Neuroscience
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A growth mindset refers to an individual's beliefs about the malleable nature of intelligence. It plays an important role in motivation and achievement. However, few studies have examined the brain mechanisms involved in the growth mindset. ⋯ Whole-brain correlation analyses showed a positive relationship between growth mindset scores and regional GMV of the medial orbitofrontal cortex (mOFC) after controlling for age, sex, and total intracranial volume. This result was robust after controlling for intelligence quotient. The mOFC was primarily related to reward processing, supporting the social-cognitive theory of motivation on growth mindset.
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Acceleration/deceleration forces are a common component of various causes of mild traumatic brain injury (mTBI) and result in strain and shear forces on brain tissue. A small quantifiable volume dubbed the compensatory reserve volume (CRV) permits energy transmission to brain tissue during acceleration/deceleration events. The CRV is principally regulated by cerebral blood flow (CBF) and CBF is primarily determined by the concentration of inspired carbon dioxide (CO2). ⋯ Ribonucleic acid (RNA) sequencing conducted four hpi revealed that CO2 exposure prevented mTBI-induced transcriptional alterations of several targets related to oxidative stress, immune, and inflammatory signaling. Quantitative real-time PCR analysis confirmed the prevention of mTBI-induced increases in mitogen-activated protein kinase kinase kinase 6 and metallothionein-2. These initial proof of concept studies reveal that increases in inspired CO2 mitigate the detrimental contributions of acceleration/deceleration events in mTBI and may feasibly be translated in the future to humans using a medical device seeking to prevent mTBI among high-risk groups.
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Parkinson's disease (PD) is one of the leading causes of neurological disability, and its prevalence is expected to increase rapidly in the following few decades. PD diagnosis heavily depends on clinical features using the patient's symptoms. Therefore, an accurate, robust, and non-invasive bio-marker is of critical clinical importance for PD. ⋯ The proposed methodology is applied to three open fMRI databases for demonstration and validation. The PD diagnosis accuracy can reach 96.4% when the proposed methodology is used. Thus, rs-fMRI and topological machine learning provide a quantifiable and verifiable bio-marker for future PD early detection and treatment evaluation.
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CuII(atsm) is a blood-brain barrier permeant copper(II) compound that is under investigation in human clinical trials for the treatment of neurodegenerative diseases of the central nervous system (CNS). Imaging in humans by positron emission tomography shows the compound accumulates in affected regions of the CNS in patients. Most therapeutic studies to date have utilised oral administration of CuII(atsm) in an insoluble form, as either solid tablets or a liquid suspension. ⋯ In all instances where treatment with CuII(atsm) resulted in elevated tissue copper, transdermal application of soluble CuII(atsm) led to higher concentrations of copper. In contrast to CuII(atsm), an equivalent dose of copper(II) chloride resulted in minimal changes to tissue copper concentrations, regardless of the administration method. Data presented herein provide quantitative insight to transdermal application of soluble CuII(atsm) as a potential alternative to oral administration of the compound in an insoluble formulation.
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Reconstructing visual stimulus images from the brain activity signals is an important research task in the field of brain decoding. Many methods of reconstructing visual stimulus images mainly focus on how to use deep learning to classify the brain activities measured by functional magnetic resonance imaging or identify visual stimulus images. Accurate reconstruction of visual stimulus images by using deep learning still remains challenging. ⋯ Secondly, the structure of original decoder is extended to a deeper network in the deep generative multiview model, which makes the features obtained by each deconvolution layer more distinguishable. Finally, we configure the parameters of the optimizer and compare the performance of various optimizers under different parameter values, and then the one with the best performance is chosen and adopted to the whole model. The performance evaluations conducted on two publicly available datasets demonstrate that the improved model has more accurate reconstruction effectiveness than the original deep generative multiview model.