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 study demonstrates that Soma and Neurite Density Imaging (SANDI) effectively detects Huntington's disease-related striatal neurodegeneration by revealing reduced soma density and increased soma size, which significantly correlate with motor impairment and striatal atrophy, positioning SANDI as a promising non-invasive biomarker for clinical trials.
This study demonstrates that incorporating simultaneous surface electromyography (EMG) into fMRI analysis enhances the specificity of detecting motor-related brain activity and differentiating spontaneous motor behavior, while the inclusion of temporal derivatives improves detection in primary sensorimotor cortices but may obscure signals in subcortical regions due to differing temporal dynamics.
This longitudinal study identifies that baseline between-network functional connectivity, particularly in the right anterior insula, serves as a prognostic marker and that longitudinal reductions in this integration within salience, frontoparietal, and default mode networks constitute a key mechanism driving symptom improvement in Functional Neurological Disorder.
This study demonstrates that structural MRI atrophy in the amygdala and entorhinal cortex, particularly in medial subregions and cortical layers, serves as an early and biologically valid indicator of preclinical Alzheimer's disease that strongly correlates with the spatial distribution of tau pathology.
A survey of 241 people with multiple sclerosis reveals that acceptance of artificial intelligence in healthcare is moderate and context-dependent, with patients most comfortable using AI for supportive, low-risk tasks like symptom screening while strongly preferring clinician-led decision-making for diagnosis and treatment, a stance driven more by prior familiarity with AI than by disease severity.
This study proposes a data-driven, transdiagnostic phenotype-severity metric derived from neuropsychological scores that outperforms traditional time-based measures in accurately mapping dementia progression and distinguishing disease stages from clinical heterogeneity.
This study validates that while lesion network mapping can be confounded by global connectome artifacts in conditions like schizophrenia, it retains genuine biological specificity for other disorders such as OCD, aphasia, and epilepsy when assessed through a rigorous low-dimensional projection framework.
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.