A symptom network structure of the psychosis spectrum

Exploring the interplay between core and mood symptoms in schizophrenia: A network analysis

BACKGROUND

Schizophrenia is a complex neuropsychiatric disorder characterized by positive symptoms, negative symptoms, cognitive deficits, and co-occurring mood symptoms. Network analysis offers a novel approach to investigate the intricate relationships between these symptom dimensions, potentially informing personalized treatment strategies.

METHODS

A cross-sectional study was conducted from November 2019 to October 2021, involving 1285 inpatients with schizophrenia in Liaoning Province, China. Symptom severity was assessed using the Positive and Negative Syndrome Scale (PANSS), Hamilton Depression Rating Scale (HAMD-17), Hamilton Anxiety Rating Scale (HAMA-14), and Montreal Cognitive Assessment (MoCA). Network analysis was conducted to investigate the network structure, central symptoms, and bridge symptoms.

RESULTS

The network analysis uncovered profound interconnectivity between core symptoms and the anxiety-depression community. Central symptoms, such as psychic anxiety, poor rapport, delusions, and attention, were identified as potential therapeutic targets. Bridge symptoms, including insomnia, depressed mood, anxiety-somatic, conceptual disorganization, and stereotyped thinking, emerged as key nodes facilitating interactions between symptom communities. The stability and reliability of the networks were confirmed through bootstrapping procedures.

DISCUSSION

The findings highlight the complex interplay between schizophrenia symptoms, emphasizing the importance of targeting affective symptoms and cognitive impairment in treatment. The identification of central and bridge symptoms suggests potential pathways for personalized interventions aimed at disrupting self-reinforcing symptom cycles. The study underscores the need for a transdiagnostic, personalized approach to schizophrenia treatment.

Hazardous drinking in young adults with co-occurring PTSD and psychosis symptoms: A network analysis

BACKGROUND

Existing literature suggests the co-occurrence of post-traumatic stress disorder (PTSD) and psychosis among young adults is related to hazardous drinking. However, the influencing mechanisms among these co-occurrences are inconclusive. Thus, this study aimed to investigate the symptomatic associations between PTSD, psychosis, and hazardous drinking.

METHODS

This study included 96,218 young Chinese adults, divided into three groups (PTSD, Psychosis, and co-occurring PTSD-Psychosis). PTSD, psychosis, and hazardous drinking were measured by the ten-item Trauma Screening Questionnaire, the seven-item Psychosis Screener Scale, and the four-item Alcohol Use Disorders Identification Test, respectively. Network analysis was utilized to explore and compare the symptomatic correlation between PTSD, psychosis, and hazardous drinking.

RESULTS

In this study, the most crucial symptom (both central and bridge) was "delusion of control" among the three networks. Hazardous drinking was another main bridge symptom. Compared to the Psychosis group and the co-occurring PTSD-Psychosis group, "Delusion of reference or persecution" to "Grandiose delusion" was the strongest edge in "the network structure of the PTSD group".

LIMITATIONS

The cross-sectional study cannot determine the causal relationship. Applying self-reporting questionnaires may cause inherent bias. Young adult participants limited the generalization of the results to other groups.

CONCLUSIONS

Among the three network structures, delusion of control was the most crucial symptom, and hazardous drinking was another bridge symptom; the edge of delusion of reference or persecution and grandiose delusion was strongest in the PTSD group's network. Efforts should be taken to develop diverse targeted interventions for these core symptoms to relieve PTSD, psychosis, and hazardous drinking in young adults.

Personal recovery in first-episode psychosis: Beyond clinical and functional recovery

BACKGROUND

The concept of personal recovery after psychotic illness focuses more on patients' social and existential needs compared to traditional outcome measures including clinical and functional recovery. This research aims to contribute to a broad framework on (personal) recovery and associated factors.

METHODS

Data from 203 persons with symptomatic remission of their first-episode psychosis from the ongoing HAMLETT study were analyzed. To determine the relative importance of several biological, clinical, psychological, and social factors in explaining personal recovery as measured by the Recovery Assessment Scale (RAS), partial Spearman correlations (controlling for clinical recovery (PANSS) and functional recovery (WHODAS 2.0)) and a bootstrapped multiple regression were performed. Indirect effects on personal recovery within these factors, clinical recovery, and functional recovery were explored using a regularized partial correlation network.

RESULTS

Of the factors that explained personal recovery beyond the effects of clinical and functional recovery, social support was the strongest predictor, followed by self-esteem, internalized stigma, and insecure attachment, collectively explaining 48.2 % of the variance. Anhedonia/apathy showed a trend towards a negative correlation. Age at onset, sex, early trauma/neglect, cognition, and being married/cohabiting did not significantly correlate with personal recovery. The network (n = 143) was consistent with these findings and indicated possible mediation pathways for early trauma/neglect, insecure attachment, cognition, and being married/cohabiting.

CONCLUSIONS

Personal recovery is an important addition to traditional measures of outcome after psychosis. Various quality of life indicators, such as self-esteem and social support, explain variance in personal recovery over clinical and functional recovery.

Unique Challenges in Biomarkers for Psychotic Disorders

Biomarkers are observations that provide information about the risk of certain conditions (predictive) or their underlying mechanisms (explanatory) [...].

[The new psychopathological paradigm of schizophrenia and schizophrenia spectrum disorders]

The basis of the new paradigm, as an attempt to modernize the systematics of psychopathological disorders, is the concept of simultaneous representation of two relatively independent domains (founded by neurobiological processes) in the clinical space of schizophrenia - negative and positive disorders. The study established the spectra of functional activity of endogenous dimensions, as well as the trajectories of their development, which determine the dominance of predominantly negative or positive symptoms (negative/positive schizophrenia) during the course of the disease. The differentiated impact of endogenous domains on constitutional characterological structures accompanied by the formation of pseudopsychiopathies and subpsychotic personality disorders is observed.

Gender differences in cannabis use disorder symptoms: A network analysis

BACKGROUND

While cannabis use in women is increasing worldwide, research into gender differences in cannabis use disorder (CUD) symptomology is lacking. In response to limited effectiveness of addiction treatment, research focus has been shifting from clinical diagnoses towards interactions between symptoms, as patterns of symptoms and their interactions could be crucial in understanding etiological mechanisms in addiction. The aim of this study was to evaluate the CUD symptom network and assess whether there are gender differences therein.

METHODS

A total of 1257 Dutch individuals reporting weekly cannabis use, including 745 men and 512 women, completed online questionnaires assessing DSM-5 CUD symptoms and additional items on plans to quit or reduce use, cigarette use, and the presence of psychological diagnoses. Gender differences were assessed for all variables and an Ising model estimation method was used to estimate CUD symptom networks in men and women using network comparison tests to assess differences.

RESULTS

There were gender differences in the prevalence of 6 of the 11 symptoms, but symptom networks did not differ between men and women. Cigarette use appeared to only be connected to the network through withdrawal, indicating a potential role of cigarette smoking in enhancing cannabis withdrawal symptoms. Furthermore, there were gender differences in the network associations of mood and anxiety disorders with CUD symptoms.

CONCLUSION

The association between smoking and withdrawal as well as gender differences in the role of comorbidities in the CUD network highlight the value of using network models to understand CUD and how symptom interactions might affect treatment.

Psychopathological networks in psychosis: Changes over time and clinical relevance. A long-term cohort study of first-episode psychosis

BACKGROUND

First-episode psychosis is a critical period for early interventions to reduce the risk of poor outcomes and relapse as much as possible. However, uncertainties about the long-term outcomes of symptomatology remain to be ascertained.

