Published papers

Copyright © The Author(s) 2020. Published by Cambridge University Press. BackgroundElectroconvulsive therapy (ECT) is the most effective antidepressant treatment for severe depression. Although recent structural magnetic resonance imaging (MRI) studies have consistently reported ECT-induced hippocampal volume increases, most studies did not find the association of the hippocampal volume changes with clinical improvement. To understand the underlying mechanisms of ECT action, we aimed to identify the longitudinal effects of ECT on hippocampal functional connectivity (FC) and their associations with clinical improvement.MethodsResting-state functional MRI was acquired before and after bilateral ECT in 27 depressed individuals. A priori hippocampal seed-based FC analysis and a data-driven multivoxel pattern analysis (MVPA) were conducted to investigate FC changes associated with clinical improvement. The statistical threshold was set at cluster-level false discovery rate-corrected p < 0.05.ResultsDepressive symptom improvement after ECT was positively associated with the change in the right hippocampus-ventromedial prefrontal cortex FC, and negatively associated with the right hippocampus-superior frontal gyrus FC. MVPA confirmed the results of hippocampal seed-based analyses and identified the following additional clusters associated with clinical improvement following ECT: the thalamus, the sensorimotor cortex, and the precuneus.ConclusionsECT-induced change in the right frontotemporal connectivity and thalamocortical connectivity, and changes in the nodes of the default mode network were associated with clinical improvement. Modulation of these networks may explain the underlying mechanisms by which ECT exert its potent and rapid antidepressant effect.

Psychotic major depression (PMD) is hypothesized to be a distinct clinical entity from nonpsychotic major depression (NPMD). However, neurobiological evidence supporting this notion is scarce. The aim of this study is to identify gray matter volume (GMV) differences between PMD and NPMD and their longitudinal change following electroconvulsive therapy (ECT). Structural magnetic resonance imaging (MRI) data from 8 independent sites in the Global ECT-MRI Research Collaboration (GEMRIC) database (n = 108; 56 PMD and 52 NPMD; mean age 71.7 in PMD and 70.2 in NPMD) were analyzed. All participants underwent MRI before and after ECT. First, cross-sectional whole-brain voxel-wise GMV comparisons between PMD and NPMD were conducted at both time points. Second, in a flexible factorial model, a main effect of time and a group-by-time interaction were examined to identify longitudinal effects of ECT on GMV and longitudinal differential effects of ECT between PMD and NPMD, respectively. Compared with NPMD, PMD showed lower GMV in the prefrontal, temporal and parietal cortex before ECT; PMD showed lower GMV in the medial prefrontal cortex (MPFC) after ECT. Although there was a significant main effect of time on GMV in several brain regions in both PMD and NPMD, there was no significant group-by-time interaction. Lower GMV in the MPFC was consistently identified in PMD, suggesting this may be a trait-like neural substrate of PMD. Longitudinal effect of ECT on GMV may not explain superior ECT response in PMD, and further investigation is needed.

Repetitive transcranial magnetic stimulation (rTMS) and intermittent theta-burst stimulation (iTBS) are evidenced-based treatments for patients with major depressive disorder (MDD) who fail to respond to standard first-line therapies. However, although various TMS protocols have been proven to be clinically effective, the response rate varies across clinical applications due to the heterogeneity of real-world psychiatric comorbidities, such as generalized anxiety disorder, posttraumatic stress disorder, panic disorder, or substance use disorder, which are often observed in patients with MDD. Therefore, individualized treatment approaches are important to increase treatment response by assigning a given patient to the most optimal TMS treatment protocol based on his or her individual profile. This literature review summarizes different rTMS or TBS protocols that have been applied in researches investigating MDD patients with certain psychiatric comorbidities and discusses biomarkers that may be used to predict rTMS treatment response. Furthermore, we highlight the need for the validation of neuroimaging and electrophysiological biomarkers associated with rTMS treatment responses. Finally, we discuss on which directions future efforts should focus for developing the personalization of the treatment of depression with rTMS or iTBS.

