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 demonstrates that GABAergic inhibition differentially recruits distinct dentate gyrus interneuron populations through feedforward and feedback mechanisms to tightly gate lateral entorhinal cortex inputs, thereby maintaining sparse granule cell activity essential for hippocampal pattern separation.
By leveraging a large-scale fMRI dataset to overcome signal artifacts, this study reveals that the anterior temporal lobe contains three distinct visual category preference profiles—differing in object versus scene selectivity and hemispheric bias—that exhibit a patchy spatial organization and dissociable functional connectivity patterns, thereby extending category sensitivity to the apex of the ventral visual stream.
This study demonstrates that disrupting FOXO4 function through knockout confers neuroprotection against oxidative stress and ischemia-reperfusion injury by reducing neuronal death, infarct volume, and neuroinflammation while improving survival and functional recovery.
This paper introduces a gaze-aware encoding model that integrates eye-tracking data with CNN features to effectively predict brain activity during naturalistic, free-viewing fMRI, achieving performance comparable to conventional models with significantly fewer parameters while enabling more ecologically valid neuroscience research.
This paper proposes a hierarchical neural circuit theory that dynamically implements divisive normalization via feedback connections, analytically deriving spectral signatures and predicting that feedback strength modulates inter-areal communication within a low-dimensional subspace while dictating dynamic functional connectivity across cortical areas.
This study demonstrates that while both deterministic and probabilistic tractography methods detect expected white matter degeneration in Alzheimer's disease, they yield substantially different bundle-specific results, highlighting the critical need for cross-method validation to ensure robust interpretation of tractometry findings.
This study demonstrates that the baseline activity of V1 fast-spiking interneurons, rather than pyramidal neurons, serves as the neural mechanism linking pupil-linked arousal to the inverted-U relationship with engaged behavioral states during perceptual decision-making in both mice and humans.
This study of a South African birth cohort reveals that perinatal HIV exposure in uninfected children is associated with persistent alterations in immune development and neurometabolites, which correlate with reduced language skills and disrupted neuroimmune pathways critical for school readiness.
This paper introduces "invertmeeg," a unified Python library and comprehensive benchmark that evaluates 106 EEG inverse solvers across diverse source configurations, revealing that while no single solver dominates all scenarios, flexible subspace and hybrid methods generally perform best overall.
This study introduces a unified hierarchical GAMLSS framework that outperforms existing ComBat-based methods in harmonizing large-scale, multi-cohort neuroimaging data by effectively removing technical batch effects while preserving complex biological signals and providing normative deviation scores across diverse, non-Gaussian distributions.