Understanding the Risk of Sexual Reoffending in Adult Men: A Network-Based Model

The predominant approach to understand dynamic risk factors of sexual reoffending has been referred to as the Propensities Model (Thornton, 2016). According to this model, dynamic risk factors can be conceptualized as latent constructs whose change alters the risk of sexual reoffending. Despite its strengths and contributions to research, this model does not offer answers to the question of how dynamic risk factors contribute to the risk of sexual reoffending, or of how sustained change in risk might take place. In this paper we introduce the Network-Based Model of Risk of Sexual Reoffending (NBM-RSR), which addresses several limitations and constraints of the Propensities Model and offers empirically testable propositions regarding the nature and development of the risk of sexual reoffending. The NBM-RSR considers risk of sexual reoffending to involve a self-sustaining network of causally connected dynamic risk factors. Consistent with this, an increased risk of sexual reoffending is characterized through a network that contains more and stronger interconnected dynamic risk factors with a higher strength. Sustained change in risk of sexual reoffending occurs when activity in the network exceeds a critical point resulting in a new self-sustaining network. Propositions based on the NBM-RSR are introduced and translated into testable hypotheses. These propositions revolve around (a) risk of sexual reoffending resulting from the construction of a network of causally connected dynamic risk factors, (b) network stability, sudden changes, and critical transitions, and (c) dynamic risk factors' relative influence on risk of sexual reoffending.

Revealing the psychopathological pathway linking trauma to post-traumatic stress disorder: longitudinal network approach

The present study investigated the psychopathological processes of post-traumatic stress disorder (PTSD) following the network approach to psychopathology. The directed acyclic graph model was employed to analyse a large longitudinal data-set of Chinese children and adolescents exposed to a destructive earthquake. It was found that intrusion symptoms were first activated by trauma exposure, and subsequently activated other PTSD symptoms. The data are consistent with the idea that symptoms may form a self-sustaining dynamic network by interacting with each other to promote or maintain the chronicity of PTSD. The findings advance the current understanding about the psychopathological processes of PTSD, and inform further research and clinical practices on post-traumatic psychopathology.

Exploring the dynamic interconnectedness of protective and perpetuating factors of cancer-related fatigue

OBJECTIVE

Approximately 25% of cancer patients suffer from cancer-related fatigue (CRF) after cancer treatment. CRF is a multi-factorial condition affected by several interrelated protective and perpetuating factors. As most studies merely assessed bivariate associations, more insight into the complex relationships among these constructs is needed. We applied the multivariate network approach to gain a better understanding of how patients' fatigue, perpetuating and protective factors are dynamically interconnected.

METHOD

Between February and August 2022, 30 cancer patients filled out a carefully developed ecological momentary assessment questionnaire (EnergyInSight) five times a day for at least 21 days while being on the waitlist for psychological care for CRF. We performed a multi-level vector autoregression analysis to examine the interconnectedness among fatigue, protective factors (allowing rest, acceptance, and self-efficacy) and perpetuating factors (worrying, catastrophizing, and feeling guilty).

RESULTS

In the contemporaneous network (concurrent associations), higher acceptance and self-efficacy were associated with lower fatigue, whereas all other factors were associated with higher fatigue. The strongest relationships were between worrying and feeling guilty and between acceptance and allowing rest. In the temporal network (lagged associations), fatigue was related to two factors: higher self-efficacy preceded lower fatigue, and higher fatigue preceded increased allowing rest.

CONCLUSIONS

Taking all included factors into account, the networks identified self-efficacy and allowing rest as key protective factors of CRF. Patients may benefit from psychological interventions that cultivate self-efficacy, as it seems to pave the way to reduced fatigue.

Anhedonia as a transdiagnostic symptom across psychological disorders: a network approach

BACKGROUND

Anhedonia is apparent in different mental disorders and is suggested to be related to dysfunctions in the reward system and/or affect regulation. It may hence be a common underlying feature associated with symptom severity of mental disorders.

METHODS

We constructed a cross-sectional graphical Least Absolute Shrinkage and Selection Operator (LASSO) network and a relative importance network to estimate the relationships between anhedonia severity and the severity of symptom clusters of major depressive disorder (MDD), anxiety sensitivity (AS), attention-deficit hyperactivity disorder (ADHD), and autism spectrum disorder (ASD) in a sample of Dutch adult psychiatric patients (N = 557).

