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.

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.

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.

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).

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.

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.

Accuracy of impression methods through the comparison of 3D deviation between implant fixtures.

AIM: To compare the accuracy of three impression methods by comparing the distance between the reference points of the implant fixture, especially in curved maxillary anterior teeth. MATERIALS AND METHODS: Implant fixtures were placed in the maxillary central incisor and canine regions. A maxillary master cast was made using a model scanner and 3D printer. Ten impressions were taken from the three experimental groups constructed (group P: pick-up impression coping; group I: scan body with an intraoral scanner; group B: bite impression coping). The distance between the reference points, the angle between the scan bodies, and displacement of the 3D surface area were measured. RESULTS: The distances between the reference points were significantly different between groups I and B in the maxillary incisors, and between group P and the other two groups in the maxillary canines. Group P had the least amount of displacement in both fixtures. Both fixtures showed the highest displacement in group B. Displacement of the 3D surface area in the maxillary incisors showed no significant difference between the groups. There was a significant difference in the maxillary canines between groups P and I. CONCLUSIONS: In the present study, all three implant impression methods showed changes in the position and angle of the fixture compared with the master cast. The highest accuracy was shown by the impression method using the pick-up impression coping, but the impression method using the intraoral scanner also showed clinically acceptable accuracy. It should be noted that errors may occur when taking impressions using a bite impression coping.

Severity identification for internet gaming disorder using heart rate variability reactivity for gaming cues: a deep learning approach.

Background: The diminished executive control along with cue-reactivity has been suggested to play an important role in addiction. Hear rate variability (HRV), which is related to the autonomic nervous system, is a useful biomarker that can reflect cognitive-emotional responses to stimuli. In this study, Internet gaming disorder (IGD) subjects' autonomic response to gaming-related cues was evaluated by measuring HRV changes in exposure to gaming situation. We investigated whether this HRV reactivity can significantly classify the categorical classification according to the severity of IGD. Methods: The present study included 70 subjects and classified them into 4 classes (normal, mild, moderate and severe) according to their IGD severity. We measured HRV for 5 min after the start of their preferred Internet game to reflect the autonomic response upon exposure to gaming. The neural parameters of deep learning model were trained using time-frequency parameters of HRV. Using the Class Activation Mapping (CAM) algorithm, we analyzed whether the deep learning model could predict the severity classification of IGD and which areas of the time-frequency series were mainly involved. Results: The trained deep learning model showed an accuracy of 95.10% and F-1 scores of 0.995 (normal), 0.994 (mild), 0.995 (moderate), and 0.999 (severe) for the four classes of IGD severity classification. As a result of checking the input of the deep learning model using the CAM algorithm, the high frequency (HF)-HRV was related to the severity classification of IGD. In the case of severe IGD, low frequency (LF)-HRV as well as HF-HRV were identified as regions of interest in the deep learning model. Conclusion: In a deep learning model using the time-frequency HRV data, a significant predictor of IGD severity classification was parasympathetic tone reactivity when exposed to gaming situations. The reactivity of the sympathetic tone for the gaming situation could predict only the severe group of IGD. This study suggests that the autonomic response to the game-related cues can reflect the addiction status to the game.

Mental Health Problems During COVID-19 and Attitudes Toward Digital Therapeutics.

OBJECTIVE: We aimed to elucidate public mental health problems and associated factors during the coronavirus disease-2019 (COVID-19). Furthermore, we evaluated people's attitudes toward digital therapeutics during the pandemic. METHODS: Data was collected online from participants, aged between 20-50 without any history of mental illness, from June 1st to June 30th 2021. The survey consisted of questions regarding demographics, changes during pandemic and attitude towards digital therapeutics, and mental health measures. RESULTS: Among the total of 445 participants, 49.2% reported significant level of stress and 13.5% and 7.0% met the screening criteria for major depressive disorder and generalized anxiety disorder, respectively. Significant predictive factors for mental health problems were-younger age group, female sex, currently being treated for medical or surgical disease, change in the amount of time spent on mobile device or computer after pandemic, change in household income, and change in work environment due to pandemic. Furthermore, 35.1% of participants, considered psychiatric consultation, at least slightly, but were hesitant to receive it due to the fear of contacting COVID-19 at the clinics. Instead, 54.4% of them preferred using digital therapeutics as an alternative to visiting offline clinics. CONCLUSION: We demonstrated that COVID-19 increased mental health problems along with access problems and identified their predictive factors. Digital therapeutics emerged as a viable solution to mental health problems and it was well-received by those in need of psychiatric consultation. Therefore, development and implementation of digital therapeutics should be considered to improve the mental health of people.

