Depression is more than the sum score of its parts: individual DSM symptoms have different risk factors

Bridging the relationship between physical exercise and mental health in adolescents based on network analysis

Although physical exercise has been recommended as a useful means of enhancing the mental health of adolescents, the exact mechanisms through which physical exercise plays a role are unclear. Both physical exercise and mental health are complex concepts with multiple facets, and traditional methods may constrain the manifestations of their mapping relationships. This research aimed to find the bridging connections between physical exercise and mental health. Mental health and physical exercise behaviors were assessed using the Symptom Checklist 90 (SCL-90) and the Adolescent Physical Activity Questionnaire (PAQ-A) in 9072 Chinese adolescents, respectively. Network analysis was utilized to construct the mental health-physical exercise network and to analyze the relationships between individual physical exercise behaviors and mental health symptoms. Core and bridging nodes were identified based on expected influence (EI) and bridge expected influence (BEI). Gender differences were also examined. The results revealed specific and distinct pathways between physical exercise and mental health (e.g., winter sports-obsessive-compulsive symptoms, winter sports-phobia). For both males and females, anxiety, depression, interpersonal sensitivity, ball sports, and evening activity were the most central symptoms/behaviors, reflecting their relative significance in their respective associations. The nodes with the highest BEI were obsessive-compulsive symptoms and physical education, showing negative associations with nodes in the other community. Furthermore, in the male group, somatization and winter sports stood out as the most positive bridge nodes. Conversely, in the female group, interpersonal sensitivity and sports games were the most positive bridge nodes. These findings illuminate the pathways linking physical exercise and mental health, supporting the implementation of physical exercise in a more elaborate way.

Psychometric properties of International Trauma Questionnaire (ITQ) and symptom structure of complex post-traumatic stress disorder among Chinese patients with MDD

AIMS

To assess the validity and internal reliability of the International Trauma Questionnaire (ITQ) among patients diagnosed with major depressive disorder (MDD) and to explore the network structure of Complex post-traumatic stress disorder (CPTSD) among MDD patients in China.

METHODS

Eligible individuals were recruited from a large tertiary hospital in Guangdong Province. Trained researchers conducted in-person interviews and administered self-report questionnaires, including demographics, medical information, and psychological assessments. Confirmatory factor analyses (CFA) and network analysis were performed, with calculations of Average Variance Extracted (AVE), Cronbach's α, and composite reliability.

RESULTS

A total of 113 patients with MDD participated in this study. The correlated six-factor one-order model was a good representation of the latent structure of ITQ (χ2= 60.114, df = 39, P = 0.017, SRMR = 0.070, RMSEA = 0.050, TLI = 0.952, CFI = 0.972, BIC = 175.508). All ITQ subscales possessed acceptable convergent validity and internal reliability, except for affective dysregulation and re-experiencing. The square root of AVE for affective dysregulation was lower than its correlations with other clusters. Network analysis revealed that node C4 ('I feel worthless'), as a core symptom, was significantly associated with the development of CPTSD.

CONCLUSIONS

The clinical applicability of the ITQ was demonstrated by its overall validity and reliability among patients with MDD. However, the affective dysregulation and re-experiencing clusters still need to be revised and enhanced. Timely screening, recognition, and diagnosis are critical due to the worse clinical outcomes seen in comorbid patients.

Characteristics of symptoms and development of psychological status in late Chinese adolescence

BACKGROUND

Many late adolescents experience a state of psychological sub-health, requiring early recognition and intervention. This study aims to assess the psychological state of late Chinese adolescents and uncover developmental trend of mental health through network analysis.

METHOD

We analyzed data from 9072 Chinese high school adolescents in Shandong Province surveyed in 2020-2021, and divided them into the normal, the suspected, and the abnormal groups based on Symptom Checklist 90 (SCL-90) scores. Network analysis was employed to identify the core symptoms and bridge symptoms across different states.

RESULTS

Anxiety and depression were the most central symptoms, without gender differences. Core symptoms, network structure, and network invulnerability varied across different psychological states. The abnormal group exhibited the highest value of natural connectivity, followed by the suspected and normal groups. This pattern extended to bridge networks. While not meeting diagnostic criteria, the suspected group demonstrated abnormalities in network edge invariance and global strength invariance.

LIMITATIONS

The cross-sectional design cannot establish causality, and biases in self-report measurements cannot be ignored.

CONCLUSION

Compared to traditional scale indicators, network structural characteristics may be a more sensitive assessment indicator.

Depressive symptoms during the transition to adolescence: Left hippocampal volume as a marker of social context sensitivity

The transition to adolescence is a critical period for mental health development. Socio-experiential environments play an important role in the emergence of depressive symptoms with some adolescents showing more sensitivity to social contexts than others. Drawing on recent developmental neuroscience advances, we examined whether hippocampal volume amplifies social context effects in the transition to adolescence. We analyzed 2-y longitudinal data from the Adolescent Brain Cognitive Development (ABCD®) study in a diverse sample of 11,832 youth (mean age: 9.914 y; range: 8.917 to 11.083 y; 47.8% girls) from 21 sites across the United States. Socio-experiential environments (i.e., family conflict, primary caregiver's depressive symptoms, parental warmth, peer victimization, and prosocial school environment), hippocampal volume, and a wide range of demographic characteristics were measured at baseline. Youth's symptoms of major depressive disorder were assessed at both baseline and 2 y later. Multilevel mixed-effects linear regression analyses showed that negative social environments (i.e., family conflict, primary caregiver's depressive symptoms, and peer victimization) and the absence of positive social environments (i.e., parental warmth and prosocial school environment) predicted greater increases in youth's depressive symptoms over 2 y. Importantly, left hippocampal volume amplified social context effects such that youth with larger left hippocampal volume experienced greater increases in depressive symptoms in more negative and less positive social environments. Consistent with brain-environment interaction models of mental health, these findings underscore the importance of families, peers, and schools in the development of depression during the transition to adolescence and show how neural structure amplifies social context sensitivity.

The same but different too: Depression profiles in young adults without a history of psychiatric treatment identified using Bayesian and partial correlation networks

Depression is a heterogenous diagnostic construct; however, dynamic interactions between specific depressive symptoms across their qualitatively different profiles remain largely unknown. The study aimed to recognize the most prevalent profiles of depressive symptoms and assess their dynamics in young adults without a history of psychiatric treatment. Depressive symptoms were recorded using the Patient Health Questionnaire-9 (PHQ-9). The data were assessed for all theoretical and empirical combinations of depressive symptoms in participants with a positive screening for depression. The profiles identified in the majority of participants were analyzed using partial correlation and Bayesian networks. Data from 3583 individuals with a positive screening for depression were analyzed. Out of 382 theoretical profiles, 150 profiles (39.3%) were present in this dataset. The majority of participants (56.8%) showed 4 profiles of depressive symptoms including the profile with all depressive symptoms present, the profile without suicidal ideation, the profile without psychomotor impairment, and the profile without both psychomotor impairment and suicidal ideation. The profiles differed largely in terms of their dynamics and symptoms that are necessary to activate the whole network. The network characteristics within specific profiles did not differ significantly across the level of difficulties attributable to depressive symptoms. Our findings indicate that depression emerging in young adults shows a limited number of symptom profiles. However, dynamics of depressive symptoms differs largely between specific profiles regardless of functional impairment indicating the need to personalize therapeutic approaches. Future studies should further disentangle the heterogeneity of depressive symptoms, e.g., by dissecting the symptoms that are combined together by single PHQ-9 items (i.e., hypersomnia and insomnia; psychomotor agitation and retardation).

Polygenic Scores and Networks of Psychopathology Symptoms

Importance

Studies on polygenic risk for psychiatric traits commonly use a disorder-level approach to phenotyping, implicitly considering disorders as homogeneous constructs; however, symptom heterogeneity is ubiquitous, with many possible combinations of symptoms falling under the same disorder umbrella. Focusing on individual symptoms may shed light on the role of polygenic risk in psychopathology.

Objective

To determine whether polygenic scores are associated with all symptoms of psychiatric disorders or with a subset of indicators and whether polygenic scores are associated with comorbid phenotypes via specific sets of relevant symptoms.

Design, Setting, and Participants

Data from 2 population-based cohort studies were used in this cross-sectional study. Data from children in the Avon Longitudinal Study of Parents and Children (ALSPAC) were included in the primary analysis, and data from children in the Twins Early Development Study (TEDS) were included in confirmatory analyses. Data analysis was conducted from October 2021 to January 2024. Pregnant women based in the Southwest of England due to deliver in 1991 to 1992 were recruited in ALSPAC. Twins born in 1994 to 1996 were recruited in TEDS from population-based records. Participants with available genetic data and whose mothers completed the Short Mood and Feelings Questionnaire and the Strength and Difficulties Questionnaire when children were 11 years of age were included.

Main Outcomes and Measures

Psychopathology relevant symptoms, such as hyperactivity, prosociality, depression, anxiety, and peer and conduct problems at age 11 years. Psychological networks were constructed including individual symptoms and polygenic scores for depression, anxiety, attention-deficit/hyperactivity disorder (ADHD), body mass index (BMI), and educational attainment in ALSPAC. Following a preregistered confirmatory analysis, network models were cross-validated in TEDS.

Results

Included were 5521 participants from ALSPAC (mean [SD] age, 11.8 [0.14] years; 2777 [50.3%] female) and 4625 participants from TEDS (mean [SD] age, 11.27 [0.69] years; 2460 [53.2%] female). Polygenic scores were preferentially associated with restricted subsets of core symptoms and indirectly associated with other, more distal symptoms of psychopathology (network edges ranged between r = -0.074 and r = 0.073). Psychiatric polygenic scores were associated with specific cross-disorder symptoms, and nonpsychiatric polygenic scores were associated with a variety of indicators across disorders, suggesting a potential contribution of nonpsychiatric traits to comorbidity. For example, the polygenic score for ADHD was associated with a core ADHD symptom, being easily distracted (r = 0.07), and the polygenic score for BMI was associated with symptoms across disorders, including being bullied (r = 0.053) and not thinking things out (r = 0.041).

