Personalized models of psychopathology as contextualized dynamic processes: An example from individuals with borderline personality disorder

Novel Approaches Toward Studying Change: Implications for Understanding and Treating Psychopathology

In this article, the authors critically evaluate contemporary models of psychopathology and therapies, underscoring the limitations of traditional symptom-based classification approaches in mental health. The authors introduce a paradigm shift in the field, toward a process-oriented and dynamic systems approach to psychotherapy that offers deeper insights into the complex interplay of symptoms and individual experiences in psychopathology. These approaches offer a more personalized and effective understanding and treatment of mental health issues, moving beyond static and 1-dimensional views. The authors discuss the implications for clinical practice, emphasizing improved assessment, diagnosis, and tailored treatment strategies.

Heterogeneity in suicide risk: Evidence from personalized dynamic models

Most theories of suicide propose within-person changes in psychological states cause suicidal thoughts/behaviors; however, most studies use between-person analyses. Thus, there are little empirical data exploring current theories in the way they are hypothesized to occur. We used a form of statistical modeling called group iterative multiple model estimation (GIMME) to explore one theory of suicide: The Interpersonal Theory of Suicide (IPTS). GIMME estimates personalized statistical models for each individual and associations shared across individuals. Data were from a real-time monitoring study of individuals with a history of suicidal thoughts/behavior (adult sample: participants = 111, observations = 25,242; adolescent sample: participants = 145, observations = 26,182). Across both samples, none of theorized IPTS effects (i.e., contemporaneous effect from hopeless to suicidal thinking) were shared at the group level. There was significant heterogeneity in the personalized models, suggesting there are different pathways through which different people come to experience suicidal thoughts/behaviors. These findings highlight the complexity of suicide risk and the need for more personalized approaches to assessment and prediction.

A complex systems perspective on chronic aggression and self-injury: case study of a woman with mild intellectual disability and borderline personality disorder

BACKGROUND

Challenging behaviors like aggression and self-injury are dangerous for clients and staff in residential care. These behaviors are not well understood and therefore often labeled as "complex". Yet it remains vague what this supposed complexity entails at the individual level. This case-study used a three-step mixed-methods analytical strategy, inspired by complex systems theory. First, we construed a holistic summary of relevant factors in her daily life. Second, we described her challenging behavioral trajectory by identifying stable phases. Third, instability and extraordinary events in her environment were evaluated as potential change-inducing mechanisms between different phases.

CASE PRESENTATION

A woman, living at a residential facility, diagnosed with mild intellectual disability and borderline personality disorder, who shows a chronic pattern of aggressive and self-injurious incidents. She used ecological momentary assessments to self-rate challenging behaviors daily for 560 days.

CONCLUSIONS

A qualitative summary of caretaker records revealed many internal and environmental factors relevant to her daily life. Her clinician narrowed these down to 11 staff hypothesized risk- and protective factors, such as reliving trauma, experiencing pain, receiving medical care or compliments. Coercive measures increased the chance of challenging behavior the day after and psychological therapy sessions decreased the chance of self-injury the day after. The majority of contemporaneous and lagged associations between these 11 factors and self-reported challenging behaviors were non-significant, indicating that challenging behaviors are not governed by mono-causal if-then relations, speaking to its complex nature. Despite this complexity there were patterns in the temporal ordering of incidents. Aggression and self-injury occurred on respectively 13% and 50% of the 560 days. On this timeline 11 distinct stable phases were identified that alternated between four unique states: high levels of aggression and self-injury, average aggression and self-injury, low aggression and self-injury, and low aggression with high self-injury. Eight out of ten transitions between phases were triggered by extraordinary events in her environment, or preceded by increased fluctuations in her self-ratings, or a combination of these two. Desirable patterns emerged more often and were less easily malleable, indicating that when she experiences bad times, keeping in mind that better times lie ahead is hopeful and realistic.

A Longitudinal Network of Borderline-Related Trait Vulnerabilities from Childhood to Adolescence

Early maladaptive traits are predictive of later borderline personality pathology (BPP), but little is known about their dynamic interplay over time. This is an important issue to address, however, as significant differences in the 'clinical weight' of various traits constituting the early BPP trait phenotype may inform the field on important target constructs from an early intervention perspective. Therefore, the current study aims to uncover the complex dependencies between BPP traits across the crucial developmental period of childhood and adolescence, by using longitudinal network analysis. Both between- and within-person networks were constructed to identify how early mother-reported borderline-related traits are connected across a timespan of six years (ntime 1 = 718, Mtime1 = 10.73 years, SDtime1 = 1.39, 55.1% girls). Overall, the temporal network suggested various trait interdependencies, with internalizing traits being particularly influential in the development of the BPP trait network structure. At the same time, externalizing traits likely inhibit the negative effects of these core traits. In addition, results also revealed that internalizing and externalizing clusters of early borderline-related traits are linked through emotional lability. Implications of these findings are discussed in view of the change mechanisms at play and potential targets for early intervention.

