Changes in interoceptive accuracy related to emotional interference in somatic symptom disorder.

OBJECTIVE: The somatic symptom disorder (SSD) is characterized by one or more distressing or disabling somatic symptoms accompanied by an excessive amount of time, energy and emotion related to the symptoms. These manifestations of SSD have been linked to alterations in perception and appraisal of bodily signals. We hypothesized that SSD patients would exhibit changes in interoceptive accuracy (IA), particularly when emotional processing is involved. METHODS: Twenty-three patients with SSD and 20 healthy controls were recruited. IA was assessed using the heartbeat perception task. The task was performed in the absence of stimuli as well as in the presence of emotional interference, i.e., photographs of faces with an emotional expression. IA were examined for correlation with measures related to their somatic symptoms, including resting-state heart rate variability (HRV). RESULTS: There was no significant difference in the absolute values of IA between patients with SSD and healthy controls, regardless of the condition. However, the degree of difference in IA without emotional interference and with neutral facial interference was greater in patients with SSD than in healthy controls (p = 0.039). The IA of patients with SSD also showed a significant correlation with low-frequency HRV (p = 0.004) and high-frequency HRV (p = 0.007). CONCLUSION: SSD patients showed more significant changes in IA when neutral facial interference was given. These results suggest that bodily awareness is more affected by emotionally ambiguous stimuli in SSD patients than in healthy controls.

Exploratory Clinical Trial of a Depression Diagnostic Software That Integrates Stress Biomarkers and Composite Psychometrics.

OBJECTIVE: This study evaluated the clinical effectiveness of Minds.NAVI, a depression screening kit combining psychometric measures and stress hormone biomarkers, in a prospective clinical trial. The objective was to assess its potential as a depression screening tool and investigate the associations between psychological assessments, salivary hormone staging, and depression severity. METHODS: Thirty-five participants with major depressive disorder and 12 healthy controls (HCs) were included. The Minds.NAVI software, utilizing the PROtective and Vulnerable factors battEry Test (PROVE) and salivary cortisol/dehydroepiandrosterone (DHEA) analysis, was employed. The PROVE test is a comprehensive self-report questionnaire that assesses depressive symptoms, suicide risk, attachment style, adverse childhood experiences, mentalization capacity, and resilience. In addition, salivary cortisol and DHEA levels were measured to evaluate the functional stage of the hypothalamic-pituitary-adrenal (HPA) axis. RESULTS: Minds.NAVI exhibited 100% sensitivity, 91.7% specificity, and 97.9% accuracy in distinguishing depression from HCs within an exploratory small group. Salivary stress hormone phases showed changes with depression stage (p=0.030), and the proportion of patients with "adrenal exhaustion stage" was higher in the moderate/severe depression group (p=0.038). Protective/vulnerable factors differed significantly between controls and depressed groups (p<0.001). Cortisol awakening response inversely correlated with depressive symptom severity (r=-0.31, p=0.034). CONCLUSION: This study suggested possible clinical effectiveness of Minds.NAVI, a depression screening tool that integrates psychometric measures and stress hormone biomarkers. The findings support the potential association between depression, chronic stress, and HPA axis hyporesponsiveness.

The mediating effect of attentional impulsivity between mindfulness and problematic smartphone use.

OBJECTIVE: Problematic smartphone use has been linked to lower levels of mindfulness, impaired attentional function, and higher impulsivity. This study aimed to identify the psychological mechanisms of problematic smartphone use by exploring the relationship between addictive smartphone use, mindfulness, attentional function and impulsivity. METHODS: Ninety participants were evaluated with the smartphone addiction proneness scale and classified into the problematic smartphone use group (n = 42; 24 women; mean age: 27.6 ± 7.2 years) or normal use group (n = 48; 22 women; mean age: 30.1 ± 5.7 years). All participants completed self-report questionnaires evaluating their trait impulsivity and mindfulness and attention tests that assessed selective, sustained and divided attention. We compared the variables between the groups and explored the relationship between mindfulness, attentional function, impulsivity and addictive smartphone use through mediation analysis. RESULTS: The problematic smartphone use group showed higher trait impulsivity and lower mindfulness than the normal use group. There were no significant group differences in performance on attention tests. Levels of addictive smartphone use were significantly correlated with higher levels of trait impulsivity and lower levels of mindfulness, but not with performance on attention tests. Mediation analysis showed that acting with awareness, an aspect of mindfulness, reduces the degree of addictive smartphone use through attentional impulsivity, one of the trait impulsivity. CONCLUSION: Acting without sufficient awareness could influence addictive smartphone use by mediating attentional impulsivity. This supports that executive control deficits, reflected in high attentional impulsivity, contribute to problematic smartphone use. Our findings imply that mindfulness-based interventions can enhance executive control over smartphone use by promoting awareness.

Neural correlates of model-based behavior in internet gaming disorder and alcohol use disorder.

Background: An imbalance between model-based and model-free decision-making systems is a common feature in addictive disorders. However, little is known about whether similar decision-making deficits appear in internet gaming disorder (IGD). This study compared neurocognitive features associated with model-based and model-free systems in IGD and alcohol use disorder (AUD). Method: Participants diagnosed with IGD (n = 22) and AUD (n = 22), and healthy controls (n = 30) performed the two-stage task inside the functional magnetic resonance imaging (fMRI) scanner. We used computational modeling and hierarchical Bayesian analysis to provide a mechanistic account of their choice behavior. Then, we performed a model-based fMRI analysis and functional connectivity analysis to identify neural correlates of the decision-making processes in each group. Results: The computational modeling results showed similar levels of model-based behavior in the IGD and AUD groups. However, we observed distinct neural correlates of the model-based reward prediction error (RPE) between the two groups. The IGD group exhibited insula-specific activation associated with model-based RPE, while the AUD group showed prefrontal activation, particularly in the orbitofrontal cortex and superior frontal gyrus. Furthermore, individuals with IGD demonstrated hyper-connectivity between the insula and brain regions in the salience network in the context of model-based RPE. Discussion and Conclusions: The findings suggest potential differences in the neurobiological mechanisms underlying model-based behavior in IGD and AUD, albeit shared cognitive features observed in computational modeling analysis. As the first neuroimaging study to compare IGD and AUD in terms of the model-based system, this study provides novel insights into distinct decision-making processes in IGD.