METHODS

The aim of the present study was to use network analysis to investigate first-episode and long-term stages of psychosis at three levels of analysis: micro, meso and macro. The sample was a cohort of 510 patients with first-episode psychoses from the SEGPEP study, who were reassessed at the long-term follow-up (n = 243). We used the Comprehensive Assessment of Symptoms and History for their assessments and lifetime outcome variables of clinical relevance.

RESULTS

Our results showed a similar pattern of clustering between first episodes and long-term follow-up in seven psychopathological dimensions at the micro level, 3 and 4 dimensions at the meso level, and one at the macro level. They also revealed significant differences between first-episode and long-term network structure and centrality measures at the three levels, showing that disorganization symptoms have more influence in long-term stabilized patients.

CONCLUSIONS

Our findings suggest a relative clustering invariance at all levels, with the presence of two domains of disorganization as the most notorious difference over time at micro level. The severity of disorganization at the follow-up was associated with a more severe course of the psychosis. Moreover, a relative stability in global strength of the interconnections was found, even though the network structure varied significantly in the long-term follow-up. The macro level was helpful in the integration of all dimensions into a common psychopathology factor, and in unveiling the strong relationships of psychopathological dimensions with lifetime outcomes, such as negative with poor functioning, disorganization with high antipsychotic dose-years, and delusions with poor adherence to treatment. These results add evidence to the hierarchical, dimensional and longitudinal structure of psychopathological symptoms and their clinical relevance in first-episode psychoses.

A network analysis of anxiety, depressive, and psychotic symptoms and functioning in children and adolescents at clinical high risk for psychosis

Objective

Youths at clinical high risk for psychosis (CHR-P) are characterized by a high prevalence of anxiety and depressive disorders. The present study aimed at developing and analyzing a network structure of CHR-P symptom domains (i.e., positive, negative, disorganization, and general subclinical psychotic symptoms), depressive and anxiety symptoms, and general functioning.

Methods

Network analysis was applied to data on 111 CHR-P children and adolescents (M age = 14.1), who were assessed using the Structured Interview for Prodromal Syndromes, the Children's Depression Inventory, the Children's Global Assessment Scale, and the Multidimensional Anxiety Scale for Children.

Results

In the network, negative and disorganization symptoms showed the strongest association (r = 0.71), and depressive and anxiety symptoms showed dense within-domain connections, with a main bridging role played by physical symptoms of anxiety. The positive symptom cluster was not associated with any other node. The network stability coefficient (CS) was slightly below 0.25, and observed correlations observed ranged from 0.35 to 0.71.

Conclusion

The lack of association between subclinical positive symptoms and other network variables confirmed the independent nature of subclinical positive symptoms from comorbid symptoms, which were found to play a central role in the analyzed network. Complex interventions should be developed to target positive and comorbid symptoms, prioritizing those with the most significant impact on functioning and the most relevance for the young individual, through a shared decision-making process. Importantly, the results suggest that negative and disorganization symptoms, as well as depressive and anxiety symptoms, may be targeted simultaneously.

Contemporaneous symptom networks of multidimensional symptom experiences in cancer survivors: A network analysis

BACKGROUND

Symptom networks can provide empirical evidence for the development of personalized and precise symptom management strategies. However, few studies have explored the symptom networks of multidimensional symptom experiences in cancer survivors. The objectives of this study were to generate symptom networks of multidimensional symptom experiences in cancer survivors and explore the centrality indices and density in these symptom networks METHODS: Data from 1065 cancer survivors were obtained from the Shanghai CANcer Survivor (SCANS) Report. The MD Anderson Symptom Inventory was used to assess the prevalence and severity of 13 cancer-related symptoms. We constructed contemporaneous networks with all 13 symptoms after controlling for covariates.

RESULTS

Distress (r_s  = 9.18, r_c  = 0.06), sadness (r_s  = 9.05, r_c  = 0.06), and lack of appetite (r_s  = 9.04, r_c  = 0.06) had the largest values for strength and closeness. The density of the "less than 5 years" network was significantly different from that of the "5-10 years" and "over 10 years" networks (p < 0.001). We found that while fatigue was the most severe symptom in cancer survivorship, the centrality of fatigue was lower than that of the majority of other symptoms.

CONCLUSION

Our study demonstrates the need for the assessment of centrality indices and network density as an essential component of cancer care, especially for survivors with <5 years of survivorship. Future studies are warranted to develop dynamic symptom networks and trajectories of centrality indices in longitudinal data to explore causality among symptoms and markers of interventions.

The network structure of cognitive deficits in first episode psychosis patients

Network analysis is an important conceptual and analytical approach in mental health research. However, few studies have used network analysis to examine the structure of cognitive performance in psychotic disorders. We examined the network structure of the cognitive scores of a sample of 207 first-episode psychosis (FEP) patients and 188 healthy controls. Participants were assessed using a battery of 10 neuropsychological tests. Fourteen cognitive scores encompassing six cognitive domains and premorbid IQ were selected to perform the network analysis. Many similarities were found in the network structure of FEP patients and healthy controls. Verbal memory, attention, working memory and executive function nodes were the most central nodes in the network. Nodes in both groups corresponding to the same tests tended to be strongly connected. Verbal memory, attention, working memory and executive function were central dimensions in the cognitive network of FEP patients and controls. These results suggest that the interplay between these core dimensions is essential for demands to solve complex tasks, and these interactions may guide the aims of cognitive rehabilitation. Network analysis of cognitive dimensions might have therapeutic implications that deserve further research.

(Attenuated) hallucinations join basic symptoms in a transdiagnostic network cluster analysis

Hallucinations are considered characteristic symptoms of psychosis and part of the 'psychosis superspectrum' of the Hierarchical Taxonomy Of Psychopathology (HiTOP) initiative. To gain insight into their psychopathological relevance, we studied their dimensional placement within a single dense transdiagnostic network constituting of basic symptoms as well as of attenuated and frank psychotic, and related symptoms. Newman's modularity analysis was used to detect symptom clusters in an earlier generated network (Jimeno, N., et al., 2020. Main symptomatic treatment targets in suspected and early psychosis: New insights from network analysis. Schizophr. Bull. 46, 884-895. https://doi.org/10.1093/schbul/sbz140). The constituting 86 symptoms were assessed with the Schizophrenia Proneness Instrument, Adult version (SPI-A), the Structured Interview for Psychosis-Risk Syndromes (SIPS), and the Positive And Negative Syndrome Scale (PANSS) in three adult samples of an early detection service: clinical high-risk (n = 203), first-episode psychosis (n = 153), and major depression (n = 104). Three clusters were detected: "subjective disturbances", "positive symptoms and behaviors", and "negative and anxious-depressive symptoms". The predominately attenuated hallucinations of both SIPS and PANSS joined the basic symptoms in "subjective disturbances", whereas other positive symptoms entered "positive symptoms and behaviors". Our results underline the importance of insight in separating true psychotic hallucinations from other hallucinatory experiences that, albeit phenomenologically similar are still experienced with some insight, i.e., are present in an attenuated form. We conclude that, strictly, hallucinations held with any degree of insight should not be used to diagnose transition to or presence of frank psychoses and, relatedly, to justify antipsychotic medication.

Identification of central symptoms in Internet addictions and depression among adolescents in Macau: A network analysis

BACKGROUND

Internet addiction (IA) and depression are common among adolescents and often are co-occurring. This study examined the network structures of IA and depressive symptoms (depression hereafter) in adolescents.