From the Individual to the Group: Using Idiographic Analyses and Two-Stage Random Effects Meta-Analysis to Obtain Population Level Inferences for within-Person Processes

In psychology, the use of portable technology and wearable devices to ease participant burden in data collection is on the rise. This creates increased interest in collecting real-time or near real-time data from individuals within their natural environments. As a result, vast amounts of observational time series data are generated. Often, motivation for collecting this data hinges on understanding within-person processes that underlie psychological phenomena. Motivated by the body of Dr. Peter Molenaar's life work calling for analytical approaches that consider potential heterogeneity and non-ergodicity, the focus of this paper is on using idiographic analyses to generate population inferences for within-person processes. Meta-analysis techniques using one-stage and two-stage random effects meta-analysis as implemented in single-case experimental designs are presented. The case for preferring a two-stage approach for meta-analysis of single-subject observational time series data is made and demonstrated using an empirical example. This provides a novel implementation of the methodology as prior implementations focus on applications to short time series with experimental designs. Inspired by Dr. Molenaar's work, we describe how an approach, two-stage random effects meta-analysis (2SRE-MA), aligns with recent calls to consider idiographic approaches when making population-level inferences regarding within-person processes.

Dynamic processes in behavioral activation therapy for anhedonic adolescents: Modeling common and patient-specific relations

OBJECTIVE

Behavioral activation (BA) is a brief intervention for depression encouraging gradual and systematic re-engagement with rewarding activities and behaviors. Given this treatment focus, BA may be particularly beneficial for adolescents with prominent anhedonia, a predictor of poor treatment response and common residual symptom. We applied group iterative multiple model estimation (GIMME) to ecological momentary assessment (EMA) treatment data to investigate common and person-specific processes during BA for anhedonic adolescents.

METHOD

Thirty-nine adolescents (Mage = 15.7 years old, 67% female, 81% White) with elevated anhedonia (Snaith-Hamilton Pleasure Scale) were enrolled in a 12-week BA trial, with weekly anhedonia assessments. EMA surveys were triggered every other week (2-3 surveys per day) throughout treatment assessing current positive affect (PA) and negative affect (NA), engagement in pleasurable activities and social interactions, anticipatory pleasure, rumination, and recent pleasurable and stressful experiences.

RESULTS

A multilevel model revealed significant decreases in anhedonia, t(25.5) = -4.76, p < .001, over the 12-week trial. GIMME results indicated substantial heterogeneity in variable networks across patients. PA was the variable with the greatest number (22% of all paths vs. 11% for NA) of predictive paths to other symptoms (i.e., highest out-degree). Higher PA (but not NA) out-degree was associated with greater anhedonia improvement, t(25.8) = -2.22, p = .035.

CONCLUSIONS

Results revealed substantial heterogeneity in variable relations across patients, which may obscure the search for common processes of change in BA. PA may be a particularly important treatment target for anhedonic adolescents in BA. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Developmental and Momentary Dynamics in the Onset and Maintenance of Nonsuicidal Self-Injurious Behavior and Borderline Personality Disorder

PURPOSE OF REVIEW

Traditional conceptualizations of both nonsuicidal self-injury (NSSI) and borderline personality disorder (BPD) typically rely on static and unidirectional, linear associations between key biopsychosocial vulnerabilities. Instead, we argue that utilizing a complex dynamic systems view of NSSI and BPD will advance the field, as such conceptual models allow for analysis of bottom-up effects for key vulnerabilities on disorder and behavior emergence, as well as top-down effects of the emergent disorder on underlying vulnerabilities.

