A sex-specific pathway linking early life maltreatment, vagal activity, and depressive symptoms

Background: Experiences of early life maltreatment (ELM) are alarmingly common and represent a risk factor for the development of psychopathology, particularly depression. Research has focused on alterations in autonomic nervous system (ANS) functioning as a mediator of negative mental health outcomes associated with ELM. Early alterations in autonomic vagal activity (vmHRV) may moderate the relationship between ELM and depression, particularly when considering forms of emotional maltreatment. Recent evidence suggests that the relationships of both ELM and vmHRV with depression may be non-linear, particularly considering females.Objective: Building on and extending theoretical considerations and previous work, the present work aims to further the current understanding of the complex relationships between ELM exposure, vmHRV, and depression.Methods: This study uses an adaptive modelling approach, combining exploratory network-based analyses with linear and quadratic moderation analyses, drawing on a large sample of males and females across adolescence (total N = 213; outpatient at-risk sample and healthy controls) and adulthood (total N = 85; community-based convenience sample).Results: Exploratory network-based analyses reveal that exposure to emotional abuse is particularly central within a network of ELM subtypes, depressive symptoms, and concurrent vmHRV in both adolescents and adults. In adults, emotional neglect shows strong associations with both emotional abuse and vmHRV and is highly central as a network node, which is not observed in adolescents. Moderator analyses reveal significant interactions between emotional maltreatment and vmHRV predicting depressive symptoms in adult females. Significant quadratic relationships of emotional maltreatment and vmHRV with depression are observed in both adolescent and adult females.Conclusions: The present findings contribute to the understanding of the psychological and physiological mechanisms by which ELM acts as a risk factor for the development of depression. Ultimately, this will contribute to the development of targeted and effective intervention strategies to mitigate the detrimental effects of early adversity.

Improving the Walktrap Algorithm Using K-Means Clustering

The walktrap algorithm is one of the most popular community-detection methods in psychological research. Several simulation studies have shown that it is often effective at determining the correct number of communities and assigning items to their proper community. Nevertheless, it is important to recognize that the walktrap algorithm relies on hierarchical clustering because it was originally developed for networks much larger than those encountered in psychological research. In this paper, we present and demonstrate a computational alternative to the hierarchical algorithm that is conceptually easier to understand. More importantly, we show that better solutions to the sum-of-squares optimization problem that is heuristically tackled by hierarchical clustering in the walktrap algorithm can often be obtained using exact or approximate methods for K-means clustering. Three simulation studies and analyses of empirical networks were completed to assess the impact of better sum-of-squares solutions.

Weight Bias Internalization and Eating Disorder Psychopathology in Treatment-Seeking Patients with Obesity

This study aimed to investigate the relationship between weight bias internalization and eating disorder psychopathology in treatment-seeking patients with severe obesity using a network approach. Two thousand one hundred and thirteen patients with obesity were consecutively admitted to a specialist clinical unit for obesity and were recruited from January 2016 to February 2023. Body mass index was measured, and each patient completed the Weight Bias Internalization Scale (WBSI) and the Eating Disorder Examination Interview (EDE). Network analysis showed that the most central and highly interconnected nodes in the network were related to the EDE items exposure avoidance, dissatisfaction with shape, and wanting an empty stomach. Bridge nodes were found, but the bootstrap difference test on expected bridge influence indicated non-significant centrality differences. Nevertheless, the eating disorder psychopathology and weight bias internalization network structure in patients seeking treatment for obesity indicate the prominent roles of body dissatisfaction and control of eating and weight in these psychological constructs. This finding, if replicated, could pave the way for a new understanding of the psychological mechanisms operating in patients with obesity.

A network analysis of subjective well-being in Chinese high school students

BACKGROUND

The psychological situation of high school students during adolescence is not promising, and the most obvious manifestation is the lack of subjective well-being (SWB). This network analysis presents a model of the interaction and correlation between different items of SWB, identifying the most central items for high school students.