Background: Although several guidelines indicate that daily pharmacotherapy is an important part of the treatment of schizophrenia and major depressive disorder, there are few reports regarding pro re nata (PRN) prescriptions. The purpose of this study is to clarify the characteristics of patients receiving psychotropic PRN prescription for the treatment of schizophrenia and major depressive disorder. Method: We used data from ‘the effectiveness of guideline for dissemination and education in psychiatric treatment’ (EGUIDE) project to evaluate the presence or absence of psychotropic PRN prescription at the time of discharge, the age and sex of patients receiving PRN prescription for each diagnosis, and the association between PRN prescription and regular daily psychotropics. Results: The psychotropic PRN prescription ratio was 29.9% among 2617 patients with schizophrenia and 31.1% among 1248 patients with major depressive disorder at discharge. In schizophrenia, the psychotropic PRN prescription ratio was 21.6% for patients aged 65 years or older, which was lower than that of all other age groups. In major depressive disorder, the psychotropic PRN prescription ratio was 34.2% for female patients, which was significantly higher than that for male patients (25.5%). In schizophrenia, there was an association between psychotropic PRN prescription and regular use of multiple psychotropic medications. Conclusions: Psychotropic PRN prescription was less common in elderly patients with schizophrenia and more common in female patients with major depressive disorder. In schizophrenia, psychotropic PRN prescription led to polypharmacy of psychotropics. Further studies are needed to accumulate evidence and to provide education on appropriate PRN prescriptions.

Background:. High-quality comprehensive data on short-/long-term physical/mental health effects of the COVID-19 pandemic are needed. Methods:. The Collaborative Outcomes study on Health and Functioning during Infection Times (COH-FIT) is an international, multi-language (n=30) project involving >230 investigators from 49 countries/territories/regions, endorsed by national/international professional associations. COH-FIT is a multi-wave, on-line anonymous, cross-sectional survey [wave 1: 04/2020 until the end of the pandemic, 12 months waves 2/3 starting 6/24 months threreafter] for adults, adolescents (14-17), and children (6-13), utilizing non-probability/snowball and representative sampling. COH-FIT aims to identify non-modifiable/modifiable risk factors/treatment targets to inform prevention/intervention programs to improve social/health outcomes in the general population/vulnerable subgrous during/after COVID-19. In adults, co-primary outcomes are change from pre-COVID-19 to intra-COVID-19 in well-being (WHO-5) and a composite psychopathology P-Score. Key secondary outcomes are a P-extended score, global mental and physical health. Secondary outcomes include health-service utilization/functioning, treatment adherence, functioning, symptoms/behaviors/emotions, substance use, violence, among others. Results:. Starting 04/26/2020, up to 14/07/2021 >151,000 people from 155 countries/territories/regions and six continents have participated. Representative samples of ≥1,000 adults have been collected in 15 countries. Overall, 43.0% had prior physical disorders, 16.3% had prior mental disorders, 26.5% were health care workers, 8.2% were aged ≥65 years, 19.3% were exposed to someone infected with COVID-19, 76.1% had been in quarantine, and 2.1% had been COVID 19-positive. Limitations:. Cross-sectional survey, preponderance of non-representative participants. Conclusions:. Results from COH-FIT will comprehensively quantify the impact of COVID-19, seeking to identify high-risk groups in need for acute and long-term intervention, and inform evidence-based health policies/strategies during this/future pandemics.

Introduction: Mental disorders are a leading cause of disability worldwide. Depression has a significant impact in the field of occupational health because it is particularly prevalent during working age. On the other hand, there are a growing number of studies on the relationship between “well-being” and employee productivity. To promote healthy and productive workplaces, this study aims to develop a technique to quantify stress and well-being in a way that does not disturb the workplace. Methods and analysis: This is a single-arm prospective observational study. The target population is adult (>20 years old) workers at companies that often engage in desk work; specifically, a person who sits in front of a computer for at least half their work hours. The following data will be collected: (a) participants' background characteristics; (b) participants' biological data during the 4-week observation period using sensing devices such as a camera built into the computer (pulse wave data extracted from the facial video images), a microphone built into their work computer (voice data), and a wristband-type wearable device (electrodermal activity data, body motion data, and body temperature); (c) stress, well-being, and depression rating scale assessment data. The analysis workflow is as follows: (1) primary analysis, comprised of using software to digitalize participants' vital information; (2) secondary analysis, comprised of examining the relationship between the quantified vital data from (1), stress, well-being, and depression; (3) tertiary analysis, comprised of generating machine learning algorithms to estimate stress, well-being, and degree of depression in relation to each set of vital data as well as multimodal vital data. Discussion: This study will evaluate digital phenotype regarding stress and well-being of white-collar workers over a 4-week period using persistently obtainable biomarkers such as heart rate, acoustic characteristics, body motion, and electrodermal activity. Eventually, this study will lead to the development of a machine learning algorithm to determine people's optimal levels of stress and well-being. Ethics and dissemination: Collected data and study results will be disseminated widely through conference presentations, journal publications, and/or mass media. The summarized results of our overall analysis will be supplied to participants. Registration: UMIN000036814

