Patterns of comorbid PTSD, depression, alcohol use disorder, and insomnia symptoms in firefighters: A latent profile analysis.

BACKGROUND: Firefighters are an at-risk population for multiple psychiatric conditions, including posttraumatic stress disorder (PTSD), depression, alcohol use disorders (AUDs), and insomnia. These disorders are likely to co-occur; however, patterns of comorbidity have scarcely been investigated in firefighters. We aimed to identify subgroups of comorbidity of PTSD, depression, AUDs, and insomnia in a nationwide population of firefighters in South Korea. METHODS: A total of 54,054 firefighters responded to an online survey. Latent classes of comorbidity were categorized using latent profile analysis (LPA) based on the symptom scores of PTSD, depression, AUDs, and insomnia. Analysis of variance was performed to compare the characteristics of the identified classes, and multinomial logistic regression was conducted to examine whether anger reactions, resilience, and number of traumatic events predicted class membership. RESULTS: The LPA identified four subgroups: minimal symptoms (n = 42,948, 79.5 %), predominant PTSD (n = 2858, 5.3 %), subthreshold symptoms and comorbidity (n = 7003, 13.0 %), and high symptoms and comorbidity (n = 1245, 2.3 %). Three comorbidity classes were defined based on severity and one class showed predominant PTSD symptoms. Number of traumatic exposures predicted predominant PTSD, while resilience and anger reactions predicted severity of comorbidities. LIMITATIONS: The cross-sectional design and usage of self-reported questionnaires are limitations of this study. CONCLUSIONS: The severity of PTSD, depression, AUDs and insomnia tend to correlate and co-occur in firefighters. Our findings highlight the need to assess comorbid symptoms in firefighters and need to reduce anger reactions and enhance resilience in those with multiple comorbidities.

The Effectiveness of a Neurofeedback-Assisted Mindfulness Training Program Using a Mobile App on Stress Reduction in Employees: Randomized Controlled Trial.

BACKGROUND: Mindfulness-based training programs have consistently shown efficacy in stress reduction. However, questions regarding the optimal duration and most effective delivery methods remain. OBJECTIVE: This research explores a 4-week neurofeedback-assisted mindfulness training for employees via a mobile app. The study's core query is whether incorporating neurofeedback can amplify the benefits on stress reduction and related metrics compared with conventional mindfulness training. METHODS: A total of 92 full-time employees were randomized into 3 groups: group 1 received mobile mindfulness training with neurofeedback assistance (n=29, mean age 39.72 years); group 2 received mobile mindfulness training without neurofeedback (n=32, mean age 37.66 years); and group 3 were given self-learning paper materials on stress management during their first visit (n=31, mean age 38.65 years). The primary outcomes were perceived stress and resilience scales. The secondary outcomes were mindfulness awareness, emotional labor, occupational stress, insomnia, and depression. Heart rate variability and electroencephalography were measured for physiological outcomes. These measurements were collected at 3 different times, namely, at baseline, immediately after training, and at a 4-week follow-up. The generalized estimating equation model was used for data analysis. RESULTS: The 4-week program showed significant stress reduction (Wald χ22=107.167, P<.001) and improvements in psychological indices including resilience, emotional labor, insomnia, and depression. A significant interaction was observed in resilience (time × group, Wald χ42=10.846, P=.02). The post hoc analysis showed a statistically significant difference between groups 1 (least squares mean [LSM] 21.62, SE 0.55) and 3 (LSM 19.90, SE 0.61) at the posttraining assessment (P=.008). Group 1 showed a significant improvement (P<.001) at the posttraining assessment, with continued improvements through the 1-month follow-up assessment period (LSM 21.55, SE 0.61). Physiological indices were analyzed only for data of 67 participants (22 in group 1, 22 in group 2, and 23 in group 3) due to the data quality. The relaxation index (ratio of alpha to high beta power) from the right electroencephalography channel showed a significant interaction (time × group, Wald χ22=6.947, P=.03), with group 1 revealing the highest improvement (LSM 0.43, SE 0.15) compared with groups 2 (LSM -0.11, SE 0.10) and 3 (LSM 0.12, SE 0.10) at the 1-month follow-up assessment. CONCLUSIONS: The study demonstrated that the neurofeedback-assisted group achieved superior outcomes in resilience and relaxation during the 4-week mobile mindfulness program. Further research with larger samples and long-term follow-up is warranted. TRIAL REGISTRATION: ClinicalTrials.gov NCT03787407; https://clinicaltrials.gov/ct2/show/NCT03787407.

Nonsuicidal Self-Injury and Its Mediation Effect on the Association Between Posttraumatic Stress Disorder, Depression, and Suicidal Behavior in Firefighters.

