1322 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 resolves conflicting findings on the relationship between alpha oscillations and mind-wandering by demonstrating that the effect of alpha power reverses depending on eye state, with higher alpha predicting increased mind-wandering when eyes are open but decreased mind-wandering when eyes are closed.
Analyzing 728,000 memory tests from 80,000 participants, this study reveals that while a small minority of older adults maintain stable episodic memory linked to preserved brain structure, most experience cognitive aging as a complex system characterized by long periods of stability punctuated by sudden, critical transitions of accelerated decline rather than continuous erosion.
This study demonstrates that zebrafish models with isoform-specific deletions of the *nrxn1a* gene exhibit distinct yet overlapping behavioral deficits, supporting a model where specific *NRXN1* isoform disruptions predict particular neuropsychiatric phenotypes.
This study demonstrates that in healthy adults, higher gut microbial alpha diversity and the inferred capacity to produce short-chain fatty acids are associated with increased acute stress reactivity, as measured by cortisol levels and subjective stress.
This study demonstrates that respiration dynamically modulates human sensory perception by orchestrating brain states and arousal levels, where aligning breathing patterns with predictable stimuli enhances perceptual sensitivity through optimized information flow within interoceptive networks.
This study uses a recursive Bayesian computational model to demonstrate that the relative weighting of velocity-based versus position-based proprioceptive feedback critically influences state estimation and endpoint errors in motor control, revealing that different internal sensory assumptions can produce similar observable behaviors in some experimental conditions while diverging significantly in others.
This study demonstrates through virtual reality experiments and Bayesian computational modeling that, in the absence of visual feedback, the human brain relies heavily on velocity-based proprioceptive cues rather than absolute positional information for state estimation, resulting in slow corrections to visual perturbations during movement.
This study demonstrates that lower levels of the vesicular glutamate transporter in female *Drosophila* dopamine neurons reduce cytosolic dopamine sequestration, thereby increasing cytosolic dopamine levels and susceptibility to -synuclein toxicity, which explains the sex-specific vulnerability to Parkinson's disease-like neurodegeneration observed in males.
This study proposes and validates a neurocomputational account of premature responding impulsivity (PRI) as an adaptive response to environmental uncertainty, demonstrating through fMRI and hierarchical Bayesian modeling that volatility modulates a distributed cortico-subcortical network involving the anterior insula, dorsal anterior cingulate cortex, striatum, and midbrain regions to regulate impulsive decision-making.
This study demonstrates that EEG-based multivariate pattern decoding of visual color and category representations is a reliable method that exhibits stability across sessions, tasks, and attentional conditions, behaving like an individual trait with minimal representational drift.