Predicting major adverse cardiovascular events within 3 years by optimization of radiomics model derived from pericoronary adipose tissue on coronary computed tomography angiography: a case-control study.

BACKGROUND: Coronary inflammation induces changes in pericoronary adipose tissue (PCAT) can be detected by coronary computed tomography angiography (CCTA). Our aim was to investigate whether different PCAT radiomics model based on CCTA could improve the prediction of major adverse cardiovascular events (MACE) within 3 years. METHODS: This retrospective study included 141 consecutive patients with MACE and matched to patients with non-MACE (n = 141). Patients were randomly assigned into training and test datasets at a ratio of 8:2. After the robust radiomics features were selected by using the Spearman correlation analysis and the least absolute shrinkage and selection operator, radiomics models were built based on different machine learning algorithms. The clinical model was then calculated according to independent clinical risk factors. Finally, an overall model was established using the radiomics features and the clinical factors. Performance of the models was evaluated for discrimination degree, calibration degree, and clinical usefulness. RESULTS: The diagnostic performance of the PCAT model was superior to that of the RCA-model, LAD-model, and LCX-model alone, with AUCs of 0.723, 0.675, 0.664, and 0.623, respectively. The overall model showed superior diagnostic performance than that of the PCAT-model and Cli-model, with AUCs of 0.797, 0.723, and 0.706, respectively. Calibration curve showed good fitness of the overall model, and decision curve analyze demonstrated that the model provides greater clinical benefit. CONCLUSION: The CCTA-based PCAT radiomics features of three major coronary arteries have the potential to be used as a predictor for MACE. The overall model incorporating the radiomics features and clinical factors offered significantly higher discrimination ability for MACE than using radiomics or clinical factors alone.

Diagnosis of coronary artery disease in patients with type 2 diabetes mellitus based on computed tomography and pericoronary adipose tissue radiomics: a retrospective cross-sectional study.

BACKGROUND: Patients with type 2 diabetes mellitus (T2DM) are highly susceptible to cardiovascular disease, and coronary artery disease (CAD) is their leading cause of death. We aimed to assess whether computed tomography (CT) based imaging parameters and radiomic features of pericoronary adipose tissue (PCAT) can improve the diagnostic efficacy of whether patients with T2DM have developed CAD. METHODS: We retrospectively recruited 229 patients with T2DM but no CAD history (146 were diagnosed with CAD at this visit and 83 were not). We collected clinical information and extracted imaging manifestations from CT images and 93 radiomic features of PCAT from all patients. All patients were randomly divided into training and test groups at a ratio of 7:3. Four models were constructed, encapsulating clinical factors (Model 1), clinical factors and imaging indices (Model 2), clinical factors and Radscore (Model 3), and all together (Model 4), to identify patients with CAD. Receiver operating characteristic curves and decision curve analysis were plotted to evaluate the model performance and pairwise model comparisons were performed via the DeLong test to demonstrate the additive value of different factors. RESULTS: In the test set, the areas under the curve (AUCs) of Model 2 and Model 4 were 0.930 and 0.929, respectively, with higher recognition effectiveness compared to the other two models (each p < 0.001). Of these models, Model 2 had higher diagnostic efficacy for CAD than Model 1 (p < 0.001, 95% CI [0.129-0.350]). However, Model 4 did not improve the effectiveness of the identification of CAD compared to Model 2 (p = 0.776); similarly, the AUC did not significantly differ between Model 3 (AUC = 0.693) and Model 1 (AUC = 0.691, p = 0.382). Overall, Model 2 was rated better for the diagnosis of CAD in patients with T2DM. CONCLUSIONS: A comprehensive diagnostic model combining patient clinical risk factors with CT-based imaging parameters has superior efficacy in diagnosing the occurrence of CAD in patients with T2DM.

Magnetic Resonance Deep Learning Radiomic Model Based on Distinct Metastatic Vascular Patterns for Evaluating Recurrence-Free Survival in Hepatocellular Carcinoma.

