Predictors of pharmacotherapy outcomes for body dysmorphic disorder: a machine learning approach.

BACKGROUND: Serotonin-reuptake inhibitors (SRIs) are first-line pharmacotherapy for the treatment of body dysmorphic disorder (BDD), a common and severe disorder. However, prior research has not focused on or identified definitive predictors of SRI treatment outcomes. Leveraging precision medicine techniques such as machine learning can facilitate the prediction of treatment outcomes. METHODS: The study used 10-fold cross-validation support vector machine (SVM) learning models to predict three treatment outcomes (i.e. response, partial remission, and full remission) for 97 patients with BDD receiving up to 14-weeks of open-label treatment with the SRI escitalopram. SVM models used baseline clinical and demographic variables as predictors. Feature importance analyses complemented traditional SVM modeling to identify which variables most successfully predicted treatment response. RESULTS: SVM models indicated acceptable classification performance for predicting treatment response with an area under the curve (AUC) of 0.77 (sensitivity = 0.77 and specificity = 0.63), partial remission with an AUC of 0.75 (sensitivity = 0.67 and specificity = 0.73), and full remission with an AUC of 0.79 (sensitivity = 0.70 and specificity = 0.79). Feature importance analyses supported constructs such as better quality of life and less severe depression, general psychopathology symptoms, and hopelessness as more predictive of better treatment outcome; demographic variables were least predictive. CONCLUSIONS: The current study is the first to demonstrate that machine learning algorithms can successfully predict treatment outcomes for pharmacotherapy for BDD. Consistent with precision medicine initiatives in psychiatry, the current study provides a foundation for personalized pharmacotherapy strategies for patients with BDD.

Rising early warning signals in affect associated with future changes in depression: a dynamical systems approach.

BACKGROUND: Predicting future states of psychopathology such as depressive episodes has been a hallmark initiative in mental health research. Dynamical systems theory has proposed that rises in certain 'early warning signals' (EWSs) in time-series data (e.g. auto-correlation, temporal variance, network connectivity) may precede impending changes in disorder severity. The current study investigates whether rises in these EWSs over time are associated with future changes in disorder severity among a group of patients with major depressive disorder (MDD). METHODS: Thirty-one patients with MDD completed the study, which consisted of daily smartphone-delivered surveys over 8 weeks. Daily positive and negative affect were collected for the time-series analyses. A rolling window approach was used to determine whether rises in auto-correlation of total affect, temporal standard deviation of total affect, and overall network connectivity in individual affect items were predictive of increases in depression symptoms. RESULTS: Results suggested that rises in auto-correlation were significantly associated with worsening in depression symptoms (r = 0.41, p = 0.02). Results indicated that neither rises in temporal standard deviation (r = -0.23, p = 0.23) nor in network connectivity (r = -0.12, p = 0.59) were associated with changes in depression symptoms. CONCLUSIONS: This study more rigorously examines whether rises in EWSs were associated with future depression symptoms in a larger group of patients with MDD. Results indicated that rises in auto-correlation were the only EWS that was associated with worsening future changes in depression.

Using Machine Learning to Examine Suicidal Ideation After Traumatic Brain Injury: A Traumatic Brain Injury Model Systems National Database Study.

OBJECTIVE: The aim of the study was to predict suicidal ideation 1 yr after moderate to severe traumatic brain injury. DESIGN: This study used a cross-sectional design with data collected through the prospective, longitudinal Traumatic Brain Injury Model Systems network at hospitalization and 1 yr after injury. Participants who completed the Patient Health Questionnaire-9 suicide item at year 1 follow-up ( N = 4328) were included. RESULTS: A gradient boosting machine algorithm demonstrated the best performance in predicting suicidal ideation 1 yr after traumatic brain injury. Predictors were Patient Health Questionnaire-9 items (except suicidality), Generalized Anxiety Disorder-7 items, and a measure of heavy drinking. Results of the 10-fold cross-validation gradient boosting machine analysis indicated excellent classification performance with an area under the curve of 0.882. Sensitivity was 0.85 and specificity was 0.77. Accuracy was 0.78 (95% confidence interval, 0.77-0.79). Feature importance analyses revealed that depressed mood and guilt were the most important predictors of suicidal ideation, followed by anhedonia, concentration difficulties, and psychomotor disturbance. CONCLUSIONS: Overall, depression symptoms were most predictive of suicidal ideation. Despite the limited clinical impact of the present findings, machine learning has potential to improve prediction of suicidal behavior, leveraging electronic health record data, to identify individuals at greatest risk, thereby facilitating intervention and optimization of long-term outcomes after traumatic brain injury.

Temporal and contemporaneous network structures of affect and physical activity in emotional disorders.

BACKGROUND: High negative affect, low positive affect, and limited physical activity figure prominently in psychopathology, but little is known about the interrelatedness of affect and physical activity in emotional disorders. METHODS: We combined ecological momentary assessment data with a network approach to examine the dynamic relations among positive affect, negative affect, and smartphone-based estimates of physical activity in 34 participants with anxiety and depressive disorders over a 2-week period. RESULTS: In the contemporaneous networks, the positive affect nodes exhibited greater overall strength centrality than negative affect nodes. The temporal networks indicated that the negative affect node 'sadness' exhibited the greatest out-strength centrality. Furthermore, physical activity was unconnected to the affect nodes in either the temporal or contemporaneous networks. CONCLUSIONS: Whereas positive affect plays a greater role in the contemporaneous experience of emotions, negative affect contributes more so to future affective states.