Conclusions and Relevance

Genetic associations observed at the disorder level may hide symptom-level heterogeneity. A symptom-level approach may enable a better understanding of the role of polygenic risk in shaping psychopathology and comorbidity.

The comorbidity mechanism of problematic internet use and depression among Chinese college students: A cross-lagged panel network analysis

Problematic internet use (PIU) and depression usually co-occur and are common among college students. According to network theory, it may be attributed to the interplay of symptoms that connect these two mental health problems. However, most studies have failed to examine complex and subtle connections at the symptom level and have not clarified how PIU and depression symptoms are intercorrelated, which symptoms serve as the source of comorbidity (i.e., the central symptoms), and whether such a comorbidity mechanism would change with higher grades. To explore these questions, this study examined four contemporaneous networks and three cross-lagged panel networks, visualizing the symptoms as nodes and the connections between symptoms as edges. A total of 2,420 college students (Mage = 18.35, SD = 0.84; 67.98 % girls) completed four annual surveys. Overall, the results of contemporaneous networks and cross-lagged panel networks indicated that (a) PIU and depression symptoms are intercorrelated; (b) the core symptoms responsible for comorbidity mostly belonged to PIU, and (c) the comorbidity mechanism would change with time. These findings explain the dynamic relation between PIU and depression and identify possible primary symptoms that comorbidity programs can mitigate at different stages of the college years.

Evaluation of symptom network density as a predictor of treatment outcome of inpatient psychotherapy

OBJECTIVE

The network approach implies that the persistence of a mental disorder is rooted in a dense causal interconnection of symptoms. This study attempts to replicate and generalize previous findings in support of the assumption that higher density predicts poorer outcomes. The study examines the predictive value of network density at admission for recovery after inpatient treatment.

METHOD

N = 1375 adult patients with various forms of mental illness were classified as recovered (28%) versus not recovered (72%) after inpatient treatment. Recovery was defined as clinically significant improvement in impairment from admission to discharge. Networks of transdiagnostic symptoms at the time of admission were estimated. Network density, measured by global strength d, was compared between the recovered and not recovered groups using a permutation test.

RESULTS

Global strength at the time of admission tended to be higher in the No-Recovery group (d = 10.83) than the Recovery group (d = 7.53) but the association was not significant (p = .12). Similar results were found after controlling for group size and symptom severity.

CONCLUSION

The predictive value of network density for treatment outcomes remains unclear. There might be structural differences between the groups that the current measure of network density does not adequately represent.

Associations between over-the-counter analgesics usage and symptoms of anxiety and depression in adolescents: a network analysis

BACKGROUND

The use of over-the-counter analgesics (OTCA) is common among adolescents and has been linked with increased symptoms of anxiety and depression. However, little is known about which specific symptoms are most strongly connected to OTCA usage. The current study assessed which anxiety and depression symptoms were most closely associated with OTCA usage in a large sample of adolescents and examined whether this differed across genders.

METHOD

The present study was based on data from 626,581 participants from the Ungdata survey in Norway. Associations between OTCA and anxiety and depression symptoms were examined using network analysis. Non-regularized partial-correlation networks were constructed to estimate the conditional dependent relations between the use of OTCA and symptoms while controlling for pain. Gender-specific networks were created for comparison.

RESULTS

OTCA usage was associated with most symptoms, even after controlling for pain, with the strongest associations with "sleep problems", "stiff or tense", "everything is a struggle" and "suddenly scared". There were some gender differences, showing that "sleep problems" and "hopeless" were more strongly related to OTCA usage in females, whereas "stiff or tense" was more strongly related to OTCA usage in males.

CONCLUSION

Overall, the somatic symptoms of anxiety and depression displayed the strongest associations with OTCA usage. When examining the gender-specific networks, both showed similar trends, although males exhibited slightly stronger associations between OTCA usage and somatic symptoms.

Post-stroke Depressive Symptoms and Cognitive Performances: A Network Analysis

OBJECTIVE

To examine the relationships between post-stroke depression and cognition using network analysis. In particular, we identified central depressive symptoms, central cognitive performances, and bridge components that connect these 2 constructs.

DESIGN

An observational study. We applied network analysis to analyze baseline data to visualize and quantify the relationships between depression and cognition.

SETTING

Home and Community.

PARTICIPANTS

202 participants with mild-to-moderate stroke (N=202; mean age: 59.7 years; 55% men; 55% Whites; 90% ischemic stroke).

INTERVENTION

Not applicable.

MAIN OUTCOME MEASURES

Patient Health Questionnaire (PHQ-8) for depressive symptoms and the NIH Toolbox Cognitive Battery for cognitive performances.

RESULTS

Depressive symptoms were positively intercorrelated with the network, with symptoms from similar domains clustered together. Mood (expected influence=1.58), concentration (expected influence=0.67), and guilt (expected influence=0.63) were the top 3 central depressive symptoms. Cognitive performances also showed similar network patterns, with executive function (expected influence=0.89), expressive language (expected influence=0.68), and processing speed (expected influence=0.48) identified as the top 3 central cognitive performances. Psychomotor functioning (bridge expected influence=2.49) and attention (bridge expected influence=1.10) were the components connecting depression and cognition.

CONCLUSIONS

The central and bridge components identified in this study might serve as targets for interventions against these deficits. Future trials are needed to compare the effectiveness of interventions targeting the central and bridge components vs general interventions treating depression and cognitive impairment as a homogenous clinical syndrome.

Characterizing the hierarchical depression phenotype in sexually diverse individuals

INTRODUCTION

Sexual diverse individuals are at high risk for internalizing psychopathologies, such as depression. Understanding how symptom profiles of heterogeneous psychiatric disorders such as depression differ for sexually diverse vs. heterosexual individuals is thus critical to advance precision psychiatry and maximize our ability to effectively treat members of this population. Research has failed to consider the possibility of hierarchical phenotypes, wherein sexual orientation status may be uniquely and simultaneously associated with both depression broadly and with individual symptoms.

METHOD

To address these issues, we conducted a moderated nonlinear factor analysis in Wave IV of the Add Health study, using sexual diversity status as a predictor of (a) latent depression, (b) factor loadings, and (c) individual symptoms, with and without controlling for race.

RESULTS

Sexual diversity status was positively and simultaneously associated with latent depression, concentration difficulties, and happiness.

DISCUSSION

These findings suggest that sexually diverse populations not only face greater depression, broadly defined, but are disproportionately more likely to experience concentration difficulties and be happier compared to heterosexual counterparts. Methodologically, these models indicate that the CES-D is scalar noninvariant as a function of sexual diversity status (i.e., identical scores on the CES-D may represent different manifestations of depression for sexually diverse and heterosexual participants). Studies examining disparities in depression across heterosexual and sexually diverse samples should thus consider depression broadly as well as specific symptoms. Further, it is critical to examine whether these relations function via different mechanisms.

On the Selection of Item Scores or Composite Scores for Clinical Prediction

Recent shifts to prioritize prediction, rather than explanation, in psychological science have increased applications of predictive modeling methods. However, composite predictors, such as sum scores, are still commonly used in practice. The motivations behind composite test scores are largely intertwined with reducing the influence of measurement error in answering explanatory questions. But this may be detrimental for predictive aims. The present paper examines the impact of utilizing composite or item-level predictors in linear regression. A mathematical examination of the bias-variance decomposition of prediction error in the presence of measurement error is provided. It is shown that prediction bias, which may be exacerbated by composite scoring, drives prediction error for linear regression. This may be particularly salient when composite scores are comprised of heterogeneous items such as in clinical scales where items correspond to symptoms. With sufficiently large training samples, the increased prediction variance associated with item scores becomes negligible even when composite scores are sufficient. Practical implications of predictor scoring are examined in an empirical example predicting suicidal ideation from various depression scales. Results show that item scores can markedly improve prediction particularly for symptom-based scales. Cross-validation methods can be used to empirically justify predictor scoring decisions.

Multilayer depressive symptom networks in adults with bodily pain living in precarious housing or homelessness

Housing insecurity is associated with co-occurring depression and pain interfering with daily activities. Network analysis of depressive symptoms along with associated risk or protective exposures may identify potential targets for intervention in patients with co-occurring bodily pain. In a community-based sample of adults (n = 408) living in precarious housing or homelessness in Vancouver, Canada, depressive symptoms were measured by the Beck Depression Inventory; bodily pain and impact were assessed with the 36-item Short Form Health Survey. Network and bootstrap permutation analyses were used to compare depressive symptoms endorsed by Low versus Moderate-to-Severe (Mod + Pain) groups. Multilayer networks estimated the effects of risk and protective factors. The overall sample was comprised of 78% men, mean age 40.7 years, with 53% opioid use disorder and 14% major depressive disorder. The Mod + Pain group was characterized by multiple types of pain, more persistent pain, more severe depressive symptoms and a higher rate of suicidal ideation. Global network connectivity did not differ between the two pain groups. Suicidal ideation was a network hub only in the Mod + Pain group, with high centrality and a direct association with exposure to lifetime trauma. Antidepressant medications had limited impact on suicidal ideation. Guilt and increased feelings of failure represented symptoms from two other communities of network nodes, and completed the shortest pathway from trauma exposure through suicidal ideation, to the non-prescribed opioid exposure node. Interventions targeting these risk factors and symptoms could affect the progression of depression among precariously housed patients.

