Psychopathological networks:Theory, methods and practice

In recent years, network approaches to psychopathology have sparked much debate and have had a significant impact on how mental disorders are perceived in the field of clinical psychology. However, there are many important challenges in moving from theory to empirical research and clinical practice and vice versa. Therefore, in this article, we bring together different points of view on psychological networks by methodologists and clinicians to give a critical overview on these challenges, and to present an agenda for addressing these challenges. In contrast to previous reviews, we especially focus on methodological issues related to temporal networks. This includes topics such as selecting and assessing the quality of the nodes in the network, distinguishing between- and within-person effects in networks, relating items that are measured at different time scales, and dealing with changes in network structures. These issues are not only important for researchers using network models on empirical data, but also for clinicians, who are increasingly likely to encounter (person-specific) networks in the consulting room

App-based support for breast cancer patients to reduce psychological distress during therapy and survivorship – a multicentric randomized controlled trial

IntroductionThe negative impact of unmanaged psychological distress on quality of life and outcome in breast cancer survivors has been demonstrated. Fortunately, studies indicate that distress can effectively be addressed and even prevented using evidence-based interventions. In Germany prescription-based mobile health apps, known as DiGAs (digital health applications), that are fully reimbursed by health insurances, were introduced in 2020. In this study, the effectiveness of an approved breast cancer DiGA was investigated: The personalized coaching app PINK! Coach supports and accompanies breast cancer patients during therapy and follow-up.MethodsPINK! Coach was specifically designed for breast cancer (BC) patients from the day of diagnosis to the time of Follow-up (aftercare). The app offers individualized, evidence-based therapy and side-effect management, mindfulness-based stress reduction, nutritional and psychological education, physical activity tracking, and motivational exercises to implement lifestyle changes sustainably in daily routine. A prospective, intraindividual RCT (DRKS00028699) was performed with n = 434 patients recruited in 7 German breast cancer centers from September 2022 until January 2023. Patients with BC were included independent of their stage of diseases, type of therapy and molecular characteristics of the tumor. Patients were randomized into one of two groups: The intervention group got access to PINK! over 12 weeks; the control group served as a waiting-list comparison to “standard of care.” The primary endpoint was psychological distress objectified by means of Patient Health Questionnaire-9 (PHQ-9). Subgroups were defined to investigate the app’s effect on several patient groups such as MBC vs. EBC patients, patients on therapy vs. in aftercare, patients who received a chemotherapy vs. patients who did not.ResultsEfficacy analysis of the primary endpoint revealed a significant reduction in psychological distress (least squares estimate -1.62, 95% confidence interval [1.03; 2.21]; p<0.001) among intervention group patients from baseline to T3 vs, control group. Subgroup analysis also suggested improvements across all clinical situations.ConclusionPatients with breast cancer suffer from psychological problems including anxiety and depression during and after therapy. Personalized, supportive care with the app PINK! Coach turned out as a promising opportunity to significantly improve psychological distress in a convenient, accessible, and low-threshold manner for breast cancer patients independent of their stage of disease (EBC/MBC), therapy phase (aftercare or therapy) or therapy itself (chemotherapy/other therapy options). The app is routinely available in Germany as a DiGA. Clinical Trial Registration: DRKS Trial Registry (DRKS00028699)

Introducing SNAC: Sparse Network and Component model for integration of multi-source data

Gaussian graphical models (GGMs) are a popular method for analysing complex data by modelling the unique relationships between variables. Recently, a shift in interest has taken place from investigating relationships within a discipline (e.g. genetics) to estimating relationships between variables from various disciplines (e.g. how gene expression relates to cognitive performance). It is thus not surprising that there is an increasing need for analysing large, so-called \textit{multi-source} datasets, each containing detailed information from many data sources on the same individuals. GGMs are a straightforward statistical candidate for estimating \textit{unique cross-source relationships} from such network-oriented data. However, the multi-source nature of the data poses two challenges: First, different sources may inherently differ from one another, biasing the estimated relations. Second, GGMs are not cut out for separating cross-source relationships from all other, source-specific relationships. In this paper we propose adding a simultaneous-component-model as a pre-pocessing step to the GGM, the combination of which is suitable for estimating cross-source relationships from multi-source data. Compared to the graphical lasso (a commonly used GGM technique), this Sparse Network And Component (SNAC) model more accurately estimates the unique cross-source relationships from multi-source data. This holds in particular when the data contains more variables than observations (p>n). Neither differences in sparseness of the underlying component structure of the data nor in the relative dominance of the cross-source compared to source-specific relationships strongly affect the relationship estimates. Sparse Network And Component analysis, a hybrid component-graphical model, is a promising tool for modelling unique relationships between different data sources, thus providing insight in how various disciplines are connected to one another

