It was previously shown by fMRI studies that unfair offers during an ultimatum bargaining game activate regions in the brain associated with emotions and conflict, leading to decisions inconsistent with standard economic theory. The temporal dynamics of emotional processing and mental attributes were not clear due to the coarse temporal resolution in those studies (∼2 s). Here, the ultimatum game was studied by EEG recorded from the responders in 19 channels. EEG time series were first in-put to independent component analysis. An equivalent current dipole model was used to localize the sources of the independent components in EEGLAB. The Talairach coordinates of the dipoles were matched with references in the Brede neuroinformatics database. Dipole magnitudes, anatomical regions, and mental attributes were used to explain the rejection of the offers by applying multiple regression as a function of time in epochs with a median resolution of 250 ms. The results are consistent with previous studies regarding responder behavior and activated regions (e.g., anterior cingulate cortex, frontal gyrus, insular cortex). There are three main findings observed with the higher temporal resolution: (1) regression results from fine-scale temporal data showed activations not captured when the analysis was done by using time-averaged data; (2) temporal analysis detected the individual significant epochs and fluctuations (positive and negative correlations) in regions and for the associated mental attributes (e.g., reward/harm perception, anger, unfairness); (3) there was a sequential activation of anterior cingulate cortex and insular cortex, respectively, leading to the rejection response. Overall, regression models could explain a large percentage (∼80%) of out-of-sample behavioral responses. The results are promising for the prospect of using EEG and source localization techniques in neuroeconomics to study finer temporal dynamics of neural activation.
Florey Institute of Neuroscience and Mental Health, University of Melbourne, Parkville, VIC, 3052, Australia; Department of Paediatrics, University of Melbourne, Parkville, VIC, 3052, Australia; Melbourne School of Population and Global Health, University of Melbourne, Parkville, VIC, 3052, Australia.
Introduction: Phthalates, chemical additives used to enhance plastic products' flexibility, are easily released into the environment, and can harm the brain development through various mechanisms including inflammation. Genetic variation influencing an individual's susceptibility to inflammation may play a role in the effects of phthalate exposure on neurodevelopment however there is no summary measure developed for genetic susceptibility to inflammation.
Methods: We developed a genetic pathway function score for inflammation (gPFS), based on the transcriptional activity of the inflammatory response pathway in the brain and other tissues.
Background: Little is known about the impact of PM on people with disabilities. We aimed to explore the association between PM and hospitalization via the emergency department (ED admission) among people with disabilities, together with the attributable ED admission cases and costs.
Methods: We applied a time-stratified case-crossover design adjusting ozone, holiday, and temperature using seven years (2015-2021) of claim-based data on ED admissions from the Korean National Health Insurance Database.
Department of Epidemiology, NHC Key Laboratory for Health Technology Assessment, Fudan University School of Public Health, 138 Yi Xue Yuan Road, Shanghai, 200032, China.
Background: Depression is a severe mental disorder commonly co-morbid with diabetes, but it remains to elucidate whether depression is associated with the risks of a wide range of vascular complications in people with type 2 diabetes mellitus (T2DM) and whether metabolic biomarkers may mediate this pathway.
Methods: We conducted this prospective analysis among the participants of the UK Biobank who were diagnosed with T2DM and free of vascular complications at baseline between March 13, 2006 and September 30, 2010. Major depressive disorder (MDD) was ascertained according to the hospital admission records and self-report of doctor-diagnosed conditions, while the presence of depressive symptoms was assessed using the Patient Health Questionnaire-2.
Background And Aims: The rapid expansion of artificial intelligence (AI) within worldwide healthcare systems is occurring at a significant rate. In this context, the Middle East has demonstrated distinctive characteristics in the application of AI within the healthcare sector, particularly shaped by regional policies. This study examined the outcomes resulting from the utilization of AI within healthcare systems in the Middle East.
