The Academic Salon of the Department of Psychology and Cognitive Sciences is held every Thursday afternoon. Welcome to all students and faculty members from every department!
Time: April 10 (Thursday) afternoon 14:30
Location: Room 1110, 11th floor, Lv Dalong Building
Department of Psychology and Cognitive Sciences YAN Chaogan's Research Group Academic Salon
Report One
"Blooming Life" Depression Research and Intervention Program: Comprehensive Prevention and Intervention Research for Depression
Presenter: Yifan Liao
Content:
The "Blooming Life" Depression Research and Intervention Program is a comprehensive clinical research project aimed at establishing objective markers for precise diagnosis and classification of depression through brain imaging and other research methods, developing novel psychological intervention programs based on Chinese culture and new non-invasive neuromodulation therapies beyond medication. Since its launch in December 2021, the project has conducted three phases of intervention research targeting populations with different degrees of depression, providing over 1,000 person-times of psychological services and physical interventions with significant results. "Blooming Life Phase I" collaborated with Professor Liu Tianjun's team from Beijing University of Chinese Medicine to provide 8 weeks of "Space Transfer Therapy" psychological intervention based on traditional Chinese culture for clinical depression patients under medication treatment; "Blooming Life Phase II" joined forces with the music therapy team led by Teacher Chen Xijing and the mindful living group team led by Teacher Long Di from the Institute of Psychology, Chinese Academy of Sciences, to provide 8 weeks of systematic group psychological support for subclinical depression populations; "Blooming Life Phase III" adopted the individualized transcranial magnetic stimulation (TMS) target localization new technology based on depression brain imaging big data independently developed by Professor Yan Chaogan's team, providing 5-day accelerated TMS intervention for treatment-resistant depression patients, with a response rate of 66.67% and recovery rate of 46.67%. Multiple research methods including clinical interviews, cognitive testing, psychological scales, and functional magnetic resonance imaging (fMRI) were used to evaluate intervention effects before and after each phase, and will further explore mechanisms of efficacy. Preliminary analysis found that participants in each phase generally showed positive changes after intervention, including significant relief of depression and anxiety symptoms, significantly reduced stress perception, and significantly enhanced self-efficacy; cognitive function improved with increased task accuracy and shortened reaction time; brain imaging analysis showed decreased default network functional connectivity. This research integrates modern neuroscience technology with traditional Chinese cultural psychological intervention methods, not only providing new intervention paradigms and multidimensional evaluation systems for precise diagnosis and treatment of depression, but also pioneering a new model of culturally adaptive mental health services, which has important value for improving the scientificity and accessibility of mental health services in China.
Report Two
Functional Connectivity Gradient Features Under Rumination States and Their Abnormalities in Depression Patients
Presenter: Zhengjiayi Hu
Content:
Rumination is a typical thought characteristic of major depressive disorder (MDD), characterized by focusing on one's past, presenting negative thinking patterns, and being confined to repetitive, narrow scenarios. Currently, the functional organizational features of rumination across the whole brain remain unclear. Therefore, we adopted a method called functional connectivity gradients to study the impact of rumination on global organizational features in the brain. We used the Beijing dataset including healthy participants from three different sites and the Suzhou dataset including healthy participants and depression patients, comparing their functional connectivity gradient global index differences and local brain differences between rumination states and distraction states. Research results found that both healthy participants and depression participants showed primary-transmodal gradients and visual-sensorimotor gradients in both rumination and distraction states, and in the first gradient, compared to distraction states, rumination states in both groups showed decreased gradient values in the default mode network (DMN) and increased gradient values in the frontoparietal control network (FPN). In global indices, the main finding was that in healthy participants, the first gradient explained variance significantly decreased in rumination states, while depression participants showed the opposite trend. Finally, we calculated correlations between participants' whole-brain functional connectivity and functional connectivity gradients, finding that brain regions showing decreased gradient values in the first gradient mainly showed negative correlations with functional connectivity, while brain regions with increased gradient values mainly showed positive correlations with functional connectivity. In healthy participants, the correlation values between functional connectivity and the first gradient in rumination states were significantly lower than in distraction states, consistent with the trend of significantly decreased first gradient explained variance. This study indicates that rumination may correspond to specific functional gradient features, and these features are altered in MDD patients. These results provide new insights for understanding the neural mechanisms of rumination, highlighting global functional features across the whole brain under rumination states.
Report Three
Depression Imaging Research from a Precision Psychiatry Perspective: Challenges, Progress, and Future Pathways
Presenter: Qinglin Gao
Content:
Magnetic resonance imaging (MRI) technology shows important potential in identifying depression biomarkers. However, its clinical translational application is still limited by traditional group-level research methods that ignore patient individual heterogeneity. Depression has high heterogeneity, leading to significant individual differences among patients in symptom presentation, treatment response, and disease prognosis, while MRI research based on group averages is difficult to support precise individualized diagnosis and treatment strategies. Precision Psychiatry, by integrating multimodal MRI, big data analysis, and artificial intelligence technologies, promotes the mining and application of individual-level biomarkers, providing new research directions and practical pathways for precise classification, efficacy prediction, and personalized intervention of depression. This report will systematically review the latest research progress of MRI in depression diagnostic classification, efficacy prediction, and individualized intervention, and further propose the construction of a closed-loop clinical framework of "precise classification–dynamic prediction–adaptive intervention," aiming to replace traditional "trial-and-error" diagnosis and treatment models and achieve full-process individualized management from diagnosis to recovery.