BACKGROUND: The metastatic vascular patterns of hepatocellular carcinoma (HCC) are mainly microvascular invasion (MVI) and vessels encapsulating tumor clusters (VETC). However, most existing VETC-related radiological studies still focus on the prediction of VETC status. PURPOSE: This study aimed to build and compare VETC-MVI related models (clinical, radiomics, and deep learning) associated with recurrence-free survival of HCC patients. STUDY TYPE: Retrospective. POPULATION: 398 HCC patients (349 male, 49 female; median age 51.7 years, and age range: 22-80 years) who underwent resection from five hospitals in China. The patients were randomly divided into training cohort (n = 358) and test cohort (n = 40). FIELD STRENGTH/SEQUENCE: 3-T, pre-contrast T1-weighted imaging spoiled gradient recalled echo (T1WI SPGR), T2-weighted imaging fast spin echo (T2WI FSE), and contrast enhanced arterial phase (AP), delay phase (DP). ASSESSMENT: Two radiologists performed the segmentation of HCC on T1WI, T2WI, AP, and DP images, from which radiomic features were extracted. The RFS related clinical characteristics (VETC, MVI, Barcelona stage, tumor maximum diameter, and alpha fetoprotein) and radiomic features were used to build the clinical model, clinical-radiomic (CR) nomogram, deep learning model. The follow-up process was done 1 month after resection, and every 3 months subsequently. The RFS was defined as the date of resection to the date of recurrence confirmed by radiology or the last follow-up. Patients were followed up until December 31, 2022. STATISTICAL TESTS: Univariate COX regression, least absolute shrinkage and selection operator (LASSO), Kaplan-Meier curves, log-rank test, C-index, and area under the curve (AUC). P < 0.05 was considered statistically significant. RESULTS: The C-index of deep learning model achieved 0.830 in test cohort compared with CR nomogram (0.731), radiomic signature (0.707), and clinical model (0.702). The average RFS of the overall patients was 26.77 months (range 1-80 months). DATA CONCLUSION: MR deep learning model based on VETC and MVI provides a potential tool for survival assessment. EVIDENCE LEVEL: 3 TECHNICAL EFFICACY: Stage 3.

Vertebral MRI-based radiomics model to differentiate multiple myeloma from metastases: influence of features number on logistic regression model performance.

OBJECTIVES: This study aimed to use the most frequent features to establish a vertebral MRI-based radiomics model that could differentiate multiple myeloma (MM) from metastases and compare the model performance with different features number. METHODS: We retrospectively analyzed conventional MRI (T1WI and fat-suppression T2WI) of 103 MM patients and 138 patients with metastases. The feature selection process included four steps. The first three steps defined as conventional feature selection (CFS), carried out 50 times (ten times with 5-fold cross-validation), included variance threshold, SelectKBest, and least absolute shrinkage and selection operator. The most frequent fixed features were selected for modeling during the last step. The number of events per independent variable (EPV) is the number of patients in a smaller subgroup divided by the number of radiomics features considered in developing the prediction model. The EPV values considered were 5, 10, 15, and 20. Therefore, we constructed four models using the top 16, 8, 6, and 4 most frequent features, respectively. The models constructed with features selected by CFS were also compared. RESULTS: The AUCs of 20EPV-Model, 15EPV-Model, and CSF-Model (AUC = 0.71, 0.81, and 0.78) were poor than 10EPV-Model (AUC = 0.84, p < 0.001). The AUC of 10EPV-Model was comparable with 5EPV-Model (AUC = 0.85, p = 0.480). CONCLUSIONS: The radiomics model constructed with an appropriate small number of the most frequent features could well distinguish metastases from MM based on conventional vertebral MRI. Based on our results, we recommend following the 10 EPV as the rule of thumb for feature selection. KEY POINTS: • The developed radiomics model could distinguish metastases from multiple myeloma based on conventional vertebral MRI. • An accurate model based on just a handful of the most frequent features could be constructed by utilizing multiple feature reduction techniques. • An event per independent variable value of 10 is recommended as a rule of thumb for modeling feature selection.

Oxytocin Modulates the Intrinsic Dynamics Between Attention-Related Large-Scale Networks.

