98 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 presents and validates an end-to-end deep learning pipeline that successfully segments white matter hypoattenuations in CT scans by combining expert-annotated and pseudo-labelled multi-centre data, achieving high volume correlation with MRI ground truth and demonstrating the clinical viability of CT for assessing white matter disease burden where MRI is unavailable.
This single-center cohort study of 404 individuals with pathogenic PRNP variants in the United States validates the accuracy of retrospective survival data while suggesting a slightly later age of onset, demonstrating that combining autopsy records with public death searches provides a highly sensitive method for tracking mortality in this population.
By integrating genome-wide association study data with brain transcriptomics and drug perturbation signatures, this study identifies robust transcriptional signatures associated with long-term ischemic stroke outcomes and prioritizes candidate compounds, including anandamide, progesterone, and Z-guggulsterone, for drug repurposing to improve functional recovery.
The Sleep Apnoea and Memory (SAM) study is a prospective multi-site observational trial designed to determine the prevalence of sleep apnoea in NHS memory clinic patients, evaluate optimal screening methods, and inform future interventional trials aimed at improving cognitive outcomes through sleep apnoea treatment.
This study demonstrates that integrating instantaneous frequency and amplitude into EEG microstate analysis reveals distinct alterations in brain state prevalence and network organization in Alzheimer's disease patients compared to healthy controls, offering a complementary approach to characterizing neurodegenerative dysfunction.
In a pilot randomized crossover trial involving nine Parkinson's disease patients, chronic adaptive and conventional deep brain stimulation demonstrated comparable population-level efficacy, though exploratory analyses suggest that individual baseline disease characteristics may influence specific treatment advantages, highlighting the need for larger studies to identify optimal patient subgroups.
This paper presents and validates a novel implantable device platform that utilizes secure large language models to enable bidirectional, real-time conversation between patients and an AI agent, allowing the system to co-manage epilepsy by detecting biomarkers, personalizing interactions, and rapidly adapting seizure detection algorithms without extensive expert intervention.
This study introduces MELD-PostOp, a deep learning tool trained on a large, multi-center cohort that achieves accurate, generalizable, and rapid (17-second) automated segmentation of post-surgical resection cavities on MRI, significantly outperforming existing methods in both accuracy and efficiency.
This study utilizes patient and family-reported data from 32 individuals to characterize the heterogeneous clinical spectrum of IRF2BPL-related disorders and provides evidence that missense variants are associated with a less severe phenotype compared to truncating variants.
This study demonstrates that a multi-session hierarchical Bayesian model (MS-HBM) can reliably map individual-specific resting-state networks in drug-resistant epilepsy patients using short fMRI sessions, enabling accurate prediction of task-based language dominance for presurgical planning.