OBJECTIVE: This study aimed to investigate the prevalence, clinical characteristics, and the correlates of nonsuicidal self-injury (NSSI) in firefighters. We also investigated the mediating role of NSSI frequency in the association between posttraumatic stress disorder (PTSD), depression, and suicidal behavior. METHODS: A total of 51,505 Korean firefighters completed a web-based self-reported survey, including demographic and occupational characteristics, NSSI, PTSD, depression, and suicidal behavior. Multivariable logistic regression analyses and serial mediation analyses were performed. RESULTS: The 1-year prevalence of NSSI was 4.67% in Korean firefighters. Female gender, the presence of recent traumatic experience, and PTSD and depression symptoms were correlated with NSSI. Serial mediation analyses revealed that NSSI frequency mediated the association between PTSD, depression, and suicidal behavior; it indicates more severe PTSD was sequentially associated with more severe depression symptoms and more frequent NSSI, leading to higher risk of suicidal behavior. CONCLUSION: NSSI is prevalent and may play a significant mediating role when PTSD is associated with suicidal behavior in firefighters. Our results imply the need for screening and early intervention of NSSI in firefighters.

Etomidate versus Propofol for Electroconvulsive Therapy in Patients with Major Depressive Disorders in Terms of Clinical Responses to Treatment: A Retrospective Analysis.

General anesthetic agents may be associated with the clinical efficacy of electroconvulsive therapy (ECT), as they may influence seizure quality and duration. Hence, a retrospective study was conducted to compare the clinical effects and seizure variables of etomidate and propofol during ECT. Patients treated with ECT under anesthesia with etomidate (n = 43) or propofol (n = 12) were retrospectively analyzed. Seizure variables (seizure duration, intensity, and threshold) and hemodynamic changes during ECT were assessed and recorded. Clinical responses to treatment were evaluated using the Clinical Global Impression scale and mood at discharge after the course of ECT. Adverse effects were also recorded. The demographic characteristics were similar between the two groups. There were no significant differences in the Clinical Global Impression scale scores, mood at discharge, and adverse effects between the two groups (p > 0.05); however, etomidate was associated with a significantly longer motor (42.0 vs. 23.65 s, p < 0.001) and electroencephalogram (51.8 vs. 33.5 s, p < 0.001) seizure duration than propofol. In conclusion, etomidate showed more favorable seizure profiles than propofol during ECT; however, both agents (etomidate and propofol) were associated with similar clinical efficacy profiles at discharge.

The mediation effect of depression and alcohol use disorders on the association between post-traumatic stress disorder and obstructive sleep apnea risk in 51,149 Korean firefighters: PTSD and OSA in Korean firefighters.

BACKGROUND: Post-traumatic stress disorder (PTSD) is shown to be linked to a higher risk of obstructive sleep apnea (OSA). Firefighters are at high risk for PTSD given the increased exposure to trauma.  However, the relationship between PTSD and OSA remains unclear in firefighters. Moreover, alcohol use disorders (AUDs) and depression - also common in firefighters - show a high comorbidity rate with both PTSD and OSA. The purpose of this study was to investigate the association of PTSD, depression, and AUDs with OSA in a national sample of Korean firefighters. METHODS: A total of 51,149 Korean firefighters completed self-reported questionnaires, assessing the severity of PTSD, OSA, depression, and alcohol misuse. Multivariable logistic regression and mediation analyses were performed. RESULTS: PTSD, depression, and AUDs were significantly associated with OSA. In the mediation analyses, PTSD had both direct and indirect effects mediated by depression and AUDs on OSA. Depression had both direct and indirect effects on OSA, with the latter mediated by AUDs. LIMITATIONS: First, high-risk of OSA was measured using the Berlin Questionnaire. Second, other medical comorbidities or mediation use were not considered. Third, self-report questionnaires were used for assessment, which are prone to subjectivity and recollection bias. Finally, the majority of the study population were male and all Korean, limiting generalizability. CONCLUSIONS: PTSD had both direct and indirect effects on OSA, mediated by depression and AUDs. In patients with both PTSD and OSA, depression and AUD symptoms should be screened routinely.

The prevalence and clinical impairment of subthreshold PTSD using DSM-5 criteria in a national sample of Korean firefighters.