Impact of depressive symptoms on medication adherence in older adults with chronic neurological diseases

BACKGROUND

Nonadherence to medication contributes substantially to worse health outcomes. Especially among older adults with chronic illness, multimorbidity leads to complex medication regimes and high nonadherence rates. In previous research, depressive symptomology has been identified as a major contributor to nonadherence, and some authors hypothesize a link via motivational deficits and low self-efficacy. However, the exact mechanisms linking depressive symptomology and nonadherence are not yet understood. This is in part because the often-employed sum scores cannot do justice to the complexity of depressive symptomology; instead, it is recommended to assess the influence of individual symptoms.

METHODS

Following this symptom-based approach, we performed correlation, network and regression analysis using depressive symptoms as depicted by the items of the revised Beck Depression Inventory II (BDI) to assess their influence with nonadherence in N = 731 older adults with chronic neurological diseases. Nonadherence was measured with the self-report Stendal Adherence to Medication Score (SAMS).

RESULTS

Even when controlling for sociodemographic and health-related covariates, the BDI remained the most influential contributor to nonadherence. Across different methods, Loss of Interest and Difficulty with Concentration were identified as particularly influential for nonadherence, linking nonadherence with other affective or somatic BDI items, respectively. Additionally, Fatigue, Problems with Decision Making, Suicidal Thoughts, and Worthlessness contribute to nonadherence.

CONCLUSION

Using a symptom-driven approach, we aimed to understand which depressive symptoms contribute to higher levels of nonadherence. Our results refine previous hypotheses about motivation and control beliefs by suggesting that it is not merely a lack of beliefs in the efficacy of medication that connects depressive symptoms and nonadherence, but rather an overall lack of interest in improving one's health due to feelings of worthlessness and suicidal tendencies. This lack of interest is further substantiated by already sparse resources caused by changes in concentration and fatigue. In order to improve health outcomes and reduce nonadherence, these associations between depressive symptoms must be further understood and targeted in tailored interventions.

Risk factors for internalizing symptoms: The influence of empathy, theory of mind, and negative thinking processes

Internalizing symptoms such as elevated stress and sustained negative affect can be important warning signs for developing mental disorders. A recent theoretical framework suggests a complex interplay of empathy, theory of mind (ToM), and negative thinking processes as a crucial risk combination for internalizing symptoms. To disentangle these relationships, this study utilizes neural, behavioral, and self-report data to examine how the interplay between empathy, ToM, and negative thinking processes relates to stress and negative affect. We reanalyzed the baseline data of N = 302 healthy participants (57% female, Mage  = 40.52, SDage  = 9.30) who participated in a large-scale mental training study, the ReSource project. Empathy and ToM were assessed using a validated fMRI paradigm featuring naturalistic video stimuli and via self-report. Additional self-report scales were employed to measure internalizing symptoms (perceived stress, negative affect) and negative thinking processes (rumination and self-blame). Our results revealed linear associations of self-reported ToM and empathic distress with stress and negative affect. Also, both lower and higher, compared to average, activation in the anterior insula during empathic processing and in the middle temporal gyrus during ToM performance was significantly associated with internalizing symptoms. These associations were dependent on rumination and self-blame. Our findings indicate specific risk constellations for internalizing symptoms. Especially people with lower self-reported ToM and higher empathic distress may be at risk for more internalizing symptoms. Quadratic associations of empathy- and ToM-related brain activation with internalizing symptoms depended on negative thinking processes, suggesting differential effects of cognitive and affective functioning on internalizing symptoms. Using a multi-method approach, these findings advance current research by shedding light on which complex risk combinations of cognitive and affective functioning are relevant for internalizing symptoms.

Characterizing depression after traumatic brain injury using a symptom-oriented approach

BACKGROUND

Progress in addressing depression after traumatic brain injury (TBI) has been limited. Traditional approaches to measuring depression classify individuals with diverse symptoms as having the same problem. We adopted a novel, symptom-oriented approach to characterize post-TBI depression, emphasizing specific symptoms rather than the number of symptoms.

METHODS

We assessed depressive symptoms cross-sectionally in 393 participants with moderate-severe TBI (range 0.4-35.4 years post-injury; M = 12.6) using the Inventory of Depression and Anxiety Symptoms - Expanded Version (IDAS-II). We analyzed symptoms of DSM-5 major depressive disorder (MDD), separating compound symptoms into sub-symptoms. We quantified depression heterogeneity across 16 specific symptoms and explored associations between each symptom and personal, injury-related, treatment, and functional/psychosocial outcome factors.

RESULTS

28 % of participants self-reported a current depression diagnosis, and 35 % met DSM-5 symptom criteria for MDD. Depressed participants (according to either self-reported diagnosis or MDD symptom criteria) were more likely to endorse each specific depressive symptom, including those that overlap with TBI. Post-TBI depression was highly heterogeneous, with 84-91 % of depressed participants (depending on classification method) showing a unique symptom profile not shared with any other individual. The most common symptom profile was shared by only three individuals. This heterogeneity was meaningful, as specific depressive symptoms had distinct associations with personal, injury-related, treatment, and outcome factors.

LIMITATIONS

Cross-sectional design. We only analyzed DSM-5 MDD symptoms, and some symptoms were assessed using only one item.

CONCLUSIONS

A symptom-oriented approach to post-TBI depression captures the individual's unique profile of depressive symptoms, which relate differently to outcomes and other factors. We recommend future studies investigating post-TBI depression analyze specific symptoms alongside overall depression scores.

Predictors for quality of life in older adults: network analysis on cognitive and neuropsychiatric symptoms

BACKGROUND

Quality of life (QoL) of older adults has become a pivotal concern of the public and health system. Previous studies found that both cognitive decline and neuropsychiatric symptoms (NPS) can affect QoL in older adults. However, it remains unclear how these symptoms are related to each other and impact on QoL. Our aim is to investigate the complex network relationship between cognitive and NPS symptoms in older adults, and to further explore their association with QoL.

METHODS

A cross-sectional study was conducted in a sample of 389 older individuals with complaints of memory decline. The instruments included the Neuropsychiatric Inventory, the Mini Mental State Examination, and the 36-item Short Form Health Survey. Data was analyzed using network analysis and mediation analysis.

RESULTS

We found that attention and agitation were the variables with the highest centrality in cognitive and NPS symptoms, respectively. In an exploratory mediation analysis, agitation was significantly associated with poor attention (β = -0.214, P < 0.001) and reduced QoL (β = -0.137, P = 0.005). The indirect effect of agitation on the QoL through attention was significant (95% confidence interval (CI) [-0.119, -0.035]). Furthermore, attention served as a mediator between agitation and QoL, accounting for 35.09% of the total effect.

CONCLUSIONS

By elucidating the NPS-cognition-QoL relationship, the current study provides insights for developing rehabilitation programs among older adults to ensure their QoL.

Networks of C-reactive protein and depression symptoms in patients with stable coronary heart disease: Findings from the Heart and Soul Study

OBJECTIVE

Research addressing the associations between C-reactive protein (CRP) and depression among patients with stable coronary heart disease (CHD) has produced inconsistent results. This might be attributable to varying associations of CRP with specific depression symptom profiles. We responded to this challenge using various network analysis techniques.

METHODS

A total of 967 outpatients with documented CHD were drawn from the baseline cross-sectional data of the Heart and Soul Study. We first estimated mixed graphical models that included CRP and individual depression symptoms, before and after adjusting for relevant covariates, to explore whether CRP is correlated with specific facets of depression. We also investigated whether CRP levels moderated the associations between specific depression symptoms using moderated network models. Finally, we performed a network comparison test and compared the symptom network properties between non-elevated and elevated CRP groups.

RESULTS

In the network model without covariates, CRP was positively associated with fatigue, appetite changes, and psychomotor problems. CRP maintained its negative association with concentration difficulty regardless of covariate adjustment. Few symptom-symptom associations, especially those involving appetite changes, were moderated by CRP. Further, the elevated CRP group showed greater overall symptom connectivity as compared to the non-elevated group.

CONCLUSION

This study segues into CRP-depression relationship with sophisticated methodology.

Psychometric properties of sum scores and factor scores differ even when their correlation is 0.98: A response to Widaman and Revelle

Commentary in Widaman and Revelle (2022) argued that sum scoring is justified as long as unidimensionality holds because sum score reliability is defined. My response begins with a review of the literature supporting the perspective we adopted in the original article. I then conduct simulation studies to assess the psychometric properties of sum scores created using Widaman and Revelle's justification relative to scores created by the weighted factor score approach in the original article. In my simulations, I generate data where sum and factor scores are correlated at 0.96 or 0.98 because high factor-sum score correlations are often used to support the contention that sum and factor scores have interchangeable psychometric properties. I explore (a) correlations between estimated scores and true scores, (b) classification accuracy of sum and factor scores, and (c) reliability of sum and factor scores. Results show that factor scores have (a) higher correlations with true scores (Δ = 0.02-0.04), (b) higher sensitivity (Δ = 4-8 percentage points), and (c) higher reliability (Δ = 0.04-0.07). Factor score performance metrics also have less sampling variability in most conditions. Psychometric properties of sum scores-even when highly correlated with factor scores-remain less desirable than those of factor scores. Additional considerations like models with multiple factors and measurement invariance are also discussed. Essentially, even if accepting Widaman and Revelle's justification for sum scoring, it is uncertain whether researchers generally would want to sum score after fitting a factor analysis unless sum and factor scores correlate at (and not merely close to) 1.00.