Multimodal-based machine learning approach to classify features of internet gaming disorder and alcohol use disorder: A sensor-level and source-level resting-state electroencephalography activity and neuropsychological study.

OBJECTIVES: Addictions have recently been classified as substance use disorder (SUD) and behavioral addiction (BA), but the concept of BA is still debatable. Therefore, it is necessary to conduct further neuroscientific research to understand the mechanisms of BA to the same extent as SUD. The present study used machine learning (ML) algorithms to investigate the neuropsychological and neurophysiological aspects of addictions in individuals with internet gaming disorder (IGD) and alcohol use disorder (AUD). METHODS: We developed three models for distinguishing individuals with IGD from those with AUD, individuals with IGD from healthy controls (HCs), and individuals with AUD from HCs using ML algorithms, including L1-norm support vector machine, random forest, and L1-norm logistic regression (LR). Three distinct feature sets were used for model training: a unimodal-electroencephalography (EEG) feature set combined with sensor- and source-level feature; a unimodal-neuropsychological feature (NF) set included sex, age, depression, anxiety, impulsivity, and general cognitive function, and a multimodal (EEG + NF) feature set. RESULTS: The LR model with the multimodal feature set used for the classification of IGD and AUD outperformed the other models (accuracy: 0.712). The important features selected by the model highlighted that the IGD group had differential delta and beta source connectivity between right intrahemispheric regions and distinct sensor-level EEG activities. Among the NFs, sex and age were the important features for good model performance. CONCLUSIONS: Using ML techniques, we demonstrated the neurophysiological and neuropsychological similarities and differences between IGD (a BA) and AUD (a SUD).

North Korean defectors with PTSD and complex PTSD show alterations in default mode network resting-state functional connectivity.

BACKGROUND: North Korean defectors (NKDs) have often been exposed to traumatic events. However, there have been few studies of neural alterations in NKDs with post-traumatic stress disorder (PTSD) and complex PTSD (cPTSD). AIMS: To investigate neural alterations in NKDs with PTSD and cPTSD, with a specific focus on alterations in resting-state functional connectivity networks, including the default mode network (DMN). METHOD: Resting-state functional connectivity was assessed using brain functional magnetic resonance imaging in three groups of NKDs: without PTSD, with PTSD and with cPTSD. Statistical tests were performed, including region of interest (ROI)-to-ROI and ROI-to-voxel analysis, followed by post hoc correlation analysis. RESULTS: In the ROI-to-ROI analysis, differences in functional connectivity were found among the components of the DMN, as well as in the thalamus and the basal ganglia. Right hippocampus-left pallidum and right amygdala-left lingual gyrus connectivity differed between groups in the ROI-to-voxel analysis, as did connectivity involving the basal ganglia. The post hoc analysis revealed negative correlations between Coping and Adaptation Processing Scale (CAPS) score and left posterior cingulate cortex-right pallidum connectivity and between CAPS score and right putamen-left angular gyrus connectivity in the control group, which were not observed in other groups. CONCLUSIONS: The results suggest that there are alterations in the functional connectivity of the DMN and the limbic system in NKDs with PTSD and cPTSD, and that these alterations involve the basal ganglia. The lower correlations of CAPS score with right basal ganglia-DMN functional connectivity in patients compared with controls further implies that these connectivities are potential targets for treatment of PTSD and cPTSD.

Effects of Game-Related Tasks for the Diagnosis and Classification of Gaming Disorder.

Gaming disorder (GD) is an addictive behavior characterized by an insatiable need to play video games and shares similar symptoms with the failure of self-control due to a decline in cognitive function. Current GD diagnostic and screening tools rely on questionnaires and behavioral observations related to cognitive functions to assess an individual's capacity to maintain self-control in everyday life. However, current GD screening approaches rely on subjective symptoms, and a reliable diagnosis requires long-term clinical follow-up. Recent studies have measured biosignals along with cognitive functional tasks to provide objectivity to GD diagnosis and to acquire immediate results. However, people with GD are hypersensitive to game-related cues, so their responses may vary depending on the type of stimuli, and the difference in response to stimuli might manifest as a difference in the degree of change in the biosignal. Therefore, it is critical to choose the correct stimulus type when performing GD diagnostic tasks. In this study, we investigated the task dependence of cognitive decline in GD by comparing two cognitive functional tasks: a continuous performance task (CPT) and video game play. For this study, 69 young male adults were classified into either the gaming disorder group (GD, n = 39) or a healthy control group (HC, n = 30). CPT score, EEG signal (theta, alpha, and beta), and HRV-HF power were assessed. We observed differences in the left frontal region (LF) of the brain between the GD and HC groups during online video game play. The GD group also showed a significant difference in HF power of HRV between CPT and online video gaming. Furthermore, LF and HRV-HF significantly correlated with Young's Internet Addiction Test (Y-IAT) score, which is positively associated with impulsivity score. The amount of change in theta band activity in LF and HRV-HF-both biomarkers for changes in cognitive function-during online video game play suggests that people with GD express task-dependent cognitive decline compared with HC. Our results demonstrate the feasibility of quantifying individual self-regulation ability for gaming and underscore its importance for GD classification.