Identifying a system of predominant negative symptoms: Network analysis of three randomized clinical trials

Conceptualization and characterization of "primary" and "secondary" cognitive impairment in schizophrenia

Cognitive impairment represents one of the core features of schizophrenia, involves both neurocognition and social cognition domains, and has a significant negative impact on real-world functioning. The present review provides a framework for the conceptualization and characterization of "primary" and "secondary" cognitive impairment in schizophrenia. In this conceptualization, primary cognitive impairment can be defined as a consequence of the neurobiological alterations that underlie psychopathological manifestations of the disorder, while secondary cognitive impairment can be defined as the results of a source issue that has a negative impact on cognitive performance. Sources of secondary cognitive impairment are frequent in people with schizophrenia and include several different factors, such as positive and negative symptoms, depressive symptoms, autistic symptoms, pharmacotherapy, substance abuse, metabolic syndrome, social deprivation, and sleep disorders. It can be hypothesized that secondary cognitive impairment may be improved by effectively resolving the source issue, while primary cognitive impairment may benefit from dedicated treatment. Further research is required to confirm this hypothesis, to better characterize the distinction between primary and secondary cognitive impairment in a clinical and in a neurobiological perspective, and to evaluate the impact of systematically assessing and treating secondary cognitive impairment.

Prevalence of prominent and predominant negative symptoms across different criteria for negative symptom severity and minimal positive symptoms: A comparison of different criteria

Negative symptoms are a source of disability in schizophrenia, but criteria for identifying patients for clinical trials are in flux. Minimum severity for negative symptoms is paired with a definition of minimal psychosis to identify predominant negative symptoms. Two previous successful negative symptoms treatment studies used very different severity and selection criteria. We compared the prevalence of participants meeting those two criteria in a large outpatient sample of participants with schizophrenia. Data from 867 outpatients with schizophrenia who participated in one of four NIMH-funded studies were analyzed. Common data elements included diagnoses, the PANSS, and an assessment of everyday functioning. We compared previous criterion for premoninant negative symptoms based on low levels of agitation and psychosis and different cut-offs for negative symptoms severity. 57 % of the participants met the agitation-based criteria for low scores and 33 % met the psychosis-based criteria. 18 % met total PANSS score ≥ 20 and 8 % met ≥24 prominent negative symptoms criteria. 14 % met low agitation and PANSS≥20 and 2 % met the low psychosis and negative symptoms ≥24 criteria. Participants who met all predominant criteria had more impairments in social functioning (all p < .001, all d > 0.37). Criteria for predominant negative symptoms from previous clinical trials identify widely different numbers of cases, with criteria for negative symptom severity and low symptoms both impacting. All criteria yield the expected profile of relatively specific social deficits. Even in unselected populations who participated in complex research protocols, 14 % meet low- agitation based criteria for predominant negative symptoms and many more participants would be expected to meet criteria with enrichment for the presence of negative symptoms.

Comparison of Negative Symptom Network Structures Between Patients With Early and Chronic Schizophrenia: A Network and Exploratory Graph Analysis

BACKGROUND AND HYPOTHESIS

Despite the clinical relevance of negative symptoms in schizophrenia, our understanding of negative symptoms remains limited. Although various courses and stages of schizophrenia have been identified, variations in the negative symptom networks between distinct stages of schizophrenia remain unexplored.

STUDY DESIGN

We examined 405 patients with early schizophrenia (ES) and 330 patients with chronic schizophrenia (CS) using the Scale for the Assessment of Negative Symptoms. Network analysis and exploratory graph analysis were used to identify and compare the network structures and community memberships of negative symptoms between the two groups. Further, associations between communities and social functioning were evaluated. The potential influences of other symptom domains and confounding factors were also examined.

STUDY RESULTS

Multidimensional differences were found in the networks of negative symptoms between ES and CS. The global connectivity strength was higher in the network of ES than in the network of CS. In ES, central symptoms were mainly related to expressive deficits, whereas in CS they were distributed across negative symptom domains. A three-community structure was suggested across stages but with different memberships and associations with social functioning. Potential confounding factors and symptom domains, including mood, positive, disorganization, and excitement symptoms, did not affect the network structures.

CONCLUSION

Our findings revealed the presence of stage-specific network structures of negative symptoms in schizophrenia, with negative symptom communities having differential significance for social functioning. These findings provide implications for the future development of tailored interventions to alleviate negative symptoms and improve functionality across stages.

Centrality statistics of symptom networks of schizophrenia: a systematic review

The network theory of psychological disorders posits that systems of symptoms cause, or are associated with, the expression of other symptoms. Substantial literature on symptom networks has been published to date, although no systematic review has been conducted exclusively on symptom networks of schizophrenia, schizoaffective disorder, and schizophreniform (people diagnosed with schizophrenia; PDS). This study aims to compare statistics of the symptom network publications on PDS in the last 21 years and identify congruences and discrepancies in the literature. More specifically, we will focus on centrality statistics. Thirty-two studies met the inclusion criteria. The results suggest that cognition, and social, and occupational functioning are central to the network of symptoms. Positive symptoms, particularly delusions were central among participants in many studies that did not include cognitive assessment. Nodes representing cognition were most central in those studies that did. Nodes representing negative symptoms were not as central as items measuring positive symptoms. Some studies that included measures of mood and affect found items or subscales measuring depression were central nodes in the networks. Cognition, and social, and occupational functioning appear to be core symptoms of schizophrenia as they are more central in the networks, compared to variables assessing positive symptoms. This seems consistent despite heterogeneity in the design of the studies.

The longitudinal structure of negative symptoms in treatment resistant schizophrenia

BACKGROUND AND HYPOTHESIS

The negative symptoms of schizophrenia are strong prognostic factors but remain poorly understood and treated. Five negative symptom domains are frequently clustered into the motivation and pleasure (MAP) and emotional expression (EE) 'dimensions', but whether this structure remains stable and behaves as a single entity or not remains unclear.

