Objective: Maumgyeol Basic service is a mental health evaluation and grade scoring software using the 2 channels EEG and photoplethysmogram (PPG). This service is supposed to assess potential at-risk groups with mental illness more easily, rapidly, and reliably. This study aimed to evaluate the clinical implication of the Maumgyeol Basic service. Methods: One hundred one healthy controls and 103 patients with a psychiatric disorder were recruited. Psychological evaluation (Mental Health Screening for Depressive Disorders [MHS-D], Mental Health Screening for Anxiety Disorders [MHS-A], cognitive stress response scale [CSRS], 12-item General Health Questionnaire [GHQ-12], Clinical Global Impression [CGI]) and digit symbol substitution test (DSST) were applied to all participants. Maumgyeol brain health score and Maumgyeol mind health score were calculated from 2 channel frontal EEG and PPG, respectively. Results: Participants were divided into three groups: Maumgyeol Risky, Maumgyeol Good, and Maumgyeol Usual. The Maumgyeol mind health scores, but not brain health scores, were significantly lower in the patients group compared to healthy controls. Maumgyeol Risky group showed significantly lower psychological and cognitive ability evaluation scores than Maumgyeol Usual and Good groups. Maumgyel brain health score showed significant correlations with CSRS and DSST. Maumgyeol mind health score showed significant correlations with CGI and DSST. About 20.6% of individuals were classified as the No Insight group, who had mental health problems but were unaware of their illnesses. Conclusion This study suggests that the Maumgyeol Basic service can provide important clinical information about mental health and be used as a meaningful digital mental healthcare monitoring solution to prevent symptom aggravation.

Differentiating between the diagnoses of mood disorders and other psychiatric disorders, and predicting treatment response in depression has long been a concern for clinicians. Machine learning (ML) is one part of artificial intelligence that focuses on instructing computers to mimic the cognitive abilities of the human brain through training. This study will review the research on the use of ML techniques to differentiate diagnoses and predict treatment responses in mood disorders based on electroencephalography (EEG) data. There have been several attempts to differentiate between the diagnoses of bipolar disorder and major depressive disorder , mood disorders, and other psychiatric disorders using ML techniques found on EEG markers. Previous studies have shown that accuracy varies depending on which EEG markers are used, the sample size, and the ML technique. Also, precise and improved ML approaches can be developed by adapting the various feature selection and validation methods that reflect each disease’s characteristics. Although ML faces some limitations and challenges in solving for consistent and improved accuracy in the diagnosis and treatment of mood disorders, it has a great potential to understand mood disorders better and provide valuable tools to personalize both identification and treatment.

Differentiating between the diagnoses of mood disorders and other psychiatric disorders, and predicting treatment response in depression has long been a concern for clinicians. Machine learning (ML) is one part of artificial intelligence that focuses on instructing computers to mimic the cognitive abilities of the human brain through training. This study will review the research on the use of ML techniques to differentiate diagnoses and predict treatment responses in mood disorders based on electroencephalography (EEG) data. There have been several attempts to differentiate between the diagnoses of bipolar disorder and major depressive disorder , mood disorders, and other psychiatric disorders using ML techniques found on EEG markers. Previous studies have shown that accuracy varies depending on which EEG markers are used, the sample size, and the ML technique. Also, precise and improved ML approaches can be developed by adapting the various feature selection and validation methods that reflect each disease’s characteristics. Although ML faces some limitations and challenges in solving for consistent and improved accuracy in the diagnosis and treatment of mood disorders, it has a great potential to understand mood disorders better and provide valuable tools to personalize both identification and treatment.

