Age Estimation by Machine Learning and CT-Multiplanar Reformation of Cranial Sutures in Northern Chinese Han Adults.

OBJECTIVES: To establish age estimation models of northern Chinese Han adults using cranial suture images obtained by CT and multiplanar reformation (MPR), and to explore the applicability of cranial suture closure rule in age estimation of northern Chinese Han population. METHODS: The head CT samples of 132 northern Chinese Han adults aged 29-80 years were retrospectively collected. Volume reconstruction (VR) and MPR were performed on the skull, and 160 cranial suture tomography images were generated for each sample. Then the MPR images of cranial sutures were scored according to the closure grading criteria, and the mean closure grades of sagittal suture, coronal sutures (both left and right) and lambdoid sutures (both left and right) were calculated respectively. Finally taking the above grades as independent variables, the linear regression model and four machine learning models for age estimation (gradient boosting regression, support vector regression, decision tree regression and Bayesian ridge regression) were established for northern Chinese Han adults age estimation. The accuracy of each model was evaluated. RESULTS: Each cranial suture closure grade was positively correlated with age and the correlation of sagittal suture was the highest. All four machine learning models had higher age estimation accuracy than linear regression model. The support vector regression model had the highest accuracy among the machine learning models with a mean absolute error of 9.542 years. CONCLUSIONS: The combination of skull CT-MPR and machine learning model can be used for age estimation in northern Chinese Han adults, but it is still necessary to combine with other adult age estimation indicators in forensic practice.

Childhood Trauma and Depressive Level Among Chinese College Students in Guangzhou: The Roles of Rumination and Perceived Stress.

OBJECTIVE: Although previous studies have validated the effect of childhood trauma on depressive level, few studies have utilized the diathesis-stress theory to investigate the specific roles of perceived stress and rumination in the pathway between childhood trauma and depression in Chinese college students. This study aims to demonstrate the mediation effect of perceived stress and the moderation effect of rumination in the pathway between childhood trauma and depressive level in Chinese college students. METHODS: A total of 995 Chinese college students in Guangzhou were included in this study by recruitment advertisement from October to December 2021. And they were asked to finish four self-report questionnaires, including Childhood Trauma Questionnaire-Short Form, Perceived Stress Scale, the 22-item Ruminative Response Scale, and Beck Depression Scale-II. Then the data were analyzed with Mplus 8.3. RESULTS: Results revealed significant correlations among childhood trauma, perceived stress, rumination and depressive level. Further analyses revealed that perceived stress played a mediation role between childhood trauma and depressive level (estimate=0.09, standard error [SE]=0.02, t=5.93, 95% confidence interval [CI]=0.06-0.12), and rumination played a moderation role between childhood trauma and perceived stress (estimate=-0.17, SE=0.06, t=-2.86, 95% CI=-0.28- -0.05]) as well as between childhood trauma and depressive level (estimate=0.10, SE=0.04, t=2.74, 95% CI=0.03-0.16). CONCLUSION: These results revealed the mediation effect of perceived stress and the moderation effect of rumination in the pathway between childhood trauma and depressive level in Chinese college students, which helped us to understand how the childhood trauma influenced the depressive level and gave us multi-dimensional indications for reducing the effect of childhood trauma on depressive level.

Large-scale network abnormality in behavioral addiction.

BACKGROUND: Researchers have endeavored to ascertain the network dysfunction associated with behavioral addiction (BA) through the utilization of resting-state functional connectivity (rsFC). Nevertheless, the identification of aberrant patterns within large-scale networks pertaining to BA has proven to be challenging. METHODS: Whole-brain seed-based rsFC studies comparing subjects with BA and healthy controls (HC) were collected from multiple databases. Multilevel kernel density analysis was employed to ascertain brain networks in which BA was linked to hyper-connectivity or hypo-connectivity with each prior network. RESULTS: Fifty-six seed-based rsFC publications (1755 individuals with BA and 1828 HC) were included in the meta-analysis. The present study indicate that individuals with BAs exhibit (1) hypo-connectivity within the fronto-parietal network (FN) and hypo- and hyper-connectivity within the ventral attention network (VAN); (2) hypo-connectivity between the FN and regions of the VAN, hypo-connectivity between the VAN and regions of the FN and default mode network (DMN), hyper-connectivity between the DMN and regions of the FN; (3) hypo-connectivity between the reward system and regions of the sensorimotor network (SS), DMN and VAN; (4) hypo-connectivity between the FN and regions of the SS, hyper-connectivity between the VAN and regions of the SS. CONCLUSIONS: These findings provide impetus for a conceptual framework positing a model of BA characterized by disconnected functional coordination among large-scale networks.