A Network Approach to Bipolar Symptomatology in Patients with Different Course Types

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.

Predicting prognosis for adults with depression using individual symptom data: a comparison of modelling approaches

BACKGROUND

This study aimed to develop, validate and compare the performance of models predicting post-treatment outcomes for depressed adults based on pre-treatment data.

METHODS

Individual patient data from all six eligible randomised controlled trials were used to develop (k = 3, n = 1722) and test (k = 3, n = 918) nine models. Predictors included depressive and anxiety symptoms, social support, life events and alcohol use. Weighted sum scores were developed using coefficient weights derived from network centrality statistics (models 1-3) and factor loadings from a confirmatory factor analysis (model 4). Unweighted sum score models were tested using elastic net regularised (ENR) and ordinary least squares (OLS) regression (models 5 and 6). Individual items were then included in ENR and OLS (models 7 and 8). All models were compared to one another and to a null model (mean post-baseline Beck Depression Inventory Second Edition (BDI-II) score in the training data: model 9). Primary outcome: BDI-II scores at 3-4 months.

RESULTS

Models 1-7 all outperformed the null model and model 8. Model performance was very similar across models 1-6, meaning that differential weights applied to the baseline sum scores had little impact.

CONCLUSIONS

Any of the modelling techniques (models 1-7) could be used to inform prognostic predictions for depressed adults with differences in the proportions of patients reaching remission based on the predicted severity of depressive symptoms post-treatment. However, the majority of variance in prognosis remained unexplained. It may be necessary to include a broader range of biopsychosocial variables to better adjudicate between competing models, and to derive models with greater clinical utility for treatment-seeking adults with depression.

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).

Structure and stability of symptoms in first episode psychosis: a longitudinal network approach

Early psychosis is characterised by heterogeneity in illness trajectories, where outcomes remain poor for many. Understanding psychosis symptoms and their relation to illness outcomes, from a novel network perspective, may help to delineate psychopathology within early psychosis and identify pivotal targets for intervention. Using network modelling in first episode psychosis (FEP), this study aimed to identify: (a) key central and bridge symptoms most influential in symptom networks, and (b) examine the structure and stability of the networks at baseline and 12-month follow-up. Data on 1027 participants with FEP were taken from the National EDEN longitudinal study and used to create regularised partial correlation networks using the 'EBICglasso' algorithm for positive, negative, and depressive symptoms at baseline and at 12-months. Centrality and bridge estimations were computed using a permutation-based network comparison test. Depression featured as a central symptom in both the baseline and 12-month networks. Conceptual disorganisation, stereotyped thinking, along with hallucinations and suspiciousness featured as key bridge symptoms across the networks. The network comparison test revealed that the strength and bridge centralities did not differ significantly between the two networks (C = 0.096153; p = 0.22297). However, the network structure and connectedness differed significantly from baseline to follow-up (M = 0.16405, p = <0.0001; S = 0.74536, p = 0.02), with several associations between psychosis and depressive items differing significantly by 12 months. Depressive symptoms, in addition to symptoms of thought disturbance (e.g. conceptual disorganisation and stereotyped thinking), may be examples of important, under-recognized treatment targets in early psychosis, which may have the potential to lead to global symptom improvements and better recovery.

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.

Network Analysis of Psychopathology: Controversies and Challenges

Empirical publications inspired by the network approach to psychopathology have increased exponentially in the twenty-first century. The central idea that an episode of mental disorder arises from causal interactions among its symptomatic elements has especially resonated with those clinical scientists whose disenchantment with traditional categorical and dimensional approaches to mental illness has become all too apparent. As the field has matured, conceptual and statistical concerns about the limitations of network approaches to psychopathology have emerged, inspiring the development of novel methods to address these concerns. Rather than reviewing the vast empirical literature, I focus instead on the issues and controversies regarding this approach and sketch directions where the field might go next.

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.

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.

Using network analysis to explore cognitive domains in patients with unipolar versus bipolar depression: a prospective naturalistic study

OBJECTIVE

Despite growing evidence in the field of cognitive function in mood disorders, the neurocognitive profiles of patients with unipolar and bipolar depression still need further characterization. In this study, we applied network analysis, hypothesizing this approach could highlight differences between major depressive disorder (MDD) and bipolar disorder (BD) from a cognitive perspective.