Orbitofrontal cortex functional connectivity changes in patients with binge eating disorder and bulimia nervosa.

We aimed to define the shared and unshared functional neurobiological underpinnings of binge eating disorder (BED) and bulimia nervosa (BN). These disorders both involve loss of control over binge eating, but differ based on purging behavior and body image distortion. BED and BN have also been found to show differences in brain activation patterns in reward sensitivity. We enrolled 13 and 12 drug-naive and medication-free women with BED and BN, respectively, and 22 age- and sex-matched healthy controls. We performed an orbitofrontal cortex (OFC)-seeded resting-state whole brain functional connectivity (FC) analysis among the groups. In this study, BED patients exhibited significantly higher impulsivity than controls, whereas the difference in impulsivity between BN and controls was not significant. Participants with BED and BN showed weaker FC between the left lateral OFC and the right precuneus than controls. In the BED only group, the FC strength between these regions was negatively correlated with self-reported impulsivity. In both BED and BN, FC between the left lateral OFC and the right dorsolateral prefrontal cortex was weaker than that in controls. In BED, FC between the left medial OFC and the right cerebellar lobule IV was stronger than that of other groups. Our current results suggest similarities and differences between BED and BN in OFC-seeded FC with respect to reward processing. In particular, FC of the OFC in BED patients showed a significant correlation with their high impulsivity, which may reflect a decline in executive control over binge eating.

Altered resting-state functional connectivity of the dorsal anterior cingulate cortex with intrinsic brain networks in male problematic smartphone users.

The excessive use of smartphones is associated with various medical complications and mental health problems. However, existing research findings on neurobiological mechanisms behind problematic smartphone use are limited. In this study, we investigated functional connectivity in problematic smartphone users, focusing on the default mode network (DMN) and attentional networks. We hypothesized that problematic smartphone users would have alterations in functional connectivity between the DMN and attentional networks and that such alterations would correlate with the severity of problematic smartphone use. This study included 30 problematic smartphone users and 35 non-problematic smartphone users. We carried out group independent component analysis (group ICA) to decompose resting-state functional magnetic resonance imaging (fMRI) data into distinct networks. We examined functional connectivity using seed-to-seed analysis and identified the nodes of networks in group ICA, which we used as region of interest. We identified greater functional connectivity of the dorsal anterior cingulate cortex (dACC) with the ventral attention network (VAN) and with the DMN in problematic smartphone users. In seed-to-seed analysis, problematic smartphone users showed atypical dACC-VAN functional connectivity which correlated with the smartphone addiction proneness scale total scores. Our resting-state fMRI study found greater functional connectivity between the dACC and attentional networks in problematic smartphone users. Our findings suggest that increased bottom-up and interoceptive attentional processing might play an important role in problematic smartphone use.

Changes in intrinsic functional brain connectivity related to occupational stress of firefighters.

Firefighters are exposed to physical and mental threats in the working environment that put them at risk for occupational stress and burnout. Stress responses can impact the functional interactions between brain regions involved in emotional and cognitive regulation. The objective of this study was to investigate brain functional connectivity (FC) related to occupational stress in firefighters. Male firefighters (n = 77) completed the Korean Occupational Stress Scale questionnaire on occupational stress and underwent brain magnetic resonance imaging. Seed-based FC analyses were conducted by setting core regions of the large-scale functional networks as seeds. Subsequent correlational analyses detected relationships between occupational stress scale scores and brain FC. The results showed that occupational stress was negatively correlated with FC between the central executive network (CEN)-related brain regions and seed regions of other networks. Additionally, occupational stress was negatively correlated with FC within the default mode network (DMN), but positively correlated with FC between the salience network (SN) and the DMN. Changes in FC in large-scale neural networks are likely involved in occupational stress responses. Taken together, these results suggest that proper management of occupational stress may help prevent the occurrence of clinical problems caused by changes in brain functional networks.

Social media use and mental health during the COVID-19 pandemic in young adults: a meta-analysis of 14 cross-sectional studies.

