1554 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.
SqueakPose Studio is an integrated software-hardware ecosystem that unifies dataset creation, model training, and real-time edge-AI deployment for accurate, whole-frame pose estimation, eliminating the need for fragmented pipelines or workstation-grade hardware.
This study demonstrates that in A53T transgenic mice modeling Parkinson's disease, astrocytes in the substantia nigra pars compacta exhibit a reactive phenotype with significantly increased numbers and accumulation of α-synuclein aggregates compared to wild-type controls, suggesting that early deficits in astrocytic clearance of these aggregates contribute to the region's vulnerability.
This paper introduces an integrated framework combining Oblique Random Forests with a novel permutation-based feature importance algorithm (NEOFIT) to achieve statistically valid, interpretable sex classification from brain imaging data across humans and macaques, overcoming the limitations of traditional methods in handling high-dimensional neuroimaging features.
This study utilizes cryo-EM to reveal that systemic delivery of a P301L/S320F double-mutant human tau into AppNL-G-F/MAPT mice induces rapid, seed-independent aggregation into a novel, compact filament fold distinct from known Alzheimer's and Pick disease structures, establishing a rapid platform for modeling tauopathies.
This study demonstrates that while directional and positional experiences independently improve movement initiation and termination respectively during de novo visuomotor learning, accurate sensorimotor mapping ultimately depends on the coordinated alignment of these two information types rather than their simple combination.
This study introduces an asymmetry-aware tractometry framework that integrates a symmetric white-matter atlas with BUAN to enable principled, segment-wise quantification of hemispheric asymmetries along homologous white-matter tracts, demonstrating its utility in differentiating cognitively normal individuals from those with mild cognitive impairment and dementia using ADNI data.
This study combines eye-tracking and fMRI to demonstrate that overt visual attention not only amplifies sampled-value signals in the vmPFC and ventral striatum but also modulates accumulated-value signals in the pre-supplementary motor area, revealing how gaze shapes both immediate and lasting decision variables in the brain.
This systematic review and meta-analysis of 43 studies reveals that female normal sleepers exhibit higher sleep spindle density and slow wave activity than males, while highlighting significant gaps in understanding sex differences specifically within insomnia disorder due to inconsistent reporting and limited interaction data.
This study presents a biophysically realistic, multiscale network transport model that bridges the gap between rapid cellular tau kinetics and slow macroscopic disease progression by integrating active axonal transport, continuous production, and exosome-mediated release and uptake, thereby identifying trans-neuronal exchange rates as the primary bottleneck governing tau spread across the brain connectome.
This study demonstrates that including lower-quality fetal MRI data systematically biases normative growth models, particularly affecting extreme centile estimates, indicating that increasing sample size at the expense of image quality can compromise the clinical utility of these models.