Published papers

Background: There is growing evidence regarding the connection between alterations in gut microbiota and their metabolites in patients with depressive disorders, suggesting a potential role in pathophysiology. Our study aimed to investigate the relationship between microbial, metabolomic features and the course of treatment for depression in a real-world clinical setting. Methods: Patients diagnosed with depressive disorders were recruited, and their stool was collected at three time points during their depression treatments. Patients were divided into three groups: non-responders, responders, and stable remitters. Gut microbiomes were analyzed using 16S rRNA gene sequencing, and gut metabolomes were analyzed by a mass spectrometry approach. Microbiomes/metabolomes were compared between groups cross-sectionally and longitudinally. Results: A total of 33 patients were recruited and divided into non-responders (n = 16), responders (n = 11), and stable remitters (n = 6). Non-responders presented lower alpha diversity in the Phylogenic Diversity index compared to responders during the treatment course (p = 0.003). Non-responders presented increased estimated glutamate synthesis functions by the microbiota compared to responders and stable remitters (p = 0.035). There were no specific microbiota or metabolome that differentiated the three groups. Limitations: Small sample size with no healthy controls. Conclusions: Our results indicate that both cross-sectional microbial features and longitudinal microbial transitions are different depending on the treatment course of depression. Controlled studies, as well as animal studies, are needed in the future to elucidate the causal relationship between microbiota and depression.

Behavioral problems directly affect the quality of life of caregivers and children with autism spectrum disorder (ASD) and/or attention-deficit/hyperactivity disorder (ADHD), and is known to be associated with clinical factors such as gastrointestinal (GI) symptoms, sensory abnormalities, intellectual abilities, and use of medication. However, previous studies have not considered these relationships comprehensively. We conducted a cross-sectional study of 6–12-year-old children with diagnoses of ASD and/or ADHD at two hospitals in Japan. Scores for the aberrant behavior checklist (ABC), autism-spectrum quotient (AQ), and Conners 3, as well as information on daily sleep and exercise, GI symptoms, and Short Sensory Profile, were collected. Each factor was subjected to a correlation analysis to investigate its effect on ABC scores. A stepwise multiple linear regression analysis for the factors with p < 0.05 was performed. Data were obtained from 60 patients with a mean age of 8.3 years; 21 had ASD alone, 18 had ADHD alone, and 21 had ASD + ADHD. The correlation analyses identified six factors associated with ABC severity: (a) methylphenidate use, (b) Conners hyperactivity score, (c) Conners inattention score, (d) AQ score, (e) SSP score, and (f) GI symptom score. The multiple regression showed that “GI symptoms” and “sensory abnormalities” were independently associated with ABC severity. Although further studies are needed to show a causal relationship, appropriate assessment of GI symptoms and sensory abnormalities may help alleviate some problematic behaviors and improve the quality of life of children with neurodevelopmental disorders and their families. Lay summary: Behavioral problems in children with neurodevelopmental disorders are known to be associated with many factors. This study aimed to comprehensively investigate the known factors. We have discovered that “gastrointestinal symptoms” and “sensory abnormalities” were independently associated with Behavioral problems. Our results suggest that it is important for clinicians and caregivers to pay more attention to children's GI symptoms and sensory abnormalities that may not present as obvious symptoms or complaints.

Introduction: The COVID-19 pandemic has had a profound impact on the mental health of people around the world. Anxiety related to infection, stress and stigma caused by the forced changes in daily life have reportedly increased the incidence and symptoms of depression, anxiety disorder and obsessive-compulsive disorder. Under such circumstances, telepsychiatry is gaining importance and attracting a great deal of attention. However, few large pragmatic clinical trials on the use of telepsychiatry targeting multiple psychiatric disorders have been conducted to date. Methods: The targeted study cohort will consist of adults (>18 years) who meet the DSM-5 diagnostic criteria for either (1) depressive disorders, (2) anxiety disorders, or (3) obsessive-compulsive and related disorders. Patients will be assigned in a 1:1 ratio to either a “telepsychiatry group” (at least 50% of treatments to be conducted using telemedicine, with at least one face-to-face treatment [FTF] within six months) or an “FTF group” (all treatments to be conducted FTF, with no telemedicine). Both groups will receive the usual treatment covered by public medical insurance. The study will utilize a master protocol design in that there will be primary and secondary outcomes for the entire group regardless of diagnosis, as well as the outcomes for each individual disorder group. Discussion: This study will be a non-inferiority trial to test that the treatment effect of telepsychiatry is not inferior to that of FTF alone. This study will provide useful insights into the effect of the COVID-19 pandemic on the practice of psychiatry. Trial Registration: jRCT1030210037, Japan Registry of Clinical Trials (jRCT).

