Neuroscience
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Alzheimer's disease (AD) is the most prevalent disorder of senile dementia mainly characterized by amyloid-beta peptide (Aβ) deposits in the brain. Cannabinoids are relevant to AD as they exert several beneficial effects in many models of this disease. Still, whether the endocannabinoid system is either up- or down-regulated in AD has not yet been fully elucidated. ⋯ Conversely, a high availability of 2-AG resulting from an increase in DAGL and lysophosphatidic acid phosphohydrolase activities occurred in the presence of Aβ1-40 fibrils although synaptosomal membrane disruption was also observed. Interestingly, neither synaptosomal mitochondrial viability assayed by MTT reduction nor membrane lipid peroxidation assayed by TBARS formation measurements were altered by Aβ1-40 oligomers or fibrils. These results show a differential effect of Aβ1-40 peptide on 2-AG metabolism depending on its conformation.
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Alcoholism is a relapsing disorder with limited treatment options, in part due to our limited understanding of the disease etiology. We have recently shown that increased ethanol-seeking in a behavioral model of relapse in a rat model of alcoholism was associated with increased oligodendrogenesis which was positively correlated with platelet/endothelial cell adhesion molecule (PECAM-1) expression in the medial prefrontal cortex (mPFC). The current study investigated whether newly born oligodendrocytes form close physical associations with endothelial cells expressing PECAM-1 and whether these changes were accompanied by altered blood-brain barrier (BBB) integrity. ⋯ Furthermore, voluntary wheel running during abstinence enhanced SMI-71 expression in endothelial cells, indicating protection against abstinence-induced reduction in BBB integrity. Taken together, these results suggest that ethanol experience and abstinence disrupts homeostasis in the oligo-vascular niche in the mPFC. Reversing these mechanisms may hold the key to reducing propensity for relapse in individuals with moderate to severe alcohol use disorder.
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The locomotor central pattern generator is a neural network located in the ventral aspect of the caudal spinal cord that underlies stepping in mammals. While many genetically defined interneurons that are thought to comprise this neural network have been identified and characterized, the dI6 cells- which express the transcription factors WT1 and/or DMRT3- are one population that settle in this region, are active during locomotion, whose function is poorly understood. These cells were originally hypothesized to be commissural premotor interneurons, however evidence in support of this is sparse. ⋯ Retrograde tracing experiments indicate that the majority of dI6 cells project descending axons, and some make monosynaptic or disynaptic contacts onto motoneurons on either side of the spinal cord. Analysis of their activity during non-resetting deletions, which occur during bouts of fictive locomotion, suggests that these cells are involved in both locomotor rhythm generation and pattern formation. This study provides a thorough characterization of the dI6 cells labeled in the TgDbx1Cre;R26EFP;Dbx1LacZ transgenic mouse, and supports previous work suggesting that these cells play multiple roles during locomotor activity.
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Attention deficit hyperactivity disorder (ADHD) is a common brain disorder with high prevalence in school-age children. Previously developed machine learning-based methods have discriminated patients with ADHD from normal controls by providing label information of the disease for individuals. Inattention and impulsivity are the two most significant clinical symptoms of ADHD. ⋯ The performance of the predictive model for inattention is r=0.79 (p<10-8), and the performance of the predictive model for impulsivity is r=0.48 (p<10-8). The ICN-related predictive patterns may provide valuable information for investigating the brain network mechanisms of ADHD. In summary, the predictive models for clinical symptoms could be beneficial for personalizing ADHD medications.
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Resting-state functional Magnetic Resonance Imaging (rs-fMRI) is frequently used as a powerful technology to detect individual differences in many cognitive functions. Recently, some studies have explored the association between scale-free dynamic properties of resting-state brain activation and individual personality traits. However, little is known about whether the scale-free dynamics of resting-state function networks is associated with delay discounting. ⋯ After controlling some covariates, including gender, age, education, personality and trait anxiety, partial correlation analysis revealed that the Hurst exponent in default mode network (DMN) and salience network (SN) was positively correlated with the delay discounting rates. No significant correlation between delay discounting and mean Hurst exponent of the whole brain was found. Thus, our results suggest the individual delay discounting is associated with the dynamics of inner-network interactions in the DMN and SN, and highlight the crucial role of scale-free dynamic properties of function networks on intertemporal choice.