Institute of Psychology, Chinese Academy of Sciences
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Relationship between elevated habituation tendencies and impulsivity, compulsivity, and childhood adversity among abstinent patients with methamphetamine use disorders
No full textElevated habituation tendencies have been identified in several subgroups of patients with substance use disorders, however, their intricate relationship with substance use disorders remains unknown. A total of 133 individuals with methamphetamine use disorder (PwMUD) and 131 healthy controls (HC) were initially recruited for this study. After exclusion of outliers, data from 128 PwMUD and 125 HC were included in the analyses. Participants reported their habituation tendencies, drug-use characteristics, impulsivity, compulsivity, and experiences of childhood adversity through self-report scales and structured interviews. The results show that PwMUD had a higher level of automatic habituation tendencies compared to healthy controls. This higher level of automaticity demonstrated a tenuous association with the duration of methamphetamine use and a relatively stronger association with compulsivity, suggesting that the imbalance between the goal-directed and habitual systems in PwMUD may be both a risk factor for and a consequence of methamphetamine use disorder
Uncovering the depressive symptom network in self-harming rural children: a Bayesian undirected network analysis
No full textSelf-harm among children is a growing public health concern, particularly in rural settings where unique socioeconomic and environmental factors increase vulnerability. This study explored depressive symptom networks in rural children with and without self-harm behaviors, using Bayesian undirected network analysis to identify symptom-level differences and inform targeted interventions. Data were collected from 2,009 rural elementary school students (Mage = 10.73 years, SD = 1.65) across seven Chinese provinces. Depressive symptoms were assessed using the Child Depression Inventory, and self-harm behaviors were identified through selfreported measures. Bayesian undirected network analysis compared depressive symptom networks between children with (N = 417) and without (N = 1592) self-harm behaviors. The results revealed that children with self-harm behaviors exhibited less densely connected symptom networks, with Anhedonia, Feeling Unloved, and Somatic Concerns as the most central symptoms. In contrast, children without self-harm behaviors displayed denser, strongly clustered networks, with Anhedonia, Permissive Worrying, and Somatic Concerns as central symptoms. Significant differences in network density and clustering coefficients were observed between the two groups. These findings suggest that self-harming children experience more fragmented emotional and social dynamics, while non-self-harming children demonstrate stronger symptom interconnectivity that may support better emotional regulation. These findings emphasize the need for rural-specific mental health policies and services, including school-based programs, telepsychiatry, and community-driven efforts to reduce mental health stigma and improve early detection of depressive symptoms and self-harm behaviors. By addressing the unique challenges of rural communities, this study contributes to improved mental health outcomes and supports the development of evidence-based, symptom-focused interventions for underserved populations
Predicting cognitive aging through brain structural covariance networks: A decade of longitudinal insights using source-based morphometry
No full textCognitive aging presents significant challenges to public health as the global population ages. While functional connectivity changes in aging have been extensively studied, the predictive value of structural covariance networks remains understudied. This longitudinal study investigated whether baseline structural covariance networks could predict cognitive decline over a 10-year period using Source-Based Morphometry (SBM). Thirty-seven participants (23 males; mean age 54.97 +/- 1.14 years) underwent structural magnetic resonance imaging (T3) and cognitive assessments at baseline (T3) and follow-up (T4). SBM analysis identified twelve independent components (ICs) representing distinct structural covariance networks. After controlling for demographics and APOE genotype, IC1 strongly predicted working memory (beta = -3.12, p < 0.001), while IC2 predicted global cognitive function (beta = 0.37, p = 0.047). Brain-cognition relationships were significantly moderated by baseline cognitive performance, with key interactions observed for working memory and IC1 (beta = 0.50, p < 0.001), executive function and IC7 (beta = -0.25, p < 0.001), and processing speed and IC8 (beta = 0.28, p = 0.003). Sex-specific effects emerged for IC8 in relation to verbal memory (beta = 1.99, p = 0.007) and IC10 in relation to processing speed (beta = 2.17, p = 0.022). APOE genotype demonstrated pronounced moderation effects between IC8 and processing speed (beta = -7.68, p < 0.001) and for IC2 and global cognitive function (beta = 0.37, p = 0.018). These findings demonstrate that structural covariance networks can serve as predictive markers for cognitive aging trajectories, potentially informing early intervention strategies for preserving cognitive health
