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 a multi-level episodic memory task reveals age- and APOE4-related declines in retrieval accuracy and confidence under increasing cognitive demands, offering a sensitive approach to detecting subtle deficits in cognitively unimpaired older adults that single-level paradigms might miss.
This study proposes a multi-representational deep learning framework that integrates voxel-level PET imaging features with region-level quantification to significantly improve the sensitivity and accuracy of detecting cognitive decline compared to single-feature models and standard clinical assessments.
In a randomized controlled trial of 45 myasthenia gravis patients, telemedical monitoring via a mobile app significantly increased perceived specialist accessibility and facilitated frequent, timely communication regarding medical and technical issues, highlighting the potential of such solutions to address the demand for remote advice in managing this rare disease.
This study demonstrates that quantitative multiparametric MRI (MPM) effectively detects stage-specific microstructural alterations, such as early demyelination in premanifest and widespread neuronal loss in manifest Huntington's disease, establishing MPM as a valuable non-invasive biomarker for tracking neurodegeneration and correlating with clinical severity.
In a large population-based cohort, this study demonstrates that while theory-driven ATN classification and data-driven machine learning phenotypes show only modest concordance—largely driven by GFAP rather than shared amyloid, tau, and neurodegeneration biomarkers—both frameworks effectively predict longitudinal cognitive decline, suggesting that integrating these complementary approaches offers a more comprehensive characterization of Alzheimer's pathology.