METHODS

Through offline and online surveys, 4,378 questionnaires were sent out and finally 4,282 Chinese high school students were available. The response rate was 97.807%. The study used the eLASSO method to estimate the network structure and centrality measures. This algorithm used the EBIC to select the best neighbor factor for each node.

RESULTS

The average age for high school students was 16.320 years old and the average SWB score was 76.680. The distribution of SWB between male and female students was significant different (P < 0.001). S8 (Have you been anxious, worried, or upset) was the node with the highest strength and expected influence. The network structure and centrality remained stable after discarding 75% of the sample at random. Except for S15 (How concerned or worried about your health have you been), all nodes were positively correlated with each other (P < 0.01). The network structure of SWB was similar for female and male students (network strength: 8.482 for male participants; 8.323 for female participants; P = 0.159), as well as for rural and urban students (network strength: 8.500 for rural students; 8.315 for urban students; P = 0.140).

CONCLUSION

Targeting S8 (Have you been anxious, worried, or upset) as a potential intervention target may increase high school students' SWB effectively.

A network approach to substance use, internalizing, and externalizing comorbidity in U.S. adults

INTRODUCTION

Use of conventional cigarettes (CIG), alcohol, marijuana, and sedatives [i.e., benzodiazepines and barbiturates]) commonly co-occur with internalizing and externalizing disorders. It is unclear how these relationships extend to electronic cigarettes (ECIGs) and prescription drugs not prescribed (i.e., sedatives, tranquilizers, and painkillers [PDNP]), and whether they differ by gender.

METHODS

Adult data (N = 30,211) from Wave 1 (2013-2014) of the Population Assessment of Tobacco and Health Study were used to estimate a network of current or past-month use for six substances, experiencing four internalizing symptoms in the past month, and experiencing seven externalizing symptoms in the past month. Visual comparisons, global strength invariance, network structure invariance, and edge strength invariance were tested to detail substance use and internalizing/externalizing symptom networks.

RESULTS

Overall, networks were consistent between men and women. The strongest substance use/mental health symptom connections estimated as edge-weights (EW) were between marijuana with lying (EW = 0.60, 95% CI = 0.49; 0.70), marijuana with engaging in fights (EW = 0.54, 95% CI = 0.27; 0.81), PDNP with having trouble sleeping (EW = 0.53, 95% CI = 0.40; 0.66), and alcohol and impulsivity (EW = 0.48, 95% CI = 0.42; 0.53).

DISCUSSION

There were many weak connections throughout the substance use and internalizing/externalizing network. A few important connections were identified and encourage future study. In particular, PDNP was most strongly associated with internalizing symptoms. Marijuana, alcohol and PDNP use were most strongly associated with externalizing symptoms.

Changes in specific and nonspecific psychopathology network structure after intensive cognitive behavior therapy in patients with anorexia nervosa

OBJECTIVE

This study aimed to compare eating disorder-specific and nonspecific clinical features in patients with anorexia nervosa before and after intensive enhanced cognitive behavior therapy (CBT-E) via network analysis.

METHODS

All consecutive patients admitted to intensive CBT-E were eligible, and the sample comprised patients aged ≥16 years who completed a 20-week intensive CBT-E program. Body mass index (BMI), Eating Disorder Examination Questionnaire and Brief Symptoms Inventory responses were gathered at baseline and end of treatment, and used to generate statistical networks of the connections between symptoms (nodes) and the strength and centrality thereof.

RESULTS

A total of 214 patients were included. Most nodes had relatively similar centrality compared to other nodes in the networks. "Eating concern" and "phobic anxiety" showed the greatest bridge centrality at both time points. No differences were found between baseline and the end of treatment in either global network or individual connection strengths.

CONCLUSION

These findings suggest that some clinical expressions not specific to eating-disorder psychopathology remain strongly connected in the generalized network of patients with anorexia nervosa after CBT-E. Future research should examine whether additional procedures specifically designed to target these symptoms should be integrated into this and other treatments.