Exposure therapy is a mainstream of treatment for social anxiety disorder (SAD). However, effort and time are required to recreate interpersonal situations that produce moderate anxiety. On the other hand, virtual reality exposure therapy can easily control anxiety-inducing conditions and allow for graduated exposure. However, artificial intelligence and animations that speak as naturally as actual humans are not yet practical, adding to the limitations of these treatments. The authors propose the use of a virtual reality technology that can transform facial expressions into smiling or sad faces in real time and display them on a monitor, potentially solving the above-mentioned problems associated with virtual reality animations. This feasibility study was conducted to determine whether this system can be safely applied to the treatment of SAD patients. A total of four SAD patients received 16 exposure therapy sessions led by an experienced therapist over a monitor; throughout the sessions, the facial expressions of the therapist were modified using software to display expressions ranging from smiling to sad on the monitor that was being viewed by the patient. Client satisfaction, treatment alliance, and symptom assessments were then conducted. Although one patient dropped out of the study, treatment satisfaction and treatment alliance were scored high in all the cases. In two of the four cases, the improvement in symptoms was sustained over time. Exposure therapy in which the interviewer’s facial expressions are modified to induce appropriate levels of anxiety in the patient can be safely used for the treatment of SAD patients and may be effective for some patients.

The Effectiveness of Guidelines for Dissemination and Education in psychiatric treatment (EGUIDE) project, which is a nationwide dissemination and implementation program for clinical practice guidelines (CPGs) in the field of psychiatry, is currently ongoing. In the current study, a subjective assessment of the participants in the EGUIDE programs was assessed using a questionnaire. Then, the relationships between the subjective assessment, the characteristics of the participants, and the clinical knowledge of the CPGs were evaluated. More than 90% of the participants gave a high rating for the components of content, recommendation, knowledge, skill, and adherence, but not for the component of confidence. A positive correlation was found between years of professional experience and the score of confidence. These results suggest that it may be necessary to apply the knowledge and skills of CPGs obtained in the education programs into practice to increase confidence in the proper use of psychiatric therapies based on CPGs.

Background The COVID-19 pandemic has transformed healthcare significantly and telepsychiatry is now the primary means of treatment in some countries. Aims To compare the efficacy of telepsychiatry and face-to-face treatment. Method A comprehensive meta-analysis comparing telepsychiatry with face-to-face treatment for psychiatric disorders. The primary outcome was the mean change in the standard symptom scale scores used for each psychiatric disorder. Secondary outcomes included all meta-analysable outcomes, such as all-cause discontinuation and safety/tolerability. Results We identified 32 studies (n = 3592 participants) across 11 mental illnesses. Disease-specific analyses showed that telepsychiatry was superior to face-to-face treatment regarding symptom improvement for depressive disorders (k = 6 studies, n = 561; standardised mean difference s.m.d. = -0.325, 95% CI -0.640 to -0.011, P = 0.043), whereas face-to-face treatment was superior to telepsychiatry for eating disorder (k = 1, n = 128; s.m.d. = 0.368, 95% CI 0.018-0.717, P = 0.039). No significant difference was seen between telepsychiatry and face-to-face treatment when all the studies/diagnoses were combined (k = 26, n = 2290; P = 0.248). Telepsychiatry had significantly fewer all-cause discontinuations than face-to-face treatment for mild cognitive impairment (k = 1, n = 61; risk ratio RR = 0.552, 95% CI 0.312-0.975, P = 0.040), whereas the opposite was seen for substance misuse (k = 1, n = 85; RR = 37.41, 95% CI 2.356-594.1, P = 0.010). No significant difference regarding all-cause discontinuation was seen between telepsychiatry and face-to-face treatment when all the studies/diagnoses were combined (k = 27, n = 3341; P = 0.564). Conclusions Telepsychiatry achieved a symptom improvement effect for various psychiatric disorders similar to that of face-to-face treatment. However, some superiorities/inferiorities were seen across a few specific psychiatric disorders, suggesting that its efficacy may vary according to disease type.

