal stability, and probabilistic modeling to identify reproducible spectral biomarkers of pathological brain aging. Two independent public EEG datasets comprising cognitively normal (CN) individuals and patients with Alzheimer's disease (AD), Parkinson's disease (PD), and frontotemporal dementia (FTD) were analyzed. Spectral features from canonical frequency bands were analyzed using SHAP to identify reproducible biomarkers in two datasets. The identified biomarker was subsequently characterized through inter-subject heterogeneity, temporal stability, and empirical distributions to assess disease-related alterations in oscillatory activity. Results : Several spectral features showed consistent disease-related associations across classification tasks. The θ/α ratio emerged as a representative biomarker, showing reduced temporal stability and broader empirical distributions in most neurodegenerative groups than in CN. Conclusions : Reduced temporal stability of reproducible EEG spectral biomarkers may represent an observable signature of disrupted oscillatory brain organization during pathological aging. The proposed framework characterizes a dimension of EEG spectral biomarkers that static summaries discard, offering an interpretable basis on which future longitudinal studies may build diagnostic and monitoring tools.
Journal IF-equivalent: 4.0 (OpenAlex 2-year mean citedness, value as of 2026-10-16, retrieved 2026-10-17; not the official Clarivate IF)Reference: Prieur-Coloma Y, Prado P, El-Deredy W, Weinstein A. An Interpretable Framework for Identifying EEG Spectral Biomarkers of Pathological Brain Aging. Diagnostics. 2026 Sep 27;16(19):3143. doi:10.3390/diagnostics16193143.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
Drug-induced parkinsonism is a potentially reversible movement disorder that may be subtle and difficult to monitor in residential dementia care. Vision-based machine learning analysis of gait may provide unobtrusive longitudinal monitoring around medication changes. We conducted a two-center cohort study of 61 residents with dementia receiving psychotropic medication. Hallway gait sequences were recorded using ceiling-mounted cameras and converted to three-dimensional body pose. A TabPFN classifier estimated UPDRS-III item 29 gait severity for each walk. We evaluated associations between acute antipsychotic (AP) and antidepressant (AD) dose reduction/cessation ( ≥ 50% dose reduction or cessation, 0–21 days after change) and daily mean predicted gait severity using linear mixed-effects models in ever-exposed participants, with complementary equal-weight paired analyses among participants observed in both medication states. In mixed-effects models, acute AP dose reduction/cessation was associated with lower predicted gait severity ( β = − 0.127 , p = 0.022), whereas AD dose reduction/cessation was not ( β = − 0.060 , p = 0.210). Equal-weight paired analyses found no statistically significant participant-average within-person change among paired participants (AP mean change = 0.031, 95% CI [ − 0.114 , 0.176], p = 0.653; AD mean change = − 0.021 , 95% CI [ − 0.098 , 0.056], p = 0.575), consistent with heterogeneous individual responses. Vision-based monitoring captured within-participant gait variation around psychotropic dose-reduction periods, but findings are associative. Larger prospective studies with documented reasons for medication changes are needed before using such monitoring to guide medication decisions.
Journal IF-equivalent: 6.6 (OpenAlex 2-year mean citedness, value as of 2026-10-16, retrieved 2026-10-17; not the official Clarivate IF)Reference: Filtjens B, Adeli V, Mehraban S, Sabo A, Taati B, Iaboni A. Assessment of change in drug-induced parkinsonism in daily-life gait using vision-based machine learning. PLOS Digit Health. 2026 Oct 15;5(10):e0001780. doi:10.1371/journal.pdig.0001780.Checked: Abstract only
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.