METHODS

The cognitive performance of 109 patients (72 unipolar and 37 bipolar depressed outpatients) was assessed through the Montreal Cognitive Assessment (MoCA), and a series of clinical variables were collected. Differences in cognitive performance between MDD and BD patients were tested using non-parametric tests. Moreover, a network graph representing MoCA domains as nodes and Spearman's rho correlation coefficients between the domains as edges was constructed for each group.

RESULTS

The presence of mild cognitive impairment was observed in both MDD and BD patients during depression. No statistical significant difference was found between the two groups in terms of overall cognitive performance and across single domains. Nonetheless, network analytic metrics demonstrated different roles of memory and executive dysfunction in MDD versus BD patients: in particular, MDD network was more densely interconnected than BD network, and memory was the node with the highest betweenness and closeness centrality in MDD, while executive function was more central in BD.

CONCLUSIONS

From a network analytic perspective, memory impairment displays a central role in the cognitive impairment of patients with unipolar depression, whereas executive dysfunction appears to be more central in bipolar depression. Further research is warranted to confirm our results.

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

OBJECTIVES

Network analyses of psychopathology examine the relationships between individual symptoms in an attempt to establish the causal interactions between symptoms that may give rise to episodes of psychiatric disorders. We conducted a network analysis of mood symptoms in adolescents with or at risk for bipolar spectrum disorders.

METHODS

The sample consisted of 272 treatment-seeking adolescents with or at high risk for bipolar disorder who had at least subsyndromal depressive or (hypo)manic symptoms. Based on symptom scores assessed via semi-structured interviews, we constructed the network of depressive and manic symptoms and identified the most central symptoms and symptom communities within the network. We used bootstrapping analyses to determine the reliability of network parameters.

RESULTS

Symptoms within the depressive and manic mood poles were more related to each other than to symptoms of the opposing mood pole. Four communities were identified, including a depressive symptom community and three manic symptom communities. Fatigue and depressed mood were the strongest individual symptoms within the overall network (ie the most highly correlated with other symptoms), followed by motor hyperactivity. Mood lability and irritability were found to be "bridge" symptoms that connected the two mood poles.

CONCLUSIONS

Symptoms of activity/energy (ie fatigue and hyperactivity) and depressed mood are the most prominent mood symptoms among youth with bipolar spectrum disorders. Mood lability and irritability represent potential warning signs of emergent episodes of either polarity. Targeting these central and bridge symptoms would lead to more efficient assessments and therapeutic interventions for bipolar disorder.

The Differential Role of Central and Bridge Symptoms in Deactivating Psychopathological Networks

The network model of psychopathology suggests that central and bridge symptoms represent promising treatment targets because they may accelerate the deactivation of the network of interactions between the symptoms of mental disorders. However, the evidence confirming this hypothesis is scarce. This study re-analyzed a convenience sample of 51 cross-sectional psychopathological networks published in previous studies addressing diverse mental disorders or clinically relevant problems. In order to address the hypothesis that central and bridge symptoms are valuable treatment targets, this study simulated five distinct attack conditions on the psychopathological networks by deactivating symptoms based on two characteristics of central symptoms (degree and strength), two characteristics of bridge symptoms (overlap and bridgeness), and at random. The differential impact of the characteristics of these symptoms was assessed in terms of the magnitude and the extent of the attack required to achieve a maximum impact on the number of components, average path length, and connectivity. Only moderate evidence was obtained to sustain the hypothesis that central and bridge symptoms constitute preferential treatment targets. The results suggest that the degree, strength, and bridgeness attack conditions are more effective than the random attack condition only in increasing the number of components of the psychopathological networks. The degree attack condition seemed to perform better than the strength, bridgeness, and overlap attack conditions. Overlapping symptoms evidenced limited impact on the psychopathological networks. The need to address the basic mechanisms underlying the structure and dynamics of psychopathological networks through the expansion of the current methodological framework and its consolidation in more robust theories is stressed.

The Study of Psychopathology from the Network Analysis Perspective: A Systematic Review

BACKGROUND

Network analysis (NA) is an analytical tool that allows one to explore the map of connections and eventual dynamic influences among symptoms and other elements of mental disorders. In recent years, the use of NA in psychopathology has rapidly grown, which calls for a systematic and critical analysis of its clinical utility.