METHODS

A total of 1,009 adolescents were recruited. IA and depression were measured using the Internet Addiction Test (IAT) and the 9 items-Patient Health Questionnaire (PHQ-9), respectively. A network analysis was conducted to identify central symptoms and bridge symptoms using centrality indices. Network stability was evaluated using the case-dropping procedure. The Network Comparison Test (NCT) was conducted to examine whether network characteristics differed by gender.

RESULTS

Network analysis revealed that nodes IAT-15 ("Preoccupation with the Internet"), IAT-2 ("Neglect chores to spend more time online"), PHQ-6 ("Guilty"), and IAT-16 ("Request an extension for longer time spent online") were the most central symptoms within the model of coexisting IA and depression. The most important bridge symptom was node IAT-11 ("Anticipation for future online activities"), followed by IAT-12 ("Fear that life is boring and empty without the Internet") and IAT-19 ("Spend more time online over going out with others"). Gender did not significantly influence the network structure. The IA and depression network model showed a high degree of stability.

CONCLUSION

The central symptoms along with key bridge symptoms identified could be potentially targeted when treating and preventing IA and depression among adolescents.

The Limits between Schizophrenia and Bipolar Disorder: What Do Magnetic Resonance Findings Tell Us?

Schizophrenia and bipolar disorder, two of the most severe psychiatric illnesses, have historically been regarded as dichotomous entities but share many features of the premorbid course, clinical profile, genetic factors and treatment approaches. Studies focusing on neuroimaging findings have received considerable attention, as they plead for an improved understanding of the brain regions involved in the pathophysiology of schizophrenia and bipolar disorder. In this review, we summarize the main magnetic resonance imaging findings in both disorders, aiming at exploring the neuroanatomical and functional similarities and differences between the two. The findings show that gray and white matter structural changes and functional dysconnectivity predominate in the frontal and limbic areas and the frontotemporal circuitry of the brain areas involved in the integration of executive, cognitive and affective functions, commonly affected in both disorders. Available evidence points to a considerable overlap in the affected regions between the two conditions, therefore possibly placing them at opposite ends of a psychosis continuum.

Network analysis of depressive and anxiety symptoms in adolescents during the later stage of the COVID-19 pandemic

Network analysis is an effective approach for examining complex relationships between psychiatric symptoms. This study was designed to examine item-level relationships between depressive and anxiety symptoms using network analysis in an adolescent sample and identified the most central symptoms within the depressive-anxiety symptoms network model. Depressive and anxiety symptoms were assessed using the Patient Health Questionire-9 (PHQ-9) and Generalized Anxiety Disorder Screener (GAD-7), respectively. The structure of depressive and anxiety symptoms was characterized using "Strength" and "Bridge Strength" as centrality indices in the symptom network. Network stability was tested using a case-dropping bootstrap procedure. Finally, a Network Comparison Test (NCT) was conducted to examine whether network characteristics differed on the basis of gender, school grade and residence. Network analysis revealed that nodes PHQ2 ("Sad mood"), GAD6 ("Irritability"), GAD3 ("Worry too much"), and PHQ6 ("Guilty") were central symptoms in the network model of adolescents. Additionally, bridge symptoms linking anxiety and depressive symptoms in this sample were nodes PHQ6 ("Guilty"), PHQ2 ("Sad mood"), and PHQ9 ("Suicide ideation"). Gender, school grade and residence did not significantly affect the network structure. Central symptoms (e.g., Sad mood, Irritability, Worry too much, and Guilty) and key bridge symptoms (e.g., Guilty, Sad mood, and Suicide ideation) in the depressive and anxiety symptoms network may be useful as potential targets for intervention among adolescents who are at risk for or suffer from depressive and anxiety symptoms.

Dependence Graphs Based on Association Rules to Explore Delusional Experiences

Methods to estimate dependence graphs among variables, have quickly gained popularity in psychopathology research. To date, multiple methods have been proposed but recent studies report several drawbacks impacting on the validity of the conclusions as it is argued that assumptions and conditions underlying the methods commonly used and the nature of the data is lacking alignment. A particularly important issue is that underlying dynamics potentially present in heterogeneous datasets are disregarded, as the methods focus on the variables but not on individuals. This work also argues that the networks may lack relevant components as current methods ignore connections beyond pairwise interactions between individual symptoms. This study addresses these issues with a novel method for constructing dependence graphs based on applying Association Rules to binary records, which is often the type of records in the psychopathology domain. To demonstrate the benefits, we examine 12 delusional experiences in a sample of 1423 subjects with psychotic disorders. We show that by extracting Association Rules using an algorithm called apriori, in addition to facilitating an intuitive interpretation, previously unseen relevant dependencies are revealed from higher order interactions among psychotic experiences in subgroups of patients.

Clusters, lines and webs-so does my patient have psychosis? reflections on the use of psychiatric conceptual frameworks from a clinical vantage point

Mental health professionals working in hospitals or community clinics inevitably face the realisation that we possess imperfect conceptual means to understand mental disorders. In this paper the authors bring together ideas from the fields of Philosophy, Psychiatry, Cognitive Psychology and Linguistics to reflect on the ways we represent phenomena of high practical importance that we often take for granted, but are nevertheless difficult to define in ontological terms. The paper follows through the development of the concept of psychosis over the last two centuries in the interplay of three different conceptual orientations: the categorical, dimensional and network approaches. Each of these represent the available knowledge and dominant thinking styles of the era in which they emerged and take markedly different stances regarding the nature of mental phenomena. Without particular commitment to any ontological positions or models described, the authors invite the reader into a thinking process about the strengths and weaknesses of these models, and how they can be reconciled in multidisciplinary settings to benefit the process of patient care.

Autistic Symptoms and Social Functioning in Psychosis: A Network Approach

Psychotic and autistic symptoms are related to social functioning in individuals with psychotic disorders (PD). The present study used a network approach to (1) evaluate the interactions between autistic symptoms, psychotic symptoms, and social functioning, and (2) investigate whether relations are similar in individuals with and without PD. We estimated an undirected network model in a sample of 504 PD, 572 familial risk for psychosis (FR), and 337 typical comparisons (TC), with a mean age of 34.9 years. Symptoms were assessed with the Autism Spectrum Quotient (AQ; 5 nodes) and the Community Assessment of Psychic Experiences (CAPE; 9 nodes). Social functioning was measured with the Social Functioning Scale (SFS; 7 nodes). We identified statistically significant differences between the FR and PD samples in global strength (P < .001) and network structure (P < .001). Our results show autistic symptoms (social interaction nodes) are negatively and more closely related to social functioning (withdrawal, interpersonal behavior) than psychotic symptoms. More and stronger connections between nodes were observed for the PD network than for FR and TC networks, while the latter 2 were similar in density (P = .11) and network structure (P = .19). The most central items in strength for PD were bizarre experiences, social skills, and paranoia. In conclusion, specific autistic symptoms are negatively associated with social functioning across the psychosis spectrum, but in the PD network symptoms may reinforce each other more easily. These findings emphasize the need for increased clinical awareness of comorbid autistic symptoms in psychotic individuals.

Network Analysis of Persistent Somatic Symptoms in Two Clinical Patient Samples

OBJECTIVE

Previous attempts to group persistent somatic symptoms (PSSs) with factor-analytic approaches have obtained heterogeneous results. An alternative approach that seems to be more suitable is the network theory. Compared with factor analysis, which focuses on the underlying factor of symptoms, network analysis focuses on the dynamic relationships and interactions among different symptoms. The main aim of this study is to apply the network approach to examine the heterogeneous structure of PSS within two clinical samples.

METHODS

The first data set consisted of n = 254 outpatients who were part of a multicenter study. The second data set included n = 574 inpatients, both with somatoform disorders. Somatic symptom severity was assessed with the Screening of Somatoform Disorder (SOMS-7T).

