Neuroscience
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The locomotor central pattern generator is a neural network located in the ventral aspect of the caudal spinal cord that underlies stepping in mammals. While many genetically defined interneurons that are thought to comprise this neural network have been identified and characterized, the dI6 cells- which express the transcription factors WT1 and/or DMRT3- are one population that settle in this region, are active during locomotion, whose function is poorly understood. These cells were originally hypothesized to be commissural premotor interneurons, however evidence in support of this is sparse. ⋯ Retrograde tracing experiments indicate that the majority of dI6 cells project descending axons, and some make monosynaptic or disynaptic contacts onto motoneurons on either side of the spinal cord. Analysis of their activity during non-resetting deletions, which occur during bouts of fictive locomotion, suggests that these cells are involved in both locomotor rhythm generation and pattern formation. This study provides a thorough characterization of the dI6 cells labeled in the TgDbx1Cre;R26EFP;Dbx1LacZ transgenic mouse, and supports previous work suggesting that these cells play multiple roles during locomotor activity.
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Recent data suggest that manipulating the muscle afferents of one arm affects both ipsilateral and contralateral perceptual estimates. Here, we used the mirror paradigm to study the bimanual integration of kinesthetic muscle afferents. The reflection of a moving hand in a mirror positioned in the sagittal plane creates an illusion of symmetrical bimanual movement. ⋯ We found that as long as the arm was still moving, the kinesthetic illusion decayed slowly after visual occlusion. These findings suggest that the mirror illusion results from the combination of visuo-proprioceptive signals from the two arms and is not purely visual in origin. Our findings also support the more general concept whereby proprioceptive afferents are integrated bilaterally for the purpose of kinesthesia during bimanual tasks.
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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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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.