1825 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 utilizes *C. elegans* to demonstrate that 50% of high-confidence autism-associated epigenetic modifier genes can be systematically categorized into three distinct behavioral profiles based on their specific impacts on sensory processing and gross development, thereby highlighting the need for refined sensory testing in human cohorts.
This study reveals that normal human brain aging involves an asymmetric remodeling of the insoluble proteome, characterized by the decline of highly stable aggregates and the progressive accumulation of intermediate-stability aggregates that are prone to phase separation and linked to Alzheimer's disease pathology, a process strongly predicted by proteasome and chaperone capacity.
This paper establishes an analytical and empirical link between Granger Causality and Effective Connectivity in fMRI by deriving a quadratic relationship between them, demonstrating that their convergence requires large datasets and offering methodological guidance for reconstructing directed brain networks.
This study utilizes population-level analysis of dendritic spine nanostructure in mouse models to distinguish schizophrenia from autism spectrum disorder phenotypes and identifies the gene Ecrg4 as a critical molecular driver of the impaired spine dynamics characteristic of schizophrenia.
Although electrophysiological recordings and modeling demonstrate that primate foveal cones effectively backpropagate signals from their terminals to their outer segments without requiring voltage-gated amplification, these signals are unlikely to influence phototransduction, suggesting that visual encoding in these cells remains compartmentalized.
This study demonstrates that the RNA editing enzyme ADARB1 is a robust, species-conserved nuclear marker for spiral ganglion neurons, enabling reliable automated cell counting as a time-efficient alternative to manual methods for assessing auditory neuron degeneration.
This study reveals that the brainstem exerts lateralized, contralateral control over hepatic glucose metabolism in mice via bilaterally projecting sympathetic pathways that cross over at the porta hepatis, a mechanism that ensures systemic glucose homeostasis through intrinsic neuroadaptive compensation.
The paper introduces DIANA, an automated pipeline that integrates long-read whole-genome sequencing data to simultaneously generate comprehensive molecular profiles—including methylation classification, copy-number variants, gene fusions, and small variants—for streamlined CNS tumor diagnosis and clinical decision-making.
This paper presents a fully self-contained, resource-efficient bidirectional wireless headstage that integrates a neuromorphic decoder on a Spartan-6 FPGA to enable real-time, untethered closed-loop optogenetic stimulation in freely moving rodents with high accuracy and sub-millisecond latency.
This study demonstrates that while resting-state fMRI data are approximately Gaussian within single scans, their distinguishability from phase-randomized surrogates by advanced analysis techniques is primarily driven by non-stationarity across scans, offering a refined statistical characterization for brain modeling.