Attention and salience processing have been linked to the intrinsic between- and within-network dynamics of large-scale networks engaged in internal (default network [DN]) and external attention allocation (dorsal attention network [DAN] and salience network [SN]). The central oxytocin (OXT) system appears ideally organized to modulate widely distributed neural systems and to regulate the switch between internal attention and salient stimuli in the environment. The current randomized placebo (PLC)-controlled between-subject pharmacological resting-state fMRI study in N = 187 (OXT, n = 94; PLC, n = 93; single-dose intranasal administration) healthy male and female participants employed an independent component analysis approach to determine the modulatory effects of OXT on the within- and between-network dynamics of the DAN-SN-DN triple network system. OXT increased the functional integration between subsystems within SN and DN and increased functional segregation of the DN with both attentional control networks (SN and DAN). Whereas no sex differences were observed, OXT effects on the DN-SN interaction were modulated by autistic traits. Together, the findings suggest that OXT may facilitate efficient attention allocation by modulating the intrinsic functional dynamics between DN components and large-scale networks involved in external attentional demands (SN and DAN).

Intrinsic, dynamic and effective connectivity among large-scale brain networks modulated by oxytocin.

The neuropeptide oxytocin is a key modulator of social-emotional behavior and its intranasal administration can influence the functional connectivity of brain networks involved in the control of attention, emotion and reward reported in humans. However, no studies have systematically investigated the effects of oxytocin on dynamic or directional aspects of functional connectivity. The present study employed a novel computational framework to investigate these latter aspects in 15 oxytocin-sensitive regions using data from randomized placebo-controlled between-subject resting state functional MRI studies incorporating 200 healthy subjects. In order to characterize the temporal dynamics, the 'temporal state' was defined as a temporal segment of the whole functional MRI signal which exhibited a similar functional interaction pattern among brain regions of interest. Results showed that while no significant effects of oxytocin were found on brain temporal state related characteristics (including temporal state switching frequency, probability of transitions between neighboring states, and averaged dwell time on each state) oxytocin extensively (n = 54 links) modulated effective connectivity among the 15 regions. The effects of oxytocin were primarily characterized by increased effective connectivity both between and within emotion, reward, salience, attention and social cognition processing networks and their interactions with the default mode network. Top-down control over emotional processing regions such as the amygdala was particularly affected. Oxytocin also increased effective homotopic interhemispheric connectivity in almost all these regions. Additionally, the effects of oxytocin on effective connectivity were sex-dependent, being more extensive in males. Overall, these findings suggest that modulatory effects of oxytocin on both within- and between-network interactions may underlie its functional influence on social-emotional behaviors, although in a sex-dependent manner. These findings may be of particular relevance to potential therapeutic use of oxytocin in psychiatric disorders associated with social dysfunction, such as autism spectrum disorder and schizophrenia, where directionality of treatment effects on causal interactions between networks may be of key importance .

Radiomics Nomograms Based on Non-enhanced MRI and Clinical Risk Factors for the Differentiation of Chondrosarcoma from Enchondroma.

BACKGROUND: Differentiating chondrosarcoma from enchondroma using conventional MRI remains challenging. An effective method for accurate preoperative diagnosis could affect the management and prognosis of patients. PURPOSE: To validate and evaluate radiomics nomograms based on non-enhanced MRI and clinical risk factors for the differentiation of chondrosarcoma from enchondroma. STUDY TYPE: Retrospective. POPULATION: A total of 103 patients with pathologically confirmed chondrosarcoma (n = 53) and enchondroma (n = 50) were randomly divided into training (n = 68) and validation (n = 35) groups. FIELD STRENGTH/SEQUENCE: Axial non-contrast-enhanced T1-weighted images (T1WI) and fat-suppressed T2-weighted images (T2WI-FS) were acquired at 3.0 T. ASSESSMENT: Clinical risk factors (sex, age, and tumor location) and diagnosis assessment based on morphologic MRI by three radiologists were recorded. Three radiomics signatures were established based on the T1WI, T2WI-FS, and T1WI + T2WI-FS sequences. Three clinical radiomics nomograms were developed based on the clinical risk factors and three radiomics signatures. STATISTICAL TESTS: The area under the receiver operating characteristic curve (AUC) was used to evaluate the performance of radiomics signatures and clinical radiomics nomograms. RESULTS: Tumor location was an important clinical risk factor (P < 0.05). The radiomics signature based on T1WI and T1WI + T2WI-FS features performed better than that based on T2WI-FS in the validation group (AUC in the validation group: 0.961, 0.938, and 0.833, respectively; P < 0.05). In the validation group, the three clinical radiomics nomograms (T1WI, T2WI-FS, and T1WI + T2WI-FS) achieved AUCs of 0.938, 0.935, and 0.954, respectively. In all patients, the clinical radiomics nomogram based on T2WI-FS (AUC = 0.967) performed better than that based on T2WI-FS (AUC = 0.901, P < 0.05). DATA CONCLUSION: The proposed clinical radiomics nomogram showed promising performance in differentiating chondrosarcoma from enchondroma. LEVEL OF EVIDENCE: 4 TECHNICAL EFFICACY: Stage 2.

