Recent studies have suggested oxytocin's therapeutic effects on deficits in social communication and interaction in autism spectrum disorder through improvement of emotion recognition with direct emotional cues, such as facial expression and voice prosody. Although difficulty in understanding of others' social emotions and beliefs under conditions without direct emotional cues also plays an important role in autism spectrum disorder, no study has examined the potential effect of oxytocin on this difficulty. Here, we sequentially conducted both a case-control study and a clinical trial to investigate the potential effects of oxytocin on this difficulty at behavioural and neural levels measured using functional magnetic resonance imaging during a psychological task. This task was modified from the Sally-Anne Task, a well-known first-order false belief task. The task was optimized for investigation of the abilities to infer another person's social emotions and beliefs distinctively so as to test the hypothesis that oxytocin improves deficit in inferring others' social emotions rather than beliefs, under conditions without direct emotional cues. In the case-control study, 17 males with autism spectrum disorder showed significant behavioural deficits in inferring others' social emotions (P = 0.018) but not in inferring others' beliefs (P = 0.064) compared with 17 typically developing demographically-matched male participants. They also showed significantly less activity in the right anterior insula and posterior superior temporal sulcus during inferring others' social emotions, and in the dorsomedial prefrontal cortex during inferring others' beliefs compared with the typically developing participants (P < 0.001 and cluster size > 10 voxels). Then, to investigate potential effects of oxytocin on these behavioural and neural deficits, we conducted a double-blind placebo-controlled crossover within-subject trial for single-dose intranasal administration of 24 IU oxytocin in an independent group of 20 males with autism spectrum disorder. Behaviourally, oxytocin significantly increased the correct rate in inferring others' social emotions (P = 0.043, one-tail). At the neural level, the peptide significantly enhanced the originally-diminished brain activity in the right anterior insula during inferring others' social emotions (P = 0.004), but not in the dorsomedial prefrontal cortex during inferring others' beliefs (P = 0.858). The present findings suggest that oxytocin enhances the ability to understand others' social emotions that have also required second-order false belief rather than first-order false beliefs under conditions without direct emotional cues in autism spectrum disorder at both the behaviour and neural levels.

Changes in brain pathology as schizophrenia progresses have been repeatedly suggested by previous studies. Meta-analyses of previous proton magnetic resonance spectroscopy ((1)H MRS) studies at each clinical stage of schizophrenia indicate that the abnormalities of N-acetylaspartate (NAA) and glutamatergic metabolites change progressively. However, to our knowledge, no single study has addressed the possible differences in (1)H MRS abnormalities in subjects at 3 different stages of disease, including those at ultrahigh risk for psychosis (UHR), with first-episode schizophrenia (FES), and with chronic schizophrenia (ChSz). In the current study, 24 patients with UHR, 19 FES, 25 ChSz, and their demographically matched 3 independent control groups (n = 26/19/28 for the UHR, FES, and ChSz control groups, respectively) underwent (1)H MRS in a 3-Tesla scanner to examine metabolites in medial prefrontal cortex. The analysis revealed significant decreases in the medial prefrontal NAA and glutamate + glutamine (Glx) levels, specifically in the ChSz group as indexed by a significant interaction between stage (UHR/FES/ChSz) and clinical status (patients/controls) (P = .008). Furthermore, the specificity of NAA and Glx reductions compared with the other metabolites in the patients with ChSz was also supported by a significant interaction between the clinical status and types of metabolites that only occurred at the ChSz stage (P = .001 for NAA, P = .004 for Glx). The present study demonstrates significant differences in (1)H MRS abnormalities at different stages of schizophrenia, which potentially correspond to changes in glutamatergic neurotransmission, plasticity, and/or excitotoxicity and regional neuronal integrity with relevance for the progression of schizophrenia.