Neurostimulation is a mainstream treatment option for major depression. Neuromodulation techniques apply repetitive magnetic or electrical stimulation to some neural target but significantly differ in their invasiveness, spatial selectivity, mechanism of action, and efficacy. Despite these differences, recent analyses of transcranial magnetic stimulation (TMS) and deep brain stimulation (DBS)-treated individuals converged on a common neural network that might have a causal role in treatment response. We set out to investigate if the neuronal underpinnings of electroconvulsive therapy (ECT) are similarly associated with this causal depression network (CDN). Our aim here is to provide a comprehensive analysis in three cohorts of patients segregated by electrode placement (N = 246 with right unilateral, 79 with bitemporal, and 61 with mixed) who underwent ECT. We conducted a data-driven, unsupervised multivariate neuroimaging analysis Principal Component Analysis (PCA) of the cortical and subcortical volume changes and electric field (EF) distribution to explore changes within the CDN associated with antidepressant outcomes. Despite the different treatment modalities (ECT vs TMS and DBS) and methodological approaches (structural vs functional networks), we found a highly similar pattern of change within the CDN in the three cohorts of patients (spatial similarity across 85 regions: r = 0.65, 0.58, 0.40, df = 83). Most importantly, the expression of this pattern correlated with clinical outcomes (t = −2.35, p = 0.019). This evidence further supports that treatment interventions converge on a CDN in depression. Optimizing modulation of this network could serve to improve the outcome of neurostimulation in depression.

In recent years, studies on the use of natural language processing (NLP) approaches to identify dementia have been reported. Most of these studies used picture description tasks or other similar tasks to encourage spontaneous speech, but the use of free conversation without requiring a task might be easier to perform in a clinical setting. Moreover, free conversation is unlikely to induce a learning effect. Therefore, the purpose of this study was to develop a machine learning model to discriminate subjects with and without dementia by extracting features from unstructured free conversation data using NLP. We recruited patients who visited a specialized outpatient clinic for dementia and healthy volunteers. Participants’ conversation was transcribed and the text data was decomposed from natural sentences into morphemes by performing a morphological analysis using NLP, and then converted into real-valued vectors that were used as features for machine learning. A total of 432 datasets were used, and the resulting machine learning model classified the data for dementia and non-dementia subjects with an accuracy of 0.900, sensitivity of 0.881, and a specificity of 0.916. Using sentence vector information, it was possible to develop a machine-learning algorithm capable of discriminating dementia from non-dementia subjects with a high accuracy based on free conversation.

Technology use is ubiquitous in the digital age, and to ensure quality care, patients, more research, and evaluation are needed of digital interventions and informatics approaches for behavioral health disorders—particularly those that have population-level impact. Clinicians, teams, organizations, and countries have to prioritize and implement systems to organize clinical care and set the stage for evidence-based interventions. Much of this is through electronic health records (EHRs), patient portals, artificial intelligence (AI), and data analytic approaches for workflow (e.g., clinical decision support). The Special Edition is geared to help clinicians/faculty, trainees, and healthcare leaders improve clinical care, by showcasing new systems, technologies, research, and evidence- and consensus-based best practices. One overarching goal is to apply the most recent health technology and evidence to promote behavioral health and to predict, assess, triage, and treat behavioral health disorders. Another goal is to describe how to shift from in-person/video care visits to longitudinal, in time care with patient and clinician decision support facilitated by artificial intelligence and other technologies. The issue focuses on effectiveness and implementation science approaches for patients across populations and settings. Evaluation of intended and unintended consequences of new technologies could facilitate or impede workflow and outcomes.

Aim: The authors applied natural language processing and machine learning to explore the disease-related language patterns that warrant objective measures for assessing language ability in Japanese patients with Alzheimer disease (AD), while most previous studies have used large publicly available data sets in Euro-American languages. Methods: The authors obtained 276 speech samples from 42 patients with AD and 52 healthy controls, aged 50 years or older. A natural language processing library for Python was used, spaCy, with an add-on library, GiNZA, which is a Japanese parser based on Universal Dependencies designed to facilitate multilingual parser development. The authors used eXtreme Gradient Boosting for our classification algorithm. Each unit of part-of-speech and dependency was tagged and counted to create features such as tag-frequency and tag-to-tag transition-frequency. Each feature's importance was computed during the 100-fold repeated random subsampling validation and averaged. Results: The model resulted in an accuracy of 0.84 (SD = 0.06), and an area under the curve of 0.90 (SD = 0.03). Among the features that were important for such predictions, seven of the top 10 features were related to part-of-speech, while the remaining three were related to dependency. A box plot analysis demonstrated that the appearance rates of content words–related features were lower among the patients, whereas those with stagnation-related features were higher. Conclusion: The current study demonstrated a promising level of accuracy for predicting AD and found the language patterns corresponding to the type of lexical-semantic decline known as ‘empty speech’, which is regarded as a characteristic of AD.

