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Sensitivity to criticism, which can be defined as a negative evaluation that a person receives from someone else, is considered a risk factor for the development of psychiatric disorders in adolescents. They may be more vulnerable to social evaluation than adults and exhibit more inadequate emotion regulation strategies such as rumination. The neural network involved in dealing with criticism in adolescents may serve as a biomarker for vulnerability to depression. However, the directions of the functional interactions between the brain regions within this neural network in adolescents are still unclear. In this study, 64 healthy adolescents (aged 14 to 17 years) were asked to listen to a series of self-referential auditory segments, which included negative (critical), positive (praising), and neutral conditions, during fMRI scanning. Dynamic Causal Modeling (DCM) with Parametric Empirical Bayesian (PEB) analysis was performed to map the interactions within the neural network that was engaged during the processing of these segments. Three regions were identified to form the interaction network: the left pregenual anterior cingulate cortex (pgACC), the left dorsolateral prefrontal cortex (DLPFC), and the right precuneus (preCUN). We quantified the modulatory effects of exposure to criticism and praise on the effective connectivity between these brain regions. Being criticized was found to significantly inhibit the effective connectivity from the preCUN to the DLPFC. Adolescents who scored high on the Perceived Criticism Measure (PCM) showed less inhibition of the preCUN-to-DLPFC connectivity when being criticized, which may indicate that they required more engagement of the Central Executive Network (which includes the DLPFC) to sufficiently disengage from negative self-referential processing. Furthermore, the inhibitory connectivity from the DLPFC to the pgACC was strengthened by exposure to praise as well as criticism, suggesting a recruitment of cognitive control over emotional responses when dealing with positive and negative evaluative feedback. Our novel findings contribute to a more profound understanding of how criticism affects the adolescent brain and can help to identify potential biomarkers for vulnerability to develop mood disorders before or during adulthood.
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http://dx.doi.org/10.1016/j.neuroimage.2024.120510 | DOI Listing |
Exp Brain Res
September 2025
School of Information Science and Technology, Yunnan Normal University, Kunming, 650500, China.
This study explores how differences in colors presented separately to each eye (binocular color differences) can be identified through EEG signals, a method of recording electrical activity from the brain. Four distinct levels of green-red color differences, defined in the CIELAB color space with constant luminance and chroma, are investigated in this study. Analysis of Event-Related Potentials (ERPs) revealed a significant decrease in the amplitude of the P300 component as binocular color differences increased, suggesting a measurable brain response to these differences.
View Article and Find Full Text PDFSoc Cogn Affect Neurosci
September 2025
Faculty of Health and Wellness, City University of Macau, Macau 999078, P.R. China.
Emotional contagion is an important aspect of social interaction. Traditional theories suggest that it relies on mimicry of facial or emotional movements. To address the question of whether there is a distinction between emotional contagion and emotional mimicry, we conducted a meta-analysis using the ALE algorithm to identify brain regions activated by the two tasks.
View Article and Find Full Text PDFJ Chem Theory Comput
September 2025
State Key Laboratory of Fine Chemicals, Frontiers Science Center for Smart Materials Oriented Chemical Engineering, Department of Pharmaceutical Sciences, Institute of Chemical Process Systems Engineering, School of Chemical Engineering, Dalian University of Technology, Dalian 116024, China.
Organometallic catalysis lies at the heart of numerous industrial processes that produce bulk and fine chemicals. The search for transition states and screening for organic ligands are vital in designing highly active organometallic catalysts with efficient reaction kinetics. However, identifying accurate transition states necessitates computationally intensive quantum chemistry calculations.
View Article and Find Full Text PDFChaos
September 2025
School of Computational Science and Engineering, Georgia Institute of Technology, Atlanta, Georgia 30332, USA.
Although many real-world time series are complex, developing methods that can learn from their behavior effectively enough to enable reliable forecasting remains challenging. Recently, several machine-learning approaches have shown promise in addressing this problem. In particular, the echo state network (ESN) architecture, a type of recurrent neural network where neurons are randomly connected and only the read-out layer is trained, has been proposed as suitable for many-step-ahead forecasting tasks.
View Article and Find Full Text PDFJ Chem Inf Model
September 2025
Songshan Lake Materials Laboratory, Dongguan 523808, PR China.
Large language models (LLMs) have demonstrated transformative potential for materials discovery in condensed matter systems, but their full utility requires both broader application scenarios and integration with ab initio crystal structure prediction (CSP), density functional theory (DFT) methods and domain knowledge to benefit future inverse material design. Here, we develop an integrated computational framework combining language model-guided materials screening with genetic algorithm (GA) and graph neural network (GNN)-based CSP methods to predict new photovoltaic material. This LLM + CSP + DFT approach successfully identifies a previously overlooked oxide material with unexpected photovoltaic potential.
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