Prediction of High-Risk Cytogenetic Status in Multiple Myeloma Based on Magnetic Resonance Imaging: Utility of Radiomics and Comparison of Machine Learning Methods.

BACKGROUND: Radiomics has shown promising results in the diagnosis, efficacy, and prognostic assessments of multiple myeloma (MM). However, little evidence exists on the utility of radiomics in predicting a high-risk cytogenetic (HRC) status in MM. PURPOSE: To develop and test a magnetic resonance imaging (MRI)-based radiomics model for predicting an HRC status in MM patients. STUDY TYPE: Retrospective. POPULATION: Eighty-nine MM patients (HRC [n: 37] and non-HRC [n: 52]). FIELD STRENGTH/SEQUENCE: A 3.0 T; fast spin-echo (FSE): T1-weighted image (T1WI) and fat-suppression T2WI (FS-T2WI). ASSESSMENT: Overall, 1409 radiomics features were extracted from each volume of interest drawn by radiologists. Three sequential feature selection steps-variance threshold, SelectKBest, and least absolute shrinkage selection operator-were repeated 10 times with 5-fold cross-validation. Radiomics models were constructed with the top three frequency features of T1 WI/T2 WI/two-sequence MRI (T1 WI and FS-T2 WI). Radiomics models, clinical data (age and visually assessed MRI pattern), or radiomics combined with clinical data were used with six classifiers to distinguish between HRC and non-HRC statuses. Six classifiers used were support vector machine, random forest, logistic regression (LR), decision tree, k-nearest neighbor, and XGBoost. Model performance was evaluated with area under the curve (AUC) values. STATISTICAL TESTS: Mann-Whitney U-test, Chi-squared test, Z test, and DeLong method. RESULTS: The LR classifier performed better than the other classifiers based on different data (AUC: 0.65-0.82; P < 0.05). The two-sequence MRI models performed better than the other data models using different classifiers (AUC: 0.68-0.82; P < 0.05). Thus, the LR two-sequence model yielded the best performance (AUC: 0.82 ± 0.02; sensitivity: 84.1%; specificity: 68.1%; accuracy: 74.7%; P < 0.05). CONCLUSION: The LR-based machine learning method appears superior to other classifier methods for assessing HRC in MM. Radiomics features based on two-sequence MRI showed good performance in differentiating HRC and non-HRC statuses in MM. EVIDENCE LEVEL: 3 TECHNICAL EFFICACY: Stage 2.

Preoperative prediction for pathological grade of hepatocellular carcinoma via machine learning-based radiomics.

OBJECTIVE: To investigate the efficacy of contrast-enhanced computed tomography (CECT)-based radiomics signatures for preoperative prediction of pathological grades of hepatocellular carcinoma (HCC) via machine learning. METHODS: In this single-center retrospective study, data collected from 297 consecutive subjects with HCC were allocated to training dataset (n = 237) and test dataset (n = 60). Manual segmentation of lesion sites was performed with ITK-SNAP, the radiomics features were extracted by the Pyradiomics, and radiomics signatures were synthesized using recursive feature elimination (RFE) method. The prediction models for pathological grading of HCC were established by using eXtreme Gradient Boosting (XGBoost). The performance of the models was evaluated using the AUC along with 95% confidence intervals (CIs) and standard deviation, sensitivity, specificity, and accuracy. RESULTS: The radiomics signatures were found highly efficient for machine learning to differentiate high-grade HCC from low-grade HCC. For the clinical factors, when they were merely applied to train a machine learning model, the model achieved an AUC of 0.6698, along with 95% CI and standard deviation of 0.5307-0.8089 and 0.0710, respectively (sensitivity, 0.6522; specificity, 0.4595; accuracy, 0.5333). Meanwhile, when the radiomics signatures were applied in association with clinical factors to train a machine learning model, the performance of the model remarkably increased with AUC of 0.8014, along with 95% CI and standard deviation of 0.6899-0.9129 and 0.0569, respectively (sensitivity, 0.6522; specificity, 0.7297; accuracy, 0.7000). CONCLUSIONS: The radiomics signatures could non-invasively explore the underlying association between CECT images and pathological grades of HCC. KEY POINTS: • The radiomics signatures may non-invasively explore the underlying association between CECT images and pathological grades of HCC via machine learning. • The radiomics signatures of CECT images may enhance the prediction performance of pathological grading of HCC, and further validation is required. • The features extracted from arterial phase CECT images may be more reliable than venous phase CECT images for predicting pathological grades of HCC.