© The Author(s) 2020. Published by Oxford University Press on behalf of CINP. BACKGROUND: The antibacterial effects of psychotropics may be part of their pharmacological effects when treating depression. However, limited studies have focused on gut microbiota in relation to prescribed medication. METHOD: We longitudinally investigated the relationship between patients' prescribed medications and intestinal bacterial diversity in a naturalistic treatment course for patients with major depressive disorders and anxiety disorders. Patients were recruited and their stool was collected at 3 time points during their usual psychiatric treatments. Gut microbiota were analyzed using 16S rRNA gene sequencing. We examined the impact of psychotropics (i.e., antidepressants, anxiolytics, antipsychotics) on their gut microbial diversity and functions. RESULTS: We collected 246 stool samples from 40 patients. Despite no differences in microbial diversity between medication groups at the baseline, over the course of treatment, phylogenic diversity whole-tree diversity decreased in patients on antipsychotics compared with patients without (P = .027), and beta diversity followed this trend. Based on a fixed-effect model, antipsychotics predicted microbial diversity; the higher doses correlated with less diversity based on the Shannon index and phylogenic diversity whole tree (estimate = -0.00254, SE = 0.000595, P < .0001; estimate = -0.02644, SE = 0.00833, P = .002, respectively). CONCLUSION: Antipsychotics may play a role in decreasing the alpha diversity of the gut microbiome among patients with depression and anxiety, and our results indicate a relationship with medication dosage. Future studies are warranted and should consider patients' types and doses of antipsychotics in order to further elucidate the mechanisms of gut-brain interactions in psychiatric disorders.

Social interaction might prevent or delay dementia, but little is known about the specific effects of various social activity interventions on cognition. This study conducted a single-site randomized controlled trial (RCT) of Photo-Integrated Conversation Moderated by Robots (PICMOR), a group conversation intervention program for resilience against cognitive decline and dementia. In the RCT, PICMOR was compared to an unstructured group conversation condition. Sixty-five community-living older adults participated in this study. The intervention was provided once a week for 12 weeks. Primary outcome measures were the cognitive functions; process outcome measures included the linguistic characteristics of speech to estimate interaction quality. Baseline and post-intervention data were collected. PICMOR contains two key features: 1) photos taken by the participants are displayed and discussed sequentially; and 2) a robotic moderator manages turn-taking to make sure that participants are allocated the same amount of time. Among the primary outcome measures, one of the subcategories of cognitive functions, verbal fluency significantly improved in the intervention group. Among the process outcome measures, a part of the subcategories of linguistic characteristics of speech, the amount of speech and richness of words, proportion of providing topics, questions, and answers in total utterances were larger for the intervention group. This study demonstrated for the first time the positive effects of a robotic social activity intervention on cognitive function in healthy older adults via RCT. The group conversation generated by PICMOR may improve participants’ verbal fluency since participants have more opportunity to provide their own topics, asking and answering questions which results in exploring larger vocabularies. PICMOR is available and accessible to community-living older adults. Clinical Trial Registration: UMIN Clinical Trials Registry, identifier UMIN000036667.

BACKGROUND: There is a lack of studies that investigated the effect of a wide range of work environmental factors on stress and depression in Japan. OBJECTIVES: To examine the association of work environment factors with stress and depression among workers in Japan. METHODS: We conducted questionnaire surveys of workers that mainly engage in desk work in Japan. Stress was assessed through the Perceived Stress Scale (PSS), depression through the Patient Health Questionnaire-9 (PHQ-9), and work environment through physical and psychological workplace environment questionnaires. Workers were divided into low and high stress groups based on PSS score (median split), and divided into non-depressed and depressed groups based on their PHQ-9 score (< 5, and ≥5); these groups were then compared with their working environment. In addition, a multiple regression analysis was performed. RESULTS: Responses were obtained from 210 subjects. Multiple regression analysis showed that 'Ability to work at one's own pace' and 'Ability to apply personal viewpoint to work,' etc., had effect on stress, while 'Workplace harassment' and 'Support from colleagues,' etc., had effect on depression. CONCLUSIONS: The results suggest that stress and depression in Japanese workers are related to factors such as job demands, control of work, workplace harassment, and psychological safety.

