Neuroscience
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Impulsivity is a personality trait of healthy individuals, but in extreme forms common in mental disorders. Previous behavioral testing of wild-caught bank voles and wood mice suggested impulsiveness in bank voles. Here, we compared behavioral performance of bank voles and wood mice in tests for response control in the IntelliCage. ⋯ Corticosterone measurements at the end of experiments suggested that IntelliCage testing did not elicit a stress response in these wild rodents. In summary, habenular size differences and altered activation of brain areas after testing might indicate differently balanced activations of cortico-limbic and cortico-hypothalamic circuits in bank voles compared to wood mice. Behavioral performance of bank voles suggest that these rodents could be a natural animal model for investigating impulsive and perseverative behaviors.
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Altruism is highly valued and cherished by human society. However, human preferences and behavior are sensitive to inequality considerations. Currently, remarkably little is known about the neurobiological mechanisms underlying the process of altruistic acts in inequity situations. ⋯ A total of 71 participants (38 female and 33 male) received anodal tDCS at 1.5 mA over the rDLPFC (n = 38) or the primary visual cortex (n = 33) and subsequently participated in a modified dictator game that measures altruism. We found that anodal tDCS over the rDLPFC decreased subjects' sensitivity to altruistic efficiency and cost in situations of advantageous inequity. Our results suggested that the rDLPFC plays an important role in overriding self-interest to enforce altruism in situations of advantageous inequity.
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Reconstructing visual stimulus images from the brain activity signals is an important research task in the field of brain decoding. Many methods of reconstructing visual stimulus images mainly focus on how to use deep learning to classify the brain activities measured by functional magnetic resonance imaging or identify visual stimulus images. Accurate reconstruction of visual stimulus images by using deep learning still remains challenging. ⋯ Secondly, the structure of original decoder is extended to a deeper network in the deep generative multiview model, which makes the features obtained by each deconvolution layer more distinguishable. Finally, we configure the parameters of the optimizer and compare the performance of various optimizers under different parameter values, and then the one with the best performance is chosen and adopted to the whole model. The performance evaluations conducted on two publicly available datasets demonstrate that the improved model has more accurate reconstruction effectiveness than the original deep generative multiview model.
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Spreading depolarizations (SD) refer to the near-complete depolarization of neurons that is associated with brain injuries such as ischemic stroke. The present gold standard for SD monitoring in humans is invasive electrocorticography (ECoG). A promising non-invasive alternative to ECoG is diffuse optical monitoring of SD-related flow and hemoglobin transients. ⋯ Flow transient morphology, positive amplitude, positive slope, and total amplitude were all strongly associated with infarction (p < 0.001). Associations with infarction were also observed for oxy-hemoglobin morphology, oxy-hemoglobin positive amplitude and slope, and deoxy-hemoglobin positive slope and duration (all p < 0.01). These results suggest that flow and hemoglobin transients accompanying SD have value for detecting infarction.
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Perceptual decisions rely on accumulating sensory evidence over time. However, the accumulation process is complicated in real life when evidence resulted from separated cues over time. ⋯ We used behavioral and EEG datasets from a visual choice task -Random dot motion- with separated evidence to investigate three candid distributed neural networks. We showed that decisions based on evidence accumulation by separated cues over time are best explained by the interplay of recurrent cortical dynamics of centro-parietal and frontal brain areas while an uncertainty-monitoring module included in the model.