STUDY DESIGN

We examined a cohort of 153 patients taking clozapine for treatment-resistant schizophrenia in a regional mental health clinic. Patients were assessed longitudinally over a mean period of 45 months using validated scales for positive, negative and mood symptoms. Network analyses were performed to identify symptom 'communities' and their stability over time. The influence of common causes of secondary negative symptoms as well as centrality measures were also examined.

STUDY RESULTS

Across patients at baseline, two distinct communities matching the clinical domains of MAP and EE were found. These communities remained highly stable and independent over time. The communities remained stabled when considering psychosis, depression, and sedation severity, and these causes of secondary negative symptoms were clustered into the MAP community. Centrality measures also remained stable over time, with similar centrality measures across symptoms.

CONCLUSIONS

Our results suggest that MAP and EE are independent dimensions that remain highly stable over time in chronic schizophrenia patients treated with clozapine. Common causes of secondary negative symptoms mapped onto the MAP dimension. Our results emphasise the need for clinical trials to address either MAP or EE, and that treating causes of secondary negative symptoms may improve MAP.

Pathological network changes in patients with social anxiety disorder before and after an Internet-based CBT

A network perspective may shed light on the understanding of Internet-based CBT efficacy for social anxiety disorder. Previous cross-sectional evidence revealed a densely interconnected network for individuals with social anxiety. Yet, longitudinal network changes before and after ICBT are lacking. This study aimed to investigate pathological network changes with Graphical Gaussian Model among patients with social anxiety disorder (n = 249). Social phobia scale (SPS) and Social interaction anxiety scale (SIAS) were measured before and after 8 weeks Internet-based CBT. Results revealed the connection between symptom tension when speaking and symptom awkward when being watched was the most robust edges during ICBT interventions. The pathological network benefited from ICBT and exhibited modification in several prominent interconnections. The overall network connectivity continues to exhibit comparable strength after ICBT. This study represents the first examination of social anxiety network changes after patients with SAD completed a systematic ICBT. Changes in critical edges and nodes provide valuable insights for the design and efficacy assessment of ICBT interventions.

The structure stability of negative symptoms: longitudinal network analysis of the Brief Negative Symptom Scale in people with schizophrenia

BACKGROUND

The structure of negative symptoms of schizophrenia is still a matter of controversy. Although a two-dimensional model (comprising the expressive deficit dimension and the motivation and pleasure dimension) has gained a large consensus, it has been questioned by recent investigations.

AIMS

To investigate the latent structure of negative symptoms and its stability over time in people with schizophrenia using network analysis.

METHOD

Negative symptoms were assessed in 612 people with schizophrenia using the Brief Negative Symptom Scale (BNSS) at baseline and at 4-year follow-up. A network invariance analysis was conducted to investigate changes in the network structure and strength of connections between the two time points.

RESULTS

The network analysis carried out at baseline and follow-up, supported by community detection analysis, indicated that the BNSS's items aggregate to form four or five distinct domains (avolition/asociality, anhedonia, blunted affect and alogia). The network invariance test indicated that the network structure remained unchanged over time (network invariance test score 0.13; P = 0.169), although its overall strength decreased (6.28 at baseline, 5.79 at follow-up; global strength invariance test score 0.48; P = 0.016).

CONCLUSIONS

The results lend support to a four- or five-factor model of negative symptoms and indicate overall stability over time. These data have implications for the study of pathophysiological mechanisms and the development of targeted treatments for negative symptoms.

Bridge centrality network structure of negative symptoms in people with schizophrenia

Negative symptoms are complex psychopathology. Although evidence generally supported the NIMH five consensus domains, research seldom examined measurement invariance of this model, and domain-specific correspondence across multiple scales. This study aimed to examine the interrelationship between negative symptom domains captured by different rating scales, and to examine the domain-specific correspondence across multiple scales. We administered the Brief Negative Symptom Scale (BNSS), the Self-evaluation of Negative Symptoms (SNS), and the Scale for Assessment of Negative Symptoms (SANS) to 204 individuals with schizophrenia. We used network analysis to examine the interrelationship between negative symptom domains. Besides regularized partial correlation network, we estimated bridge centrality indices to investigate domain-specific correspondence, while taking each scale as an independent community. The regularized partial correlation network showed that the SNS nodes clustered together, whereas the SANS and the BNSS nodes intermingled together. The SANS attention domain lied at the periphery of the network according to the Fruchterman-Reingold algorithm. The SANS anhedonia-asociality (strength = 1.48; EI = 1.48) and the SANS affective flattening (strength = 1.06; EI = 1.06) had the highest node strength and EI. Moreover, the five nodes of the BNSS bridged the nodes of the SANS and the SNS. BNSS blunted affect (strength = 0.76; EI = 0.76) and SANS anhedonia-asociality (strength = 0.76; EI = 0.74) showed the highest bridge strength and bridge EI. The BNSS captures negative symptoms and bridges the symptom domains measured by the SANS and the SNS. The three scales showed domain-specific correspondence.

Longitudinal Network Analysis Reveals Interactive Change of Schizophrenia Symptoms During Acute Antipsychotic Treatment

BACKGROUND AND HYPOTHESIS

Complex schizophrenia symptoms were recently conceptualized as interactive symptoms within a network system. However, it remains unknown how a schizophrenia network changed during acute antipsychotic treatment. The present study aimed to evaluate the interactive change of schizophrenia symptoms under seven antipsychotics from individual time series.

STUDY DESIGN

Data on 3030 schizophrenia patients were taken from a multicenter randomized clinical trial and used to estimate the partial correlation cross-sectional networks and longitudinal random slope networks based on multivariate multilevel model. Thirty symptoms assessed by The Positive and Negative Syndrome Scale clustered the networks.

