Neuroscience
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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.
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Primary visual cortex, the first cortical stage of visual information processing, is represented by diverse functional maps that demonstrate the selectivity for specific visual features such as spatial frequency (SF). Although the local organization of SF maps in cat area 17 (A17) has been largely investigated, the global arrangement remains elusive. ⋯ In particular, we found the highest SF preference within the global distribution concentrated around the horizontal meridian. These results significantly contribute to a more comprehensive understanding of the SF organization in visual cortex.
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Previous studies have shown a close relationship between the serotonin system and working memory (WM), but the neural mechanism for the role of the serotonin system on the WM is unclear. The frontoparietal network is involved in WM and is associated with the serotonin system. ⋯ Moreover, the mean connectivity in the right inferior parietal lobule was positively correlated with WM performance. These brain network analysis findings could provide a new perspective on the neural mechanisms of gene-gene interactions and on individual differences in cognitive functions.