153 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 systematic review of 59,621 adults demonstrates that the mechanism of traumatic brain injury significantly influences long-term functional independence, with recreational injuries yielding the best outcomes and penetrating trauma resulting in the most adverse functional recovery.
This study demonstrates that a data-driven transdiagnostic approach using single-visit MRI can identify three distinct brain atrophy subtypes across Alzheimer's disease and dementia with Lewy bodies, offering a promising tool for in vivo biological staging and subtyping despite imperfect alignment with clinical diagnoses.
Using high-throughput SomaScan assays on large cohorts, this study identified specific blood proteins associated with synaptic plasticity and lysosomal function that predict the progression to dementia in Parkinson's disease years before clinical symptoms appear, with Mendelian Randomisation suggesting a causal link between the Nogo receptor RTN4R and Lewy body dementia.
By employing novel tasks and mixed-model lesion-symptom mapping in frontotemporal dementia patients, this study demonstrates that the anterior temporal lobe serves as a domain-general semantic hub while ventral prefrontal and insular regions specifically support social concepts.
This study demonstrates that applying a machine learning classifier to wrist accelerometry data in the UK Biobank effectively identifies individuals with REM sleep behavior disorder who have a significantly elevated, dose-dependent risk of developing Parkinson's disease, offering a scalable and superior alternative to questionnaire-based screening for prodromal risk enrichment.
This study demonstrates that an interpretable, L1-regularized logistic regression model utilizing hemispheric asymmetry features from structural MRI can achieve statistically significant accuracy in detecting focal cortical dysplasia, offering a transparent and anatomically grounded alternative to complex deep learning approaches for presurgical epilepsy evaluation.
This paper presents the development and validation of a specialized Dravet Syndrome ontology, created through expert-guided expansion of an existing epilepsy framework, which serves as a durable infrastructure for data harmonization, knowledge representation, and AI-driven translational informatics in rare disease research.
This nationwide study establishes a baseline neurological exposome profile for a Roma movement-disorder screening cohort, revealing that marginalized participants and those with positive motor symptom screens face significantly greater environmental and socioeconomic disadvantages compared to their integrated and screen-negative counterparts.
This paper proposes and evaluates a multivariate mixed model that combines MRI whole brain atrophy with three clinical outcomes to enhance the resilience and statistical power of interim treatment response assessments in multi-arm multi-stage trials for progressive multiple sclerosis.
This community-based study in Lagos, Nigeria, reveals an age-standardized Parkinson's disease prevalence of 193 per 100,000 with significant male predominance and high diagnostic and treatment gaps, providing crucial contemporary data to inform health system planning in sub-Saharan Africa.