Neuroscience
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Procrastination is generally recognized as a problematic behavior and the consequences of which spread to various aspects of an individual's life such as academic performance, social accomplishment, well-being, and health. Previous studies have indicated that neuroticism is positively correlated with procrastination; however, little is known about the neural substrates underlying the link between neuroticism and procrastination. To address this issue, we employed voxel-based morphometry (VBM) and resting-state functional connectivity (RSFC) methods to investigate the neural underpinning for their relationship in the present study (N = 153). ⋯ Moreover, results from RSFC analysis suggested that the functional connectivity between RMTG and the right superior frontal gyrus (RSFG) was positively associated with neuroticism. More importantly, a mediation analysis demonstrated that neuroticism played a full mediating role in the impact of RMTG-RSFG functional connectivity on procrastination. Overall, the present study offered new insights into the relation between neuroticism and procrastination from a neural basis perspective, which also suggested the importance of emotional regulation with regard to the link between such an association.
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Mainstream theories of first and second language (L1, L2) processing in bilinguals are crucially informed by word translation research. A core finding is the translation asymmetry effect, typified by slower performance in forward translation (FT, from L1 into L2) than in backward translation (BT, from L2 into L1). Yet, few studies have explored its neural bases and none has employed (de)synchronization measures, precluding the integration of bilingual memory models with neural (de)coupling accounts of word processing. ⋯ Relative to BT, FT yielded slower responses, higher frontal theta (4-7 Hz) power in an early window (0-300 ms), reduced centro-posterior lower-beta (14-20 Hz) and centro-frontal upper-beta (21-30 Hz) power in a later window (300-600 ms), and lower fronto-parietal connectivity below 10 Hz in the early window. Also, the greater the behavioral difference between FT and BT, the greater the power of the early theta cluster for FT over BT. These results reveal key (de)coupling dynamics underlying translation asymmetry, offering frequency-specific constraints for leading models of bilingual lexical processing.
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The primary sensory modality for probing spatial perception can vary among psychophysical paradigms. In the subjective visual vertical (SVV) task, the brain must account for the position of the eye within the orbit to generate an estimate of a visual line orientation, whereas in the subjective haptic vertical (SHV) task, the position of the hand is used to sense the orientation of a haptic bar. Here we investigated whether a hand sensory bias can affect SHV measurement. ⋯ Midline SHV measures using the left and right hands were different, confirming a laterality effect (left hand -4.5 ± 1.7°, right hand 6.4 ± 2.0°). These results demonstrate a sensory bias in SHV measurement related to the effects of both hand-in-body (i.e., right vs left hand) and hand-in-space positions. Such modality-specific bias may result in disparity between SHV and SVV measurements, and therefore cannot be generalized to vertical or spatial perception.
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Electroencephalogram (EEG)-based quantitative pain measurement is valuable in the field of clinical pain treatment, providing objective pain intensity assessment especially for nonverbal patients who are unable to self-report. At present, a key challenge in modeling pain events from EEG is to find invariant representations for intra- and inter-subject variations, where current methods based on hand-crafted features cannot provide satisfactory results. Hence, we propose a novel method based on deep learning to learn such invariant representations from multi-channel EEG signals and demonstrate its great advantages in EEG-based pain classification tasks. ⋯ The proposed method aims to jointly preserve the spatial-spectral-temporal structures of EEG, for learning representations with high robustness against intra-subject and inter-subject variations, making it more conducive to multi-class and subject-independent scenarios. Empirical evaluation on 4-level pain intensity assessment within the subject-independent scenario demonstrated significant improvement over baseline and state-of-the-art methods in this field. Our approach applies deep neural networks (DNNs) to pain intensity assessment for the first time and demonstrates its potential advantages in modeling pain events from EEG.
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Fragmentation of the daily sleep-wake rhythm with increased nighttime awakenings and more daytime naps is correlated with the risk of development of Alzheimer's disease (AD). To explore whether a causal relationship underlies this correlation, the present study tested the hypothesis that chronic fragmentation of the daily sleep-wake rhythm stimulates brain amyloid-beta (Aβ) levels and neuroinflammation in the 3xTg-AD mouse model of AD. Female 3xTg-AD mice were allowed to sleep undisturbed or were subjected to chronic sleep fragmentation consisting of four daily sessions of enforced wakefulness (one hour each) evenly distributed during the light phase, five days a week for four weeks. ⋯ Sleep fragmentation also stimulated neuroinflammation as shown by increased expression of markers of microglial activation and proinflammatory cytokines measured by q-RT-PCR analysis of hippocampal samples. No significant effects of sleep fragmentation on Aβ, tau, or neuroinflammation were observed in the cerebral cortex. These studies support the concept that improving sleep consolidation in individuals at risk for AD may be beneficial for slowing the onset or progression of this devastating neurodegenerative disease.