Self-concept mediates the relationships between childhood traumatic experiences and adolescent depression in both clinical and community samples.

BACKGROUND: Childhood trauma is a pivotal risk factor for adolescent depression. While the association between childhood trauma and depression is well-established, the mediating role of self-concept has not been acknowledged. Specifically, limited attention has been paid to how childhood maltreatment impacts adolescent depression through physical and social self-concept, both in clinical and community samples. This study aims to investigate how distinct and cumulative childhood trauma affects adolescent depression, as well as the potential mediating role of self-concept in their relationships. METHODS: We recruited 227 depressed adolescents (dataset 1, 45 males, age = 15.34 ± 1.96) and 574 community adolescents (dataset 2, 107 males, age = 16.79 ± 0.65). Each participant was assessed on five subtypes of childhood trauma severity, cumulative trauma index, physical and social self-concept, and depression. Mediation models were tested separately in the clinical and community samples. RESULTS: Clinically depressed adolescents experienced a higher level of trauma severity, a greater number of trauma subtypes, and had lower levels of physical and social self-concept compared to community adolescents. Analyses on childhood trauma severity and cumulative trauma index jointly indicated that physical and social self-concept played mediation roles in the relationships between childhood trauma experiences and depression. Moreover, the mediating effects of self-concept were stronger in depressed adolescents when compared to community samples. CONCLUSIONS: Our findings suggest that physical and social self-concept play mediating roles in the pathway linking childhood trauma and adolescent depression, particularly in clinically depressed individuals.

Computational markers of risky decision-making predict for relapse to alcohol.

BACKGROUND: Relapse remains the major challenge in treatment of alcohol use disorder (AUD). Aberrant decision-making has been found as important cognitive mechanism underlying relapse, but factors associated with relapse vulnerability are unclear. Here, we aim to identify potential computational markers of relapse vulnerability by investigating risky decision-making in individuals with AUD. METHODS: Forty-six healthy controls and fifty-two individuals with AUD were recruited for this study. The risk-taking propensity of these subjects was investigated using the balloon analog risk task (BART). After completion of clinical treatment, all individuals with AUD were followed up and divided into a non-relapse AUD group and a relapse AUD group according to their drinking status. RESULTS: The risk-taking propensity differed significantly among healthy controls, the non-relapse AUD group, and the relapse AUD group, and was negatively associated with the duration of abstinence in individuals with AUD. Logistic regression models showed that risk-taking propensity, as measured by the computational model, was a valid predictor of alcohol relapse, and higher risk-taking propensity was associated with greater risk of relapse to drink. CONCLUSION: Our study presents new insights into risk-taking measurement and identifies computational markers that provide prospective information for relapse to drink in individuals with AUD.

RClaNet: An explainable Alzheimer's disease diagnosis framework by joint registration and classification.

Alzheimer's disease (AD) is a degenerative mental disorder of the central nervous system that affects people's ability of daily life. Unfortunately, there is currently no known cure for AD. Thus, the early detection of AD plays a key role in preventing and controlling its progression. Magnetic resonance imaging (MRI)-based measures of cerebral atrophy are regarded as valid markers of the AD state. As one of representative methods for measuring brain atrophy, image registration technique has been widely adopted for AD diagnosis. However, AD detection is sensitive to the accuracy of image registration. To address this problem, an AD assistant diagnosis framework based on joint registration and classification is proposed. Specifically, in order to capture more local deformation information, we propose a novel patch-based joint brain image registration and classification network (RClaNet) to estimate the local dense deformation fields (DDF) and disease risk probability maps that explain high-risk areas for AD patients. RClaNet consists of a registration network and a classification network, in which the deformation field from registration network is fed into the classification network to enhance the prediction accuracy of the disease. Then, the exponential distance weighting method is used to obtain the global DDF and the global disease risk probability map, and it can remove grid-like artifacts by uniformly weighting method. Finally, the global classification network uses the global disease risk probability map for the early detection of AD. We evaluate the proposed method on the OASIS-3, AIBL and ADNI datasets, and experimental results show that the proposed RClaNet achieves superior registration performances than several state-of-the-art methods. Early diagnosis of AD using the global disease risk probability map also yielded competitive results. To demonstrate the generality, we also evaluate the proposed method on a COVID-19 dataset and achieve decent registration and classification results. These experiments prove that the deformation information in the registration process can be used to characterize subtle changes of degenerative diseases and further assist clinicians in diagnosis.

Behavioral Effects of Repetitive Transcranial Magnetic Stimulation in Disorders of Consciousness: A Systematic Review and Meta-Analysis.

