OBJECTIVE: Previous studies indicate that emotion regulation problems in major depressive disorder (MDD) may be caused by difficulties in preferring useful emotions according to their goals. We investigated expected emotional usefulness and emotional preference in individuals with MDD (MDDs) and healthy controls (HCs). METHODS: Participants were given an interpersonal scenario with two different goals (confrontation and collaboration) and rated their willingness to participate in emotion-provoking activities and the expected usefulness of a particular emotion. RESULTS: MDDs were similar to HCs in expected emotional usefulness but showed different patterns of emotional preference. HCs preferred happiness to negative emotions across goals whereas MDDs did not show such pattern. In addition, HCs displayed goal-appropriate preferences whereas MDDs did not prefer certain emotions for specific goals. CONCLUSION: Although MDDs seemed to understand how useful an emotion can be, they did not show preference for goal-appropriate emotions. Interventions should address why MDDs have difficulty engaging in goal-appropriate emotions despite having full knowledge of the utility of emotions in achieving goals.
Depression ; Depressive Disorder, Major* ; Happiness

Objective: Loudness of dependence of the auditory evoked potential (LDAEP) is an electroencephalogram-based measure that represents amplitude changes of auditory evoked potentials in primary auditory cortex. Several narrative reviews argued that pre-treatment LDAEP values predict responses to selective serotonin reuptake inhibitors (SSRIs). This study aims to quantify the overall relationship between baseline LDAEP values and treatment response to SSRIs in patients with depression and generalized anxiety disorders, evidenced by clinical symptoms reductions, across multiple studies. Methods: In our meta-analysis, seven articles with a total sample of 241 patients were included. Results: Our results showed that stronger baseline LDAEP values predicted favorable response to SSRIs for depression and anxiety, with a moderate effect size. Conclusion The current results support the idea that LDAEP is a promising biomarker for SSRIs treatment prediction in patients with depression and generalized anxiety disorder.

Objective: Loudness of dependence of the auditory evoked potential (LDAEP) is an electroencephalogram-based measure that represents amplitude changes of auditory evoked potentials in primary auditory cortex. Several narrative reviews argued that pre-treatment LDAEP values predict responses to selective serotonin reuptake inhibitors (SSRIs). This study aims to quantify the overall relationship between baseline LDAEP values and treatment response to SSRIs in patients with depression and generalized anxiety disorders, evidenced by clinical symptoms reductions, across multiple studies. Methods: In our meta-analysis, seven articles with a total sample of 241 patients were included. Results: Our results showed that stronger baseline LDAEP values predicted favorable response to SSRIs for depression and anxiety, with a moderate effect size. Conclusion The current results support the idea that LDAEP is a promising biomarker for SSRIs treatment prediction in patients with depression and generalized anxiety disorder.

The aim of this article was to determine clinical significance of neurofeedback training of children with attention-deficit/hyperactivity disorder (ADHD). Many previous studies showed that the characteristic pattern of electroencephalogram (EEG) can be found in ADHD children. Neurofeedback training is developed based on this characteristic EEG pattern. Theta power and theta/beta power ratio have been known as useful indicators of neurofeedback training. However, in Korea, direct analysis of EEG change patterns before and after neurofeedback training is very rare. We reviewed the current findings of neurofeedback training of children with ADHD, and its clinical implications were discussed.
Child* ; Electroencephalography ; Humans ; Korea ; Neurofeedback*

Objective: The development of individual subtypes based on biomarkers offers a cost-effective and timely avenue to comprehending individual differences pertaining to mental health, independent from individuals’ subjective insights. Incorporating 2-channel electroencephalography (EEG) and photoplethysmogram (PPG), we sought to establish a subtype classification system with clinical relevance. Methods: One hundred healthy participants and 99 patients with psychiatric disorders were recruited. Classification thresholds were determined using the EEG and PPG data from 2,278 individuals without mental disorders, serving to classify subtypes in our sample of 199 participants. Multivariate analysis of variance was applied to examine psychological distinctions among these subtypes. K-means clustering was employed to verify the classification system. Results: The distribution of subtypes differed between healthy participants and those with psychiatric disorders. Cognitive abilities were contingent upon brain subtypes, while mind subtypes exhibited significant differences in symptom severity, overall health, and cognitive stress. K-means clustering revealed that the results of our theory-based classification and data-driven classification are comparable. The synergistic assessment of both brain and mind subtypes was also explored. Conclusion Our subtype classification system offers a concise means to access individuals’ mental health. The utilization of EEG and PPG signals for subtype classification offers potential for the future of digital mental healthcare.