Objective Bayesian Edge Screening and Structure Selection for Ising Networks

The Ising model is one of the most widely analyzed graphical models in network psychometrics. However, popular approaches to parameter estimation and structure selection for the Ising model cannot naturally express uncertainty about the estimated parameters or selected structures. To address this issue, this paper offers an objective Bayesian approach to parameter estimation and structure selection for the Ising model. Our methods build on a continuous spike-and-slab approach. We show that our methods consistently select the correct structure and provide a new objective method to set the spike-and-slab hyperparameters. To circumvent the exploration of the complete structure space, which is too large in practical situations, we propose a novel approach that first screens for promising edges and then only explore the space instantiated by these edges. We apply our proposed methods to estimate the network of depression and alcohol use disorder symptoms from symptom scores of over 26,000 subjects.

Using network analysis to improve understanding and utility of the 10-item Autism-Spectrum Quotient

The 10-item Autism-Spectrum Quotient (AQ10) is a measure of autistic traits used in research and clinical practice. Recently, the AQ10 has garnered critical attention, with research questioning its psychometric properties and clinical cutoff value. To help inform the utility of the measure, we conducted the first network analysis of the AQ10, with a view to gain a better understanding of its individual items. Using a large dataset of 6,595 participants who had completed the AQ10, we found strongest inter-subscale connections between communication, imagination, and socially relevant items. The nodes with greatest centrality concerned theory of mind differences. Together, these findings align with cognitive explanations of autism and provide clues about which AQ10 items show greatest utility for informing autism-related clinical practice.

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.

Change in eating-disorder psychopathology network structure in patients with anorexia nervosa treated with intensive cognitive behavior therapy

OBJECTIVE

This study was designed to compare the change in eating-disorder feature networks in patients with anorexia nervosa after treatment with intensive enhanced cognitive behavior therapy (CBT-E).

METHODS

Patients seeking treatment for anorexia nervosa were consecutively recruited from January 2016 to September 2020. All patients aged ≥16 years who completed a 20-week intensive CBT-E program (13 weeks of inpatient followed by 7 weeks of day-hospital treatment) were included in the study. Body mass index (BMI) was measured, and the Eating Disorder Examination Questionnaire completed for each patient, both at baseline and the end of treatment.

RESULTS

The sample comprised 214 patients with anorexia nervosa. Treated patients showed significant improvements in BMI and eating-disorder psychopathology. Network analysis revealed a significant reduction in the network global and connection strengths at the end of treatment. The most central and highly interconnected nodes in the network at baseline were related to the drive for thinness, but at the end of treatment to body image concerns. Some edge connections were significantly stronger at baseline than at the end of treatment, while others were significantly stronger at the end of treatment than at baseline.

DISCUSSION

CBT-E reduces the psychopathology network connectivity over time in patients with anorexia nervosa. The differences in central nodes and edge connections between baseline and end of treatment, not detected by classical inferential analysis, may be informative for understanding the centrality of symptoms in the psychopathology network, and how a specific treatment may act to reduce symptoms and change their connections over time.

In situ functional cell phenotyping reveals microdomain networks in colorectal cancer recurrence

Tumors are dynamic ecosystems comprising localized niches (microdomains), possessing distinct compositions and spatial configurations of cancer and non-cancer cell populations. Microdomain-specific network signaling coevolves with a continuum of cell states and functional plasticity associated with disease progression and therapeutic responses. We present LEAPH, an unsupervised machine learning algorithm for identifying cell phenotypes, which applies recursive steps of probabilistic clustering and spatial regularization to derive functional phenotypes (FPs) along a continuum. Combining LEAPH with pointwise mutual information and network biology analyses enables the discovery of outcome-associated microdomains visualized as distinct spatial configurations of heterogeneous FPs. Utilization of an immunofluorescence-based (51 biomarkers) image dataset of colorectal carcinoma primary tumors (n = 213) revealed microdomain-specific network dysregulation supporting cancer stem cell maintenance and immunosuppression that associated selectively with the recurrence phenotype. LEAPH enables an explainable artificial intelligence platform providing insights into pathophysiological mechanisms and novel drug targets to inform personalized therapeutic strategies.