RESULTS

Both these networks revealed anhedonia severity and depression symptom severity as central to the network. Results suggest that anhedonia severity may be predictive of the severity of symptom clusters of MDD, AS, ADHD, and ASD. MDD symptom severity may be predictive of AS and ADHD symptom severity.

CONCLUSIONS

The results suggest that anhedonia may serve as a common underlying transdiagnostic psychopathology feature, predictive of the severity of symptom clusters of depression, AS, ADHD, and ASD. Thus, anhedonia may be associated with the high comorbidity between these symptom clusters and disorders. If our results will be replicated in future studies, it is recommended for clinicians to be more vigilant about screening for anhedonia and/or depression severity in individuals diagnosed with an anxiety disorder, ADHD and/or ASD.

Modeling psychopathology: From data models to formal theories

Over the past decade, there has been a surge of empirical research investigating mental disorders as complex systems. In this article, we investigate how to best make use of this growing body of empirical research and move the field toward its fundamental aims of explaining, predicting, and controlling psychopathology. We first review the contemporary philosophy of science literature on scientific theories and argue that fully achieving the aims of explanation, prediction, and control requires that we construct formal theories of mental disorders: theories expressed in the language of mathematics or a computational programming language. We then investigate three routes by which one can use empirical findings (i.e., data models) to construct formal theories: (a) using data models themselves as formal theories, (b) using data models to infer formal theories, and (c) comparing empirical data models to theory-implied data models in order to evaluate and refine an existing formal theory. We argue that the third approach is the most promising path forward. We conclude by introducing the abductive formal theory construction (AFTC) framework, informed by both our review of philosophy of science and our methodological investigation. We argue that this approach provides a clear and promising way forward for using empirical research to inform the generation, development, and testing of formal theories both in the domain of psychopathology and in the broader field of psychological science. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Personalizing Psychological Care for Chronic Cancer-Related Fatigue: A Case Study on Symptom Dynamics

Approximately 25% of cancer patients suffer from chronic cancer-related fatigue (CCRF), which is a complex, multifactorial condition. While there are evidence-based interventions, it remains unclear what treatment works best for the individual patient. Psychological network models can offer a schematic representation of interrelations among fatigue and protective and perpetuating factors for the individual patient. We explored whether feedback based on these individual fatigue networks can help personalize psychological care for CCRF. A 34-year old woman with CCRF was referred to our mental healthcare institute for psycho-oncology. During the waitlist period, she filled out an experience sampling app for 101 days, including five daily assessments of fatigue, pain, mood, activity and fatigue coping. The interplay between items was visualized in network graphs at the moment-level and day-level, which were discussed with the patient. For example, acceptance of fatigue in the past three hours was associated with less hopelessness and less fatigue in the following moment. At the day-level, acceptance was also being associated with less fatigue, less hopelessness, a better mood, and more motivation to do things. The patient recognized these patterns and explained how unexpected waves of fatigue can make her feel hopeless. This started a dialogue on how cultivating acceptance could potentially help her handle the fatigue. The patient would discuss this with her therapist. Feedback based on individual fatigue networks can provide direct insight into how one copes with CCRF and subsequently offer directions for treatment. Further research is needed in order to implement this in clinical practice.

Symptom network analysis of the sleep disorders diagnostic criteria based on the clinical text of the ICSD-3

The third edition of the International Classification of Sleep Disorders (ICSD-3) is the authoritative clinical text for the diagnosis of sleep disorders. An important issue of sleep nosology is to better understand the relationship between symptoms found in conventional diagnostic manuals and to compare classifications. Nevertheless, to our knowledge, there is no specific exhaustive work on the general structure of the networks of symptoms of sleep disorders as described in diagnostic manuals. The general aim of the present study was to use symptom network analysis to explore the diagnostic criteria in the ICSD-3 manual. The ICSD-3 diagnostic criteria related to clinical manifestations were systematically identified, and the units of analysis (symptoms) were labelled from these clinical manifestation diagnostic criteria using three rules ("Conservation", "Splitting", "Lumping"). A total of 37 of the 43 main sleep disorders with 160 units of analysis from 114 clinical manifestations in the ICSD-3 were analysed. A symptom network representing all individual ICSD-3 criteria and connections between them was constructed graphically (network estimation), quantified with classical metrics (network inference with global and local measures) and tested for robustness. The global measure of the sleep symptoms network shows that it can be considered as a small world, suggesting a strong interconnection between symptoms in the ICSD-3. Local measures show the central role of three kinds of bridge sleep symptoms: daytime sleepiness, insomnia, and behaviour during sleep symptoms. Such a symptom network analysis of the ICSD-3 structure could provide a framework for better systematising and organising symptomatology in sleep medicine.

