A network view on psychiatric disorders: network clusters of symptoms as elementary syndromes of psychopathology

Structural Study of Anxiety and Mood-related Symptomatology in Psychiatric Outpatients

INTRODUCTION

Knowledge of the symptomatological structure of mental disorders is relevant for their understanding and classification. In the absence of previous Latin American research on the simultaneous structural exploration of various types of psychiatric symptomatologies, the objective of this study is to examine the structure of anxious and mood-related symptoms, resulting syndromes, and their correlations.

METHOD

Several instruments for the evaluation of anxious, depressive, and manic symptoms were administered to 305 psychiatric outpatients. Using factor analysis and network graphs based on polychoric correlations between the symptoms, their clustering patterns (syndromes) were explored. Further, correlations between the scores of each resulting syndrome were performed.

RESULTS

The symptom grouping process led to a total of fifteen generally overlapping syndromes: fear of evaluation, fear of people, agoraphobia, general anxiety, somatization, anergy, depressive core, lack of positive mood, cognitive difficulties, mania, post-traumatic stress/obsessions, fear of madness/loss of control, acrophobia, irritability, and sleep disturbances. General anxiety was at the center of the structure. Morning/matinal pole, hypersomnia, and increased appetite were relatively isolated symptoms.

CONCLUSION

Overlapping and/or highly correlated psychiatric syndromes were prominent findings, underlining the pertinence of a dimensional approach as a substantial strategy toward a more inclusive understanding of mental disorders.

Network Structure of Autism Spectrum Disorder Behaviors and Its Evolution in Preschool Children: Insights from a New Longitudinal Network Analysis Method

Network modeling of the social, communication and restrictive/repetitive behaviors (RRBs) included in the definition of Autism Spectrum Disorder was performed. The Autism Diagnostic Interview-Revised (ADI-R) assessed behaviors in 139 pre-school cases at two cross-sections that averaged 34.8 months apart. Cross-sectional networks were based on the correlation matrix of the ADI-R behavioral items and the "bootCross" method was developed and enabled the estimation of a longitudinal network. At both stages, RRB items/nodes formed a consistent peripheral cluster, while social and communication nodes formed a core cluster that diverged with time. These differences in the nature and evolution of the RRB and socio-communicative dimensions indicate that their inter-behavior dynamics are very different. The most central behaviors across stages are proposed as prime targets for efficient therapeutic intervention.

The associations between depressive symptoms, functional impairment, and quality of life, in patients with major depression: undirected and Bayesian network analyses

BACKGROUND

Depressive symptoms, functional impairment, and decreased quality of life (QOL) are three important domains of major depressive disorder (MDD). However, the possible causal relationship between these factors has yet to be elucidated. Moreover, it is not known whether certain symptoms of MDD are more impairing than others. The network approach is a promising solution to these shortfalls.

METHODS

The baseline data of a multicenter prospective project conducted in 11 governances of China were analyzed. In total, 1385 patients with MDD were included. Depressive symptoms, functioning disability, and QOL were evaluated by the 17-item Hamilton Depression Rating Scale (HAMD-17), the Sheehan Disability Scale (SDS), and the Quality of Life Enjoyment and Satisfaction Questionnaire-Short Form (Q-LES-Q-SF). The network was estimated through the graphical Least Absolute Shrinkage and Selection Operator (LASSO) technique in combination with the directed acyclic graph.

RESULTS

Three centrality metrics of the graphical LASSO showed that social life dysfunction, QOL, and late insomnia exhibited the highest strength centrality. The network accuracy and stability were estimated to be robust and stable. The Bayesian network indicated that some depressive symptoms were directly associated with QOL, while other depressive symptoms showed an indirect association with QOL mediated by impaired function. Depressed mood was positioned at the highest level in the model and predicted the activation of functional impairment and anxiety.

CONCLUSIONS

Functional disability mediated the relationship between depressive symptoms and QOL. Family functionality and suicidal symptoms were directly related to QOL. Depressed mood played the predominant role in activating both anxiety symptom and functional impairment.

Network Analysis of ADHD Symptoms and Cognitive Profiles in Children

Purpose

Although many studies have reported the cognitive profiles in attention-deficit/hyperactivity disorder (ADHD), the interactions between ADHD symptoms and the patients' cognitive profiles have not been carefully examined through the network analysis. Here, in this study, we systematically analyzed the ADHD patents' symptoms and cognitive profiles, and identified a set of interactions between ADHD symptoms and cognitive domains using the network approach.

Patients and Methods

A total of 146 children with ADHD, 6 to 15 years of age, were included in the study. All participants were assessed by the Wechsler Intelligence Scale for Children-Fourth Edition (WISC-IV) test. The patients' ADHD symptoms were evaluated by the Vanderbilt ADHD parent and teacher rating scales. GraphPad Prism 9.1.1 software was used for descriptive statistics and R 4.2.2 was used for network model construction.

Results

The ADHD children in our sample showed lower scores for full scale intelligence quotient (FSIQ), verbal comprehension index (VCI), processing speed index (PSI) and working memory index (WMI). Among all the ADHD core symptoms and comorbid symptoms, the academic ability, inattention symptoms and mood disorder showed direct interaction with the cognitive domains of WISC-IV. In addition, oppositional defiant of the ADHD comorbid symptoms, and perceptual reasoning of the cognitive domains exhibited the highest strength centrality in the ADHD-Cognition network based on parent ratings. Classroom behaviors of the ADHD functional impairment, and verbal comprehension of the cognitive domains exhibited the highest strength centrality in the network based on teacher ratings.

Conclusion

We highlighted the importance of considering the interactions between the ADHD symptoms and cognitive properties when designing the intervention plans for the ADHD children.

Network Analysis of the Structure of the Core Symptoms and Clinical Correlates in Comorbid Schizophrenia and Gambling Disorder

Few studies have analyzed the clinical profile of treatment-seeking patients with the comorbid presence of schizophrenia (SCZ) and gambling disorder (GD), which warrants new research to assess the network structure of this complex mental condition. The aim of this study was to explore the organization of the symptoms and other clinical correlates of SCZ with GD. Network analysis was applied to a sample of N = 179 SCZ patients (age range: 19-70 years, mean=39.5, SD=9.9) who met clinical criteria for gambling disorder-related problems. Variables included in the network were the core GD symptoms according to the DSM-5, psychotic and paranoid ideation levels, global psychological distress, GD severity measures (debts and illegal behavior related with gambling), substances (tobacco, alcohol, and illegal drugs), and personality profile. The nodes with the highest authority in the network (variables of highest relevance) were personality traits and psychological distress. Four empirical modules/clusters were identified, and linkage analysis identified the nodes with the highest closeness (bridge nodes) to be novelty seeking and reward dependence (these traits facilitate the transition between the modules). Identification of the variables with the highest centrality/linkage can be particularly useful for developing precise management plans to prevent and treat SCZ with GD.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11469-022-00983-y.