Differentiating between the diagnoses of mood disorders and other psychiatric disorders, and predicting treatment response in depression has long been a concern for clinicians. Machine learning (ML) is one part of artificial intelligence that focuses on instructing computers to mimic the cognitive abilities of the human brain through training. This study will review the research on the use of ML techniques to differentiate diagnoses and predict treatment responses in mood disorders based on electroencephalography (EEG) data. There have been several attempts to differentiate between the diagnoses of bipolar disorder and major depressive disorder , mood disorders, and other psychiatric disorders using ML techniques found on EEG markers. Previous studies have shown that accuracy varies depending on which EEG markers are used, the sample size, and the ML technique. Also, precise and improved ML approaches can be developed by adapting the various feature selection and validation methods that reflect each disease’s characteristics. Although ML faces some limitations and challenges in solving for consistent and improved accuracy in the diagnosis and treatment of mood disorders, it has a great potential to understand mood disorders better and provide valuable tools to personalize both identification and treatment.

Differentiating between the diagnoses of mood disorders and other psychiatric disorders, and predicting treatment response in depression has long been a concern for clinicians. Machine learning (ML) is one part of artificial intelligence that focuses on instructing computers to mimic the cognitive abilities of the human brain through training. This study will review the research on the use of ML techniques to differentiate diagnoses and predict treatment responses in mood disorders based on electroencephalography (EEG) data. There have been several attempts to differentiate between the diagnoses of bipolar disorder and major depressive disorder , mood disorders, and other psychiatric disorders using ML techniques found on EEG markers. Previous studies have shown that accuracy varies depending on which EEG markers are used, the sample size, and the ML technique. Also, precise and improved ML approaches can be developed by adapting the various feature selection and validation methods that reflect each disease’s characteristics. Although ML faces some limitations and challenges in solving for consistent and improved accuracy in the diagnosis and treatment of mood disorders, it has a great potential to understand mood disorders better and provide valuable tools to personalize both identification and treatment.

Objective: Frontal alpha asymmetry (FAA) of electroencephalography (EEG) has been studied to differentiate patients with major depressive disorder (MDD) from healthy controls (HC). However, inconsistent results have been obtained thus far. Suicidal ideation (SI) has been known to alter frontal lobe activity, and could be an important covariate in FAA studies. This study aimed to explore the influence of FAA on the relationship among MDD patients with SI and without SI, and HC. Methods: Sixty-seven patients with MDD (44 without and 23 with SI) and 60 HCs were recruited. Resting state EEG was recorded with their eyes open, and FAA as a lateralized index of alpha power was calculated in the frontal brain region. Hamilton Rating Scale for Anxiety and Depression scores were estimated. Results: FAA was higher (increased alpha power in the left frontal region) in the MDD group than in the HC group. The FAA was lower (reduced alpha power in the left frontal region) in MDD patients with SI than in MDD patients without SI. The severity of depression and anxiety symptoms were significantly correlated with FAA only in MDD patients with SI. SI moderated the effects of depressive symptom on FAA in the MDD group. Conclusion Our results suggest that SI is a clinically important moderator of frontal alpha asymmetry in patients with MDD.

OBJECTIVE: The aim of this study is to examine social functioning in patients with schizophrenia and bipolar disorder and explore the psychological and neurophysiological predictors of social functioning. METHODS: Twenty-seven patients with schizophrenia and thirty patients with bipolar disorder, as well as twenty-five healthy controls, completed measures of social functioning (questionnaire of social functioning), neurocognition (Verbal fluency, Korean-Auditory Verbal Learning Test), and social cognition (basic empathy scale and Social Attribution Task-Multiple Choice), and the childhood trauma questionnaire (CTQ). For neurophysiological measurements, mismatch negativity and heart rate variability (HRV) were recorded from all participants. Multiple hierarchical regression was performed to explore the impact of factors on social functioning. RESULTS: The results showed that CTQ-emotional neglect significantly predicted social functioning in schizophrenia group, while HRV-high frequency significantly predicted social functioning in bipolar disorder patients. Furthermore, emotional neglect and HRV-HF still predicted social functioning in all of the subjects after controlling for the diagnostic criteria. CONCLUSION: Our results implicated that even though each group has different predictors of social functioning, early traumatic events and HRV could be important indicators of functional outcome irrespective of what group they are.
Bipolar Disorder ; Cognition ; Empathy ; Heart Rate ; Humans ; Schizophrenia ; Verbal Learning

