Neuroscience
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Methamphetamine (MA), neurotoxic drug of abuse, causes cell death in vitro and in vivo via several mechanisms such as mitochondrial dysfunction. In this study we evaluated the effect of MA on cell viability and mitochondrial biogenesis in primary midbrain culture. Primary mesencephalon cells prepared from E14.5 rat embryo were treated with 0.2-5 mM MA concentrations for 24, 48, and 72 h. ⋯ The results indicated that MA effect on cell viability occurs in a dose-dependent manner. While moderate concentrations increased cell viability, the higher ones reduced it and caused cell death. Mitochondrial biogenesis activation, as a compensatory mechanism, did not prevent neuronal and glial cell death following high MA concentration.
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Neural proliferation in the dentate gyrus (DG) is closely linked with learning and memory, but the transcriptional programming that drives adult proliferation remains incompletely understood. Our lab previously elucidated the critical role of the transcription factor ΔFosB in the dorsal hippocampus (dHPC) in learning and memory, and the FosB gene has been suggested to play a role in neuronal proliferation. ⋯ Here, we crossed neurotensin receptor-2 (NtsR2) Cre mice, which express Cre within the subgranular zone (SGZ) of dHPC DG, with floxed FosB mice to show that knockout of ΔFosB in hippocampal SGZ neurons reduces antidepressant-induced neurogenesis and impedes hippocampus-dependent learning in the novel object recognition task. Taken together, these data indicate that FosB gene expression in SGZ is necessary for both hippocampal neurogenesis and memory formation.
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Sensory perception is neither static nor simple. The senses influence each other during multisensory stimulation and can be both suppressive and super-additive. As most knowledge of human olfactory perception is derived from functional neuroimaging studies, in particular fMRI, our current understanding of olfactory perception has systematically been investigated in an environment with concurrent loud sounds. ⋯ For this, 50 subjects were tested in both a silent setting and an fMRI-noise setting, in a randomised order. We found that fMRI-related acoustic noise had a significant negative effect on the olfactory detection threshold score. No significant effects were identified on olfactory discrimination, identification, identification certainty, hedonic rating, or intensity rating.
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Brain-derived neurotrophic factor (BDNF) expression and signaling activity in brain are influenced by chronic ethanol and stress. We previously demonstrated reduced Bdnf mRNA levels in the medial prefrontal cortex (mPFC) following chronic ethanol treatment and forced swim stress (FSS) enhanced escalated drinking associated with chronic ethanol exposure. The present study examined the effects of chronic ethanol and FSS exposure, alone and in combination, on Bdnf mRNA expression in different brain regions, including mPFC, central amygdala (CeA), and hippocampus (HPC). ⋯ In general, CIE and FSS exposure reduced Bdnf mRNA expression while miR-206 levels were increased in the mPFC, CeA, and HPC. Further, in many instances, these effects were more robust in mice that experienced both CIE and FSS treatments. These results have important implications for the potential link between BDNF signaling in the brain and ethanol consumption related to stress interactions with chronic ethanol experience.
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The electroencephalogram (EEG) is an informative neuroimaging tool for studying attention-deficit/hyperactivity disorder (ADHD); one main goal is to characterize the EEG of children with ADHD. In this study, we employed the power spectrum, complexity and bicoherence, biomarker candidates for identifying ADHD children in a machine learning approach, to characterize resting-state EEG (rsEEG). We built support vector machine classifiers using a single type of feature, all features from a method (relative spectral power, spectral power ratio, complexity or bicoherence), or all features from all four methods. ⋯ Bicoherence features had significant between-group differences, but classifier performance was sensitive to brain region used. rsEEG complexity of ADHD children was significantly lower than controls and may be a suitable biomarker candidate. Through a machine learning approach, 14 features from various brain regions using different methods were selected; the classifier based on these features had an AUC of 0.9158 and an accuracy of 84.59%. These findings strongly suggest that the combination of rsEEG characteristics obtained by various methods may be a tool for identifying ADHD.