BACKGROUND: Firefighters are at high risk for subthreshold and full-threshold posttraumatic stress disorder (PTSD) due to their frequent exposure to various traumatic events. Although individuals with subthreshold PTSD have increased impairment, often needing treatment, the rates of subthreshold PTSD in firefighters remain unknown. Moreover, there is currently no consensus regarding the definition of subthreshold PTSD. The purpose of this study was to investigate the rates and clinical impairment of subthreshold PTSD according to various definitions in a national sample of firefighters. METHODS: A total of 45,698 Korean firefighters completed self-reported questionnaires to assess the severity of PTSD symptoms, suicidal behavior, depression, alcohol use problems, and PTS-related functional impairment. Six different definitions used in the literature were used to calculate the prevalence rate of subthreshold PTSD. Multivariable linear regression and analysis of variance were performed to identify the relationship of subthreshold PTSD with suicidal behavior, depression, alcohol use problems, and PTS-related functional impairment. RESULTS: The rate of full-threshold PTSD was 2.63%, and the rate of subthreshold PTSD ranged from 1.79% to 17.98%. The individuals with subthreshold PTSD most commonly failed the D criteria, which included negative alterations in cognition and mood. Regardless of the definition used, subthreshold PTSD was found to be associated with increased suicidal behavior, depression, alcohol use problems, and functional impairment. CONCLUSIONS: Subthreshold PTSD appears to be equal to or more frequent than full-threshold PTSD in Korean firefighters and associated with various negative clinical outcomes. Further effort to detect and treat subthreshold PTSD in firefighters may be essential.

Prediction of individual responses to electroconvulsive therapy in patients with schizophrenia: Machine learning analysis of resting-state electroencephalography.

BACKGROUND: Electroconvulsive therapy (ECT) has strong efficacy in patients with treatment refractory schizophrenia. However, access to ECT has been limited by high costs, professional labor, treatment duration, and significant adverse effects. To provide support for the decision to perform ECT, we aimed to predict individual responses to ECT among patients with schizophrenia using machine learning analysis of resting-state electroencephalography (EEG). METHODS: Forty-seven patients diagnosed with schizophrenia or schizoaffective disorder with EEG recordings before the course of ECT were treated at Seoul National University Hospital. Among these patients, 29 were responders who showed scores of 3 or less on the Clinical Global Impression Severity scale after the course of ECT. Transfer entropy (TE), which represents information flow, was extracted from baseline EEG data and used as a feature. Feature selection was performed with four methods, including Random Subset Feature Selection (RSFS). The random forest classifier was used to predict individual ECT responses. RESULTS: The averaged TE, especially in frontal regions, was higher in ECT responders than in nonresponders. A predictive model using the RSFS method classified ECT responders and nonresponders with 85.3% balanced accuracy, 85.2% accuracy, 88.7% sensitivity, and 81.8% specificity. The positive predictive value was 82.6%, and the negative predictive value was 88.2%. CONCLUSIONS: The results of the current study suggest that higher effective connectivity in frontal areas may be associated with a favorable ECT response. Furthermore, personalized decisions to perform ECT in clinical practice could be augmented by resting-state EEG biomarkers of the ECT response in schizophrenia patients.

Prevalence and correlates of suicidal ideation in Korean firefighters: a nationwide study.

BACKGROUND: It is generally known that firefighters are at increased risk of suicide. However, the prevalence and correlates of suicidal ideation in firefighters have not been thoroughly described to date. The aim of this study was to measure the 1-year prevalence of suicidal ideation in firefighters and to investigate the correlates of past-year suicidal ideation among the demographic, occupational and clinical characteristics. METHOD: A web-based survey was conducted using a self-reported questionnaire. A total of 45,698 Korean firefighters were included for analysis. The prevalence of suicidal ideation in the past year was calculated and its correlates were elucidated using a multivariable logistic regression analysis. RESULTS: The 1-year prevalence of suicidal ideation was 10.66% in Korean firefighters. Recent traumatic experience, high levels of occupational stress from physical work environment and emotional labor, as well as current duty of officer were significant correlates of suicidal ideation in the previous year, even after controlling for the effects of PTSD and depressive symptoms. With respect to demographic factors, female gender and marital status of divorced/separated/widowed were identified to be associated with suicidal ideation in the previous year among firefighters. CONCLUSIONS: The 1-year prevalence of suicidal ideation was high in Korean firefighters and was associated with various occupational factors as well as psychiatric symptoms. Early detection and management of these risk factors could reduce the risk of suicidal ideation in firefighters.

Multiclass Classification of Word Imagination Speech with Hybrid Connectivity Features.

OBJECTIVE: In this study, electroencephalography data of imagined words were classified using four different feature extraction approaches. Eight subjects were recruited for the recording of imagination with five different words, namely; 'go', 'back', 'left', 'right', and 'stop'.

Corrigendum: Multi-modal, Multi-measure, and Multi-class Discrimination of ADHD with Hierarchical Feature Extraction and Extreme Learning Machine Using Structural and Functional Brain MRI.

[This corrects the article on p. 157 in vol. 11, PMID: 28420972.].

Multi-modal, Multi-measure, and Multi-class Discrimination of ADHD with Hierarchical Feature Extraction and Extreme Learning Machine Using Structural and Functional Brain MRI.