The influence of psychological network on the willingness to communicate in a second language

This study investigated the effect of the psychological network on the willingness to communicate in English among Japanese people. Previous studies have shown that psychological factors affect the willingness to communicate in English for Japanese people. However, the network structure of psychological factors and their effects have not been revealed yet. The present study conducted a network analysis with 644 Japanese people. Consequently, the edge between perceived communication competence and the willingness to communicate in the first or second language was very strong. Node centrality strength showed that these factors were central in the network structure. The results of the network analysis show the effect of psychological networks on the willingness to communicate in a second language, which will be beneficial for language education.

The Influence of Socio-Ecological Networks on Willingness to Communicate in English for Japanese People

This study investigates the effect of socio-ecological networks on the willingness to communicate (WTC) in English among Japanese people. Previous studies have shown that relational mobility (socio-ecological factor), which is defined as the availability of opportunities to choose new relationship partners, positively affects the WTC in English for Japanese people. However, the network structure of the variables of relational mobility and its effects have not been revealed yet. The present study conducted network analysis with 474 Japanese university students and found the two clusters that correspond to the dimensions of relational mobility in the partial correlation network. Three variables regarding opportunities to meet new people and leave current relationships positively affected the WTC in English; one had a negative effect. Centrality indices, such as nodes strength, betweenness, and closeness, revealed the centrality of several variables in the network. Bootstrapping methods showed the trustworthiness of the estimated network structure and centrality indices as well as edges and variables whose effects differed significantly from that of others. Contrary to the regression analysis results, the network analysis findings can help us understand the in-depth effect of relational mobility on the WTC in a second language, which will prove useful for intervention studies.

Investigating the Utility of Fixed-margin Sampling in Network Psychometrics

Steinley, Hoffman, Brusco, and Sher (2017) proposed a new method for evaluating the performance of psychological network models: fixed-margin sampling. The authors investigated LASSO regularized Ising models (eLasso) by generating random datasets with the same margins as the original binary dataset, and concluded that many estimated eLasso parameters are not distinguishable from those that would be expected if the data were generated by chance. We argue that fixed-margin sampling cannot be used for this purpose, as it generates data under a particular null-hypothesis: a unidimensional factor model with interchangeable indicators (i.e., the Rasch model). We show this by discussing relevant psychometric literature and by performing simulation studies. Results indicate that while eLasso correctly estimated network models and estimated almost no edges due to chance, fixed-margin sampling performed poorly in classifying true effects as "interesting" (Steinley et al. 2017, p. 1004). Further simulation studies indicate that fixed-margin sampling offers a powerful method for highlighting local misfit from the Rasch model, but performs only moderately in identifying global departures from the Rasch model. We conclude that fixed-margin sampling is not up to the task of assessing if results from estimated Ising models or other multivariate psychometric models are due to chance.

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.

Responses to Stress: Investigating the Role of Gender, Social Relationships, and Touch Avoidance in Italy

Stress is a physiological response to internal and external events we call “stressors”. Response to the same daily stressors varies across individuals and seems to be higher for women. A possible explanation for this phenomenon is that women perceive sociality, relationships, and intimacy—important sources of both stress and wellbeing—differently from how men experience them. In this study, we investigate how gender, attachment, and touch avoidance predict stress responses on a sample of 335 Italians (216 females; age = 35.82 ± 14.32). Moreover, we analyze the network of relationships between these variables through multiple linear regression and exploratory network analysis techniques. The results recontextualize the role of gender in determining stress responses in terms of (lack of) confidence and touch avoidance toward family members; attitudes toward relationships seem to be the main determinants of stress responses. These results have implications for reducing stress in both clinical settings and at a social level.

