BACKGROUND: The processing of emotional visual stimulation involves the processing of emotional and visuoperceptual information. It is not completely revealed how the valence and arousal affect these two aspects. The objective was to investigate the effects of valence and arousal on spatiotemporal characteristics of cortical information processing using distributed source imaging of event-related current density (ERCD). METHODS: Electroencephalograms (64 channels) were recorded from 19 healthy men while presenting affective pictures. Distributed source localization analysis was adopted to obtain the spatiotemporal pattern of ERCD on cortical surface in response to emotional visual stimulation. A nonparametric cluster-based permutation test was used to find meaningful time and space without prior knowledge. RESULTS: Significant changes of ERCD in 400–800 ms among positive, negative, and neutral emotional conditions were found in left posterior cingulate cortex (PCC) and right inferior temporal cortex (ITC). In the PCC, the stimuli with higher arousal levels showed more negative ERCD than neutral stimuli. In the ITC, the ERCD for negative stimuli was significantly more negative than those of positive and neutral ones. CONCLUSION: Arousal and valence had strong influence on memory encoding and visual analysis at late period. The location and time showing significant change in neural activity according to arousal and valence would provide valuable information for understanding the changes of cortical function by neuropsychiatric disorders.
Arousal ; Automatic Data Processing ; Electroencephalography ; Gyrus Cinguli ; Humans ; Male ; Memory ; Photic Stimulation ; Spatio-Temporal Analysis ; Temporal Lobe

Background: Worldwide, sepsis is the leading cause of death in hospitals. If mortality rates in patients with sepsis can be predicted early, medical resources can be allocated efficiently. We constructed machine learning (ML) models to predict the mortality of patients with sepsis in a hospital emergency department. Methods: This study prospectively collected nationwide data from an ongoing multicenter cohort of patients with sepsis identified in the emergency department. Patients were enrolled from 19 hospitals between September 2019 and December 2020. For acquired data from 3,657 survivors and 1,455 deaths, six ML models (logistic regression, support vector machine, random forest, extreme gradient boosting [XGBoost], light gradient boosting machine, and categorical boosting [CatBoost]) were constructed using fivefold cross-validation to predict mortality. Through these models, 44 clinical variables measured on the day of admission were compared with six sequential organ failure assessment (SOFA) components (PaO 2 /FIO 2 [PF], platelets (PLT), bilirubin, cardiovascular, Glasgow Coma Scale score, and creatinine).The confidence interval (CI) was obtained by performing 10,000 repeated measurements via random sampling of the test dataset. All results were explained and interpreted using Shapley’s additive explanations (SHAP). Results: Of the 5,112 participants, CatBoost exhibited the highest area under the curve (AUC) of 0.800 (95% CI, 0.756–0.840) using clinical variables. Using the SOFA components for the same patient, XGBoost exhibited the highest AUC of 0.678 (95% CI, 0.626–0.730). As interpreted by SHAP, albumin, lactate, blood urea nitrogen, and international normalization ratio were determined to significantly affect the results. Additionally, PF and PLTs in the SOFA component significantly influenced the prediction results. Conclusion Newly established ML-based models achieved good prediction of mortality in patients with sepsis. Using several clinical variables acquired at the baseline can provide more accurate results for early predictions than using SOFA components. Additionally, the impact of each variable was identified.