A Novel Network Approach to Capture Cognition and Affect: COVID-19 Experiences in Canada and Germany

We tested a novel method for studying human experience (thoughts and affect). We utilized Cognitive-Affective Maps (CAMs)-an approach to visually represent thoughts and their affective connotations as networks of concepts that individuals associate with a given event. Using an innovative software tool, we recruited a comparative sample of (n = 93) Canadians and (n = 100) Germans to draw a CAM of their experience (events, thoughts, feelings) with the Covid-19 pandemic. We treated these CAM networks as a series of directed graphs and examined the extent to which their structural properties (latent and emotional) are predictive for the perceived coronavirus threat (PCT). Across multiple models, we found consistent and significant relationships between these network variables and the PCT in both the Canadian and German sample. Our results provide unique insights into individuals' thinking and perceptions of the viral outbreak. Our results also demonstrate that a network analysis of CAMs' properties is a promising method to study the relationship between human thought and affective connotation. We suggest that CAMs can bridge several gaps between qualitative and quantitative methods. Unlike when using quantitative tools (e.g., questionnaires), participants' answers are not restricted by response items as participants are free to incorporate any thoughts and feelings on the given topic. Furthermore, as compared to traditional qualitative measures, such as structured interviews, the CAM technique may better enable researchers to objectively assess and integrate the substance of a shared experience for large samples of participants.

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.

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

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

The 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.

Upcoming diagnostic biomarkers with promising prospects in neurological disorders

An exponential increase in the prevalence of neurological disorders requires substantial steps to be taken for their prevention and treatment. Neurodiagnostic biomarkers are gaining momentum presently in order to enhance the diagnostic accuracy of neurodegenerative disorders, to precisely assess their advancement and to monitor the efficiency of therapeutic interventions. Therefore, the primary focus of the present review is the recent development in this field of neurodiagnostic biomarkers, and the current state of biomarker exploration in the context of various neurodegenerative diseases. This review encompasses an updated and detailed account of specific (β-Amyloid, Tau and Phospho-tau 181, Tar-DNA binding protein-43, Progranulin, a-synuclein, Clusterin, etc) and non-specific (genetic, synaptic, inflammatory and coagulation) neurodiagnostic biomarkers and the recent advances in this growing field. This comprehensive review also suggests the utilization of neurodiagnostic markers in network approaches and personalized medication that will eventually improvise the existing diagnostic and therapeutic complexities of neurodiagnostic biomarkers.

The replicability of ICD-11 complex post-traumatic stress disorder symptom networks in adults

BACKGROUND

The ICD-11 includes a new disorder, complex post-traumatic stress disorder (CPTSD). A network approach to CPTSD will enable investigation of the structure of the disorder at the symptom level, which may inform the development of treatments that target specific symptoms to accelerate clinical outcomes.AimsWe aimed to test whether similar networks of ICD-11 CPTSD replicate across culturally different samples and to investigate possible differences, using a network analysis.

METHOD

We investigated the network models of four nationally representative, community-based cross-sectional samples drawn from Germany, Israel, the UK, and the USA (total N = 6417). CPTSD symptoms were assessed with the International Trauma Questionnaire in all samples. Only those participants who reported significant functional impairment by CPTSD symptoms were included (N = 1591 included in analysis; mean age 43.55 years, s.d. 15.10, range 14-99, 67.7% women). Regularised partial correlation networks were estimated for each sample and the resulting networks were compared.

RESULTS

Despite differences in traumatic experiences, symptom severity and symptom profiles, the networks were very similar across the four countries. The symptoms within dimensions were strongly associated with each other in all networks, except for the two symptom indicators assessing aspects of affective dysregulation. The most central symptoms were 'feelings of worthlessness' and 'exaggerated startle response'.

CONCLUSIONS

The structure of CPTSD symptoms appears very similar across countries. Addressing symptoms with the strongest associations in the network, such as negative self-worth and startle reactivity, will likely result in rapid treatment response.Declaration of interestA.M. and M.C. were members of the World Health Organization (WHO) ICD-11 Working Group on the Classification of Disorders Specifically Associated with Stress, reporting to the WHO International Advisory Group for the Revision of ICD-10 Mental and Behavioural Disorders. The views expressed in this article are those of the authors and do not represent the official policies or positions of the International Advisory Group or the WHO.

