UNASSIGNED: Sexual victimization (SV) is common among college women, with approximately half of those who have experienced SV meeting criteria for posttraumatic stress disorder (PTSD) within a year. Both SV and PTSD are associated with alcohol misuse among college women, often explained by the self-medication hypothesis. Existing literature focuses on overall PTSD severity rather than potential day-to-day fluctuations in specific symptoms, which might play a crucial role in understanding alcohol misuse risk. Studies also examine only same-day or next-day associations between PTSD and drinking, neglecting the potential for longer-term changes.
UNASSIGNED: This study explores the short-term longitudinal stability and time-lagged predictive dynamics of PTSD symptoms, affect, and drinking behavior among 174 female college heavy episodic drinkers over four weeks. Participants were categorized into three groups: those with a history of SV and PTSD (n = 77), women with SV but without PTSD (n = 59), and women without prior trauma history (n = 38) to be able to examine differences by trauma exposure, and PTSD. We compared the longitudinal stability of PTSD symptom networks, affect (arousal, positive affect, and negative affect), and drinking behavior across groups. Support vector regression determined which PTSD symptom networks and affect best predict drinking behavior at specific time lags within a 0-7 day range.
UNASSIGNED: The PTSD group showed higher longitudinal stability for PTSD symptom networks (adjusted ps <.049) and arousal (adjusted ps <.048), but lower stability for negative affect (adjusted p =.013) and drinking behavior, including alcohol cravings (adjusted p =.019) and consumption (adjusted ps =.012), compared to the comparison groups. This suggests individuals with PTSD have more stable symptoms and arousal levels but greater fluctuations in negative affect and alcohol-related behaviors. Secondary analysis revealed PTSD symptom networks optimally predicted alcohol cravings with a three-day time lag (r=.88, p <.001) and consumption with a four-day time lag (r=.82, p <.001).
UNASSIGNED: These findings challenge assumptions regarding immediate effects of PTSD and affect on drinking behavior and underscore the need for therapeutic approaches that consider longer-range effects. Future research should expand on these findings by incorporating longer-range assessments and exploring a broader range of symptom interactions.

The network approach to psychopathology, which assesses associations between individual symptoms, has recently been applied to evaluate treatments for mental disorders. While various options for conducting network analyses in intervention research exist, an overview and an evaluation of the various approaches are currently missing. Therefore, we conducted a review on network analyses in intervention research. Studies were included if they constructed a symptom network, analyzed data that were collected before, during or after treatment of a mental disorder, and yielded information about the treatment effect. The 56 included studies were reviewed regarding their methodological and analytic strategies. About half of the studies based on data from randomized trials conducted a network intervention analysis, while the other half compared networks between treatment groups. The majority of studies estimated cross-sectional networks, even when repeated measures were available. All but five studies investigated networks on the group level. This review highlights that current methodological practices limit the information that can be gained through network analyses in intervention research. We discuss the strength and limitations of certain methodological and analytic strategies and propose that further work is needed to use the full potential of the network approach in intervention research.

Adolescent mental health is difficult to capture in categories such as depression or specific anxiety disorders. An alternative is to approach psychiatric symptoms as causal networks, potentially revealing feedback loops that maintain a pathological state. One approach to creating such networks, implemented in the PECAN methodology, is to ask adolescents about their perceptions of the causes to their symptoms. For this purpose, a transdiagnostic item list was created, and adolescents who screened positive for depression (N = 55) completed twice in two weeks a survey quantifying perceptions of causality between their mental health problems. A network that was averaged across all participants was reliable and revealed three strong feedback loops: a first loop running through stress, insomnia, fatigue, procrastination, and back to stress; a second loop between stress and overthinking; and a third loop between stress and procrastination. Although all adolescents in the study screened positive for depression, symptoms of depression were not particularly central to the network. Instead, the most central symptoms were procrastination and overthinking. The average test-retest reliability for individual networks was low, limiting clinical application. In conclusion, PECAN was found to be reliable and useful when creating a group-level network of adolescent mental health problems. While informative at a group level, the method should be improved before it can be used to inform treatment at the individual level.

