Neuroscience
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Schizophrenia, a complex psychiatric disorder with diverse symptoms, has been linked to ketamine, known for its N-methyl-D-aspartate (NMDA) receptor antagonistic properties. Understanding the distinct roles and mechanisms of ketamine is crucial, especially regarding its induction of schizophrenia-like symptoms. Recent research highlights the impact of ketamine on key brain regions associated with schizophrenia, specifically the prefrontal cortex (PFC) and hippocampus (Hip). ⋯ In the Hip, 129 differentially expressed proteins were screened, mainly related to synaptic plasticity proteins and mitochondrial respiratory chain complex-associated proteins. Additionally, we investigated key proteins within the glutamatergic synapse pathway and observed decreased expression levels of phosphorylated CaMKII and CREB. Overall, the study unveiled a significant proteomic signature in the chronic ketamine-induced schizophrenia mouse model, characterized by anxiety and cognitive impairment in both the PFC and Hip, and this comprehensive proteomic dataset may not only enhance our understanding of the molecular mechanisms underlying ketamine-related mental disorders but also offer valuable insights for future disease treatments.
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Face processing includes two crucial processing levels - face detection and face recognition. However, it remains unclear how human brains organize the two processing levels sequentially. While some studies found that faces are recognized as fast as they are detected, others have reported that faces are detected first, followed by recognition. ⋯ Our findings showed that the networks trained on face recognition also exhibited the "detection-first, recognition-later" pattern. Moreover, this sequential organization mechanism developed gradually during the training of the networks and was observed only for correctly predicted images. These findings collectively support the computational account as to why the brain organizes them in this way.
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Contests may be highly effective in eliciting high levels of effort, but they also carry the risk of inefficient resource allocation due to excessive effort (overbidding), squandering valuable social resources. While a growing body of research has focused on how group identity exacerbates out-group conflict, its influence on in-group conflict remains relatively unexplored. This study endeavors to explore the impact of group identity on conflicts within and between groups in competitive environments, thereby addressing gaps in the current research landscape and dissecting the involved neurobiological mechanisms. ⋯ Subsequently, after the task, additional activation was observed in the right temporal lobe. Results from functional connectivity studies indicated that group identity tasks modify decision-making processes by promoting group norms, empathy, and blurred self-other boundaries for in-group decisions, while out-group decisions after the group identity task see heightened cognitive control, an increased dependence on rational judgment, introspection of self-environment relationships, and a greater focus on anticipating others' behaviors. This study reveals the widespread occurrence of overbidding behavior and demonstrates the role of group identity in mitigating this phenomenon, concurrently providing a comprehensive analysis of the underlying neural mechanisms.
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The precise electroencephalogram (EEG) signal classification with the highest possible accuracy is a key goal in the brain-computer interface (BCI). Considering the complexity and nonstationary nature of the EEG signals, there is an urgent need for effective feature extraction and data mining techniques. Here, we introduce a novel pipeline based on Bat and genetic algorithms for feature construction and dimension reduction of EEG signals. ⋯ Compared to the previously introduced methods, our proposed framework demonstrates a superior balance of high accuracy and short runtime. The minimum achieved accuracies for balanced and unbalanced classes are 100% and 75.9%, respectively. This approach has the potential for direct applications in clinics, enabling accurate and rapid analysis of the epilepsy EEG signals obtained from patients.
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Innate defensive behavior is important for animal survival. The Vglut2+ neurons in the ventral tegmental area (VTA) have been demonstrated to play important roles in innate defensive behaviors, but the neural circuit mechanism is still unclear. Here, we find that VTA - zona incerta (ZI) glutamatergic projection is involved in regulating innate fear responses. ⋯ Using viral tracing and immunofluorescence, we show that VTA - Vglut2+ neurons send direct excitatory outputs to the ZI. Moreover, we find that the activity of VTAVglut2 - ZI projection is pivotal in modulating fear response. Together, our study reveals a new VTA - ZI glutamatergic circuit in mediating innate fear response and provides a potential target for treating post-traumatic stress disorder.