Network dynamics of depressive symptoms in antidepressant medication treatment: secondary analysis of eight clinical trials

How Coparenting Is Linked to Depression among Chinese Young Girls and Boys: Evidence from a Network Analysis

This study aimed to explore the relationship between parental coparenting and depression among Chinese young adolescents and potential gender differences via network analysis. Thus, 793 fourth-grade students (girls: 281 (35.40%), Mage = 9.99 years, SD = 0.59 years) were recruited from three primary schools in Northern China. The young adolescents rated their depression and perceived paternal and maternal coparenting. Network analysis was used to detect the central nodes and bridge mechanisms among coparenting and depressive components. The results indicated that paternal and maternal consistency as well as maternal conflict were the most central components in the coparenting-depression network. Paternal consistency, maternal conflict and paternal disparagement in coparenting, as well as somatic complaints and positive affect in adolescents' depression, exhibited high bridge strengths, suggesting those constructs served as vital bridges to connect the two subnetworks. Moreover, paternal consistency showed a higher bridge strength in the boys' network than the girls' one, whereas the edge linking adolescents' positive affect to paternal disparagement and integrity was stronger in the girls' network. This study contributes to the understanding of associations between parental coparenting and young adolescents' depression and offered insights into targeted interventions for early adolescent depression by enhancing parental coparenting.

Direction of dependence analysis for pre-post assessments using non-Gaussian methods: a tutorial

OBJECTIVE

We introduced methods for solving causal direction of dependence between variables observed in pre- and post-psychotherapy assessments, showing how to apply them and investigate their properties via simulations. In addition, we investigated whether changes in depressive symptoms drive changes in social and occupational functioning as suggested by the phase model of psychotherapy or vice versa, or neither.

METHOD

As a Gaussian (normal-distribution) model is unidentifiable here, we used an identifiable linear non-Gaussian structural vector autoregression model, conceptualizing instantaneous effects as during-psychotherapy causation and lagged effects as pre-treatment predictors of change. We tested six alternative estimators in six simulation settings that captured different real-world scenarios, and used real psychotherapy data from 1428 adult patients (Finnish Psychotherapy Quality Registry; assessments on Patient Health Questionnaire-9 and Social and Occupational Functioning Assessment Schedule).

RESULTS

The methodology was successful in identifying causal directions in simulated data. The real-data results provided no evidence for single direction of dependence, suggesting shared or reciprocal causation.

CONCLUSIONS

A powerful new tool was presented to investigate the process of psychotherapy using observational data. Application to patient data suggested that depression symptoms and functioning may reciprocate or reflect third variables instead of one predominantly driving the other during psychotherapy.

Associations between specific depressive symptoms and psychosocial functioning in psychotherapy

BACKGROUND

Psychotherapy for depression aims to reduce symptoms and to improve psychosocial functioning. We examined whether some symptoms are more important than others in the association between depression and functioning over the course of psychotherapy treatment.

METHODS

We studied associations between specific symptoms of depression (PHQ-9) and change in social and occupational functioning (SOFAS), both with structural equation models (considering liabilities of depression and each specific symptom) and with logistic regression models (considering the risk for individual patients). The study sample consisted of adult patients (n = 771) from the Finnish Psychotherapy Quality Registry (FPQR) who completed psychotherapy treatment between September 2018 and September 2021.

RESULTS

Based on our results of logistic regression analyses and SEM models, the baseline measures of depression symptoms were not associated with changes in functioning. Changes in depressed mood or hopelessness, problems with sleep, feeling tired, and feeling little interest or pleasure were associated with improved functioning during psychotherapy. The strongest evidence for symptom-specific effects was found for the symptom of depressed mood or hopelessness.

LIMITATIONS

Due to our naturalistic study design containing only two measurement points, we were unable to study the causal relationship between symptoms and functioning.

CONCLUSIONS

Changes in certain symptoms during psychotherapy may affect functioning independently of underlying depression. Knowledge about the dynamics between symptoms and functioning could be used in treatment planning or implementation. Depressed mood or hopelessness appears to have a role in the dynamic relationship between depression and functioning.