Bayesian information criterion approximations to Bayes factors for univariate and multivariate logistic regression models

Schwarz's criterion, also known as the Bayesian Information Criterion or BIC, is commonly used for model selection in logistic regression due to its simple intuitive formula. For tests of nested hypotheses in independent and identically distributed data as well as in Normal linear regression, previous results have motivated use of Schwarz's criterion by its consistent approximation to the Bayes factor (BF), defined as the ratio of posterior to prior model odds. Furthermore, under construction of an intuitive unit-information prior for the parameters of interest to test for inclusion in the nested models, previous results have shown that Schwarz's criterion approximates the BF to higher order in the neighborhood of the simpler nested model. This paper extends these results to univariate and multivariate logistic regression, providing approximations to the BF for arbitrary prior distributions and definitions of the unit-information prior corresponding to Schwarz's approximation. Simulations show accuracies of the approximations for small samples sizes as well as comparisons to conclusions from frequentist testing. We present an application in prostate cancer, the motivating setting for our work, which illustrates the approximation for large data sets in a practical example.

Caught in a web of trauma: Network analysis of childhood adversity and adult mental ill-health

BACKGROUND

Current interventions for adverse childhood experiences have only limited effectiveness.

OBJECTIVE

We sought to identify optimal targets for the development of new interventions against adverse childhood experiences (ACE), that is, ACEs that a) are so central in the network of childhood adversity that curbing them is likely to impact other types of adversity, too, and b) are so central to the link of childhood adversity and adult mental ill-health that curbing them is likely to prevent this negative long-term effect from developing.

PARTICIPANTS AND SETTING

384 adult psychiatric inpatients.

METHODS

Using the R packages qgraph and IsingFit, we analyzed the ACE network and the common network of ACEs and adult mental disorders.

RESULTS

We found two clusters of ACEs: direct interactions with the child and indirect traumatization via adverse circumstances. When controlling for interrelatedness, the associations of sexual abuse with posttraumatic stress disorder and borderline personality disorder were the only direct links between ACEs and adult mental disorders.

CONCLUSIONS

As neglect and violence against the mother were the most influential ACEs, curbing them is likely to destabilize the whole network of adversity. Thus, neglect and violence against the mother lend themselves as candidate targets for the development of new interventions. As sexual abuse was the only link between childhood adversity and adult mental ill-health, interventions against it seem most likely to keep this negative long-term effect from developing. Further, ideally prospective, research is needed to corroborate these findings.

Eating disorder psychopathology in adults and adolescents with anorexia nervosa: A network approach

OBJECTIVE

The aim of this study was to assess and compare eating disorder feature networks in adult and adolescent patients with anorexia nervosa.

METHODS

Patients seeking treatment for anorexia nervosa in inpatient and outpatient settings were consecutively recruited from January 2008 to September 2019. Body mass index was measured, and each patient completed the Eating Disorder Examination Questionnaire.

RESULTS

The sample comprised 547 adolescent and 724 adult patients with anorexia nervosa. Network analysis showed that in both adults and adolescents, the most central and highly interconnected nodes in the network were related to shape overvaluation and desiring weight loss. The network comparison test identified similar global strength and network invariance, confirming the similarity of the two network structures.

DISCUSSION

The network structures in adult and adolescent patients with anorexia nervosa are similar, and lend weight to the cognitive behavioral theory that overvaluation of shape and weight is the core feature of anorexia nervosa psychopathology.

Quality of Life and Physical Performance in Patients with Obesity: A Network Analysis

BACKGROUND

The aim of this study was to investigate the interconnections between specific quality-of-life domains in patients with obesity and high or low physical performance using a network approach.

METHODS

716 consecutive female and male patients (aged 18-65 years) with obesity seeking weight-loss treatment were included. The 36-item Short Form Health Survey (SF-36) and the six-minute walking test (6MWT) were used to assess quality of life and physical performance, respectively. The sample was split into two groups according to the distance walked in the 6MWT. Network structures of the SF-36 domains in the two groups were assessed and compared, and the relative importance of individual items in the network structures was determined using centrality analyses.

RESULTS

35.3% (n = 253) of participants covered more distance than expected, and 64.7% (n = 463) did not. Although low-performing patients showed lower quality of life domain scores, the network structures were similar in the two groups, with the SF-36 Vitality representing the central domain in both networks. Mental Health was a node with strong connections in patients who walked less distance.

