Neuroscience
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Short-term synaptic plasticity refers to the regulation of synapses by their past activity on time scales of milliseconds to minutes. Hippocampal mossy fiber synapses onto CA3 pyramidal cells (Mf-CA3 synapses) are endowed with remarkable forms of short-term synaptic plasticity expressed as facilitation of synaptic release by a factor of up to ten-fold. Three main forms of short-term plasticity are distinguished: 1) Frequency facilitation, which includes low frequency facilitation and train facilitation, operating in the range of tens of milliseconds to several seconds; 2) Post-tetanic potentiation triggered by trains of high frequency stimulation, which lasts several minutes; 3) Finally, depolarization-induced potentiation of excitation, based on retrograde signaling, with an onset and offset of several minutes. ⋯ We then review evidence for a physiological function of short-term plasticity of Mf-CA3 synapses in information transfer between the dentate gyrus and CA3 in conditions of natural spiking, and discuss how short-term plasticity counteracts robust feedforward inhibition in a frequency-dependent manner. Although DG-CA3 connections have long been proposed to play a role in memory, direct evidence for an implication of short-term plasticity at Mf-CA3 synapses is mostly lacking. The mechanistic knowledge gained on short-term plasticity at Mf-CA3 synapses should help in designing future experiments to directly test how this evolutionary conserved feature controls hippocampal circuit function in behavioural conditions.
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Widespread structural changes have been observed in patients with stroke in previous diffusion tensor imaging studies. However, the topological organization of white matter structural networks after acute ischemic stroke (AIS) in the right basal ganglia (BG) remains unknown. The aim of our study is to investigate whether the topological structure of the white matter structural network is altered in patients with AIS in the right BG, and its relationship with cognition. ⋯ Reduced structural connectivity detected by NBS in AIS patients were primarily located in the lesional hemisphere. Furthermore, altered nodal properties were correlated with cognitive scores. Documenting the alterations in the topological patterns of white matter structural networks will help to promote the understanding of the neural mechanisms of cognitive impairment after AIS in the right BG.
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Cancer and depression are closely interrelated, particularly in patients with advanced cancer, who often present with comorbid anxiety and depression for various reasons. Recently, there has been a growing interest in the study of depression in cancer patients, with the aim of assessing the possible triggers, predictors, adverse events, and possible treatment options for depression in several common cancers. The objective of this narrative review is to synthesize the extant literature on the relationship between the occurrence and progression of depression in several common patient categories. ⋯ The current research findings indicate a strong association between cancer and depression. However, the direction of causality remains unclear. Focusing on depression in cancer patients may, therefore, be beneficial for these patients.
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Major depressive disorder (MDD) has demonstrated its negative impact on various aspects of the lives of those affected. Although several therapies have been developed over the years, it remains a challenge for mental health professionals. Thus, understanding the pathophysiology of MDD is necessary to improve existing treatment options or seek new therapeutic alternatives. ⋯ However, synaptic plasticity involves a cascade of events, including the action of presynaptic proteins such as synaptophysin and synapsins and postsynaptic proteins such as postsynaptic density-95 (PSD-95). Additionally, several factors can negatively impact the process of spinogenesis/neurogenesis, which are related to many outcomes, including MDD. Thus, this narrative review aims to deepen the understanding of the involvement of synaptic formations and their components in the pathophysiology and treatment of MDD.
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Brain-computer interface (BCI) is a technology that directly connects signals between the human brain and a computer or other external device. Motor imagery electroencephalographic (MI-EEG) signals are considered a promising paradigm for BCI systems, with a wide range of potential applications in medical rehabilitation, human-computer interaction, and virtual reality. Accurate decoding of MI-EEG signals poses a significant challenge due to issues related to the quality of the collected EEG data and subject variability. ⋯ We conducted experiments on the BCI Competition IV-2a and IV-2b datasets, and the proposed network outperformed the current state-of-the-art techniques with an accuracy of 84.58% and 90.94%, respectively, for the subject-dependent mode. In addition, we used t-SNE to visualize the features extracted by the proposed network, further demonstrating the effectiveness of the feature extraction framework. We also conducted extensive ablation and hyperparameter tuning experiments to construct a robust network architecture that can be well generalized.