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 utilizes Bayesian nonparametrics and machine learning to demonstrate that categorical speech perception emerges from temporally discrete neural microstates within a distributed left-hemisphere cortical network, which not only accurately decode speech tokens but also robustly predict individual behavioral identification patterns.
This longitudinal study of a rural Gambian population demonstrates that early physical growth before five months of age drives optimal developmental trajectories of long-range interhemispheric functional connectivity, which subsequently predicts cognitive flexibility in preschool-aged children.
This study demonstrates that integrating structural connectivity into a hierarchical empirical Bayes model of resting-state effective connectivity not only improves model accuracy and reliability but also reveals that the relationship between structural and effective connectivity follows a biologically plausible unimodal-transmodal cortical hierarchy.
This study describes the development and preclinical validation of a potent divalent siRNA drug candidate, 2439-s4, which significantly lowers human prion protein levels and extends survival in mouse models of prion disease, leading to FDA clearance for clinical trials.
This paper introduces the Sparse Cognitive Graph (SCG) framework, which reconciles gradual predictive learning with efficient goal-directed control by demonstrating how reward-dependent, nonlinear selection of transition statistics drives discrete graph reorganization to explain diverse behavioral regimes and neural activity patterns across humans and mice.
This study reveals that maternal age in *C. elegans* adaptively modulates offspring social behavior by altering an ERI-1/mir-8207/flp-26 small RNA-neuropeptide axis in AVH interneurons, which subsequently tunes ADL sensory circuit activity to adjust pheromone avoidance strategies.
This study demonstrates that while PARK2-knockout mice exhibit age-dependent behavioral and biochemical changes, they fail to fully recapitulate the specific pathological manifestations of human Parkinson's disease.
By analyzing standardized datasets from approximately 100 mice, this study reveals that individual differences in decision-making strategies, ranging from anticipatory to inattentive behaviors, are driven by distinct, low-dimensional cortical dynamics characterized by specific population activity timescales.
By combining fMRI studies in healthy adults with behavioral assessments of individuals with severe aphasia, this research demonstrates that logical reasoning operates independently of natural language, engaging distinct neural networks rather than linguistic representations.
This study demonstrates that sustained alpha oscillations in working memory specifically reflect the attentional prioritization of task-relevant information rather than serving as a general mechanism for maintaining all stored items, which may instead rely on activity-silent synaptic traces.