The development of depressive symptoms in older adults from a network perspective in the English Longitudinal Study of Ageing

An increased understanding of the interrelations between depressive symptoms among older populations could help improve interventions. However, studies often use sum scores to understand depression in older populations, neglecting important symptom dynamics that can be elucidated in evolving depressive symptom networks. We computed Cross-Lagged Panel Network Models (CLPN) of depression symptoms in 11,391 adults from the English Longitudinal Study of Ageing. Adults aged 50 and above (mean age 65) were followed over 16 years throughout this nine-wave representative population study. Using the eight-item Center for Epidemiological Studies Depression Scale, we computed eight CLPNs covering each consecutive wave. Across waves, networks were consistent with respect to the strength of lagged associations (edge weights) and the degree of interrelationships among symptoms (centrality indices). Everything was an effort and could not get going displayed the strongest reciprocal cross-lagged associations across waves. These two symptoms and loneliness were core symptoms as reflected in strong incoming and outgoing connections. Feeling depressed was strongly predicted by other symptoms only (incoming but not strong outgoing connections were observed) and thus was not related to new symptom onset. Restless sleep had outgoing connections only and thus was a precursor to other depression symptoms. Being happy and enjoying life were the least central symptoms. This research underscores the relevance of somatic symptoms in evolving depression networks among older populations. Findings suggest the central symptoms from the present study (everything was an effort, could not get going, loneliness) may be potential key intervention targets to mitigate depression in older adults.

Distinct latent symptom profiles in late-onset depressive symptoms in community-dwelling older adults

OBJECTIVES

To examine the symptom profiles of late-onset depressive symptoms in a sample of older adults.

METHOD

The sample included 1,192 participants from the National Alzheimer's Coordinating Center Data Set. Participants were ≥65 years old, community-dwelling, and without cognitive impairment or a prior history of depression. Depressive symptoms were assessed using the Geriatric Depression Scale, 15-item (GDS-15). Latent class analysis (LCA) was used to identify and group participants based on profiles of depressive symptoms.

RESULTS

LCA revealed three distinct symptom profiles: (1) an Anhedonia/Amotivation profile with a higher probability of endorsing a combination of low positive emotion and amotivation (6%), (2) an Amotivation/Withdrawal profile with a high probability of endorsing only amotivational depressive symptoms (35%), and (3) an asymptomatic profile with no probability of endorsing any depressive symptoms (59%). Amotivational depressive symptoms were observed across both symptomatic profiles, while depressed mood (e.g. sadness) did not predominantly characterize any profile in this sample. There were also significant differences among symptom profiles in terms of demographic and clinical characteristics.

CONCLUSIONS

Findings highlight the importance of understanding depression at the symptom pattern level. A profile-based diagnostic approach may help improve the recognition of depressive symptoms in older adults.

Longitudinal interactions between residual symptoms and physiological stress in the remitted symptom network structure of depression

Residual symptoms and stress are amongst the most reliable predictors of relapse in remitted depression. Standard methodologies often preclude continuous stress sampling or the evaluation of complex symptom interactions. This limits knowledge acquisition relative to the day-to-day interactions between residual symptoms and stress. The study aims to explore the interactions between physiological stress and residual symptoms network structure in remitted depression. Twenty-two individuals remitted from depression completed baseline, daily diary (DD), and post-DD assessments. Self-reported stress and residual symptoms were measured at baseline and post-DD. Daily diaries required participants to use a wearable electrodermal activity (EDA) device during waking hours and complete residual symptom measures twice daily for 3-weeks. Two-step multilevel vector auto-regression models were used to estimate contemporaneous and dynamic networks. Depressed mood and concentration problems were central across networks. Skin conductance responses (SCRs), suicide, appetite, and sleep problems were central in the temporal and energy loss in the contemporaneous network. Increased SCRs predicted decreased energy loss. Residual symptoms and stress showed bi-directional interactions. Overall, depressed mood and concentration problems were consistently central, thus potentially important intervention targets. Non-obtrusive bio-signal measures should be used to provide the clinical evidence-base for modelling the interactions between depressive residual symptoms and stress. Practical implications are discussed throughout related to focusing on symptom-specific interactions in clinical practice, simultaneously reducing residual symptom and stress occurrences, EDA as pioneering signal for stress detection, and the central role of specific residual symptoms in remitted depression.

Electroconvulsive therapy improves somatic symptoms before mood in patients with depression: A directed network analysis

BACKGROUND

The recent network perspective of depression conceptualizes depression as a dynamic network of causally related symptoms, that contrasts with the traditional view of depression as a discrete latent entity that causes all symptoms. Electroconvulsive therapy (ECT) is an effective treatment for severe depression, but little is known about the temporal trajectories of symptom improvement during a course of ECT.

OBJECTIVE

To gain insight into the dynamics of depressive symptoms in individuals treated with ECT.

METHODS

The Quick Inventory of Depressive Symptomatology (QIDS) was used to assess symptoms twice a week in 68 participants with a unipolar or bipolar depression treated with ECT, with an average of 12 assessments per participant. Dynamic time warping (DTW) was used to analyze individual time series data, which were subsequently aggregated to calculate a directed symptom network and the in- and out-strength for each symptom.

RESULTS

Participants had a mean age of 49.6 (SD = 12.8) and 60% were female. Somatic symptoms (e.g., decreased weight) and suicidal ideation showed the highest out-strength values, indicating that their improvement tended to precede improvements in mood symptoms, which showed high in-strength. Sad mood had the highest in-strength, and thus appeared to be the last symptom to improve during ECT treatment (p < 0.001).

CONCLUSION

This study addresses a gap in the existing literature on ECT, by first analysing the temporal trajectories of symptoms within individual patients and subsequently aggregating them to the group level. The results show that somatic symptoms tend to improve before mood symptoms during ECT.

Neuroanatomy of post-stroke depression: the association between symptom clusters and lesion location

Post-stroke depression affects about 30% of stroke patients and often hampers functional recovery. The diagnosis of depression encompasses heterogeneous symptoms at emotional, motivational, cognitive, behavioural or somatic levels. Evidence indicates that depression is caused by disruption of bio-aminergic fibre tracts between prefrontal and limbic or striatal brain regions comprising different functional networks. Voxel-based lesion-symptom mapping studies reported discrepant findings regarding the association between infarct locations and depression. Inconsistencies may be due to the usage of sum scores, thereby mixing different symptoms of depression. In this cross-sectional study, we used multivariate support vector regression for lesion-symptom mapping to identify regions significantly involved in distinct depressive symptom domains and global depression. MRI lesion data were included from 200 patients with acute first-ever ischaemic stroke (mean 0.9 ± 1.5 days of post-stroke). The Montgomery-Åsberg Depression Rating interview assessed depression severity in five symptom domains encompassing motivational, emotional and cognitive symptoms deficits, anxiety and somatic symptoms and was examined 8.4 days of post-stroke (±4.3). We found that global depression severity, irrespective of individual symptom domains, was primarily linked to right hemispheric lesions in the dorsolateral prefrontal cortex and inferior frontal gyrus. In contrast, when considering distinct symptom domains individually, the analyses yielded much more sensitive results in regions where the correlations with the global depression score yielded no effects. Accordingly, motivational deficits were associated with lesions in orbitofrontal cortex, dorsolateral prefrontal cortex, pre- and post-central gyri and basal ganglia, including putamen and pallidum. Lesions affecting the dorsal thalamus, anterior insula and somatosensory cortex were significantly associated with emotional symptoms such as sadness. Damage to the dorsolateral prefrontal cortex was associated with concentration deficits, cognitive symptoms of guilt and self-reproach. Furthermore, somatic symptoms, including loss of appetite and sleep disturbances, were linked to the insula, parietal operculum and amygdala lesions. Likewise, anxiety was associated with lesions impacting the central operculum, insula and inferior frontal gyrus. Interestingly, symptoms of anxiety were exclusively left hemispheric, whereas the lesion-symptom associations of the other domains were lateralized to the right hemisphere. In conclusion, this large-scale study shows that in acute stroke patients, differential post-stroke depression symptom domains are associated with specific structural correlates. Our findings extend existing concepts on the neural underpinnings of depressive symptoms, indicating that differential lesion patterns lead to distinct depressive symptoms in the first weeks of post-stroke. These findings may facilitate the development of personalized treatments to improve post-stroke rehabilitation.

Understanding suicidality in Pacific adolescents in New Zealand using network analysis

INTRODUCTION

Pacific adolescents in New Zealand (NZ) are three to four times more likely than NZ European adolescents to report suicide attempts and have higher rates of suicidal plans. Suicidal thoughts, plans, and attempts, termed suicidality in this study, result from a complex dynamic interplay of factors, which emerging methodologies like network analysis aim to capture.

METHODS

This study used cross-sectional network analysis to model the relationships between suicidality, self-harm, and individual depression symptoms, whilst conditioning on a multi-dimensional set of variables relevant to suicidality. A series of network models were fitted to data from a community sample of New Zealand-born Pacific adolescents (n = 550; 51% male; Mean age (SD) = 17 (0.35)).

RESULTS

Self-harm and the depression symptom measuring pessimism had the strongest associations with suicidality, followed by symptoms related to having a negative self-image about looks and sadness. Nonsymptom risk factors for self-harm and suicidality differed markedly.

CONCLUSIONS

Depression symptoms varied widely in terms of their contribution to suicidality, highlighting the valuable information gained from analysing depression at the symptom-item level. Reducing the sources of pessimism and building self-esteem presented as potential targets for alleviating suicidality amongst Pacific adolescents in New Zealand. Suicide prevention strategies need to include risk factors for self-harm.

Measuring the active elements of cognitive-behavioral therapies

Understanding how and for whom cognitive-behavioral therapies work is central to the development and improvement of mental health interventions. Suboptimal quantification of the active elements of cognitive-behavioral therapies has hampered progress in elucidating mechanisms of change. To advance process research on cognitive-behavioral therapies, we describe a theoretical measurement framework that focuses on the delivery, receipt, and application of the active elements of these interventions. We then provide recommendations for measuring the active elements of cognitive-behavioral therapies aligned with this framework. Finally, to support measurement harmonization and improve study comparability, we propose the development of a publicly available repository of assessment tools: the Active Elements of Cognitive-Behavioral Therapies Measurement Kit.

