Objective In this study,we investigated the correlation between the scores for different Liebowitz Social Anxiety Scale(LSAS)subscale models and the metabolic activity in specific regions of the brain using 18F-fluorodeoxyglucose(FDG)positron emission tomography/computed tomography(PET/CT)(18F-FDG PET),thereby improving the understanding of the neurobiological characteristics of social anxiety.Methods A total of 39 cognitively normal participants(29 men and 10 women,aged 30-63 years)were enrolled.All participants underwent LSAS assessment and brain 18F-FDG PET scanning.Correlations between metabolic activities in various brain regions and scores from the different LSAS subscales were analyzed accordingly.Results LSAS subscale scores were significantly correlated with metabolic activity in specific brain regions.In the Safren model,the score for the observation by others subscale was positively correlated with the left fusiform gyrus(P<0.001,false discovery rate[FDR]-corrected)and the left caudate tail(P<0.001,FDR-corrected),suggesting a close association between mood states related to observation by others and the metabolic activity in these regions.In the Baker model,the score for the eating and drinking subscale was negatively correlated with the right precuneus(P<0.001,FDR-corrected),while the score for the assertiveness subscale was positively correlated with the left caudate nucleus(P<0.001,FDR-corrected).These findings revealed the complex associative patterns between various mood and behavioral dimensions and metabolic activities in specific brain regions.Conclusion Social anxiety symptoms are closely associated with metabolic changes in specific brain regions,including the left insula,left caudate tail,and right precuneus.Moreover,different social situations activate distinct brain regions.Compared with individuals with social anxiety disorder,normal individuals exhibit involvement of fewer brain regions when experiencing social anxiety.These findings provide new experimental evidence for understanding the neural mechanisms underlying social anxiety.

Objective To evaluate the effect of deep learning reconstruction algorithm combined with smart metal artifact reduction(DLMAR)on the quality of abdominal CT images in critically ill patients who are unable to raise their arms and require electrocardiographic(ECG)monitoring.Methods A total of 102 patients were retrospectively enrolled.All subjects were critically ill patients who were unable to raise their arms and required ECG monitoring.Images were reconstructed using 6 algorithms,including filtered back projection(FBP),iterative reconstruction(IR),deep learning(DL),FBP combined with smart metal artifact reduction(FBPMAR),adaptive statistical iterative reconstruction-V combined with smart metal artifact reduction(IRMAR),and DLMAR.A quantitative analysis of CT values,noise,signal-to-noise ratio(SNR),and contrast-to-noise ratio(CNR)was conducted in regions without metal artifacts and regions with metal artifacts in the liver,as well as the tissues,including those from the liver,spleen,pancreas,and aorta,between the two arms.Qualitative analysis of electrode metal artifacts,the visualization of the structures between the two arms,and image noise was performed with a 5-point scoring system(l=worst and 5=best).Results In the regions of the liver with metal artifacts,there was a significant difference between the CT values of the DLMAR group([98.5±9.8]Hounsfield units[H[J])and those of the FBP group([73.7±5.6]HU),the IR group([75.3±7.5]HU),and the DL group([66.3±11.4]HU)(P＜0.01).There was no significant difference between the CT values of the DLMAR group and those of the FBPMAR group([99.8±4.8]HU)and the IRMAR group([99.6±3.4]HU)(P＞0.05).The noise of the DLMAR group was found to be significantly lower than that of the other groups(P＜0.01).Furthermore,the SNR and CNR of the DLMAR group were also found to be higher than those of the other groups(P＜0.01).In the tissue region between the two arms,the differences in CT values among the six groups were not statistically significant(P＞0.05).The noise of the DLMAR group was lower than those of the other groups(P＜0.01),and the SNR and CNR of the DLMAR group were higher than those of the other groups(P＜0.01).In terms of the removal of metal artifacts,the scores of the FBPMAR,IRMAR,and DLMAR groups(4.27±0.32,4.44±0.34,and 4.61±0.28,respectively)were higher than those of the FBP,IR,and DL groups(1.36±0.54,1.32±0.45,and 1.24±0.46,respectively)(P＜0.01).The DLMAR group also had a higher score of 4.62±0.37 in the visualization of structures between the two arms and 4.53±0.39 in the noise reduction of images,both of which were higher than those of the other groups(P＜0.01).Conclusion DLMAR reduces artifacts,decreases noise,and improves the quality of abdominal CT imaging in critically ill patients who are unable to raise their arms and require ECG monitoring.

Objective Present study aimed to characterize the alteration of cortical thickness in first-episode, never-medicated, adult patients with major depressive disorder (MDD), and explore whether such deficits were related with their disease duration and clinical symptom severity. Methods Thirty-seven adult MDD patients were recruited from March 2013 to August 2015 as patient group, and 41 healthy volunteers were as control group. All the patients underwent three-dimensional spoiled gradient recalled (3D-SPGR) sequences, and the images were acquired. Constructions of the cortical surface were developed from 3D-SPGR images using FreeSurfer software, and the thickness of the entire cortex was measured according to the automated surface reconstruction, transformation, and high-resolution inter-subject alignment procedures. Finally, cortical thickness was compared between the two groups, and the relativity between clinical symptom severity, disease progression and clinical scores were analyzed using the General Linear Model (GLM). Results Our results revealed a significant increase in cortical thickness(P<0.05, false discovery rate corrected) in the left anterior and middle cingulate cortex, bilateral precentral cortex, left paracentral cortex, bilateral superior parietal cortex, left temporal pole, and right lateral occipital cortex (cortical thickness 1.89-2.87 mm, cortical volume 34-384 mm2, P<0.05) in MDD patients compared to healthy controls, while no reversed alternation was found. In addition, clinical symptom severity and disease progression showed no correlation with the cortical thickness abnormalities in MDD group(P>0.05). Conclusion Excluding the impact of treatment, our study showed that the cortical thickness change was mainly located in the prefrontal-limbic system in the in early course of MDD.