Background: Several guidelines recommend monotherapy in pharmacotherapy for schizophrenia and major depressive disorder. The content of regular prescriptions has been reported in several studies, but not enough research has been conducted on the content of pharmacotherapy, including pro re nata (PRN) medications. The purpose of this study was to evaluate the content of pharmacotherapy, including PRN medications, and to clarify the relationship with regular prescriptions. Methods: We used data from the “Effectiveness of Guidelines for Dissemination And Education in psychiatric treatment” (EGUIDE) project to investigate the presence or absence of PRN psychotropic medications at discharge for each drug category. We compared the PRN psychotropic prescription ratio at discharge by diagnosis for each drug category. The antipsychotic monotherapy ratio and no prescription ratio of other psychotropics for schizophrenia at discharge and the antidepressant monotherapy ratio and no prescription ratio of other psychotropics for major depressive disorder at discharge were calculated for each regular prescription, including PRN psychotropic medications, as quality indicators (QIs). Spearman's rank correlation test was performed for QI values of regular prescriptions and the QI ratio between regular prescriptions and prescriptions including PRN medications for each diagnosis. Results: The PRN psychotropic prescription ratio at discharge was 28.7% for schizophrenia and 30.4% for major depressive disorder, with no significant differences by diagnosis. The prescription ratios of PRN antipsychotic medications and PRN antiparkinsonian medications were significantly higher for schizophrenia. The prescription ratios of PRN anxiolytic and hypnotic and PRN antidepressant medications were significantly higher for patients with major depressive disorder. For both schizophrenia and major depressive disorder, the QI was lower for discharge prescriptions, including PRN medications, than for regular prescriptions. QI values for regular prescriptions and the QI ratio were positively correlated. Conclusions: Considering PRN psychotropic medications, the monotherapy ratio and no prescription ratio of other psychotropics at discharge decreased in pharmacotherapy for schizophrenia and major depressive disorder. A higher ratio of monotherapy and no prescription of other psychotropics on regular prescriptions may result in less concomitant use of PRN psychotropic medications. Further studies are needed to optimize PRN psychotropic prescriptions.

ChatGPT has sparked extensive discussions within the healthcare community since its November 2022 release. However, potential applications in the field of psychiatry have received limited attention. Deep learning has proven beneficial to psychiatry, and GPT is a powerful deep learning-based language model with immense potential for this field. Despite the convenience of ChatGPT, this advanced chatbot currently has limited practical applications in psychiatry. It may be used to support psychiatrists in routine tasks such as completing medical records, facilitating communications between clinicians and with patients, polishing academic writings and presentations, and programming and performing analyses for research. The current training and application of ChatGPT require using appropriate prompts to maximize appropriate outputs and minimize deleterious inaccuracies and phantom errors. Moreover, future GPT advances that incorporate empathy, emotion recognition, personality assessment, and detection of mental health warning signs are essential for its effective integration into psychiatric care. In the near future, developing a fully-automated psychotherapy system trained for expert communication (such as psychotherapy verbatim) is conceivable by building on foundational GPT technology. This dream system should integrate practical ‘real world’ inputs and friendly AI user and patient interfaces via clinically validated algorithms, voice comprehension/generation modules, and emotion discrimination algorithms based on facial expressions and physiological inputs from wearable devices. In addition to the technology challenges, we believe it is critical to establish generally accepted ethical standards for applying ChatGPT-related tools in all mental healthcare environments, including telemedicine and academic/training settings.

