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 introduces a scalable multivariate blood-based framework that successfully identifies molecularly distinct resilient and resistant subgroups across the Alzheimer's disease spectrum, offering a new approach for pathology-informed stratification and mechanistic investigation.
This study demonstrates that a machine learning model analyzing 876 physiological features derived from multi-night wrist accelerometry can accurately detect isolated REM sleep behavior disorder (iRBD) with an AUC of 0.955, offering a scalable alternative to polysomnography for identifying prodromal synucleinopathy cohorts.
In a standardized comparison of 2,430 MCI participants, cognitive assessments emerged as the most predictive individual modality for 24-month progression to Alzheimer's disease, outperforming genetic, imaging, and fluid biomarkers, while a multimodal model achieved the highest overall accuracy but with significantly reduced data availability.
This systematic review and meta-analysis of 83,560 participants across 24 countries reveals a global diabetic peripheral neuropathy prevalence of 57.42%, identifies microvascular alteration as the sole significant mechanistic hallmark, and highlights peripheral vascular disease, male sex, and height as key risk factors while challenging the primacy of glycemic control in disease progression.
This study demonstrates that patients with persistent postural-perceptual dizziness (PPPD) exhibit distinct cerebro-cerebellar functional connectivity alterations, characterized by reduced visual-vestibular cortical coupling and increased connectivity between visual/multisensory areas and cerebellar regions involved in sensory prediction, which likely underlie the maladaptive multisensory integration and aberrant motion perception observed in the disorder.
This study demonstrates that imperfect seizure detection devices, characterized by reduced sensitivity or elevated false alarm rates, significantly amplify regression-to-the-mean and inflate the apparent placebo response in simulated epilepsy clinical trials.
This study demonstrates that an interleaved Hierarchical Attention Transformer (HAT) model, which explicitly captures cross-trial dependencies in single-pulse electrical stimulation responses without averaging, achieves superior concordance with the clinical seizure onset zone compared to traditional averaging methods while remaining robust to limited trial availability, though its association with postsurgical seizure freedom remains inconclusive.
Using a large-scale simulation of epilepsy patients, this study demonstrates that regression to the mean can artificially inflate apparent placebo responses through distinct mechanisms—including transient illness worsening, stricter eligibility criteria, reduced measurement sensitivity, and false alarms—thereby providing critical insights for improving trial design and interpreting endpoints.
This study demonstrates that the lesion-specific T1w/T2w-FLAIR ratio in white matter hyperintensities serves as a distinct microstructural biomarker differentiating vascular dementia, characterized by vascular-gliotic injury, from Alzheimer's disease, characterized by amyloid-linked demyelination, thereby capturing tissue integrity loss that volumetric measures miss.
The PRECISE-TCD study is a prospective observational protocol designed to evaluate whether serial Transcranial Doppler-derived hemodynamic parameters can predict early neurological deterioration and hemorrhagic transformation in acute ischemic stroke patients following endovascular thrombectomy, with the ultimate goal of guiding individualized blood pressure management strategies.