CONCLUSIONS

These findings indicate that psychosocial variables represent the most influential and interconnected features as regards quality of life in both groups.

Psychological features in obesity: A network analysis

OBJECTIVE

Enhanced understanding of psychosocial factors associated with obesity may improve knowledge of their interplay mechanisms. The aim of this study was to assess the relationship between psychosocial variables in individuals with obesity using a network analysis.

METHODS

Patients seeking treatment for obesity were consecutively recruited from a rehabilitative residential treatment program for severe obesity between January 2016 and March 2019. Each patient completed the following questionnaires: Eating Disorder Examination Questionnaire, Symptom Checklist-90, Obesity Related Well-Being, and Weight Bias Internalization Scale. In addition, current body mass index (BMI) was measured, and maximum acceptable and dream BMI were assessed.

RESULTS

The sample comprised 996 patients with obesity (age 52.3 [SD = 16.0] years; BMI 41.8 [SD = 7.8] kg/m² ; 65.7% women; 52.2% married or living with a partner). Network analysis showed that interpersonal sensitivity and shape-weight concern, but also internalized weight stigma, were the most central and highly interconnected nodes in the network. In contrast, objective binge-eating episodes and dietary restraint were the most peripheral and least connected nodes. Eating disorder features and psychological distress formed two clearly separate clusters. No difference in network structure was found between men and women.

CONCLUSIONS

The pattern of network node connections supports the importance of assessing psychological distress, interpersonal sensitivity, shape-weight concern, and internalized weight stigma in patients seeking treatment for obesity.

Patterns of Anatomic Injury in Critically Injured Combat Casualties: A Network Analysis

A mortality review of death caused by injury requires a determination of injury survivability prior to a determination of death preventability. If injuries are nonsurvivable, only non-medical primary prevention strategies have potential to prevent the death. Therefore, objective measures are needed to empirically inform injury survivability from complex anatomic patterns of injury. As a component of injury mortality reviews, network structures show promise to objectively elucidate survivability from complex anatomic patterns of injury resulting from explosive and firearm mechanisms. In this network analysis of 5,703 critically injured combat casualties, patterns of injury among fatalities from explosive mechanisms were associated with both a higher number and severity of anatomic injuries to regions such as the extremities, abdomen, and thorax. Patterns of injuries from a firearm were more isolated to individual body regions with fatal patterns involving more severe injuries to the head and thorax. Each injury generates a specific level of risk as part of an overall anatomic pattern to inform injury survivability not always captured by traditional trauma scoring systems. Network models have potential to further elucidate differences between potentially survivable and nonsurvivable anatomic patterns of injury as part of the mortality review process relevant to improving both the military and civilian trauma care systems.

Foundations of idiographic methods in psychology and applications for psychotherapy

Researchers have long called for greater recognition and use of longitudinal, individual-level research in the study of psychopathology and psychotherapy. Much of our current research attempts to indirectly investigate individual-level, or idiographic, psychological processes via group-based, or nomothetic, designs. However, results from nomothetic research do not necessarily translate to the individual-level. In this review, we discuss how idiographic analyses can be integrated into psychotherapy and psychotherapy research. We examine and review key statistical methods for conducting idiographic analyses. These methods include factor-based and vector autoregressive approaches using longitudinal data. The theoretical framework behind each approach is reviewed and critically evaluated. Empirical examples of each approach are discussed, with the aim of helping interested readers consider how they may use idiographic methods to analyze longitudinal data and psychological processes. Finally, we conclude by citing key limitations of the idiographic approach, calling for greater development of these analyses to ease their successful integration into clinical settings.