METHODS

Following PRISMA guidelines, a systematic review of published empirical studies applying NA in psychopathology, between 2010 and 2017, was conducted. We included the literature published in PubMed and PsycINFO using as keywords any combination of "network analysis" with the terms "anxiety," "affective disorders," "depression," "schizophrenia," "psychosis," "personality disorders," "substance abuse" and "psychopathology."

RESULTS

The review showed that NA has been applied in a plethora of mental disorders in adults (i.e., 13 studies on anxiety disorders; 19 on mood disorders; 7 on psychosis; 1 on substance abuse; 1 on borderline personality disorder; 18 on the association of symptoms between disorders), and 6 on childhood and adolescence.

CONCLUSIONS

A critical examination of the results of each study suggests that NA helps to identify, in an innovative way, important aspects of psychopathology like the centrality of the symptoms in a given disorder as well as the mutual dynamics among symptoms. Yet, despite these promising results, the clinical utility of NA is still uncertain as there are important limitations on the analytic procedures (e.g., reliability of indices), the type of data included (e.g., typically restricted to secondary analysis of already published data), and ultimately, the psychometric and clinical validity of the results.

Impulsivity predicts illness severity in long-term course of bipolar disorder: A prospective approach

BACKGROUND

Bipolar disorder is a common, severe and chronic mental illness. Despite this, predictors of illness severity remain poorly understood. Impulsivity is reported to be associated with bipolar disorder and aggravating comorbidities. This study therefore sought to examine the predictive value of impulsivity for determining illness severity in euthymic bipolar disorder patients.

METHODS

Baseline trait impulsivity of 120 bipolar euthymic patients (81 bipolar disorder I [68%], 80 female [67%]) and 51 healthy controls was assessed using Barratt Impulsiveness Scale 11. The impact of impulsivity on illness severity (measured with morbidity index) was prospectively tested in 97 patients with sufficient follow-up data (average observation time: 54.4 weeks), using linear regression analysis.

RESULTS

Barratt Impulsiveness Scale 11 total (β = 0.01; p < 0.01) and in particular Barratt Impulsiveness Scale 11 attentional subscale scores (β = 0.04; p < 0.001) predicted illness severity in bipolar disorder, while controlling for other clinical variables. Only age at onset persisted as an additional, but less influential predictor. Barratt Impulsiveness Scale 11 total scores and Barratt Impulsiveness Scale 11 attentional subscale scores were significantly higher in euthymic patients compared to controls. This was not observed for the motor or non-planning subscale scores.

LIMITATIONS

The average year-long observation time might not be long enough to account for the chronic course of bipolar disorder.

CONCLUSION

Trait impulsivity and particularly attentional impulsivity in euthymic bipolar patients can be strong predictors of illness severity in bipolar disorder. Future studies should explore impulsivity as a risk assessment for morbidity and as a therapeutic target in bipolar disorder patients.

The clinical course of late-life bipolar disorder, looking back and forward

OBJECTIVES

Little is known about the course of late-life bipolar disorder (LLBD). First, we studied patients with LLBD retrospectively with regard to age at first mood episode, onset polarity, predominant polarity and episode density and its associations with other clinical variables. Next, we examined prospectively the clinical course and its associated factors.

METHODS

Data were used from a dynamic cohort (Dutch Older Bipolars [DOBi]) including 101 patients with LLBD (mean age of 68.9 years) at baseline in 2012, with 3-year follow-up measurements available for 64 of these patients. Retrospective course was assessed by diagnostic interviews, and at follow-up polarity and duration for each consecutive episode were noted. Linear and logistic analyses were performed to assess associations between relevant factors and outcome.

RESULTS

The mean age at the first episode was 33.0 years. Onset polarity was depression in 44.6% of patients, with a predominant polarity of depression in 47.5%. At 3-year follow-up, 37.5% of patients reported at least one mood episode, mainly depression. Life events, somatic illness, use of lithium and other factors were not associated with recurrence during the 3-year follow-up.

DISCUSSION

A relapse rate of 37.5% in 3 years is high, considering that LLBD patients generally have a longer history of disease and were receiving care and medication. The course of LLBD can provide important information on which clinical factors are associated with recurrence. Further phenotyping may reveal unique predictors for outcome, and both course specifiers and clinical variables should be included.