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.

Validity, reliability and clinical utility of mental disorders: The case of ICD-11 schizophrenia

Diagnostic classification systems categorise mental psychopathology in mental disorders. Although these entities are clinical constructs developed by consensus, it has been pointed out that in practice they are usually managed as natural entities and without evaluating aspects related to their nosological construction. The objectives of the study are to review a) the conceptualisation of mental disorders, b) the indicators of validity, reliability and clinical utility, and c) the values ​​of these indicators in ICD-11 schizophrenia. The results show that mental disorders are conceptualised as discrete entities, like the diseases of other areas of medicine; however, differences are observed between these diagnostic categories in clinical practice. The reliability and clinical utility of mental disorders are adequate; however, the validity is not yet clarified. Similarly, ICD-11 schizophrenia demonstrates adequate reliability and clinical utility, but its validity remains uncertain. The conceptualisation of psychopathology in discrete entities may be inadequate for its study, therefore dimensional and mixed models have been proposed. The indicators of validity, reliability and clinical utility enable us to obtain an accurate view of the nosological state of mental disorders when evaluating different aspects of their nosological construction.

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.

A systematic review and meta-analysis of the effect of whole body cryotherapy on mental health problems

OBJECTIVE

To give an overview of the nature and methodological quality of studies on whole body cryotherapy (WBC) as add-on intervention for mental health problems.

METHODS

A meta-analysis according to PRISMA guidelines was conducted (Prospero registration: CRD42020167443). Databases MEDLINE, PsycINFO and the Cochrane Library were searched. Risk of bias was scored according to the Cochrane ROBINS-I-tool to which an extra bias-dimension of allegiance bias was added. Within and between Hedges' g pooled effect sizes were calculated for the main aspect of mental health measured. Treatment efficacy was examined using a random effects model. Heterogeneity was examined through identification of visual outliers and by I² statistics.

RESULTS

Out of 196 articles coming up from the search, ten studies met all inclusion criteria, six of which were (randomized) controlled trials. Together these studies report on a total of 294 participants receiving WBC. The within-group pooled effect size for mental health problems is large (Hedges' g = 1.63, CI: 1.05-2.21), with high heterogeneity (I² = 93%). Subgroup analyses on depressive symptoms and quality of life (QOL) showed a diminution of heterogeneity to moderate. Effect sizes for depressive symptoms are very large (Hedges' g = 2.95, CI: 2.44-3.45) and for QOL medium (Hedges' g = 0.70, CI: 0.15-1.24). The between-group pooled effect size is medium (Hedges' g = 0.76, CI: 0.17-1.36).

CONCLUSIONS

Results indicate preliminary evidence for WBC as efficacious add-on intervention for mental health problems, especially depressive symptoms. Further research in the form of RCTs with larger numbers of participants is needed.

Profile of Treatment-Seeking Gaming Disorder Patients: A Network Perspective

The increasing presence of gaming disorder in recent years has led to major efforts to identify the specific predictors that have a high impact on the profile of people seeking treatment for this mental condition. The purpose of this study was to explore the network structure of the correlates of gaming disorder considering sociodemographic features and other clinical symptoms. Network analysis was applied to a sample of patients who met clinical criteria for gaming disorder (n = 117, of ages ranging from 15 to 70 yrs-old). Variables considered in the network included sex, age, socioeconomic position, global emotional distress, age of onset and duration of the gaming disorder, personality traits and the presence of other addictive behaviors (tobacco, alcohol and behavioral addictions). The central nodes in the network were global psychological distress, chronological age, and age of onset of gaming related problems. Linkage analysis also identified psychopathological status and age as the variables with the most valuable information in the model. The poorest relevance in the analysis was for the duration of gaming problems and socioeconomic levels. Modularity analysis grouped the nodes within four clusters. Identification of the variables with the highest centrality/linkage can be particularly useful for developing precise management plans to prevent and treat gaming disorder related problems.

A network analysis of post-traumatic stress and psychosis symptoms

BACKGROUND

Understanding the interplay between trauma-related psychological mechanisms and psychotic symptoms may improve the effectiveness of interventions for post-traumatic stress reactions in psychosis. Network theory assumes that mental health problems persist not because of a common latent variable, but from dynamic feedback loops between symptoms, thereby addressing the heterogeneous and overlapping nature of traumagenic and psychotic diagnoses. This is a proof-of-concept study examining interactions between post-traumatic stress symptoms, which were hypothesized to reflect trauma-related psychological mechanisms, and auditory hallucinations and delusions.

METHOD

Baseline data from two randomised controlled trials (N = 216) of trauma-focused therapy in people with post-traumatic stress symptoms (87.5% met diagnostic criteria for PTSD) and psychotic disorder were analysed. Reexperiencing, hyperarousal, avoidance, trauma-related beliefs, auditory hallucinations and delusional beliefs were used to estimate a Gaussian graphical model along with expected node influence and predictability (proportion of explained variance).

RESULTS

Trauma-related beliefs had the largest direct influence on the network and, together with hypervigilance, were implicated in the shortest paths from flashbacks to delusions and auditory hallucinations.

CONCLUSIONS

These findings are in contrast to previous research suggesting a central role for re-experiencing, emotional numbing and interpersonal avoidance in psychosis. Trauma-related beliefs were the psychological mechanism most associated with psychotic symptoms, although not all relevant mechanisms were measured. This work demonstrates that investigating multiple putative mediators may clarify which processes are most relevant to trauma-related psychosis. Further research should use network modelling to investigate how the spectrum of traumatic stress reactions play a role in psychotic symptoms.

Perfectionism and Difficulty Controlling Thoughts Bridge Eating Disorder and Obsessive-Compulsive Disorder Symptoms: A Network Analysis

BACKGROUND

Eating disorders (ED) and obsessive-compulsive disorder (OCD) are highly comorbid, but little is known about how this comorbidity is maintained. Prior research suggests that obsessive thoughts and perfectionism may be shared maintenance factors for EDs and OCD.

METHODS

The current study used network analysis to (1) identify bridge pathways in an ED-OCD comorbidity network and (2) test if perfectionism symptoms bridge between ED-OCD symptoms in a combined network model including ED, OCD, and Perfectionism symptoms. Participants (N = 1,619) were a mixed sample of undergraduate students and individuals diagnosed with EDs.

RESULTS

Difficulty controlling thoughts was the symptom with the highest bridge centrality in both models, connecting with ED-related worry and doubts. In the ED-OCD-Perfectionism comorbidity network, doubts about simple everyday things and repeating things over and over bridged between ED and OCD symptoms. Additionally, specific and distinct pathways were identified between OCD and two types of ED pathology: restricting (checking compulsions and rigidity around food) and binge eating (hoarding and binge eating symptoms).

