Background:Fear of negative evaluation(FNE),referring to negative expectation and feelings toward other people’s social evaluation,is closely associated with social anxiety that plays an important role in our social ...Background:Fear of negative evaluation(FNE),referring to negative expectation and feelings toward other people’s social evaluation,is closely associated with social anxiety that plays an important role in our social life.Exploring the neural markers of FNE may be of theoretical and practical significance to psychiatry research(e.g.,studies on social anxiety).Methods:To search for potentially relevant biomarkers of FNE in human brain,the current study applied multivariate relevance vector regression,a machine-learning and data-driven approach,on brain morphological features(e.g.,cortical thickness)derived from structural imaging data;further,we used these features as indexes to predict self-reported FNE score in each participant.Results:Our results confirm the predictive power of multiple brain regions,including those engaged in negative emotional experience(e.g.,amygdala,insula),regulation and inhibition of emotional feeling(e.g.,frontal gyrus,anterior cingulate gyrus),and encoding and retrieval of emotional memory(e.g.,posterior cingulate cortex,parahippocampal gyrus).Conclusions:The current findings suggest that anxiety represents a complicated construct that engages multiple brain systems,from primitive subcortical mechanisms to sophisticated cortical processes.展开更多
This paper presents a relevance vector regression(RVR) based on parametric approach to the bias field estimation in brain magnetic resonance(MR) image segmentation. Segmentation is a very important and challenging tas...This paper presents a relevance vector regression(RVR) based on parametric approach to the bias field estimation in brain magnetic resonance(MR) image segmentation. Segmentation is a very important and challenging task in brain analysis,while the bias field existed in the images can significantly deteriorate the performance.Most of current parametric bias field correction techniques use a pre-set linear combination of low degree basis functions, the coefficients and the basis function types of which completely determine the field. The proposed RVR method can automatically determine the best combination for the bias field, resulting in a good segmentation in the presence of noise by combining with spatial constrained fuzzy C-means(SCFCM)segmentation. Experiments on simulated T1 images show the efficiency.展开更多
基金This work was supported by the National Natural Science Foundation of China(Nos.31900757,32071083 and 32020103008)the Major Program of the Chinese National Social Science Foundation(No.17ZDA324)the Youth Innovation Promotion Association,CAS(No.2019088).
文摘Background:Fear of negative evaluation(FNE),referring to negative expectation and feelings toward other people’s social evaluation,is closely associated with social anxiety that plays an important role in our social life.Exploring the neural markers of FNE may be of theoretical and practical significance to psychiatry research(e.g.,studies on social anxiety).Methods:To search for potentially relevant biomarkers of FNE in human brain,the current study applied multivariate relevance vector regression,a machine-learning and data-driven approach,on brain morphological features(e.g.,cortical thickness)derived from structural imaging data;further,we used these features as indexes to predict self-reported FNE score in each participant.Results:Our results confirm the predictive power of multiple brain regions,including those engaged in negative emotional experience(e.g.,amygdala,insula),regulation and inhibition of emotional feeling(e.g.,frontal gyrus,anterior cingulate gyrus),and encoding and retrieval of emotional memory(e.g.,posterior cingulate cortex,parahippocampal gyrus).Conclusions:The current findings suggest that anxiety represents a complicated construct that engages multiple brain systems,from primitive subcortical mechanisms to sophisticated cortical processes.
基金National Natural Science Foundation of Chinagrant number:10971190+1 种基金National Natural Science Foundation of Chinagrant number:11001239 and 11101365
文摘This paper presents a relevance vector regression(RVR) based on parametric approach to the bias field estimation in brain magnetic resonance(MR) image segmentation. Segmentation is a very important and challenging task in brain analysis,while the bias field existed in the images can significantly deteriorate the performance.Most of current parametric bias field correction techniques use a pre-set linear combination of low degree basis functions, the coefficients and the basis function types of which completely determine the field. The proposed RVR method can automatically determine the best combination for the bias field, resulting in a good segmentation in the presence of noise by combining with spatial constrained fuzzy C-means(SCFCM)segmentation. Experiments on simulated T1 images show the efficiency.