STUDY RESULTS

Five stable communities were detected in cross-sectional networks and random slope networks that describe symptoms change over time. Delusions, emotional withdrawal, and lack of spontaneity and flow of conversation featured as central symptoms, and conceptual disorganization, hostility, uncooperativeness, and difficulty in abstract thinking featured as bridge symptoms, all showing high centrality in the random slope network. Acute antipsychotic treatment changed the network structure (M-test = 0.116, P < .001) compared to baseline, and responsive subjects showed lower global strength after treatment (11.68 vs 14.18, S-test = 2.503, P < .001) compared to resistant subjects. Central symptoms and bridge symptoms kept higher centrality across random slope networks of different antipsychotics. Quetiapine treatment network showed improvement in excitement symptoms, the one featured as both central and bridge symptom.

CONCLUSION

Our findings revealed the central symptoms, bridge symptoms, cochanging features, and individualized features under different antipsychotics of schizophrenia. This brings implications for future targeted drug development and search for pathophysiological mechanisms.

Dynamic networks of psychotic symptoms in adults living in precarious housing or homelessness

BACKGROUND

People living in precarious housing or homelessness have higher than expected rates of psychotic disorders, persistent psychotic symptoms, and premature mortality. Psychotic symptoms can be modeled as a complex dynamic system, allowing assessment of roles for risk factors in symptom development, persistence, and contribution to premature mortality.

METHOD

The severity of delusions, conceptual disorganization, hallucinations, suspiciousness, and unusual thought content was rated monthly over 5 years in a community sample of precariously housed/homeless adults (n = 375) in Vancouver, Canada. Multilevel vector auto-regression analysis was used to construct temporal, contemporaneous, and between-person symptom networks. Network measures were compared between participants with (n = 219) or without (n = 156) history of psychotic disorder using bootstrap and permutation analyses. Relationships between network connectivity and risk factors including homelessness, trauma, and substance dependence were estimated by multiple linear regression. The contribution of network measures to premature mortality was estimated by Cox proportional hazard models.

RESULTS

Delusions and unusual thought content were central symptoms in the multilevel network. Each psychotic symptom was positively reinforcing over time, an effect most pronounced in participants with a history of psychotic disorder. Global connectivity was similar between those with and without such a history. Greater connectivity between symptoms was associated with methamphetamine dependence and past trauma exposure. Auto-regressive connectivity was associated with premature mortality in participants under age 55.

CONCLUSIONS

Past and current experiences contribute to the severity and dynamic relationships between psychotic symptoms. Interrupting the self-perpetuating severity of psychotic symptoms in a vulnerable group of people could contribute to reducing premature mortality.

Longitudinal symptom network structure in first-episode psychosis: a possible marker for remission

BACKGROUND

Network approach has been applied to a wide variety of psychiatric disorders. The aim of the present study was to identify network structures of remitters and non-remitters in patients with first-episode psychosis (FEP) at baseline and the 6-month follow-up.

METHODS

Participants (n = 252) from the Korean Early Psychosis Study (KEPS) were enrolled. They were classified as remitters or non-remitters using Andreasen's criteria. We estimated network structure with 10 symptoms (three symptoms from the Positive and Negative Syndrome Scale, one depressive symptom, and six symptoms related to schema and rumination) as nodes using a Gaussian graphical model. Global and local network metrics were compared within and between the networks over time.

RESULTS

Global network metrics did not differ between the remitters and non-remitters at baseline or 6 months. However, the network structure and nodal strengths associated with positive-self and positive-others scores changed significantly in the remitters over time. Unique central symptoms for remitters and non-remitters were cognitive brooding and negative-self, respectively. The correlation stability coefficients for nodal strength were within the acceptable range.

CONCLUSION

Our findings indicate that network structure and some nodal strengths were more flexible in remitters. Negative-self could be an important target for therapeutic intervention.

Cognitive impairment networks in Alzheimer's disease: Analysis of three double-blind randomized, placebo-controlled, clinical trials of donepezil

Psychometric network analysis is an alternative theoretically-driven analytic approach that has the potential to conceptualize cognitive impairment in Alzheimer's disease differently than was previously assumed and consequently detect unknown treatment effects. Based on individual participant data, extracted from three double-blind, randomized placebo-controlled clinical trials, psychometric networks were computed on observed Alzheimer's Disease Assessment Scale Cognitive Subscale scores at baseline (N=1,554) and on predicted change scores at 24 weeks of follow-up for participants who received donepezil (N=797) or placebo (N=484). A novel conceptualization of cognitive impairment in Alzheimer's disease was displayed through the baseline network, that had 90% (n=27) positive statistically significant (p<0.05) associations, and a most central aspect of ideational praxis. Following 24 weeks, treatment effects emerged via the differences between the change score networks. The donepezil network had more statistically significant (p<0.05) positive associations and a higher global strength (n=15; S=1.22; p=0.03), than the placebo network (n=8; S=0.57). This suggests that for those who were treated with donepezil compared with placebo, cognition is a more unified construct. The main aspects of change in cognitive impairment were comprehension of spoken language for the donepezil network and spoken language ability for the placebo network. Comprehension of spoken language apears to be most sensitive to psychopharmaceutical interventions and should therefore be closely monitored. Overall, our psychometric network analysis presents a new conceptualization of cognitive impairment in Alzheimer's disease, points to previously unknown treatment effects and highlights well-defined aspects of cognitive impairment  that may translate into future treatment targets.

