1795 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 disrupting molecular circadian rhythms specifically in nucleus accumbens astrocytes of mice leads to a significant increase in binge-like alcohol consumption, revealing a novel role for these glial cells in regulating reward circuitry and susceptibility to alcohol misuse.
This study establishes a practical framework for precise MRI-guided stereotaxic targeting in nonhuman primates using skull-fixed fiducials, demonstrating that rigid registration offers the optimal balance between correcting positioning differences and maintaining accuracy at internal targets compared to offset translation and affine methods.
This study demonstrates that bumetanide treatment strengthens residual, functionally compromised brain-bladder communication mediated by spared thoracic spinal neurons after spinal cord injury, thereby improving neurogenic bladder function in both mouse models and a clinical cohort.
This study demonstrates that while MScanFit is broadly robust to substantial motor unit loss and heterogeneous remodelling, its estimation accuracy is systematically influenced by specific physiological factors such as denervation patterns, reinnervation methods, and neuromuscular resilience, indicating that performance variability has a physiological basis rather than being purely technical.
This study demonstrates that in mice trained on an orientation discrimination task, the primary visual cortex (V1) undergoes a geometric reshaping of population activity—characterized by static manifold compression and dynamic separation—that enhances the linear separability and stability of task-relevant representations to support perceptual decisions.
This study utilizing mobile EEG and behavioral observation reveals that student-led learning environments most effectively enhance attentional engagement in children aged 6 to 10, with neural and behavioral patterns of attention remaining largely consistent across ADHD and non-ADHD groups despite differences in motor behaviors.
This study demonstrates that in rats navigating in darkness, a significant portion of primary visual cortex (V1) neurons encodes path-invariant cognitive maps of movement rather than visual inputs, with vision subsequently modulating rather than replacing this internal representation.
This study identifies the dorsomedial prefrontal cortex (dmPFC) as a critical neural substrate for top-down, expectancy-dependent control that regulates how reward-predictive cues influence the vigor of instrumental behavior, specifically by constraining pursuit when rewards are imminent and allowing invigoration when rewards are uncertain.
Using high-precision fMRI, this study reveals that while imagined and experienced pain share widespread distributed neural activity in transmodal regions, imagined pain fails to reactivate the specific body-site-selective patterns found in nociceptive areas, thereby maintaining a distinct neural signature that may explain the difficulty of vividly simulating pain.
This paper introduces "framed RSA," a novel analysis technique that enhances model-selection accuracy and mechanistic interpretability by augmenting traditional representational similarity analysis with reference patterns to simultaneously honor both the geometry of neural activity and population-mean response preferences.