1296 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 paper introduces a novel voxel-wise O-information framework that bridges high-order information theory with standard neuroimaging pipelines to capture complex multivariate brain interactions, demonstrating its utility in revealing age-related reductions in redundancy within the default mode network.
This study demonstrates that low-intensity focused ultrasound (LIFU) non-invasively enhances meningeal lymphatic drainage to prevent cognitive decline and reduce Alzheimer's pathology by activating the Piezo1 ion channel in lymphatic endothelial cells, offering a promising and clinically translatable therapeutic strategy.
This study characterizes YX84, a novel neuroactive steroid with a unique structure that acts as a potent GABA_A receptor full agonist and positive allosteric modulator while also inhibiting NMDA receptors, demonstrating favorable behavioral tolerability in animal models despite requiring further pharmacokinetic optimization.
This study establishes a scalable, optimized human neuron-microglia tri-culture platform using deterministically programmed cells that enables the systematic investigation of neuroimmune interactions and the modeling of genetic variants, such as the TREM2 R47H mutation associated with Alzheimer's disease, on developing neuronal network dynamics.
This paper introduces EmoFB, a biologically inspired deep neural network model that integrates intrinsic affective appraisal and external contextual steering to demonstrate how top-down feedback dynamically reshapes visual representations, improves recognition under ambiguity, and enhances alignment with human brain activity across key emotional and visual processing regions.
This study demonstrates that peripherally administered GLP-1R agonists activate the central amygdala (CeA) to induce hypophagia, a process mediated by multiple CeA neuron populations including Prkcd, Glp1r, and Sst-expressing cells, with Glp1r neurons specifically playing a crucial role in suppressing the consumption of energy-dense, palatable diets.
The study introduces Perturb-CLEAR, a method combining pooled CRISPR screening with whole-mount imaging and transcriptomics to reveal how neurodevelopmental disorder risk genes drive gene-specific, concordant remodeling of both dendritic morphology and gene expression in the developing mouse brain.
This study reveals that in rhesus macaques, a transient burst of activity in foveal superior colliculus neurons precedes and likely facilitates the rapid representational transformation required for peripheral saccadic orienting by triggering a brief pause in target-related neurons before their motor bursts.
This paper proposes that continuous temporal difference learning, which integrates fast model-based value change computations with a slower model-free cache, unifies diverse dopamine signaling patterns—including phasic errors, tonic modulation, and activity ramps—into a single computational framework validated across independent rodent datasets.
Using all-optical methods in mouse retrosplenial cortex, this study demonstrates that causal connectivity between excitatory neurons is selectively and rapidly modulated during specific phases of a decision-making task, suggesting that dynamic connectivity adjustments rather than fixed anatomy underlie distinct computational stages.