1523 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 demonstrates that while the modulation index (MI) effectively quantifies the strength of phase-amplitude coupling, it fails to encode temporal polarity, meaning it cannot distinguish between opposite temporal organizations of cross-frequency coupling and thus requires complementary phase-sensitive measures for mechanistic interpretations of temporal alignment.
This study demonstrates that auditory spatial attention in humans involves a coordinated interplay between neural alpha oscillations, which filter relevant and irrelevant stimuli, and active head rotations that reduce sensory uncertainty and improve task accuracy.
This study provides the first direct electrophysiological evidence from human intracranial recordings that the ventral tegmental area encodes reward-related signals consistent with reinforcement learning mechanisms, including the differentiation of rewarding outcomes and the representation of expected value during decision-making.
This study presents the first comprehensive, age- and sex-specific normative charts for cervical spinal cord morphometry across the human lifespan (0–100 years), derived from over 78,000 MRI scans using advanced deep learning and statistical modeling to establish a critical framework for distinguishing typical development and aging from pathological changes.
This cross-sectional experimental study demonstrates that movement-contingent pain does not significantly disrupt motor learning or alter the development of efficient attentional strategies compared to tonic pain or pain-free conditions, as all groups showed similar performance improvements and attentional shifts despite the presence of pain.
Using high-density recordings in macaque area MT, this study reveals that the computation of object motion relies on a modular, hierarchical neural circuit where spatially segregated pattern neurons integrate inputs from distinct component neurons to resolve motion direction.
Using magnetoencephalography, this study demonstrates that synchronized -band neural activity between the prefrontal and visual cortices coordinates the maintenance of visuospatial working memory, linking prefrontal control to sensory representations and predicting memory accuracy.
Using mouse hippocampal slices, this study demonstrates that the direction of synaptic plasticity is determined by the temporal derivative of neural activity over a theta cycle, where increasing activity induces long-term potentiation and decreasing activity induces long-term depression, while stable activity levels result in no net change.
This study challenges the prevailing view of U-fibers as ubiquitous, uniform connectors between adjacent gyri by demonstrating through macaque tract tracing that short association fibers exhibit diverse, sulcus-dependent organizational patterns and that the superficial white matter comprises multiple distinct fiber classes rather than a single canonical system.
This paper proposes a joint modeling framework that links non-decision time to external EEG measurements to resolve parameter estimation unreliability in time-dependent threshold diffusion models, thereby enabling robust inference of individual differences in decision dynamics.