The association between different sources of distraction and symptoms of attention deficit hyperactivity disorder

Introduction

Adults with Attention-Deficit/Hyperactivity Disorder (ADHD) are generally distractible. Yet, the precise relationship between ADHD and distractibility remains under-specified in two respects. First, different sources of distraction, such as background noise or mind wandering, may not be equally associated with ADHD. Second, ADHD itself comprises a variety of symptoms that show considerable heterogeneity and it is unclear which ADHD symptoms are associated with which type of distraction.

Methods

The current study addresses these questions using one clinically evaluated sample (N = 69) and two large non-clinically evaluated samples (N = 569, N = 651). In all samples, participants completed questionnaires about their susceptibility to external distraction, unwanted intrusive thoughts, spontaneous mind-wandering and ADHD symptomatology.

Results

Traditional regression and novel network analyses revealed an overwhelming contribution of spontaneous mind-wandering in explaining ADHD symptoms, although external distraction and unwanted intrusive thoughts were also associated with a small number of ADHD symptoms.

Discussion

Findings support a growing body of literature linking spontaneous mind-wandering and ADHD, and they highlight the heterogeneity in the association between ADHD symptoms and different sources of distraction.

Trajectories of depression and generalised anxiety symptoms over the course of cognitive behaviour therapy in primary care: an observational, retrospective cohort

BACKGROUND

Cognitive-behavioural therapy (CBT) has been shown to be an effective treatment for depression and anxiety. However, most research has focused on the sum scores of symptoms. Relatively little is known about how individual symptoms respond.

METHODS

Longitudinal models were used to explore how depression and generalised anxiety symptoms behave over the course of CBT in a retrospective, observational cohort of patients from primary care settings (n = 5306). Logistic mixed models were used to examine the probability of being symptom-free across CBT appointments, using the 9-item Patient Health Questionnaire and the 7-item Generalised Anxiety Disorder scale as measures.

RESULTS

All symptoms improve across CBT treatment. The results suggest that low mood/hopelessness and guilt/worthlessness improved quickest relative to other depressive symptoms, with sleeping problems, appetite changes, and psychomotor retardation/agitation improving relatively slower. Uncontrollable worry and too much worry were the anxiety symptoms that improved fastest; irritability and restlessness improved the slowest.

CONCLUSIONS

This research suggests there is a benefit to examining symptoms rather than sum scores alone. Investigations of symptoms provide the potential for precision psychiatry and may explain some of the heterogeneity observed in clinical outcomes when only sum scores are considered.

The interplay of family history of depression and early trauma: associations with lifetime and current depression in the German national cohort (NAKO)

Introduction

Family history of depression and childhood maltreatment are established risk factors for depression. However, how these factors are interrelated and jointly influence depression risk is not well understood. The present study investigated (i) if childhood maltreatment is associated with a family history of depression (ii) if family history and childhood maltreatment are associated with increased lifetime and current depression, and whether both factors interact beyond their main effects, and (iii) if family history affects lifetime and current depression via childhood maltreatment.

Methods

Analyses were based on a subgroup of the first 100,000 participants of the German National Cohort (NAKO), with complete information (58,703 participants, mean age = 51.2 years, 53% female). Parental family history of depression was assessed via self-report, childhood maltreatment with the Childhood Trauma Screener (CTS), lifetime depression with self-reported physician's diagnosis and the Mini-International Neuropsychiatric Interview (MINI), and current depressive symptoms with the depression scale of the Patient Health Questionnaire (PHQ-9). Generalized linear models were used to test main and interaction effects. Mediation was tested using causal mediation analyses.

Results

Higher frequencies of the childhood maltreatment measures were found in subjects reporting a positive family history of depression. Family history and childhood maltreatment were independently associated with increased depression. No statistical interactions of family history and childhood maltreatment were found for the lifetime depression measures. For current depressive symptoms (PHQ-9 sum score), an interaction was found, with stronger associations of childhood maltreatment and depression in subjects with a positive family history. Childhood maltreatment was estimated to mediate 7%-12% of the effect of family history on depression, with higher mediated proportions in subjects whose parents had a depression onset below 40 years. Abuse showed stronger associations with family history and depression, and higher mediated proportions of family history effects on depression than neglect.

Discussion

The present study confirms the association of childhood maltreatment and family history with depression in a large population-based cohort. While analyses provide little evidence for the joint effects of both risk factors on depression beyond their individual effects, results are consistent with family history affecting depression via childhood maltreatment to a small extent.

Depressive symptom dimensions predict the treatment effect of repetitive transcranial magnetic stimulation for post-stroke depression

OBJECTIVE

Repetitive transcranial magnetic stimulation (rTMS) has attracted considerable attention because of its non-invasiveness, minimal side effects, and treatment efficacy. Despite an adequate duration of rTMS treatment, some patients with post-stroke depression (PSD) do not achieve full symptom response or remission.

METHODS

This was a prospective randomized controlled trial. Participants receiving rTMS were randomly assigned to the ventromedial prefrontal cortex (VMPFC), left dorsolateral prefrontal cortex (DLPFC), or contralateral motor area (M1) groups in a ratio of 1:1:1. Enrollment assessments and data collection were performed in weeks 0, 2, 4, and 8. The impact of depressive symptom dimensions on treatment outcomes were tested using a linear mixed-effects model fitted with maximum likelihood. Univariate analysis of variance (ANOVA) and back-testing were used to analyze the differences between the groups.

RESULTS

In total, 276 patients were included in the analysis. Comparisons across groups showed that 17-item Hamilton Rating Scale for Depression (HAMD-17) scores of the DLPFC group significantly differed from those of the VMPFC and M1 groups at 2, 4, and 8 weeks after treatment (p < 0.05). A higher observed mood score (β = -0.44, 95% confidence interval [CI]: -0.85-0.04, p = 0.030) could predict a greater improvement in depressive symptoms in the DLPFC group. Higher neurovegetative scores (β = 0.60, 95% CI: 0.25-0.96, p = 0.001) could predict less improvement of depressive symptoms in the DLPFC group.

CONCLUSION

Stimulation of the left DLPFC by high-frequency rTMS (HF-rTMS) could significantly improve depressive symptoms in the subacute period of subcortical ischemic stroke, and the dimension of depressive symptoms at admission might predict the treatment effect.

Prevalence and patterns of major depressive disorder and subthreshold depressive symptoms in south China

BACKGROUND

Information on major depressive disorder (MDD) and subthreshold depressive symptoms (SDS) is rarely reported in south China. This study examines the prevalence rates and patterns of MDD and SDS of a large representative sample of adult residents in south China.

METHODS

The Guangdong Mental Health Survey was conducted on adults (over 18 years) from September to December 2021. Multistage stratified cluster sampling was used and face-to-face interviews were done with a two-stage design by trained lay interviewers and psychiatrists. A total of 16,377 inhabitants were interviewed using standardized assessment tools. Data were weighted to adjust for differential probabilities of selection and differential response.

RESULTS

The weighted prevalence rates of MDD and SDS were 2.5 % (95%CI: 2.2 %-2.9 %) and 14.7 % (95%CI: 14.0 %-15.5 %), respectively. Multinomial logistic regression analysis revealed that female, younger age, living in urban area, higher education, unmarried, irregular meal pattern, lack of physical exercise, chronic diseases, irregular napping pattern and short sleep were positively associated with SDS. Besides, female, younger age, unmarried, irregular meal pattern, lack of physical exercise, chronic diseases, short sleep and poor mental health were positively associated with MDD.

LIMITATIONS

The cross-sectional nature of the study limited causal inferences.

CONCLUSIONS

The prevalence of MDD in Guangdong province in 2021 is higher than in mainland China in 2013. Given the higher prevalence of SDS, and high burden of depression, it also offers valuable opportunities for policymakers and health-care professionals to explore the factors affecting mental health in Guangdong province, especially during the COVID-19 epidemic.

An optimization for postpartum depression risk assessment and preventive intervention strategy based machine learning approaches

BACKGROUND

Postpartum depression (PPD) is one of the most common psychiatric disorders for women after delivery. The establishment of an effective PPD prediction model helps to distinguish high-risk groups, and verifying whether such high-risk groups can benefit from drug intervention is very important for clinical guidance.

METHODS

We collected data of parturients that underwent a cesarean delivery. The Control group was divided into a training cohort and a testing cohort. Six different ML models were constructed and we compared their prediction performance in the testing cohort. For model interpretation, we introduced SHapley Additive exPlanations (SHAP). Then, training cohort, ketamine group and dexmedetomidine (DEX) group were classified as high or low risk for PPD by the model. A 1:1 propensity score matching (PSM) was performed to compare the incidence of PPD between two groups in different risk cohorts.

RESULTS

Extreme gradient enhancement (XGB) had the best recognition effect, with an area under the receiver operating characteristic curve (AUROC) of 0.789 (95 % CI 0.742-0.836) in the training cohort and 0.744 (95 % CI 0.655-0.823) in the testing cohort, respectively. A threshold of 21.5 % PPD risk probability was determined. After PSM, the results showed that the incidence of PPD in the two intervention groups was significantly different from the control group in the high-risk cohort (P < 0.001) but not in the low-risk cohort (P > 0.001).

CONCLUSION

Our study demonstrated that the XGB algorithm provided a more accurate in prediction of PPD risk, and it was beneficial to receive early intervention for the high-risk groups distinguished by the model.

Prevalence of depression and its association with quality of life in patients after pacemaker implantation during the COVID-19 pandemic: A network analysis

Background

This study was designed to investigate the prevalence and predictors of depression in patients after pacemaker implantation during the COVID-19 pandemic in addition to identifying specific depressive symptoms associated with quality of life (QOL) using network analysis (NA).

Methods

This cross-sectional, observational study was conducted in China between July 1, 2021, and May 17, 2022. Descriptive analysis was used to calculate depression prevalence. Univariate analyses were used to compare differences in demographic and clinical characteristics between depressed and non-depressed patients following pacemaker implantation. Binary logistic regression analysis was used to assess factors independently associated with depression. Network analysis “expected influence,” and flow function indexes were used to identify symptoms central to the depression network of the sample and depressive symptoms that were directly associated with QOL, respectively. Network stability was examined using a case-dropping bootstrap procedure.

