1527 papers
Neuroscience explores the intricate machinery of the brain and nervous system, seeking to understand how we think, feel, and move. From the microscopic dance of individual neurons to the complex networks that shape our memories and behaviors, this field peels back the layers of our biological selves to reveal the origins of consciousness and disease.
At Gist.Science, we bring these discoveries directly from bioRxiv, the leading preprint server for biological sciences, to a broader audience. We process every new neuroscience preprint as it is uploaded, transforming dense academic manuscripts into clear, plain-language explanations alongside detailed technical summaries. This ensures that both curious readers and specialists can stay current with the latest breakthroughs before they are formally published.
Below are the latest neuroscience papers we have processed from bioRxiv, offering fresh insights into the workings of the mind.
This study utilizes a large cohort of healthy adults and comprehensive source-reconstructed MEG analysis, while controlling for key confounding factors, to characterize healthy ageing as a frequency-specific shift in oscillatory power and increased coherence, alongside declining frontal network dynamics that appear to play a compensatory role in maintaining cognitive performance.
This study optimizes and applies molecularly-guided single-cell spatial proteomics to the mammalian brain, successfully characterizing region-specific neuronal proteomes, non-neuronal responses to injury, and disease-specific disruptions in dopaminergic neurons associated with Parkinson's disease.
This paper introduces and validates a real-time closed-loop TMS-EEG system that directly detects oscillatory phase without prediction, demonstrating superior triggering probability and phase precision compared to traditional prediction-based methods for targeting both spontaneous and event-related brain oscillations during active cognition.
This study presents an electrodiffusive computational framework demonstrating that ionic ephaptic effects primarily increase neuronal population firing rates, whereas electric ephaptic effects drive subtle spike timing shifts that lead to a stable, unique phase preference among neurons.
This study utilizes a *C. elegans* model to demonstrate that conserved homologs of understudied late-onset Alzheimer's disease risk genes causally regulate neuron-class-selective aging and neurodegeneration, often independently of organismal lifespan, by modulating early endosomal and lipid-related pathways.
This study demonstrates that reticulospinal tract hyperexcitability in stroke survivors, measured via the StartReact paradigm, is associated with greater motor impairment and spasticity rather than serving as a functional compensatory mechanism, particularly in severely affected individuals.
This paper proposes an adversarial approach that uses realistically simulated signals injected into real EEG data as ground truth to objectively evaluate and select preprocessing pipelines, thereby optimizing noise removal while preserving neural signal integrity to enhance the reproducibility and interpretability of ERP studies.
This study demonstrates that adult female mice exhibit a stronger preference for fat and greater overall food intake compared to males, who favor fat-carbohydrate combinations, with female feeding behavior further modulated by the estrous cycle.
This retrospective public benchmark demonstrates that healthy-to-stroke lower-limb EEG transport is currently weak and constrained more by source construction and adaptation burden than by model complexity, suggesting a need for harmonized prospective validation over further retrospective model iteration.
This study introduces MAMGL, a memory-augmented meta-graph learning framework (BrainMetaGCN) that leverages rs-fMRI data to achieve robust and interpretable diagnosis of adolescent major depressive disorder by effectively balancing individual-specific brain connectivity patterns with population-level generalization.