1541 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 paper introduces ELA/GAopt, a novel framework that employs genetic algorithms to automate the selection of optimal brain regions for energy landscape analysis, thereby overcoming the subjectivity of manual selection and revealing reproducible, condition-specific neurodynamic signatures in both creative and autism spectrum disorder populations.
This paper introduces SynaptoTagMe, a versatile genetic toolkit in *C. elegans* that enables the in vivo fluorescent labeling and conditional ablation of specific neurotransmitter vesicular transporters to map co-transmission patterns and dissect synaptic communication at single-cell resolution.
This study demonstrates that GBA1 mutations in Parkinson's disease impair lysosomal acidification via mTORC1-driven disruption of V-ATPase assembly, leading to defective mitophagy and mitochondrial dysfunction, which can be therapeutically rescued by restoring lysosomal pH using rapamycin or acidic nanoparticles.
This paper proposes a Physics-Informed Neural Network (PINN) framework that integrates finite element modeling with electromagnetic laws to solve the ill-posed MEG inverse problem, demonstrating a 30.2% improvement in accuracy over the standard Minimum Norm Estimation baseline.
This study reveals that the diverse electrophysiological parameters of neurons are not random but are organized along a hidden manifold representing a Pareto front of energy-efficient solutions, which explains the prevalence of low firing rates and adapts to metabolic demands like food restriction.
This study demonstrates that in non-human primates, the emergence and persistence of post-stroke flexor synergies are driven primarily by the extent of damage to descending corticofugal pathways rather than by lesions to specific cortical regions, as severe and persistent synergies only occurred following large internal capsule lesions that disrupted these outputs.
By integrating computational modeling and event-related potential (ERP) techniques, this study reveals that the emotional aversion to betrayal exerts a stronger influence on reducing cooperation than the financial loss itself, with neural data indicating that betrayal is processed earlier in decision-making than loss.
By applying a Hodge Laplacian framework to a large-scale ENIGMA-OCD fMRI dataset, this study reveals that obsessive-compulsive disorder is characterized by disrupted higher-order multi-region interactions within frontoparietal, default mode, and sensorimotor networks that are not detectable through traditional pairwise connectivity analyses.
This study demonstrates that the protein Rubicon exacerbates acute neuroinflammation and oxidative stress following traumatic brain injury by suppressing autophagy and interacting with NRROS, while its genetic deletion in mice attenuates these damaging responses and improves motor recovery.
Using Slide-tags spatial transcriptomics on 1.1 million cells from 19 human donors, this study reveals a molecularly defined mesoscale architecture in the striatum comprising six distinct zones with unique neuron-astrocyte signaling and differential susceptibility to age-related transcriptional changes.