Introducing SNAC: Sparse Network and Component model for integration of multi-source data

Gaussian graphical models (GGMs) are a popular method for analysing complex data by modelling the unique relationships between variables. Recently, a shift in interest has taken place from investigating relationships within a discipline (e.g. genetics) to estimating relationships between variables from various disciplines (e.g. how gene expression relates to cognitive performance). It is thus not surprising that there is an increasing need for analysing large, so-called \textit{multi-source} datasets, each containing detailed information from many data sources on the same individuals. GGMs are a straightforward statistical candidate for estimating \textit{unique cross-source relationships} from such network-oriented data. However, the multi-source nature of the data poses two challenges: First, different sources may inherently differ from one another, biasing the estimated relations. Second, GGMs are not cut out for separating cross-source relationships from all other, source-specific relationships. In this paper we propose adding a simultaneous-component-model as a pre-pocessing step to the GGM, the combination of which is suitable for estimating cross-source relationships from multi-source data. Compared to the graphical lasso (a commonly used GGM technique), this Sparse Network And Component (SNAC) model more accurately estimates the unique cross-source relationships from multi-source data. This holds in particular when the data contains more variables than observations (p>n). Neither differences in sparseness of the underlying component structure of the data nor in the relative dominance of the cross-source compared to source-specific relationships strongly affect the relationship estimates. Sparse Network And Component analysis, a hybrid component-graphical model, is a promising tool for modelling unique relationships between different data sources, thus providing insight in how various disciplines are connected to one another

Introducing SNAC:Sparse Network and Component model for integration of multi-source data

Gaussian graphical models (GGMs) are a popular method for analysing complex data by modelling the unique relationships between variables. Recently, a shift in interest has taken place from investigating relationships within a discipline (e.g. genetics) to estimating relationships between variables from various disciplines (e.g. how gene expression relates to cognitive performance). It is thus not surprising that there is an increasing need for analysing large, so-called \textit{multi-source} datasets, each containing detailed information from many data sources on the same individuals. GGMs are a straightforward statistical candidate for estimating \textit{unique cross-source relationships} from such network-oriented data. However, the multi-source nature of the data poses two challenges: First, different sources may inherently differ from one another, biasing the estimated relations. Second, GGMs are not cut out for separating cross-source relationships from all other, source-specific relationships. In this paper we propose adding a simultaneous-component-model as a pre-pocessing step to the GGM, the combination of which is suitable for estimating cross-source relationships from multi-source data. Compared to the graphical lasso (a commonly used GGM technique), this Sparse Network And Component (SNAC) model more accurately estimates the unique cross-source relationships from multi-source data. This holds in particular when the data contains more variables than observations ($p>n$). Neither differences in sparseness of the underlying component structure of the data nor in the relative dominance of the cross-source compared to source-specific relationships strongly affect the relationship estimates. Sparse Network And Component analysis, a hybrid component-graphical model, is a promising tool for modelling unique relationships between different data sources, thus providing insight in how various disciplines are connected to one another

Comparing network structures on three aspects: A permutation test

The network approach, in which psychological constructs are modeled in terms of interactions between their constituent factors, have rapidly gained popularity in psychology. Applications of such network approaches to various psychological constructs have recently moved from a descriptive stance, in which the goal is to estimate the network structure, to a more comparative stance, in which the goal is to compare network structures across groups. However, the statistical tools to do so are lacking. In this article, we present the network comparison test (NCT). NCT is a statistical test that compares two network structures on three types of characteristics. Performance of NCT is evaluated by means of a simulation study. Simulated data shows that NCT performs well in various circumstances for all three tests: when the groups are simulated to be similar, the error rate (i.e., NCT indicating that they are different, while the simulated networks are similar) is adequately low, and when the groups are simulated to be different, the ability to detect a difference is sufficiently high when the difference between simulated networks and the sample size are substantial. We illustrate NCT by comparing depression symptom networks of males and females. Possible extensions of NCT are discussed.Network approaches to psychometric constructs, in which constructs are modeled in terms of interactions between their constituent factors, have rapidly gained popularity in psychology. Applications of such network approaches to various psychological constructs have recently moved from a descriptive stance, in which the goal is to estimate the network structure that pertains to a construct, to a more comparative stance, in which the goal is to compare network structures across populations. However, the statistical tools to do so are lacking. In this article, we present the network comparison test (NCT), which uses resampling-based permutation testing to compare network structures from two independent, cross-sectional data sets on invariance of (a) network structure, (b) edge (connection) strength, and (c) global strength. Performance of NCT is evaluated in simulations that show NCT to perform well in various circumstances for all three tests: The Type I error rate is close to the nominal significance level, and power proves sufficiently high if sample size and difference between networks are substantial. We illustrate NCT by comparing depression symptom networks of males and females. Possible extensions of NCT are discussed