On the Control of Psychological Networks

The combination of network theory and network psychometric methods has opened up a variety of new ways to conceptualize and study psychological disorders. The idea of psychological disorders as dynamic systems has sparked interest in developing interventions based on results of network analytic tools. However, simply estimating a network model is not sufficient for determining which symptoms might be most effective to intervene upon, nor is it sufficient for determining the potential efficacy of any given intervention. In this paper, we attempt to remedy this gap by introducing fundamental concepts of control theory to both psychometricians and applied psychologists. We introduce two controllability statistics to the psychometric literature, average and modal controllability, to facilitate selecting the best set of intervention targets. Following this introduction, we show how intervention scientists can probe the effects of both theoretical and empirical interventions on networks derived from real data and demonstrate how simulations can account for intervention cost and the desire to reduce specific symptoms. Every step is based on rich clinical EMA data from a sample of subjects undergoing treatment for complicated grief, with a focus on the outcome suicidal ideation. All methods are implemented in an open-source R package netcontrol, and complete code for replicating the analyses in this manuscript are available online.

A network analysis of interoception, self-awareness, empathy, alexithymia, and autistic traits

Altered interoception has been consistently found in people with autism spectrum disorder (ASD), and this impairment may contribute to social cognitive dysfunctions. However, little is known regarding the intercorrelations between interoceptive sensibility, autistic, alexithymic, empathic, and self-related traits. We recruited 1360 non-clinical college students and adults to investigate the complex inter-relationship between these variables using network analysis. The resultant network revealed patterns connecting autistic traits to interoceptive sensibility, empathy, alexithymia, and self-awareness, with reasonable stability and test-retest consistency. The node of alexithymia exhibited the highest centrality and expected influence. As revealed by the network comparison test, networks constructed in high- and low-autistic subgroups were comparable in global strength and structure. Our findings suggested that alexithymia serves as an important node, bridging interoceptive deficits, self-awareness, and empathic impairments of autism spectrum disorder. The co-morbidity of alexithymia should be considered carefully in future studies of interoceptive impairments and social deficits in ASD.

The network analysis of depressive symptoms before and after two weeks of antidepressant treatment

BACKGROUND

A substantial proportion of patients do not benefit from selective serotonin reuptake inhibitors (SSRIs). We used network analysis to examine changes in symptom associations over time to identify SSRIs treatment targets for patients with major depressive disorder (MDD).

METHODS

This study was a post-hoc analysis of data originated from the 2-week open-label phase of a multicenter clinical trial. A total of 474 participants who completed 2-week paroxetine treatment and subsequent evaluation were included in this analysis. The sample was divided into early improvement (a reduction of the HAMD-17 total score ⩾20% at week 2) and not early improvement. The network analysis was performed to compare the pattern of relationships among depressive symptoms at baseline and endpoint. In addition, we compared the network structure of the participants who achieved early improvement with those without early improvement.

RESULTS

We found that the network structure and global strength increased significantly from baseline to endpoint (P<0.05). The baseline network of early improvers was more strongly connected than that of the participants who did not reach early improvement, and the global strength was significantly different (P = 0.049). Psychological anxiety and depressed mood were central symptoms of the early improvers, while somatic anxiety, insomnia, gastrointestinal symptoms and feelings of guilt were central in the network among the participants who did not show early improvement.

CONCLUSIONS

The connectivity of symptom network significantly increased with treatment. The baseline network connectivity of symptoms is tighter in early improvers than those without early improvement, and their central symptoms are different.

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.

The importance of recognizing worthlessness for suicide prevention in adolescents with Attention-deficit/hyperactivity disorder

BACKGROUND

Attention-deficit/hyperactivity disorder (ADHD) is one of the most common psychiatric diagnoses among children and adolescents. Depression and general anxiety disorder (GAD) are often co-occurring with ADHD among children and adolescents. Previous studies have found that ADHD, depression and GAD are all strongly correlated with suicidal thoughts and planning.

AIM

The current study aimed to further explore the association between ADHD, GAD and depressive symptoms as well as their association with suicidal thoughts and planning among adolescents.

METHOD

Adolescents with ADHD diagnosis were involved from child psychiatry outpatient clinics and adolescents without a psychiatric treatment or diagnosis were enrolled from high schools in Hungary. The Mini International Neuropsychiatric Interview for Children and Adolescents was used to evaluate psychiatric symptoms and disorders as well as suicidal thoughts and planning. Regularized psychological networks were used to investigate the associations.

RESULTS

Altogether 185 adolescents (58 females and 127 males; mean age 14.79 years, SD = 1.48), 89 with ADHD and 96 without ADHD were enrolled. Depression symptom worthlessness was directly related to suicidal thoughts and planning, CI95 of the logit B between worthlessness and suicidal thought (0.72, 1.66). Both ADHD and anxiety were indirectly related to suicidal thoughts and planning through depression: CI95 of the logit B between being disorganized and feeling worthless is (0.38, 3.02), and CI95 of the logit B between being distressed and feeling worthless is (0.57, 2.52).

CONCLUSIONS

This study draws the attention of clinicians to the importance of recognizing "worthlessness" for suicide prevention in adolescents with ADHD. Furthermore, the results support previous studies, whereby symptoms of depression and anxiety mediate the relationship between ADHD and suicidal thoughts and planning. These results highlight the importance of ADHD comorbidities with depression and GAD and their effect on suicidal thoughts and planning.

Network analysis of trauma in patients with early-stage psychosis

Childhood trauma (ChT) is a risk factor for psychosis. Negative lifestyle factors such as rumination, negative schemas, and poor diet and exercise are common in psychosis. The present study aimed to perform a network analysis of interactions between ChT and negative lifestyle in patients and controls. We used data of patients with early-stage psychosis (n = 500) and healthy controls (n = 202). Networks were constructed using 12 nodes from five scales: the Brief Core Schema Scale (BCSS), Brooding Scale (BS), Dietary Habits Questionnaire, Physical Activity Rating, and Early Trauma Inventory Self Report-Short Form (ETI). Graph metrics were calculated. The nodes with the highest predictability and expected influence in both patients and controls were cognitive and emotional components of the BS and emotional abuse of the ETI. The emotional abuse was a mediator in the shortest pathway connecting the ETI and negative lifestyle for both groups. The negative others and negative self of the BCSS mediated emotional abuse to other BCSS or BS for patients and controls, respectively. Our findings suggest that rumination and emotional abuse were central symptoms in both groups and that negative others and negative self played important mediating roles for patients and controls, respectively.

