Network analysis of mood symptoms in adolescents with or at high risk for bipolar disorder

Manic Residual Symptoms Also Deserve Attention: A Symptom Network Analysis of Residual Symptoms in Bipolar Disorder

Background

Lots of patients with bipolar disorder (BD) continue to have residual symptoms after treatment in their remission, BD exhibits intricate characteristics and transformation patterns in its residual symptoms, residual symptoms of different polarities and degrees can mix with and transform to each other. There is a need for further investigation of BD as a comprehensive multivariate disease system. The current research lacks network analyses focusing on BD's residual and subsyndromal symptoms.

Methods

242 patients were included with bipolar disorder in remission. We compared demographic data and differences in symptoms between populations with and without residual symptoms using t-tests and chi-square tests, with FDR applied for multiple comparison correction. Logistic regression was used to identify influencing factors for residual symptoms. Symptom networks were compared by network analysis to analyze the relationships between different types of residual symptoms.

Results

Depressive residual symptoms (N=111) were more common than manic residual symptoms (n=29) in the patients included. The comparison between two groups with and without residual symptoms shows no difference in demographic data and medical history information. The main influencing factors related to residual symptoms were time from diagnosis to first treatment (OR=0.88), the first(OR=1.51) and second (OR=17.1)factors of the Mood Disorder Questionnaire (MDQ), the Quick Inventory of Depressive Symptomatology Self-Report (QIDS)(OR=5.28), the psychological(OR=0.68) and environment (OR=1.53) subscale of the World Health Organization Quality of Life Short Form (WHOQOL-BREF). There was a significant difference in network structure between the groups with and without residual symptoms (network invariance difference=0.4, p =0.025). At the same time, there was no significant difference between the groups with and without depressive residual symptoms. However, the symptom network in patients with depressive residual symptoms is more loosely structured than in those without, with symptoms exhibiting weaker interconnections. When there is no depressive or manic residual symptom, it can still form a symptom network and cause an impact on social function.

Conclusion

This study underscores the complexity of bipolar disorder's residual symptoms. Although it primarily manifests as loosely structured depressive residual symptoms, manic residual symptoms should not be ignored. Future research should explore network-based interventions targeting specific symptom clusters or connections to improve residual symptom management and patient outcomes.

Network Structure and Clustering Analysis Relating to Individual Symptoms of Problematic Internet Use in a Community Adolescent Population

INTRODUCTION

Problematic internet use (PIU) is a psychopathology that includes multiple symptoms and psychological constructs. Because no studies have considered both network structures and clusters among individual symptoms in the context of PIU in a Korean adolescent population, this study aimed to investigate network structures and clustering in relation to PIU symptoms in adolescents.

METHODS

Overall, 73,238 adolescents were included. PIU severity was assessed using a self-rating scale comprising 20 items and 6 subscales, namely, the Internet Addiction Proneness Scale for Youth-Short Form; KS scale. Network structures and clusters among symptoms were analyzed using a Gaussian graphical model and exploratory graph analysis, respectively. Centrality of strength, closeness, and betweenness scores was also calculated.

RESULTS

Our study identified four clusters: disturbance in adaptive functioning, virtual interpersonal relationships, withdrawal, and tolerance. The symptom of confidence served as a node bridging the cluster of virtual interpersonal relationships and other clusters of withdrawal and disturbances of adaptive function. The symptom of craving served as a bridge between the clusters of withdrawal and tolerance with high betweenness centrality.

CONCLUSION

This study identified network structures and clustering among PIU symptoms in adolescents and revealed that positive experiences derived from online interpersonal relationships were an important mechanism underlying PIU. These are novel insights concerning the interconnection among multiple symptoms and related clustering for the mechanism of adolescent PIU in terms of KS-scale PIU assessment.

