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 reveals that visuomotor flexibility in the marmoset frontal cortex arises from a mosaic organization of visual and motor maps with distinct spatial scales, where their superposition creates a moiré pattern that enables flexible transformations between visual inputs and saccade outputs.
This study demonstrates that volumetric reductions in specific thalamic nuclei, detectable via standard T1-weighted MRI, serve as practical biomarkers for identifying preclinical and symptomatic Alzheimer's disease and improving clinical classification accuracy.
The paper introduces the Decisions From Experience Database (DfE-DB), a comprehensive open-access resource harmonizing 3.8 million trial-level decisions from 11,921 participants across 168 studies to enable systematic comparisons of how design features shape human decision-making tendencies and foster reproducible research.
This study demonstrates that intranasal delivery of hypoxia-primed Wharton's jelly mesenchymal stem cell-derived small extracellular vesicles (WJ-H-sEVs) effectively ameliorates behavioral deficits and neuroinflammation in a valproic acid-induced mouse model of autism spectrum disorder by reprogramming neuroimmune signaling through the activation of the Nrf2 pathway and suppression of NF-κB and JAK-STAT signaling.
This study constructs a single-cell atlas of dorsal root ganglion remodeling following neuroma-forming nerve injury to compare the effects of proximal crush and nerve resection interventions, revealing distinct glial and immune transcriptional programs that differentiate permissive regeneration from persistent pain states and offering new targets for mechanism-guided therapies.
Using in vivo calcium imaging in rats, this study reveals that distinct neural ensembles in the prelimbic cortex independently track different facets of motivation for both cocaine and water rewards, rather than relying on a shared neural population to drive behavior across various tasks.
This study presents the first large-scale comparative analysis of 26 hybrid deep learning architectures for reconstructing speech envelopes from EEG signals, demonstrating that combining CNNs with LSTMs and GCNs effectively captures complex spatio-temporal patterns and offers practical guidelines for advancing robust non-invasive brain-computer interfaces.
This study demonstrates that closed-loop neurofeedback training to increase spontaneous gamma oscillations in the parieto-occipital region causally reduces pain perception, unpleasantness, and laser-evoked potentials in healthy subjects, establishing a novel therapeutic strategy for pain management.
This study utilized structural MRI and machine learning on 674 participants to identify distinct typical and atypical brainstem volume loss patterns, finding that while aging affects these systems, age itself explains more variance in functional decline than the specific volume loss patterns.
This paper addresses the underutilization of functional templates in fMRI by comprehensively evaluating various alignment methods and construction strategies across multiple tasks, ultimately demonstrating that population templates built using Optimal Transport achieve superior decoding accuracy, ensure generalization to new subjects, and preserve cortical signal topography.