1795 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 the pigeon ventral lateral geniculate nucleus (GLv) contains a retinotopically organized map of fixed-vector head movements, revealing a tectum-independent pathway that links visual spatial representations directly to premotor orienting circuits.
This study reveals that bone-derived osteocalcin sustains spinal motoneuron function and locomotion by signaling through the GPR158 receptor to regulate mitochondrial activity and autophagy, thereby offering a potential mechanism to reverse age-related motor decline.
This study reveals that paclitaxel-induced cold hypersensitivity in peripheral neuropathy is driven by epigenetic alterations that suppress myelin maintenance genes, leading to myelin degradation and persistent pathological cold pain.
This paper introduces BakingTray, an open-source, modular software platform that extends ScanImage to enable flexible, high-throughput block-face serial two-photon tomography (STPT) systems (BrainSaws), which have been successfully deployed in multiple facilities worldwide to support diverse neuroscience applications ranging from whole-brain cell counting to Alzheimer's disease research.
This paper presents the first draft of a complete, gap-free connectome for the first instar female larval *Drosophila melanogaster* central nervous system, providing an enhanced FIB-SEM volume with largely automated segmentations of neurons, mitochondria, synapses, and glia that are publicly available for further machine learning development and human proofreading.
This study reveals that multiscale structural MRI differences among collegiate American football players, cross-country runners, and non-athletes vary significantly by anatomical scale, comparison group, and head-size adjustment, indicating that these variations cannot be uniquely attributed to collision-sport exposure.
This study identifies a specific, widespread brain network called EN-Withdrawal, characterized by synchronized delta/theta and regional gamma oscillations, which encodes the internal state of fentanyl withdrawal and predicts both the severity of withdrawal symptoms and the subsequent escalation of drug-taking behavior across diverse animal cohorts.
This study demonstrates that while recalibration and fixed-weight learning significantly improve target-position estimation under changing measurement accuracy, input-dependent weighting offers a marginal additional benefit primarily when dealing with occasional large errors, though its effectiveness is constrained by correlated reference errors and shared coordinate biases.
This study integrates magnetoencephalography (MEG) and multivariate pattern analysis to demonstrate that while the N400 component reflects semantic context modulation, semantic decoding reveals distinct, time-limited neural representations of semantic vectors that are reshaped by linguistic context and provide information beyond what the N400 alone can infer.
This study utilizes wearable high-density fNIRS and participant-specific audiovisual recordings to establish a feature-based framework that successfully maps and predicts cortical responses to naturally unfolding luminance, sound, and face features during unconstrained real-world walking.