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.
In a pragmatic randomized controlled trial involving 470 people with multiple sclerosis and impaired quality of life, the digital therapeutic levidex, when added to standard care, significantly improved disease-specific quality of life, depressive symptoms, and social functioning compared to standard care alone.
By integrating post-mortem tissue data, in vivo PET imaging, and kinetic modeling, this study reveals that a profound defect in vesicular dopamine storage, rather than just synthesis or release, is the primary cause of dopamine depletion in the residual terminals of Parkinson's disease patients.
This study demonstrates that transcranial electrical stimulation with temporal interference (TES-TI) targeting the thalamus at a fixed 10 Hz difference frequency significantly enhances sleep spindle activity and sigma band power during daytime naps, outperforming both individually matched frequency and stimulation without a difference frequency.
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.