Ultralow-cost piezoelectric sensor constructed by thermal compression bonding for long-term biomechanical signal monitoring in chronic mental disorders

Wearable bioelectronic devices, which circumvent issues related to the large size and high cost of clinical equipment, have emerged as powerful tools for the auxiliary diagnosis and long-term monitoring of chronic psychiatric diseases. Current devices often integrate multiple intricate and expensive devices to ensure accurate diagnosis. However, their high cost and complexity hinder widespread clinical application and long-term user compliance. Herein, we developed an ultralow-cost poly(vinylidene fluoride)/zinc oxide nanofiber film-based piezoelectric sensor in a thermal compression bonding process. Our piezoelectric sensor exhibits remarkable sensitivity (13.4 mV N-1), rapid response (8 ms), and exceptional stability over 2000 compression/release cycles, all at a negligibly low fabrication cost. We demonstrate that pulse wave, blink, and speech signals can be acquired by the sensor, proposing a single biomechanical modality to monitor multiple physiological traits associated with bipolar disorder. This ultralow-cost and mass-producible piezoelectric sensor paves the way for extensive long-term monitoring and immediate feedback for bipolar disorder management.

Affect dimensions and variability during major depressive episodes: Ecological momentary assessment of unipolar, bipolar, and borderline patients and healthy controls

Differentiating major depressive episodes (MDEs) of major depressive disorder (MDD), bipolar disorder (MDE/BD) and the MDEs comorbid with borderline personality disorder (MDE/BPD) is crucial for appropriate treatment, and knowledge of phenomenological differences may aid this. However, studies comparing affect experiences of these three patient groups and healthy subjects are scarce. In our study, participants (N = 114), including patients with MDD (n = 34), MDE/BD (n = 27), and MDE/BPD (n = 24), and healthy controls (HC, n = 29) responded to ecological momentary assessment (EMA) with ten circumplex model affect items ten times daily for seven days (7709 recordings). Explorative factor analysis resulted in two affect dimensions. The positive dimension included active, excited, cheerful (high arousal), and content (low arousal) affects, and the negative dimension irritated, angry, and nervous (high arousal) affects. Relative to HC, patients reported 3.5-fold negative affects (mean MDD 1.36 (SD 0.92), MDE/BD 1.43 (0.76), MDE/BPD 1.81 (0.95) vs. HC 0.44 (0.49) (p < 0.01)) but 0.5-fold positive affects (2.01 (0.90), 1.95 (0.89), 2.24 (1.03), vs. 3.2 (0.95), respectively (p < 0.01)). We used multilevel modelling. Negative-affect within-individual stability was lowest in MDE/BPD and highest in MDD. Negative affect predicted concurrent positive affect more in MDE/BPD than in MDD. Moderate size of subcohorts and no inpatients were limitations. Despite apparently similar MDEs, affective experiences may differ between BPD, BD, and MDD patients. Clinical subgroups of patients with depression may vary in affective instability and concurrent presence of negative and positive affects during depression.

Dynamic time warp analysis of individual symptom trajectories in individuals with bipolar disorder

BACKGROUND

Manic and depressive mood states in bipolar disorder (BD) may emerge from the non-linear relations between constantly changing mood symptoms exhibited as a complex dynamic system. Dynamic Time Warp (DTW) is an algorithm that may capture symptom interactions from panel data with sparse observations over time.

METHODS

The Young Mania Rating Scale and Quick Inventory of Depressive Symptomatology were repeatedly assessed in 141 individuals with BD, with on average 5.5 assessments per subject every 3-6 months. Dynamic Time Warp calculated the distance between each of the 27 × 27 pairs of standardized symptom scores. The changing profile of standardized symptom scores of BD participants was analyzed in individual subjects, yielding symptom dimensions in aggregated group-level analyses. Using an asymmetric time-window, symptom changes that preceded other symptom changes (i.e., Granger causality) yielded a directed network.

RESULTS

The mean age of the BD participants was 40.1 (SD 13.5) years old, and 60% were female participants. Idiographic symptom networks were highly variable between subjects. Yet, nomothetic analyses showed five symptom dimensions: core (hypo)mania (6 items), dysphoric mania (5 items), lethargy (7 items), somatic/suicidality (6 items), and sleep (3 items). Symptoms of the "Lethargy" dimension showed the highest out-strength, and its changes preceded those of "somatic/suicidality," while changes in "core (hypo)mania" preceded those of "dysphoric mania."

CONCLUSION

Dynamic Time Warp may help to capture meaningful BD symptom interactions from panel data with sparse observations. It may increase insight into the temporal dynamics of symptoms, as those with high out-strength (rather than high in-strength) could be promising targets for intervention.

The network structure of mania symptoms differs between people with and without binge eating

OBJECTIVES

People with bipolar disorder who also report binge eating have increased psychopathology and greater impairment than those without binge eating. Whether this co-occurrence is related to binge eating as a symptom or presents differently across full-syndrome eating disorders with binge eating is unclear.

