Neuroscience
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The post-stroke angiogenic response is accompanied by changes of tight junctions (TJs) of the blood-brain barrier (BBB). However, the precise dynamic change of TJ proteins (TJPs) in the different stages of stroke-induced vascular remodeling and the molecules mediating these processes have yet to be fully defined. To investigate the temporal relationship between changes in TJPs, the pro-angiogenic factor α5β1 integrin and the anti-permeability factor Ang1 in cerebral vessels following cerebral ischemic stroke, male C57Bl/6 mice were subject to 90min of ischemia by temporary occlusion of the middle cerebral artery followed by reperfusion and their brains analyzed 0, 1, 2, 4, 7 and 14days post-ischemia. ⋯ In the penumbra, Ang1 expression was induced, peaking at the same time point as α5β1 expression. Consistent with these findings, oxygen glucose deprivation/reperfusion induced expression of α5β1 and Ang1 on brain endothelial cell (BEC) in a similar manner in vitro, which correlated closely with BEC proliferation and increased expression of TJPs. Our results demonstrate that in the post-ischemic penumbra, a tight temporal correlation exists between the angiogenic markers α5β1 and Ang1 and the TJPs, suggesting a potential role for Ang1 and α5β1 in promoting BBB integrity following ischemic stroke.
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The fact that interference from peripheral distracting information can be reduced in high perceptual load tasks has been widely demonstrated in previous research. The modulation from the perceptual load is known as perceptual load effect (PLE). Previous functional magnetic resonance imaging (fMRI) studies on perceptual load have reported the brain areas implicated in attentional control. ⋯ DC-PLE correlation analysis revealed that PLE was positively associated with the right middle temporal visual area (MT)-one of dorsal attention network (DAN) nodes. Furthermore, the right MT functionally connected to the conventional DAN and the RSFCs between right MT and DAN nodes were also positively associated with individual difference in PLE. The results suggest an important role of attentional control in perceptual load tasks and provide novel insights into the understanding of the neural correlates underlying PLE.
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Studies have indicated that a cortical sensory system is capable of processing information from different sensory modalities. However, it still remains unclear when and how a cortical system integrates and retains information across sensory modalities during learning. Here we investigated the neural dynamics underlying crossmodal associations and memory by recording event-related potentials (ERPs) when human participants performed visuo-tactile (crossmodal) and visuo-visual (unimodal) paired-associate (PA) learning tasks. ⋯ Additional behavioral experiments suggested that these ERP components were not relevant to the participants' familiarity with stimuli per se. Further, by shortening the delay length (from 1300ms to 400ms or 200 ms) between the first and second stimulus in the crossmodal task, declines in participants' task performance were observed accordingly. Taken together, these results provide insights into the cortical plasticity (induced by PA learning) of neural networks involved in crossmodal associations in working memory.
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Attention deficit hyperactivity disorder (ADHD) is a common brain disorder with high prevalence in school-age children. Previously developed machine learning-based methods have discriminated patients with ADHD from normal controls by providing label information of the disease for individuals. Inattention and impulsivity are the two most significant clinical symptoms of ADHD. ⋯ The performance of the predictive model for inattention is r=0.79 (p<10-8), and the performance of the predictive model for impulsivity is r=0.48 (p<10-8). The ICN-related predictive patterns may provide valuable information for investigating the brain network mechanisms of ADHD. In summary, the predictive models for clinical symptoms could be beneficial for personalizing ADHD medications.
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Resting-state functional Magnetic Resonance Imaging (rs-fMRI) is frequently used as a powerful technology to detect individual differences in many cognitive functions. Recently, some studies have explored the association between scale-free dynamic properties of resting-state brain activation and individual personality traits. However, little is known about whether the scale-free dynamics of resting-state function networks is associated with delay discounting. ⋯ After controlling some covariates, including gender, age, education, personality and trait anxiety, partial correlation analysis revealed that the Hurst exponent in default mode network (DMN) and salience network (SN) was positively correlated with the delay discounting rates. No significant correlation between delay discounting and mean Hurst exponent of the whole brain was found. Thus, our results suggest the individual delay discounting is associated with the dynamics of inner-network interactions in the DMN and SN, and highlight the crucial role of scale-free dynamic properties of function networks on intertemporal choice.