Antipsychotic Monotherapy for Major Depressive Disorder: A Systematic Review and Meta-Analysis.

Although several randomized controlled trials (RCTs) have compared the effectiveness, efficacy, and safety of antipsychotic monotherapy (APM) versus placebo in patients with major depressive disorder (MDD), no meta-analysis has examined this topic. We conducted a systematic literature search using MEDLINE and Embase to identify relevant RCTs and performed a meta-analysis to compare the following outcomes between APM and placebo: response and remission rates, study discontinuation due to all causes, lack of efficacy, and adverse events, changes in total scores on depression severity scales, and individual adverse event rates. A total of 13 studies were identified, with 14 comparisons involving 3,197 participants that met the eligibility criteria. There were significant differences between APM and placebo in response and remission rates and changes in the primary depression severity scale in favor of APM, and study discontinuation due to adverse events and several individual adverse events in favor of placebo. No significant difference was observed in discontinuation due to all causes. APM could have antidepressant effects in the acute phase of MDD, although clinicians should be aware of an increased risk of some adverse events.

Understanding psychiatric illness through natural language processing (UNDERPIN): Rationale, design, and methodology.

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.

Effectiveness of Mindfulness-Based Cognitive Therapy for Improving Subjective and Eudaimonic Well-Being in Healthy Individuals: A Randomized Controlled Trial.

Objectives: Better subjective and eudaimonic well-being fosters better health conditions. Several studies have confirmed that mindfulness-based interventions are effective for improving well-being; however, the samples examined in these studies have been limited to specific populations, and the studies only measured certain aspects of well-being rather than the entire construct. Additionally, few studies have examined the effect of mindfulness-based cognitive therapy on well-being. The present study examines the feasibility of mindfulness-based cognitive therapy and its effectiveness for improving subjective and eudaimonic well-being among community residents. Methods: The study design featured an 8-week randomized, waiting-list controlled, parallel-group study. 8 weekly mindfulness classes, followed by 2 monthly classes, were provided for healthy individuals aged 20-65 years who had a Satisfaction with Life Scale score of ≤ 24 indicating average to low cognitive aspect of subjective well-being. This trial was registered with the University Hospital Medical Information Network Clinical Trials Registry (ID: UMIN000031885, URL: https://upload.umin.ac.jp/cgi-open-bin/ctr_e/ctr_view.cgi?recptno=R000036376). Results: The results showed that cognitive aspect of subjective well-being and mindfulness skills were significantly improved at 8 weeks, and this effect was enhanced up to the end of the follow-up period. Positive affective aspect of subjective and eudaimonic well-being were significantly improved at 16 weeks. Conclusions: Eight weeks of mindfulness-based cognitive therapy with a 2-month follow-up period improves cognitive and affective aspects of subjective and eudaimonic well-being in healthy individuals. The order of improvement was cognitive, positive affective, and eudaimonic well-being. To verify these findings, multi-center randomized controlled trials with active control groups and longer follow-up periods are warranted.

Neuronal network mechanisms associated with depressive symptom improvement following electroconvulsive therapy.

BACKGROUND: Electroconvulsive 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. METHODS: Resting-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. RESULTS: Depressive 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. CONCLUSIONS: ECT-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.

Virtual reality exposure therapy for social anxiety disorder: a systematic review and meta-analysis.

BACKGROUND: Virtual reality exposure therapy (VRET) is currently being used to treat social anxiety disorder (SAD); however, VRET's magnitude of efficacy, duration of efficacy, and impact on treatment discontinuation are still unclear. METHODS: We conducted a meta-analysis of studies that investigated the efficacy of VRET for SAD. The search strategy and analysis method are registered at PROSPERO (#CRD42019121097). Inclusion criteria were: (1) studies that targeted patients with SAD or related phobias; (2) studies where VRET was conducted for at least three sessions; (3) studies that included at least 10 participants. The primary outcome was social anxiety evaluation score change. Hedges' g and its 95% confidence intervals were calculated using random-effect models. The secondary outcome was the risk ratio for treatment discontinuation. RESULTS: Twenty-two studies (n = 703) met the inclusion criteria and were analyzed. The efficacy of VRET for SAD was significant and continued over a long-term follow-up period: Hedges' g for effect size at post-intervention, -0.86 (-1.04 to -0.68); three months post-intervention, -1.03 (-1.35 to -0.72); 6 months post-intervention, -1.14 (-1.39 to -0.89); and 12 months post-intervention, -0.74 (-1.05 to -0.43). When compared to in vivo exposure, the efficacy of VRET was similar at post-intervention but became inferior at later follow-up points. Participant dropout rates showed no significant difference compared to in vivo exposure. CONCLUSION: VRET is an acceptable treatment for SAD patients that has significant, long-lasting efficacy, although it is possible that during long-term follow-up, VRET efficacy lessens as compared to in vivo exposure.

Using speech recognition technology to investigate the association between timing-related speech features and depression severity.

