Acta Psychiatrica Scandinavica最新文献

Introduction: The aim of this systematic review is to assess the functional magnetic resonance imaging (fMRI) studies of bipolar disorder (BD) patients that characterize differences in terms of structural, functional, and effective connectivity between the patients with BD, patients with other psychiatric disorders and healthy controls as possible biomarkers for diagnosing the disorder using neuroimaging.

Methods: Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA), guidelines a systematic search for recent (since 2015) original studies on connectivity in bipolar disorder was conducted in PUBMED and SCOPUS.

Results: A total of 60 studies were included in this systematic review: 20 of the structural connectivity, 33 of the functional connectivity, and only 7 of the studies focused on effective connectivity complied with the inclusion and exclusion criteria.

Discussion: Despite the great heterogeneity in the findings, there are several trends that emerge. In structural connectivity studies, the main abnormalities in bipolar disorder patients were in the frontal gyrus, anterior, as well as posterior cingulate cortex and differences in emotion and reward-related networks. Cerebellum (vermis) to cerebrum functional connectivity was found to be the most common finding in BD. Moreover, prefrontal cortex and amygdala connectivity as part of the rich-club hubs were often reported to be disrupted. The most common findings based on effective connectivity were alterations in salience network, default mode network and executive control network. Although more studies with larger sample sizes are needed to ascertain altered brain connectivity as diagnostic biomarker, there is a perspective that the method could be used as a single marker of diagnosis in the future, and the process of adoption could be accelerated by using approaches such as semiunsupervised machine learning.

Background: Facial expressions are a core aspect of non-verbal communication. Reduced emotional expressiveness of the face is a common negative symptom of schizophrenia, however, quantifying negative symptoms can be clinically challenging and involves a considerable element of rater subjectivity. We used computer vision to investigate if (i) automated assessment of facial expressions captures negative as well as positive and general symptom domains, and (ii) if automated assessments are associated with treatment response in initially antipsychotic-naïve patients with first-episode psychosis.

Method: We included 46 patients (mean age 25.4 (6.1); 65.2% males). Psychopathology was assessed at baseline and after 6 weeks of monotherapy with amisulpride using the Positive and Negative Syndrome Scale (PANSS). Baseline interview videos were recorded. Seventeen facial action units (AUs), that is, activation of muscles, from the Facial Action Coding System were extracted using OpenFace 2.0. A correlation matrix was calculated for each patient. Facial expressions were identified using spectral clustering at group-level. Associations between facial expressions and psychopathology were investigated using multiple linear regression.

Results: Three clusters of facial expressions were identified related to different locations of the face. Cluster 1 was associated with positive and general symptoms at baseline, Cluster 2 was associated with all symptom domains, showing the strongest association with the negative domain, and Cluster 3 was only associated with general symptoms. Cluster 1 was significantly associated with the clinically rated improvement in positive and general symptoms after treatment, and Cluster 2 was significantly associated with clinical improvement in all domains.

Conclusion: Using automated computer vision of facial expressions during PANSS interviews did not only capture negative symptoms but also combinations of the three overall domains of psychopathology. Moreover, automated assessments of facial expressions at baseline were associated with initial antipsychotic treatment response. The findings underscore the clinical relevance of facial expressions and motivate further investigations of computer vision in clinical psychiatry.

Introduction: Affective instability represents an important, transdiagnostic biobehavioural dimension of mental ill health and clinical outcome. The causes of affective instability remain unclear. This systematic review and meta-analysis evaluated the extent to which exposure to childhood adversity is associated with affective instability across psychiatric disorders, and which forms of adversity are most strongly associated with affective instability.

Methods: The review followed a published protocol (PROSPERO: CRD42020168676). Searches in Medline, Embase and PsychInfo identified studies using quantitative measures of childhood adversity and affective instability, published between January 1980 and July 2023. Data were analysed using a random effects meta-analysis separately for each outcome, namely affective lability, emotion dysregulation, and rapid cycling. The Mixed-Methods Appraisal Tool was used to appraise the quality of the literature.