How predictable are symptoms in psychopathological networks? A reanalysis of 18 published datasets

BACKGROUND

Network analyses on psychopathological data focus on the network structure and its derivatives such as node centrality. One conclusion one can draw from centrality measures is that the node with the highest centrality is likely to be the node that is determined most by its neighboring nodes. However, centrality is a relative measure: knowing that a node is highly central gives no information about the extent to which it is determined by its neighbors. Here we provide an absolute measure of determination (or controllability) of a node - its predictability. We introduce predictability, estimate the predictability of all nodes in 18 prior empirical network papers on psychopathology, and statistically relate it to centrality.

METHODS

We carried out a literature review and collected 25 datasets from 18 published papers in the field (several mood and anxiety disorders, substance abuse, psychosis, autism, and transdiagnostic data). We fit state-of-the-art network models to all datasets, and computed the predictability of all nodes.

RESULTS

Predictability was unrelated to sample size, moderately high in most symptom networks, and differed considerable both within and between datasets. Predictability was higher in community than clinical samples, highest for mood and anxiety disorders, and lowest for psychosis.

CONCLUSIONS

Predictability is an important additional characterization of symptom networks because it gives an absolute measure of the controllability of each node. It allows conclusions about how self-determined a symptom network is, and may help to inform intervention strategies. Limitations of predictability along with future directions are discussed.

The centrality of affective instability and identity in Borderline Personality Disorder: Evidence from network analysis

We argue that the series of traits characterizing Borderline Personality Disorder samples do not weigh equally. In this regard, we believe that network approaches employed recently in Personality and Psychopathology research to provide information about the differential relationships among symptoms would be useful to test our claim. To our knowledge, this approach has never been applied to personality disorders. We applied network analysis to the nine Borderline Personality Disorder traits to explore their relationships in two samples drawn from university students and clinical populations (N = 1317 and N = 96, respectively). We used the Fused Graphical Lasso, a technique that allows estimating networks from different populations separately while considering their similarities and differences. Moreover, we examined centrality indices to determine the relative importance of each symptom in each network. The general structure of the two networks was very similar in the two samples, although some differences were detected. Results indicate the centrality of mainly affective instability, identity, and effort to avoid abandonment aspects in Borderline Personality Disorder. Results are consistent with the new DSM Alternative Model for Personality Disorders. We discuss them in terms of implications for therapy.

Moving Forward: Challenges and Directions for Psychopathological Network Theory and Methodology

Since the introduction of mental disorders as networks of causally interacting symptoms, this novel framework has received considerable attention. The past years have resulted in over 40 scientific publications and numerous conference symposia and workshops. Now is an excellent moment to take stock of the network approach: What are its most fundamental challenges, and what are potential ways forward in addressing them? After a brief conceptual introduction, we first discuss challenges to network theory: (1) What is the validity of the network approach beyond some commonly investigated disorders such as major depression? (2) How do we best define psychopathological networks and their constituent elements? And (3) how can we gain a better understanding of the causal nature and real-life underpinnings of associations among symptoms? Next, after a short technical introduction to network modeling, we discuss challenges to network methodology: (4) heterogeneity of samples studied with network analytic models, and (5) a lurking replicability crisis in this strongly data-driven and exploratory field. Addressing these challenges may propel the network approach from its adolescence into adulthood and promises advances in understanding psychopathology both at the nomothetic and idiographic level.

Bipolarity and suicidal ideation in children and adolescents: a systematic review with meta-analysis

BACKGROUND

Affective disorders in children and adolescents have received growing attention in the world scenario of mental health. Additionally, there has been an increasing prevalence of suicidal ideation in this population.

OBJECTIVE

A systematic review with meta-analysis was conducted to demonstrate the main risk factors regarding the development of suicidal ideation in the bipolar disorder.

METHODS

This is a systematic review with meta-analysis using the PRISMA protocol (http://www.prisma-statement.org/). This study included secondary data. Original data in mental health were collected by mapping the evidence found in the following electronic databases: MEDLINE/PubMed, LILACS, SciELO, and ScienceDirect in the period from 2005 to 2015.

RESULTS

We found 1418 registrations in such databases, and 46 of them were selected to comprise this review. The result introduces a joint risk between the studies of 2.94 CI [2.29-3.78]. A significant correlation was verified between the risk factors and the suicidal ideation. The result was r (Pearson) = 0.7103 and p value <0.001.

CONCLUSION

Children and adolescents living with bipolar disorder are more vulnerable to suicidal ideation. These results reinforce the need of a more effective public policy directed toward this population.

