Psychiatry Investigation最新文献

Objective: Low-intensity transcranial focused ultrasound (tFUS) has emerged as a promising non-invasive brain stimulation modality with high spatial selectivity and the ability to reach deep brain areas. The present study aimed to investigate the safety and effectiveness of low-intensity tFUS in treating major depressive disorder.

Methods: Participants were recruited in an outpatient clinic and randomly assigned to either the verum tFUS or sham stimulation group. The intervention group received six sessions of tFUS stimulation to the left dorsolateral prefrontal cortex over two weeks. Neuropsychological assessments were conducted before and after the sessions. Resting-state functional magnetic resonance imaging (rsfMRI) was also performed to evaluate changes in functional connectivity (FC). The primary outcome measure was the change in depressive symptoms, assessed with the Montgomery-Åsberg Depression Rating Scale (MADRS).

Results: The tFUS stimulation sessions were well tolerated without any undesirable side effects. The analysis revealed a significant main effect of session sequence on the MADRS scores and significant interactions between the session sequences and groups. The rsfMRI analysis showed a higher FC correlation between the right superior part of the subgenual anterior cingulate cortex (sgACC) and several other brain regions in the verum group compared with the sham group.

Conclusion: Our results reveal that tFUS stimulation clinically improved MADRS scores with network-level modulation of a sgACC subregion. This randomized, sham-controlled clinical trial, the first study of its kind, demonstrated the safety and probable efficacy of tFUS stimulation for the treatment of depression.

Objective: Extrapyramidal symptoms (EPS) are common side effects of antipsychotic drugs. Despite the growing interest in exploring objective biomarkers for EPS prevention and the potential use of voice in detecting clinical disorders, no studies have demonstrated the relationships between vocal changes and EPS. Therefore, we aimed to determine the associations between voice changes and antipsychotic dosage, and further investigated whether speech characteristics could be used as predictors of EPS.

Methods: Forty-two patients receiving or expected to receive antipsychotic drugs were recruited. Drug-induced parkinsonism of EPS was evaluated using the Simpson-Angus Scale (SAS). Participants' voice data consisted of 16 neutral sentences and 2 second-long /Ah/utterances. Thirteen voice features were extracted from the obtained voice data. Each voice feature was compared between groups categorized based on SAS total score of below and above "0.6." The associations between antipsychotic dosage and voice characteristics were examined, and vocal trait variations according to the presence of EPS were explored.

Results: Significant associations were observed between specific vocal characteristics and antipsychotic dosage across both datasets of 1-16 sentences and /Ah/utterances. Notably, Mel-Frequency Cepstral Coefficients (MFCC) exhibited noteworthy variations in response to the presence of EPS. Specifically, among the 13 MFCC coefficients, MFCC1 (t=-4.47, p<0.001), MFCC8 (t=-4.49, p<0.001), and MFCC12 (t=-2.21, p=0.029) showed significant group differences in the overall statistical values.

Conclusion: Our results suggest that MFCC may serve as a predictor of detecting drug-induced parkinsonism of EPS. Further research should address potential confounding factors impacting the relationship between MFCC and antipsychotic dosage, possibly improving EPS detection and reducing antipsychotic medication side effects.

Objective: Herbal and natural supplements have gained popularity as alternative treatments to insomnia and sleep disorders due to their perceived safety and potential effectiveness. This literature review summarizes the current evidence on the efficacy, safety, and mechanisms of action of commonly used supplements for sleep, including valerian, hops, kava, German chamomile, cherry, tryptophan, theanine, melatonin, magnesium, and zinc.

Methods: We conducted literature review of clinical research on herbal and supplements for sleep reported to date. We summarized key findings and reviewed outcomes related to clinical efficacy and side effects.

Results: Findings suggest that certain supplements, particularly valerian, hops, and melatonin, could be effective in improving sleep quality and reducing insomnia symptoms through modulation of neurotransmitter systems and regulation of sleep-wake cycles. However, the strength of the evidence varies with unestablished optimal dosages, formulations, and treatment durations. Although generally considered safe, these supplements are not without risks, such as rare but serious adverse effects associated with kava and potential interactions with prescription medications. The quality and purity of supplements also vary widely due to a lack of strict regulations.