BACKGROUND: Public isolated due to the early quarantine regarding coronavirus disease 2019 (COVID-19) increasingly used more social media platforms. Contradictory claims regarding the effect of social media use on mental health needs to be resolved. The purpose of the study was to summarise the association between the time spent on social media platform during the COVID-19 quarantine and mental health outcomes (i.e., anxiety and depression). METHODS: Studies were screened from the PubMed, Embase, and Cochrane Library databases. Regarding eligibility criteria, studies conducted after the declaration of the pandemic, studies that measured mental health symptoms with validated tools, and studies that presented quantitative results were eligible. The studies after retrieval evaluated the association between time spent on social media platform and mental health outcomes (i.e. anxiety and depression). The pooled estimates of retrieved studies were summarised in odds ratios (ORs). Data analyses included a random-effect model and an assessment of inter-study heterogeneity. Quality assessment was conducted by two independent researchers using the Risk of Bias Assessment Tool for Nonrandomized Studies (RoBANS). This meta-analysis review was registered in PROSPERO ( https://www.crd.york.ac.uk/PROSPERO/ , registration No CRD42021260223, 15 June 2021). RESULTS: Fourteen studies were included. The increase in the time spent using social media platforms were associated with anxiety symptoms in overall studies (pooled OR = 1.55, 95% CI: 1.30-1.85), and the heterogeneity between studies was mild (I2 = 26.77%). Similarly, the increase in social media use time was also associated with depressive symptoms (pooled OR = 1.43, 95% CI: 1.30-1.85), and the heterogeneity between studies was moderate (I2 = 67.16%). For sensitivity analysis, the results of analysis including only the "High quality" studies after quality assessment were similar to those of the overall study with low heterogeneity (anxiety: pooled OR = 1.45, 95% CI: 1.21-1.96, I2 = 0.00%; depression: pooled OR = 1.42, 95% CI: 0.69-2.90, I2 = 0.00%). CONCLUSIONS: The analysis demonstrated that the excessive time spent on social media platform was associated with a greater likelihood of having symptoms of anxiety and depression.

Insular activation and functional connectivity in firefighters with post-traumatic stress disorder.

BACKGROUND: Firefighters are frequently exposed to stressful situations and are at high risk of developing post-traumatic stress disorder (PTSD). Hyperresponsiveness to threatening and emotional stimuli and diminishment of executive control have been suggested as manifestations of PTSD. AIMS: To examine brain activation in firefighters with PTSD by conducting an executive control-related behavioural task with trauma-related interferences. METHOD: Twelve firefighters with PTSD and 14 healthy firefighters underwent functional magnetic resonance imaging (fMRI) while performing a Stroop match-to-sample task using trauma-related photographic stimuli. Seed-based functional connectivity analysis was conducted using regions identified in fMRI contrast analysis. RESULTS: Compared with the controls, the participants with PTSD had longer reaction times when the trauma-related interferences were presented. They showed significantly stronger brain activation to interfering trauma-related stimuli in the left insula, and had weaker insular functional connectivity in the supplementary motor area and the anterior cingulate cortex than the controls. They also showed a significant correlation between left insula-supplementary motor area connectivity strength and the hyperarousal subscale of the Clinician-Administered PTSD Scale. CONCLUSIONS: Our findings indicate that trauma-related stimuli elicit excessive brain activation in the left insula among firefighters with PTSD. Firefighters with PTSD also appear to have weak left insular functional connectivity with executive control-related brain regions. This aberrant insular activation and functional connectivity could be related to the development and maintenance of PTSD symptoms in firefighters.

Structural and functional brain alterations associated with cancer-associated cognitive decline in gastric cancer patients: A preliminary longitudinal neuroimaging study.