Traumatic brain injury, cardiac arrest, intracerebral hemorrhage, and ischemic stroke may cause disorders of consciousness (DoC). Repetitive transcranial magnetic stimulation (rTMS) has been used to promote the recovery of disorders of consciousness (DoC) patients. In this meta-analysis, we examined whether rTMS can relieve DoC patient symptoms. We searched through journal articles indexed in PubMed, the Web of Science, Embase, Scopus, and the Cochrane Library until 20 April 2023. We assessed whether studies used rTMS as an intervention and reported the pre- and post-rTMS coma recovery scale-revised (CRS-R) scores. A total of 207 patients from seven trials were included. rTMS significantly improved the recovery degree of patients; the weighted mean difference (WMD) of the change in the CRS-R score was 1.89 (95% confidence interval (CI): 1.39-2.39; p < 0.00001) in comparison with controls. The subgroup analysis showed a significant improvement in CRS-R scores in rTMS over the dorsolateral prefrontal cortex (WMD = 2.24; 95% CI: 1.55-2.92; p < 0.00001; I2 = 31%) and the primary motor cortex (WMD = 1.63; 95% CI: 0.69-2.57; p = 0.0007; I2 = 14%). Twenty-hertz rTMS significantly improved CRS-R scores in patients with DoC (WMD = 1.61; 95% CI: 0.39-2.83; p = 0.010; I2 = 31%). Furthermore, CRS-R scores in rTMS over 20 sessions significantly improved (WMD = 1.75; 95% CI: 0.95-2.55; p < 0.0001; I2 = 12%). rTMS improved the symptoms of DoC patients; however, the available evidence remains limited and inadequate.

Top-down and bottom-up stimulation techniques combined with action observation treatment in stroke rehabilitation: a perspective.

Stroke is a central nervous system disease that causes structural lesions and functional impairments of the brain, resulting in varying types, and degrees of dysfunction. The bimodal balance-recovery model (interhemispheric competition model and vicariation model) has been proposed as the mechanism of functional recovery after a stroke. We analyzed how combinations of motor observation treatment approaches, transcranial electrical (TES) or magnetic (TMS) stimulation and peripheral electrical (PES) or magnetic (PMS) stimulation techniques can be taken as accessorial physical therapy methods on symptom reduction of stroke patients. We suggest that top-down and bottom-up stimulation techniques combined with action observation treatment synergistically might develop into valuable physical therapy strategies in neurorehabilitation after stroke. We explored how TES or TMS intervention over the contralesional hemisphere or the lesioned hemisphere combined with PES or PMS of the paretic limbs during motor observation followed by action execution have super-additive effects to potentiate the effect of conventional treatment in stroke patients. The proposed paradigm could be an innovative and adjunctive approach to potentiate the effect of conventional rehabilitation treatment, especially for those patients with severe motor deficits.

Posttraumatic stress disorder symptoms among healthcare workers during the Omicron era.

Background: The COVID-19 pandemic has caused significant psychological stress among healthcare workers. This study aimed to clarify the factors that influenced health workers' posttraumatic stress disorder (PTSD) symptoms. Method: A total of 443 healthcare workers from eight Mental Health Centers in Shandong were recruited to attend an online survey. Participants completed self-evaluation measures of exposure to the COVID-19 environment and PTSD symptoms, as well as measures of potential protective factors such as euthymia and perceived social support. Results: About 45.37% of healthcare workers had severe symptoms of PTSD symptoms. Healthcare workers with more serious PTSD symptoms were significantly related to higher exposure to COVID-19 (r = 0.177, p < 0.001), as well as lower levels of euthymia (r = -0.287, p < 0.001) and perceived social support (r = -0.236, p < 0.001). The structural equation model (SEM) further revealed that the impact of exposure to COVID-19 on PTSD symptoms was partially mediated by euthymia, and moderated by perceived social support, especially from others (e.g., friends, leaders, relatives and colleagues). Conclusion: These findings suggested that improving the state of euthymia, getting social support from others could alleviate PTSD symptoms among healthcare workers during the COVID-19.

Post-error slowing predicts for relapse in individuals with alcohol use disorder.

BACKGROUND: Relapse characterizes addiction. The cognitive phenotype underlying relapse in individuals with alcohol use disorder (AUD) remain unelucidated. Here we aimed to investigate the potential changes in behavioral adjustment in AUD, and the association with relapse. METHOD: Forty-seven subjects with AUD at Shandong Mental Health Center completed the stop-signal task, the PACS, the Beck Depression Inventory and the State-Trait anxiety questionnaires. Thirty age-matched male healthy subjects served as the control group (HC). In the follow-up, twenty-one subjects remained abstinent, while twenty-six subjects relapsed. Independent sample t-test was applied to measure differences between two groups and logistic regression analysis was conducted to explore the potential predictors on relapse. RESULTS: The results showed that there were significant differences in stop signal reaction time (SSRT) and trigger failure between the AUD and HC groups. Relapsed group showed longer post-error slowing (PES) when compared to the non-relapsed group. The PES could predict relapse in alcohol use disorder. CONCLUSION: Individuals with AUD showed impaired inhibitory control, which may predict relapse.

Clean-lifting transfer of large-area residual-free graphene films.

A unique "clean-lifting transfer" (CLT) technique that applies a controllable electrostatic force to transfer large-area and high-quality CVD-grown graphene onto various rigid or flexible substrates is reported. The CLT technique without using any organic support or adhesives can produce residual-free graphene films with large-area processability, and has great potential for future industrial production of graphene-based electronics or optoelectronics.