Conclusion: Healthcare providers should remain informed about the latest research and work closely with patients to develop personalized treatment plans. Herbal and natural supplements may offer promising alternatives or adjunct treatments for insomnia and sleep disorders, but their use should be guided by the best available evidence and individual patient requirements. Larger, well-designed clinical trials are needed to establish the efficacy and safety of these supplements for clinical decision-making.

Objective: Healthcare workers (HCWs) suffered from a heavy mental health burden during the coronavirus disease-2019 (COVID-19) pandemic. We aimed to explore the differences in sleep disturbance in three waves of the COVID-19 pandemic in Taiwan among HCWs. Moreover, factors associated with sleep disturbances in the third wave were investigated.

Methods: This study, with three waves of cross-sectional surveys, recruited first-line and second-line HCWs. The level of sleep disturbance and related demographic variables were collected through self-report questionnaires. Differences in sleep disturbance across the three waves were compared with analysis of variance. Factors associated with the level of sleep disturbance were identified using univariate linear regression and further used for multivariate stepwise and bootstrap linear regression to identify the independent predictors.

Results: In total, 711, 560, and 747 HCWs were included in the first, second, and third waves, respectively. For first-line HCWs, sleep disturbance was significantly higher in the third wave than in the first wave. The level of sleep disturbance gradually increased across the three waves for all HCWs. In addition, sleep disturbance was associated with depression, posttraumatic stress disorder (PTSD) symptoms, anxiety about COVID-19, vaccine mistrust, and poorer physical and mental health among first-line HCWs. Among second-line HCWs, sleep disturbance was associated with younger age, depression, PTSD symptoms, lower preference for natural immunity, and poorer physical health.

Conclusion: The current study identified an increase in sleep disturbance and several predictors among HCWs. Further investigation is warranted to extend the application and generalizability of the current study.

Objective: This study aimed to use deep learning (DL) to develop a cost-effective and accessible screening tool to improve the detection of cognitive decline, a precursor of Alzheimer's disease (AD). This study integrating a comprehensive battery of neuropsychological tests adjusted for individual demographic variables such as age, sex, and education level.

Methods: A total of 2,863 subjects with subjective cognitive complaints who underwent a comprehensive neuropsychological assessment were included. A random forest classifier was used to discern the most predictive test combinations to distinguish between dementia and nondementia cases. The model was trained and validated on this dataset, focusing on feature importance to determine the cognitive tests that were most indicative of decline.

Results: Subjects had a mean age of 72.68 years and an average education level of 7.62 years. The DL model achieved an accuracy of 82.42% and an area under the curve of 0.816, effectively classifying dementia. Feature importance analysis identified significant tests across cognitive domains: attention was gauged by the Trail Making Test Part B, language by the Boston Naming Test, memory by the Rey Complex Figure Test delayed recall, visuospatial skills by the Rey Complex Figure Test copy score, and frontal function by the Stroop Test Word reading time.

Conclusion: This study showed the potential of DL to improve AD diagnostics, suggesting that a wide range of cognitive assessments could yield a more accurate diagnosis than traditional methods. This research establishes a foundation for future broader studies, which could substantiate the approach and further refine the screening tool.

Objective: Bipolar and depressive disorders are distinct disorders with clearly different clinical courses, however, distinguishing between them often presents clinical challenges. This study investigates the utility of self-report questionnaires, the Mood Disorder Questionnaire (MDQ) and Bipolar Spectrum Diagnostic Scale (BSDS), with machine learning-based multivariate analysis, to classify patients with bipolar and depressive disorders.

Methods: A total of 189 patients with bipolar disorders and depressive disorders were included in the study, and all participants completed both the MDQ and BSDS questionnaires. Machine-learning classifiers, including support vector machine (SVM) and linear discriminant analysis (LDA), were exploited for multivariate analysis. Classification performance was assessed through cross-validation.

Results: Both MDQ and BSDS demonstrated significant differences in each item and total scores between the two groups. Machine learning-based multivariate analysis, including SVM, achieved excellent discrimination levels with area under the ROC curve (AUC) values exceeding 0.8 for each questionnaire individually. In particular, the combination of MDQ and BSDS further improved classification performance, yielding an AUC of 0.8762.