METHODS

We first compared networks of 13 lifetime mania symptoms in 34,226 participants from the United Kingdom's National Institute for Health and Care Research BioResource with (n = 12,104) and without (n = 22,122) lifetime binge eating. Second, in the subsample with binge eating, we compared networks of mania symptoms in participants with lifetime anorexia nervosa binge-eating/purging (n = 825), bulimia nervosa (n = 3737), and binge-eating disorder (n = 3648).

RESULTS

People with binge eating endorsed every mania symptom significantly more often than those without binge eating. Within the subsample, people with bulimia nervosa most often had the highest endorsement rate of each mania symptom. We found significant differences in network parameter statistics, including network structure (M = 0.25, p = 0.001) and global strength (S = 1.84, p = 0.002) when comparing the binge eating with no binge-eating participants. However, network structure differences were sensitive to reductions in sample size and the greater density of the latter network was explained by the large proportion of participants (34%) without mania symptoms. The structure of the anorexia nervosa binge-eating/purging network differed from the bulimia nervosa network (M = 0.66, p = 0.001), but the result was unstable.

CONCLUSIONS

Our results suggest that the presence and structure of mania symptoms may be more associated with binge eating as a symptom rather than any specific binge-type eating disorder. Further research with larger sample sizes is required to confirm our findings.

Identifying the bridge between depression and mania: A machine learning and network approach to bipolar disorder

OBJECTIVES

Although the cyclic nature of bipolarity is almost by definition a network system, no research to date has attempted to scrutinize the relationship of the two bipolar poles using network psychometrics. We used state-of-the-art network and machine learning methodologies to identify symptoms, as well as relations thereof, that bridge depression and mania.

METHODS

Observational study that used mental health data (12 symptoms for depression and 12 for mania) from a large, representative Canadian sample (the Canadian Community Health Survey of 2002). Complete data (N = 36,557; 54.6% female) were analysed using network psychometrics, in conjunction with a random forest algorithm, to examine the bidirectional interplay of depressive and manic symptoms.

RESULTS

Centrality analyses pointed to symptoms relating to emotionality and hyperactivity as being the most central aspects of depression and mania, respectively. The two syndromes were spatially segregated in the bipolar model and four symptoms appeared crucial in bridging them: sleep disturbances (insomnia and hypersomnia), anhedonia, suicidal ideation, and impulsivity. Our machine learning algorithm validated the clinical utility of central and bridge symptoms (in the prediction of lifetime episodes of mania and depression), and suggested that centrality, but not bridge, metrics map almost perfectly onto a data-driven measure of diagnostic utility.

CONCLUSIONS

Our results replicate key findings from past network studies on bipolar disorder, but also extend them by highlighting symptoms that bridge the two bipolar poles, while also demonstrating their clinical utility. If replicated, these endophenotypes could prove fruitful targets for prevention/intervention strategies for bipolar disorders.

Psychomotor agitation and irritability in adolescents with manic episode: Clinical data from three inpatient units

OBJECTIVES

We aimed to investigate the characteristics of adolescents with Bipolar disorder-I with irritability and agitation (Mania+IA) compared to those without irritability and agitation (Mania-IA) in a multi-center representative sample.

METHODS

Data of 145 patients from three tertiary-care inpatient units between 2016 and 2021 were obtained. Psychomotor agitation was defined as a score of ≥3 on the YMRS "Increased Motor Activity--Energy" item, irritability as a score of ≥4 on the YMRS 'irritability' item, and severity anchors of speech and thought disturbance on the YMRS '6 and 7' items.

RESULTS

Previous manic episodes (p = 0.013), involuntary hospitalization (p = 0.006), psychotic features (p = 0.001), formal thought disorder (p = 0.010) and aggressive/disruptive behavior (p = 0.021) were more frequent in the Mania+IA group. Conversely, depressive episodes (p = 0.006) and family history of depression (p = 0.024) were more frequent in the Mania-IA group. The Mania+IA had poorer functioning at the time of discharge.

CONCLUSIONS

Irritability and agitation were closely related to complications, psychotic symptoms and thought disorder. Assessment and monitoring of psychomotor agitation and irritability may help child and adolescent psychiatrists to predict clinical difficulties and appropriate interventions.

