In a trial involving patients with bipolar I disorder and a recently remitted depressive episode, adjunctive treatment with escitalopram or bupropion XL that continued for 52 weeks did not show a significant benefit as compared with treatment for 8 weeks in preventing relapse of any mood episode. The trial was stopped early owing to slow recruitment and funding limitations. (Funded by the Canadian Institutes of Health Research; ClinicalTrials.gov number, NCT00958633.).

A core challenge in the genetic analysis of major depressive disorder (MDD) is how to recruit large numbers of stringently diagnosed cases with sufficient information to explore the interplay between genetic and environmental risk factors and evaluate genetic influences on putative MD subtypes and key clinical features. Currently, most genome-wide association studies of MDD rely on self-administered questionnaires or electronic health records, both of which are limited in diagnostic accuracy and introduce systematic, heritable biases that confound the interpretation of genetic analyses. Here, we describe how to address this problem through a combination of targeted ascertainment and in-depth phenotyping by clinical interview. We increase the homogeneity of the sample, reducing the number of cases needed to detect genetic signals, by recruiting only women with recurrent depressive episodes, ascertained through hospitals. Structured interviews capture detailed information on the known and putative risk factors for the disorder. We trained 347 interviewers working at 47 participating hospitals across South Korea and recruited 5704 cases and 4995 screened controls over 4 years toward a total target sample of 10,000 cases and 10,000 controls. We met and overcame a series of logistic challenges, including restrictions due to COVID-19 and an ongoing medical crisis. We confirmed that our cases have recurrent, severe MDD and are suitable to explore the causes of recurrent episodes of disturbances of sleep and appetite, suicidality, guilty ruminations, anhedonia, and low mood. Our study design provides deeply phenotyped cases and screened controls at scale and can be adapted for deployment in other countries to yield cohorts for the genetic analysis of MDD.

Between- and within-person comparisons of speech markers can be leveraged in detecting and monitoring bipolar depression. We demonstrate the feasibility of applying Linguistic Inquiry and Word Count-based psycholinguistic analysis to machine-transcribed and translated speech, supporting the replicability of this approach across languages. Automated multimodal voice analysis can be integrated into digital health platforms, providing a scalable and effective approach for accessing mental health monitoring and care.

<sec> <title>BACKGROUND</title> Depressive episodes in bipolar disorder are frequent, prolonged, and contribute substantially to functional impairment and reduced quality of life. Therefore, early and objective detection of bipolar depression is critical for timely intervention and improved outcomes. Multimodal speech analyses hold promise for capturing psychomotor, cognitive, and affective changes associated with bipolar depression. </sec> <sec> <title>OBJECTIVE</title> This study aims to develop between- and within-person classifiers to screen for bipolar depression and monitor longitudinal changes to detect depressive recurrence in patients with bipolar disorder. A secondary objective was to compare the predictive performance across speech modalities. </sec> <sec> <title>METHODS</title> We collected 304 voice audio recordings obtained during semistructured interviews with 92 patients diagnosed with bipolar disorder over a 1-year period. Depression severity was assessed using the Hamilton Depression Rating Scale. Acoustic features were extracted using the openSMILE toolkit, and linguistic features were extracted using the Linguistic Inquiry and Word Count frameworks following automatic speech recognition and machine translation. Mixed-effects multivariate linear regression evaluated the associations between speech markers and Hamilton Depression Rating Scale scores adjusting for demographic variables, diagnosis, and feature-specific covariates. Extreme gradient boosting and light gradient boosting were used as base learners. We developed a between-person classifier to detect moderate to severe depression and a within-person classifier to detect recurrence. Hyperparameter tuning and 95% CI estimation were performed using a bootstrap bias-corrected cross-validation (&lt;i&gt;k&lt;/i&gt;=5) approach combined with a grid search. Feature contributions were interpreted using Shapley additive explanations. </sec> <sec> <title>RESULTS</title> Patients with depression showed reduced energy modulation, prolonged monotony, and more frequent use of words related to death and negative emotions. The between-person classifier combining acoustic and linguistic features detected moderate to severe depression with an area under the curve of 0.76 compared to 0.54 for the demographic model. The within-person classifier based on speech features detected depression recurrence with an area under the curve of 0.70 compared to 0.55 for the demographic model. </sec> <sec> <title>CONCLUSIONS</title> Between- and within-person comparisons of speech markers can be leveraged in detecting and monitoring bipolar depression. We demonstrate the feasibility of applying Linguistic Inquiry and Word Count–based psycholinguistic analysis to machine–transcribed and translated speech, supporting the replicability of this approach across languages. Automated multimodal voice analysis can be integrated into digital health platforms, providing a scalable and effective approach for accessing mental health monitoring and care. </sec> <sec> <title>CLINICALTRIAL</title> <p/> </sec>

The field of psychiatry has encountered ongoing challenges in understanding the intricate nature of psychotic symptoms, particularly when they manifest in individuals diagnosed with bipolar disorder or schizophrenia. In this study, we employed manifold and network analyses to investigate whether the pattern of symptom occurrence differs between schizophrenia and bipolar I disorder. We analyzed data collected from 555 individuals, 282 of whom were diagnosed with schizophrenia-related disorders and 273 with bipolar I disorder. In the context of schizophrenia, negative symptoms, particularly avolition, were prominent with manifold and network analyses, identifying avolition as a high central symptom associated with clozapine use, patterns of deterioration, tendency toward remission, and illness severity. Conversely, bipolar I disorder exhibits discernible patterns where positive symptoms play a central role in network analysis. Unexpectedly, manifold analysis revealed two distinct clusters of patients, suggesting variability in psychotic symptom profiles within bipolar I disorder. In conclusion, schizophrenia and bipolar I disorder, while sharing psychotic symptoms, exhibit distinct co-occurrence patterns. Schizophrenia demonstrates negative symptoms, whereas bipolar I disorder exhibits a stronger interconnectivity of psychotic symptoms, highlighting the complexity of psychotic symptom patterns and their relevance for understanding psychiatric disorders. These findings highlight the complexity of psychotic symptom patterns and their relevance for understanding psychiatric disorders.

In a trial involving patients with bipolar I disorder and a recently remitted depressive episode, adjunctive treatment with escitalopram or bupropion XL that continued for 52 weeks did not show a significant benefit as compared with treatment for 8 weeks in preventing relapse of any mood episode. The trial was stopped early owing to slow recruitment and funding limitations. (Funded by the Canadian Institutes of Health Research; ClinicalTrials.gov number, NCT00958633.).