1829 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 glutamatergic synapse maturation in the developing neocortex follows a region- and layer-specific pattern where feedforward pathways mature early while top-down feedback circuits in layer 1 undergo a delayed, global maturation during adolescence, representing the final stage of outside-in brain development.
This study demonstrates that *Acropora millepora* coral larvae exhibit an adaptive light-off response characterized by ciliary arrest and body contraction, which regulates their swimming behavior and positioning within complex reef environments to enhance recruitment and survival.
This study utilizing a combined offset analgesia and onset hyperalgesia protocol with fMRI found that women with non-suicidal self-injury exhibit a weaker onset hyperalgesia response compared to healthy controls, providing partial support for the hypothesis of enhanced pain modulation in this population.
By integrating whole-brain computational modeling with biophysical principles and mouse connectomics, this study elucidates how cellular excitatory-inhibitory imbalances and specific regional disruptions (particularly in the insula and retrosplenial cortex) govern the robustness, suppression, and distinct failure modes of the default mode network, thereby linking microscopic neuronal dysregulation to macroscopic network dysfunction in brain disorders.
Using high-resolution precision fMRI, this study reveals that the lateral prefrontal cortex is organized into densely interdigitated, individual-specific network patches with conserved motifs and sharp functional boundaries, challenging the dominant view of smooth, domain-general gradients derived from group-averaged data.
Through three studies involving 702 participants, the authors demonstrate that humans improve epistemic inference by employing a distinct two-stage policy—initially "streaking" to test hypotheses before shifting to uncertainty-guided exploration—which outperforms standard neural network strategies and reveals unique psychological traits beyond traditional reward-based exploration.
This study reveals that during active eating, caudal nucleus of the solitary tract (cNTS) neurons primarily encode rapid, pregastric oral and pharyngeal signals rather than the gradual gut-derived feedback previously thought to drive satiety, with descending hypothalamic projections playing a key role in tracking ingestion dynamics to regulate meal termination.
This study utilizing two large developmental cohorts found that while transdiagnostic latent factor models can distinguish more distinct neural signatures between psychopathology dimensions, they do not systematically improve the reliability or strength of brain-psychopathology associations compared to traditional summary scores, suggesting that fundamental limits in explainable symptom variance may exist regardless of phenotypic modeling approaches.
This paper introduces a behavior-informed modeling framework that integrates continuous measurements of running speed and head motion into functional ultrasound imaging analysis, effectively disentangling motion-related confounds from neural signals to enable reliable brain-wide mapping during naturalistic, high-motion behaviors in awake mice.
This paper proposes a computational framework combining information-theoretic and network science principles to demonstrate that population-level neural phenomena like oscillations and avalanches are genuine emergent properties driven by network-level interactions, with distinct temporal characteristics supported by both in-vivo data and in-silico simulations.