RESULTS

Results indicate that there are five main symptom groups that were found in both samples: neurological, gastrointestinal, urogenital, cardiovascular, and musculoskeletal symptoms. Although patterns of symptoms with high connection to each other look quite similar in both networks, the order of the most central symptoms (e.g., symptoms with a high connection to other symptoms in the network) differs.

CONCLUSIONS

This work is the first to estimate the structure of PSS using network analysis. A next step could be first to replicate our findings before translating them into clinical practice. Second, results may be useful for generating hypotheses to be tested in future studies, and the results open new opportunities for a better understanding for etiology, prevention, and intervention research.

Dynamic symptom networks across different at-risk stages for psychosis: An individual and transdiagnostic perspective

The clinical staging model distinguishes different stages of mental illness. Early stages, are suggested to be more mild, diffuse and volatile in terms of expression of psychopathology than later stages. This study aimed to compare individual transdiagnostic symptom networks based on intensive longitudinal data between individuals in different early clinical stages for psychosis. It was hypothesized that with increasing clinical stage (i) density of symptom networks would increase and (ii) psychotic experiences would be more central in the symptom networks. Data came from a 90-day diary study, resulting in 8640 observations within N = 96 individuals, divided over four subgroups representing different early clinical stages (n₁ = 25, n₂ = 27, n₃ = 24, n₄ = 20). Sparse Time Series Chain Graphical Models were used to create individual contemporaneous and temporal symptom networks based on 10 items concerning symptoms of depression, anxiety, psychosis, non-specific and vulnerability domains. Network density and symptom centrality (strength) were calculated individually and compared between and within the four subgroups. Level of psychopathology increased with clinical stage. The symptom networks showed large between-individual variation, but neither network density not psychotic symptom strength differed between the subgroups in the contemporaneous (p_density = 0.59, p_strength > 0.51) and temporal (p_density = 0.75, p_strength > 0.35) networks. No support was found for our hypothesis that higher clinical stage comes with higher symptom network density or a more central role for psychotic symptoms. Based on the high inter-individual variability, our results highlight the importance of individualized assessment of symptom networks.

Structure of Hypomanic Symptoms in Adolescents With Bipolar Disorders: A Network Approach

BACKGROUND

Bipolar disorders (BD) are severe mental illnesses that are often misdiagnosed or under-diagnosed. The self-report 33-item Hypomania Checklist (HCL-33) and the 33-item Hypomania Checklist - external assessment (HCL-33-EA) are well-validated scales for BD symptom detection. This study compared the network structure, central symptoms, and network stability of hypomanic symptoms measured by the HCL-33 vs. the HCL-33-EA.

METHODS

This cross-sectional study was conducted from January to December 2019. Adolescents (aged between 12 and 18 years) with BD were recruited from the outpatient department of Child Psychiatry, First Affiliated Hospital of Zhengzhou University. All participants were asked to complete the HCL-33, and their caregivers completed the HCL-33-EA. Network analyses were conducted.

RESULTS

A total of 215 adolescents with BD and their family caregivers were recruited. Node HCL17 ("talk more," node strength = 4.044) was the most central symptom in the HCL-33 network, followed by node HCL2 ("more energetic," node strength = 3.822), and HCL18 ("think faster," node strength = 3.801). For the HCL-33-EA network model, node HCL27 ("more optimistic," node strength = 3.867) was the most central node, followed by node HCL18 ("think faster," node strength = 3.077), and HCL17 ("talk more," node strength = 2.998). In the network comparison test, there was no significant difference at the levels of network structure (M = 0.946, P = 0.931), global strength (S: 5.174, P = 0.274), or each specific edge (all P's > 0.05 after Holm-Bonferroni corrections) between HCL-33 and HCL-33-EA items. Network stabilities for both models were acceptable.

CONCLUSION

The nodes "talk more" and "think faster" acted as central symptoms in BD symptom network models based on the HCL-33 and HCL-33-EA. Although the most prominent central symptom differed between the two models ("talk more" in HCL-33 vs. "more optimistic" in HCL-33-EA model), networks based on each measure were highly similar and underscored similarities in BD symptom relations perceived by adolescents and their caregivers. This research provides foundations for future studies with larger sample sizes toward improving the accuracy and robustness of observed network structures.

Insight-related beliefs and controllability appraisals contribute little to hallucinated voices: a transdiagnostic network analysis study

Hallucinated voices cause high levels of distress and disability. Current theories suggest that insight-related beliefs, about internal or external origin, perceived source location, and appraisals of controllability are important in mediating the impact of these experiences but previous findings have been mixed. We report two open code and open data network analytic studies of items in the Psychotic Symptoms Ratings Scale for auditory verbal hallucinations (PSYRATS-AH) in a large sample of patients with hallucinated voices to examine the network structure of items at (1) first assessment, and (2) differences over two consecutive assessments during a wait-list period. Networks were generated using least absolute shrinkage and selection operator (LASSO) and extended Bayesian information criterion (EBIC) with node predictability. In Study 1 (N = 386), we report that insight-related items made a negligible contribution to hallucinated voices and the controllability appraisal made at most a modest contribution. Items relating to distress and negative content were the most central and most predicted by the wider network. In Study 2 (N = 204), we tested the longitudinal stability of the structure of hallucinated voices over a period of several months, finding a small change in total hallucination score and global strength but no clear evidence for an alteration in the structural relationship. The insight-related and controllability items remained as least influential over time. Insight-related beliefs and controllability appraisals may contribute less than previously thought to distressing hallucinated voices although we do not discount that other appraisals may remain important.

A Network of Psychopathological, Cognitive, and Motor Symptoms in Schizophrenia Spectrum Disorders

Schizophrenia spectrum disorders (SSDs) are complex syndromes involving psychopathological, cognitive, and also motor symptoms as core features. A better understanding of how these symptoms mutually impact each other could translate into diagnostic, prognostic, and, eventually, treatment advancements. The present study aimed to: (1) estimate a network model of psychopathological, cognitive, and motor symptoms in SSD; (2) detect communities and explore the connectivity and relative importance of variables within the network; and (3) explore differences in subsample networks according to remission status. A sample of 1007 patients from a multisite cohort study was included in the analysis. We estimated a network of 43 nodes, including all the items from the Positive and Negative Syndrome Scale, a cognitive assessment battery and clinical ratings of extrapyramidal symptoms. Methodologies specific to network analysis were employed to address the study's aims. The estimated network for the total sample was densely interconnected and organized into 7 communities. Nodes related to insight, abstraction capacity, attention, and suspiciousness were the main bridges between network communities. The estimated network for the subgroup of patients in remission showed a sparser density and a different structure compared to the network of nonremitted patients. In conclusion, the present study conveys a detailed characterization of the interrelations between a set of core clinical elements of SSD. These results provide potential novel clues for clinical assessment and intervention.

A network analysis approach to functioning problems in first psychotic episodes and their relationship with duration of untreated illness: Findings from the PAFIP cohort

BACKGROUND

The domains of functioning affected by first episode of psychosis (FEP) could be analysed as forming a network of interacting or even reinforcing elements. The reasons why longer duration of untreated psychosis (DUP) might be related to higher disability are not still clear. The aim of the present study is to evaluate how different areas of functioning are inter-related according to the length of DUP in patients with FEP, with a particular focus on studying the relative influence of each other according to lengthy delays in initial treatment.

METHOD

441 participants in an epidemiological and intervention program of first episode psychosis (PAFIP) were included in our study. Functioning problems at baseline were assessed with the WHO Disability Assessment Schedule (DAS). Three networks of functioning domains have been estimated according to the length of DUP.

