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 employs a Wilson-Cowan model to demonstrate that the heterogeneous gamma oscillation responses to deep brain stimulation in Parkinson's disease arise from bifurcations in underlying neural dynamics, where the specific response pattern depends on the pre-existing damping state of the system and exhibits hysteresis.
The paper introduces GRAD, a two-stage machine learning algorithm that combines a p-tau217 "Gatekeeper" with a multi-marker "Reflex" classifier to effectively resolve diagnostic uncertainty in the plasma p-tau217 gray zone, achieving high accuracy and significantly reducing healthcare costs compared to universal PET screening.
This study demonstrates that Visual Snow Syndrome patients exhibit distinct neurophysiological markers, including more diffuse TMS-evoked phosphene patterns and altered oculomotor dynamics, suggesting that combining phosphene mapping with eye-tracking offers a promising objective tool for assessing the condition's underlying cortical hyperexcitability.
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.