1519 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 a semi-supervised deep learning model utilizing EEG microstate sequences, combined with transfer learning, can effectively classify motor imagery tasks to achieve high accuracy with reduced calibration time, offering a promising pathway for user-convenient and calibration-free Brain-Computer Interfaces.
This study reveals that in Fragile X syndrome, altered development of the axonal initial segment renders Kv7 channels functionally inert, causing transient early-life deficits in hippocampal circuit activity that resolve by adulthood.
By validating source-localized magnetoencephalography against concurrent intracranial EEG in epileptic patients, this study demonstrates that MEG accurately captures spontaneous oscillatory dynamics across various frequencies and depths, including deep subcortical structures like the hippocampus, thereby confirming its utility for studying human cognition in health and disease.
Using finite impulse response modeling on large-scale fMRI data, this study reveals that human amygdala subregions exhibit distinct spatiotemporal dynamics during negative affect, with laterobasal and superficial areas peaking early while the centromedial region shows sustained activation, whereas instructed cognitive reappraisal failed to significantly alter these response patterns.
This study demonstrates that neuronal survival during mitochondrial stress relies on a compensatory upregulation of glycolytic activity and LDH function rather than merely restoring the NAD+/NADH ratio, representing a broader metabolic reprogramming essential for maintaining neuronal function.
The authors have withdrawn their manuscript titled "Multisensory learning binds modality-specific neurons into a cross-modal memory engram" because they discovered that the voltage imaging results were not reproducible and contained data contamination, rendering the core findings unreliable despite the replication of behavioral and connectomic data.
This study utilizes single-particle tracking to demonstrate that synaptic activity enhances the nucleocytoplasmic shuttling of ARC/ARG3.1 protein between perinuclear cytoplasmic clusters and the nucleus in a manner dependent on its N-terminal oligomerization, thereby revealing a mechanism for coordinating ARC's dual synaptic and nuclear functions during neuronal plasticity.
A forward genetic screen in a Drosophila model of alpha-synucleinopathy identified that glial knockdown of adenosine metabolism genes increases brain adenosine levels, which activates neuronal adenosine receptors to reduce alpha-synuclein toxicity and neurodegeneration, establishing glial adenosine as a promising therapeutic target for Parkinson's disease.
This preregistered multi-center study found that while combining SSILD training with multisensory REM stimulation successfully induced lucid dreaming in over half of the participants, the sensory cues did not significantly increase lucidity rates compared to sham conditions, though they did enhance the duration and stability of lucid episodes.
This paper introduces trial-level Representational Similarity Analysis (tRSA) as a robust, multi-level modeling framework that overcomes the limitations of classic RSA by enabling the assessment of trial-level variance and demonstrating superior sensitivity and theoretical appropriateness in both simulated and real fMRI data.