RESULTS

All the DAS items took part in the different networks. We have not found differences across the edge weights in the short, medium and long DUP groups. The domains "social withdrawal", "participation in the household activities", "general interest and information", and "low level of activity" seem to act as bridge items with other areas of functioning in people with longer DUP.

CONCLUSIONS

Our results could have clinical implications for patients with longer DUP, in which case, social withdrawal, household activities, level of activity and general interest in the world around them, could be high-priority target areas of treatment, since they seem to be mediating the relation between others areas of functioning.

The interplay between functioning problems and symptoms in first episode of psychosis: An approach from network analysis

The relationship between psychotic symptoms and global measures of functioning has been widely studied. No previous study has assessed so far the interplay between specific clinical symptoms and particular areas of functioning in first-episode psychosis (FEP) using network analysis methods. A total of 191 patients with FEP (age 24.45 ± 6.28 years, 64.9% male) participating in an observational and longitudinal study (AGES-CM) comprised the study sample. Functioning problems were assessed with the WHO Disability Assessment Schedule (WHODAS), whereas the Positive and Negative Syndrome Scale (PANSS) was used to assess symptom severity. Network analysis were conducted with the aim of analysing the patterns of relationships between the different dimensions of functioning and PANSS symptoms and factors at baseline. According to our results, the most important nodes were "conceptual disorganization", "emotional withdrawal", "lack of spontaneity and flow of conversation", "delusions", "unusual thought content", "dealing with strangers" and "poor rapport". Our findings suggest that these symptoms and functioning dimensions should be prioritized in the clinical assessment and management of patients with FEP. These areas may also become targets of future early intervention strategies, so as to improve quality of life in this population.

How does neighbourhood socio-economic status affect the interrelationships between functioning dimensions in first episode of psychosis? A network analysis approach

The links between psychosis and socio-economic disadvantage have been widely studied. No previous study has analysed the interrelationships and mutual influences between functioning dimensions in first episode of psychosis (FEP) according to their neighbourhood household income, using a multidimensional and transdiagnostic perspective. 170 patients and 129 controls, participants in an observational study (AGES-CM), comprised the study sample. The WHO Disability Assessment Schedule (WHODAS 2.0) was used to assess functioning, whereas participants' postcodes were used to obtain the average household income for each neighbourhood, collected by the Spanish National Statistics Institute (INE). Network analyses were conducted with the aim of defining the interrelationships between the different dimensions of functioning according to the neighbourhood household income. Our results show that lower neighbourhood socioeconomic level is associated with lower functioning in patients with FEP. Moreover, our findings suggest that "household responsibilities" plays a central role in the disability of patients who live in low-income neighbourhoods, whereas "dealing with strangers" is the most important node in the network of patients who live in high-income neighbourhoods. These results could help to personalize treatments, by allowing the identification of potential functioning areas to be prioritized in the treatment of FEP according to the patient's neighbourhood characteristics.

Bayesian Estimation for Gaussian Graphical Models: Structure Learning, Predictability, and Network Comparisons

Gaussian graphical models (GGM; "networks") allow for estimating conditional dependence structures that are encoded by partial correlations. This is accomplished by identifying non-zero relations in the inverse of the covariance matrix. In psychology the default estimation method uses ℓ1-regularization, where the accompanying inferences are restricted to frequentist objectives. Bayesian methods remain relatively uncommon in practice and methodological literatures. To date, they have not yet been used for estimation and inference in the psychological network literature. In this work, I introduce Bayesian methodology that is specifically designed for the most common psychological applications. The graphical structure is determined with posterior probabilities that can be used to assess conditional dependent and independent relations. Additional methods are provided for extending inference to specific aspects within- and between-networks, including partial correlation differences and Bayesian methodology to quantify network predictability. I first demonstrate that the decision rule based on posterior probabilities can be calibrated to the desired level of specificity. The proposed techniques are then demonstrated in several illustrative examples. The methods have been implemented in the R package BGGM.

The centrality of secure attachment within an interacting network of symptoms, cognition, and attachment dimensions in persons with schizophrenia-spectrum disorders: A preliminary study

BACKGROUND

Research in the field of psychosis broadly suggests that symptoms, neurocognitive deficits, social cognition, cognitive biases, and attachment experiences influence each other. However, little is known if any of these constructions play a more central role than others as they interact.

METHOD

To clarify this issue, we conducted a "network" analysis to explore the interplay among a set of variables related to attachment, cognition domains, and psychotic symptoms in a small sample of outpatients with stabilised schizophrenia-spectrum disorders (n = 25). Eighteen participants (72%) were first-episode patients. We assessed psychotic symptoms, attachment dimensions, neurocognitive performance, "theory of mind", emotion recognition, and "jumping to conclusions" bias using standardised instruments.

RESULTS

The study provides preliminary evidence about a network structure in which the secure attachment (SA) is the most central "node" within the interacting network considering all centrality measures, followed by general psychopathology. SA was closely connected to self-sufficiency (avoidant attachment) and child traumatism, as well as with neurocognition. Emotion recognition impairment was the most robust connection to positive symptoms and mediated the influence of SA on psychotic symptoms.

CONCLUSIONS

Beyond the importance of symptoms, our results, although preliminary, suggest the need to assess attachment experiences and cognition domains to improve specific interventions that can promote recovery in outpatients with psychosis.

Course and symptom and functional correlates of passivity symptoms in schizophrenia: an 18-year multi-follow-up longitudinal study

BACKGROUND

Change in the experience of oneself may lay the groundwork for the development of additional hallucinations and delusions in individuals with schizophrenia. However, to date, the course and symptom and functioning correlates of passivity symptoms (cf. thought insertion, thought withdrawal) have not been measured consistently over long periods of time. Information on the course and correlates of passivity symptoms is essential for developing models of their contribution to schizophrenic illness.

METHOD

Eighty-two individuals diagnosed with schizophrenia or schizoaffective disorder were recruited at an index hospitalization and reassessed at three or more follow-ups over the following 18 years.

RESULTS

The results indicate that a small group of participants report passivity symptoms at all follow-ups, many reported passivity symptoms at some follow-ups, and the majority of individuals never reported passivity symptoms. The prevalence of passivity symptoms was similar to that for delusions of reference and persecutory delusions. Notably, when individuals did experience passivity symptoms, they also had a greater number of additional psychotic symptoms than individuals without passivity symptoms. Further, the presence of passivity symptoms was associated with work impairment at some assessments.

CONCLUSIONS

Passivity symptoms present episodically, at a similar rate as delusions of reference and persecutory delusions, and when present, they are associated with having a higher number of additional psychotic symptoms, as well as having some impact on work functioning. These results suggest that passivity symptoms may increase vulnerability to additional psychotic symptoms and greater work impairment.

Maternal and Paternal Depression During Pregnancy in China: Prevalence, Correlates, and Network Analysis

BACKGROUND

Depression is a commonly seen mental health concern for mothers and fathers during their transition to parenthood. This study aims to provide new insights into the prevalence of maternal and paternal depression, its demographic and clinical correlates, and its symptom network among Chinese pregnant women and their partners.

METHODS

In this multicenter, cross-sectional study, 769 pregnant women and their partners were assessed by Edinburgh Postnatal Depression Scale (EPDS) from June 15th to Sep 15th, 2020 in southern China. Convenient sampling method was used. Univariate analyses, multivariate logistic regression, and network analyses were conducted. Networks of maternal and paternal depression were compared.