OBJECTIVE: Despite the clinical significance of cancer-associated cognitive decline (CACD), no longitudinal study has evaluated CACD in gastric cancer patients. This preliminary study explored structural and functional neural changes of CACD in gastric cancer patients focusing on the effects of chemotherapy. METHODS: 13 gastric cancer patients who received adjuvant chemotherapy (CTx+ group), 9 gastric cancer patients who did not receive adjuvant chemotherapy (CTx- group), and 10 healthy controls (HCs) were enrolled in this study. We performed self-report questionnaires, neurocognitive tests, voxel-based morphometry (VBM), and resting-state functional magnetic resonance imaging (rsfMRI) analyses before and 3 months after chemotherapy. RESULTS: Compared to the CTx- group, the CTx+ group exhibited statistically significant decrease in attention and executive function over time and dysfunction in delayed recognition performance. The results of the rsfMRI analysis showed a significant group-by-time interaction in the left hippocampus-anterior thalamus. However, no significant structural change was observed in the VBM analysis. CONCLUSION: To the best of our knowledge, this is the first longitudinal neuroimaging study on CACD in gastric cancer patients. Based on the results of our preliminary study, we suggest that the neuropathological processes and clinical presentation of CACD in gastric cancer patients is similar to those of patients associated with age-related neurodegenerative disorders.

Regional Gray Matter Volume Related to High Occupational Stress in Firefighters.

BACKGROUND: Firefighters inevitably encounter emotionally and physically stressful situations at work. Even firefighters without diagnosed post-traumatic stress disorder receive clinical attention because the nature of the profession exposes them to repetitive trauma and high occupational stress. This study investigated gray matter abnormalities related to high occupational stress in firefighters using voxel-based morphometry (VBM) and surface-based morphometry (SBM). METHODS: We assessed 115 subjects (112 males and 3 females) using magnetic resonance imaging and evaluated occupational stress by the Korean Occupational Stress Scale-26 (KOSS-26). Subjects were classified into highly or lowly stressed groups based on the median value of the KOSS-26. RESULTS: In VBM analysis, we found that firefighters with high occupational stress had lower gray matter volume (GMV) in both sides of the insula, the left amygdala, the right medial prefrontal cortex (mPFC), and the anterior cingulate cortex than firefighters with low occupational stress. In SBM analysis based on regions of interest, the GMV of the bilateral insula and right mPFC were also lower in the highly stressed group. Within the highly stressed group, low GMV of the insula was significantly correlated with the length of service (left: r = -0.347, P = 0.009; right: r = -0.333, P = 0.012). CONCLUSION: Our findings suggest that regional GMV abnormalities are related to occupational stress. Regional gray matter abnormalities and related emotional dysregulation may contribute to firefighter susceptibility to burnout.

Altered thalamic gray matter volume in firefighters with heavy alcohol use.

BACKGROUND: Alcohol, a known addictive substance, affects the structural properties of the brain. In this study, we explored associations between alcohol use and gray matter properties among firefighters, who are often exposed to significant occupational stress. METHODS: Gray matter volume (GMV) was evaluated using voxel-based morphometry in 287 male firefighters (mean age: 48.8 ± 7.7 years). Firefighters were classified into 32 never-drinkers, 162 non-heavy alcohol users, and 93 heavy alcohol users according to their alcohol consumption. GMV was compared between groups, and the correlations between GMV and alcohol use were investigated. A voxel-wise height threshold of p < 0.001 (uncorrected) was used, with small volume correction applied on cluster level. RESULTS: Heavy alcohol users had lower GMV in the bilateral thalamus than non-heavy alcohol users or never-drinkers. Heavy alcohol users also showed lower GMV in the left insula, compared to other groups. The higher the alcohol consumption among firefighters, the lower the GMV of the right thalamus. CONCLUSIONS: The results of this study show that heavy alcohol use has an association with lower GMV in several core regions, including the thalamus. When considering the impact of these brain regions on cognitive and behavioral control, our findings suggest a need for concern about heavy alcohol use among firefighters.

Altered Functional Connectivity of the Salience Network in Problematic Smartphone Users.

Smartphones provide convenience in everyday life. Smartphones, however, can elicit adverse effects when used excessively. The purpose of this study was to examine the underlying neurobiological alterations that arise from problematic smartphone use. We performed resting state seed-based functional connectivity (FC) analysis of 44 problematic smartphone users and 54 healthy controls. This analysis assessed the salience, central executive, default mode, and affective networks. Compared to controls, problematic smartphone users showed enhanced FC within the salience network and between the salience and default mode network. Moreover, we observed decreased FC between the salience and central executive network in problematic smartphone users, compared to controls. These results imply that problematic smartphone use is associated with aberrant FC in key brain networks. Our results suggest that changes in FC of key networks centered around the salience network might be associated with problematic smartphone use.