LIMITATIONS

Due to the cross-sectional nature of the data, no directional inferences can be made. Due to a higher OCD symptom prevalence rate than reported in previous studies, our undergraduate sample may not be representative of other college populations.

CONCLUSIONS

The presence of intrusive cognitions and maladaptive perfectionism may contribute to the maintenance of co-occurring ED and OCD symptoms. These findings begin to delineate specific pathways among OCD and ED symptoms, which can be used in the development of interventions to disrupt connections among these disorders.

ADHD Comorbid With Major Depression on Parents and Teachers Perceptions

Objective: Examine the Strengths and Difficulties Questionnaire (SDQ) responses of parents and teachers for children with ADHD comorbid with major depressive disorder (MDD), with an emphasis on determining how well the respondent groups' responses correlate, and how well the results obtained perform as predictors of clinical diagnosis. Method: The SDQ was completed by parents and teachers of (n = 215 participants, 7-12 years old) in ADHD, MDD, ADHD + MDD, and healthy control groups. Agreement between parent and teacher SDQs and their concordance with Diagnostic and Statistical Manual of Mental Disorders (4th ed.; DSM-IV) diagnoses were assessed. Receiver operating characteristic (ROC) and Kappa concordance analyses were used to compare the groups with the health control group. Results: The comorbid group presented greater impairments than the ADHD, MDD, and control groups (p < .001). Conclusion: The presence of psychiatric comorbidity causes greater impairment for school children with ADHD. The SDQ has good sensitivity for detecting these children and correlates well with DSM diagnosis.

Symptom-network dynamics in irritable bowel syndrome with comorbid panic disorder using electronic momentary assessment: A randomized controlled trial of escitalopram vs. placebo

INTRODUCTION

Momentary ecological assessment indicated alleviated abdominal pain in escitalopram treatment of irritable bowel syndrome (IBS) with comorbid panic disorder. Hitherto, little is known about symptom formation, i.e., how psychological impact physical symptoms, and vice versa, and about the effect of SSRI-treatment on symptom formation.

OBJECTIVE

To investigate how psychological and somatic symptoms co-vary over time in IBS patients with comorbid panic disorder and how they are affected by escitalopram treatment.

METHODS

Experience sampling data from 14 IBS patients with panic disorder were obtained from a single-centre, double-blind, parallel-group, randomized controlled trial on escitalopram versus placebo. At baseline, after three and six months, multilevel time-lagged linear regression analysis was used to construct symptom networks. Network connections represented coefficients between various affect and gastrointestinal items.

RESULTS

Connectivity increased up to 3 months in both groups. Between 3 and 6 months, connectivity decreased for placebo and further increased in the escitalopram group. Additionally, a steep increase in node strength for negative affect nodes was observed in the escitalopram network and the opposite for positive affect nodes. Over time, group symptom networks became increasingly different from each other. Anxious-anxious and enthusiastic-relaxed became significantly different between groups at 6 months. The connection that changed significantly in all analyses was anxious-anxious.

CONCLUSIONS

Escitalopram treatment was associated with changes in the symptom networks in IBS patients with panic disorder. While mood and physical symptoms improve over time, mainly connectivity between mood nodes changed, possibly pointing towards a healthier emotion regulation resulting in alleviation of physical symptoms.

How higher goals are constructed and collapse under stress: A hierarchical Bayesian control systems perspective

In this paper, we show that organisms can be modeled as hierarchical Bayesian control systems with small world and information bottleneck (bow-tie) network structure. Such systems combine hierarchical perception with hierarchical goal setting and hierarchical action control. We argue that hierarchical Bayesian control systems produce deep hierarchies of goal states, from which it follows that organisms must have some form of 'highest goals'. For all organisms, these involve internal (self) models, external (social) models and overarching (normative) models. We show that goal hierarchies tend to decompose in a top-down manner under severe and prolonged levels of stress. This produces behavior that favors short-term and self-referential goals over long term, social and/or normative goals. The collapse of goal hierarchies is universally accompanied by an increase in entropy (disorder) in control systems that can serve as an early warning sign for tipping points (disease or death of the organism). In humans, learning goal hierarchies corresponds to personality development (maturation). The failure of goal hierarchies to mature properly corresponds to personality deficits. A top-down collapse of such hierarchies under stress is identified as a common factor in all forms of episodic mental disorders (psychopathology). The paper concludes by discussing ways of testing these hypotheses empirically.

High-frequency follow-up studies in musculoskeletal disorders: a scoping review

OBJECTIVES

This scoping review identifies research in musculoskeletal disorders that uses high frequency follow-up of symptoms. The aim was to investigate whether symptom variability is investigated as a predictor of disease outcome and how intensive follow-up methods are used in musculoskeletal research.

METHODS

Embase, MEDLINE and PsycInfo were searched using OVID, and the Institute of Electrical and Electronic Engineers was also searched using the Institute of Electrical and Electronic Engineers Xplore search engine. Studies were systematically reviewed in accordance with Preferred Reporting Items for Systematic Reviews and Meta-Analyses, but no meta-analysis was done because the priority in this study is to identify gaps in available literature.

RESULTS

Twenty-one papers were included. There was a mean of 54 patients per study (s.d. of 27.7). Two-thirds of the papers looked at how a symptom influences another in the short-term (subsequent assessment in the same day or next day), but none looked at the long-term. Only one study considered symptom variability investigating how higher variability in pain (defined by the s.d.) is associated with higher average pain severity and lower average sleep quality.

CONCLUSION

The methodology of musculoskeletal disorder research has changed from completing paper booklets to using electronic data capture (smartphones). There has also been a trend of collecting more intensive longitudinal data, but very little research utilizes these data to look at how symptom variability affects symptom outcomes. This demonstrates a gap in research where furthering understanding of this will help clinicians decide on the most important symptom to address in future patients.