Results: The search identified 36 studies involving 8431 participants. All reports focused on cross-sectional associations. We did not identify any prospective longitudinal research. The analysis showed small, but statistically significant effects of childhood adversity on affective lability (r = 0.09, 95% CI 0.02, 0.17), emotion dysregulation (r = 0.25, 95% CI 0.19, 0.32), and rapid cycling (OR = 1.39; 95% CI 1.14, 1.70). When considering adversity subtypes, emotional abuse showed the strongest effect on affective lability (r = 0.16, 95% CI 0.07, 0.24) and emotion dysregulation (r = 0.32, 95% CI 0.19, 0.44). Quality assessment scores were generally low. Most studies failed to control for confounding factors or offer assurances around the representativeness of the samples.

Conclusions: The findings suggest that childhood adversity, particularly emotional abuse, is associated emotional instability in adulthood, but further prospective longitudinal research is needed to confirm causality. The findings have implications for the prevention and treatment of affective instability across psychiatric disorders.

Introduction: Voice features could be a sensitive marker of affective state in bipolar disorder (BD). Smartphone apps offer an excellent opportunity to collect voice data in the natural setting and become a useful tool in phase prediction in BD.

Aims of the study: We investigate the relations between the symptoms of BD, evaluated by psychiatrists, and patients' voice characteristics. A smartphone app extracted acoustic parameters from the daily phone calls of n = 51 patients. We show how the prosodic, spectral, and voice quality features correlate with clinically assessed affective states and explore their usefulness in predicting the BD phase.

Methods: A smartphone app (BDmon) was developed to collect the voice signal and extract its physical features. BD patients used the application on average for 208 days. Psychiatrists assessed the severity of BD symptoms using the Hamilton depression rating scale -17 and the Young Mania rating scale. We analyze the relations between acoustic features of speech and patients' mental states using linear generalized mixed-effect models.

Results: The prosodic, spectral, and voice quality parameters, are valid markers in assessing the severity of manic and depressive symptoms. The accuracy of the predictive generalized mixed-effect model is 70.9%-71.4%. Significant differences in the effect sizes and directions are observed between female and male subgroups. The greater the severity of mania in males, the louder (β = 1.6) and higher the tone of voice (β = 0.71), more clearly (β = 1.35), and more sharply they speak (β = 0.95), and their conversations are longer (β = 1.64). For females, the observations are either exactly the opposite-the greater the severity of mania, the quieter (β = -0.27) and lower the tone of voice (β = -0.21) and less clearly (β = -0.25) they speak - or no correlations are found (length of speech). On the other hand, the greater the severity of bipolar depression in males, the quieter (β = -1.07) and less clearly they speak (β = -1.00). In females, no distinct correlations between the severity of depressive symptoms and the change in voice parameters are found.

Conclusions: Speech analysis provides physiological markers of affective symptoms in BD and acoustic features extracted from speech are effective in predicting BD phases. This could personalize monitoring and care for BD patients, helping to decide whether a specialist should be consulted.

Objective: This study addresses the limitations of existing interventions for depression, such as a deficit-oriented focus, overlooking the utilization of positive elements such as nature, and neglecting the incorporation of group effects. The present feasibility study examines FlowVR, a resource-oriented, nature-inspired virtual reality (VR)-based group therapy. Previously tested individually in a pilot study for non-clinical participants, FlowVR has demonstrated positive effects on depressive symptoms. This study assesses the impact and safety of FlowVR in a group setting within a clinical sample using a one-armed study design.

Method: Forty-two inpatients and day patients with depression were recruited. Before and after the FlowVR intervention period of 4 weeks (two sessions per week), depressive symptoms were assessed (Beck Depression Inventory-II; BDI). Symptomatology (i.e., depressive symptoms), depression-associated variables (i.e., self-efficacy), intervention-specific variables (feeling of flow), and VR-specific variables (simulator sickness) were assessed before and after each session.

Results: Linear mixed effect models showed that symptomatology (depression, negative affect, current anxiety), depression-associated constructs (self-efficacy, motivation), and intervention-specific variables (flow) improved over the course of the sessions. No variable deteriorated more in one session compared to any other session. The lasso regression identified five potential predictors for the change in depression (BDI-II), yet these could not be validated in a subsequent linear regression analysis.

Conclusion: To conclude, FlowVR had the hypothesized positive impact over the course of the sessions, showing, for example, improvements in symptomatology. The sessions have demonstrated safety with no notable deteriorations. Therefore, FlowVR is deemed safe for clinical patients and group settings. However, further research is needed to explore predictors for the change in depression.