Neuroscience
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Neonatal treatment with monosodium glutamate causes profound deficits in place learning and memory in adult rats evaluated in the Morris maze. Theta activity has been related to hippocampal learning, and increased high-frequency theta activity occurs through efficient place learning training in the Morris maze. We wondered whether the place learning deficits observed in adult rats that had been neonatally treated with monosodium glutamate (MSG), were related to altered theta patterns in the hippocampus and prelimbic cortex, which were recorded during place learning training in the Morris maze. ⋯ Learning-related changes were observed in the relative power distribution in control and MSG-treated groups in the hippocampal EEG, but not in the prelimbic cortex. Increased prefrontal and reduced hippocampal absolute power that appeared principally during the final days of training, and reduced coherence between regions throughout the training (4-12 Hz), were observed in the MSG-treated rats, thereby suggesting a misfunction of the circuits rather than a hyperexcitable general state. In conclusion, neonatal administration of MSG, which caused a profound deficit in place learning at the adult age, also altered the theta pattern both in the hippocampus and prelimbic cortex.
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Accumulating evidence suggests that glutamatergic signaling and synaptic plasticity underlie one of a number of ways psychiatric disorders appear. The present study reveals a possible mechanism by which this occurs, through highlighting the importance of LMTK3, in the brain. Behavioral analysis of Lmtk3-KO mice revealed a number of abnormalities that have been linked to psychiatric disease such as hyper-sociability, PPI deficits and cognitive dysfunction. ⋯ As synaptic dysfunction is implicated in human psychiatric disease, we analyzed the LTP of Lmtk3-KO mice and found that induction is severely impaired. Further investigation revealed abnormalities in GluA1 trafficking after AMPA stimulation in Lmtk3-KO neurons, along with a reduction in GluA1 expression in the post-synaptic density. Therefore, we hypothesize that LMTK3 is an important factor involved in the trafficking of GluA1 during LTP, and that disruption of this pathway contributes to the appearance of behavior associated with human psychiatric disease in mice.
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Subjective well-being (SWB) is closely related to our physical and mental health. Existing studies show that neural or genetic basis underpins individual difference in SWB. Moreover, researchers have found high enrichment of SWB-related mutations in the central nervous system, but the relationship between the genetic architecture of SWB and brain morphology has not been explored. ⋯ In whole-brain analyses, we found that a higher PGS was significantly associated with increased CT in the right superior temporal gyrus (STG) and GMV in the right insula, both of which are involved in social cognition and emotional processing. More importantly, these findings were repeatable at some different thresholds. The results may suggest that the brain morphology of right STG and insula is partly regulated by SWB-related genetic factors.
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Trigeminal neuropathic pain (TGN) is an attacking, abrupt, electric-shock headache involving abnormal cortical activity. The neural mechanism underlying TGN remains elusive. In this study, we explored the role of microglia in the primary somatosensory barrel cortex (S1BF), which is a critical region for TGN, of a mouse model of TGN that displayed significant pain-related behaviors. ⋯ In addition, we found that microglia in the S1BF (microgliaS1BF) were significantly activated, with density and morphology changes. Intraperitoneal administration of minocycline, a microglia inhibitor, attenuated pain sensitization, and decreased GluS1BF neuronal activity. Together, these findings demonstrate the putative importance of microglia as a key regulator in TGN through actions on GluS1BF neuronal adaptation.
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Computer-aided diagnosis has become a widely-used auxiliary tool for the diagnosis of Alzheimer's disease (AD). In this study, we developed an extreme learning machine (ELM) model to discriminate between patients with AD and normal controls (NCs) using voxel-based morphometry (VBM) obtained from magnetic resonance imaging. Support vector machine (SVM), Gaussian process regression (GPR), and partial least squares (PLS) regression were compared with the ELM model. ⋯ We applied the proposed methods to data from 58 patients with AD and 94 NCs, and achieved a classification accuracy of up to 0.96 with all classification features of the ELM model, while the results of the other three models were 0.82 (PLS), 0.79 (GPR), and 0.75 (SVM). Furthermore, the effect of VBM parameter modeling is better than texture parameter. Thus, our method was optimal in distinguishing patients with AD from NCs, and may therefore be useful for the diagnosis of AD.