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

This study aimed to assess whether adding information on psychological experiences derived from a daily diary to baseline cross-sectional data could improve short- (1-year) and long-term (3-years) prediction of psychopathology and positive psychotic experiences (PEs). We used 90-day daily diary data from 96 individuals in early subclinical risk stages for psychosis. Stepwise linear regression models were built for psychopathology and PEs at 1- and 3-years follow-up, adding: (1) baseline questionnaires, (2) the mean and variance of daily psychological experiences, and (3) individual symptom network density. We assessed whether similar results could be achieved with a subset of the data (7-14- and 30-days). The mean and variance of the diary improved model prediction of short- and long-term psychopathology and PEs, compared to prediction based on baseline questionnaires solely. Similar results were achieved with 7-14- and 30-day subsets. Symptom network density did not improve model prediction except for short-term prediction of PEs. Simple metrics, i.e., the mean and variance from 7 to 14 days of daily psychological experiences assessments, can improve short- and long-term prediction of both psychopathology and PEs in individuals in early subclinical stages for psychosis. Diary data could be a valuable addition to clinical risk prediction models for psychopathology development.

Background: Concurrent use (co-use) of cannabis and tobacco is common and associated with worse clinical outcomes compared with cannabis use only. The mechanisms and interactions of cannabis use disorder (CUD) symptoms underlying co-use remain poorly understood. Methods: We examined differences in the symptom presence and symptom network configurations between weekly cannabis users who use tobacco daily (co-users, n=789) or non- or nondaily (nondaily co-users, n=428). Results: First, we identified a range of symptoms (craving, failed reduce or quit attempts, neglected responsibilities, and negative social effects) that are most central to the highly interconnected CUD symptom network. Risky cannabis use was mostly associated with negative social and health effects, and independent of other CUD symptoms. Craving symptoms act as a bridge between different CUD and withdrawal symptoms. Among co-users, (1) craving is more strongly associated with negative psychosocial effects, (2) feelings of depression and negative health effects are more central to the network, and (3) the negative health effects are more strongly associated with failed attempts to reduce or quit attempts compared with nondaily co-users. Discussion: Our results go beyond existing findings focused on the mere increase in CUD symptom presence, and speak to the potential synergistic effects of co-use on dependence and withdrawal symptoms. We outline clinical implications with respect to targeting specific CUD symptoms in co-users, and point to future research to disentangle tobacco and cannabis craving symptoms.

Recurrent gene dosage disorders impart substantial risk for psychopathology. Yet, understanding that risk is hampered by complex presentations that challenge classical diagnostic systems. Here, we present a suite of generalizable analytic approaches for parsing this clinical complexity, which we illustrate through application to XYY syndrome.
We gathered high-dimensional measures of psychopathology in 64 XYY individuals and 60 XY controls, plus additional interviewer-based diagnostic data in the XYY group. We provide the first comprehensive diagnostic description of psychiatric morbidity in XYY syndrome and show how diagnostic morbidity relates to functioning, subthreshold symptoms, and ascertainment bias. We then map behavioral vulnerabilities and resilience across 67 behavioral dimensions before borrowing techniques from network science to resolve the mesoscale architecture of these dimensions and links to observable functional outcomes.
Carriage of an extra Y-chromosome increases risk for diverse psychiatric diagnoses, with clinically impactful subthreshold symptomatology. Highest rates are seen for neurodevelopmental and affective disorders. A lower bound of < 25% of carriers are free of any diagnosis. Dimensional analysis of 67 scales details the profile of psychopathology in XYY, which survives control for ascertainment bias, specifies attentional and social domains as the most impacted, and refutes stigmatizing historical associations between XYY and violence. Network modeling compresses all measured symptom scales into 8 modules with dissociable links to cognitive ability, adaptive function, and caregiver strain. Hub modules offer efficient proxies for the full symptom network.
This study parses the complex behavioral phenotype of XYY syndrome by applying new and generalizable analytic approaches for analysis of deep-phenotypic psychiatric data in neurogenetic disorders.