Results

In total, 206 patients implanted with a pacemaker met the study entry criteria and completed the assessment. The overall prevalence of depression (PHQ-9 total score ≥ 5) was 39.92% [95% confidence interval (CI) = 29.37−42.47%]. A binary logistic regression analysis revealed that patients with depression were more likely to report a poor health status (p = 0.031), severe anxiety symptoms (p &lt; 0.001), and fatigue (p &lt; 0.001). In the network model for depression, “Sad mood,” “Poor Energy,” and “Guilt” were the most influential symptoms. “Fatigue” had the strongest negative association with QOL, followed by “Sad mood” and “Appetite”.

Conclusion

Depression is common among patients having undergone pacemaker implantation during the COVID-19 pandemic. Anxiety, central symptoms of depression (i.e., “Sad mood”, “Poor Energy”, and “Guilt”) and depressive symptoms linked to QOL (i.e., “Sad mood”, “Appetite”, and “Fatigue”) identified in this study are promising targets for interventions and preventive measures for depression in patients who have undergone pacemaker implants.

Suicide ideation and anhedonia among clinically stable adolescents with the recurrent depressive disorder during the COVID-19 pandemic: A network perspective

BACKGROUND

Anhedonia is a suicide risk factor among adolescent patients with recurrent depressive disorder (depression hereafter). This study examined associations between suicidal ideation (SI) and residual depressive symptoms (RSD), including anhedonia, among clinically stable adolescents with depression.

METHOD

A network analysis was performed to examine the association between RDS and SI among adolescents with depression. Node-specific predictive betweenness was computed to examine short paths between anhedonia and SI. Additionally, a Network Comparison Test (NCT) was conducted to examine gender differences in derived network model characteristics.

RESULTS

The network analysis identified close associations of PHQ9 ("Suicide ideation") with PHQ1 ("Anhedonia") as well as some other RDS including PHQ6 ("Guilt"), PHQ2 ("Sad mood") and PHQ8 ("Motor disturbances"). Additionally, PHQ2 ("Sad mood") and PHQ4 ("Fatigue") were the main bridge nodes linking anhedonia and SI. Comparisons of network models did not find significant differences in network global strength or edge weights.

LIMITATION

Causal relations between anhedonia and SI could not be determined due to the cross-sectional study design.

CONCLUSIONS

SI was directly related to Anhedonia in addition to Guilt, Sad mood and Motor disturbances. Sad mood and Fatigue were the main bridge nodes linking Anhedonia and SI. To reduce the risk of SI among clinically stable adolescents with depression during the COVID-19 pandemic, specific RDS including Anhedonia, Guilt, Sad mood, Motor disturbances and Fatigue should be targeted in interventions.

Inter-relationships of depressive and anxiety symptoms with suicidality among adolescents: A network perspective

BACKGROUND

Persons with suicidality including suicidal ideation (SI), suicide plans (SP) and/or suicide attempts (SA) are at higher risk for future suicide than those without suicidality. To reduce the risk of future suicide, it is important to understand symptoms of emotional distress that have the strongest links with SI, SP and SA. This network analysis examined item-level relations of depressive and anxiety symptoms with suicidality among adolescents during the COVID-19 pandemic.

METHODS

Adolescents between 12 and 20 years of age were assessed with the Patient Health Questionnaire (PHQ-9), Generalized Anxiety Disorder Scale (GAD-7), and individual binary reponse (no/yes) items assessing SI, SP, and SA during the pandemic. The structure of depressive symptoms, anxiety symptoms and suicidality was characterized using "Expected Influence" and "Bridge Expected Influence" as centrality indices in the symptom network. Network stability was tested using a case-dropping bootstrap procedure. Node-specific predictive betweenness was computed to examine short paths of anhedonia, other depressive symptoms and anxiety symptoms with suicidality. A Network Comparison Test (NCT) was conducted to examine whether network characteristics differed based on gender.

RESULTS

Prevalence rates of depressive symptoms, anxiety symptoms, and suicidality were 44.60 % (95% confidence interval (CI) = 41.53-47.67 %), 31.12 % (95%CI = 28.26-33.98 %), and 16.95 % (95%CI = 14.63-19.26 %), respectively, in the study sample. The network analysis identified GAD3 ("Worry too much") as the most central symptom, followed by GAD6 ("Irritability") and PHQ6 ("Guilt") in the sample. Additionally, PHQ6 ("Guilt"), GAD6 ("Irritability"), and PHQ2 ("Sad mood") were bridge nodes linking depressive and anxiety symptoms with suicidality. A flow network indicated that the connection between S ("Suicidality") and PHQ6 ("Guilt") reflected the strongest connection, followed by connections of S ("Suicidality") with GAD2 ("Uncontrollable worrying"), and S ("Suicidality") with PHQ2 ("Sad mood"). Finally, PHQ2 ("Sad mood") was the main bridge node linking anhedonia with other depressive and anxiety symptoms and suicidality in the sample.

CONCLUSIONS

Findings highlight the potential importance of reducing specific depressive and anxiety symptoms as possible means of reducing suicidality among adolescents during the pandemic. Central symptoms and key bridge symptoms identified in this study should be targeted in suicide prevention for at-risk adolescents.

The association between trauma exposure, polygenic risk and individual depression symptoms

BACKGROUND

Traumatic experiences are associated with increased risk for major depressive disorder (MDD). This study sought to determine the extent that trauma exposure, depression polygenic risk scores (PRS), and their interaction are associated with MDD and individual depression symptoms.

METHODS

Data from 102,182 individuals from the large-scale UK Biobank population cohort was analysed. A series of regression analyses were conducted to estimate the association between trauma, depression PRS and 1) current depression, 2) lifetime MDD case-control status, 3) nine individual current depressive symptoms, and 4) thirteen individual symptoms experienced during a major depressive episode. Additive and multiplicative PRS-by-trauma interactions were also assessed.

RESULTS

Trauma and depression PRS were significantly associated with both current depression and lifetime MDD. A positive, additive interaction effect was observed on depression, but multiplicative interactions were not significant. Trauma exposure and depression PRS were associated with specific patterns of depression symptoms; Trauma was associated with low self-esteem, suicidal ideation, and atypical (but not typical) neurovegetative symptoms. Additive interaction effects were observed on six out of nine current depressive symptoms.

CONCLUSIONS

Trauma exposure and genetic predisposition to depression may lead to particular symptomatology, which may contribute to the extreme clinical heterogeneity observed in individuals with major depression.

Psychopathological network for early-onset post-stroke depression symptoms

BACKGROUND

Post-stroke depression (PSD) can be conceptualized as a complex network where PSD symptoms (PSDS) interact with each other. The neural mechanism of PSD and interactions among PSDS remain to be elucidated. This study aimed to investigate the neuroanatomical substrates of, as well as the interactions between, individual PSDS to better understand the pathogenesis of early-onset PSD.

METHODS

A total of 861 first-ever stroke patients admitted within 7 days poststroke were consecutively recruited from three independent hospitals in China. Sociodemographic, clinical and neuroimaging data were collected upon admission. PSDS assessment with Hamilton Depression Rating Scale was performed at 2 weeks after stroke. Thirteen PSDS were included to develop a psychopathological network in which central symptoms (i.e. symptoms most strongly correlated with other PSDS) were identified. Voxel-based lesion-symptom mapping (VLSM) was performed to uncover the lesion locations associated with overall PSDS severity and severities of individual PSDS, in order to test the hypothesis that strategic lesion locations for central symptoms could significantly contribute to higher overall PSDS severity.

RESULTS

Depressed mood, Psychiatric anxiety and Loss of interest in work and activities were identified as central PSDS at the early stage of stroke in our relatively stable PSDS network. Lesions in bilateral (especially the right) basal ganglia and capsular regions were found significantly associated with higher overall PSDS severity. Most of the above regions were also correlated with higher severities of 3 central PSDS. The other 10 PSDS could not be mapped to any certain brain region.

CONCLUSIONS

There are stable interactions among early-onset PSDS with Depressed mood, Psychiatric anxiety and Loss of interest as central symptoms. The strategic lesion locations for central symptoms may indirectly induce other PSDS via the symptom network, resulting in higher overall PSDS severity.

TRIAL REGISTRATION

URL: http://www.chictr.org.cn/enIndex.aspx ; Unique identifier: ChiCTR-ROC-17013993.

Cyberbully victimization and its association with residual depressive symptoms among clinically stable adolescents with psychiatric disorders during the COVID-19 pandemic: A perspective from network analysis

Objective

This study examined the prevalence of cyberbullying and its relationship with residual depressive symptoms in this patient population during the COVID-19 outbreak using network analysis.

Methods

This was a multicenter, cross-sectional study. Adolescent patients attending maintenance treatment at outpatient departments of three major psychiatric hospitals were included. Experience of cyberbullying was measured with a standard question, while the severity of Internet addiction and depressive symptoms were measured using the Internet Addiction Test and the Patient Health Questionnaire-9, respectively. The network structure of depression and cyberbully were characterized and indices of "Expected Influence" was used to identify symptoms central to the network. To identify particular symptoms that were directly associated with cyberbully, the flow function was used.

Results

Altogether 1,265 patients completed the assessments. The overall prevalence of cyberbullying was 92.3% (95% confidence interval (CI): 90.8-93.7%). Multiple logistic regression analysis revealed that male gender (p = 0.04, OR = 1.72, 95%CI: 1.04-2.85) was significantly associated with higher risk of cyberbullying, while a relapse of illness during the COVID-19 pandemic was significantly associated with a lower risk of cyberbullying (p = 0.03, OR = 0.50, 95%CI: 0.27-0.93). In the network of depression and cyberbully, "Sad mood," "Anhedonia" and "Energy" were the most central (influential) symptoms. Furthermore, "Suicidal ideation" had the strongest negative association with cyberbully followed by "Guilt".

