Neuroscience
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In 2017, the Food and Drug Administration published a safety recommendation to limit the exposure to general anesthesia as much as possible below the age of three. Indeed, several preclinical and clinical studies have questioned the possible toxicity of general anesthesia on the developing brain. Since then, recent clinical studies tried to mitigate this alarming issue. ⋯ Only stronger translational research will allow scientists to provide concrete answers to this public health issue. In this review, we will provide and discuss the more recent data in this field, including the point of view of preclinical researchers, neuropsychologists and pediatric anesthesiologists. Through translational research, preclinical researchers have more than ever a role to play to better understand and identify long-term effects of general anesthesia for pediatric surgery on brain development in order to minimize it.
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Emotion plays an important role in people's lives. However, the neural mechanism of affective perception is still unclear. In this study, steady-state visual evoked potentials (SSVEPs) were used to explore information processing speed and interactions among cortical structures involved in affective perception. ⋯ Unpleasant emotions had the fastest information processing speed in the ventral stream compared with pleasant and neutral emotions, including the middle occipital gyrus and the middle temporal gyrus, with a right hemisphere bias. In addition, unpleasant emotions were stronger than pleasant emotions in long-term causal connections in the bilateral middle temporal gyrus, and the direction was from the right hemisphere to the left hemisphere. These results provide unique insights into the cortical activities for affective perception and support the view that unpleasant emotions have priority in information perception in the middle temporal gyrus compared with pleasant and neutral emotions, with a right hemisphere bias.
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Glutamate (Glu) is known as the main excitatory neurotransmitter in the central nervous system. It can trigger a series of processes ranging from synaptic plasticity to neurophysiological regulation. To carry out its functions, Glu acts via interaction with its cognate receptors, which are ligand-dependent. ⋯ Therefore, the aim of this review was to summarize the current knowledge regarding iGluRs, while describing their structures and molecular mechanisms of action, including their role in excitotoxicity, as well as the current strategies to reduce excitotoxic damage. Particularly, strategies mediated by prolactin, a somatotropin family-related hormone that displays a significant neuroprotective effect against both Glu and kainic acid-induced excitotoxicity in the hippocampus, are described. Finally, the role of prolactin as a possible molecule in the treatment of excitotoxicity in neurological diseases is discussed.
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Performance of supercomputers has been steadily and exponentially increasing for the past 20 years, and is expected to increase further. This unprecedented computational power enables us to build and simulate large-scale neural network models composed of tens of billions of neurons and tens of trillions of synapses with detailed anatomical connections and realistic physiological parameters. Such "human-scale" brain simulation could be considered a milestone in computational neuroscience and even in general neuroscience. ⋯ Then, we direct our attention to the cerebellum, with a review of more simulation studies specific to that region. Furthermore, we present recent simulation results of a human-scale cerebellar network model composed of 86 billion neurons on the Japanese flagship supercomputer K (now retired). Finally, we discuss the necessity and importance of human-scale brain simulation, and suggest future directions of such large-scale brain simulation research.
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As a tribute to Masao Ito, we propose a model of cerebellar learning that incorporates and extends his original model. We suggest four principles that align well with conclusions from multiple cerebellar learning systems. (1) Climbing fiber inputs to the cerebellum drive early, fast, poorly-retained learning in the parallel fiber to Purkinje cell synapse. (2) Learned Purkinje cell outputs drive late, slow, well-retained learning in non-Purkinje cell inputs to neurons in the cerebellar nucleus, transferring learning from the cortex to the nucleus. (3) Recurrent feedback from Purkinje cells to the inferior olive, through interneurons in the cerebellar nucleus, limits the magnitude of fast, early learning in the cerebellar cortex. (4) Functionally different inputs are subjected to plasticity in the cerebellar cortex versus the cerebellar nucleus. A computational neural circuit model that is based on these principles mimics a large amount of neural and behavioral data obtained from the smooth pursuit eye movements of monkeys.