RECENT FINDINGS

Following the presentation of a novel framework highlighting momentary and developmental dynamics, we explore several advances in the field that exhibit key dynamic qualities or inform dynamic conceptualizations of NSSI and BPD. At the momentary dynamic level, several advances are being made with multimethod and repeated assessment approaches, as well as advanced bidirectional and complex modeling procedures. Additional progress is being made at the developmental dynamic level, although several questions have arisen regarding the problem of onset and subsequent trajectory, particularly with issues such as pain perception and the interplay between interpersonal, emotional, and behavioral symptoms before and after treatment. Self-injury and BPD both exhibit substantial momentary and developmental dynamics in underlying vulnerabilities, including potential variance in momentary dynamics as a function of psychopathological developmental stage (e.g., onset versus maintenance versus recovery). Recent work has highlighted the necessity of utilizing multimodal research to encapsulate a holistic view of the interplay of several vulnerability factors, the developmental importance of assessment timing, and the need to examine the dynamic interplay between affect, behavior, and interpersonal experiences in BPD and/or NSSI. Research also indicated substantial variation in key vulnerability factors at both between- and within-person levels, highlighting the utility of harnessing statistical models that allow for the simultaneous incorporation of numerous variables at both levels and across several time points. As such, by using a complex dynamic systems conceptualization, we can begin to better understand integrated connections between key vulnerabilities, how they collectively interact in the short term, and how changes in the dynamic interplay between vulnerabilities may arise over the long term and with successful treatment.

Attachment Manifestations in Daily Interpersonal Interactions

The predominant focus in attachment research on trait-like individual differences has overshadowed investigation of the ways in which working models of attachment represent dynamic, interpersonally responsive socio-affective systems. Intensive longitudinal designs extend previous work by evaluating to what extent attachment varies over social interactions and the functional processes that underlie its fluctuation. We examined momentary activation of attachment orientations in the stream of peoples' daily lives and how those patterns were linked to interpersonal behavior. Based on an event-contingent, ambulatory 7-day assessment protocol (N=263; 3,971 interactions) operationalized using Contemporary Integrative Interpersonal Theory, we examined whether contextually activated working models accounted for patterns of interpersonal (anti-)complementarity. Our analyses revealed that the situational activation of working models varied as a function of interpersonal perceptions of warmth, which were linked to greater state security and lower levels of anxious or avoidant expectations. These reactivity patterns, in turn, accounted for interpersonal complementarity. Avoidant attachment was linked to diminished and secure attachment to enhanced expressions of warmth. The analyses were robust even when controlling for momentary positive and negative affect and closeness of the relationship. Attachment expectations wax and wane across daily social interactions, and such fluctuations are reflective primarily of a process in which perceptions of others' warmth activate secure attachment expectations and lower insecure ones.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1007/s42761-022-00117-6.

Integrating a functional view on suicide risk into idiographic statistical models

Acute risk of death by suicide manifests in heightened suicidal ideation in certain contexts and time periods. These increases are thought to emerge from complex and mutually reinforcing relationships between dispositional vulnerability factors and individually suicidogenic short-term stressors. Together, these processes inform clinical safety planning and our therapeutic tools accommodate a reasonable degree of idiosyncrasy when we individualize interventions. Unraveling these multifaceted factors and processes on a quantitative level, however, requires estimation frameworks capable of representing idiosyncrasies relevant to intervention and psychotherapy. Using, data from a 21-day ambulatory assessment protocol that included six random prompts per day, we developed personalized (i.e., idiographic) models of interacting risk factors and suicidal ideation via Group Iterative Multiple Model Estimation (GIMME) in a sample of people diagnosed with borderline personality disorder (N = 95) stratified for a history of high lethality suicide attempts. Our models revealed high levels of heterogeneity in state risk factors related to suicidal ideation, with no features shared among the majority of participants or even among relatively homogenous clusters of participants (i.e., empirically derived subgroups). We discuss steps toward clinical implementation of personalized models, which can eventually capture suicidogenic changes in proximal risk factors and inform safety planning and interventions.

Integrating and distinguishing personality and psychopathology

OBJECTIVE

We asked authors of this Special Issue to answer the following four questions: (1) Is there evidence that personality and psychopathology can be integrated? (2) Is integration important? (3) Can they be distinguished? and (4) How can the difference be measured?

METHOD

We review each of the papers and place the special issue in a historical context.

RESULTS

Authors uniformly agreed that personality and psychopathology can be integrated within a common structure and that this is important. The third and fourth questions were more challenging. Though authors generally agreed that there is a distinction between the person and their mental health problems, articulations of that distinction were fuzzy and it is clear that current methods cannot adequately delineate these domains.

CONCLUSIONS

We summarize the issue by offering five directions for future research: (1) develop measurement tools that distinguish between the person, the context, and their transaction, (2) measure behavior and context at multiple timescales, (3) distinguish behavior and dysfunction in measurement, (4) use multimethod data to tap different levels of behavior, and (5) examine person-specific processes. Each of these directions comes with challenges, but the payoff of resolving them will be a more principled, evidence-based, and clinically useful model for the distinction between personality and psychopathology.