Mechanism of Curcuma wenyujin Rhizoma on Acute Blood Stasis in Rats Based on a UPLC-Q/TOF-MS Metabolomics and Network Approach

Rhizome of Curcuma wenyujin, which is called EZhu in China, is a traditional Chinese medicine used to treat blood stasis for many years. However, the underlying mechanism of EZhu is not clear at present. In this study, plasma metabolomics combined with network pharmacology were used to elucidate the therapeutic mechanism of EZhu in blood stasis from a metabolic perspective. The results showed that 26 potential metabolite markers of acute blood stasis were screened, and the levels were all reversed to different degrees by EZhu preadministration. Metabolic pathway analysis showed that the improvement of blood stasis by Curcuma wenyujin rhizome was mainly related to lipid metabolism (linoleic acid metabolism, ether lipid metabolism, sphingolipid metabolism, glycerophospholipid metabolism, and arachidonic acid metabolism) and amino acid metabolisms (tryptophan metabolism, lysine degradation). The component-target-pathway network showed that 68 target proteins were associated with 21 chemical components in EZhu. Five metabolic pathways of the network, including linoleic acid metabolism, sphingolipid metabolism, glycerolipid metabolism, arachidonic acid metabolism, and steroid hormone biosynthesis, were consistent with plasma metabolomics results. In conclusion, plasma metabolomics combined with network pharmacology can be helpful to clarify the mechanism of EZhu in improving blood stasis and to provide a literature basis for further research on the therapeutic mechanism of EZhu in clinical practice.

Metacognitive Therapy Versus Cognitive Behavioral Therapy:A Network Approach

A network perspective on mental problems represents a new alternative to the latent variable perspective. Diagnoses are assumed to refer to a causal network of observable mental problems or symptoms (observables). The observable symptoms that traditionally have been considered indicators of latent traits (disorders) are taken to be directly related causal entities. Few studies have investigated how different therapies affect a network-structure of symptoms and processes. In this study, three anxiety symptoms, three depression symptoms and mechanisms in the form of cognitions, metacognitions, worry and threat monitoring were selected. The network structure over the course of therapy for metacognitive therapy (MCT) and Cognitive behavioral therapy (CBT) was investigated. It was hypothesized that worry, attention, and metacognition would be important nodes in MCT and that cognitions would be important in CBT. The data used in the analysis are from a RCT where 74 patients with comorbid anxiety disorders were randomized to either transdiagnostic MCT or disorder-specific CBT. Symptoms and mechanisms were measured every week. The data was analyzed using the multilevel vector autoregressive (mlVAR) model, which is currently the most developed method to analyze multivariate time series in multiple subjects and construct networks. The results indicate that there were different networks of symptoms and mechanisms in MCT and CBT. Central nodes in both treatments are worry and attention, however, the node of negative metacognitive beliefs about uncontrollability was more central in the MCT treatment. The results are consistent with predictions from the S-REF model.

Psychotic Experiences and Related Distress: A Cross-national Comparison and Network Analysis Based on 7141 Participants From 13 Countries

Psychotic experiences (PEs) are common in the general population but do not necessarily reflect a risk status if they occur in relative isolation or are not distressing. Emerging evidence suggests that PEs might be experienced as more benign for individuals from collectivistic low- and middle-income countries (LAMIC) compared with individualistic high-income countries (HIC). The aim of this study was to determine whether: (1) self-reported PEs are less distressing in community samples from LAMIC than from HIC; (2) the network of PEs is significantly less connected in a sample from LAMIC than from HIC. Adults from 8 HIC (n = 4669) and 5 LAMIC (n = 2472) were compared. The lifetime frequency of PEs and related distress were assessed with the Community Assessment of Psychic Experiences. We analyzed the associations of PEs with distress and country type. The interconnection of PEs was visualized by a network analysis and tested for differences in global connection strengths. The average endorsement rates of PEs were significantly higher in LAMIC than in HIC (χ2 = 1772.87, P < .01, Φcramer = 0.50). There was a universal positive correlation between higher frequency of PEs and more distress, but the distress levels controlled for frequency were significantly higher in HIC (R2 = 0.11; b = 0.26; SE = 0.01; T = 17.68; P < .001). Moreover, the network of PEs was significantly less connected in LAMIC (S = 0.40, P < .05). The findings indicate that PEs are of less clinical relevance in LAMIC compared with HIC. The universal use of current high-risk criteria might thus not be adequate without consideration of associated distress and cultural values.