Childhood trauma and depressive symptoms in bipolar disorder: A network analysis

BACKGROUND

Childhood trauma is related to an increased number of depressive episodes and more severe depressive symptoms in bipolar disorder. The evaluation of the networks of depressive symptoms-or the patterns of relationships between individual symptoms-among people with bipolar disorder with and without a history of childhood trauma may assist in further clarifying this complex relationship.

METHODS

Data from over 500 participants from the Heinz C. Prechter Longitudinal Study of Bipolar Disorder were used to construct a series of regularised Gaussian Graphical Models. The networks of individual depressive symptoms-self-reported (Patient Health Questionnaire-9; n = 543) and clinician-rated (Hamilton Depression Rating Scale-17; n = 529)-among participants with bipolar disorder with and without a history of childhood trauma (Childhood Trauma Questionnaire) were characterised and compared.

RESULTS

Across the sets of networks, depressed mood consistently emerged as a central symptom (as indicated by strength centrality and expected influence); regardless of participants' history of childhood trauma. Additionally, feelings of worthlessness emerged as a key symptom in the network of self-reported depressive symptoms among participants with-but not without-a history of childhood trauma.

CONCLUSION

The present analyses-although exploratory-provide nuanced insights into the impact of childhood trauma on the presentation of depressive symptoms in bipolar disorder, which have the potential to aid detection and inform targeted intervention development.

Validation of the Bipolar Prodrome Symptom Interview and Scale-Abbreviated Prospective (BPSS-AP) in a clinical sample and healthy controls

BACKGROUND

After the existence of a bipolar disorder (BD) prodrome was established, the development of clinical rating instruments has become relevant that are sufficiently brief to be implemented in real-world clinical practice and that are designed to identify individuals at-risk for BD. This study aimed to validate a shorter version of the Bipolar Prodrome Symptom Interview and Scale (BPSS), the BPSS-Abbreviated Prospective (BPSS-AP), for use among clinical populations.

METHODS

Altogether, 104 adults, comprising individuals diagnosed with BD (n = 17, mania: n = 8, hypomania: n = 9), with major depressive disorder (MDD, n = 38, all currently depressed), and healthy controls (HCs, n = 49), underwent BPSS-AP interviews. The psychometric properties of the BPSS-AP were evaluated, including internal consistency, convergent validity, discriminant validity, and factor structure.

RESULTS

The median (IQR) age was 29 (23-38), 40 (23-55), and 25 (22-28) years, for the BD, MDD, and HC groups, respectively. The BPSS-AP showed excellent internal consistency (Cronbach's α = 0.95). Convergent validity between the BPSS-AP and Young Mania Rating Scale (YMRS) was high (r > 0.7). The BPSS-AP discriminated patients with BD from those with MDD (P < .001) and from HCs (P < .001).

LIMITATIONS

The study design precludes assessment of the predictive validity of the BPSS-AP.

CONCLUSIONS

This study found that the BPSS-AP, a more concise and feasible version of the semi-structured interview for identifying individuals at risk of developing BD, has satisfactory psychometric properties. There is room for further validation and application of the BPSS-AP in clinical settings to evaluate its utility in research and clinical care.

Altered patterns of central executive, default mode and salience network activity and connectivity are associated with current and future depression risk in two independent young adult samples

Neural markers of pathophysiological processes underlying the dimension of subsyndromal-syndromal-level depression severity can provide objective, biologically informed targets for novel interventions to help prevent the onset of depressive and other affective disorders in individuals with subsyndromal symptoms, and prevent worsening symptom severity in those with these disorders. Greater functional connectivity (FC) among the central executive network (CEN), supporting emotional regulation (ER) subcomponent processes such as working memory (WM), the default mode network (DMN), supporting self-related information processing, and the salience network (SN), is thought to interfere with cognitive functioning and predispose to depressive disorders. We examined in young adults (1) relationships among activity and FC in these networks and current depression severity, using a paradigm designed to examine WM and ER capacity in n = 90, age = 21.7 (2.0); (2) the extent to which these relationships were specific to depression versus mania/hypomania; (3) whether findings in a first, "discovery" sample could be replicated in a second, independent, "test" sample of young adults n = 96, age = 21.6 (2.1); and (4) whether such relationships also predicted depression at up to 12 months post scan and/or mania/hypomania severity in (n = 61, including participants from both samples, age = 21.6 (2.1)). We also examined the extent to which there were common depression- and anxiety-related findings, given that depression and anxiety are highly comorbid. In the discovery sample, current depression severity was robustly predicted by greater activity and greater positive functional connectivity among the CEN, DMN, and SN during working memory and emotional regulation tasks (all ps < 0.05 qFDR). These findings were specific to depression, replicated in the independent sample, and predicted future depression severity. Similar neural marker-anxiety relationships were shown, with robust DMN-SN FC relationships. These data help provide objective, neural marker targets to better guide and monitor early interventions in young adults at risk for, or those with established, depressive and other affective disorders.

