Neuroscience
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Review
Lactate supply from astrocytes to neurons and its role in ischemic stroke-induced neurodegeneration.
Glucose transported to the brain is metabolized to lactate in astrocytes and supplied to neuronal cells via a monocarboxylic acid transporter (MCT). Lactate is used in neuronal cells for various functions, including learning and memory formation. Furthermore, lactate can block stroke-induced neurodegeneration. ⋯ These findings suggest that the lack of lactate supply may strongly contribute to hypoxia-induced neurodegeneration. Furthermore, diminished lactate supply from astrocytes could facilitate stroke-induced neurodegeneration. Therefore, astrocyte-derived lactate may contribute to stroke prevention.
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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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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 study of the effects of fear and disgust on the capture of automatic attention is gaining interest. Most findings reveal a more efficient capture of exogenous attention by disgust than by fear stimuli, although the underlying mechanisms are not completely understood. The manipulation of their spatial frequency may provide new insight that may contribute to clarify this issue. ⋯ The results showed that disgust and fear distractors captured exogenous attention equally early, as indicated by the augmented amplitude of the N2p, and later disgust distractors are the ones eliciting the highest amplitude of the LPP component. While in an initial stage, both stimuli seem to have similar preferential access to further processing allowing fast responding in both cases, disgust is more deeply processed at a later stage probably facilitating its examination. These findings suggest that exploring the temporal course of processing is relevant for the understanding of the differential capture of exogenous attention by disgust and fear distractors.
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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.