Oxytocin differentially modulates specific dorsal and ventral striatal functional connections with frontal and cerebellar regions.

Interactions between oxytocin and the basal ganglia are central in current overarching conceptualizations of its broad modulatory effects on behavior. Whereas evidence from animal models emphasizes the critical role of the ventral striatum in the behavioral effects of oxytocin, region-specific contributions of the basal ganglia have not been systematically explored in humans. The present study combined the randomized placebo-controlled administration of oxytocin versus placebo in healthy men (n = 144) with fMRI-based resting-state functional connectivity to determine the modulatory role of oxytocin on the major basal ganglia pathways. Oxytocin specifically increased connectivity between ventral striatal and pallidal nodes with upstream frontal regions, whereas it decreased the strengths of downstream pathways between the dorsal striatum and posterior cerebellum. These pathways have previously been implicated in salience, reward and behavioral flexibility, thus shaping goal-directed behavior. Given the importance of aberrant striatal intrinsic organization in autism, addiction and schizophrenia the present findings may suggest new mechanistic perspectives for the therapeutic potential of oxytocin in these disorders.

Oxytocin, the peptide that bonds the sexes also divides them.

Facilitation of social attraction and bonding by the evolutionarily conserved neuropeptide oxytocin is well-established in female mammals. However, accumulating behavioral evidence suggests that oxytocin may have evolved sex-specific functional roles in the domain of human social cognition. A critical question is how oxytocin differentially modulates neural processing of social information in men and women, leading to divergent behavioral responses. Here we show that intranasal oxytocin treatment produces sex- and valence-dependent increases in amygdala activation when women view individuals identified as praising others but in men those who criticize them. Women subsequently show increased liking for the faces of these individuals, whereas in men it is reduced. Thus, oxytocin may act differentially via the amygdala to enhance the salience of positive social attributes in women but negative ones in men. We hypothesize that oxytocin may have evolved different but complementary roles to help ensure successful reproduction by encouraging mothers to promote a prosocial rearing environment for offspring and fathers to protect against antisocial influences.

Sex- and context-dependent effects of oxytocin on social sharing.

We interact socially and form bonds with others because such experiences are rewarding. However, an insecure attachment style or social anxiety can reduce these rewarding effects. The neuropeptide oxytocin (OXT) may facilitate social interactions either by increasing their rewarding experience or by attenuating anxiety, although effects can be sex- and attachment-style dependent. In this study, 128 pairs of same-sex friends completed a social sharing paradigm in a double-blind, placebo-controlled, between-subject design with one friend inside an MRI scanner and the other in a remote behavioral testing room. In this way we could examine whether intranasal-OXT differentially modulated the emotional impact of social sharing and associated neural processing. Additionally, we investigated if OXT effects were modulated by sex and attachment style. Results showed that in women, but not men, OXT increased ratings for sharing stimuli with their friend but not with a stranger, particularly in the friend in the scanner. Corresponding neuroimaging results showed that OXT decreased both amygdala and insula activity as well as their functional connectivity in women when they shared with friends but had the opposite effect in men. On the other hand, OXT did not enhance responses in brain reward circuitry. In the PLC treated group amygdala responses in women when they shared pictures with their friend were positively associated with attachment anxiety and OXT uncoupled this. Our findings demonstrate that OXT facilitates the impact of sharing positive experiences with others in women, but not men, and that this is associated with differential effects on the amygdala and insula and their functional connections. Furthermore, OXT particularly reduced increased amygdala responses during sharing in individuals with higher attachment anxiety. Thus, OXT effects in this context may be due more to reduced anxiety when sharing with a friend than to enhanced social reward.

