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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Individuals with Highly Superior Autobiographical Memory (HSAM) provide the opportunity to investigate the neurobiological substrates of enhanced memory performance. While previous studies started to assess the neural correlates of memory retrieval in HSAM, here we assessed for the first time the intrinsic connectivity of a core memory region, the hippocampus, with the whole brain, in 8 HSAM subjects (HSAMs) and 21 controls during resting-state functional neuroimaging. ⋯ This altered pattern of hippocampal rsFC might be interpreted as a reduced capability of HSAMs to discriminate and select salient information, with a subsequent increase in the probability to encode and consolidate sensory information irrespective of their task-relevancy. Ultimately, these findings provide evidence that HSAM might be paradoxically enabled by an altered hippocampal rsFC that bypasses regions involved with salience detection in favor of specialized sensory regions.
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Astrocytes experience significant metabolic shifts in the "sensitive period" of neurological function recovery following cerebral ischemia. However, the changes in astrocyte lipid metabolism and their implications for neurological recovery remain unknown. In the present study, we employed a mouse middle cerebral artery occlusion model to investigate the changes in de novo lipogenesis and interleukin-33 (IL-33) production in astrocytes and elucidate their role in blood-brain barrier (BBB) repair in the subacute phase of cerebral ischemia. ⋯ Inhibition of lipogenesis in astrocytes decreased IL-33 production in the peri-infarct area, deteriorated BBB damage and interfered with neurological recovery. In addition, supplementation of IL-33 alleviated BBB destruction and improved neurological recovery worsened by lipogenesis inhibition. These findings indicate that astrocyte lipogenesis increases the production of IL-33 in the peri-infarct area, which promotes BBB repair in the subacute phase of cerebral ischemia injury and improves long-term functional recovery.
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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.