OBJECTIVE: The objective of this study is to scrutinize whether psychopathology symptom networks differ between those with and without lifetime: treatment seeking, treatment and treatment of longer duration.
METHODS: We created non-exclusive groups of subjects with versus without lifetime treatment seeking, treatment and treatment of mid-long-term duration. We estimated Ising models and carried out network comparison tests (NCTs) to compare (a) overall connectivity and (b) network structure. Furthermore, we examined node strength. We used propensity score matching (PSM) to minimize potential confounding by indication for service use.
RESULTS: Based on data from 9,172 participants, there were no statistically significant differences in overall connectivity and network structure in those with versus without lifetime: treatment seeking (p = .75 and p = .82, respectively), treatment (p = .63 and p = .49, respectively) and treatment of mid-longterm duration (p = .15 and p = .62, respectively). Notably, comparing networks with versus without service use consistently revealed higher node strength in \'obsessions\' and \'aggression\' and lower node strength in \'elevated mood\' in all networks with service use.
CONCLUSIONS: Findings suggest that after adjusting for potential confounding by indication for service use, there was no indication of an association in overall connectivity or network structure for lifetime treatment seeking, treatment and treatment of longer duration. However, selected structurally important symptoms differed consistently in all three comparisons. Our findings highlight the potential of network analysis methods to examine treatment mechanisms and outcomes. Specifically, more granular network characteristics on the node level may complement and enrich traditional outcomes in clinical research.

BACKGROUND: Prevention in psychiatry provides a promising way to address the burden of mental illness. However, established approaches focus on specific diagnoses and do not address the heterogeneity and manifold potential outcomes of help-seeking populations that present at early recognition services. Conceptualizing the psychopathology manifested in help-seeking populations from a network perspective of interacting symptoms allows transdiagnostic investigations beyond binary disease categories. Furthermore, modern technologies such as smartphones facilitate the application of the Experience Sampling Method (ESM).
OBJECTIVE: This study is a combination of ESM with network analyses to provide valid insights beyond the established assessment instruments in a help-seeking population.
METHODS: We will examine 75 individuals (aged 18-40 years) of the help-seeking population of the Cologne early recognition center. For a maximally naturalistic sample, only minimal exclusion criteria will be applied. We will collect data for 14 days using a mobile app to assess 10 transdiagnostic symptoms (ie, depressive, anxious, and psychotic symptoms) as well as distress level 5 times a day. With these data, we will generate average group-level symptom networks and personalized symptom networks using a 2-step multilevel vector autoregressive model. Additionally, we will explore associations between symptom networks and sociodemographic, risk, and resilience factors, as well as psychosocial functioning.
RESULTS: The protocol was designed in February 2020 and approved by the Ethics Committee of the University Hospital Cologne in October 2020. The protocol was reviewed and funded by the Köln Fortune program in September 2020. Data collection began in November 2020 and was completed in November 2021. Of the 258 participants who were screened, 93 (36%) fulfilled the inclusion criteria and were willing to participate in the study. Of these 93 participants, 86 (92%) completed the study. The first results are expected to be published in 2022.
CONCLUSIONS: This study will provide insights about the feasibility and utility of the ESM in a help-seeking population of an early recognition center. Providing the first explorative phenotyping of transdiagnostic psychopathology in this population, our study will contribute to the innovation of early recognition in psychiatry. The results will help pave the way for prevention and targeted early intervention in a broader patient group, and thus, enable greater intended effects in alleviating the burden of psychiatric disorders.
UNASSIGNED: DERR1-10.2196/35206.

Modern theoretical models of Alcohol Use Disorder (AUD) highlight the different functional roles played by various mechanisms associated with different symptoms. Symptom network models (SNMs) offer one approach to modeling AUD symptomatology in a way that could reflect these processes and provide important information on the progression and persistence of disorder. However, much of the research conducted using SNMs relies on cross-sectional data, which has raised questions regarding the extent they reflect dynamic processes. The current study aimed to (a) examine symptom networks of AUD and (b) compare the extent to which cross-sectional network models had similar structures and interpretations as longitudinal network models. 17,360 participants from Wave 1 (2001-2002) and Wave 2 (2003-2004) of the National Epidemiological Survey on Alcohol and Related Conditions (NESARC) were used to model cross-sectional and longitudinal AUD symptom networks. The cross-sectional analyses demonstrate high replicability across waves and central symptoms consistent with other cross-sectional studies on addiction networks. The longitudinal network shared much less similarity than the cross-sectional networks and had a substantially different structure. Given the increasing attention given to the network perspective in psychopathology research, the results of this study raise concerns about interpreting cross-sectional symptom networks as representative of temporal changes occurring within a psychological disorder. We conclude that the psychological symptom network literature should be bolstered with additional research on longitudinal network models.