BACKGROUND: There are no reliable and validated objective biomarkers for the assessment of depression severity. We aimed to investigate the association between depression severity and timing-related speech features using speech recognition technology. METHOD: Patients with major depressive disorder (MDD), those with bipolar disorder (BP), and healthy controls (HC) were asked to engage in a non-structured interview with research psychologists. Using automated speech recognition technology, we measured three timing-related speech features: speech rate, pause time, and response time. The severity of depression was assessed using the Hamilton Depression Rating Scale 17-item version (HAMD-17). We conducted the current study to answer the following questions: 1) Are there differences in speech features among MDD, BP, and HC? 2) Do speech features correlate with depression severity? 3) Do changes in speech features correlate with within-subject changes in depression severity? RESULTS: We collected 1058 data sets from 241 individuals for the study (97 MDD, 68 BP, and 76 HC). There were significant differences in speech features among groups; depressed patients showed slower speech rate, longer pause time, and longer response time than HC. All timing-related speech features showed significant associations with HAMD-17 total scores. Longitudinal changes in speech rate correlated with changes in HAMD-17 total scores. CONCLUSIONS: Depressed individuals showed longer response time, longer pause time, and slower speech rate than healthy individuals, all of which were suggestive of psychomotor retardation. Our study suggests that speech features could be used as objective biomarkers for the assessment of depression severity.

Predicting Individual Remission After Electroconvulsive Therapy Based on Structural Magnetic Resonance Imaging: A Machine Learning Approach.

OBJECTIVE: To identify important clinical or imaging features predictive of an individual's response to electroconvulsive therapy (ECT) by utilizing a machine learning approach. METHODS: Twenty-seven depressed patients who received ECT were recruited. Clinical demographics and pretreatment structural magnetic resonance imaging (MRI) data were used as candidate features to build models to predict remission and post-ECT Hamilton Depression Rating Scale scores. Support vector machine and support vector regression with elastic-net regularization were used to build models using (i) only clinical features, (ii) only MRI features, and (iii) both clinical and MRI features. Consistently selected features across all individuals were identified through leave-one-out cross-validation. RESULTS: Compared with models that include only clinical variables, the models including MRI data improved the prediction of ECT remission: the prediction accuracy improved from 70% to 93%. Features selected consistently across all individuals included volumes in the gyrus rectus, the right anterior lateral temporal lobe, the cuneus, and the third ventricle, as well as 2 clinical features: psychotic features and family history of mood disorder. CONCLUSIONS: Pretreatment structural MRI data improved the individual predictive accuracy of ECT remission, and only a small subset of features was important for prediction.

Levels of glutamatergic neurometabolites in patients with severe treatment-resistant schizophrenia: a proton magnetic resonance spectroscopy study.

Approximately 30% of patients with schizophrenia do not respond to antipsychotics and are thus considered to have treatment-resistant schizophrenia (TRS). To date, only four studies have examined glutamatergic neurometabolite levels using proton magnetic resonance spectroscopy (1H-MRS) in patients with TRS, collectively suggesting that glutamatergic dysfunction may be implicated in the pathophysiology of TRS. Notably, the TRS patient population in these studies had mild-to-moderate illness severity, which is not entirely reflective of what is observed in clinical practice. In this present work, we compared glutamate + glutamine (Glx) levels in the dorsal anterior cingulate cortex (dACC) and caudate among patients with TRS, patients with non-TRS, and healthy controls (HCs), using 3T 1H-MRS (PRESS, TE = 35 ms). TRS criteria were defined by severe positive symptoms (i.e., ≥5 on 2 Positive and Negative Syndrome Scale (PANSS)-positive symptom items or ≥4 on 3 PANSS-positive symptom items), despite standard antipsychotic treatment. A total of 95 participants were included (29 TRS patients [PANSS = 111.2 ± 20.4], 33 non-TRS patients [PANSS = 49.8 ± 13.7], and 33 HCs). dACC Glx levels were higher in the TRS group vs. HCs (group effect: F[2,75] = 4.74, p = 0.011; TRS vs. HCs: p = 0.012). No group differences were identified in the caudate. There were no associations between Glx levels and clinical severity in either patient group. Our results are suggestive of greater heterogeneity in TRS relative to non-TRS with respect to dACC Glx levels, necessitating further research to determine biological subtypes of TRS.

Thalamic volume, resting-state activity, and their association with the efficacy of electroconvulsive therapy.

Electroconvulsive therapy (ECT) is the most effective antidepressant treatment. Biological predictors of clinical outcome to ECT are valuable. We aimed to examine multimodal magnetic resonance imaging (MRI) data that correlates to the efficacy of ECT. Structural and resting-state functional MRI data were acquired from 46 individuals (25 depressed individuals who received ECT, and 21 healthy controls). Whole-brain grey matter volume (GMV) and fractional amplitude of low frequency fluctuations (fALFF) were investigated to identify brain regions associated with post-ECT Hamilton Depression Rating Scale (HAM-D) total scores. GMV and fALFF values were compared with those in healthy controls using analysis of covariance (ANCOVA). Remission was defined by HAM-D ≤7. A multiple regression analysis revealed that pretreatment smaller GMV in the left thalamus was associated with worse response to ECT (i.e. higher post-ECT HAM-D). Pretreatment higher fALFF in the right anterior insula, and lower fALFF in the left thalamus and the cerebellum were associated with worse outcomes. The left thalamus was identified in both GMV and fALFF analyses. Nonremitters showed significantly smaller thalamic GMV compared to remitters and controls. We found that pretreatment thalamic volume and resting-state activity were associated with the efficacy of ECT. Our results highlight the importance of the thalamus as a possible biological predictor and its role in the underlying mechanisms of ECT action.