自我面孔优势加工及其机制
No full textSelf-face is a unique and highly distinctive stimulus, not shared with others, and serves as a reliable marker of self-awareness. Compared to other faces, self-face processing exhibits several advantages, including the self-face recognition advantage, self-face attention advantage, and self-face positive processing advantage. The self-face recognition advantage manifests as faster and more accurate identification across different orientations and spatial frequency components, supported by enhanced early event-related potential (ERP) components, such as N170. Attentional biases toward self-face are evident in target detection during spatial tasks and the attentional blink effect in temporal paradigms. However, measurement sensitivity, perceptual load, and task demands contribute to some mixed findings. Positive biases further characterize the self-face processing advantage, with individuals perceiving their faces as more attractive or trustworthy than objective representations. These biases even extend to self-similar others, influencing social behaviors such as trust and voting preferences. Self-face processing advantages have been observed at an unconscious level and are regulated by several factors, including self-esteem, cultural differences, and multisensory integration. Cultural and individual differences play a crucial role in shaping self-face advantages. Individuals from Western cultures, which emphasize independent selfconstrual, exhibit stronger self-face biases compared to those from East Asian collectivist contexts. Self-esteem also modulates self-face advantages: high-self-esteem individuals generally maintain their self-face recognition advantage despite interference, exhibit attentional prioritization of self-faces, and demonstrate enhanced positive associations with subliminal self-faces. In contrast, low-self-esteem individuals display recognition vulnerabilities to social cues, show context-dependent attentional divergence (prioritizing others' faces in task-oriented settings while prioritizing self-face in free-viewing tasks), and exhibit reversed positive associations with subliminal selffaces. Multisensory integration, such as synchronized visual-tactile cues, enhances self-face advantages and induces perceptual plasticity. This phenomenon is exemplified by the enfacement illusion, in which synchronous visual and tactile inputs update the mental representation of the self-face, leading to assimilation with another face. Neuroanatomically, self-face processing is predominantly lateralized to the right hemisphere and involves a network of brain regions, including the occipital lobe, temporal lobe, frontal lobe, insula, and cingulate gyrus. Disruptions in these networks are linked to self-face processing deficits in socio-cognitive disorders. For instance, autism spectrum disorder (ASD) and schizophrenia are associated with attenuated self-face advantages and abnormal neural activity in regions such as the right inferior frontal gyrus, insula, and posterior cingulate cortex. These findings suggest that self-face processing could serve as a potential biomarker for the early diagnosis and intervention of such disorders. In recent years, researchers have proposed various theoretical explanations for selfface processing and its advantage effects. However, some studies have reported no significant behavioral or neural advantages of self-faces over familiar faces, leaving the specificity of self-face a subject of debate. Further elucidation of self-face specificity requires the adoption of a face association paradigm, which controls for facial familiarity and helps determine whether qualitative differences exist between self-faces and familiar faces. Given the close relationship between self-face processing advantages and socio-cognitive disorders (e. g., ASD, schizophrenia), a deeper understanding of self-face specificity has the potential to provide critical insights into the early identification, classification, and intervention of these disorders. This research holds both theoretical significance and substantial social value.</p
Depression May Not Be Related to Impaired Interoceptive Sensibility: The Role of Alexithymia
No full textInteroceptive impairments are increasingly recognized as psychopathology processes underlying emotional disorders. However, their relationship with depression remains inconclusive. Alexithymia may influence the association between interoception and depressive symptoms. This study aimed to examine the role of alexithymia between interoception and depression. Eighty-eight depressed patients (DEPs) and fifty healthy controls (HCs) were recruited. Interoceptive sensibility was assessed using the Multidimensional Assessment of Interoceptive Awareness, and interoceptive accuracy and interoceptive awareness were evaluated through heartbeat counting tasks. Alexithymia was measured with the Toronto Alexithymia Scale. In the DEP group, depressive symptoms were assessed using the Hamilton Depression Scale. In DEPs, none of the three dimensions of interoception were associated with depressive symptoms. The alexithymic depressed patients exhibited lower interoceptive sensibility than their non-alexithymic counterparts, while the latter did not differ from the HC group. Moreover, alexithymia mediated the link between interoceptive sensibility and depressive symptoms. These results suggested that impaired interoceptive sensibility may primarily contribute to alexithymia, which, in turn, leads to depression. This highlights the importance of addressing alexithymia in therapeutic interventions aimed at improving the interoceptive process in depressed individuals.</p