Trial Registration: ClinicalTrials.gov identifier: CUH201411002.

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.

Disentangling the symptoms of schizophrenia: Network analysis in acute phase patients and in patients with predominant negative symptoms

Background

The Positive and Negative Syndrome Scale (PANSS) is widely used in schizophrenia and has been divided into distinct factors (5-factor models) and subfactors. Network analyses are newer in psychiatry and can help to better understand the relationships and interactions between the symptoms of a psychiatric disorder. The aim of this study was threefold: (a) to evaluate connections between schizophrenia symptoms in two populations of patients (patients in the acutely exacerbated phase of schizophrenia and patients with predominant negative symptoms [PNS]), (b) to test whether network analyses support the Mohr 5 factor model of the PANSS and the Kahn 2 factor model of negative symptoms, and finally (c) to identify the most central symptoms in the two populations.

Methods

Using pooled baseline data from four cariprazine clinical trials in patients with acute exacerbation of schizophrenia (n = 2193) and the cariprazine–risperidone study in patients with PNS (n = 460), separate network analyses were performed. Network structures were estimated for all 30 items of the PANSS.

Results

While negative symptoms in patients with an acute exacerbation of schizophrenia are correlated with other PANSS symptoms, these negative symptoms are not correlated with other PANSS symptoms in patients with PNS. The Mohr factors were partially reflected in the network analyses. The two most central symptoms (largest node strength) were delusions and uncooperativeness in acute phase patients and hostility and delusions in patients with PNS.

Conclusions

This network analysis suggests that symptoms of schizophrenia are differently structured in acute and PNS patients. While in the former, negative symptoms are mainly secondary, in patients with PNS, they are mainly primary. Further, primary negative symptoms are better conceptualized as distinct negative symptom dimensions of the PANSS.

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.

Bridging Brain and Cognition: A Multilayer Network Analysis of Brain Structural Covariance and General Intelligence in a Developmental Sample of Struggling Learners

Network analytic methods that are ubiquitous in other areas, such as systems neuroscience, have recently been used to test network theories in psychology, including intelligence research. The network or mutualism theory of intelligence proposes that the statistical associations among cognitive abilities (e.g., specific abilities such as vocabulary or memory) stem from causal relations among them throughout development. In this study, we used network models (specifically LASSO) of cognitive abilities and brain structural covariance (grey and white matter) to simultaneously model brain-behavior relationships essential for general intelligence in a large (behavioral, N = 805; cortical volume, N = 246; fractional anisotropy, N = 165) developmental (ages 5-18) cohort of struggling learners (CALM). We found that mostly positive, small partial correlations pervade our cognitive, neural, and multilayer networks. Moreover, using community detection (Walktrap algorithm) and calculating node centrality (absolute strength and bridge strength), we found convergent evidence that subsets of both cognitive and neural nodes play an intermediary role 'between' brain and behavior. We discuss implications and possible avenues for future studies.

The Association of Nonsuicidal Self-Injury with Quality of Life and Mental Disorders in Clinical Adolescents-A Network Approach

Although earlier research has highlighted that psychiatric disorders significantly impair patients' quality of life (QoL), few studies have examined the relationship between nonsuicidal self-injury (NSSI) and QoL. Our aim was to investigate whether QoL mediates the mental disorder-NSSI relationship, and to study the QoL ratings agreement of self and parents in a clinical population of adolescents. We involved 202 adolescents from Vadaskert Child Psychiatric Hospital and Outpatient Clinic, Budapest, aged 13-18 years. All participants completed the Deliberate Self-Harm Inventory, Inventar zur Erfassung der Lebensqualität bei Kindern und Jugendlichen, and the Mini International Neuropsychiatric Interview Kid. To map the interrelationship between the NSSI, mental disorders, and QoL dimensions, Mixed Graphical Models were estimated. Adolescents with a history of NSSI rated their QoL to be significantly lower than adolescents without NSSI. Self and parents' QoL ratings are closer in the NSSI sample than in the no-NSSI sample. Among all QoL dimensions, only family problems had a direct significant association with NSSI engagement. Our results highlight that, contrary to our hypothesis, the presence of mental disorders mediates the relationship between most QoL dimensions and the occurrence of NSSI. Our results draw attention to the potential causal effect of environmental factors (e.g., peer problems) on mental disorders that, in turn, result in NSSI. The present paper highlights the importance of network modelling in clinical research.

Emotion Network Analysis During COVID-19 Quarantine - A Longitudinal Study

Introduction: The coronavirus disease 2019 (COVID-19) emergency has imposed important challenges in the lives of individuals, particularly since the restriction of free movement. In Spain, this mandatory home confinement started on March 14, 2020. In this scenario, some calls have been made to better understand the exact impact of the quarantine on the emotional status of individuals across time. Materials and Methods: On the first day that the Spanish government imposed the quarantine, our team launched an online longitudinal study to monitor emotional responses to the COVID-19 emergency over time. For 2 weeks, 187 people have responded to a daily diary on emotion functioning. An emotion network analysis was performed to study the network structure of 30 mood states and its changes during the first 2 weeks of the quarantine. Results: The emotional network showed critical changes in the interactions of emotions over time. An analysis of mean emotional levels did not show statistically significant changes in mood over time. Interestingly, two different network patterns were found when the sample was divided between those with favorable responses and those with unfavorable responses. Discussion: This new approach to the study of longitudinal changes of the mood state network of the population reveals different adaptation strategies reflected on the sample's emotional network. This network approach can help identify most fragile individuals (more vulnerable to external stressors) before they develop clear and identifiable psychopathology and also help identify anti-fragile individuals (those who improve their functioning in the face of external stressors). This is one of the first studies to apply an emotional network approach to study the psychological effects of pandemics and might offer some clues to psychologists and health administrators to help people cope with and adjust to this critical situation.