Structural and functional MRI unveil many hidden properties of the human brain. We performed this multi-class classification study on selected subjects from the publically available attention deficit hyperactivity disorder ADHD-200 dataset of patients and healthy children. The dataset has three groups, namely, ADHD inattentive, ADHD combined, and typically developing. We calculated the global averaged functional connectivity maps across the whole cortex to extract anatomical atlas parcellation based features from the resting-state fMRI (rs-fMRI) data and cortical parcellation based features from the structural MRI (sMRI) data. In addition, the preprocessed image volumes from both of these modalities followed an ANOVA analysis separately using all the voxels. This study utilized the average measure from the most significant regions acquired from ANOVA as features for classification in addition to the multi-modal and multi-measure features of structural and functional MRI data. We extracted most discriminative features by hierarchical sparse feature elimination and selection algorithm. These features include cortical thickness, image intensity, volume, cortical thickness standard deviation, surface area, and ANOVA based features respectively. An extreme learning machine performed both the binary and multi-class classifications in comparison with support vector machines. This article reports prediction accuracy of both unimodal and multi-modal features from test data. We achieved 76.190% (p < 0.0001) classification accuracy in multi-class settings as well as 92.857% (p < 0.0001) classification accuracy in binary settings. In addition, we found ANOVA-based significant regions of the brain that also play a vital role in the classification of ADHD. Thus, from a clinical perspective, this multi-modal group analysis approach with multi-measure features may improve the accuracy of the ADHD differential diagnosis.

EEG-Based Prediction of Epileptic Seizures Using Phase Synchronization Elicited from Noise-Assisted Multivariate Empirical Mode Decomposition.

In this study, we examined the phase locking value (PLV) for seizure prediction, particularly, in the gamma frequency band. We prepared simulation data and 65 clinical cases of seizure. In addition, various filtering algorithms including bandpass filtering, empirical mode decomposition, multivariate empirical mode decomposition and noise-assisted multivariate empirical mode decomposition (NA-MEMD) were used to decompose spectral components from the data. Moreover, in the case of clinical data, the PLVs were used to classify between interictal and preictal stages using a support vector machine. The highest PLV was achieved with NA-MEMD with 0-dB white noise algorithm (0.9988), which exhibited statistically significant differences compared to other filtering algorithms. Moreover, the classification rate was the highest for the NA-MEMD with 0-dB algorithm (83.17%). In terms of frequency components, examining the gamma band resulted in the highest classification rates for all algorithms, compared to other frequency bands such as theta, alpha, and beta bands. We found that PLVs calculated with the NA-MEMD algorithm could be used as a potential biological marker for seizure prediction. Moreover, the gamma frequency band was useful for discriminating between interictal and preictal stages.

Multiclass Classification for the Differential Diagnosis on the ADHD Subtypes Using Recursive Feature Elimination and Hierarchical Extreme Learning Machine: Structural MRI Study.

The classification of neuroimaging data for the diagnosis of certain brain diseases is one of the main research goals of the neuroscience and clinical communities. In this study, we performed multiclass classification using a hierarchical extreme learning machine (H-ELM) classifier. We compared the performance of this classifier with that of a support vector machine (SVM) and basic extreme learning machine (ELM) for cortical MRI data from attention deficit/hyperactivity disorder (ADHD) patients. We used 159 structural MRI images of children from the publicly available ADHD-200 MRI dataset. The data consisted of three types, namely, typically developing (TDC), ADHD-inattentive (ADHD-I), and ADHD-combined (ADHD-C). We carried out feature selection by using standard SVM-based recursive feature elimination (RFE-SVM) that enabled us to achieve good classification accuracy (60.78%). In this study, we found the RFE-SVM feature selection approach in combination with H-ELM to effectively enable the acquisition of high multiclass classification accuracy rates for structural neuroimaging data. In addition, we found that the most important features for classification were the surface area of the superior frontal lobe, and the cortical thickness, volume, and mean surface area of the whole cortex.

Vowel Imagery Decoding toward Silent Speech BCI Using Extreme Learning Machine with Electroencephalogram.

The purpose of this study is to classify EEG data on imagined speech in a single trial. We recorded EEG data while five subjects imagined different vowels, /a/, /e/, /i/, /o/, and /u/. We divided each single trial dataset into thirty segments and extracted features (mean, variance, standard deviation, and skewness) from all segments. To reduce the dimension of the feature vector, we applied a feature selection algorithm based on the sparse regression model. These features were classified using a support vector machine with a radial basis function kernel, an extreme learning machine, and two variants of an extreme learning machine with different kernels. Because each single trial consisted of thirty segments, our algorithm decided the label of the single trial by selecting the most frequent output among the outputs of the thirty segments. As a result, we observed that the extreme learning machine and its variants achieved better classification rates than the support vector machine with a radial basis function kernel and linear discrimination analysis. Thus, our results suggested that EEG responses to imagined speech could be successfully classified in a single trial using an extreme learning machine with a radial basis function and linear kernel. This study with classification of imagined speech might contribute to the development of silent speech BCI systems.