Network structure of the Wisconsin Schizotypy Scales-Short Forms: Examining psychometric network filtering approaches

Schizotypy is a multidimensional construct that provides a useful framework for understanding the etiology, development, and risk for schizophrenia-spectrum disorders. Past research has applied traditional methods, such as factor analysis, to uncovering common dimensions of schizotypy. In the present study, we aimed to advance the construct of schizotypy, measured by the Wisconsin Schizotypy Scales-Short Forms (WSS-SF), beyond this general scope by applying two different psychometric network filtering approaches-the state-of-the-art approach (lasso), which has been employed in previous studies, and an alternative approach (information-filtering networks; IFNs). First, we applied both filtering approaches to two large, independent samples of WSS-SF data (ns = 5,831 and 2,171) and assessed each approach's representation of the WSS-SF's schizotypy construct. Both filtering approaches produced results similar to those from traditional methods, with the IFN approach producing results more consistent with previous theoretical interpretations of schizotypy. Then we evaluated how well both filtering approaches reproduced the global and local network characteristics of the two samples. We found that the IFN approach produced more consistent results for both global and local network characteristics. Finally, we sought to evaluate the predictability of the network centrality measures for each filtering approach, by determining the core, intermediate, and peripheral items on the WSS-SF and using them to predict interview reports of schizophrenia-spectrum symptoms. We found some similarities and differences in their effectiveness, with the IFN approach's network structure providing better overall predictive distinctions. We discuss the implications of our findings for schizotypy and for psychometric network analysis more generally.

Differential Markov random field analysis with an application to detecting differential microbial community networks

Micro-organisms such as bacteria form complex ecological community networks that can be greatly influenced by diet and other environmental factors. Differential analysis of microbial community structures aims to elucidate systematic changes during an adaptive response to changes in environment. In this paper, we propose a flexible Markov random field model for microbial network structure and introduce a hypothesis testing framework for detecting differences between networks, also known as differential network analysis. Our global test for differential networks is particularly powerful against sparse alternatives. In addition, we develop a multiple testing procedure with false discovery rate control to identify the structure of the differential network. The proposed method is applied to data from a gut microbiome study on U.K. twins to evaluate how age affects the microbial community network.

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.

Capturing Aesthetic Experiences With Installation Art: An Empirical Assessment of Emotion, Evaluations, and Mobile Eye Tracking in Olafur Eliasson's "Baroque, Baroque!"

Installation art is one of the most important and provocative developments in the visual arts during the last half century and has become a key focus of artists and of contemporary museums. It is also seen as particularly challenging or even disliked by many viewers, and—due to its unique in situ, immersive setting—is equally regarded as difficult or even beyond the grasp of present methods in empirical aesthetic psychology. In this paper, we introduce an exploratory study with installation art, utilizing a collection of techniques to capture the eclectic, the embodied, and often the emotionally-charged viewing experience. We present results from an investigation of two pieces, both part of Olafur Eliasson’s exhibition “Baroque, Baroque” held at the Belvedere museum in Vienna. These were assessed by pre- and post-viewing questionnaires focusing on emotion, meaning-making, and appraisals, in tandem with mobile eye tracking to consider viewers’ attention to both installed artworks and/or to the museum environment. The data showed differences in participants’ emotional states, appraisals, and visual exploration, which together paint a picture of the aesthetic reactions to the works. These differences also showed how viewers’ appraisal strategies, meaning making, and physical actions facilitated relatively more or less deep engagement with, and enjoyment of, the art. The results are discussed in terms of their implications for museum studies, art education, and theory in empirical aesthetics.

The network structure of paranoia in the general population

PURPOSE

Bebbington and colleagues' influential study on 'the structure of paranoia in the general population' used data from the British National Psychiatric Morbidity Survey and latent variable analysis methods. Network analysis is a relatively new approach in psychopathology research that considers mental disorders to be emergent phenomena from causal interactions among symptoms. This study re-analysed the British National Psychiatric Morbidity Survey data using network analysis to examine the network structure of paranoia in the general population.

METHODS

We used a Graphical Least Absolute Shrinkage and Selection Operator (glasso) method that estimated an optimal network structure based on the Extended Bayesian Information Criterion. Network sub-communities were identified by spinglass and EGA algorithms and centrality metrics were calculated per item and per sub-community.

