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.
By integrating promoter capture Hi-C with ATAC-seq and ChIP-seq data in mouse cerebellar granule cell progenitors, this study maps a vast landscape of long-range gene regulatory interactions and reveals that the transcription factor Atoh1 and the chromatin remodeller CHD7 physically interact and co-regulate key developmental genes.
This paper introduces and evaluates two methods for mapping hierarchical fMRI timescales, demonstrating that a time-domain autoregressive (AR1) approach provides more accurate, computationally efficient, and statistically robust estimates than traditional autocorrelation-domain methods.
This study demonstrates that motor imagery involves a dynamic reconfiguration of the brain-heart axis, where task-dependent modulations of cardiac activity are linked to specific cortical and cerebellar connectivity patterns that support sensorimotor function and longitudinal motor learning.
To overcome the issue of spurious longitudinal changes caused by varying streamline trajectories, this paper introduces a novel quantitative streamline tractography framework that keeps individual trajectories fixed while allowing only their density weights to vary, thereby enhancing the sensitivity and robustness of detecting true biological differences in white matter connectivity.
This paper demonstrates that LLM-based program evolution can discover adaptive regularization schedules for Latent Factor Analysis via Dynamical Systems (LFADS) that prevent posterior collapse more efficiently than traditional population-based training methods.
This study demonstrates that invisible visual flashes can bias the perception of sound location even without conscious awareness, suggesting that sensory integration occurs at a neural level before information reaches subjective consciousness.
This study demonstrates that the variability in motor unit reflex amplitude estimates is driven by complex interactions between intrinsic motor neuron properties and extrinsic factors like muscle force, suggesting that PSF-based estimation is a more reliable method for capturing motor neuron heterogeneity than PSTH-based methods.
By analyzing deep neural network models, this study demonstrates that "what" and "where" representations form geometrically organized manifolds that align with human perception, revealing that task-irrelevant object attributes are learned alongside spatial information and that the emergence of a spatial map can actually reduce localization accuracy.
This study found that higher levels of self-regulation are associated with increased activity in the cholinergic substrates of the basal forebrain, suggesting that this system plays a key role in sustaining attention.
This study systematically evaluates the numerical variability of fMRI-derived graph measures using the fMRIPrep pipeline, finding that while variability is measurable and influenced by processing choices, it typically remains lower than population-level differences.