We aimed to investigate the impact of aging on the relationship among the composition of gut microbiota, gastrointestinal (GI) symptoms, and the course of treatment for major depressive disorder (MDD) by analyzing the datasets from our previous study. Patients with MDD were recruited, and their stools were collected at three time points (baseline, midterm, and endpoint) following the usual antidepressant treatment. Gut microbiota were analyzed using 16S rRNA gene sequencing. Patients were categorized into two groups based on their age: the late-life group over 60 years and the middle-aged group under 60 years. GI symptoms were assessed with scores of item 11 of the Hamilton Anxiety Rating Scale. One hundred and ninety samples were collected from 32 patients with MDD. Several gut microbes had higher relative abundances in the late-life group than in the middle-aged group. In addition, the late-life group showed significantly higher diversity in the Chao1 index at baseline compared with the middle-aged group. We further found possible microbial taxa related to GI symptoms in patients with late-life depression. The abundance of several bacterial taxa may contribute to GI symptoms in the late-life depression, and our findings suggest that the therapeutic targets for the application of gut microbiota may differ depending on the age group of patients with depression.

We have been creating our UNDERPIN mental disease dialogue corpus, which includes more than 1000 hours of recorded voice. Our corpus consists of patients' information (disease name, drugs, etc.), disease test results, and dialogue data. We classified the disease types (bipolar disorder, schizophrenia, dementia, depression, anxiety) versus healthy people, using audio and linguistic features extracted from the corpus. We achieved around 75-91 points in f-score depending on the disease types, which feature importance suggested that formants, fillers, laughs, and questions are important indicators to predict mental diseases.

Introduction: Psychiatric disorders are diagnosed through observations of psychiatrists according to diagnostic criteria such as the DSM-5. Such observations, however, are mainly based on each psychiatrist's level of experience and often lack objectivity, potentially leading to disagreements among psychiatrists. In contrast, specific linguistic features can be observed in some psychiatric disorders, such as a loosening of associations in schizophrenia. Some studies explored biomarkers, but biomarkers have yet to be used in clinical practice. Aim: The purposes of this study are to create a large dataset of Japanese speech data labeled with detailed information on psychiatric disorders and neurocognitive disorders to quantify the linguistic features of those disorders using natural language processing and, finally, to develop objective and easy-to-use biomarkers for diagnosing and assessing the severity of them. Methods: This study will have a multi-center prospective design. The DSM-5 or ICD-11 criteria for major depressive disorder, bipolar disorder, schizophrenia, and anxiety disorder and for major and minor neurocognitive disorders will be regarded as the inclusion criteria for the psychiatric disorder samples. For the healthy subjects, the absence of a history of psychiatric disorders will be confirmed using the Mini-International Neuropsychiatric Interview (M.I.N.I.). The absence of current cognitive decline will be confirmed using the Mini-Mental State Examination (MMSE). A psychiatrist or psychologist will conduct 30-to-60-min interviews with each participant; these interviews will include free conversation, picture-description task, and story-telling task, all of which will be recorded using a microphone headset. In addition, the severity of disorders will be assessed using clinical rating scales. Data will be collected from each participant at least twice during the study period and up to a maximum of five times at an interval of at least one month. Discussion: This study is unique in its large sample size and the novelty of its method, and has potential for applications in many fields. We have some challenges regarding inter-rater reliability and the linguistic peculiarities of Japanese. As of September 2022, we have collected a total of >1000 records from >400 participants. To the best of our knowledge, this data sample is one of the largest in this field. Clinical Trial Registration: Identifier: UMIN000032141.