Gene Discovery of Characteristic Metabolic Pathways in the Tea Plant (Camellia sinensis) Using 'Omics'-Based Network Approaches: A Future Perspective

Characteristic secondary metabolites, including flavonoids, theanine and caffeine, in the tea plant (Camellia sinensis) are the primary sources of the rich flavors, fresh taste, and health benefits of tea. The decoding of genes involved in these characteristic components is still significantly lagging, which lays an obstacle for applied genetic improvement and metabolic engineering. With the popularity of high-throughout transcriptomics and metabolomics, 'omics'-based network approaches, such as gene co-expression network and gene-to-metabolite network, have emerged as powerful tools for gene discovery of plant-specialized (secondary) metabolism. Thus, it is pivotal to summarize and introduce such system-based strategies in facilitating gene identification of characteristic metabolic pathways in the tea plant (or other plants). In this review, we describe recent advances in transcriptomics and metabolomics for transcript and metabolite profiling, and highlight 'omics'-based network strategies using successful examples in model and non-model plants. Further, we summarize recent progress in 'omics' analysis for gene identification of characteristic metabolites in the tea plant. Limitations of the current strategies are discussed by comparison with 'omics'-based network approaches. Finally, we demonstrate the potential of introducing such network strategies in the tea plant, with a prospects ending for a promising network discovery of characteristic metabolite genes in the tea plant.

Gut microbiota, metabolism and psychopathology: A critical review and novel perspectives

Psychiatric disorders are often associated with metabolic comorbidities. However, the mechanisms through which metabolic and psychiatric disorders are connected remain unclear. Pre-clinical studies in rodents indicate that the bidirectional signaling between the intestine and the brain, the so-called microbiome-gut-brain axis, plays an important role in the regulation of both metabolism and behavior. The gut microbiome produces a vast number of metabolites that may be transported into the host and play a part in homeostatic control of metabolism as well as brain function. In addition to short chain fatty acids, many of these metabolites have been identified in recent years. To what extent both microbiota and their products control human metabolism and behavior is a subject of intense investigation. In this review, we will discuss the most recent findings concerning alterations in the gut microbiota as a possible pathophysiological factor for the co-occurrence of metabolic comorbidities in psychiatric disorders.

[Investigation of internet-communities' influence on the social inclusion of the elderly people through leisure practices.]

Modern society is characterized by the intensification of new interactions and new communities, in parallel with the interest in social exclusion-inclusion topic. As the Internet develops, the research interest is directed to studying the specifics of emerging communities in a virtual environment called as «online communities», «virtual communities» etc. The article presents the results of studying 7 online communities of older people in the social network VKontakte about leisure. The research was carried out on the basis of a network approach using automated data analysis tools that allows to evaluate communities characteristics, as well as information exchange processes between the participants. The method of network data analysis, automated online community analysis, as well as Gephi software packages and Socstat social network analysis service were used. In addition, qualitative data on content published in communities and offline activities were obtained. The study conducted came to conclusions about the formation of communities characterized by weak online connections between users of older age groups in VKontakte. In the studied groups, the real social community is formed initially in an offline environment where all key activities take place, and their organizers use social networks as one of the channels for informing the target audience. For effective social inclusion, it is necessary to maintain a certain level of trust in what is happening in the group and the information published, as well as relevant content that illuminates activities in an offline environment.

Application of network methods for understanding mental disorders: pitfalls and promise

Galvanized with the availability of sophisticated statistical techniques and large datasets, network medicine has emerged as an active area of investigation. Following this trend, network methods have been utilized to understand the interplay between symptoms of mental disorders. This realistic approach that may provide an improved framework into understanding mental conditions and underlying mechanisms is certainly to be welcomed. However, we have noticed that symptom network studies tend to lose sight of the fundamentals, overlook major limitations embedded in study designs, and make inferences that are difficult to justify with current findings. There is concern that disregarding these flaws may halt the progress of the network approach in psychiatry. Therefore, in this paper, we first attempt to identify the pitfalls: (1) a reductionist understanding of medicine and psychiatry, thereby inadvertently reintroducing the dichotomy of medicine (lung cancer) and psychiatry (depression), (2) a shortsighted view of signs and symptoms, (3) overlooking the limitations of available datasets based on scales with embedded latent class structures, (4) overestimating the importance of the current findings beyond what is supported by the study design. By addressing current issues, the hope is to navigate this rapidly growing field to a more methodologically sound and reproducible path that will contribute to our understanding of mental disorders and its underlying mechanisms.