Predictors of functional outcome in patients with major depression and bipolar disorder: A dynamic network approach to identify distinct patterns of interacting symptoms

The purpose of this study is to use a dynamic network approach as an innovative way to identify distinct patterns of interacting symptoms in patients with Major Depressive Disorder (MDD) and patients with Bipolar Type I Disorder (BD). More precisely, the hypothesis will be testing that the phenotype of patients is driven by disease specific connectivity and interdependencies among various domains of functioning even in the presence of underlying common mechanisms. In a prospective observational cohort study, hundred-forty-three patients were recruited at the Psychiatric Clinic "Villa dei Gerani" (Catania, Italy), 87 patients with MDD and 56 with BD with a depressive episode. Two nested sub-groups were treated for a twelve-week period, which allowed us to explore differences in the pattern of symptom distribution (central vs. peripheral) and their connectedness (strong vs weak) before (T0) and after (T1) treatment. All patients underwent a complete neuropsychological evaluation at baseline (T0) and at T1. A network structure was computed for MDD and BD patients at T0 and T1 from a covariance matrix of 17 items belonging to three domains-neurocognitive, psychosocial, and mood-related (affective) to identify what symptoms were driving the networks. Clinically relevant differences were observed between MDD and BD, at T0 and after 12 weeks of pharmacological treatment. At time T0, MDD patients displayed an affective domain strongly connected with the nodes of psychosocial functioning, while direct connectivity of the affective domain with the neurocognitive cluster was absent. The network of patients with BD, in contrast, revealed a cluster of highly interconnected psychosocial nodes but was guided by neurocognitive functions. The nodes related to the affective domain in MDD are less connected and placed in the periphery of the networks, whereas in BD they are more connected with psychosocial and neurocognitive nodes. Noteworthy is that, from T0 to T1 the "Betweenness" centrality measure was lower in both disorders which means that fewer "shortest paths" between nodes pass through the affective domain. Moreover, fewer edges were connected directly with the nodes in this domain. In MDD patients, pharmacological treatment primarily affected executive functions which seem to improve with treatment. In contrast, in patients with BD, treatment resulted in improvement of overall connectivity and centrality of the affective domain, which seems then to affect and direct the overall network. Though different network structures were observed for MDD and BD patients, data suggest that treatment should include tailored cognitive therapy, because improvement in this central domain appeared to be fundamental for better outcomes in other domains. In sum, the advantage of network analysis is that it helps to predict the trajectory of future phenotype related disease manifestations. In turn, this allows new insights in how to balance therapeutic interventions, involving different fields of function and combining pharmacological and non-pharmacological treatment modalities.

Positive moods are all alike? Differential affect amplification effects of 'elated' versus 'calm' mental imagery in young adults reporting hypomanic-like experiences

Positive mood amplification is a hallmark of the bipolar disorder spectrum (BPDS). We need better understanding of cognitive mechanisms contributing to such elevated mood. Generation of vivid, emotionally compelling mental imagery is proposed to act as an 'emotional amplifier' in BPDS. We used a positive mental imagery generation paradigm to manipulate affect in a subclinical BPDS-relevant sample reporting high (n = 31) vs. low (n = 30) hypomanic-like experiences on the Mood Disorder Questionnaire (MDQ). Participants were randomized to an 'elated' or 'calm' mental imagery condition, rating their momentary affect four times across the experimental session. We hypothesized greater affect increase in the high (vs. low) MDQ group assigned to the elated (vs. calm) imagery generation condition. We further hypothesized that affect increase in the high MDQ group would be particularly apparent in the types of affect typically associated with (hypo)mania, i.e., suggestive of high activity levels. Mixed model and time-series analysis showed that for the high MDQ group, affect increased steeply and in a sustained manner over time in the 'elated' imagery condition, and more shallowly in 'calm'. The low-MDQ group did not show this amplification effect. Analysis of affect clusters showed high-MDQ mood amplification in the 'elated' imagery condition was most pronounced for active affective states. This experimental model of BPDS-relevant mood amplification shows evidence that positive mental imagery drives changes in affect in the high MDQ group in a targeted manner. Findings inform cognitive mechanisms of mood amplification, and spotlight prevention strategies targeting elated imagery, while potentially retaining calm imagery to preserve adaptive positive emotionality.

