195 papers
Neurology explores the intricate workings of the brain and nervous system, tackling everything from memory and movement to complex conditions like epilepsy and Alzheimer's. This field seeks to understand how our minds function and what happens when that delicate machinery falters, aiming to improve lives through better diagnosis and treatment.
At Gist.Science, we make the latest research from medRxiv accessible to everyone. We process every new preprint in this category, offering both straightforward plain-language explanations and detailed technical summaries so you can grasp the science at your own pace. Below are the latest papers in neurology, ready for you to explore.
This study demonstrates that cerebrospinal fluid dynamics can be accurately reconstructed from noninvasive sleep physiology signals, such as ECG and PPG, establishing a scalable, wearable-compatible digital biomarker that overcomes the limitations of current invasive or expensive imaging methods.
Using the Epic Cosmos federated electronic health record network, this study characterizes population-level trends in psychiatric medication co-prescriptions among persons with epilepsy from 2018 to 2025, revealing stable antiseizure medication patterns, higher rates of concurrent psychiatric drug use compared to asthma patients, and distinct age-specific prescribing profiles that establish a framework for monitoring psychiatric comorbidity and drug interactions.
This study establishes a validated per-subject calibration framework for the Zmax wearable EEG headband using the Wearanize+ dataset, demonstrating that while the device systematically underestimates spectral power and requires specific adjustments for spindle detection, it achieves strong agreement with polysomnography after N2-referenced calibration, thereby enabling reliable home-based sleep biomarker research.
This regression discontinuity analysis of English and Welsh data finds that while eligibility for the live-attenuated shingles vaccine significantly reduced hospital-coded shingles diagnoses, it had no detectable effect on hospital-coded dementia diagnoses.
This study demonstrates that automated detection of motor speech disorders is feasible and clinically promising, with pretrained models like HuBERT and articulatory-informed Phonet features significantly outperforming static acoustic features in binary classification while showing more limited stability for multi-label subtype classification across independent datasets.
This study identifies reduced plasma levels of the brain-derived metabolite 24S-hydroxycholesterol in manifest Huntington's disease patients compared to premanifest and healthy groups, suggesting its potential utility as a biomarker for monitoring disease progression and distinguishing disease stages through diagnostic modeling.
This retrospective study of 291 patients at a tertiary center found that while routine brain CTs for acute dizziness have a low diagnostic yield, specific clinical predictors such as ataxia, headache, and diabetes can effectively identify patients at higher risk for central pathology, whereas isolated vestibular symptoms like nausea and vomiting suggest a lower likelihood of abnormal findings.
This study demonstrates that deep-learning algorithms, particularly PLAn-FL and nnU-Net, outperform machine-learning methods in accurately segmenting white matter lesions on portable 64mT MRI scans, with the resulting volume measurements showing significant correlations to clinical disability scores in multiple sclerosis patients.
This study demonstrates that Mini Mental State Examination (MMSE) scores systematically extracted from the VA Million Veteran Program's electronic health records are valid for cognitive aging research, showing significant associations with APOE ε4 status and dementia diagnoses across diverse ancestral groups.
This study demonstrates that goodness of pronunciation scores derived from self-supervised speech representations, particularly WavLM, serve as superior objective markers for assessing speech severity in motor speech disorders compared to traditional acoustic features and phonological posterior probabilities, while showing strong alignment with perceptual ratings of distortion and intelligibility.