Conclusion

During the COVID-19 pandemic, the experience of cyberbullying was highly prevalent among clinically stable adolescent psychiatric patients, particularly male patients. This finding should raise awareness of this issue emphasizing the need for regular screening and interventions for adolescent patients. Central symptoms (e.g., "Sad mood," "Anhedonia" and "Energy") identified in this study should be targeted in interventions and preventive measures.

C-Reactive Protein and Specific Depression Symptoms Among Older Adults: An Exploratory Investigation of Multi-Plane Networks Using Cross-Sectional Data From NHANES (2017-2020)

INTRODUCTION

Studies investigating the association between C-reactive protein (CRP) and depression among older adults have yielded inconsistent results. We suspect that this may be due to varying associations between CRP and particular depression symptom criteria, and we addressed this challenge using network analysis.

METHODS

We used cross-sectional data from prepandemic National Health and Nutrition Examination Survey questionnaires (2017-2020) and included a sample of 1698 adults aged 65 years or older. Depression symptoms were assessed using the Patient Health Questionnaire-9. Unregularized Mixed Graphical Models were estimated using the R package mgm before and after adjusting for relevant sociodemographic, clinical, and lifestyle covariates.

RESULTS

In the model with no covariates, the only symptom criterion associated with CRP was "appetite problems." This association remained robust after controlling for all covariates. Although not associated with CRP, other criteria such as "fatigue" and "concentration difficulty" showed associations with important covariates for older adults such as white blood cell count or hemoglobin, respectively.

DISCUSSION

The CRP-related variability in the depression symptom network that we have demonstrated may help explain the reported inconsistencies. The present study stands as exploratory, and future research should focus on applying longitudinal designs and including several other inflammatory proteins and covariates that were not measured in the current network model.

Agomelatine in the treatment of anhedonia, somatic symptoms, and sexual dysfunction in major depressive disorder

Objective

This study evaluated the treatment outcomes of agomelatine on anhedonic state, anxiety/somatic symptoms, and sexual function in Chinese patients with major depressive disorder (MDD).

Method

In total, 93 adult patients with MDD were enrolled, and 68 of them were included in a prospective, open-label, multicenter clinical study. All patients received agomelatine monotherapy during a 9-week treatment phase. The effectiveness of the treatment was reflected by the improvement of anhedonia and somatic symptoms based on the 17-item Hamilton Depression Rating Scale (HAMD-17). In addition, the Arizona Sexual Dysfunction Scale (ASEX), Sheehan Disability Scale (SDS), and Short Form of Quality-of-Life Enjoyment and Satisfaction Questionnaire (Q-LES-Q-SF) were administered to all participants at baseline and at the 3-, 6-, and 9-week follow-ups.

Results

After 9 weeks of treatment with agomelatine, the response and remission rates were 73.5% and 39.7%, respectively. Somatic symptoms significantly improved at week 9 (p < 0.001), and significant effects were also observed on the HAMD anhedonia items (p < 0.001). The patients exhibited lower levels of disease severity (the SDS score dropped from 15.52 ± 4.7 to 7.09 ± 5.62 at week 9; the ASEX score dropped from 21.89 ± 4.06 to 16.19 ± 4.79, p < 0.001) and higher levels of QOL (the Q-LES-Q-SF score dropped from 41.02 ± 5.99 to 50.49 ± 8.57, p < 0.001) during the follow-up. Furthermore, treatment with agomelatine improved depressive symptoms without causing serious adverse events.

Conclusion

These analyses indicate that agomelatine is a treatment option for improving anhedonic status, anxiety/somatic symptoms, and sexual dysfunction in MDD patients.

The relationship between dimensions of empathy and symptoms of depression among university students during the COVID-19 pandemic: A network analysis

Background

The relationship between different dimensions of empathy and individual symptoms of depression during the COVID-19 pandemic remains unclear, despite the established link between empathy and depression. The network analysis offers a novel framework for visualizing the association between empathy and depression as a complex system consisting of interacting nodes. In this study, we investigated the nuanced associations between different dimensions of empathy and individual symptoms of depression using a network model during the pandemic.

Methods

1,177 students completed the Chinese version of the Interpersonal Reactivity Index (IRI), measuring dimensions of empathy, and the Chinese version of the Patient Health Questionnaire-9 (PHQ-9), measuring symptoms of depression. First, we investigated the nuanced associations between different dimensions of empathy and individual depressive symptoms. Then, we calculated the bridge expected influence to examine how different dimensions of empathy may activate or deactivate the symptoms of depression cluster. Finally, we conducted a network comparison test to explore whether network characteristics such as empathy-depression edges and bridge nodes differed between genders.

Results

First, our findings showed that personal distress was positively linked to symptoms of depression. These symptoms involved psychomotor agitation or retardation (edge weight = 0.18), sad mood (edge weight = 0.12), trouble with concentrating (edge weight = 0.11), and guilt (edge weight = 0.10). Perspective-taking was found to be negatively correlated with trouble with concentrating (edge weight = -0.11). Empathic concern was negatively associated with suicidal thoughts (edge weight = -0.10) and psychomotor agitation or retardation (edge weight = -0.08). Fantasy was not connected with any symptoms of depression. Second, personal distress and empathic concern were the most positive and negative influential nodes that bridged empathy and depression (values of bridge expected influence were 0.51 and -0.19 and values of predictability were 0.24 and 0.24, respectively). The estimates of the bridge expected influence on the nodes were adequately stable (correlation stability coefficient = 0.75). Finally, no sex differences in the studied network characteristics were observed.

Conclusions

This study applied network analysis to reveal potential pathways between different dimensions of empathy and individual symptoms of depression. The findings supported the existing theoretical system and contribute to the theoretical mechanism. We have also made efforts to suggest interventions and preventions based on personal distress and empathic concern, the two most important dimensions of empathy for depressive symptoms. These efforts may help Chinese university students to adopt better practical methods to overcome symptoms of depression during the COVID-19 pandemic.

Intersecting identities and adolescent depression: Patterns of depressed mood and anhedonia in the past decade

BACKGROUND

Research suggests adolescent depression is increasing and certain adolescents may be uniquely vulnerable. However, limited conceptualizations of identity and time, as well as the reliance on unitary conceptualizations of depression, inhibits a nuanced perspective on these trends. In response, we examined how adolescent depressive symptoms, depressed mood, and anhedonia, vary across intersecting identities over time.

METHODS

Secondary data analysis on the National Survey on Drug Use and Health between 2009 and 2017 was conducted. In total 145,499 nationally representative adolescents (ages 12-17) completed a diagnostic assessment for depression. Lifetime and past year reports of depressive symptoms, depressed mood, and anhedonia were treated as separate variables. A novel, mixed-level model in which participants were nested within identity (defined by one's age, gender, race/ethnicity, poverty level) and time was used to test our aims.

RESULTS

Overall, the relation between depression outcomes and identity did not vary over time (p > .01). Further, identity's impact on depression was approximately ten-fold that of temporal effects. Multiracial, late adolescent, female adolescents were at particular risk. Findings concerning depressed mood and anhedonia were similar across analyses.

LIMITATIONS

All facets of identity (e.g., sexual identity) were not included in the model and a unidimensional measure of poverty may have underestimated its depressogenic influence.

CONCLUSION

Adolescent depression outcomes are mostly consistent across criterial symptom subtypes and time, but vary as a function of identity. Prevention protocols that highlight mechanisms of risk tethered to social identity, and include salient experiences of females, late adolescents, and multiracial youth in particular, need to be prioritized in mental health initiatives.

Symptomatology in 1,112 women screened positive and negative using the Edinburgh postnatal depression scale (EPDS): longitudinal observations from the first trimester to 6 weeks postpartum of a Chinese cohort

PURPOSE

To compare the characteristics of depression-related symptoms identified by individual EPDS items in women who screened positive and negative, and to describe the occurrence of thoughts of self-harm in these women.

METHODS

Based on a Chinese cohort of 1,112 women, scores on each EPDS item were analyzed at 7 time points from the first trimester to 6 weeks postpartum. Scores greater than 0 indicated the presence of symptoms, and higher scores indicated more severe symptoms. We defined the most frequent, serious and important symptoms for screening-positive and screening-negative groups as the item with the highest proportion of respondents scoring 1 or higher, highest proportion scoring 3, and highest average score, respectively.

RESULTS

In screened positive women the most frequent symptom was feeling sad or miserable, and the most serious and important symptoms were both sleeping problems. Among those screened negative, self-blame was the most frequent, serious and important item. For women who screened positive in the first trimester, only self-blame and feeling overwhelmed showed stability over time. Symptoms in women screened negative were relatively stable. Four in ten women who had self-harm thoughts were screened negative.

CONCLUSION

Women who screened positive in EPDS differed from those screened negative in the characteristics in depressive symptoms. Intervention strategies focusing on the most frequent, serious and important symptoms (such as sadness and insomnia) may be worthwhile. Health practitioners should be trained to respond to a positive response to thoughts of self-harm, regardless of whether the women are screened positive or negative.