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

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

Topological Analysis of the Language Networks of Ancient Traditional Chinese Medicine Books

This study aims to explore the topological regularities of the character network of ancient traditional Chinese medicine (TCM) book. We applied the 2-gram model to construct language networks from ancient TCM books. Each text of the book was separated into sentences and a TCM book was generated as a directed network, in which nodes represent Chinese characters and links represent the sequential associations between Chinese characters in the sentences (the occurrence of identical sequential associations is considered as the weight of this link). We first calculated node degrees, average path lengths, and clustering coefficients of the book networks and explored the basic topological correlations between them. Then, we compared the similarity of network nodes to assess the specificity of TCM concepts in the network. In order to explore the relationship between TCM concepts, we screened TCM concepts and clustered them. Finally, we selected the binary groups whose weights are greater than 10 in Inner Canon of Huangdi (ICH, ) and Treatise on Cold Pathogenic Disease (TCPD, ), hoping to find the core differences of these two ancient TCM books through them. We found that the degree distributions of ancient TCM book networks are consistent with power law distribution. Moreover, the average path lengths of book networks are much smaller than random networks of the same scale; clustering coefficients are higher, which means that ancient book networks have small-world patterns. In addition, the similar TCM concepts are displayed and linked closely, according to the results of cosine similarity comparison and clustering. Furthermore, the core words of Inner Canon of Huangdi and Treatise on Cold Pathogenic Diseases have essential differences, which might indicate the significant differences of language and conceptual patterns between theoretical and clinical books. This study adopts language network approach to investigate the basic conceptual characteristics of ancient TCM book networks, which proposes a useful method to identify the underlying conceptual meanings of particular concepts conceived in TCM theories and clinical operations.

Network approach of mood and functional gastrointestinal symptom dynamics in relation to childhood trauma in patients with irritable bowel syndrome and comorbid panic disorder

OBJECTIVE

Irritable bowel syndrome (IBS) has a high comorbidity with mental disorders. The present paper aims to visualise the interplay between IBS and affect (anxiety and mood) in daily life. Furthermore, this interplay may be different depending on risk factors such as childhood trauma.

METHODS

Using momentary assessment (Experience Sampling Method), data of 24 individuals diagnosed with both IBS and panic disorder were analysed (15 non-trauma and 9 low-trauma-score patients). Networks were constructed, based on multilevel time-lagged linear regression analysis. Regression coefficients present network connections including three negative affect items (down, irritated, rushed), three positive affect items (happy, enthusiastic, cheerful), three abdominal complaints (abdominal pain, bloating, nausea) and one social item (feeling lonely). Those networks were stratified by levels of childhood trauma based on the Childhood Trauma Questionnaire.

RESULTS

Connections within the group of mood items and within the group of abdominal complaints were more frequent than between abdominal complaints and mood items. When data were stratified by childhood trauma, networks were different. In addition, node strengths were stronger in low-trauma than in non-trauma, although only one was significantly different (enthusiastic). Overall, there were mainly non-significant connections and a clear pattern was not visible.

CONCLUSIONS

A time-lagged network provides additional insight in connections between abdominal complaints and affective complaints, in patients with IBS and panic disorder, with different levels of childhood trauma. More research is needed to gain a better understanding of symptom formation and the impact of variation in context on individual symptom experiences in IBS with affective comorbidity. Baseline data of a clinical trial: NCT01551225 (http://www.clinicaltrials.gov).

Validity, Reliability and Clinical Utility of Mental Disorders: The Case of ICD-11 Schizophrenia

Diagnostic classification systems categorise mental psychopathology in mental disorders. Although these entities are clinical constructs developed by consensus, it has been pointed out that in practice they are usually managed as natural entities and without evaluating aspects related to their nosological construction. The objectives of the study are to review a) the conceptualisation of mental disorders, b) the indicators of validity, reliability and clinical utility, and c) the values of these indicators in ICD-11 schizophrenia. The results show that mental disorders are conceptualised as discrete entities, like the diseases of other areas of medicine; however, differences are observed between these diagnostic categories in clinical practice. The reliability and clinical utility of mental disorders are adequate; however, the validity is not yet clarified. Similarly, ICD-11 schizophrenia demonstrates adequate reliability and clinical utility, but its validity remains uncertain. The conceptualisation of psychopathology in discrete entities may be inadequate for its study, therefore dimensional and mixed models have been proposed. The indicators of validity, reliability and clinical utility enable us to obtain an accurate view of the nosological state of mental disorders when evaluating different aspects of their nosological construction.

The proportion of non-depressed subjects in a study sample strongly affects the results of psychometric analyses of depression symptoms

Background

Recent studies have uncovered a peculiar finding: that the strength and dimensionality of depression symptoms’ inter-relationships vary systematically across study samples with different average levels of depression severity. Our aim was to examine whether this phenomenon is driven by the proportion of non-affected subjects in the sample.

Methods

Cross-sectional data from the “Cohort Study on Substance Use Risk Factors” was analyzed. Self-reported depression symptoms were assessed via the Major Depressive Inventory. Symptom data were analyzed via polychoric correlations, principal component analysis, confirmatory factor analysis, Mokken scale analysis, and network analysis. Analyses were carried out across 22 subsamples containing increasingly higher proportions of non-depressed participants. Results were examined as a function of the proportion of non-depressed participants.

Results

A strong influence of the proportion of non-depressed participants was uncovered: the higher the proportion, the stronger the symptom correlations, higher their tendency towards unidimensionality, better their scalability, and higher the network edge strengths. Comparing the depressed sample with the general population sample, the average symptom correlation increased from 0.29 to 0.51; variance explained by the first eigenvalue increased from 0.36 to 0.56; fit measures from confirmatory one-factor analysis increased from 0.81 to 0.97; the H coefficient of scalability increased from 0.26 to 0.48; and the median network edge increased from 0.00 to 0.07.

Conclusions

Results of psychometric analyses vary substantially as a function of the proportion of non-depressed participants in the sample being studied. This provides a possible explanation for the lack of reproducibility of previous psychometric studies.

The Network Structure of Personality Pathology in Adolescence With the 100-Item Personality Inventory for DSM-5 Short-Form (PID-5-SF)

There is currently a lack of understanding of the structure of personality disorder (PD) trait facets. The network approach may be useful in providing additional insights, uncovering the unique association of each PD trait facet with every other facet. A unique feature of network analysis is centrality, which indicates the importance of the role a trait facet plays in the context of other trait facets. Using data from 1,940 community Dutch adolescents, we applied network analysis to the 25 trait facets from the 100-item Personality Inventory for DSM-5 Short-Form (PID-5-SF) to explore their associations. We found that some trait facets only seem to be core indicators of their pre-ordained domains, whereas we observed that other trait facets were strongly associated with trait facets outside of their hypothesized domains. Importantly, anxiousness and callousness were identified as highly central facets, being uniquely associated with many other trait facets. Future longitudinal network studies could therefore further examine the possibility of anxiousness and callousness as risk marker trait facets among other PD trait facets.