Subgenual anterior cingulate cortex functional connectivity abnormalities in depression: insights from brain imaging big data and precision-guided personalized intervention via transcranial magnetic stimulation
No full textThe subgenual anterior cingulate cortex (sgACC) plays a central role in the pathophysiology of major depressive disorder (MDD). Its functional interactive profile with the left dorsal lateral prefrontal cortex (DLPFC) is associated with transcranial magnetic stimulation (TMS) treatment outcomes. Previous research on sgACC functional connectivity (FC) in MDD has yielded inconsistent results, partly due to small sample sizes and limited statistical power. Furthermore, calculating sgACC-FC to target TMS individually is challenging. We used a large multi-site cross-sectional sample (1660 patients with MDD vs. 1341 healthy controls) from Phase II of the Depression Imaging REsearch ConsorTium (DIRECT) to systematically delineate case-control difference maps of sgACC-FC. We explored the potential impact of group-level abnormality profiles on TMS target localization and clinical efficacy. Next, we developed an MDD big data-guided, individualized TMS targeting algorithm to integrate group-level statistical maps with individual-level brain activity to individually localize TMS targets. We found enhanced sgACC-DLPFC FC in patients with MDD compared with healthy controls (HC). These group differences altered the position of the sgACC anti-correlation peak in the left DLPFC. We showed that the magnitude of case-control differences in the sgACC-FC was related to clinical improvement in two independent clinical samples. This targeting algorithm may generate targets demonstrating stronger associations with clinical efficiency than group-level targets. We reliably delineated MDD-related abnormalities of sgACC-FC profiles in a large, independently ascertained sample and demonstrated the potential impact of such case-control differences on FC-guided localization of TMS targets. (c) 2025 The Authors. Published by Elsevier B.V. and Science China Press. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).</p
The Assessment of Body Image Based on Large Language Model
No full textAssessing adolescent body image is crucial for mental health interventions, yet traditional methods suffer from limited dimensional coverage, poor dynamic tracking, and weak ecological validity. To address these gaps, this study proposes a multidimensional evaluation using large language models (LLMs) and compares its criterion validity against a dictionary-based method and expert ratings. We defined four dimensions-perception, positive attitude, negative attitude, behavior-by reviewing the body-image literature and built a validated dictionary through expert ratings and iterative refinement. A four-step prompt-engineering process, incorporating role-playing and other optimization techniques, produced tailored prompts for LLM-based recognition. To validate these tools, we collected self-reported texts and scale scores from 194 university students, performed semantic analyses with Llama-3.1-70B, Qwen-Max, and DeepSeek-R1 using these prompts, and confirmed ecological validity on social media posts. Results indicate that our multidimensional dictionary correlated significantly with expert ratings across all four dimensions (r = 0.515-0.625), providing a solid benchmark. LLM-based assessments then outperformed both the dictionary and human ratings, with zero-shot LLMs achieving r = 0.664 in positive attitude (vs. expert r = 0.657) and DeepSeek-R1 reaching r = 0.722 in perception. Role-playing techniques significantly improved the validity in the perception dimension (Delta r = +0.117). Consistency checks revealed that the DeepSeek model reduced error dispersion in extreme score ranges by 48.4% compared to human ratings, with the 95% consistency limits covering the fluctuations of human scores. Incremental validity analysis showed that LLMs could replace human evaluations in the perception dimension (Delta R 2 = 0.220). In ecological validity checks, the Qwen model achieved a correlation of 0.651 in the social media behavior dimension-53.1% higher than the dictionary method. We found that LLMs demonstrated significant advantages in the multidimensional assessment of body image, offering a new intelligent approach to mental health measurement
Assessing the rereading effect of digital reading through eye movements using artificial neural networks