Attitudes Toward Electroconvulsive Therapy Among Involuntary and Voluntary Patients.

OBJECTIVE: We aimed to examine attitudes toward electroconvulsive therapy (ECT) among involuntary patients, voluntary patients, and their relatives. METHODS: Patients experiencing a major depressive episode and receiving ECT and their relatives were recruited for the survey. Patients and their relatives answered the self-rating questionnaires with a 7-point Likert scale. We explored differences in the survey results between involuntary and voluntary patients, as well as differences in the survey results between patients and their relatives. RESULTS: We recruited 97 participants (53 patients and 44 relatives) for the survey. Approximately 80% of the patients showed positive attitudes toward ECT. There were no statistically significant differences between involuntary (n = 23) and voluntary (n = 30) patients across multiple aspects of the ECT experience, including treatment satisfaction, positive or adverse effects of ECT, and treatment preference in the future. Relatives were more satisfied with the positive effects of ECT and with the information offered before ECT treatment than the patients themselves. CONCLUSIONS: Approximately 80% of the patients showed overall satisfaction with ECT irrespective of consent status. Relatives were more satisfied with ECT than patients. Electroconvulsive therapy can be a lifesaving treatment for severely depressed patients, and the subjective experience of involuntary patients should be taken into consideration when discussing involuntary ECT treatment.

Impact of subjective vs. objective remission status on subjective cognitive impairments in depression.

OBJECTIVE: The impact of subjective vs. objective illness severity on subjective cognitive impairment in patients with depression has not been addressed. METHODS: This study is a post-hoc analysis of our cross-sectional study in Japanese outpatients with depressive disorder (ICD-10) (Ozawa et al., 2017). The participants received assessments with the Japanese version of the Perceived Deficits Questionnaire (J-PDQ), Quick Inventory of Depressive Symptomatology (QIDS), and Montgomery-Asberg Depression Rating Scale (MADRS). First, multiple regression analysis was conducted to examine the effects of demographic and clinical characteristics, including illness severity and medications (e.g., antidepressants and benzodiazepines), on the PDQ total score. Next, we categorized the participants into 4 groups based on the presence/absence of subjective and objective symptom remission (i.e., QIDS total score of ≤5 and MADRS total score of ≤9, respectively), and compared the differences in PDQ total scores between the QIDS- and MADRS-remitted group and the QIDS-non-remitted but MADRS-remitted group. RESULTS: 102 participants were included (45 men; mean ±â€¯SD age, 50.5 ±â€¯14.7 years). Higher QIDS and MADRS total scores were significantly associated with a greater PDQ total score (both p's < 0.001), while other factors did not exhibit any associations. The QIDS-non-remitted but MADRS-remitted group showed a significantly higher PDQ total score than that of the QIDS- and MADRS-remitted group (median 10.0 [8.0-12.0] vs. 3.0 [range: 2.0-4.0], p < 0.001). CONCLUSIONS: These findings suggest that objective remission in the absence of subjective remission may not be adequate to improve subjective cognitive functioning.

Effect of Education on Alzheimer's Disease-Related Neuroimaging Biomarkers in Healthy Controls, and Participants with Mild Cognitive Impairment and Alzheimer's Disease: A Cross-Sectional Study.

BACKGROUND: Cognitive reserve is the acquired capacity reflecting a functional brain adaptability/flexibility in the context of aging. Educational attainment is thought to be among the most important factors that contribute to cognitive reserve. OBJECTIVE: The aim of this study is to investigate the relationships among duration of education and Alzheimer's disease (AD) related neuroimaging biomarkers such as amyloid-ß deposition, glucose metabolism, and brain volumes in each stage of AD. METHODS: We reanalyzed a part of the datasets of the Alzheimer's Disease Neuroimaging Initiative. Participants were between 55 and 90 years of age and diagnosed as one of the following: healthy controls (HC), mild cognitive impairment (MCI), or AD. Multiple regression analyses were conducted to examine the relationships among duration of education and amyloid-ß deposition (n = 825), brain metabolism (n = 1,304), and brain volumes (n = 1,606) among three groups using data for 18F-Florbetapir (AV-45) imaging, fludeoxyglucose (FDG) positron emission tomography, and T1-weighted magnetic resonance imaging. RESULTS: Duration of education had no correlations with amyloid-ß deposition or brain metabolism in any groups. However, duration of education was positively associated with the total brain volume only in participants with MCI. CONCLUSIONS: Our findings suggest that education may exert a protective effect on total brain volume in the MCI stage but not in HC or AD. Thus, education may play an important role in preventing the onset of dementia through brain reserve in MCI.