A network theory of mental disorders

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

Mapping the manuals of madness: Comparing the ICD-10 and DSM-IV-TR using a network approach

The International Classification of Diseases and Related Health Problems (ICD) and the Diagnostic and Statistical Manual of Mental Disorders (DSM) represent dominant approaches to diagnosis of mental disorders. However, it is unclear how these alternative systems relate to each other when taking into account the symptoms that make up the disorders. This study uses a network approach to investigate the overlap in structure between diagnostic networks pertaining to ICD-10 and DSM-IV-TR. Networks are constructed by representing individual symptoms as nodes, and connecting nodes whenever the corresponding symptoms feature as diagnostic criteria for the same mental disorder. Results indicate that, relative to the DSM-IV-TR network, the ICD-10 network contains (a) more nodes, (b) lower level of clustering, and (c) a higher level of connectivity. Both networks show features of a small world, and have similar (of "the same") high centrality nodes. Comparison to empirical data indicates that the DSM-IV-TR network structure follows comorbidity rates more closely than the ICD-10 network structure. We conclude that, despite their apparent likeness, ICD-10 and DSM-IV-TR harbour important structural differences, and that both may be improved by matching diagnostic categories more closely to empirically determined network structures. Copyright © 2016 John Wiley & Sons, Ltd.

A Snapshot on MRSA Epidemiology in a Neonatal Intensive Care Unit Network, Palermo, Italy

Objectives: We performed a 1-year prospective surveillance study on MRSA colonization within the five NICUs of the metropolitan area of Palermo, Italy. The purpose of the study was to assess epidemiology of MRSA in NICU from a network perspective.

Methods: Transfer of patients between NICUs during 2014 was traced based on the annual hospital discharge records. In the period February 2014–January 2015, in the NICU B, at the University teaching hospital, nasal swabs from all infants were collected weekly, whereas in the other four NICUs (A, C, D, E) at 4 week-intervals of time. MRSA isolates were submitted to antibiotic susceptibility testing, SCCmec typing, PCR to detect lukS-PV and lukF-PV (lukS/F-PV) genes and the gene encoding the toxic shock syndrome toxin (TSST-1), multilocus variable number tandem repeat fingerprinting (MLVF), and multilocus sequence typing (MLST).

Results: In the period under study, 587 nasal swabs were obtained from NICU B, whereas 218, 180, 157, and 95 from NICUs A, C, D, and E, respectively. Two groups of NICUs at high prevalence and low prevalence of MRSA colonization were recognized. Overall, 113 isolates of MRSA were identified from 102 infants. Six MLVF types (A–F) were detected, with type C being subdivided into five subtypes. Five sequence types (STs) were found with ST22-IVa being the most frequent type in all NICUs. All the MRSA molecular subtypes, except for ST1-IVa, were identified in NICU B.

Conclusions: Our findings support the need to approach surveillance and infection control in NICU in a network perspective, prioritizing referral healthcare facilities.

Exploring the underlying structure of mental disorders: cross-diagnostic differences and similarities from a network perspective using both a top-down and a bottom-up approach

BACKGROUND

It has been suggested that the structure of psychopathology is best described as a complex network of components that interact in dynamic ways. The goal of the present paper was to examine the concept of psychopathology from a network perspective, combining complementary top-down and bottom-up approaches using momentary assessment techniques.

METHOD

A pooled Experience Sampling Method (ESM) dataset of three groups (individuals with a diagnosis of depression, psychotic disorder or no diagnosis) was used (pooled N = 599). The top-down approach explored the network structure of mental states across different diagnostic categories. For this purpose, networks of five momentary mental states ('cheerful', 'content', 'down', 'insecure' and 'suspicious') were compared between the three groups. The complementary bottom-up approach used principal component analysis to explore whether empirically derived network structures yield meaningful higher order clusters.

RESULTS

Individuals with a clinical diagnosis had more strongly connected moment-to-moment network structures, especially the depressed group. This group also showed more interconnections specifically between positive and negative mental states than the psychotic group. In the bottom-up approach, all possible connections between mental states were clustered into seven main components that together captured the main characteristics of the network dynamics.

CONCLUSIONS

Our combination of (i) comparing network structure of mental states across three diagnostically different groups and (ii) searching for trans-diagnostic network components across all pooled individuals showed that these two approaches yield different, complementary perspectives in the field of psychopathology. The network paradigm therefore may be useful to map transdiagnostic processes.