No full textObjective This study aimed to investigate the differences in eye movement characteristics between first reading and rereading and to develop a neural network model for classifying these reading practices. The primary goal was to enhance the understanding of rereading identification and provide insights into assessing students' text familiarity.Methods We compared eye movement metrics during first reading and rereading, focusing on parameters such as total reading time, fixation duration, regression size, regression count, and local eye movement behaviors within areas of interest (AOIs). Pupil size, the proportion of fixation duration, and regression duration within and across lines were also examined. A neural network model was constructed to classify the reading practices based on these metrics.Results During rereading, students exhibited shorter total reading time, fixation durations, and fewer regression counts compared to first reading. Regression size was longer during rereading. Local eye movement behaviors within AOIs were also reduced. However, pupil size, the proportion of fixation duration, and regression duration within and across lines were not useful in identifying rereading. The neural network model achieved an accuracy of 0.769, precision of 0.774, recall of 0.788, and an F1-score of 0.781.Conclusion The findings demonstrate distinct eye movement patterns between first reading and rereading, highlighting the effectiveness of certain metrics in differentiating these practices. The neural network model provides a promising tool for rereading identification. These results expand our understanding of rereading behavior and offer valuable insights for assessing students' text familiarity.</p
A whole-brain voxel-based analysis of structural abnormalities in PTSD: An ENIGMA-PGC study
No full textBackground: Patients with posttraumatic stress disorder (PTSD) exhibit smaller regional brain volumes in commonly reported regions including the amygdala and hippocampus, regions associated with fear and memory processing. In the current study, we have conducted a voxel-based morphometry (VBM) meta-analysis using whole-brain statistical maps with neuroimaging data from the ENIGMA-PGC PTSD working group. Methods: T1-weighted structural neuroimaging scans from 36 cohorts (PTSD n = 1309; controls n = 2198) were processed using a standardized VBM pipeline (ENIGMA-VBM tool). We meta-analyzed the resulting statistical maps for voxel-wise differences in gray matter (GM) and white matter (WM) volumes between PTSD patients and controls, performed subgroup analyses considering the trauma exposure of the controls, and examined associations between regional brain volumes and clinical variables including PTSD (CAPS-4/5, PCL-5) and depression severity (BDI-II, PHQ-9). Results: PTSD patients exhibited smaller GM volumes across the frontal and temporal lobes, and cerebellum, with the most significant effect in the left cerebellum (Hedges' g = 0.22, p(corrected) = .001), and smaller cerebellar WM volume (peak Hedges' g = 0.14, p(corrected) = .008). We observed similar regional differences when comparing patients to trauma-exposed controls, suggesting these structural abnormalities may be specific to PTSD. Regression analyses revealed PTSD severity was negatively associated with GM volumes within the cerebellum (p(corrected) = .003), while depression severity was negatively associated with GM volumes within the cerebellum and superior frontal gyrus in patients (p(corrected) = .001). Conclusions: PTSD patients exhibited widespread, regional differences in brain volumes where greater regional deficits appeared to reflect more severe symptoms. Our findings add to the growing literature implicating the cerebellum in PTSD psychopathology.</p
FED-PsyAU: Privacy-Preserving Micro-Expression Recognition via Psychological AU Coordination and Dynamic Facial Motion Modeling
No full textMicro-expressions (MEs) are brief, low-intensity, often localized facial expressions. They could reveal genuine emotions individuals may attempt to conceal, valuable in contexts like criminal interrogation and psychological counseling. However, ME recognition (MER) faces challenges, such as small sample sizes and subtle features, which hinder efficient modeling. Additionally, real-world applications encounter ME data privacy issues, leaving the task of enhancing recognition across settings under privacy constraints largely unexplored. To address these issues, we propose a FED-PsyAU research framework. We begin with a psychological study on the coordination of upper and lower facial action units (AUs) to provide structured prior knowledge of facial muscle dynamics. We then develop a DPK-GAT network that combines these psychological priors with statistical AU patterns, enabling hierarchical learning of facial motion features from regional to global levels, effectively enhancing MER performance. Additionally, our federated learning (FL) framework advances MER capabilities across multiple clients without data sharing, preserving privacy and alleviating the limited-sample issue for each client. Extensive experiments on commonly-used ME databases demonstrate the effectiveness of our approach.</p
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