Acta Psychiatrica Scandinavica最新文献

Background: Affective states influence the sympathetic nervous system, inducing variations in electrodermal activity (EDA), however, EDA association with bipolar disorder (BD) remains uncertain in real-world settings due to confounders like physical activity and temperature. We analysed EDA separately during sleep and wakefulness due to varying confounders and potential differences in mood state discrimination capacities.

Methods: We monitored EDA from 102 participants with BD including 35 manic, 29 depressive, 38 euthymic patients, and 38 healthy controls (HC), for 48 h. Fifteen EDA features were inferred by mixed-effect models for repeated measures considering sleep state, group and covariates.

Results: Thirteen EDA feature models were significantly influenced by sleep state, notably including phasic peaks (p < 0.001). During wakefulness, phasic peaks showed different values for mania (M [SD] = 6.49 [5.74, 7.23]), euthymia (5.89 [4.83, 6.94]), HC (3.04 [1.65, 4.42]), and depression (3.00 [2.07, 3.92]). Four phasic features during wakefulness better discriminated between HC and mania or euthymia, and between depression and euthymia or mania, compared to sleep. Mixed symptoms, average skin temperature, and anticholinergic medication affected the models, while sex and age did not.

Conclusion: EDA measured from awake recordings better distinguished between BD states than sleep recordings, when controlled by confounders.

Background: Knowledge graphs (KGs) remain an underutilized tool in the field of psychiatric research. In the broader biomedical field KGs are already a significant tool mainly used as knowledge database or for novel relation detection between biomedical entities. This review aims to outline how KGs would further research in the field of psychiatry in the age of Artificial Intelligence (AI) and Large Language Models (LLMs).

Methods: We conducted a thorough literature review across a spectrum of scientific fields ranging from computer science and knowledge engineering to bioinformatics. The literature reviewed was taken from PubMed, Semantic Scholar and Google Scholar searches including terms such as "Psychiatric Knowledge Graphs", "Biomedical Knowledge Graphs", "Knowledge Graph Machine Learning Applications", "Knowledge Graph Applications for Biomedical Sciences". The resulting publications were then assessed and accumulated in this review regarding their possible relevance to future psychiatric applications.

Results: A multitude of papers and applications of KGs in associated research fields that are yet to be utilized in psychiatric research was found and outlined in this review. We create a thorough recommendation for other computational researchers regarding use-cases of these KG applications in psychiatry.

Conclusion: This review illustrates use-cases of KG-based research applications in biomedicine and beyond that may aid in elucidating the complex biology of psychiatric illness and open new routes for developing innovative interventions. We conclude that there is a wealth of opportunities for KG utilization in psychiatric research across a variety of application areas including biomarker discovery, patient stratification and personalized medicine approaches.

Background: Virtual Reality cognitive behavioral therapy (VR-CBT) has proven to be an effective treatment method for paranoia and anxiety in psychosis. However, it is unknown, which individuals benefit most from VR-CBT. Previous studies examined factors affecting the treatment effect of regular CBTp, including illness duration, paranoia, depression, and pre-therapy avoidance behaviors, but results are inconsistent. The study aims to investigate the factors that influence the effectiveness of VR-CBT.

Methods: A total of 95 participants with a psychotic disorder and at least moderate paranoia (GTPS >40) were included in this explorative study. Data were collected as part of a multicenter randomized controlled trial in which participants were assigned to VR-CBT or treatment as usual (TAU). The VR-CBT group received 16 sessions of individual treatment. A moderator analysis was conducted to examine the influence of baseline demographic (age, gender, and education level) and clinical characteristics (duration of illness, paranoia, anxiety, depression, safety behavior, self-esteem, and social functioning) on treatment effects of paranoia and anxiety as measured with questionnaires and the experience sampling method (ESM) directly after treatment (12 weeks after baseline).

Results: More use of safety behavior at baseline resulted in greater benefits of VR-CBT on paranoid ideation and ESM paranoia. A higher age was associated with greater benefits of VR-CBT on social anxiety but not paranoia outcomes. There was no consistent evidence of moderation by any of the other sociodemographic or clinical variables for paranoid ideation and social anxiety.

Conclusions: Our findings suggest that a diverse spectrum of patients, with different backgrounds and symptom severity may be able to benefit from VR-CBT. VR-CBT can be recommended to a broad spectrum of patients with psychotic disorders, and particularly those with high levels of safety behaviors, including severe avoidance, seem to benefit more.

Introduction: Clinical assessment of mood and anxiety change often relies on clinical assessment or self-reported scales. Using smartphone digital phenotyping data and resulting markers of behavior (e.g., sleep) to augment clinical symptom scores offers a scalable and potentially more valid method to understand changes in patients' state. This paper explores the potential of using a combination of active and passive sensors in the context of smartphone-based digital phenotyping to assess mood and anxiety changes in two distinct cohorts of patients to assess the preliminary reliability and validity of this digital phenotyping method.

Methods: Participants from two different cohorts, each n = 76, one with diagnoses of depression/anxiety and the other schizophrenia, utilized mindLAMP to collect active data (e.g., surveys on mood/anxiety), along with passive data consisting of smartphone digital phenotyping data (geolocation, accelerometer, and screen state) for at least 1 month. Using anomaly detection algorithms, we assessed if statistical anomalies in the combination of active and passive data could predict changes in mood/anxiety scores as measured via smartphone surveys.

Results: The anomaly detection model was reliably able to predict symptom change of 4 points or greater for depression as measured by the PHQ-9 and anxiety as measured for the GAD-8 for both patient populations, with an area under the ROC curve of 0.65 and 0.80 for each respectively. For both PHQ-9 and GAD-7, these AUCs were maintained when predicting significant symptom change at least 7 days in advance. Active data alone predicted around 52% and 75% of the symptom variability for the depression/anxiety and schizophrenia populations respectively.

Conclusion: These results indicate the feasibility of anomaly detection for predicting symptom change in transdiagnostic cohorts. These results across different patient groups, different countries, and different sites (India and the US) suggest anomaly detection of smartphone digital phenotyping data may offer a reliable and valid approach to predicting symptom change. Future work should emphasize prospective application of these statistical methods.