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 study presents the first single-cell transcriptional map of the chick intermediate medial mesopallium (IMM), revealing over 30 distinct cell clusters and identifying specific lncRNAs and protein-coding genes that correlate with visual imprinting memory strength.
Inspired by biological mechanisms, this paper proposes a modular Echo State Network that utilizes noradrenaline and acetylcholine gain modulation to dynamically reconfigure functional connectivity between integration and segregation, thereby enhancing performance on context-dependent tasks without altering structural connectivity.
This randomized controlled trial demonstrates that suppressive transcranial focused ultrasound targeting the posterior cingulate cortex can accelerate neuroplastic changes in novice meditators by increasing segregation between the default mode and central executive networks, thereby enhancing equanimity and meditation adherence.
This study demonstrates that stress-enhanced fear learning (SEFL) potentiates fear memory by specifically increasing the reactivation of fear engram cells in the ventral (caudal) dentate gyrus, while leaving cell numbers and reactivation in the dorsal (rostral) dentate gyrus unaffected.
This study pioneers the development of GPR52-1.0, a genetically encoded fluorescent sensor that enables real-time monitoring of GPR52 activation and neuronal ligand release, thereby providing a critical tool for deorphanizing this receptor and advancing the discovery of GPR52-targeted therapeutics.
This study demonstrates that dopamine release deficits in human Parkinson's disease neurons carrying the SNCA-triplication mutation stem from impaired vesicular monoamine transporter 2 (VMAT2) function, which reduces dopamine storage capacity, disrupts vesicle recycling, and elevates cytosolic dopamine levels, thereby contributing to both symptomatic dysfunction and neuronal degeneration.
This study introduces a probabilistic deep learning framework using EEGNet to quantify visual mental imagery vividness on a shared neural-behavioral scale with visual perception, revealing that imagery is a "barely depictive" process that is substantially weaker than perception yet scales with individual subjective ratings.
The paper introduces "Hyperface," a high-quality, publicly available naturalistic fMRI dataset featuring 707 unique face video clips with systematic variations in identity and expression, designed to overcome the limitations of static stimuli and enable robust investigation of human face processing under ecologically valid conditions alongside benchmarking computational models.
This paper presents a low-cost, optimized Golgi Cox staining protocol that addresses traditional limitations such as inconsistent impregnation and slice fragility by adjusting temperature, fixation, and sectioning conditions to achieve robust, high-resolution visualization of cortical neuronal morphology.
This paper introduces Hybrid eTFCE-GRF, a novel method that combines exact cluster-size retrieval via a union-find data structure with analytical Gaussian random field inference to achieve significantly faster, permutation-free voxel-based morphometry analysis while maintaining strict family-wise error control and high sensitivity.