Mental disorders as networks of problems: a review of recent insights

PURPOSE

The network perspective on psychopathology understands mental disorders as complex networks of interacting symptoms. Despite its recent debut, with conceptual foundations in 2008 and empirical foundations in 2010, the framework has received considerable attention and recognition in the last years.

METHODS

This paper provides a review of all empirical network studies published between 2010 and 2016 and discusses them according to three main themes: comorbidity, prediction, and clinical intervention.

RESULTS

Pertaining to comorbidity, the network approach provides a powerful new framework to explain why certain disorders may co-occur more often than others. For prediction, studies have consistently found that symptom networks of people with mental disorders show different characteristics than that of healthy individuals, and preliminary evidence suggests that networks of healthy people show early warning signals before shifting into disordered states. For intervention, centrality-a metric that measures how connected and clinically relevant a symptom is in a network-is the most commonly studied topic, and numerous studies have suggested that targeting the most central symptoms may offer novel therapeutic strategies.

CONCLUSIONS

We sketch future directions for the network approach pertaining to both clinical and methodological research, and conclude that network analysis has yielded important insights and may provide an important inroad towards personalized medicine by investigating the network structures of individual patients.

Brain-derived neurotrophic factor, impaired glucose metabolism, and bipolar disorder course

OBJECTIVES

The neurotrophin brain-derived neurotrophic factor (BDNF) has been proposed as a potential biomarker in bipolar disorder (BD). However, current evidence is limited and results have been highly heterogeneous. This study aimed to assess the moderating effect of impaired glucose metabolism (IGM) on plasma levels of BDNF in individuals with BD, and on the relationship between BDNF and variables of illness course.

METHODS

We measured and compared the plasma levels of BDNF in individuals with BD (n=57) and healthy controls (n=26). IGM was operationalized as pre-diabetes or type 2 diabetes mellitus. Information related to current and past psychiatric/medical history, as well as prescription of pharmacological treatments was also captured.

RESULTS

Individuals with BD had lower levels of BDNF, relative to healthy controls, after adjustment for age, gender, current medications, smoking, alcohol use, and IGM (P=.046). There was no effect of IGM (P=.860) and no interaction between BD diagnosis and IGM (P=.893). Peripheral BDNF levels were positively correlated with lifetime depressive episodes (P<.001), psychiatric hospitalizations (P=.001) and suicide attempts (P=.021). IGM moderated the association between BDNF and the number of previous mood episodes (P<.001), wherein there was a positive correlation in euglycemic participants and a negative correlation in individuals with IGM.

CONCLUSIONS

BD is independently associated with lower levels of BDNF; IGM may modify the relationship between BDNF and BD course, suggesting an interactive effect of BDNF with metabolic status on illness progression.

Impaired glucose metabolism moderates the course of illness in bipolar disorder

BACKGROUND

The longitudinal course of bipolar disorder (BD) is highly heterogeneous, and is moderated by the presence of general medical comorbidities. This study aimed to investigate the moderating effects of impaired glucose metabolism (IGM) on variables of illness course and severity in a BD population.

METHODS

Fifty-five patients with BD were evaluated. All subjects were evaluated with respect to current and past psychiatric and medical disorders, as well as lifetime use of any medication. Body mass index (BMI) and metabolic parameters were obtained. IGM was operationalized as pre-diabetes or type 2 diabetes mellitus.

RESULTS

Thirty (54.5%) individuals had IGM. After adjustment for age, gender, ethnicity, alcohol use, smoking, BMI and past and current exposure to psychotropic medications, individuals with IGM, when compared to euglycemic participants, had an earlier age of onset (RR: 0.835, p=0.024), longer illness duration (RR: 1.754, p=0.007), a higher number of previous manic/hypomanic episodes (RR: 1.483, p=0.002) and a higher ratio of manic/hypomanic to depressive episodes (RR: 1.753, p=0.028). Moreover, we observed a moderating effect of IGM on the association between number of mood episodes and other variables of illness course, with the correlation between lifetime mood episodes and frequency of episodes being significantly greater in the IGM subgroup (RR: 1.027, p=0.029). All associations observed herein remained significant after adjusting for relevant confounding factors (e.g. age, alcohol and tobacco use, exposure to psychotropic agents, BMI).

LIMITATIONS

Cross-sectional design, small sample size.

CONCLUSIONS

Comorbid IGM may be a key moderator of illness progression in BD.