Neuroscience
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With the improvement of cancer treatment techniques, increasing attention has been given to chemotherapy-induced cognitive impairment through white matter injury. Clemastine fumarate has been shown to enhance white matter integrity in cuprizone- or hypoxia-induced demyelination mouse models. However, whether clemastine can be beneficial for reversing chemotherapy-induced cognitive impairment remains unexplored. ⋯ Clemastine enhanced myelination, promoted oligodendrocyte precursor cell differentiation and increased the neurofilament 200 protein levels in the corpus callosum and hippocampus. We concluded that clemastine rescues cognitive function damage caused by chemotherapy through improving white matter integrity. Remyelination, oligodendrocyte differentiation and the increase of neurofilament protein promoted by clemastine are potential strategies for reversing the cognitive dysfunction caused by chemotherapy.
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A spontaneous mutation of the disrupted in schizophrenia 1 (Disc1) gene is carried by the 129S inbred mouse strain. Truncated DISC1 protein in 129S mouse synapses impairs the scaffolding of excitatory postsynaptic receptors and leads to progressive spine dysgenesis. In contrast, C57BL/6 inbred mice carry the wild-type Disc1 gene and exhibit more typical cognitive performance in spatial exploration and executive behavioral tests. ⋯ Analysis of pyr/int connectivity revealed a significant delay in synaptic transmission for 129S putative pairs. Sampled 129S pyr/int pairs also had lower detectability index scores than B6 putative pairs. Therefore, the spontaneous Disc1 mutation in the 129S strain attenuates the firing of putative pyr CA1 neurons and impairs spike timing fidelity during cognitive tasks.
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A new method for analyzing brain complex dynamics and states is presented. This method constructs functional brain graphs and is comprised of two pylons: (a) Operational architectonics (OA) concept of brain and mind functioning. (b) Network neuroscience. In particular, the algorithm utilizes OA framework for a non-parametric segmentation of EEG signals, which leads to the identification of change points, namely abrupt jumps in EEG amplitude, called Rapid Transition Processes (RTPs). ⋯ The classification results, based on a Naïve Bayes classifier, show that the overall accuracies were found to be above chance level in all tested cases. This method was also compared with other state-of-the-art computational approaches commonly used for functional network generation, exhibiting competitive performance. The method can be useful to neuroscientists wishing to enhance their repository of brain research algorithms.
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Persistent improvement of cognitive deficits in Alzheimer's disease (AD), a common form of dementia, is an unattained therapeutic objective. Gene therapy holds promise for treatment of familial and sporadic forms of AD. p38γ, a member of the p38 mitogen-activated protein (MAP) kinase family, inhibits amyloid-β toxicity through regulation of tau phosphorylation. We recently showed that a gene delivery approach increasing p38γ resulted in markedly better learning and memory performance in mouse models of AD at advanced stages of amyloid-β- and tau-mediated cognitive impairment. ⋯ Moreover, their learning and memory function was markedly impaired compared to control-treated aged APP mice. These results suggest that high neuronal levels of active p38γ emphasize a stress kinase role of p38γ, perturbing circuit function in motivation, navigation, and spatial learning. Overall, this work shows excessive neuronal p38γ levels can aggravate circuit dysfunction and advises adjustable expression systems will be required for sustainable AD gene therapy based on p38γ activity.
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Recent studies show that overlapping community structure is an important feature of the brain functional network. However, alterations in such overlapping community structure in Alzheimer's disease (AD) patients have not been examined yet. In this study, we investigate the overlapping community structure in AD by using resting-state functional magnetic resonance imaging (rs-fMRI) data. ⋯ In particular, the frontal-parietal and basal ganglia networks exhibit significant differences between the two groups. A machine learning framework proposed in this paper for AD detection achieved an accuracy of 76.7% when using the detected community strengths of the frontal-parietal and basal ganglia networks only as input features. These findings provide novel insights into the understanding of pathological changes in the brain functional network organization of AD and show the potential of the community structure-related features for AD detection.