Neuroscience
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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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The superior temporal sulcus (STS) encompasses a complex set of regions involved in a wide range of cognitive functions. To understand its functional properties, neuromodulation approaches such brain stimulation or neurofeedback can be used. We investigated whether the posterior STS (pSTS), a core region in the face perception and imagery network, could be specifically identified based on the presence of dynamic facial expressions (and not just on simple motion or static face signals), and probed with neurofeedback. ⋯ Our results provide evidence that a facial expression-selective cluster in pSTS can be identified and may represent a suitable target for neurofeedback approaches, aiming at social and emotional cognition. These findings highlight the presence of a highly selective region in STS encoding dynamic aspects of facial expressions. Future studies should elucidate its role as a mechanistic target for neurofeedback strategies in clinical disorders of social cognition such as autism.
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Neurons containing melanin-concentrating hormone (MCH) in the lateral hypothalamic area (LH) have been shown to promote rapid eye movement sleep (REMs) in mice. However, the downstream neural pathways through which MCH neurons influence REMs remained unclear. ⋯ We found that inhibition of MCH terminals in the vlPAG/LPT significantly reduced transitions into REMs during spontaneous sleep-wake cycles and prevented the increase in REMs transitions observed after chemogenetic activation of MCH neurons. These results strongly suggest that the vlPAG/LPT may be an essential relay through which MCH neurons modulate REMs.
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High-definition transcranial direct current stimulation (HD-tDCS) is a variant of tDCS, which produces more focal stimulation, delimiting brain current flow to a defined region compared to conventional tDCS. To date, only one study has been conducted to investigate HD-tDCS effects on language recovery in aphasia. Here, we aimed to assess the effects of cathodal HD-tDCS on verb naming by comparing two current intensities: 1 vs 2 mA. ⋯ Our findings showed that HD-tDCS applied to the right intact hemisphere are efficacious for language recovery. These results indicate that HD-tDCS represents a promising new technique for language rehabilitation. However, systematic determination of stimulation intensity appears to be crucial for obtaining relevant effects.
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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.