RESULTS

In total, 60 (EPDS total score ≥13, 7.80%, 95% CI: 5.90-9.70%) women and 23 (2.99%, 95% CI: 1.78-4.20%) of these women's partners reported depression. Physical comorbidities (OR=2.664, P=0.003) was the only factor that was found to significantly correlate with maternal depression. Network analyses showed that the resulting networks were well connected and that there was significant difference of network structure between maternal and paternal depression (M=0.330, P<0.001). Centrality plot indicated that "sad or miserable" (strength=1.097) was the most central symptom in the maternal depression network, while "scared or panicky" (strength=1.091) was the most central node in the paternal network. The edge between "things have been getting on top of me" - "able to laugh and see the funny side of things" (difference: 0.153, P=0.020), and "scared or panicky" - "the thought of harming myself" (difference: 0.084, P<0.001) was significantly stronger in women's partners than that in pregnant women.

CONCLUSION

Maternal and paternal depression during pregnancy could result in significant negative consequences. Symptoms like "sad or miserable" and "scared or panicky" are critical and might be potential targets for further interventions. Evidence-based treatments, such as pharmacology, psychotherapy, community reinforcement and family training, might be beneficial for parents with depression during and after the pregnancy.

Language as a biomarker for psychosis: A natural language processing approach

Human ratings of conceptual disorganization, poverty of content, referential cohesion and illogical thinking have been shown to predict psychosis onset in prospective clinical high risk (CHR) cohort studies. The potential value of linguistic biomarkers has been significantly magnified, however, by recent advances in natural language processing (NLP) and machine learning (ML). Such methodologies allow for the rapid and objective measurement of language features, many of which are not easily recognized by human raters. Here we review the key findings on language production disturbance in psychosis. We also describe recent advances in the computational methods used to analyze language data, including methods for the automatic measurement of discourse coherence, syntactic complexity, poverty of content, referential coherence, and metaphorical language. Linguistic biomarkers of psychosis risk are now undergoing cross-validation, with attention to harmonization of methods. Future directions in extended CHR networks include studies of sources of variance, and combination with other promising biomarkers of psychosis risk, such as cognitive and sensory processing impairments likely to be related to language. Implications for the broader study of social communication, including reciprocal prosody, face expression and gesture, are discussed.

Negative Symptoms of Schizophrenia: New Prospects of Cariprazine Treatment

Background. Cariprazine is a new piperazine derivative atypical antipsychotic, like aripiprazole and brexpiprazole. It has been approved for treating schizophrenia in many countries and has recently been included on the List of Essential Medicines in Russia. Unlike most other atypical antipsychotics, it shows high in vivo occupancy of dopamine D2 and D3 receptors at clinically relevant doses. In animal models, cariprazine has demonstrated dopamine D3 receptor- dependent pro-cognitive and anti-anhedonic effects, suggesting its potential for treating negative symptoms. This review summarizes the efficacy of cariprazine in the treatment of negative symptoms of schizophrenia. Methods. A literature search of databases covering international and Russian journals, for articles published between 1st January 2010 and 1stJune 2020. Results. Cariprazine demonstrated at least comparable efficacy in the treatment of schizophrenia symptoms to active comparators including risperidone, olanzapine or aripiprazole. The drug has a good safety profile. It appeared to be associated with a lower risk of metabolic syndromes and most extrapyramidal symptoms. The positive effect of cariprazine on the negative symptoms of schizophrenia may be associated with the elimination of secondary negative symptoms. However, of all the atypical antipsychotics to date, only cariprazine has a convincingly, methodologically robust proven advantage over risperidone in eliminating the predominant negative symptoms of schizophrenia. Yet only four studies have investigated the effect of cariprazine on the negative symptoms of schizophrenia. There is a lack of research into its direct impact on emotional-volitional disorders, anhedonia, cognitive symptoms and personality changes. However, there is evidence to suggest cariprazine is effective in treatment-resistant cases, but this requires further confirmation. Conclusion. Cariprazine is an effective and well-tolerated agent for the treatment of schizophrenia and may be effective in cases where other antipsychotics have failed. Cariprazine has been shown to have a positive effect on negative symptoms. Further studies are needed to collect more data on long-term treatment of schizophrenia and especially negative symptoms.

Network Analysis of Internet Addiction Symptoms Among a Clinical Sample of Japanese Adolescents with Autism Spectrum Disorder

In the present study, we employed network analysis that conceptualizes internet addiction (IA) as a complex network of mutually influencing symptoms in 108 adolescents with autism spectrum disorder (ASD) to examine the network architecture of IA symptoms and identify central/influential symptoms. Our analysis revealed that defensive and secretive behaviors and concealment of internet use were identified as central symptoms in this population, suggesting that mitigating these symptoms potentially prevent the development and/or maintenance of IA in adolescents with ASD. Providing adolescents and their caregivers with psychoeducation on the role of central symptoms above in IA can be a salient intervention. Doing so may facilitate nonconflicting conversations between them about adolescents' internet use and promote more healthy adolescents' internet use behavior.

A Temporal Network Approach to Paranoia: A Pilot Study

Paranoid beliefs have been conceptualized as a central psychological process linked to schizophrenia and many mental disorders. Research on paranoia has indicated that it is pivotal to consider not only levels but also dynamic aspects of incriminated related mechanisms over time. In the present study, we conceptualized paranoia as a system of interacting elements. To do so, we used temporal network analysis to unfold the temporal dynamics between core psychological paranoia-related mechanisms, such as self-esteem, sadness, feeling close to others, and experiential avoidance. Time-series data of 23 participants with high scores in paranoia and/or interpersonal sensitivity were collected via experience sampling methodology (ESM). We applied a multilevel vector autoregressive (mlVAR) model approach and computed three distinct and complementary network models (i.e., contemporaneous, temporal, and between-subject) to disentangle associations between paranoia-related mechanisms in three different time frames. The contemporaneous model indicated that paranoia and sadness co-occurred within the same time frame, while sadness was associated with both low self-esteem and lack of closeness to others. The temporal model highlighted the importance of feeling close to others in predicting low paranoia levels in the next time frame. Finally, the between-subject model largely replicated an association found in both contemporaneous and temporal models. The current study reveals that the network approach offers a viable data-driven methodology for elucidating how paranoia-related mechanisms fluctuate over time and may determine its severity. Moreover, this novel perspective may open up new directions toward identifying potential targets for prevention and treatment of paranoia-related problems.

Toward a Complex Network of Risks for Psychosis: Combining Trauma, Cognitive Biases, Depression, and Psychotic-like Experiences on a Large Sample of Young Adults

Although the linkage between traumatic life events and psychotic-like experiences (PLEs) is well established, the knowledge of potential mechanisms of this relationship is scarce. The aim of the present study was to better understand the structure of connections between traumatic life events and PLEs by considering at the same time the role of cognitive biases and depressive symptoms in the population of young adults (18-35 years of age, M = 26.52, SD = 4.74, n = 6772). Our study was conducted within a framework of network analysis. PLEs were measured with the Prodromal Questionnaire (PQ-16), cognitive biases were measured with nine items from the Davos Assessment of Cognitive Biases Scale-18 (DACOBS-18), depressive symptoms were assessed with the Center for Epidemiologic Studies-Depression Scale (CESD-R) and exposure to traumatic life events was measured with a combination of Childhood Experience of Care and Abuse Questionnaire (CECA.Q) and Traumatic Experience Checklist (TEC). The results present a network of all nodes being interconnected within and between domains, with no isolated factors. Exposures to sexual trauma were the most central node in the network. Pathways were identified from trauma to PLEs via cognitive biases and depressive symptoms. However, the shortest pathway between the most central traumatic life event and PLEs was through other traumatic life events, without cognitive biases or depressive symptoms along the way. Our findings suggest the importance of environmental adversities as well as dysfunctional information processing and depression in the network of psychosis risks.