RESULTS

We replicated Bebbington's four component structure of paranoia, identifying 'interpersonal sensitivities', 'mistrust', 'ideas of reference' and 'ideas of persecution' as sub-communities in the network. In line with previous experimental findings, worry was the most central item in the network. However, 'mistrust' and 'ideas of reference' were the most central sub-communities.

CONCLUSIONS

Rather than a strict hierarchy, we argue that the structure of paranoia is best thought of as a heterarchy, where the activation of high-centrality nodes and communities is most likely to lead to steady state paranoia. We also highlight the novel methodological approach used by this study: namely, using network analysis to re-examine a population structure of psychopathology previously identified by latent variable approaches.

Post-conflict struggles as networks of problems: A network analysis of trauma, daily stressors and psychological distress among Sri Lankan war survivors

A growing body of literature indicates that the mental distress experienced by survivors of war is a function of both experienced trauma and stressful life events. However, the majority of these studies are limited in that they 1) employ models of psychological distress that emphasize underlying latent constructs and do not allow researchers to examine the unique associations between particular symptoms and various stressors; and 2) use one or more measures that were not developed for that particular context and thus may exclude key traumas, stressful life events and symptoms of psychopathology. The current study addresses both these limitations by 1) using a novel conceptual model, network analysis, which assumes that symptoms covary with each other not because they stem from a latent construct, but rather because they represent meaningful relationships between the symptoms; and 2) employing a locally developed measure of experienced trauma, stressful life problems and symptoms of psychopathology. Over the course of 2009-2011, 337 survivors of the Sri Lankan civil war were administered the Penn-RESIST-Peradeniya War Problems Questionnaire (PRPWPQ). Network analysis revealed that symptoms of psychopathology, problems pertaining to lack of basic needs, and social problems were central to the network relative to experienced trauma and other types of problems. After controlling for shared associations, social problems in particular were the most central, significantly more so than traumatic events and family problems. Several particular traumatic events, stressful life events and symptoms of psychopathology that were central to the network were also identified. Discussion emphasizes the utility of such network models to researchers and practitioners determining how to spend limited resources in the most impactful way possible.

Estimating psychopathological networks: Be careful what you wish for

Network models, in which psychopathological disorders are conceptualized as a complex interplay of psychological and biological components, have become increasingly popular in the recent psychopathological literature (Borsboom, et. al., 2011). These network models often contain significant numbers of unknown parameters, yet the sample sizes available in psychological research are limited. As such, general assumptions about the true network are introduced to reduce the number of free parameters. Incorporating these assumptions, however, means that the resulting network will lead to reflect the particular structure assumed by the estimation method-a crucial and often ignored aspect of psychopathological networks. For example, observing a sparse structure and simultaneously assuming a sparse structure does not imply that the true model is, in fact, sparse. To illustrate this point, we discuss recent literature and show the effect of the assumption of sparsity in three simulation studies.

Estimation of High-Dimensional Graphical Models Using Regularized Score Matching

Graphical models are widely used to model stochastic dependences among large collections of variables. We introduce a new method of estimating undirected conditional independence graphs based on the score matching loss, introduced by Hyvärinen (2005), and subsequently extended in Hyvärinen (2007). The regularized score matching method we propose applies to settings with continuous observations and allows for computationally efficient treatment of possibly non-Gaussian exponential family models. In the well-explored Gaussian setting, regularized score matching avoids issues of asymmetry that arise when applying the technique of neighborhood selection, and compared to existing methods that directly yield symmetric estimates, the score matching approach has the advantage that the considered loss is quadratic and gives piecewise linear solution paths under ℓ₁ regularization. Under suitable irrepresentability conditions, we show that ℓ₁-regularized score matching is consistent for graph estimation in sparse high-dimensional settings. Through numerical experiments and an application to RNAseq data, we confirm that regularized score matching achieves state-of-the-art performance in the Gaussian case and provides a valuable tool for computationally efficient estimation in non-Gaussian graphical models.