Individual variation is increasingly recognized as important to psychopathology research. Concurrently, new methods of analysis based on network models are bringing new perspectives on mental (dys)function. This current work analyzed idiographic multivariate time series data using a novel network methodology that incorporates contemporaneous and lagged associations in mood and anxiety symptomatology. Data were taken from 40 individuals with generalized anxiety disorder (GAD), major depressive disorder (MDD), or comorbid GAD and MDD, who answered questions about 21 descriptors of mood and anxiety symptomatology 4 times a day over a period of approximately 30 days. The model provided an excellent fit to the intraindividual symptom dynamics of all 40 individuals. The most central symptoms in contemporaneous systems were those related to positive and negative mood. The temporal networks highlighted the importance of anger to symptomatology, while also finding that depressed mood and worry-the principal diagnostic criteria for GAD and MDD-were the least influential nodes across the sample. The method's potential for analysis of individual symptom patterns is demonstrated by 3 exemplar participants. Idiographic network-based analysis may fundamentally alter the way psychopathology is assessed, classified, and treated, allowing researchers and clinicians to better understand individual symptom dynamics. (PsycINFO Database Record

Psychosocial treatments for mood and anxiety disorders are generally effective, however, a number of treated individuals fail to demonstrate clinically-significant change. Consistent the decades-old aim to identify 'what works for whom,' personalized and precision treatments have become a recent area of interest in medicine and psychology. The present study followed the recommendations of Fisher (2015) to employ a personalized modular model of cognitivebehavioral therapy. Employing the algorithms provided by Fernandez, Fisher, and Chi (2017), the present study collected intensive repeated measures data prior to therapy in order to perform person-specific factor analyses and dynamic factor models. The results of these analyses were then used to generated personalized modular treatment plans on a person-by-person basis.Thirty-two participants completed therapy. The average number of sessions was 10.38. Hedges g's for the Hamilton Rating Scale for Depression (HRSD) and Hamilton Anxiety Rating Scale (HARS) were 2.33 and 1.62, respectively. The change per unit time was g=.24/session for the HRSD and g=.17/session for the HARS. The current open trial provides promising data in support of personalization, modularization, and idiographic research paradigms. Personalized Modular CBT 1

Clinical psychological science has seen an exciting shift toward the use of person-specific (idiographic) approaches to studying psychopathology and change in treatment at the level of the individual. One commonly used method in idiographic research is ecological momentary assessment (EMA). EMA offers a way to sample individuals intensivelyoften multiple times per dayas they go about their lives. While these methods offer benefits such as greater ecological validity and streamlined data collection, many share concerns about their feasibility across diverse clinical populations. To investigate the feasibility of using EMA to study psychological processes idiographically both in-and out of the context of therapy, the present study aggregated participants across seven studies spanning diverse clinical and community populations (N = 496), all of which utilized an idiographic EMA approach to study symptoms of psychopathology (e.g., PTSD, mood and anxiety, substance abuse). In a series of linear regression models, participant and study design characteristics were used to predict compliance with EMA surveys. Across study designs, we found that (1) participants were willing to report on symptoms and mechanisms relating to a wide range of psychopathological domains; (2) on average, participants completed 82.21% (SD = 16.34%) of all EMA surveys; and (3) compliance with EMA surveys was not significantly related to participant demographics, psychological diagnosis, personality characteristics, or most study characteristics (e.g., number of surveys per day). These findings suggest feasibility of idiographic EMA for collecting the data needed to understand psychopathology and change in treatment at the level of the individual.

Physiologic investigations of generalized anxiety disorder (GAD) have skewed toward assessment of the autonomic nervous system, largely neglecting hypothalamic-pituitary-adrenal (HPA) axis variables. Although these systems coordinate-suggesting a degree of symmetry-to promote adaptive functioning, most studies opt to monitor either one system or the other. Using a ratio of salivary alpha-amylase (sAA) over salivary cortisol, the present study examined symmetry between the sympathetic nervous system (SNS) and HPA axis in individuals with GAD (n = 71) and healthy controls (n = 37). Compared to healthy controls, individuals with GAD exhibited greater baseline ratios of sAA/cortisol and smaller ratios of sAA/cortisol following a mental arithmetic challenge. We propose that the present study provides evidence for SNS-HPA asymmetry in GAD. Further, these results suggest that increased SNS suppression in GAD may be partially mediated by cortisol activity.

Although the application of network theory to posttraumatic stress disorder (PTSD) has yielded promising insights, the lack of equivalence between inter‐ and intraindividual variation limits the generalizability of these findings to any one individual with PTSD. Instead, a better understanding of how PTSD symptoms occur and vary over time within an individual requires exploring the idiographic network structure of PTSD. To do so, the present study used an intensive repeated‐measures design to estimate intraindividual networks of PTSD symptoms on a person‐by‐person basis. Participants were 20 individuals who met criteria for PTSD and completed daily surveys assessing PTSD symptoms; surveys were completed four times per day for approximately 30 days. Employing a recently validated method provided by Fisher, Reeves, Lawyer, Medaglia, and Rubel (2017), we used these data to estimate a contemporaneous and temporal network of PTSD symptoms for individuals on a person‐by‐person basis. We then calculated centrality metrics to determine the relative importance of each symptom in each idiographic network. Across all contemporaneous networks, negative trauma‐related cognitions and emotions were most commonly the most central symptoms. Further, across all temporal networks, (a) negative trauma‐related emotions were the most common driver of variation in other symptoms over time and (b) distressing trauma‐related dreams and sleep disturbance were the most common downstream consequences of variation in other PTSD symptoms over time. We also reviewed data from two randomly selected participants to illustrate how this approach could be used to identify maintenance factors of PTSD for each individual and guide individual treatment planning.