The Symptom and Domain Structure of Psychotic Disorders: A Network Analysis Approach

Little is understood about the symptom network structure of psychotic disorders. In the current study, we aimed to examine the network structure of psychotic symptoms in a broad and transdiagnostic sample of subjects with psychotic disorders (n = 2240) and to determine whether network structure parameters vary across demographic, sampling method and clinical variables. Gaussian graphical models were estimated for 73 psychotic symptoms assessed using the Comprehensive Assessment of Symptoms and History. A 7-cluster solution (reality distortion, disorganization, catatonia, diminished expressivity, avolition/anhedonia, mania, and depression) best explained the underlying symptom structure of the network. Symptoms with the highest centrality estimates pertained to the disorganization and, to a lesser extent, negative domains. Most bridge symptoms pertained to the disorganization domain, which had a central position within the network and widespread connections with other psychopathological domains. A comparison of networks in subgroups of subjects defined by premorbid adjustment levels, treatment response, and course pattern significantly influenced both network global strength and network structure. The sampling method and diagnostic class influenced network structure but not network global strength. Subgroups of subjects with less densely connected networks had poorer outcomes or more illness severity than those with more densely connected networks. The network structure of psychotic features emphasizes the importance of disorganization symptoms as a central domain of psychopathology and raises the possibility that interventions that target these symptoms may prove of broad use across psychopathology. The network structure of psychotic disorders is dependent on the sampling method and important clinical variables.

Schizophrenia in ICD-11: Comparison of ICD-10 and DSM-5

The conceptualization of schizophrenia has changed from its initial conception in the 19th century to the recent publication of the ICD-11. The changes incorporated in this latest version were made based on the evaluation of the current ICD, the available scientific evidence, and the consensus reached by its developers. In this paper we describe the conceptualization changes (diagnostic criteria and specifiers) of ICD-11 schizophrenia with respect to those of ICD-10 and DSM-5. The changes found are discussed based on the scientific literature published in Medline, Scopus and Scielo until July 2019 and the information on the Wordl Health Organization and American Psychiatric Association websites. Given that the diagnosis of schizophrenia is based on the diagnostic criteria of the diagnostic classification systems, it is important to know the changes made in its conceptualization and the evidence supporting such modifications.

Psychosocial assessment of families caring for a child with acute lymphoblastic leukemia, epilepsy or asthma: Psychosocial risk as network of interacting symptoms

The purpose of this study is to assess psychosocial risk across several pediatric medical conditions and test the hypothesis that different severe or chronic pediatric illnesses are characterized by disease specific enhanced psychosocial risk and that risk is driven by disease specific connectivity and interdependencies among various domains of psychosocial function using the Psychosocial Assessment Tool (PAT). In a multicenter prospective cohort study of 195 patients, aged 5–12, 90 diagnosed with acute lymphoblastic leukemia (ALL), 42 with epilepsy and 63 with asthma, parents completed the PAT2.0 or the PAT2.0 generic version. Multivariate analysis was performed with disease as factor and age as covariate. Graph theory and network analysis was employed to study the connectivity and interdependencies among subscales of the PAT while data-driven cluster analysis was used to test whether common patterns of risk exist among the various diseases. Using a network modelling approach analysis, we observed unique patterns of interconnected domains of psychosocial factors. Each pathology was characterized by different interdependencies among the most central and most connected domains. Furthermore, data-driven cluster analysis resulted in two clusters: patients with ALL (89%) mostly belonged to cluster 1, while patients with epilepsy and asthma belonged primarily to cluster 2 (83% and 82% respectively). In sum, implementing a network approach improves our comprehension concerning the character of the problems central to the development of psychosocial difficulties. Therapy directed at problems related to the most central domain(s) constitutes the more rational one because such an approach will inevitably carry over to other domains that depend on the more central function.

Transdiagnostic approaches to mental health problems: Current status and future directions

Despite a longstanding and widespread influence of the diagnostic approach to mental ill health, there is an emerging and growing consensus that such psychiatric nosologies may no longer be fit for purpose in research and clinical practice. In their place, there is gathering support for a "transdiagnostic" approach that cuts across traditional diagnostic boundaries or, more radically, sets them aside altogether, to provide novel insights into how we might understand mental health difficulties. Removing the distinctions between proposed psychiatric taxa at the level of classification opens up new ways of classifying mental health problems, suggests alternative conceptualizations of the processes implicated in mental health, and provides a platform for novel ways of thinking about onset, maintenance, and clinical treatment and recovery from experiences of disabling mental distress. In this Introduction to a Special Section on Transdiagnostic Approaches to Psychopathology, we provide a narrative review of the transdiagnostic literature in order to situate the Special Section articles in context. We begin with a brief history of the diagnostic approach and outline several challenges it currently faces that arguably limit its applicability in current mental health science and practice. We then review several recent transdiagnostic approaches to classification, biopsychosocial processes, and clinical interventions, highlighting promising novel developments. Finally, we present some key challenges facing transdiagnostic science and make suggestions for a way forward. (PsycINFO Database Record (c) 2020 APA, all rights reserved).

MorbiNet: multimorbidity networks in adult general population. Analysis of type 2 diabetes mellitus comorbidity

Multimorbidity has great impact on health care. We constructed multimorbidity networks in the general population, extracted subnets focused on common chronic conditions and analysed type 2 diabetes mellitus (T2DM) comorbidity network. We used electronic records from 3,135,948 adult people in Catalonia, Spain (539,909 with T2DM), with at least 2 coexistent chronic conditions within the study period (2006-2017). We constructed networks from odds-ratio estimates adjusted by age and sex and considered connections with OR > 1.2 and p-value < 1e-5. Directed networks and trajectories were derived from temporal associations. Interactive networks are freely available in a website with the option to customize characteristics and subnets. The more connected conditions in T2DM undirected network were: complicated hypertension and atherosclerosis/peripheral vascular disease (degree: 32), cholecystitis/cholelithiasis, retinopathy and peripheral neuritis/neuropathy (degree: 31). T2DM has moderate number of connections and centrality but is associated with conditions with high scores in the multimorbidity network (neuropathy, anaemia and digestive diseases), and severe conditions with poor prognosis. The strongest associations from T2DM directed networks were to retinopathy (OR: 23.8), glomerulonephritis/nephrosis (OR: 3.4), peripheral neuritis/neuropathy (OR: 2.7) and pancreas cancer (OR: 2.4). Temporal associations showed the relevance of retinopathy in the progression to complicated hypertension, cerebrovascular disease, ischemic heart disease and organ failure.

