Publications by authors named "Adela-Maria Isvoranu"

13 Publications

  • Page 1 of 1

Network analysis of physical and psychiatric symptoms of hospital discharged patients infected with COVID-19.

J Affect Disord 2021 Nov 16;294:707-713. Epub 2021 Jul 16.

Department of Communication, Renmin University of China, China.

In the current study, we aimed to investigate the network structure of COVID-19 symptoms and its related psychiatric symptoms, using a network approach. Specifically, we examined how COVID-19 symptoms relate to psychiatric symptoms and highlighted potential pathways between COVID-19 severity and psychiatric symptoms. With a sample of six hundred seventy-five recovered COVID-19 patients recruited 1 month after hospital discharge, we respectively integrated COVID-19 symptoms with PTSD, depression, and anxiety symptoms and analyzed the three network structures. In all three networks, COVID-19 severity and ICU admission are not linked directly to COVID-19 symptoms after hospitalization, while COVID-19 severity (but not ICU admission) is linked directly to one or more psychiatric symptoms. Specific pathways between COVID-19 symptoms and psychiatric symptoms were discussed. Finally, we used directed acyclic graph estimation to show potential causal effects between COVID-19 related variables and demographic characteristics.
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http://dx.doi.org/10.1016/j.jad.2021.07.043DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8284061PMC
November 2021

Autistic Symptoms and Social Functioning in Psychosis: A Network Approach.

Schizophr Bull 2021 Jul 27. Epub 2021 Jul 27.

Department of Psychiatry, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands.

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.
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http://dx.doi.org/10.1093/schbul/sbab084DOI Listing
July 2021

A Network Perspective on the Comorbidity of Personality Disorders and Mental Disorders: An Illustration of Depression and Borderline Personality Disorder.

Front Psychol 2021 6;12:680805. Epub 2021 Jul 6.

Department of Psychology, University of Amsterdam, Amsterdam, Netherlands.

The comorbidity of personality disorders and mental disorders is commonly understood through three types of theoretical models: either a) personality disorders precede mental disorders, b) mental disorders precede personality disorders, c) mental disorders and personality disorders share common etiological grounds. Although these hypotheses differ with respect to their idea of causal direction, they all imply a latent variable perspective, in which it is assumed that either personality and mental disorders are latent variables that have certain causal relations [models a) and b)]; or that, as in model c), the common etiology is in fact a latent variable that causes symptomatology of both personality and mental disorders. We aim to provide another perspective on the comorbidity between personality and mental disorders, namely a network perspective. To this end, we investigated Major Depression (MD) and Borderline Personality Disorder (BPD) and hypothesized that symptoms of BPD and MD could interact with one another rather than being caused by a latent variable (e.g., identity diffusion → unstable relationships → depressed mood). To illustrate this theoretical network conceptualization of the comorbidity of BPD and MD we analyzed a cross-sectional clinical dataset of 376 patients who were asked to complete the Structured Clinical Interview for DSM-IV Axis II Disorders and the Beck Depression Inventory II. The results identify direct associations between symptoms of MD and BPD. If we take the links in this empirical network to be 'substantive', this suggests a radical shift of our current conceptualization of the comorbidity of mental disorders and personality disorders.
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http://dx.doi.org/10.3389/fpsyg.2021.680805DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8290338PMC
July 2021

Meta-analytic Gaussian Network Aggregation.

Psychometrika 2021 Jul 15. Epub 2021 Jul 15.

Department of Psychology, National University of Singapore, Singapore, Singapore.

A growing number of publications focus on estimating Gaussian graphical models (GGM, networks of partial correlation coefficients). At the same time, generalizibility and replicability of these highly parameterized models are debated, and sample sizes typically found in datasets may not be sufficient for estimating the underlying network structure. In addition, while recent work emerged that aims to compare networks based on different samples, these studies do not take potential cross-study heterogeneity into account. To this end, this paper introduces methods for estimating GGMs by aggregating over multiple datasets. We first introduce a general maximum likelihood estimation modeling framework in which all discussed models are embedded. This modeling framework is subsequently used to introduce meta-analytic Gaussian network aggregation (MAGNA). We discuss two variants: fixed-effects MAGNA, in which heterogeneity across studies is not taken into account, and random-effects MAGNA, which models sample correlations and takes heterogeneity into account. We assess the performance of MAGNA in large-scale simulation studies. Finally, we exemplify the method using four datasets of post-traumatic stress disorder (PTSD) symptoms, and summarize findings from a larger meta-analysis of PTSD symptom.
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http://dx.doi.org/10.1007/s11336-021-09764-3DOI Listing
July 2021

Extended network analysis: from psychopathology to chronic illness.

BMC Psychiatry 2021 02 27;21(1):119. Epub 2021 Feb 27.

Research Division, Institute of Mental Health, Singapore, Singapore.

Background: Understanding complex associations between psychopathology and chronic illness is instrumental in facilitating both research and treatment progress. The current study is the first and only network-based study to provide such an encompassing view of unique associations between a multitude of mental and physical health-related domains.