Oxytocin Facilitates Approach Behavior to Positive Social Stimuli via Decreasing Anterior Insula Activity.

Background: The neuropeptide oxytocin can extensively modulate human social behavior and affective processing, and its effects can be interpreted in terms of mediating approach-avoidance motivational processes. However, little is known about how oxytocin mediates approach-avoidance behavior and particularly the underlying neural mechanisms. Methods: In a randomized, double-blind, between-subject design, the present pharmaco-fMRI study used an approach-avoidance paradigm to investigate oxytocin's effects on approach-avoidance behavior and associated neural mechanisms. Results: Results revealed that oxytocin generally decreased activity in the right striatum irrespective of response (approach/avoidance) and social context, suggesting an inhibitory effect on motivational representation during both appetitive approach and aversive avoidance. Importantly, while on the behavioral level oxytocin selectively enhanced accuracy when approaching social positive stimuli, on the neural level it decreased left ventral and right dorsal anterior insula activity in response to social vs nonsocial positive stimuli compared with the placebo treatment. The left ventral anterior insula activity was negatively correlated with the corresponding accuracy difference scores in the oxytocin but not in the placebo group. Conclusion: Given the role of the ventral anterior insula in emotional processing and the dorsal anterior insula in salience processing, the oxytocin-induced suppression of activity in these regions may indicate that oxytocin is acting to reduce interference from hyper-activity in core regions of the emotional and salience networks when approaching salient positive social stimuli and thereby to promote social interaction. Thus, oxytocin may be of potential therapeutic benefit for psychiatric disorders exhibiting avoidance of social stimuli.

Sex-dependent neural effect of oxytocin during subliminal processing of negative emotion faces.

In line with animal models indicating sexually dimorphic effects of oxytocin (OXT) on social-emotional processing, a growing number of OXT-administration studies in humans have also reported sex-dependent effects during social information processing. To explore whether sex-dependent effects already occur during early, subliminal, processing stages the present pharmacological fMRI-study combined the intranasal-application of either OXT or placebo (n = 86-43 males) with a backward-masking emotional face paradigm. Results showed that while OXT suppressed inferior frontal gyrus, dorsal anterior cingulate and anterior insula responses to threatening face stimuli in men it increased them in women. In women increased anterior cingulate reactivity during subliminal threat processing was also positively associated with trait anxiety. On the network level, sex-dependent effects were observed on amygdala, anterior cingulate and inferior frontal gyrus functional connectivity that were mainly driven by reduced coupling in women following OXT. Our findings demonstrate that OXT produces sex-dependent effects even at the early stages of social-emotional processing, and suggest that while it attenuates neural responses to threatening social stimuli in men it increases them in women. Thus in a therapeutic context OXT may potentially produce different effects on anxiety disorders in men and women.

Voluntary control of anterior insula and its functional connections is feedback-independent and increases pain empathy.

Real-time functional magnetic resonance imaging (rtfMRI)-assisted neurofeedback (NF) training allows subjects to acquire volitional control over regional brain activity. Emerging evidence suggests its potential clinical utility as an effective non-invasive treatment approach in mental disorders. The therapeutic potential of rtfMRI-NF training depends critically upon whether: (1) acquired self-regulation produces functionally relevant changes at behavioral and brain network levels and (2) training effects can be maintained in the absence of feedback. To address these key questions, the present study combined rtfMRI-NF training for acquiring volitional anterior insula (AI) regulation with a sham-controlled between-subject design. The functional relevance of acquired AI control was assessed using both behavioral (pain empathy) and neural (activity, functional connectivity) indices. Maintenance of training effects in the absence of feedback was assessed two days later. During successful acquisition of volitional AI up-regulation subjects exhibited stronger empathic responses, increased AI-prefrontal coupling in circuits involved in learning and emotion regulation and increased resting state connectivity within AI-centered empathy networks. At follow-up both self-regulation and increased connectivity in empathy networks were fully maintained, although without further increases in empathy ratings. Overall these findings support the potential clinical application of rtfMRI-NF for inducing functionally relevant and lasting changes in emotional brain circuitry.