Pathway and network embedding methods for prioritizing psychiatric drugs

One in five Americans experience mental illness, and roughly 75% of psychiatric prescriptions do not successfully treat the patient's condition. Extensive evidence implicates genetic factors and signaling disruption in the pathophysiology of these diseases. Changes in transcription often underlie this molecular pathway dysregulation; individual patient transcriptional data can improve the efficacy of diagnosis and treatment. Recent large-scale genomic studies have uncovered shared genetic modules across multiple psychiatric disorders - providing an opportunity for an integrated multi-disease approach for diagnosis. Moreover, network-based models informed by gene expression can represent pathological biological mechanisms and suggest new genes for diagnosis and treatment. Here, we use patient gene expression data from multiple studies to classify psychiatric diseases, integrate knowledge from expert-curated databases and publicly available experimental data to create augmented disease-specific gene sets, and use these to recommend disease-relevant drugs. From Gene Expression Omnibus, we extract expression data from 145 cases of schizophrenia, 82 cases of bipolar disorder, 190 cases of major depressive disorder, and 307 shared controls. We use pathway-based approaches to predict psychiatric disease diagnosis with a random forest model (78% accuracy) and derive important features to augment available drug and disease signatures. Using protein-protein-interaction networks and embedding-based methods, we build a pipeline to prioritize treatments for psychiatric diseases that achieves a 3.4-fold improvement over a background model. Thus, we demonstrate that gene-expression-derived pathway features can diagnose psychiatric diseases and that molecular insights derived from this classification task can inform treatment prioritization for psychiatric diseases.

A Transdisciplinary Integrative Approach for Precision Psychiatry

Theoretical psychiatry considers theoretical problems in psychiatry as well as the quality and effectiveness of mental health care. This chapter addresses the idea of predictive, preventive, precision, personalized, and participatory medicine in psychiatry from a theoretical transdisciplinary integrative perspective and systems networking. The aim of the chapter is to bring together some current ideas and concepts such as computational neuroscience, network theory, multi-omics profile, precision medicine, and person-centered psychiatry as a coherent system of theory and practice.

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.

In search of disorders: internalizing symptom networks in a large clinical sample

BACKGROUND

The co-occurrence of internalizing disorders is a common form of psychiatric comorbidity, raising questions about the boundaries between these diagnostic categories. We employ network psychometrics in order to: (a) determine whether internalizing symptoms cluster in a manner reflecting DSM diagnostic criteria, (b) gauge how distinct these diagnostic clusters are and (c) examine whether this network structure changes from childhood to early and then late adolescence.

METHOD

Symptom-level data were obtained for service users in publicly funded mental health services in England between 2011 and 2015 (N = 37,162). A symptom network (i.e. Gaussian graphical model) was estimated, and a community detection algorithm was used to explore the clustering of symptoms.

RESULTS

The estimated network was densely connected and characterized by a multitude of weak associations between symptoms. Six communities of symptoms were identified; however, they were weakly demarcated. Two of these communities corresponded to social phobia and panic disorder, and four did not clearly correspond with DSM diagnostic categories. The network structure was largely consistent by sex and across three age groups (8-11, 12-14 and 15-18 years). Symptom connectivity in the two older age groups was significantly greater compared to the youngest group and there were differences in centrality across the age groups, highlighting the age-specific relevance of certain symptoms.

CONCLUSIONS

These findings clearly demonstrate the interconnected nature of internalizing symptoms, challenging the view that such pathology takes the form of distinct disorders.

Testing Cold and Hot Cognitive Control as Moderators of a Network of Comorbid Psychopathology Symptoms in Adolescence

Comorbidity is pervasive across psychopathological symptoms, diagnoses, and domains. Network analysis is a method for investigating symptom-level associations that underlie comorbidity, particularly through bridge symptoms connecting diagnostic syndromes. We applied network analyses of comorbidity to data from a population-based sample of adolescents (n = 849). We implemented a method for assessing nonparametric moderation of psychopathology networks to evaluate differences in network structure across levels of intelligence and emotional control. Symptoms generally clustered by clinical diagnoses, but specific between-cluster bridge connections emerged. Internalizing symptoms demonstrated unique connections with aggression symptoms of interpersonal irritability, whereas externalizing symptoms showed more diffuse interconnections. Aggression symptoms identified as bridge nodes in the cross-sectional network were enriched for longitudinal associations with internalizing symptoms. Cross-domain connections did not significantly vary across intelligence but were weaker at lower emotional control. Our findings highlight transdiagnostic symptom relationships that may underlie co-occurrence of clinical diagnoses or higher-order factors of psychopathology.

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.

Can we predict the direction of sudden shifts in symptoms? Transdiagnostic implications from a complex systems perspective on psychopathology

Recently, there has been renewed interest in the application of assumptions from complex systems theory in the field of psychopathology. One assumption, with high clinical relevance, is that sudden transitions in symptoms may be anticipated by rising instability in the system, which can be detected with early warning signals (EWS). Empirical studies support the idea that this principle also applies to the field of psychopathology. The current manuscript discusses whether assumptions from complex systems theory can additionally be informative with respect to the specific symptom dimension in which such a transition will occur (e.g. whether a transition towards anxious, depressive or manic symptoms is most likely). From a complex systems perspective, both EWS measured in single symptom dynamics and network symptom dynamics at large are hypothesized to provide clues regarding the direction of the transition. Challenging research designs are needed to provide empirical validation of these hypotheses. These designs should be able to follow sudden transitions 'live' using frequent observations of symptoms within individuals and apply a transdiagnostic approach to psychopathology. If the assumptions proposed are supported by empirical studies then this will signify a large improvement in the possibility for personalized estimations of the course of psychiatric symptoms. Such information can be extremely useful for early intervention strategies aimed at preventing specific psychiatric problems.

Distress, Impairment and the Extended Psychosis Phenotype: A Network Analysis of Psychotic Experiences in an US General Population Sample

It has been proposed that subclinical psychotic experiences (PEs) may causally impact on each other over time and engage with one another in patterns of mutual reinforcement and feedback. This subclinical network of experiences in turn may facilitate the onset of psychotic disorder. PEs, however, are not inherently distressing, nor do they inevitably lead to impairment. The question arises therefore, whether nondistressing PEs, distressing PEs, or both, meaningfully inform an extended psychosis phenotype. The current study first aimed to exploit valuable ordinal data that captured the absence, occurrence and associated impairment of PEs in the general population to construct a general population based severity network of PEs. The study then aimed to partition the available ordinal data into 2 sets of binary data to test whether an occurrence network comprised of PE data denoting absence (coded 0) and occurrence/impairment (coded 1) was comparable to an impairment network comprised of binary PE data denoting absence/occurrence (coded 0) and impairment (coded 1). Networks were constructed using state-of-the-art regularized pairwise Markov Random Fields (PMRF). The severity network revealed strong interconnectivity between PEs and nodes denoting paranoia were among the most central in the network. The binary PMRF impairment network structure was similar to the occurrence network, however, the impairment network was characterized by significantly stronger PE interconnectivity. The findings may help researchers and clinicians to consider and determine how, when, and why an individual might transition from experiences that are nondistressing to experiences that are more commonly characteristic of psychosis symptomology in clinical settings.

New insights into the correlation structure of DSM-IV depression symptoms in the general population v. subsamples of depressed individuals

AIMS

Previous research failed to uncover a replicable dimensional structure underlying the symptoms of depression. We aimed to examine two neglected methodological issues in this research: (a) adjusting symptom correlations for overall depression severity; and (b) analysing general population samples v. subsamples of currently depressed individuals.