Structural disconnection-based prediction of poststroke depression

Poststroke depression (PSD) is a common complication of stroke. Brain network disruptions caused by stroke are potential biological determinants of PSD but their conclusive roles are unavailable. Our study aimed to identify the strategic structural disconnection (SDC) pattern for PSD at three months poststroke and assess the predictive value of SDC information. Our prospective cohort of 697 first-ever acute ischemic stroke patients were recruited from three hospitals in central China. Sociodemographic, clinical, psychological and neuroimaging data were collected at baseline and depression status was assessed at three months poststroke. Voxel-based disconnection-symptom mapping found that SDCs involving bilateral temporal white matter and posterior corpus callosum, as well as white matter next to bilateral prefrontal cortex and posterior parietal cortex, were associated with PSD. This PSD-specific SDC pattern was used to derive SDC scores for all participants. SDC score was an independent predictor of PSD after adjusting for all imaging and clinical-sociodemographic-psychological covariates (odds ratio, 1.25; 95% confidence interval, 1.07, 1.48; P = 0.006). Split-half replication showed the stability and generalizability of above results. When added to the clinical-sociodemographic-psychological prediction model, SDC score significantly improved the model performance and ranked the highest in terms of predictor importance. In conclusion, a strategic SDC pattern involving multiple lobes bilaterally is identified for PSD at 3 months poststroke. The SDC score is an independent predictor of PSD and may improve the predictive performance of the clinical-sociodemographic-psychological prediction model, providing new evidence for the brain-behavior mechanism and biopsychosocial theory of PSD.

Relationship between adjustment disorder symptoms and probable diagnosis before and after second lockdown in Israel: longitudinal symptom network analysis

BACKGROUND

There is cumulative evidence of the importance of exploring the change of dynamics between symptoms over time as reflective of consolidation of psychopathology.

AIMS

To explore the interactions between symptoms of ICD-11 adjustment disorder before and after the second lockdown of the COVID-19 pandemic in Israel and identify the most central symptoms and their concurrent and prospective associations with probable adjustment disorder.

METHOD

This is a population-based study drawn from a probability-based internet panel. A representative sample of the adult Israeli population was assessed at two time points (T1, pre-second lockdown, n = 1029, response rate 76.17%; T2, post-second lockdown, n = 764, response rate 74.24%). Symptoms of adjustment disorder were assessed by the International Adjustment Disorder Questionnaire (IADQ).

RESULTS

Although the overall strength of associations at the two measurement points was similar and two same communities were found, there was a significant change in their structure, with a more consolidated network at T2. The most central item was 'difficult to relax' in both networks. Cross-sectionally, all symptoms of failure to adapt significantly predicted adjustment disorder. 'Worry a lot more' (preoccupation) and 'difficult to adapt to life' (failure to adapt) at T1 significantly predicted this diagnosis at T2.

CONCLUSIONS

Adjustment disorder symptoms consolidated during the second lockdown of the pandemic. In line with the ICD-11 conceptualisation of adjustment disorder, both preoccupation and failure-to-adapt symptoms have prognostic validity. This highlights the importance of identifying and targeting adjustment disorder symptoms during a period of stress such as the COVID-19 pandemic.

Impact of cognitive-affective and somatic symptoms in subthreshold depression transition in adults: Evidence from Depression Cohort in China (DCC)

BACKGROUND

Symptoms of subthreshold depression may differentially affect the illness transition. We examined the impact of cognitive-affective and somatic symptoms on different subthreshold depression transitions as well as risk factors influencing the aforementioned symptoms changes.

METHODS

Adults with subthreshold depression in the Depression Cohort in China were enrolled. Data collection was conducted at baseline, 6 and 12 months from 2019 to 2020. Cognitive-affective and somatic symptoms were assessed using the Patient Health Questionnaire-9. A total of 993 participants completed 12-month follow-up and were divided into persistent, intermittent and remission groups according to change in depressive symptoms. The longitudinal change of cognitive-affective and somatic symptoms in the three groups, as well as risk factors was analyzed using the generalized linear mixed-model.

RESULTS

There were 24.07 %, 34.04 % and 41.89 % of participants proceeding into persistent, intermittent and remission subthreshold depression groups, respectively. Cognitive-affective symptoms were the core symptoms for predicting the deterioration in persistent subthreshold depression (t = 2.48, P = 0.013), whereas somatic symptoms improved over time (t = -2.82, P = 0.005). Anxiety symptoms were the primary risk factors for worsening cognitive-affective symptoms (P < 0.001), following by insomnia symptoms, age, marital status, resilience and social functions. Somatic symptoms were affected by insomnia symptoms, anxiety symptoms and Body Mass Index successively.

LIMITATIONS

Major Depressive Episode was not explored in follow-up.

CONCLUSION

Cognitive-affective symptoms in subthreshold depression are at greater risk of illness deterioration. Future studies should endeavor to identify specific risk factors in different symptoms to forestall the transition from subthreshold to Major Depressive Disorder.

Status and Determinants of Symptoms of Anxiety and Depression among Food Delivery Drivers in Shanghai, China

(1) Background: The psychological status of employees, especially vulnerable populations, has received considerable research attention. However, as a newly emerging and popular occupation in the gig industry, food delivery drivers have received little attention. The majority of these workers are immigrants who are already in a precarious position due to a lack of available jobs, inadequate medical care, poor diets, and communication and acculturation difficulties even before they take these jobs, which involve long working hours and exposure to the elements. (2) Methods: To examine the anxiety and depression symptoms of these workers and possible influencing factors, a cross-sectional study was conducted with a sample of food delivery drivers working for the Meituan Company (one of the largest e-platform companies in China). Anxiety and depression scales were adapted from the GAD-7, and the PHQ-9 was used to assess participants' related symptoms. Differences were compared in terms of sociodemographic, work situation, and lifestyle variables. Binary logistic regressions were conducted to analyze the effects of various factors on the two psychological dimensions. (3) Results: Among the 657 participants, the proportions of participants reporting anxiety and depression symptoms were 46.0% and 18.4%, respectively. Lack of communication with leaders (OR_AN = 2.620, 95% CI: 1.528-4.493, p < 0.001; OR_DE = 1.928, 95% CI: 1.039-3.577, p = 0.037) and poor sleep quality (OR_AN = 2.152, 95% CI: 1.587-2.917, p < 0.001; OR_DE = 2.420, 95% CI: 1.672-3.504, p < 0.001) were significant risk factors for both anxiety and depression symptoms. Women (OR = 2.679, 95% CI: 1.621-4.427, p < 0.001), those who climbed ≥31 floors per day (OR = 2.415, 95% CI: 1.189-4.905, p = 0.015), and those with a high frequency of breakfast consumption (OR = 3.821, 95% CI: 1.284-11.369, p = 0.016) were more likely to have anxiety symptoms. Participants who earned less than 5000 RMB (OR = 0.438, 95% CI: 0.204-0.940, p = 0.034), were unwilling to seek medical help (OR = 3.549, 95% CI: 1.846-6.821, p < 0.001), or had a high frequency of smoking (OR = 5.107, 95% CI: 1.187-21.981, p = 0.029) were more likely to be depressive. (4) Conclusion: The existence of communication channels with leaders and good sleep quality are protective factors for anxiety and depression symptoms. Participants who were female, climbed ≥31floors per day, and had a high frequency of eating breakfast were more likely to have anxiety symptoms, while earning less, unwillingness to seek medical help, and a high frequency of smoking were risk factors for depression symptoms.

COVID-19 vaccine acceptance and perceived stigma in patients with depression: a network perspective

The association between coronavirus disease (COVID-19) vaccine acceptance and perceived stigma of having a mental illness is not clear. This study examined the association between COVID-19 vaccine acceptance and perceived stigma among patients with recurrent depressive disorder (depression hereafter) using network analysis. Participants were 1149 depressed patients (842 men, 307 women) who completed survey measures of perceived stigma and COVID-19 vaccine attitudes. T-tests, chi-square tests, and Kruskal-Wallis tests were used to compare differences in demographic and clinical characteristics between depressed patients who indented to accepted vaccines and those who were hesitant. Hierarchical multiple regression analyses assessed the unique association between COVID-19 vaccine acceptance and perceived stigma, independent of depression severity. Network analysis examined item-level relations between COVID-19 vaccine acceptance and perceived stigma after controlling for depressive symptoms. Altogether, 617 depressed patients (53.7%, 95 confidence intervals (CI) %: 50.82-56.58%) reported they would accept future COVID-19 vaccination. Hierarchical multiple regression analyses indicated higher perceived stigma scores predicted lower levels of COVID-19 vaccination acceptance (β = -0.125, P < 0.001), even after controlling for depression severity. In the network model of COVID-19 vaccination acceptance and perceived stigma nodes, "Feel others avoid me because of my illness", "Feel useless", and "Feel less competent than I did before" were the most influential symptoms. Furthermore, "COVID-19 vaccination acceptance" had the strongest connections with illness stigma items reflecting social rejection or social isolation concerns ("Employers/co-workers have discriminated", "Treated with less respect than usual", "Sense of being unequal in my relationships with others"). Given that a substantial proportion of depressed patients reported hesitancy with accepting COVID-19 vaccines and experiences of mental illness stigma related to social rejection and social isolation, providers working with this group should provide interventions to reduce stigma concerns toward addressing reluctance in receiving COVID-19 vaccines.

Computational approaches to treatment response prediction in major depression using brain activity and behavioral data: A systematic review

Major depressive disorder is a heterogeneous diagnostic category with multiple available treatments. With the goal of optimizing treatment selection, researchers are developing computational models that attempt to predict treatment response based on various pretreatment measures. In this paper, we review studies that use brain activity data to predict treatment response. Our aim is to highlight and clarify important methodological differences between various studies that relate to the incorporation of domain knowledge, specifically within two approaches delineated as data-driven and theory-driven. We argue that theory-driven generative modeling, which explicitly models information processing in the brain and thus can capture disease mechanisms, is a promising emerging approach that is only beginning to be utilized in treatment response prediction. The predictors extracted via such models could improve interpretability, which is critical for clinical decision-making. We also identify several methodological limitations across the reviewed studies and provide suggestions for addressing them. Namely, we consider problems with dichotomizing treatment outcomes, the importance of investigating more than one treatment in a given study for differential treatment response predictions, the need for a patient-centered approach for defining treatment outcomes, and finally, the use of internal and external validation methods for improving model generalizability.