Methods: The current analyses were based on the Singapore Mental Health Study, a cross-sectional study of adult Singapore residents. The study sample consisted of 6616 respondents, of which 49.8% were male and 50.2% female. A network structure was constructed to examine associations between psychopathology, alcohol use, gambling, major chronic conditions, and functioning.

Results: The network structure identified what we have labeled a Cartesian graph: a network visibly split into a psychopathological domain and a physical health domain. The borders between these domains were fuzzy and bridged by various cross-domain associations, with functioning items playing an important role in bridging chronic conditions to psychopathology.

Conclusions: Current results deliver a comprehensive overview of the complex relation between psychopathology, functioning, and chronic illness, highlighting potential pathways to comorbidity.
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http://dx.doi.org/10.1186/s12888-021-03128-yDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7913444PMC
February 2021

Obsessive-Compulsive Symptoms and Other Symptoms of the At-risk Mental State for Psychosis: A Network Perspective.

Schizophr Bull 2021 07;47(4):1018-1028

Department of Psychology, Psychological Methods, University of Amsterdam, Amsterdam, the Netherlands.

Background: The high prevalence of obsessive-compulsive symptoms (OCS) among subjects at Ultra-High Risk (UHR) for psychosis is well documented. However, the network structure spanning the relations between OCS and symptoms of the at risk mental state for psychosis as assessed with the Comprehensive Assessment of At Risk Mental States (CAARMS) has not yet been investigated. This article aimed to use a network approach to investigate the associations between OCS and CAARMS symptoms in a large sample of individuals with different levels of risk for psychosis.

Method: Three hundred and forty-one UHR and 66 healthy participants were included, who participated in the EU-GEI study. Data analysis consisted of constructing a network of CAARMS symptoms, investigating central items in the network, and identifying the shortest pathways between OCS and positive symptoms.

Results: Strong associations between OCS and anxiety, social isolation and blunted affect were identified. Depression was the most central symptom in terms of the number of connections, and anxiety was a key item in bridging OCS to other symptoms. Shortest paths between OCS and positive symptoms revealed that unusual thought content and perceptual abnormalities were connected mainly via anxiety, while disorganized speech was connected via blunted affect and cognitive change.

Conclusions: Findings provide valuable insight into the central role of depression and the potential connective component of anxiety between OCS and other symptoms of the network. Interventions specifically aimed to reduce affective symptoms might be crucial for the development and prospective course of symptom co-occurrence.
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http://dx.doi.org/10.1093/schbul/sbaa187DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8266672PMC
July 2021

Exploring multicultural classroom dynamics: A network analysis.

J Sch Psychol 2019 Jun 3;74:90-105. Epub 2019 Jun 3.

Department of Psychology, Psychological Methods, University of Amsterdam, Nieuwe Achtergracht 129, 1018 WS Amsterdam, the Netherlands. Electronic address:

Students' relationships with peers and teachers strongly influence their motivation to engage in learning activities. Ethnic minority students, however, are often victimized in schools, and their educational achievement lags behind that of their majority group counterparts. The aim of the present study was to explore teachers' multicultural approach within their classrooms as a possible factor of influence over students' peer relationships and motivation. We utilized the novel methodology of estimating psychological networks in order to map out the interactions between these constructs within multicultural classrooms. Results indicate that a multicultural approach is directly connected to student motivation for both ethnic majority and minority students. Social integration within peer groups, however, seems to be a possible mediator of this relationship for the ethnic minority students. Due to the hypothesis generating nature of the psychological network approach, a more thorough investigation of this generated mediation hypothesis is called for.
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http://dx.doi.org/10.1016/j.jsp.2019.02.003DOI Listing
June 2019

Toward incorporating genetic risk scores into symptom networks of psychosis.

Psychol Med 2020 03 14;50(4):636-643. Epub 2019 Mar 14.

Department of Psychology, Psychological Methods, University of Amsterdam, Amsterdam, The Netherlands.

Background: Psychosis spectrum disorder is a heterogeneous, multifactorial clinical phenotype, known to have a high heritability, only a minor portion of which can be explained by molecular measures of genetic variation. This study proposes that the identification of genetic variation underlying psychotic disorder may have suffered due to issues in the psychometric conceptualization of the phenotype. Here we aim to open a new line of research into the genetics of mental disorders by explicitly incorporating genes into symptom networks. Specifically, we investigate whether links between a polygenic risk score (PRS) for schizophrenia and measures of psychosis proneness can be identified in a network model.

Methods: We analyzed data from n = 2180 subjects (controls, patients diagnosed with a non-affective psychotic disorder, and the first-degree relatives of the patients). A network structure was computed to examine associations between the 42 symptoms of the Community Assessment of Psychic Experiences (CAPE) and the PRS for schizophrenia.

Results: The resulting network shows that the PRS is directly connected to the spectrum of positive and depressive symptoms, with the items conspiracy and no future being more often located on predictive pathways from PRS to other symptoms.

