1541 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 large-scale study combining quantitative MRI and computational modeling reveals that individual differences in reward-based decision-making are driven by distinct microstructural correlates, where cerebellar myelination predicts learning rate while precentral gyrus myelination and iron concentration determine learning noise.
This study utilizes The Virtual Brain framework to implement a dose-dependent NMDAR antagonism model, revealing that low-dose ketamine primarily impairs excito-inhibitory transmission via disinhibition while high doses additionally affect excito-excitatory connections, thereby elucidating the distinct neural mechanisms underlying its varying clinical effects.
This study demonstrates that right posterior theta oscillations, measured via scalp EEG during virtual navigation, significantly predict memory encoding and recall performance, suggesting this neural signal serves as a promising biomarker for spatial memory function in both health and disease.
This study demonstrates that SYNGAP1 haploinsufficiency and disruption of its PDZ ligand motif accelerate the differentiation and maturation of human iPSC-derived GABAergic neurons through dysregulation of synaptic proteins and transcriptional control, establishing SYNGAP1 as a critical regulator of neuronal development across both excitatory and inhibitory lineages.
This study demonstrates that perceptual expectations selectively modulate lateral and feedback processing of task-relevant visual features, while NMDA receptor blockade via memantine specifically enhances feedback mechanisms associated with perceptual inference without altering expectation or attention effects.
This study demonstrates that G51D alpha-synuclein mice, a model of Parkinson's disease, exhibit a selective loss of primary cilia and neurotrophic signaling in specific neuronal populations, revealing a convergent pathogenic mechanism that contributes to dopaminergic neuron degeneration across both inherited and common forms of the disease.
This study identifies a conserved central neural circuit in which glucose-responsive neuropeptidergic neurons (expressing Hugin in flies and Neuromedin U in mice) directly detect internal energy states to suppress sweet taste sensation via specific downstream signaling pathways.
By leveraging the complete *Drosophila* connectome, this study maps the circuit-scale logic of axo-axonic synapses onto descending neurons, revealing a non-clustered network architecture and experimentally validating that a specific cohort of ascending neurons modulates escape circuit excitability through these synapses.
This paper introduces a large-scale benchmark aligning over 100 deep video models with dynamic EEG recordings using Cross-Temporal Representational Similarity Analysis, revealing that the human brain functions as a dynamic mixture of expert models that differentially integrate temporal and static features across posterior and frontal regions.
This paper introduces OSCAR, a one-class SVM-based normative modeling framework that effectively detects subtle, condition-specific reorganizations in functional brain networks by identifying multivariate connectivity deviations in both cognitive tasks and clinical populations, outperforming traditional methods like perMANOVA in sensitivity and alignment with independent findings.