Oxytocin blurs the self-other distinction during trait judgments and reduces medial prefrontal cortex responses.

The neuropeptide oxytocin (OXT) may act either to increase or blur the distinction between self and other and thereby promote either more selfish or altruistic behaviors. To attempt to distinguish between these two possibilities we performed a double-blind, between-subject, placebo-controlled design study to investigate the effect of intranasal OXT on self and other (mother, classmate, or stranger) trait judgments in conjunction with functional magnetic resonance imaging. Results showed that OXT reduced response times for making both self and other judgments, but also reduced the accuracy of their subsequent recall, thereby abolishing the normal self-bias observed in this task. OXT also abolished the positive correlation between response and self-esteem scale scores seen in the PLC group, suggesting that its effects were strongest in individuals with higher levels of self-esteem. A whole-brain functional magnetic resonance imaging analysis revealed that OXT also reduced responses during both self and other trait judgments in the dorsal (dmPFC) and ventral (vmPFC) medial prefrontal cortex. A subsequent region of interest analysis revealed that behavioral performance and self-esteem scale scores were associated with dmPFC activation and its functional connectivity with the anterior cingulate and between the vmPFC and posterior cingulate. Thus overall, while OXT may improve speed of decision making in self -vs. other trait judgments it also blunts the normal bias towards remembering self-attributes and reduces mPFC responses and connectivity with other cortical midline regions involved in self-processing. This is consistent with the view that OXT can reduce self-centered behavior. Hum Brain Mapp 37:2512-2527, 2016. © 2016 Wiley Periodicals, Inc.

Oxytocin makes females, but not males, less forgiving following betrayal of trust.

Although oxytocin has been shown to enhance trust behavior, to date no study has directly established whether oxytocin can modulate the effect of repair strategies on restoring damaged trust. In the current double-blind, between-subjects, placebo-controlled design study, two repair strategies were used to examine the effect of intranasal oxytocin administration on modulating trust restoration in a revised trust game. The results showed that although oxytocin had no overall effect on modulating trust restoration, it did have a significant gender specific effect. Female subjects showed less evidence for trust repair in the oxytocin compared with the placebo treatment group. This suggests that oxytocin may make female subjects exhibit more punitive behavior towards partners who violate their trust and less sensitive to repair strategies provided by them. Interestingly, this gender specific effect was more evident in the context of attempted trust repair using financial compensation. However, it also extended to both apology alone and no compensation conditions, but not to the fair one, in females exhibiting high trait forgiveness. Thus females with a more forgiving attitude towards betrayal may actually be more likely to punish betrayal following oxytocin treatment.

Preoperative Prediction of Meningioma Subtype by Constructing a Clinical-Radiomics Model Nomogram Based on Magnetic Resonance Imaging.

OBJECTIVE: We sought to investigate the value of a clinical-radiomics model based on magnetic resonance imaging in differentiating fibroblastic meningiomas from non-fibroblastic meningiomas. METHODS: Clinical, imaging, and postoperative pathologic data of 423 patients (128 fibroblastic meningiomas and 295 non-fibroblastic meningiomas) were randomly categorized into training (n = 296) and validation (n = 127) groups at a 7:3 ratio. The Selectpercentile and LASSO were used to selected the highly correlated features from 3376 radiomics features. Different classifiers were used to train and verify the model. The receiver operating characteristic curves, accuracy (ACC), sensitivity (SEN), and specificity (SPE) were drawn to evaluate the performance. The optimal radiomics model was selected. Calibration curves and decision curve analysis were used to verify the clinical utility and consistency of the nomogram constructed from the radiomics features and clinical factors. RESULTS: Thirteen radiomics features were selected from contrast-enhanced T1-weighted imaging and T2-weighted imaging after dimensionality reduction. The prediction performance of random forest radiomics model is slightly lower than that of the clinical-radiomics model. The area under the curve, SEN, SPE, and ACC of the clinical-radiomics model training set were 0.836 (95% confidence interval, 0.795-0.878), 0.922, 0.583, and 0.686, respectively. The area under the curve, SEN, SPE, and ACC of the validation set were 0.756 (95% confidence interval, 0.660-0.846), 0.816, 0.596, and 0.661, respectively. CONCLUSIONS: The diagnostic efficacy of the clinical-radiomics model of fibroblastic meningioma and non-fibroblastic meningioma was better than that of the radiomics prediction model alone and can be used as a potential tool for clinical surgical planning and evaluation of patient prognosis.