Conclusion: This study suggests the application of machine learning to MDQ and BSDS can assist in distinguishing between bipolar and depressive disorders. The potential of combining high-dimensional psychiatric data with machine learning-based multivariate analysis as an effective approach to psychiatric disorders.

Objective: Cognition impairments are considered as a fundamental characteristic of severe mental disorders (SMD). Recent studies suggest that hyperprolactinemia may exert a detrimental influence on cognitive performance in patients with SMD. The objective of this study was to investigate the correlation between serum prolactin levels and cognitive function in female individuals diagnosed with SMD.

Methods: We conducted a study on 294 patients with SMD and 195 healthy controls, aged between 14 to 55 years old. Cognitive function was assessed using the Repeatable Battery for the Assessment of Neuropsychological Status (RBANS), while prolactin levels were measured in serum. Descriptive analysis and comparative analysis were performed to compare cognitive function and prolactin levels between groups, and linear regression models were used to explore the relationship between prolactin and cognitive function.

Results: Compared to the healthy control, individuals with SMD exhibited significantly higher levels of prolactin, while scoring lower on RBANS total and every index scores. Furthermore, a negative association between prolactin levels and cognitive function (RBANS total index score, attention, and delayed memory) was observed in SMD patients. Importantly, this inverse correlation between prolactin and cognition function (RBANS total index score, total scale score, and attention) persisted in patients who were not taking medications that could potentially influence serum prolactin levels.

Conclusion: Our study reveals a significant correlation between elevated prolactin levels and cognitive impairment in female patients with SMD, underscoring the importance of monitoring prolactin levels in order to prevent cognitive deterioration among female SMD patients.

Objective: The Impact of Event Scale-Revised (IES-R) is a widely used self-report for assessing posttraumatic stress disorder (PTSD), originally aligned with Diagnostic and Statistical Manual of Mental Disorders (DSM)-IV diagnostic criteria. This study aimed to evaluate the applicability of the IES-R under the DSM-5 guidelines and establish a cutoff point for DSM-5 PTSD diagnosis.

Methods: A total of 238 participants recruited from multiple psychiatric centers, including 67 patients with PTSD, 72 patients with psychiatric controls, and 99 healthy controls, were included in the study. All participants completed the Korean version of the Structured Clinical Interview for the DSM-5 research version to confirm the presence of PTSD, the Korean version of PTSD Checklist for DSM-5 (PCL-5), the Beck Depression Inventory-II, the Beck Anxiety Inventory, and the Spielberger State Trait Anxiety Inventory.

Results: The IES-R demonstrated good internal consistency and a high correlation with the PCL-5. Through factor analysis, 5 distinct dimensions emerged within the IES-R: sleep disturbance, intrusion, hyperarousal, avoidance, and numbness-dissociation. A proposed cutoff score of 25 on the IES-R was suggested for identifying patients with PTSD.

Conclusion: These findings underscore the scale's concurrent validity with the DSM-5 PTSD criteria and its effectiveness as a screening tool. Implementing a cutoff score of 25 on the IES-R can enhance its utility in identifying DSM-5 PTSD cases.

The circadian rhythm for mood (CRM) is a digital therapeutic, which aims to prevent mood episode and improve clinical course in patients with major mood disorders. Developed on the circadian rhythm hypothesis of mood disorder, CRM predicts the impending risk of mood episode with its built-in algorithm, utilizing wearable devices data and daily self-reports, and provides personalized feedback. In a pilot study of the CRM, the users experienced less frequent and shorter duration of mood episodes than the non-users. To investigate the efficacy of the upgraded CRM, a double-blind, randomized, sham-controlled, parallel-group trial is designed. Patients aged between 19 and 70, diagnosed with bipolar I disorder, bipolar II disorder, or major depressive disorder, in a euthymic state for more than two months, can participate. During this 12-month trial, participants are assessed for episode recurrence every three months, and the efficacy of the CRM as a potential digital therapeutic is evaluated. Trial registration: ClinicalTrials.gov Identifier: NCT05400785.