Inter-relationships among psychopathology, premorbid adjustment, cognition and psychosocial functioning in first-episode psychosis: a network analysis approach

BACKGROUND

Better understanding of interplay among symptoms, cognition and functioning in first-episode psychosis (FEP) is crucial to promoting functional recovery. Network analysis is a promising data-driven approach to elucidating complex interactions among psychopathological variables in psychosis, but has not been applied in FEP.

METHOD

This study employed network analysis to examine inter-relationships among a wide array of variables encompassing psychopathology, premorbid and onset characteristics, cognition, subjective quality-of-life and psychosocial functioning in 323 adult FEP patients in Hong Kong. Graphical Least Absolute Shrinkage and Selection Operator (LASSO) combined with extended Bayesian information criterion (BIC) model selection was used for network construction. Importance of individual nodes in a generated network was quantified by centrality analyses.

RESULTS

Our results showed that amotivation played the most central role and had the strongest associations with other variables in the network, as indexed by node strength. Amotivation and diminished expression displayed differential relationships with other nodes, supporting the validity of two-factor negative symptom structure. Psychosocial functioning was most strongly connected with amotivation and was weakly linked to several other variables. Within cognitive domain, digit span demonstrated the highest centrality and was connected with most of the other cognitive variables. Exploratory analysis revealed no significant gender differences in network structure and global strength.

CONCLUSION

Our results suggest the pivotal role of amotivation in psychopathology network of FEP and indicate its critical association with psychosocial functioning. Further research is required to verify the clinical significance of diminished motivation on functional outcome in the early course of psychotic illness.

[Negative and positive disorders of schizophrenia (issues of co-dependence, psychopathology and pathogenesis)]

The authors consider current and own conceptions about correlations of the processes underlying the pathogenesis of schizophrenia presented by negative and positive disorders. With growth of academic psychiatry, evaluation of a correlation between positive and negative dimensions has changed dramatically: on the one hand presenting in clinical unity - simultaneous psychopathologic structures, and on the other hand being clinically and pathogenetically heterogenic in dimensional structure. According to our clinical and biological findings and an analysis of fundamental neurobiological studies, positive and negative disorders present in the clinical picture of schizophrenia as two separate psychopathological and pathogenetic structures. A new paradigm of the correlation between positive and negative structures - the interaction between positive and deficit symptoms - reveals psychopathological functions differentiated for each of dimensional structures. Negative disorders act as «transformers» modifying characteristics of primary transnosological positive disorders to the level of psychopathological structures preferable for schizophrenia; positive disorders, in their turn, act as «moderators» augmenting, amplifying manifestations of negative symptoms. This psychopathological construct of the correlation between dimensional structures paves a way for the development of a new concept of psychopharmacological treatment of schizophrenic deficit: both negative symptoms and amplifying positive symptoms are considered as «target symptoms» for pharmacological interventions.

Main Symptomatic Treatment Targets in Suspected and Early Psychosis: New Insights From Network Analysis

The early detection and intervention in psychoses prior to their first episode are presently based on the symptomatic ultra-high-risk and the basic symptom criteria. Current models of symptom development assume that basic symptoms develop first, followed by attenuated and, finally, frank psychotic symptoms, though interrelations of these symptoms are yet unknown. Therefore, we studied for the first time their interrelations using a network approach in 460 patients of an early detection service (mean age = 26.3 y, SD = 6.4; 65% male; n = 203 clinical high-risk [CHR], n = 153 first-episode psychosis, and n = 104 depression). Basic, attenuated, and frank psychotic symptoms were assessed using the Schizophrenia Proneness Instrument, Adult version (SPI-A), the Structured Interview for Psychosis-Risk Syndromes (SIPS), and the Positive And Negative Syndrome Scale (PANSS). Using the R package qgraph, network analysis of the altogether 86 symptoms revealed a single dense network of highly interrelated symptoms with 5 discernible symptom subgroups. Disorganized communication was the most central symptom, followed by delusions and hallucinations. In line with current models of symptom development, the network was distinguished by symptom severity running from SPI-A via SIPS to PANSS assessments. This suggests that positive symptoms developed from cognitive and perceptual disturbances included basic symptom criteria. Possibly conveying important insight for clinical practice, central symptoms, and symptoms "bridging" the association between symptom subgroups may be regarded as the main treatment targets, in order to prevent symptomatology from spreading or increasing across the whole network.

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.

Exposure to Traumatic Events and Development of Psychotic Symptoms in a Prison Population: A Network Analysis Approach

Previous studies consistently observed an association between exposure to traumatic events and psychotic symptoms. However, little is known about the differential impact of distinct traumatic events and the role of general symptoms in mediating this relationship. Thus, our study aimed to explore the differential association of several traumatic events to the psychotic symptoms in a sample of prisoners and whether this association is mediated by general symptoms. The total sample from the Survey of Psychiatric Morbidity Among Prisoners in England and Wales (N = 3039; 75.4% male) was used. Participants completed a list of traumatic events experienced before reclusion, the Psychosis Screening Questionnaire, Clinical Review Schedule-Revised. Network analysis was used to estimate the network of interactions between traumatic events and general and psychotic symptoms. Shortest paths analysis was performed to identify the different development trajectories. Results suggested that memory problems, compulsions, and irritability might be key mediating symptoms for most traumatic events. However, sexual abuse showed alternative mediators that might be specific of this traumatic event. Finally, the traumatic events, suffered from violence at work, separation/divorce and been homeless showed direct associations with specific psychotic symptoms.

A time-series network approach to auditory verbal hallucinations: Examining dynamic interactions using experience sampling methodology

BACKGROUND

Identifying variables that influence daily-life fluctuations in auditory verbal hallucinations (AVHs) provides insight into potential mechanisms and targets for intervention. Network analysis, that uses time-series data collected by Experience Sampling Method (ESM), could be used to examine relations between multiple variables over time.

METHODS

95 daily voice-hearing individuals filled in a short questionnaire ten times a day for six consecutive days at pseudo-random moments. Using multilevel vector auto-regression, relations between voice-hearing and negative affect, positive affect, uncontrollable thoughts, dissociation, and paranoia were analysed in three types of networks: between-subjects (between persons, undirected), contemporaneous (within persons, undirected), and temporal (within persons, directed) networks. Strength centrality was measured to identify the most interconnected variables in the models.

RESULTS

Voice-hearing co-occurred with all variables, while on a 6-day period voice-hearing was only related to uncontrollable thoughts. Voice-hearing was not predicted by any of the factors, but it did predict uncontrollable thoughts and paranoia. All variables showed large autoregressions, i.e. mainly predicted themselves in this severe voice-hearing sample. Uncontrollable thoughts was the most interconnected factor, though relatively uninfluential.

DISCUSSION

Severe voice-hearing might be mainly related to mental state factors on the short-term. Once activated, voice-hearing appears to maintain itself. It is important to assess possible reactivity of AVH to triggers at the start of therapy; if reactive, therapy should focus on the triggering factor. If not reactive, Cognitive Behavioural interventions could be used first to reduce the negative effects of the voices. Limitations are discussed.