On the validity of the centrality hypothesis in cross-sectional between-subject networks of psychopathology

BACKGROUND

In the network approach to psychopathology, psychiatric disorders are considered networks of causally active symptoms (nodes), with node centrality hypothesized to reflect symptoms' causal influence within a network. Accordingly, centrality measures have been used in numerous network-based cross-sectional studies to identify specific treatment targets, based on the assumption that deactivating highly central nodes would proliferate to other nodes in the network, thereby collapsing the network structure and alleviating the overall psychopathology (i.e., the centrality hypothesis).

METHODS

Here, we summarize three types of evidence pertaining to the centrality hypothesis in psychopathology. First, we discuss the validity of the theoretical assumptions underlying the centrality hypothesis in psychopathology. We then summarize the methodological aspects of extant studies using centrality measures as predictors of symptom change following treatment, while delineating their main findings and several of their limitations. Finally, using a specific dataset of 710 treatment-seeking patients with posttraumatic stress disorder (PTSD) as an example, we empirically examine node centrality as a predictor of therapeutic change, replicating the approach taken by previous studies, while addressing some of their limitations. Specifically, we investigated whether three pre-treatment centrality indices (strength, predictability, and expected influence) were significantly correlated with the strength of the association between a symptom's change and the change in the severity of all other symptoms in the network from pre- to post-treatment (Δnode-Δnetwork association). Using similar analyses, we also examine the predictive validity of two simple non-causal node properties (mean symptom severity and infrequency of symptom endorsement).

RESULTS

Of the three centrality measures, only expected influence successfully predicted how strongly changes in nodes/symptoms were associated with change in the remainder of the nodes/symptoms. Importantly, when excluding the amnesia node, a well-documented outlier in the phenomenology of PTSD, none of the tested centrality measures predicted symptom change. Conversely, both mean symptom severity and infrequency of symptom endorsement, two standard non-network-derived indices, were found to be more predictive than expected influence and remained significantly predictive also after excluding amnesia from the network analyses.

CONCLUSIONS

The centrality hypothesis in its current form is ill-defined, showing no consistent supporting evidence in the context of cross-sectional, between-subject networks.

Network analysis of the structure and change in the mini-mental state examination: a nationally representative sample

PURPOSE

The structure of the mini-mental state examination (MMSE) is inconsistent across factor analytic studies, and yet to be examined based on network analysis. The current study aims to identify the (I) cross-sectional network structure and (II) longitudinal network changes of the MMSE.

METHODS

The MMSE was administered to a nationally representative sample of older adults (age 50 and over) in Ireland twice over 4 years (2012-2013: N = 7207; 2016: N = 5715). Psychometric network analysis was computed at each time point to identify structure, strength and magnitude of the network associations. Item clustering was examined, and modularity scores were computed to measure the overall strength of clustering. Centrality indices were used to identify the main aspects of the MMSE. Longitudinal differences between the networks were examined.

RESULTS

Cross-sectionally, the MMSE network structure clustered into a single community (modularity score = 0) with orientation items identified as most central. Longitudinally, the MMSE was time invariant regarding structure, centrality and magnitude of the positive associations between the items. The average magnitude of the negative associations increased over time[(t(65.15) = 3.78, p < 0.001; time 1: M = - 0.59, SD = 0.58 time 2: M = - 1.65, SD = 1.97] as did their percentage.

CONCLUSION

Network analysis of the MMSE showed that the measure consisted of a single entity of cognitive functioning irrespective of time. Orientation items were repeatedly identified as most central. Longitudinal changes of the network were evident in increased negative associations between selected cognitive components after 4 years of follow-up. These changes may be explained by neuro-cognitive compensation processes.

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.

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 utility of network analysis in the context of Indigenous Australian oral health literacy

BACKGROUND

The study of oral health literacy (OHL) is likely to gain new and interesting insights with the use of network analysis, a powerful analytical tool that allows the investigation of complex systems of relationships. Our aim was to investigate the relationships between oral health literacy and oral health-related factors in a sample of Indigenous Australian adults using a network analysis approach.

METHODS

Data from 400 Indigenous Australian adults was used to estimate four regularised partial correlation networks. Initially, a network with the 14 items of the Health Literacy in Dentistry scale (HeLD-14) was estimated. In a second step, psychosocial, sociodemographic and oral health-related factors were included in the network. Finally, two networks were estimated for participants with high and low oral health literacy. Participants were categorised into 'high' or 'low' OHL networks based on a median split. Centrality measures, clustering coefficients, network stability, and edge accuracy were evaluated. A permutation-based test was used to test differences between networks.

RESULTS

Solid connections among HeLD-14 items followed the structure of theoretical domains across all networks. Oral health-related self-efficacy, sporting activities, and self-rated oral health status were the strongest positively associated nodes with items of the HeLD-14 scale. HeLD-14 items were the four most central nodes in both HeLD-14 + covariates network and high OHL network, but not in the low OHL network. Differences between high and low OHL models were observed in terms of overall network structure, edge weight, and clustering coefficient.

CONCLUSION

Network models captured the dynamic relationships between oral health literacy and psychosocial, sociodemographic and oral health-related factors. Discussion on the implications of these findings for informing the development of targeted interventions to improve oral health literacy is presented.

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.