Antidepressant medications in dementia: evidence and potential mechanisms of treatment-resistance

Depression in dementia is common, disabling and causes significant distress to patients and carers. Despite widespread use of antidepressants for depression in dementia, there is no evidence of therapeutic efficacy, and their use is potentially harmful in this patient group. Depression in dementia has poor outcomes and effective treatments are urgently needed. Understanding why antidepressants are ineffective in depression in dementia could provide insight into their mechanism of action and aid identification of new therapeutic targets. In this review we discuss why depression in dementia may be a distinct entity, current theories of how antidepressants work and how these mechanisms of action may be affected by disease processes in dementia. We also consider why clinicians continue to prescribe antidepressants in dementia, and novel approaches to understand and identify effective treatments for patients living with depression and dementia.

Two distinct subtypes of obsessive compulsive disorder revealed by a framework integrating multimodal neuroimaging information

Patients with obsessive compulsive disorder (OCD) exhibit tremendous heterogeneity in structural and functional neuroimaging aberrance. However, most previous studies just focus on group‐level aberrance of a single modality ignoring heterogeneity and multimodal features. On that account, we aimed to uncover OCD subtypes integrating structural and functional neuroimaging features with the help of a multiview learning method and examined multimodal aberrance for each subtype. Ninety‐nine first‐episode untreated patients with OCD and 104 matched healthy controls (HCs) undergoing structural and functional MRI were included in this study. Voxel‐based morphometric and amplitude of low‐frequency fluctuation (ALFF) were adopted to assess gray matter volumes (GMVs) and the spontaneous neuronal fluctuations respectively. Structural/functional distance network was obtained by calculating Euclidean distance between pairs of regional GMVs/ALFF values across patients. Similarity network fusion, one of multiview learning methods capturing shared and complementary information from multimodal data sources, was used to fuse multimodal distance networks into one fused network. Then spectral clustering was adopted to categorize patients into subtypes. As a result, two robust subtypes were identified. These two subtypes presented opposite GMV aberrance and distinct ALFF aberrance compared with HCs while shared indistinguishable clinical and demographic features. In addition, these two subtypes exhibited opposite structure–function difference correlation reflecting distinct adaptive modifications between multimodal aberrance. Altogether, these results uncover two objective subtypes with distinct multimodal aberrance and provide a new insight into taxonomy of OCD.

Depressive symptom complexes of community-dwelling older adults: a latent network model

Late-life depression has multiple, heterogeneous clinical presentations. The aim of the study was to identify higher-order homogeneous clinical features (symptom complexes), while accounting for their potential causal interactions within the network approach to psychopathology. We analyzed cross-sectional data from community-dwelling adults aged 65-85 years recruited by the European MentDis_ICF65+ study (n = 2623, mean age 74, 49% females). The severity of 33 depressive symptoms was derived from the age-adapted Composite International Diagnostic Interview. Symptom complexes were identified using multiple detection algorithms for symptom networks, and their fit to data was assessed with latent network models (LNMs) in exploratory and confirmatory analyses. Sensitivity analyses included the Partial Correlation Likelihood Test (PCLT) to investigate the data-generating structure. Depressive symptoms were organized by the Walktrap algorithm into eight symptom complexes, namely sadness/hopelessness, anhedonia/lack of energy, anxiety/irritability, self-reproach, disturbed sleep, agitation/increased appetite, concentration/decision making, and thoughts of death. An LNM adequately fit the distribution of individual symptoms' data in the population. The model suggested the presence of reciprocal interactions between the symptom complexes of sadness and anxiety, concentration and self-reproach and between self-reproach and thoughts of death. Results of the PCLT confirmed that symptom complex data were more likely generated by a network, rather than a latent-variable structure. In conclusion, late-life depressive symptoms are organized into eight interacting symptom complexes. Identification of the symptom complexes of late-life depression may streamline clinical assessment, provide targets for personalization of treatment, and aid the search for biomarkers and for predictors of outcomes of late-life depression.