Lesion-specific pericoronary adipose tissue CT attenuation improves risk prediction of major adverse cardiovascular events in coronary artery disease.

OBJECTIVES: To determine whether lesion-specific pericoronary adipose tissue CT attenuation (PCATa) is superior to PCATa around the proximal right coronary artery (PCATa-RCA) and left anterior descending artery (PCATa-LAD) for major adverse cardiovascular events (MACE) prediction in coronary artery disease (CAD). METHODS: Six hundred and eight CAD patients who underwent coronary CTA from January 2014 to December 2018 were retrospectively included, with clinical risk factors, plaque features, lesion-specific PCATa, PCATa-RCA, and PCATa-LAD collected. MACE was defined as cardiovascular death, non-fatal myocardial infarction, unplanned revascularization, and hospitalization for unstable angina. Four models were established, encapsulating traditional factors (Model A), traditional factors and PCATa-RCA (Model B), traditional factors and PCATa-LAD (Model C), and traditional factors and lesion-specific PCATa (Model D). Prognostic performance was evaluated with C-statistic, area under receiver operator characteristic curve (AUC), and net reclassification index (NRI). RESULTS: Lesion-specific PCATa was an independent predictor for MACE (adjusted hazard ratio = 1.108, P < .001). The C-statistic increased from 0.750 for model A to 0.762 for model B (P = .078), 0.773 for model C (P = .046), and 0.791 for model D (P = .005). The AUC increased from 0.770 for model A to 0.793 for model B (P = .027), 0.793 for model C (P = .387), and 0.820 for model D (P = .019). Compared with model A, the NRIs for models B, C, and D were 0.243 (-0.323 to 0.792, P = .392), 0.428 (-0.012 to 0.835, P = .048), and 0.708 (0.152-1.016, P = .001), respectively. CONCLUSIONS: Lesion-specific PCATa improves risk prediction of MACE in CAD, which is better than PCATa-RCA and PCATa-LAD. ADVANCES IN KNOWLEDGE: Lesion-specific PCATa was superior to PCATa-RCA and PCATa-LAD for MACE prediction.

Nomogram using intratumoral and peritumoral radiomics for the preoperative prediction of visceral pleural invasion in clinical stage IA lung adenocarcinoma.

BACKGROUND: Accurate prediction of visceral pleural invasion (VPI) in lung adenocarcinoma before operation can provide guidance and help for surgical operation and postoperative treatment. We investigate the value of intratumoral and peritumoral radiomics nomograms for preoperatively predicting the status of VPI in patients diagnosed with clinical stage IA lung adenocarcinoma. METHODS: A total of 404 patients from our hospital were randomly assigned to a training set (n = 283) and an internal validation set (n = 121) using a 7:3 ratio, while 81 patients from two other hospitals constituted the external validation set. We extracted 1218 CT-based radiomics features from the gross tumor volume (GTV) as well as the gross peritumoral tumor volume (GPTV5, 10, 15), respectively, and constructed radiomic models. Additionally, we developed a nomogram based on relevant CT features and the radscore derived from the optimal radiomics model. RESULTS: The GPTV10 radiomics model exhibited superior predictive performance compared to GTV, GPTV5, and GPTV15, with area under the curve (AUC) values of 0.855, 0.842, and 0.842 in the three respective sets. In the clinical model, the solid component size, pleural indentation, solid attachment, and vascular convergence sign were identified as independent risk factors among the CT features. The predictive performance of the nomogram, which incorporated relevant CT features and the GPTV10-radscore, outperformed both the radiomics model and clinical model alone, with AUC values of 0.894, 0.828, and 0.876 in the three respective sets. CONCLUSIONS: The nomogram, integrating radiomics features and CT morphological features, exhibits good performance in predicting VPI status in lung adenocarcinoma.