The intestinal immune system interacts with commensal microbiota to maintain gut homeostasis. Furthermore, stress alters the microbiome composition, leading to impaired brain function; yet how the intestinal immune system mediates these effects remains elusive. Here we report that colonic γδ T cells modulate behavioral vulnerability to chronic social stress via dectin-1 signaling. We show that reduction in specific Lactobacillus species, which are involved in T cell differentiation to protect the host immune system, contributes to stress-induced social-avoidance behavior, consistent with our observations in patients with depression. Stress-susceptible behaviors derive from increased differentiation in colonic interleukin (IL)-17-producing γδ T cells (γδ17 T cells) and their meningeal accumulation. These stress-susceptible cellular and behavioral phenotypes are causally mediated by dectin-1, an innate immune receptor expressed in γδ T cells. Our results highlight the previously unrecognized role of intestinal γδ17 T cells in the modulation of psychological stress responses and the importance of dectin-1 as a potential therapeutic target for the treatment of stress-induced behaviors.

Alzheimer’s disease (AD) is a type of dementia that is more likely to occur as people age. It currently has no known cure. As the world’s population is aging quickly, early screening for AD has become increasingly important. Traditional screening methods such as brain scans or psychiatric tests are stressful and costly. The patients are likely to feel reluctant to such screenings and fail to receive timely intervention. While researchers have been exploring the use of language in dementia detection, less attention has been given to face-related features. The paper focuses on investigating how face-related features can aid in detecting dementia by exploring the PROMPT dataset that contains video data collected from patients with dementia during interviews. In this work, we extracted three types of features from the videos, including face mesh, Histogram of Oriented Gradients (HOG) features, and Action Units (AU). We trained traditional machine learning models and deep learning models on the extracted features and investigated their effectiveness in dementia detection. Our experiments show that the use of HOG features achieved the highest accuracy of 79% in dementia detection, followed by AU features with 71% accuracy, and face mesh features with 66% accuracy. Our results show that face-related features have the potential to be a crucial indicator in automated computational dementia detection.

Aim: Continued antipsychotic treatment is the key to preventing relapse. Maintenance antipsychotic monotherapy and optimal dose use are recommended for individuals with stable schizophrenia because of their undesirable effects. Decision aids (DAs) are clinical conversation tools that facilitate shared decision-making (SDM) between patients and health-care providers. This study aimed to describe the development process and results of acceptability testing of a DA for individuals with stable schizophrenia, considering (i) whether to continue high-dose antipsychotics or reduce to the standard dose and (ii) whether to continue two antipsychotics or shift to monotherapy. Methods: A DA was developed according to the guidelines for the appropriate use of psychotropic medications and International Patient Decision Aid Standards (IPDAS). First, a DA prototype was developed based on a previous systematic review and meta-analysis conducted for identifying the effects of continuing or reducing antipsychotic treatment. Second, mixed-method survey was performed among individuals with schizophrenia and health-care providers to modify and finalize the DA. Results: The DA consisted of an explanation of schizophrenia, options to continue high-dose antipsychotics or reduce to the standard dose, options to continue two antipsychotics or shift to monotherapy, pros and cons of each option, and a value-clarification worksheet for each option. The patients (n = 20) reported acceptable language use (75%), adequate information (75%), and well-balanced presentation (79%). Health-care providers (n = 20) also provided favorable overall feedback. The final DA covered six IPDAS qualifying criteria. Conclusion: A DA was successfully developed for schizophrenia, considering whether to reduce antipsychotics, which can be used in the SDM process.

Aim: Treatment guidelines are designed to assist patients and health care providers and are used as tools for making treatment decisions in clinical situations. The treatment guidelines of the Japanese Society of Mood Disorders establish treatment recommendations for each severity of depression. The individual fitness score (IFS) was developed as a simple and objective indicator to assess whether individual patients are practicing treatment by the recommendations of the depression treatment guidelines of the Japanese Society of Mood Disorders. Methods: The EGUIDE project members determined the IFS through the modified Delphi method. In this article, the IFS was calculated based on the treatment of depressed patients treated and discharged between 2016 and 2020 at facilities participating in the EGUIDE project. In addition, we compared scores at admission and discharge. Results: The study included 428 depressed patients (mild n = 22, moderate/severe n = 331, psychotic n = 75) at 57 facilities. The mean IFS scores by severity were statistically significantly higher at discharge than at admission with moderate/severe depression (mild 36.1 ± 34.2 vs. 41.6 ± 36.9, p = 0.49; moderate/severe 50.2 ± 33.6 vs. 55.7 ± 32.6, p = 2.1 × 10–3; psychotic 47.4 ± 32.9 versus 52.9 ± 36.0, p = 0.23). Conclusion: We developed the IFS based on the depression treatment guideline, which enables us to objectively determine how close the treatment is to the guideline at the time of evaluation in individual cases. Therefore, the IFS may influence guideline-oriented treatment behavior and lead to the equalization of depression treatment in Japan, including pharmacotherapy.