Individualized learning potential in stressful times: How to leverage intensive longitudinal data to inform online learning

Societal events - such as natural disasters, political shifts, or economic downturns - are time-varying and impact the learning potential of students in unique ways. These impacts are likely accentuated during the COVID-19 pandemic, which precipitated an abrupt and wholesale transition to online education. Unfortunately, the individual-level consequences of these events are difficult to determine because the extant literature focuses on single-occasion surveys that produce only group-level inferences. To better understand individual-level variability in stress and learning, intensive longitudinal data can be leveraged. The goal of this paper is to illustrate this by discussing three different techniques for the analysis of intensive longitudinal data: (1) regression analyses; (2) multilevel models; and (3) person-specific network models, (e.g., group iterative multiple model estimation; GIMME). For each technique, a brief background in the context of education research is provided, an illustrative analysis is presented using data from college students who completed a 75-day intensive longitudinal study of cognition, somatic symptoms, anxiety, and intellectual interests during the 2016 U.S. Presidential election - a period of heightened sociopolitical stress - and strengths and limitations are considered. The paper ends with recommendations for future research, especially for intensive longitudinal studies of online education during COVID-19.

Specifying exogeneity and bilinear effects in data-driven model searches

Data-driven model searches provide the opportunity to quantify person-specific processes using ambulatory assessment data. Here, the search space typically includes all potential relations among variables, meaning that all variables can potentially explain variability in all other variables. Oftentimes, this is unrealistic. For example, weather is unlikely to be predicted by someone's emotional state, whereas the reverse might be true. Allowing for specification of exogenous variables, or variables that are not predicted within the system, permits more realistic models and allows the researcher to model contextual change processes via the use of moderation variables. We use two sets of daily diary data to demonstrate the capabilities of allowing for the specification of exogenous variables in GIMME (Group Iterative Multiple Model Estimation), a model search algorithm that allows for models with idiographic, individual-level as well as subgroup- and group-level processes with intensive longitudinal data. First, using data collected from individuals diagnosed with personality disorders, we show results where weather-related and temporal basis variables are specified as exogenous, and reports on affect and behavior are endogenous. Next, we demonstrate the modeling of treatment effects in an intervention study, looking at data from a 6-week meditation workshop in midlife adults. Finally, we use the meditation intervention data to demonstrate modeling moderation effects, where relationships between two endogenous variables are dependent on the current stage of the study for a given participant (i.e., currently attending meditation classes or not). We end by presenting adaptive LASSO as a method for probing results obtained from GIMME.

Symptoms as rapidly fluctuating over time: Revealing the close psychological interconnections among borderline personality disorder symptoms via within-person structures

Despite the clinical emphasis on processes happening within individuals, investigations into the psychological, structural connections between mental health symptoms have almost exclusively analyzed differences between people. These investigations have revealed important findings; however, they do not reveal the close connections among symptoms in an individuals' psychology. This study thus examined the psychological connections between symptoms directly, using borderline personality disorder (BPD) symptoms as an example. Participants (252; 74 with BPD) reported their momentary BPD symptoms five times daily, and 165 did so again 18 months later. In support of personalized medicine (Wright & Woods, 2020), individuals' BPD symptom structures differed considerably from each other and from the between-person structure. A novel technique revealed that differences were greater than expected by chance. Within-person structures tended to exhibit more symptom granularity (more factors and lower variance explained) and differing symptom meanings (patterns of loadings). For example, some individuals exhibited close connections between relationship turmoil and identity uncertainty, whereas other individuals exhibited close connections between relationship turmoil and impulsivity. Thus, conceptions of any given person's psychopathological processes using between-person structural findings will most likely be inaccurate. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

Six assumptions of contemporary integrative interpersonal theory of personality and psychopathology

Contemporary integrative interpersonal theory is an evidence-based model of personality, psychopathology, and intervention. In this article, we review six assumptions of contemporary integrative interpersonal theory that distinguish it from other frameworks and suggest five particularly promising and important areas for future research.

Assessing the Mental Health of Emerging Adults Through a Mental Health App: Protocol for a Prospective Pilot Study

BACKGROUND

Individuals can experience different manifestations of the same psychological disorder. This underscores the need for a personalized model approach in the study of psychopathology. Emerging adulthood is a developmental phase wherein individuals are especially vulnerable to psychopathology. Given their exposure to repeated stressors and disruptions in routine, the emerging adult population is worthy of investigation.

OBJECTIVE

In our prospective study, we aim to conduct multimodal assessments to determine the feasibility of an individualized approach for understanding the contextual factors of changes in daily affect, sleep, physiology, and activity. In other words, we aim to use event mining to predict changes in mental health.