The network approach to psychopathology: a review of the literature 2008-2018 and an agenda for future research

The network approach to psychopathology posits that mental disorders can be conceptualized and studied as causal systems of mutually reinforcing symptoms. This approach, first posited in 2008, has grown substantially over the past decade and is now a full-fledged area of psychiatric research. In this article, we provide an overview and critical analysis of 363 articles produced in the first decade of this research program, with a focus on key theoretical, methodological, and empirical contributions. In addition, we turn our attention to the next decade of the network approach and propose critical avenues for future research in each of these domains. We argue that this program of research will be best served by working toward two overarching aims: (a) the identification of robust empirical phenomena and (b) the development of formal theories that can explain those phenomena. We recommend specific steps forward within this broad framework and argue that these steps are necessary if the network approach is to develop into a progressive program of research capable of producing a cumulative body of knowledge about how specific mental disorders operate as causal systems.

Pretreatment Posttraumatic Stress Disorder Symptom Network Metrics Predict the Strength of the Association Between Node Change and Network Change During Treatment

Network analysis has been increasingly applied in an effort to understand complex interactions among symptoms in posttraumatic stress disorder (PTSD). Although methods that initially focused on identifying central symptoms in cross-sectional networks have been extended to longitudinal data that can reveal the relative roles of acute symptoms in the emergence of the PTSD syndrome, the association between network metrics and symptom change during treatment have yet to be explored in PTSD. To address this gap, we estimated pretreatment PTSD symptom networks in a sample of patients from a multisite clinical trial for women with full or subthreshold PTSD and substance use. We tested the hypothesis that node metrics calculated in the pretreatment network would be predictive of the strength of the association between a symptom's change and the change in the severity of all other symptoms through the course of treatment. A symptom node's strength and predictability in the pretreatment network were each strongly correlated with the association between that symptom's change and overall change across the symptom network, r(15) = .79, p < .001 and r(15) = .75, p < .001, respectively, whereas a symptom's mean severity at pretreatment was not, r(15) = .27, p = .292. These findings suggest that a node's centrality prior to treatment engagement is a predictor of its association with overall symptom change throughout the treatment process.

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.

Associations between moral injury, PTSD clusters, and depression among Israeli veterans: a network approach

Background: Moral Injury (MI) is one of the adverse consequences of combat. Following exposure to potentially morally injurious events (PMIEs)--events perceived as violations of deep moral beliefs by oneself or trusted individuals--a significant minority of veterans could develop posttraumatic stress disorder (PTSD) and depression. Objective: The current study represents the first attempt to apply a network analysis model to examine an exploratory empirical conceptualization of a network of PMIEs during military service, post-traumatic stress disorder (PTSD) symptom clusters, depression, and combat exposure among Israel Defence Forces veterans. Method: A volunteer sample of 191 Israeli combat veterans were recruited during 2017, and completed validated self-report questionnaires tapping PMIEs, PTSD, and depression in a cross-sectional design study. A regularized Gaussian graphical model was estimated. Results: Network analysis revealed strong bridge associations between the PTSD nodes and most of the PMIEs nodes. The nodes of PMIE-betrayal and PTSD negative alterations in cognitions and mood (NACM) symptom cluster were found to have a bridging function between other PMIEs and PTSD. Depression was found to be connected to most of the PMIEs and PTSD nodes. Conclusions: The study's findings offer an overview of the complex relationships between PMIEs and PTSD clusters among Israeli veterans. PMIEs--notably, betrayal-based experiences--are related to PTSD clusters directly and through depressive symptoms. Some possible mechanisms for the links between PMIEs and PTSD and the clinical implications related to specific interventions are discussed.

A Network Analysis of Post-traumatic Stress Disorder Symptoms and Correlates During the COVID-19 Pandemic

Background and Objective: The coronavirus disease 2019 (COVID-19) outbreak has been suggested as a collective trauma, which presents a continuing crisis. However, the specific post-traumatic implication of this crisis has not been adequately studied yet. The current study was aimed to ascertain the most central symptom and the strong connections between symptoms of post-traumatic stress disorder (PTSD). At the same time, exploring the relationship between covariates and the network of PTSD symptoms, by taking sex, anxiety, depression, suicidal ideation, quality of life, and social support as covariates, may help us to know the arise and maintenance of PTSD symptoms and give specified suggestions to people under the shadow of COVID-19. Method: The Post-traumatic Stress Disorder Checklist for Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5), was used to assess the PTSD symptoms extent of 338 healthy participants over the past month. Networks were analyzed using state-of-the-art regularized partial correlation models. In addition, the centrality of the symptoms and the robustness of the results were analyzed. Results: The network analysis revealed that the especially strong connections emerged between avoidance of thoughts and avoidance of reminders, hypervigilance and exaggerated startle response, intrusive thoughts and nightmares, flashbacks and emotional cue reactivity, and detachment and restricted affect. The most central symptoms were self-destructive/reckless behavior. Incorporation of covariates into the network revealed the strong connections path between self-destructive/reckless behavior and loss of interest and depression. Conclusion: Self-destructive/reckless behavior was the most central symptom in the network of PTSD symptoms related to the COVID-19 pandemic, which as an important target of interfere may have great benefits.

Application of network analysis to investigate sex differences in interactive systems of eating-disorder psychopathology

OBJECTIVE

Although men comprise 25% of persons with eating disorders (EDs), most research has focused on understanding EDs in women. The theoretical framework underlying common ED treatment has not been rigorously tested in men. The purpose of this study was to compare the interconnectivity among ED symptoms in men versus women.

METHOD

Participants (N = 1,348; 50% men) were individuals with anorexia nervosa, bulimia nervosa, binge-eating disorder, or other specified feeding or eating disorder who were users of Recovery Record, a smartphone app for monitoring ED symptoms. Participants were matched on age and duration of illness. Network analysis was used to create networks of symptoms for both sexes. Strength centrality, network stability, and bootstrapped centrality differences were tested. The network comparison test (NCT) was used to identify sex differences between networks. Key players analysis was used to compare fragmentation of each network.