Individuals with autism spectrum disorders (ASD) tend to make inadequate social judgments, particularly when the nonverbal and verbal emotional expressions of other people are incongruent. Although previous behavioral studies have suggested that ASD individuals have difficulty in using nonverbal cues when presented with incongruent verbal-nonverbal information, the neural mechanisms underlying this symptom of ASD remain unclear. In the present functional magnetic resonance imaging study, we compared brain activity in 15 non-medicated adult males with high-functioning ASD to that of 17 age-, parental-background-, socioeconomic-, and intelligence-quotient-matched typically-developed (TD) male participants. Brain activity was measured while each participant made friend or foe judgments of realistic movies in which professional actors spoke with conflicting nonverbal facial expressions and voice prosody. We found that the ASD group made significantly less judgments primarily based on the nonverbal information than the TD group, and they exhibited significantly less brain activity in the right inferior frontal gyrus, bilateral anterior insula, anterior cingulate cortex/ventral medial prefrontal cortex (ACC/vmPFC), and dorsal medial prefrontal cortex (dmPFC) than the TD group. Among these five regions, the ACC/vmPFC and dmPFC were most involved in nonverbal-information-biased judgments in the TD group. Furthermore, the degree of decrease of the brain activity in these two brain regions predicted the severity of autistic communication deficits. The findings indicate that diminished activity in the ACC/vmPFC and dmPFC underlies the impaired abilities of individuals with ASD to use nonverbal content when making judgments regarding other people based on incongruent social information.

Purpose To compare the performance and interobserver agreement of the COVID-19 Reporting and Data System (CO-RADS), the COVID-19 imaging reporting and data system (COVID-RADS), the RSNA expert consensus statement, and the British Society of Thoracic Imaging (BSTI) guidance statement. Materials and Methods In this case-control study, total of 100 symptomatic patients suspected of having COVID-19 were included: 50 patients with COVID-19 (59±17 years, 38 men) and 50 patients without COVID-19 (65±24 years, 30 men). Eight radiologists independently scored chest CT images of the cohort according to each reporting system. The area under the receiver operating characteristic curves (AUC) and interobserver agreements were calculated and statistically compared across the systems. Results A total of 800 observations were made for each system. The level of suspicion of COVID-19 correlated with the RT-PCR positive rate except for the “negative for pneumonia” classifications in all the systems (Spearman’s coefficient: ρ=1.0, P =<.001 for all the systems). Average AUCs were as follows: CO-RADS, 0.84 (95% confidence interval, 0.83–0.85): COVID-RADS, 0.80 (0.78–0.81): the RSNA statement, 0.81 (0.79–0.82): and the BSTI statement, 0.84 (0.812-0.86). Average Cohen’s kappa across observers was 0.62 (95% confidence interval, 0.58–0.66), 0.63 (0.58–0.68), 0.63 (0.57–0.69), and 0.61 (0.58-0.64) for CO-RADS, COVID-RADS, the RSNA statement and the BSTI statement, respectively. CO-RADS and the BSTI statement outperformed COVID-RADS and the RSNA statement in diagnostic performance ( P =.<.05 for all the comparison). Conclusions CO-RADS, COVID-RADS, the RSNA statement and the BSTI statement provided reasonable performances and interobserver agreements in reporting CT findings of COVID-19.

Neuropsychiatric disorders are diagnosed based on behavioral criteria, which makes the diagnosis challenging. Objective biomarkers such as neuroimaging are needed, and when coupled with machine learning, can assist the diagnostic decision and increase its reliability. Sixty-four schizophrenia, 36 autism spectrum disorder (ASD), and 106 typically developing individuals were analyzed. FreeSurfer was used to obtain the data from the participant's brain scans. Six classifiers were utilized to classify the subjects. Subsequently, 26 ultra-high risk for psychosis (UHR) and 17 first-episode psychosis (FEP) subjects were run through the trained classifiers. Lastly, the classifiers' output of the patient groups was correlated with their clinical severity. All six classifiers performed relatively well to distinguish the subject groups, especially support vector machine (SVM) and Logistic regression (LR). Cortical thickness and subcortical volume feature groups were most useful for the classification. LR and SVM were highly consistent with clinical indices of ASD. When UHR and FEP groups were run with the trained classifiers, majority of the cases were classified as schizophrenia, none as ASD. Overall, SVM and LR were the best performing classifiers. Cortical thickness and subcortical volume were most useful for the classification, compared to surface area. LR, SVM, and DT's output were clinically informative. The trained classifiers were able to help predict the diagnostic category of both UHR and FEP Individuals.