METHODS

Using population-based cross-sectional and longitudinal data from two nations (Switzerland, 5883 young men; USA, 2174 young men and 2244 young women) we assessed the dimensions of the nine DSM-IV depression symptoms in young adults. In each general-population sample and each subsample of currently depressed participants, we conducted a standardised process of three analytical steps, based on exploratory and confirmatory factor and bifactor analysis, to reveal any replicable dimensional structure underlying symptom correlations while controlling for overall depression severity.

RESULTS

We found no evidence of a replicable dimensional structure across samples when adjusting symptom correlations for overall depression severity. In the general-population samples, symptoms correlated strongly and a single dimension of depression severity was revealed. Among depressed participants, symptom correlations were surprisingly weak and no replicable dimensions were identified, regardless of severity-adjustment.

CONCLUSIONS

First, caution is warranted when considering studies assessing dimensions of depression because general population-based studies and studies of depressed individuals generate different data that can lead to different conclusions. This problem likely generalises to other models based on the symptoms' inter-relationships such as network models. Second, whereas the overall severity aligns individuals on a continuum of disorder intensity that allows non-affected individuals to be distinguished from affected individuals, the clinical evaluation and treatment of depressed individuals should focus directly on each individual's symptom profile.

Concordance networks and application to clustering cancer symptomology

Symptoms of complex illnesses such as cancer often present with a high degree of heterogeneity between patients. At the same time, there are often core symptoms that act as common drivers for other symptoms, such as fatigue leading to depression and cognitive dysfunction. These symptoms are termed bridge symptoms and when combined with heterogeneity in symptom presentation, are difficult to detect using traditional unsupervised clustering techniques. This article develops a method for identifying patient communities based on bridge symptoms termed concordance network clustering. An empirical study of breast cancer symptomatology is presented, and demonstrates the applicability of this method for identifying bridge symptoms.

Psychosis as an Evolutionary Adaptive Mechanism to Changing Environments

Background: From an evolutionary perspective it is remarkable that psychotic disorders, mostly occurring during fertile age and decreasing fecundity, maintain in the human population. Aim: To argue the hypothesis that psychotic symptoms may not be viewed as an illness but as an adaptation phenomenon, which can become out of control due to different underlying brain vulnerabilities and external stressors, leading to social exclusion. Methods: A literature study and analysis. Results: Until now, biomedical research has not unravelld the definitive etiology of psychotic disorders. Findings are inconsistent and show non-specific brain anomalies and genetic variation with small effect sizes. However, compelling evidence was found for a relation between psychosis and stressful environmental factors, particularly those influencing social interaction. Psychotic symptoms may be explained as a natural defense mechanism or protective response to stressful environments. This is in line with the fact that psychotic symptoms most often develop during adolescence. In this phase of life, leaving the familiar, and safe home environment and building new social networks is one of the main tasks. This could cause symptoms of "hyperconsciousness" and calls on the capacity for social adaptation. Conclusions: Psychotic symptoms may be considered as an evolutionary maintained phenomenon.Research investigating psychotic disorders may benefit from a focus on underlying general brain vulnerabilities or prevention of social exclusion, instead of psychotic symptoms.

Genetic and Environmental Influences on the Affective Regulation Network: A Prospective Experience Sampling Analysis

Background: The study of networks of affective mental states that play a role in psychopathology may help model the influence of genetic and environmental risks. The aim of the present paper was to examine networks of affective mental states (AMS: "cheerful," "insecure," "relaxed," "anxious," "irritated," and "down") over time, stratified by genetic liability for psychopathology and exposure to environmental risk, using momentary assessment technology. Methods: Momentary AMS, collected using the experience sampling method (ESM) as well as childhood trauma and genetic liability (based on the level of shared genes and psychopathology in the co-twin) were collected in a population-based sample of female-female twin pairs and sisters (585 individuals). Networks were generated using multilevel time-lagged regression analysis, and regression coefficients were compared across three strata of childhood trauma severity and three strata of genetic liability using permutation testing. Regression coefficients were presented as network connections. Results: Visual inspection of network graphs revealed some suggestive changes in the networks with more exposure to either childhood trauma or genetic liability (i.e., stronger reinforcing loops between the three negative AMS anxious, insecure, and down both under higher early environmental, and under higher genetic liability exposure, stronger negative association between AMS of different valences: i.e., between "anxious" at t-1 and "relaxed" at t, "relaxed" at t-1 and "down" at t, under intermediate genetic liability exposure when compared to both networks under low and high genetic liability). Yet, statistical evaluation of differences across exposure strata was inconclusive. Conclusions: Although suggestive of a difference in the emotional dynamic, there was no conclusive evidence that genetic and environmental factors may impact ESM network models of individual AMS.

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.

A symptom network structure of the psychosis spectrum

Current diagnostic systems mainly focus on symptoms needed to classify patients with a specific mental disorder and do not take into account the variation in co-occurring symptoms and the interaction between the symptoms themselves. The innovative network approach aims to further our understanding of mental disorders by focusing on meaningful connections between individual symptoms of a disorder and has thus far proven valuable insights to psychopathology. The aims of current study were to I) construct a symptom network and investigate interactions between a wide array of psychotic symptoms; II) identify the most important symptoms within this network and III) perform an explorative shortest pathway analysis between depressive and delusional symptoms. We analyzed interview data from n=408 male patients with non-affective psychosis using the Comprehensive Assessment of Symptoms and History (CASH). A network structure of 79 symptoms was computed to explore partial correlations between positive, negative, catatonia and affective symptoms. The resulting network showed strong connectivity between individual symptoms of the CASH, both within- and between-domains. Most central symptoms included 'loss of interest', 'chaotic speech', 'inability to enjoy recreational interest in activities', 'inability to form or maintain relationships with friends' and 'poverty of content of speech'. The shortest pathway analysis between depressive and delusional symptoms displayed an important role for 'persecutory delusions'. In conclusion, this study showed that individual psychotic symptoms are meaningfully related to each other not only within their own cluster, but also between different clusters and that important information may be acquired by investigating interactions at a symptom level.