On Nonregularized Estimation of Psychological Networks

An important goal for psychological science is developing methods to characterize relationships between variables. Customary approaches use structural equation models to connect latent factors to a number of observed measurements, or test causal hypotheses between observed variables. More recently, regularized partial correlation networks have been proposed as an alternative approach for characterizing relationships among variables through off-diagonal elements in the precision matrix. While the graphical Lasso (glasso) has emerged as the default network estimation method, it was optimized in fields outside of psychology with very different needs, such as high dimensional data where the number of variables (p) exceeds the number of observations (n). In this article, we describe the glasso method in the context of the fields where it was developed, and then we demonstrate that the advantages of regularization diminish in settings where psychological networks are often fitted ( p≪n ). We first show that improved properties of the precision matrix, such as eigenvalue estimation, and predictive accuracy with cross-validation are not always appreciable. We then introduce nonregularized methods based on multiple regression and a nonparametric bootstrap strategy, after which we characterize performance with extensive simulations. Our results demonstrate that the nonregularized methods can be used to reduce the false-positive rate, compared to glasso, and they appear to provide consistent performance across sparsity levels, sample composition (p/n), and partial correlation size. We end by reviewing recent findings in the statistics literature that suggest alternative methods often have superior performance than glasso, as well as suggesting areas for future research in psychology. The nonregularized methods have been implemented in the R package GGMnonreg.

Network analysis of schizotypal personality traits and their association with other subclinical psychiatric features

Schizotypal personality (SP) traits have been found to be correlated with autistic traits, obsessive-compulsive traits, depressive symptoms and anxiety symptoms. However, the overall pattern of the relationship remains unclear. The purpose of this study was to investigate the network structure between SP traits and other subclinical features (symptoms or traits) and test the replicability of these relationships. A total of 2204 college students completed measurements for SP traits, autistic traits, obsessive-compulsive traits, depressive symptoms and anxiety symptoms, and a validated subsample of 814 completed the same questionnaires again three months later. Using network analysis, we constructed the network structure of subclinical features and then tested its replicability. We found that interpersonal features were the bridge node connecting SP traits and autistic traits (social skill: r = 0.50; attention switching: r = 0.14; communication: r = 0.12), while cognitive-perceptual (obsessing: r = 0.05; neutralizing: r = 0.06) and disorganization (obsessing: r = 0.11) features were the SP traits associated with obsessive-compulsive traits. In addition to interpersonal features (r = 0.10), disorganization (r = 0.12) and cognitive-perceptual (r = 0.05) features were also the overlap between depressive symptoms and SP traits. Anxiety symptoms only connected with interpersonal (r = 0.05) but not cognitive-perceptual features of SP traits. The network showed high predictability (43%) and interpersonal features of SP traits had the highest expected influence (1.67) among all nodes, which may be a potential target for intervention. High similarities were found on network structure (r = 0.86) and expected influence (r = 0.96), and no significant difference on global connectivity was found between these two networks (difference value = 0.45, p = 0.135), suggesting the replicability of the network structure.

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.

A machine learning approach to predicting psychosis using semantic density and latent content analysis

Subtle features in people’s everyday language may harbor the signs of future mental illness. Machine learning offers an approach for the rapid and accurate extraction of these signs. Here we investigate two potential linguistic indicators of psychosis in 40 participants of the North American Prodrome Longitudinal Study. We demonstrate how the linguistic marker of semantic density can be obtained using the mathematical method of vector unpacking, a technique that decomposes the meaning of a sentence into its core ideas. We also demonstrate how the latent semantic content of an individual’s speech can be extracted by contrasting it with the contents of conversations generated on social media, here 30,000 contributors to Reddit. The results revealed that conversion to psychosis is signaled by low semantic density and talk about voices and sounds. When combined, these two variables were able to predict the conversion with 93% accuracy in the training and 90% accuracy in the holdout datasets. The results point to a larger project in which automated analyses of language are used to forecast a broad range of mental disorders well in advance of their emergence.

A combination of self-disturbances and psychotic-like experiences. A cluster analysis study on a non-clinical sample in Poland

We aimed to perform a cluster analysis to investigate the group structure of a combination of psychotic-like experiences (PLEs) and self-disturbances in a non-clinical sample. Non-clinical adults (n = 677) were assessed with the Community Assessment of Psychic Experiences (CAPE), the Davos Assessment of Cognitive Biases Scale (DACOBS) and the Inventory of Psychotic-Like Anomalous Self-Experiences (IPASE). Cluster analysis was conducted based on the positive and negative dimension of CAPE and a total score of IPASE. Four distinct groups were revealed by the cluster analysis. The High Profile group had the highest means, and the Low Profile had the lowest scores of positive and negative subscales of the CAPE and IPASE. The Positive Profile group had a significantly higher level of self-disturbances (in 'Cognition', 'Consciousnesses and 'Somatization' dimensions) from participants with the 'Negative Profile'. The High Profile group had more cognitive biases (i.e., inadequate cognitive inference about internal and external events) related to psychosis as assessed with DACOBS, had the highest means on each IPASE subscale and had a higher level of emotional distress. A combination of high level of PLEs and self-disturbances may capture the highest risk of psychosis in the general population associated with cognitive biases characteristic for psychosis.

The Network Structure of Schizotypal Personality Traits

Elucidating schizotypal traits is important if we are to understand the various manifestations of psychosis spectrum liability and to reliably identify individuals at high risk for psychosis. The present study examined the network structures of (1) 9 schizotypal personality domains and (2) 74 individual schizotypal items, and (3) explored whether networks differed across gender and culture (North America vs China). The study was conducted in a sample of 27001 participants from 12 countries and 21 sites (M age = 22.12; SD = 6.28; 37.5% males). The Schizotypal Personality Questionnaire (SPQ) was used to assess 74 self-report items aggregated in 9 domains. We used network models to estimate conditional dependence relations among variables. In the domain-level network, schizotypal traits were strongly interconnected. Predictability (explained variance of each node) ranged from 31% (odd/magical beliefs) to 55% (constricted affect), with a mean of 43.7%. In the item-level network, variables showed relations both within and across domains, although within-domain associations were generally stronger. The average predictability of SPQ items was 27.8%. The network structures of men and women were similar (r = .74), node centrality was similar across networks (r = .90), as was connectivity (195.59 and 199.70, respectively). North American and Chinese participants networks showed lower similarity in terms of structure (r = 0.44), node centrality (r = 0.56), and connectivity (180.35 and 153.97, respectively). In sum, the present article points to the value of conceptualizing schizotypal personality as a complex system of interacting cognitive, emotional, and affective characteristics.

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.

A state-independent network of depressive, negative and positive symptoms in male patients with schizophrenia spectrum disorders

Depressive symptoms occur frequently in patients with schizophrenia. Several factor analytical studies investigated the associations between positive, negative and depressive symptoms and reported difficulties differentiating between these symptom domains. Here, we argue that a network approach may offer insights into these associations, by exploring interrelations between symptoms. The aims of current study were to I) construct a network of positive, negative and depressive symptoms in male patients with schizophrenia to investigate interactions between individual symptoms; II) identify the most central symptoms within this network and III) examine group-level differences in network connectivity between remitted and non-remitted patients. We computed a network of depressive, positive and negative symptoms in a sample of 470 male patients diagnosed with a psychotic disorder. Depressive symptoms were assessed with the Calgary Depression Rating Scale for Schizophrenia, while psychotic symptoms were assessed with the Positive and Negative Syndrome Scale. Networks of male patients who fulfilled remission criteria (Andreasen et al., 2005) and non-remitters for psychosis were compared. Our results indicate that depressive symptoms are mostly associated with suicidality and may act as moderator between psychotic symptoms and suicidality. In addition, 'depressed mood', 'observed depression', 'poor rapport', 'stereotyped thinking' and 'delusions' were central symptoms within the network. Finally, although remitted male patients had a similar network structure compared to non-remitters the networks differed significantly in terms of global strength. In conclusion, clinical symptoms of schizophrenia were linked in a stable way, independent of symptomatic remission while the number of connections appears to be dependent on remission status.