The Network Structure of Trauma Symptoms of Abuse-exposed Children and Adolescents in South Africa

Network theory promises new ways for conceptualizing, methods for investigating, and state-of-the-art lines of research that will improve our knowledge of mental health in high-risk children and adolescents. This study constructed a symptom network to examine associations between a wide range of trauma symptoms in a sample of children and adolescents (N = 270; M_age = 12.55 yrs, SD = 1.19; 67% = Female) who experienced different forms of abuse (i.e., sexual, physical, emotional and neglect). Symptom-pairs regularized partial correlations, with the Extended Bayesian Information Criterion Graphical Least Absolute Shrinkage and Selection Operator (EBICglasso), were computed to estimate the network structure and centrality measures of the TSCC-SF items. Results show sadness, dissociative amnesia, and sexual arousal to be the most central symptoms in the network, while suicidality was found to be the shortest pathway across all other symptoms (domains). By providing clinicians with specific symptoms to target in interventions, the network framework has the potential to guide and enhance the effectiveness of psychological therapies in high-risk populations.

Estimating the symptom structure of bipolar disorder via network analysis: Energy dysregulation as a central symptom

Using network analysis, we estimated the structure of relations among manic and depressive symptoms, respectively, in 486 patients (59% women; age: M = 37, SD = 12.1) with bipolar disorder prior to their entering a clinical trial. We computed three types of networks: (a) Gaussian graphical models (GGMs) depicting regularized partial correlations, (b) regression-based GGMs depicting nonregularized partial correlations, and (c) directed acyclic graphs (DAGs) via a Bayesian hill-climbing algorithm. Low energy and elevated energy were consistently identified as central nodes in the GGMs and as key parent nodes in the DAGs. Across analyses, pessimism about the future and depressed mood were the symptoms most strongly associated with suicidal thoughts and behavior. These exploratory analyses provide rich information about how bipolar disorder symptoms relate to one another, thereby furnishing a foundation for investigating how bipolar disorder symptoms may operate as a causal system. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Disorder agnostic network structure of psychopathology symptoms in youth

BACKGROUND

Youth mental health disorders are strong predictors of adult mental health disorders. Early identification of mental health disorders in youth is important as it could aid early intervention and prevention. In a disorder agnostic manner, we aimed to identify influential psychopathology symptoms that could impact mental health in youth.

METHODS

This study sampled 6063 participants from the Philadelphia Neurodevelopmental Cohort and comprised of youth of ages 12-21 years. A mixed graphical model was used to estimate the network structure of 115 symptoms corresponding to 16 psychopathology domains. Importance of individual symptoms in the network were assessed using node influence measures such as strength centrality and predictability.

RESULTS

The generated network had stronger associations between symptoms within a psychopathological domain; overall had no negative associations. A conduct disorder symptom eliciting threatening others and a depression symptom - persistent sadness or depressed mood - had the greatest strength centralities (β = 2.85). Fear of traveling in a car and compulsively going in and out a door had the largest predictability (classification accuracy = 0.99). Conduct disorder, depression, and obsessive compulsive disorder symptoms generally had the largest strength centralities. Suicidal thoughts had the largest bridge strength centrality (β = 2.85). Subgroup networks revealed that network structure differed by socioeconomic status (low versus high, p = 0.04) and network connectivity patterns differed by sex (p = 0.01), but not for age or race.

CONCLUSIONS

Psychopathology symptom networks offer insights that could be leveraged for early identification, intervention, and possibly prevention of mental health disorders.