Evidence that psychopathology symptom networks have limited replicability

Network analysis is quickly gaining popularity in psychopathology research as a method that aims to reveal causal relationships among individual symptoms. To date, 4 main types of psychopathology networks have been proposed: (a) association networks, (b) regularized concentration networks, (c) relative importance networks, and (d) directed acyclic graphs. The authors examined the replicability of these analyses based on symptoms of major depression and generalized anxiety between and within 2 highly similar epidemiological samples (i.e., the National Comorbidity Survey-Replication [n = 9282] and the National Survey of Mental Health and Wellbeing [n = 8841]). Although association networks were stable, the 3 other types of network analysis (i.e., the conditional independence networks) had poor replicability between and within methods and samples. The detailed aspects of the models-such as the estimation of specific edges and the centrality of individual nodes-were particularly unstable. For example, 44% of the symptoms were estimated as the "most influential" on at least 1 centrality index across the 6 conditional independence networks in the full samples, and only 13-21% of the edges were consistently estimated across these networks. One of the likely reasons for the instability of the networks is the predominance of measurement error in the assessment of individual symptoms. The authors discuss the implications of these findings for the growing field of psychopathology network research, and conclude that novel results originating from psychopathology networks should be held to higher standards of evidence before they are ready for dissemination or implementation in the field. (PsycINFO Database Record

A network theory of mental disorders

In recent years, the network approach to psychopathology has been advanced as an alternative way of conceptualizing mental disorders. In this approach, mental disorders arise from direct interactions between symptoms. Although the network approach has led to many novel methodologies and substantive applications, it has not yet been fully articulated as a scientific theory of mental disorders. The present paper aims to develop such a theory, by postulating a limited set of theoretical principles regarding the structure and dynamics of symptom networks. At the heart of the theory lies the notion that symptoms of psychopathology are causally connected through myriads of biological, psychological and societal mechanisms. If these causal relations are sufficiently strong, symptoms can generate a level of feedback that renders them self-sustaining. In this case, the network can get stuck in a disorder state. The network theory holds that this is a general feature of mental disorders, which can therefore be understood as alternative stable states of strongly connected symptom networks. This idea naturally leads to a comprehensive model of psychopathology, encompassing a common explanatory model for mental disorders, as well as novel definitions of associated concepts such as mental health, resilience, vulnerability and liability. In addition, the network theory has direct implications for how to understand diagnosis and treatment, and suggests a clear agenda for future research in psychiatry and associated disciplines.

A Transdiagnostic Network Approach to Psychosis

Our ability to accurately predict development and outcome of early expression of psychosis is limited. To elucidate the mechanisms underlying psychopathology, a broader, transdiagnostic approach that acknowledges the complexity of mental illness is required. The upcoming network paradigm may be fruitful here. In this study, we applied a transdiagnostic network approach to psychosis. Data pertain to the third wave (second follow-up) of a sample of adolescents originally recruited at age 7-8 years. At baseline, N = 347 children with auditory verbal hallucinations (AVH) and N = 347 control children were included. N = 293 of these N = 694 children participated in the second follow-up (mean age 18.9 years; 59% women). Participants completed the Community Assessment of Psychic Experiences (CAPE) and the Depression, Anxiety and Stress Scale (DASS-21). A specific type of network model, the Ising model, was applied to dichotomized CAPE and DASS items. Interconnections of experiences within the same domain were observed, as well as interconnections between experiences of multiple domains of psychopathology. Quantitative and qualitative differences in network architecture were found in networks of psychopathological experiences in individuals with or without AVH at age 7-8 years. Although adolescents with or without previous AVH did not differ in their current CAPE scores, differences in the interconnectedness of psychopathology items were still found, possibly mirroring a difference in psychosis liability. This study showed that it is possible to map transdiagnostic experiences of psychopathology as a network and that important information can be derived from this approach in comparison to regular approaches.

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.

Persistence or recurrence of non-psychotic comorbid mental disorders associated with 6-year poor functional outcomes in patients at ultra high risk for psychosis

BACKGROUND

Patients at ultra-high risk for psychosis (UHR) are a highly heterogeneous group in terms of clinical and functional outcomes. Several non-psychotic mental disorders co-occur together with the UHR state. Little is known about the impact of non-psychotic comorbid mental disorders on clinical and functional outcomes of UHR patients.

METHODS

The sample included 154 UHR help-seeking patients (identified with the CAARMS, comprehensive assessment of the at-risk mental state), evaluated at baseline on the Ham-D, Ham-A (Hamilton depression/anxiety rating scale), and PANSS (positive and negative syndrome scale). 74 patients completed the 6-year follow-up assessment (mean=6.19, SD=1.87). Comorbid disorders at follow-up were assessed with the SCID I and II. Global functioning was rated on the global assessment of functioning (GAF) scale.

RESULTS

In the present sample, 6-year risk of psychosis transition was 28.4%. Among non-transitioned UHR patients, 28.3% reported attenuated psychotic symptoms (APS) and 45.3% remained functionally impaired at follow-up (GAF<60). 56.8% patients were affected by at least one comorbid disorder at follow-up. Among UHR patients who presented with some comorbid disorder at baseline, 61.5% had persistent or recurrent course. Incident comorbid disorders emerged in 45.4% of baseline UHR patients. The persistence or recurrence of non-psychotic comorbid mental disorders was associated with poorer global functional outcomes at follow-up.

LIMITATIONS

A substantial proportion of the initial sample was not available for follow-up interviews and some groups in the analyses had small sample size. Predictors of longitudinal outcomes were not explored.

CONCLUSIONS

Among UHR patients, persistence or recurrence of non-psychotic comorbid mental disorders, mostly affective disorders, is associated with 6-year poor functional outcomes.

An n=1 Clinical Network Analysis of Symptoms and Treatment in Psychosis

INTRODUCTION

Dynamic relationships between the symptoms of psychosis can be shown in individual networks of psychopathology. In a single patient, data collected with the Experience Sampling Method (ESM-a method to construct intensive time series of experience and context) can be used to study lagged associations between symptoms in relation to illness severity and pharmacological treatment.

METHOD

The patient completed, over the course of 1 year, for 4 days per week, 10 daily assessments scheduled randomly between 10 minutes and 3 hours apart. Five a priori selected symptoms were analysed: 'hearing voices', 'down', 'relaxed', 'paranoia' and 'loss of control'. Regression analysis was performed including current level of one symptom as the dependent variable and all symptoms at the previous assessment (lag) as the independent variables. Resulting regression coefficients were printed in graphs representing a network of symptoms. Network graphs were generated for different levels of severity: stable, impending relapse and full relapse.

RESULTS

ESM data showed that symptoms varied intensely from moment to moment. Network representations showed meaningful relations between symptoms, e.g. 'down' and 'paranoia' fuelling each other, and 'paranoia' negatively impacting 'relaxed'. During relapse, symptom levels as well as the level of clustering between symptoms markedly increased, indicating qualitative changes in the network. While 'hearing voices' was the most prominent symptom subjectively, the data suggested that a strategic focus on 'paranoia', as the most central symptom, had the potential to bring about changes affecting the whole network.

CONCLUSION

Construction of intensive ESM time series in a single patient is feasible and informative, particularly if represented as a network, showing both quantitative and qualitative changes as a function of relapse.