Neural basis of the attention bias during addiction stroop task in methamphetamine-dependent patients with and without a history of psychosis: an ERP study

The Role and Mechanism of TRIM13 Regulation of TRAF6 Ubiquitination in the Synergy of Inflammatory Responses and Neurotoxicity Induced by METH and HIV- 1 Tat Protein in Astrocytes

Methamphetamine (METH) abuse and HIV infection are major public health concerns worldwide. While both METH and HIV- 1 Tat proteins can induce neurotoxicity and synergistic effects on the nervous system, the mechanisms by which they act synergistically remain unclear. Our recent research shows that neuroinflammation plays an important role in neurotoxicity induced by METH and HIV- 1 Tat proteins, but the regulatory mechanism has not been clarified. Tripartite Motif Containing 13 (TRIM13) is a protein known to regulate the inflammatory response through ubiquitination of Tumor Necrosis Factor Receptor Associated Factor 6 (TRAF6). This study investigated the role of TRIM13 and TRAF6 in the inflammatory response of U- 87 MG cells induced by METH and HIV- 1 Tat proteins. U- 87 MG cells were treated with 2 mM METH and/or 100 nM HIV- 1 Tat protein. Western blot (WB), immunofluorescence (IF), and co-immunoprecipitation (Co-IP) experiments were employed to elucidate the role of TRIM13 and TRAF6. The results demonstrated that METH and HIV- 1 Tat protein could synergistically induce an inflammatory response in U- 87 MG cells. Furthermore, the knockdown of TRIM13 significantly enhanced this inflammatory response, while the inhibition of TRAF6 significantly weakened it. Additionally, the study revealed that TRIM13 could degrade TRAF6 via ubiquitination. In conclusion, this study suggests that TRIM13 regulates TRAF6 ubiquitination to dampen the inflammatory response of U- 87 MG cells induced by METH and HIV- 1 Tat proteins. These findings highlight TRIM13 and TRAF6 as potential targets for therapeutic intervention in the context of METH and HIV- 1 Tat protein-induced inflammatory responses and neurotoxic effects.

Functional Connectivity Differences in the Resting-state of the Amygdala in Alcohol-dependent Patients with Depression

RATIONALE AND OBJECTIVES

The mechanism of comorbidity between alcohol dependence and depressive disorders are not well understood. This study investigated differences in the brain function of alcohol-dependent patients with and without depression by performing functional connectivity analysis using resting-state functional magnetic resonance imaging.

MATERIALS AND METHODS

A total of 29 alcohol-dependent patients with depression, 31 alcohol-dependent patients without depression and 31 healthy control subjects were included in this study. The resting-state functional connectivity between the amygdala and the whole brain was compared among the three groups. Additionally, we examined the correlation between functional connectivity values in significantly different brain regions and levels of alcohol dependence and depression.

RESULTS

The resting-state functional connectivity between the left amygdala and the right caudate nucleus was decreased in alcohol-dependent patients. Additionally, the resting-state functional connectivity of the right amygdala with the right caudate nucleus, right transverse temporal gyrus, right temporal pole: superior temporal gyrus were also decreased. In alcohol-dependent patients with depression, not only was functional connectivity between the above brain regions significantly decreased, but so was functional connectivity between the right amygdala and the left middle temporal gyrus. Also, there was no significant correlation between the resting-state functional connectivity values in statistically significant brain regions and the levels of alcohol dependence and depression.

CONCLUSION

The impairment of the functional connectivity of the amygdala with caudate nucleus and partial temporal lobe may be involved in the neural mechanism of alcohol dependence comorbidity depressive disorders.

Identification of Methamphetamine Abusers Can Be Supported by EEG-Based Wavelet Transform and BiLSTM Networks

Methamphetamine (MA) is a neurological drug, which is harmful to the overall brain cognitive function when abused. Based on this property of MA, people can be divided into those with MA abuse and healthy people. However, few studies to date have investigated automatic detection of MA abusers based on the neural activity. For this reason, the purpose of this research was to investigate the difference in the neural activity between MA abusers and healthy persons and accordingly discriminate MA abusers. First, we performed event-related potential (ERP) analysis to determine the time range of P300. Then, the wavelet coefficients of the P300 component were extracted as the main features, along with the time and frequency domain features within the selected P300 range to classify. To optimize the feature set, F_score was used to remove features below the average score. Finally, a Bidirectional Long Short-term Memory (BiLSTM) network was performed for classification. The experimental result showed that the detection accuracy of BiLSTM could reach 83.85%. In conclusion, the P300 component of EEG signals of MA abusers is different from that in normal persons. Based on this difference, this study proposes a novel way for the prevention and diagnosis of MA abuse.

Effects of 15-Day -6° Head-Down Bed Rest and HD-tDCS on Cognitive Functions

A favorable cognitive state, efficient attention, and sharp alertness are essential for successful space missions. To explore the effects of microgravity on working memory, attention, and alertness and to find effective approaches to improve them, we recruited 17 healthy participants (control: 9, active: 8) to be involved in a 15-day -6° head-down bed rest (HDBR). A 30 min HD-tDCS was applied to the active group every other day during HDBR. We adopted three types of tasks: N-back, SCWT, and PVT to evaluate the effects, which were completed before, during, and after HDBR. The results showed that: (1) there was no significant difference in accuracy or in response time between the control and the active group. (2) The accuracy of SCWT before HDBR was significantly lower than that during and after HDBR. (3) Regardless of groups, the response time of all tasks showed a gradual decrease during HDBR. For the N-back and PVT tasks, the response time increased significantly in the control group and decreased significantly in the active group after HDBR. Those demonstrated that the working memory, attention, and alertness were elevated during 15-day -6° HDBR. HD-tDCS enhanced readaptation of cognitive functions after HDBR exposure.

EEG-based major depressive disorder recognition by neural oscillation and asymmetry

Background

Major Depressive Disorder (MDD) is a pervasive mental health issue with significant diagnostic challenges. Electroencephalography (EEG) offers a non-invasive window into the neural dynamics associated with MDD, yet the diagnostic efficacy is contingent upon the appropriate selection of EEG features and brain regions.

Methods

In this study, resting-state EEG signals from both eyes-closed and eyes-open conditions were analyzed. We examined band power across various brain regions, assessed the asymmetry of band power between the hemispheres, and integrated these features with clinical characteristics of MDD into a diagnostic regression model.

Results

Regression analysis found significant predictors of MDD to be beta2 (16-24 Hz) power in the Prefrontal Cortex (PFC) with eyes open (B = 20.092, p = 0.011), beta3 (24-40 Hz) power in the Medial Occipital Cortex (MOC) (B = -12.050, p < 0.001), and beta2 power in the Right Medial Frontal Cortex (RMFC) with eyes closed (B = 24.227, p < 0.001). Asymmetries in beta1 (12-16 Hz) power with eyes open (B = 28.047, p = 0.018), and in alpha (8-12 Hz, B = 9.004, p = 0.013) and theta (4-8 Hz, B = -13.582, p = 0.008) with eyes closed were also significant predictors.

Conclusion

The study confirms the potential of multi-region EEG analysis in improving the diagnostic precision for MDD. By including both neurophysiological and clinical data, we present a more robust approach to understanding and identifying this complex disorder.

Limitations

The research is limited by the sample size and the inherent variability in EEG signal interpretation. Future studies with larger cohorts and advanced analytical techniques are warranted to validate and refine these findings.