1527 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 fine-scale exogenous attention within the high-acuity foveola selectively enhances contrast sensitivity for low-to-mid spatial frequencies (4–8 CPD) while leaving higher frequencies unaffected, indicating that this involuntary mechanism remains inflexible and frequency-specific even at the center of vision.
This study challenges the view that cognition automatically influences early perception by demonstrating that neural responses to color changes, previously attributed to categorical processing in speakers with multiple blue terms, are actually driven by low-level contrast adaptation rather than linguistic categories.
This study identifies the exocyst complex as a critical, insulin-regulated mediator of amyloid precursor protein (APP) trafficking in neurons, demonstrating that insulin modulates exocyst-cargo interactions to control APP processing and amyloid-beta secretion.
This coordinate-based fMRI meta-analysis reveals that depression is associated with stage- and content-specific alterations in brain activity during memory tasks, characterized by reduced salience network integration during encoding and autobiographical retrieval, but increased frontal vigilance during non-autobiographical retrieval.
This study proposes a novel pipeline for generating subject-specific, voxel-resolved brain biomechanical models by mapping diffusion tensor imaging fractional anisotropy to tissue stiffness, demonstrating that this microstructure-informed approach captures regional deformation differences more accurately than traditional coarse regional parameterizations despite similar global volume loss.
This study demonstrates that individuals with high schizotypy exhibit reduced top-down fronto-temporal connectivity and altered cortical excitability patterns similar to those found in chronic schizophrenia, supporting the psychosis-continuum hypothesis and suggesting these neural mechanisms as potential biomarkers for psychosis risk.
This study reveals that superagers' superior memory performance is supported by adaptive hippocampal responses to expectation violations and habituation to expected events, rather than by preserved dopaminergic integrity, distinguishing their cognitive aging from that of typical older adults.
This study establishes a conserved vertebrate framework for brain energy metabolism, revealing that across humans, macaques, and other species, the brain transitions from a prenatal anabolic state to a postnatal oxidative energy-producing state, with these distinct metabolic programs persisting into adulthood.
This study demonstrates that in freely moving macaques, the mid-superior temporal sulcus (mSTS) encodes a joint representation of spatial context and behavioral state, dynamically shifting between allocentric and body-centric coordinates while tracking the sequential structure of natural behaviors.
This paper proposes a model of structural plasticity, simulating how de novo synapse formation enables the creation of persistent concept engrams and facilitates a "percept–concept loop" in the human brain, addressing the limitations of traditional Hebbian learning in sparse neural networks.