Objective: Mismatch negativity (MMN) is known to be associated with neuro-cognition and functional outcomes. Remission and recovery rates are related to the neuro-cognition of patients with schizophrenia. The present study explored the relationship of MMN with remission in patients with schizophrenia. Methods: Forty patients with schizophrenia were recruited and divided into two groups, with or without remission, according to the Remission in Schizophrenia Working Group criteria (RSWGcr). Symptom severity (Positive and Negative Syndrome Scale, PANSS), cognitive function, functional outcome, and MMN of the patients were evaluated. A regression analysis was used to identify the factors that significantly predicted symptom improvement and remission including MMN at frontal site assessed at baseline, and anticipated clinical variables as predictive factors. Results: MMN amplitudes in frontal sites were further decreased in the groups without remission compared to the groups with remission. MMN amplitude was significantly correlated with measures of symptom change and functional outcome measurements in patients with schizophrenia. Regression analysis revealed that symptom severity and MMN significantly predicted remission in patients with schizophrenia. Symptom improvement significantly predicted PANSS at baseline, illness duration, and antipsychotic dose, as did MMN amplitude at frontal site. Conclusion Our results suggest that MMN reflected symptom improvement and remission in patients with schizophrenia. MMN indices appear to be promising candidates as predictive factors for schizophrenia remission.

Objective: Alpha wave of electroencephalography (EEG) is known to be related to behavioral inhibition. Both the alpha wave and default mode network (DMN) are predominantly activated during resting-state. To study the mechanisms of the trait inhibition, this research investigating the relations among alpha wave, DMN and behavioral inhibition in resting-state. Methods: We explored the relationship among behavioral inhibition, resting-state alpha power, and DMN. Resting-state EEG, behavioral inhibition/behavioral activation scale (BIS/BAS), Barratt impulsivity scale, and no-go accuracy were assessed in 104 healthy individuals. Three groups (i.e., participants with low/middle/high band power) were formed based on the relative power of each total-alpha, low-alpha (LA), and high-alpha band. Source-reconstructed EEG and functional network measures of 25 DMN regions were calculated. Results: Significant differences and correlations were found based on LA band power alone. The high LA group had significantly greater BIS, clustering coefficient, efficiency, and strength, and significantly lower path length than low/middle LA group. BIS score showed a significant correlation with functional network measures of DMN. Conclusion Our study revealed that LA power is related to behavioral inhibition and functional network measures of DMN of LA band appear to represent significant inhibitory function.

OBJECTIVE: Based on the constant associations made between major depressive disorder (MDD) and alpha asymmetry, and MDD and suicide, this study aimed to examine the relationship between frontal alpha asymmetry and suicide in MDD patients. METHODS: Sixty-six MDD patients, of whom fifteen were male and fifty-one were female, were recruited. Independent groups were created based on the median score of frontal alpha asymmetry: the left dominant (LD) group and the right dominant (RD) group. The alpha band (8–12 Hz) and its sub-bands (i.e., low alpha band: 8–10 Hz; high alpha band: 10–12 Hz) were of interest. Source level alpha asymmetry was calculated as well. RESULTS: Suicidal behavior was positively correlated with the asymmetry indices of the low alpha band and the alpha band in the LD group and that of the high alpha band in the RD group. Source level analysis revealed positive correlations between suicidal behavior and the asymmetry index of the low alpha band in the LD group. CONCLUSION: Frontal alpha asymmetry, especially that of the low alpha band, might reflect the cognitive deficits associated with suicidal behaviors in MDD patients.
Cognition Disorders ; Depression ; Depressive Disorder, Major ; Electroencephalography ; Female ; Humans ; Male ; Suicide