Aims: The Guidelines for the Pharmacotherapy of Schizophrenia were established to improve the quality of medical care, and the EGUIDE project was conducted to train clinicians on guideline usage. A quality indicator (QI) was established to measure the prevalence of the guidelines, and a survey was conducted, which revealed a gap between the guidelines and actual clinical practice (evidence-practice-gap). The purpose of this study was to develop an individual fitness score (IFS) formula that expresses the degree to which prescribers adhere to the Guidelines for Pharmacological Therapy of Schizophrenia in a simple manner, and to determine the validity of this formula from a survey of the prescriptions of the EGUIDE project participants'. Methods: To establish appropriate scores, members discussed the proposed formula and then voted on them. The IFS formula developed was set up so that antipsychotic monotherapy would be given 100 points, with points deducted if concomitant or adjunctive antipsychotic medications were used, and a minimum score of 0. To validate this formula, prescriptions of hospitalized schizophrenic patients at admission and at discharge were scored and compared. Result: IFS points vary and ranged from 0 to100. The average pre-admission score for all subjects was 45.6, and the average score at discharge was 54, those were significantly higher during discharge. Conclusions: We developed an IFS formula, a tool to easily visualize the degree to which current prescriptions conform to the guidelines for the pharmacological treatment of schizophrenia.

Introduction: Few biomarkers can be used clinically to diagnose and assess the severity of depression. However, a decrease in activity and sleep efficiency can be observed in depressed patients, and recent technological developments have made it possible to measure these changes. In addition, physiological changes, such as heart rate variability, can be used to distinguish depressed patients from normal persons; these parameters can be used to improve diagnostic accuracy. The proposed research will explore and construct machine learning models capable of detecting depressive episodes and assessing their severity using data collected from wristband-type wearable devices. Methods and analysis: Patients with depressive symptoms and healthy subjects will wear a wristband-type wearable device for 7 days; data on triaxial acceleration, pulse rate, skin temperature, and ultraviolet light will be collected. On the seventh day of wearing, the severity of depressive episodes will be assessed using Structured Clinical Interview for DSM-5 (SCID-5), Hamilton Depression Rating Scale (HAMD), and other scales. Data for up to five 7-day periods of device wearing will be collected from each subject. Using wearable device data associated with clinical symptoms as supervisory data, we will explore and build a machine learning model capable of identifying the presence or absence of depressive episodes and predicting the HAMD scores for an unknown data set. Discussion: Our machine learning model could improve the clinical diagnosis and management of depression through the use of a wearable medical device. Clinical trial registration: [https://jrct.niph.go.jp/latest-detail/jRCT1031210478], identifier [jRCT1031210478].

Background: Electroconvulsive therapy (ECT) is provided for patients with severe and often life-threatening illness, who lack decision making capacity to consent to treatment (DMC-T) in clinical settings. Objective: The aim of this study is to summarize previous studies investigating clinical outcomes of ECT in patients lacking DMC-T. Methods: A systematic review and meta-analysis of studies reporting clinical outcomes of ECT in patients lacking DMC-T with any psychiatric diagnoses was conducted. The primary outcome was clinical improvement. Secondary outcomes were cognitive outcomes and six month readmission rate. Hedges’ g and odds ratios were calculated using a random-effects model. The protocol was registered in Open Science Framework (https://osf.io/rxjkm). Results: Of 3552 identified articles, 41 studies (n = 1299) were included. Approximately 80% of patients lacking DMC-T responded to ECT, and part of the patients regained capacity to consent and consented to further treatment with ECT. A total of seven studies (n = 1081) were included for meta-analysis. Patients without DMC-T showed superior clinical improvement and less cognitive side effects compared with those with DMC-T, whereas the groups did not show any difference in readmission rate. Several clinical characteristics at baseline and ECT techniques were significantly different between the groups. Conclusion: ECT is equally, if not superiorly, effective in patients lacking DMC-T compared to patients with DMC-T. ECT can potentially enhance patients’ autonomy, without increasing the risk of cognitive side effects. These results support the clinical and ethical legitimacy of ECT provision for patients with the most severe illness who lack DMC-T at start of treatment.