RESULTS

For both sexes, items related to binge eating and restricting emerged as highest in strength centrality. The NCT identified significant differences global strength (p = .03) but not network invariance (p = .06) suggesting that although the structure of the networks was not statistically different, the strength of the connections within the network was greater for women. Key players analysis indicated that both networks were similarly disrupted when important nodes within the network were removed.

DISCUSSION

Findings suggested that there are more similarities than differences in networks of EDs in men and women. Results have important clinical implications by supporting theoretical underpinnings of cognitive-behavioral models of EDs in both men and women.

Spanish validation of the self-evaluation of negative symptoms scale SNS in an adolescent population

BACKGROUND

Negative symptoms (NS) may be observed in the general population in an attenuated form and in high-risk mental states. However, they have been less studied in the general population than positive symptoms, in spite of their importance at the insidious onset of schizophrenia and their appearance before positive symptoms. This study aimed to analyze the empirical structure of the Spanish version of the Self-Evaluation of Negative Symptoms (SNS) Scale and find its psychometric properties and invariance of measurement across sex and age in a sample of adolescents.

METHODS

The sample consisted of 4521 adolescents (53.6% female) from 11 to 18 years of age.

RESULTS

Confirmatory Factor Analysis of the SNS confirmed an internal structure of five first-order factors by the characteristic dimensions of NS: avolition, social withdrawal, diminished emotional range, anhedonia, alogia, and one second-order factor which includes the total NS score. Multi-group confirmatory factor analysis showed that the scale was invariant across sex and age. Total scale reliability was adequate. A strong relationship was found between the SNS with depressive symptomatology, moderate with ideas of reference and low with aberrant salience.

CONCLUSION

The results back use of the Spanish version of the SNS scale for detection of NS in the general population of adolescents.

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.

The role of gender in the structure of networks of childhood adversity

The objective of present study is to investigate the relationship between different childhood adversities. The potential impact of early adversity on prevention programs is discussed. Data on twelve childhood adversities was collected from a representative sample of 5037 members of the general population living in a large metropolitan area. Data were analyzed through network analysis, to estimate and compare network connectivity and centrality measures by gender. Over half the respondents had been exposed to at least one adversity during their earlier developmental stage. Among adversity-exposed persons, 48.4% presented simultaneous adversities, most of which were related to 'family dysfunction' and 'maltreatment' (mean = 2.9 adversities). Women reported more adversities than men (59.0% vs. 47.6%). Although the 'global' network connectivity across adversities was similar in both genders, 'regional' distinctions in the network structure were found. While 'neglect' and 'parental death' were more important for women than men, 'parental mental disorders' was more important for men. Gender-related childhood adversities were clustered experiences. Adversities related to 'early family dysfunction' and 'maltreatment' were prominent features in the networks of both boys and girls. Differential preventive and intervention programs should take into account gender-related patterns of exposure and reporting patterns of early adversity.

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.

Dynamic networks of PTSD symptoms during conflict

BACKGROUND

Conceptualizing posttraumatic stress disorder (PTSD) symptoms as a dynamic system of causal elements could provide valuable insights into the way that PTSD develops and is maintained in traumatized individuals. We present the first study to apply a multilevel network model to produce an exploratory empirical conceptualization of dynamic networks of PTSD symptoms, using data collected during a period of conflict.

METHODS

Intensive longitudinal assessment data were collected during the Israel-Gaza War in July-August 2014. The final sample (n = 96) comprised a general population sample of Israeli adult civilians exposed to rocket fire. Participants completed twice-daily reports of PTSD symptoms via smartphone for 30 days. We used a multilevel vector auto-regression model to produce contemporaneous and temporal networks, and a partial correlation network model to obtain a between-subjects network.

RESULTS

Multilevel network analysis found strong positive contemporaneous associations between hypervigilance and startle response, avoidance of thoughts and avoidance of reminders, and between flashbacks and emotional reactivity. The temporal network indicated the central role of startle response as a predictor of future PTSD symptomatology, together with restricted affect, blame, negative emotions, and avoidance of thoughts. There were some notable differences between the temporal and contemporaneous networks, including the presence of a number of negative associations, particularly from blame. The between-person network indicated flashbacks and emotional reactivity to be the most central symptoms.

CONCLUSIONS

This study suggests various symptoms that could potentially be driving the development of PTSD. We discuss clinical implications such as identifying particular symptoms as targets for interventions.

Quality of life disparities between persons with schizophrenia and their professional caregivers: Network analysis in a National Cohort

BACKGROUND

Disparities between mental health patients and their professional caregivers in quality of life appraisals have been identified, however, the structure that such disparities assume is unknown.

AIMS

To examine the network structure of quality of life appraisals and disparities using network analysis.

METHODS

Participants were 1639 persons with schizophrenia using psychiatric rehabilitation services and their primary professional caregivers (N=582). Quality of life for persons with schizophrenia was measured based on an abbreviated version of the Manchester Short Assessment of Quality of Life. Appraisals were made self-reported and by professional caregivers. Disparities scores between the aforementioned were computed. Network analysis was performed on all quality of life appraisals. Sensitivity analyses were conducted.

RESULTS

The self-appraised network significantly (p<0.05) differed by network strength compared to the caregiver-appraised network. Self-appraised network communities (clusters of quality of life items) were health conditions and socioeconomic system, whereas caregiver-appraised network communities were social activities, and combined socioeconomic and health conditions. Strength centrality was highest for self-appraised social status and for caregiver-appraised residential status (Z=1.63, Z=1.12, respectively). The disparity scores network clustered into two communities: social relations and combined financial and health conditions. The most central appraisal disparities were in social status.

CONCLUSIONS

Quality of life differed when self-appraised by persons with schizophrenia compared to when appraised by their professional caregivers, yet the salient role of social relations was shared. The latter may be an initial focus of discussion by persons with schizophrenia and their caregivers.

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.

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.