Conclusions: To our knowledge, the current exploratory study provides a first application of the network framework to the field of behavior genetics research. This allows for a novel outlook on the investigation of the relations between genome-wide association study-based PRSs and symptoms of mental disorders, by focusing on the dependencies among variables.
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http://dx.doi.org/10.1017/S003329171900045XDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7093319PMC
March 2020

Personalized Network Modeling in Psychopathology: The Importance of Contemporaneous and Temporal Connections.

Clin Psychol Sci 2018 May 19;6(3):416-427. Epub 2018 Jan 19.

Neuroimaging Center, Department of Neuroscience, University of Groningen, University Medical Center Groningen.

Recent literature has introduced (a) the network perspective to psychology and (b) collection of time series data to capture symptom fluctuations and other time varying factors in daily life. Combining these trends allows for the estimation of intraindividual network structures. We argue that these networks can be directly applied in clinical research and practice as hypothesis generating structures. Two networks can be computed: a , in which one investigates if symptoms (or other relevant variables) predict one another over time, and a , in which one investigates if symptoms predict one another in the same window of measurement. The contemporaneous network is a partial correlation network, which is emerging in the analysis of cross-sectional data but is not yet utilized in the analysis of time series data. We explain the importance of partial correlation networks and exemplify the network structures on time series data of a psychiatric patient.
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http://dx.doi.org/10.1177/2167702617744325DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5952299PMC
May 2018

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

Schizophr Res 2018 03 23;193:232-239. Epub 2017 Aug 23.

University of Amsterdam, Academic Medical Center, Department of Psychiatry, Meibergdreef 5, 1105 AZ Amsterdam, The Netherlands. Electronic address:

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.
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http://dx.doi.org/10.1016/j.schres.2017.07.035DOI Listing
March 2018

A symptom network structure of the psychosis spectrum.

Schizophr Res 2017 11 22;189:75-83. Epub 2017 Feb 22.

University of Amsterdam, Academic Medical Center, Department of Psychiatry, Meibergdreef 5, 1105 AZ Amsterdam, The Netherlands. Electronic address:

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.
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http://dx.doi.org/10.1016/j.schres.2017.02.018DOI Listing
November 2017

A Network Approach to Environmental Impact in Psychotic Disorder: Brief Theoretical Framework.

Schizophr Bull 2016 07 13;42(4):870-3. Epub 2016 May 13.

Department of Psychiatry, Yale University School of Medicine, New Haven, CT.

The spectrum of psychotic disorder represents a multifactorial and heterogeneous condition and is thought to result from a complex interplay between genetic and environmental factors. In the current paper, we analyze this interplay using network analysis, which has been recently proposed as a novel psychometric framework for the study of mental disorders. Using general population data, we construct network models for the relation between 3 environmental risk factors (cannabis use, developmental trauma, and urban environment), dimensional measures of psychopathology (anxiety, depression, interpersonal sensitivity, obsessive-compulsive disorder, phobic anxiety, somatizations, and hostility), and a composite measure of psychosis expression. Results indicate the existence of specific paths between environmental factors and symptoms. These paths most often involve cannabis use. In addition, the analyses suggest that symptom networks are more strongly connected for people exposed to environmental risk factors, implying that environmental exposure may lead to less resilient symptom networks.
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http://dx.doi.org/10.1093/schbul/sbw049DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4903069PMC
July 2016

A Network Approach to Psychosis: Pathways Between Childhood Trauma and Psychotic Symptoms.

Schizophr Bull 2017 01 10;43(1):187-196. Epub 2016 May 10.

Department of Psychology, Psychological Methods, University of Amsterdam, Amsterdam, The Netherlands.

Childhood trauma (CT) has been identified as a potential risk factor for the onset of psychotic disorders. However, to date, there is limited consensus with respect to which symptoms may ensue after exposure to trauma in early life, and whether specific pathways may account for these associations. The aim of the present study was to use the novel network approach to investigate how different types of traumatic childhood experiences relate to specific symptoms of psychotic disorders and to identify pathways that may be involved in the relationship between CT and psychosis. We used data of patients diagnosed with a psychotic disorder (n = 552) from the longitudinal observational study Genetic Risk and Outcome of Psychosis Project and included the 5 scales of the Childhood Trauma Questionnaire-Short Form and all original symptom dimensions of the Positive and Negative Syndrome Scale. Our results show that all 5 types of CT and positive and negative symptoms of psychosis are connected through symptoms of general psychopathology. These findings are in line with the theory of an affective pathway to psychosis after exposure to CT, with anxiety as a main connective component, but they also point to several additional connective paths between trauma and psychosis: eg, through poor impulse control (connecting abuse to grandiosity, excitement, and hostility) and motor retardation (connecting neglect to most negative symptoms). The results of the current study suggest that multiple paths may exist between trauma and psychosis and may also be useful in mapping potential transdiagnostic processes.
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http://dx.doi.org/10.1093/schbul/sbw055DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5216845PMC
January 2017
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