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.
This study demonstrates that focal hippocampal CA2/3 damage causes time-invariant deficits in episodic detail retrieval across the lifespan by impairing sequential narrative binding, thereby challenging classical systems consolidation theories and supporting a model of parallel hippocampal-cortical contributions to long-term memory.
This study demonstrates that personalized whole-brain Hopf models fitted to MEG data reveal that optimal brain stimulation targets for state transitions are determined by individual dynamical regimes—specifically oscillation radius, temporal variability, and frequency-dependent network properties—rather than by static functional anatomical labels.
This study demonstrates that the efficacy of intracranial brain stimulation in enhancing episodic memory depends not only on the timing of delivery but also on whether the stimulation target is structurally embedded within a specific fronto-temporo-parietal network relevant to verbal encoding.
This study introduces an individualized dual-axis network framework for focal epilepsy that reveals distinct topological phenotypes—correspondence disruption linked to neurocognitive deficits and k-hubness reconfigurations tied to clinical features—across diverse patient cohorts.
This study demonstrates that beyond the effects of aging, chronic insomnia and sex independently shape NREM brain oscillations, with females suffering from insomnia exhibiting reduced spindle and slow oscillation density as well as lower sigma power compared to their male counterparts, potentially explaining their higher vulnerability to and severity of insomnia.
By applying change-point analysis to individual participant data, this study reveals that reversal learning is more difficult than initial learning due to a hippocampus-dependent replay mechanism that delays behavioral adaptation, challenging traditional averaging methods that obscure such trial-by-trial dynamics.
This paper introduces the Energy-efficient Attention Network (EAN) model to demonstrate that attentional control mechanisms can substantially improve the overall energy efficiency of visual processing by dynamically allocating neural resources, thereby achieving up to 50% energy savings without compromising accuracy.
This study introduces a geodesic cortical flow framework to map millisecond-resolved propagation patterns in MEG data, revealing that spontaneous brain activity follows a hierarchical, frequency-dependent gradient that shifts with aging and correlates with fluid intelligence.
This study combines a human pilot trial and a computational model to demonstrate that low-frequency tibial nerve stimulation (1 Hz) excites bladder activity while high-frequency stimulation (20 Hz) inhibits it, supporting a hypothesis that supraspinal mechanisms act as a high-pass filter to explain the frequency-dependent dual effects of TNS on voiding.
This study applies co-activation pattern (CAP) analysis to high-density diffuse optical tomography (HD-DOT) data from sleeping newborns to reveal distinct, transient cortical network states and early frontoparietal connectivity that are not detectable through conventional static functional connectivity methods.