Network Analysis of Neurobehavioral and Posttraumatic Stress Disorder Symptoms One Year after Traumatic Brain Injury: A Veterans Affairs TBI Model Systems Study

Traumatic brain injury (TBI) is often experienced under stressful circumstances that can lead to both symptoms of posttraumatic stress disorder (PTSD) and neurobehavioral symptoms of brain injury. There is considerable symptom overlap in the behavioral expression of these conditions. Psychometric network analysis is a useful approach to investigate the role of specific symptoms in connecting these two disorders and is thus well-suited to explore their interrelatedness. This study applied network analysis to examine the associations among PTSD and TBI symptoms in a sample of Service Members and Veterans (SM/Vs) with a history of TBI one year after injury. Responses to the Neurobehavioral Symptom Inventory (NSI) and PTSC Checklist-Civilian version (PCL-C) were obtained from participants who completed comprehensive inpatient rehabilitation services at five VA polytrauma rehabilitation centers. Participants (N = 612) were 93.1% male with an average age of 36.98 years at injury. The analysis produced a stable network. Within the NSI symptom groups, the frustration symptom was an important bridge between the affective and cognitive TBI symptoms. The PCL-C nodes formed their own small cluster with hyperarousal yielding connections with the affective, cognitive, and somatic symptom groups. Consistent with this observation, the hyperarousal node had the second strongest bridge centrality in the network. Hyperarousal appears to play a key role in holding together this network of distress and thus represents a prime target for intervention among individuals with elevated symptoms of PTSD and a history of TBI. Network analysis offers an empirical approach to visualizing and quantifying the associations among symptoms. The identification of symptoms that are central to connecting multiple conditions can inform diagnostic precision and treatment selection.

Can network analysis of self-reported psychopathology shed light on the core phenomenology of bipolar disorders in adolescents and young adults?

OBJECTIVES

Network analysis is increasingly applied to psychopathology research. We used it to examine the core phenomenology of emerging bipolar disorder (BD I and II) and 'at risk' presentations (major depression with a family history of BD).

METHODOLOGY

The study sample comprised a community cohort of 1867 twin and nontwin siblings (57% female; mean age ~26) who had completed self-report ratings of (i) depression-like, hypomanic-like and psychotic-like experiences; (ii) family history of BD; and (iii) were assessed for mood and psychotic syndromes using the Composite International Diagnostic Interview (CIDI). Symptom networks were compared for recent onset BD versus other cohort members and then for individuals at risk of BD (depression with/without a family history of BD).

RESULTS

The four key symptoms that differentiated recent onset BD from other cohort members were: anergia, psychomotor speed, hypersomnia and (less) loss of confidence. The four key symptoms that differentiated individuals at high risk of BD from unipolar depression were anergia, psychomotor speed, impaired concentration and hopelessness. However, the latter network was less stable and more error prone.

CONCLUSIONS

We are encouraged by the overlaps between our findings and those from two recent publications reporting network analyses of BD psychopathology, especially as the studies recruited from different populations and employed different network models. However, the advantages of applying network analysis to youth mental health cohorts (which include many individuals with multimorbidity) must be weighed against the disadvantages including basic issues such as judgements regarding the selection of items for inclusion in network models.

Irritability and mood symptoms in adolescent girls: Trait anxiety and emotion dysregulation as mediators

BACKGROUND

Irritability is a common symptom in youth that is thought to be predictive of mood disorders. Its effects on mood are likely to be age-dependent, with direct and indirect mediators. We assessed age-related effects and mediators of irritability in adolescent girls with subthreshold depressive and manic symptoms.

METHODS

We analysed the irritability item from the Mood Disorder Questionnaire in 3 cohorts of girls aged 12-18years (N=229); 12-13years (N=82); 14-15years (N=68); and 16-18years (N=79). They also completed mood, anxiety and emotion regulation questionnaires. MANOVA, correlations and bootstrapped mediation analyses were performed with SPSS®v25 and Hayes Processv3.5®.

RESULTS

Overall, irritable girls had higher depressive and manic symptoms, trait anxiety and emotion dysregulation than those who were not irritable. Significantly higher rates of irritability were observed in mid-adolescents (aged 14-15years; p = 0.001). Notably, irritability exerted effects on depressive symptoms via trait anxiety, non-acceptance of emotions and dysregulation in emotion clarity throughout adolescence. However, irritability directly exerted effects on manic symptoms in mid-adolescence but in older adolescents, their relationship was indirect via impulse control dysregulation.