METHODS

We expect to have a final sample size of 20 participants. Recruited participants will be monitored for a period of time (ie, between 3 and 12 months). Participants will download the Personicle app on their smartphone to track their activities (eg, home events and cycling). They will also be given wearable sensor devices (ie, devices that monitor sleep, physiology, and physical activity), which are to be worn continuously. Participants will be asked to report on their daily moods and provide open-ended text responses on a weekly basis. Participants will be given a battery of questionnaires every 3 months.

RESULTS

Our study has been approved by an institutional review board. The study is currently in the data collection phase. Due to the COVID-19 pandemic, the study was adjusted to allow for remote data collection and COVID-19-related stress assessments.

CONCLUSIONS

Our study will help advance research on individualized approaches to understanding health and well-being through multimodal systems. Our study will also demonstrate the benefit of using individualized approaches to study interrelations among stress, social relationships, technology, and mental health.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

DERR1-10.2196/25775.

On the validity of the centrality hypothesis in cross-sectional between-subject networks of psychopathology

BACKGROUND

In the network approach to psychopathology, psychiatric disorders are considered networks of causally active symptoms (nodes), with node centrality hypothesized to reflect symptoms' causal influence within a network. Accordingly, centrality measures have been used in numerous network-based cross-sectional studies to identify specific treatment targets, based on the assumption that deactivating highly central nodes would proliferate to other nodes in the network, thereby collapsing the network structure and alleviating the overall psychopathology (i.e., the centrality hypothesis).

METHODS

Here, we summarize three types of evidence pertaining to the centrality hypothesis in psychopathology. First, we discuss the validity of the theoretical assumptions underlying the centrality hypothesis in psychopathology. We then summarize the methodological aspects of extant studies using centrality measures as predictors of symptom change following treatment, while delineating their main findings and several of their limitations. Finally, using a specific dataset of 710 treatment-seeking patients with posttraumatic stress disorder (PTSD) as an example, we empirically examine node centrality as a predictor of therapeutic change, replicating the approach taken by previous studies, while addressing some of their limitations. Specifically, we investigated whether three pre-treatment centrality indices (strength, predictability, and expected influence) were significantly correlated with the strength of the association between a symptom's change and the change in the severity of all other symptoms in the network from pre- to post-treatment (Δnode-Δnetwork association). Using similar analyses, we also examine the predictive validity of two simple non-causal node properties (mean symptom severity and infrequency of symptom endorsement).

RESULTS

Of the three centrality measures, only expected influence successfully predicted how strongly changes in nodes/symptoms were associated with change in the remainder of the nodes/symptoms. Importantly, when excluding the amnesia node, a well-documented outlier in the phenomenology of PTSD, none of the tested centrality measures predicted symptom change. Conversely, both mean symptom severity and infrequency of symptom endorsement, two standard non-network-derived indices, were found to be more predictive than expected influence and remained significantly predictive also after excluding amnesia from the network analyses.

CONCLUSIONS

The centrality hypothesis in its current form is ill-defined, showing no consistent supporting evidence in the context of cross-sectional, between-subject networks.

Concluding commentary: Treating perfectionism transdiagnostically with an eye on the future

The current piece of work is a commentary section for the special issue regarding the treatment of perfectionism. It discusses analytically the similarities and the differences between the six clinical case formulations included in the special issue to emphasize that there are different routes to beneficial outcomes. All the psychotherapeutic approaches included here are either transdiagnostic or have transdiagnostic applications. As such, the second part of the commentary focuses on the transdiagnostic future of the treatment of perfectionism. Concluding remarks evidence a collection of clinical case studies covering multiple forms of psychotherapy and an integrating overview of the literature from which practitioners can extract best practices examples. Though there is still a lot to be done with and for interventions on perfectionism, the current collection of case studies is a good starting point.

Personalized Models of Psychopathology

The personalized approach to psychopathology conceptualizes mental disorder as a complex system of contextualized dynamic processes that is nontrivially specific to each individual, and it seeks to develop formal idiographic statistical models to represent these individual processes. Although the personalized approach draws on long-standing influences in clinical psychology, there has been an explosion of research in recent years following the development of intensive longitudinal data capture and statistical techniques that facilitate modeling of the dynamic processes of each individual's pathology. Advances are also making idiographic analyses scalable and generalizable. We review emerging research using the personalized approach in descriptive psychopathology, precision assessment, and treatment selection and tailoring, and we identify future challenges and areas in need of additional research. The personalized approach to psychopathology holds promise to resolve thorny diagnostic issues, generate novel insights, and improve the timing and efficacy of interventions.