Risk factors for suicidal behaviour in late-life depression: A systematic review

BACKGROUND

Suicide is a leading cause of preventable death worldwide, with its peak of maximum incidence in later life. Depression often puts an individual at higher risk for suicidal behaviour. In turn, depression deserves particular interest in old age due to its high prevalence and dramatic impact on health and wellbeing.

AIM

To gather integrated evidence on the potential risk factors for suicide behaviour development in depressive older adults, and to examine the effects of depression treatment to tackle suicide behaviour in this population.

METHODS

A systematic review of empirical studies, published from 2000 onwards, was conducted. Suicidal behaviour was addressed considering its varying forms (i.e., wish to die, ideation, attempt, and completed suicide).

RESULTS

Thirty-five papers were selected for review, comprising both clinical and epidemiological studies. Most of studies focused on suicidal ideation (60%). The studies consistently pointed out that the risk was related to depressive episode severity, psychiatric comorbidity (anxiety or substance use disorders), poorer health status, and loss of functionality. Reduced social support and loneliness were also associated with suicide behaviour in depressive older adults. Finally, the intervention studies showed that suicidal behaviour was a robust predictor of depression treatment response. Reductions in suicidal ideation were moderated by reductions in risk factors for suicide symptoms.

CONCLUSION

To sum up, common and age-specific risk factors seem to be involved in suicide development in depressive older adults. A major effort should be made to tackle this serious public health concern so as to promote older people to age healthily and well.

Evaluation of major depression symptom networks using clinician-rated and patient-rated data

BACKGROUND

Major depressive disorder (MDD) is heterogeneous, but official diagnostic classifications and widely used rating scales are based on the premise that MDD is a single disorder and that symptoms are equally important to assess severity. Also, patients and clinicians frequently diverge in how they evaluate MDD severity. In order to better understand the differences between MDD scales used by clinicians and patients in the context of MDD heterogeneity, we performed a network analysis from an approach that focuses on the interaction of symptoms rather than total score.

METHODS

The Hamilton Depression Rating Scale (HDRS) and the Beck Depression Inventory with 21 items (BDI) scored by the clinician or patient, respectively, were used to estimate the networks based on 794 MDD patients. The networks were estimated using software R 4.0.2 and Graphical Lasso, identifying communities of symptoms by the clique percolation method, and the mixed graphical models were used to evaluate the explained variance of each symptom.

RESULTS

The networks presented different communities of symptoms and connection structure (M = 0.177, p = 0.0028). The guilt connection strength and its association with suicidal ideation was greater in the BDI network.

LIMITATIONS

Transversal data from severe, chronic, or treatment resistant depression patients.

CONCLUSIONS

The present study suggests that the self-rated scale may perform better when assessing association between guilt and other symptoms, especially suicidal ideation. Communities of symptoms and edges between symptoms suggest that insomnia may be an independent symptom, thus requiring specific interventions. Some similar items are strongly connected and could be collapsed.

A Narrative Review of Network Studies in Depression: What Different Methodological Approaches Tell Us About Depression

The network theory of psychopathology proposes that mental disorders arise from direct interactions between symptoms. This theory provides a promising framework to understand the development and maintenance of mental disorders such as depression. In this narrative review, we summarize the literature on network studies in the field of depression. Four methodological network approaches are distinguished: (i) studies focusing on symptoms at the macro-level vs. (ii) on momentary states at the micro-level, and (iii) studies based on cross-sectional vs. (iv) time-series (dynamic) data. Fifty-six studies were identified. We found that different methodological approaches to network theory yielded largely inconsistent findings on depression. Centrality is a notable exception: the majority of studies identified either positive affect or anhedonia as central nodes. To aid future research in this field, we outline a novel complementary network theory, the momentary affect dynamics (MAD) network theory, to understand the development of depression. Furthermore, we provide directions for future research and discuss if and how networks might be used in clinical practice. We conclude that more empirical network studies are needed to determine whether the network theory of psychopathology can indeed enhance our understanding of the underlying structure of depression and advance clinical treatment.