LIMITATIONS

The cross-sectional design and non-clinical sample limit generalisability of our findings.

CONCLUSIONS

Irritability is involved in subthreshold depressive symptoms, via trait anxiety and perceptual emotion dysregulation. On the other hand, irritability is directly and indirectly associated with subthreshold manic symptoms via dysregulated impulse control depending on age. Therefore, screening for irritability, trait anxiety and emotion dysregulation throughout adolescence may facilitate the early detection of subthreshold depressive and manic symptoms, and the implementation of preventive strategies.

A network structure of manic symptoms

OBJECTIVES

The aim of this study is to explore mania as a network of its symptoms, inspired by the network approach to mental disorders.

METHODS

Network structures of both cross-sectional and temporal effects were measured at three time points (admission, middle of hospital stay, and discharge) in a sample of 100 involuntarily committed patients diagnosed with bipolar I disorder with severe manic features and hospitalized in a specialized psychiatric ward.

RESULTS

Elevated mood is the most interconnected symptom in the network on admission, while aggressive behavior and irritability are highly predictive of each other, as well as language-thought disorder and "content" (the presence of abnormal ideas or delusions). Elevated mood is influenced by many symptoms in the temporal network.

CONCLUSIONS

The investigation of manic symptoms with network analysis allows for identifying important symptoms that are better connected to other symptoms at a given moment and over time. The connectivity of the manic symptoms evolves over time. Central symptoms could be considered as targets for clinical intervention when treating severe mania.

Phenotype Network and Brain Structural Covariance Network of Major Depression

Phenotype networks enable clinicians to elucidate the patterns of coexistence and interactions among the clinical symptoms, negative cognitive styles , neurocognitive performance, and environmental factors in major depressive disorder (MDD). Results of phenotype network approach could be used in finding the target symptoms as these are tightly connected or associated with many other phenomena within the phenotype network of MDD specifically when comorbid psychiatric disorder(s) is/are present. Further, by comparing the differential patterns of phenotype networks before and after the treatment, changing or enduring patterns of associations among the clinical phenomena in MDD have been deciphered.Brain structural covariance networks describe the inter-regional co-varying patterns of brain morphologies, and overlapping findings have been reported between the brain structural covariance network and coordinated trajectories of brain development and maturation. Intra-individual brain structural covariance illustrates the degrees of similarities among the different brain regions for how much the values of brain morphological features are deviated from those of healthy controls. Inter-individual brain structural covariance reflects the degrees of concordance among the different brain regions for the inter-individual distribution of brain morphologic values. Estimation of the graph metrics for these brain structural covariance networks uncovers the organizational profile of brain morphological variations in the whole brain and the regional distribution of brain hubs.

The network and dimensionality structure of affective psychoses: an exploratory graph analysis approach

BACKGROUND

The dimensional symptom structure of classes of affective psychoses, and more specifically the relationships between affective and mood symptoms, has been poorly researched. Here, we examined these questions from a network analysis perspective.

METHODS

Using Exploratory Graph Analysis (EGA) and network centrality parameters, we examined the dimensionality and network structure of 28 mood and psychotic symptoms in subjects diagnosed with schizoaffective disorder (n=124), psychotic bipolar disorder (n=345) or psychotic depression (n=245), such as in the global sample of affective psychoses.

RESULTS

EGA identified four dimensions in subjects with schizoaffective or bipolar disorders (depression, mania, positive and negative) and three dimensions in subjects with psychotic depression (depression, psychosis and activation). The item composition of dimensions and the most central symptoms varied substantially across diagnoses. The most central (i.e., interconnected) symptoms in schizoaffective disorder, psychotic bipolar disorder and psychotic depression were hallucinations, delusions and depressive mood, respectively. Classes of affective psychoses significantly differed in terms of network structure but not in network global strength.

LIMITATIONS

The cross-sectional nature of this study precludes conclusions about the causal dynamics between affective and psychotic symptoms.

CONCLUSION

EGA is a powerful tool for examining the dimensionality and network structure of symptoms in affective psychoses showing that both the interconnectivity pattern between affective and psychotic symptoms and the most central symptoms vary across classes of affective psychoses. The findings outline the value of specific diagnoses in explaining the relationships between mood and affective symptoms.