Background: The Collaborative Outcome study on Health and Functioning during Infection Times (COH-FIT; www.coh-fit.com) is an anonymous and global online survey measuring health and functioning during the COVID-19 pandemic. The aim of this study was to test concurrently the validity of COH-FIT items and the internal validity of the co-primary outcome, a composite psychopathology “P-score”. Methods: The COH-FIT survey has been translated into 30 languages (two blind forward-translations, consensus, one independent English back-translation, final harmonization). To measure mental health, 1–4 items (“COH-FIT items”) were extracted from validated questionnaires (e.g. Patient Health Questionnaire 9). COH-FIT items measured anxiety, depressive, post-traumatic, obsessive-compulsive, bipolar and psychotic symptoms, as well as stress, sleep and concentration. COH-FIT Items which correlated r ≥ 0.5 with validated companion questionnaires, were initially retained. A P-score factor structure was then identified from these items using exploratory factor analysis (EFA) and confirmatory factor analyses (CFA) on data split into training and validation sets. Consistency of results across languages, gender and age was assessed. Results: From >150,000 adult responses by May 6th, 2022, a subset of 22,456 completed both COH-FIT items and validated questionnaires. Concurrent validity was consistently demonstrated across different languages for COH-FIT items. CFA confirmed EFA results of five first-order factors (anxiety, depression, post-traumatic, psychotic, psychophysiologic symptoms) and revealed a single second-order factor P-score, with high internal reliability (ω = 0.95). Factor structure was consistent across age and sex. Conclusions: COH-FIT is a valid instrument to globally measure mental health during infection times. The P-score is a valid measure of multidimensional mental health.

© 2021 The Authors. Neuropsychopharmacology Reports published by John Wiley & Sons Australia, Ltd on behalf of the Japanese Society of NeuropsychoPharmacology. Background: To implement clinical practice guidelines (CPGs), it is necessary for psychiatrists to deepen their understanding of the CPGs. The Effectiveness of Guidelines for Dissemination and Education in Psychiatric Treatment (EGUIDE) project is a nationwide dissemination and implementation study of two sets of CPGs for schizophrenia and major depressive disorder (MDD). Methods: A total of 413 psychiatrists (n = 212 in 2016; n = 201 in 2017) learned the two CPGs in the education program of the EGUIDE project, and clinical knowledge of these CPGs was evaluated at baseline and after the programs. To improve the correct answer rate for clinical knowledge after the programs, we revised the lecture materials associated with items that had a low correct answer rate in 2016 and used the revised lecture materials with the CPGs in 2017. The rates of correct answers after the programs between the 2016 and 2017 groups were compared. Results: The correct answer rate of one item on the schizophrenia CPG and one item on the MDD CPG tended to be improved (S-D5 and D-C6) and that of one on the MDD CPG was significantly improved (D-D3, P = 0.0008) in the 2017 group compared to those in the 2016 group. Conclusions: We reported improvements in clinical knowledge of CPGs after the EGUIDE program in the 2017 group following revision of the lecture materials based on results from the 2016 group. These attempts to improve the degree of understanding of CPGs may facilitate the successful dissemination and implementation of psychiatric guidelines in everyday practice.

Background: Obesity is a frequent somatic comorbidity of major depression, and it has been associated with worse clinical outcomes and brain structural abnormalities. Converging evidence suggests that electroconvulsive therapy (ECT) induces both clinical improvements and increased subcortical grey matter volume in patients with depression. However, it remains unknown whether increased body weight modulates the clinical response and structural neuroplasticity that occur with ECT. Methods: To address this question, we conducted a longitudinal investigation of structural MRI data from the Global ECT-MRI Research Collaboration (GEMRIC) in 223 patients who were experiencing a major depressive episode (10 scanning sites). Structural MRI data were acquired before and after ECT, and we assessed change in subcortical grey matter volume using FreeSurfer and Quarc. Results: Higher body mass index (BMI) was associated with a significantly lower increase in subcortical grey matter volume following ECT. We observed significant negative associations between BMI and change in subcortical grey matter volume, with pronounced effects in the thalamus and putamen, where obese participants showed increases in grey matter volume that were 43.3% and 49.6%, respectively, of the increases found in participants with normal weight. As well, BMI significantly moderated the association between subcortical grey matter volume change and clinical response to ECT. We observed no significant association between BMI and clinical response to ECT. Limitations: Because only baseline BMI values were available, we were unable to study BMI changes during ECT and their potential association with clinical and grey matter volume change. Conclusion: Future studies should take into account the relevance of body weight as a modulator of structural neuroplasticity during ECT treatment and aim to further explore the functional relevance of this novel finding.