Cognitive-Behavioral Treatments for Anxiety and Stress-Related Disorders.

Cognitive-behavioral therapy (CBT) is a first-line, empirically supported intervention for anxiety disorders. CBT refers to a family of techniques that are designed to target maladaptive thoughts and behaviors that maintain anxiety over time. Several individual CBT protocols have been developed for individual presentations of anxiety. The article describes common and unique components of CBT interventions for the treatment of patients with anxiety and related disorders (i.e., panic disorder, social anxiety disorder, generalized anxiety disorder, obsessive-compulsive disorder, posttraumatic stress disorder, prolonged grief). Recent strategies for enhancing the efficacy of CBT protocols are highlighted as well.

Change processes in cognitive behavioral therapy and motivational interviewing for depression and heavy alcohol use: A network approach.

Objective: Both cognitive behavioral therapy for depression (CBT-D) combined with brief motivational interviewing (CBT-D + BMI) and alone are associated with symptom improvement among college students with co-occurring depression and heavy episodic drinking (HED). However, little is known about change processes underlying these different treatments.The current study uses a network approach to examine change process that may differentially underlie CBT-D + BMI relative to CBT alone. Methods: Participants included 94 college students with depression and HED who were randomized to either eight weeks of CBT-D + BMI or CBT alone. A network approach was adopted to examine how treatment condition influenced changes in the network structure of depression symptoms, heavy drinking, drinking motives, and consequences of alcohol. Network analyses were conducted using change scores representing the eight-week difference from pre-treatment to post-treatment assessments. Results: Relative to CBT-D alone, the combined CBT-D + BMI treatment influenced the symptom network structure by preferentially targeting reductions in drinking to cope motives and in the depression symptom 'loss of interest'. Conclusion: The current study revealed that combined CBT-D + BMI may confer therapeutic benefit through different network structure pathways than CBT-D alone. Specifically, augmenting CBT-D with BMI may influence change processes related to drinking motives, such as drinking to cope.

Anhedonia is central for the association between quality of life, metacognition, sleep, and affective symptoms in generalized anxiety disorder: A complex network analysis.

BACKGROUND: Poor quality of life, sleep problems, anhedonia, and negative metacognitions are common in anxiety and depression. To examine the nature of the relationship between these features and the role of metacognitions, anhedonia, and quality of life in anxiety and depression, we conducted a complex network analysis with items of self-report measures assessing quality of life, sleep, negative thinking styles, anxiety, and depression. METHODS: Participants were 226 treatment seeking individuals with a primary DSM-5 diagnosis of generalized anxiety disorder. Node centrality, strength, expected influence, community, and bridge estimation were calculated using partial correlation coefficients and glasso regularization. RESULTS: Results revealed that anhedonia was the most central node followed by quality of life nodes. Moreover, anhedonia exhibited the highest strength and expected influence, which were both stable, reliable metrics within the network. Metacognitions were not central nodes in the network, but were strong bridge symptoms between communities. LIMITATIONS: The results are limited by the cross-sectional nature of the data and the administration of self-report scales at one time-point, despite different rating anchors. CONCLUSION: These findings suggest that anhedonia is a crucial element for the association between quality of life, sleep problems, and negative cognitions.

Emotion Regulation Flexibility in Generalized Anxiety Disorder.

BACKGROUND: People differ in their affective styles, which refers to habitual use of emotion regulation (ER) strategies. Previous research has shown that mental health is associated with an individual's adaptive flexibility of emotion regulation strategies rather than any one particular ER strategy. METHODS: The present study employed a person-centered approach using latent profile analyses to distinguish patients with generalized anxiety disorder based on their responses on an affective styles measure. RESULTS: Results of the latent profile analysis supported a three-class solution. Class 1 (26% of participants) identified individuals with the lowest scores of each affective style; class 2 (10%) included individuals with the highest scores of each style; and class 3 (64%) consisted of individuals who scored in the mid-range of each affective style. Greater ER flexibility was associated with better emotional functioning and quality of life. CONCLUSIONS: Patients with GAD differ in ER flexibility. The vast majority of patients appear to have only moderate or low ER flexibility. Those individuals with high ER flexibility show a greater quality of life and less emotional distress.

Beyond linear mediation: Toward a dynamic network approach to study treatment processes.

Few clinical scientists would disagree that more research is needed on the underlying mechanisms and processes of change in psychological therapies. In the dominant current approach, processes of change are studied through mediation. The study of mediation has been largely structured around a distinction between moderation and mediation first popularized by Baron and Kenny's (1986) seminal article, which is based on a nomothetic and cross-sectional framework. In this article, we argue that this approach is unable to adequately address change processes in psychological therapies, because it falsely assumes that treatment change is a linear, unidirectional, pauci-variate process and that the statistical assumptions are met to study processes of change in an individual using a nomothetic approach. In contrast, we propose that treatment is a dynamic process involving numerous variables that may form bi-directional and complex relationships that differ between individuals. Such relationships can best be studied using an individual dynamic network approach connected to nomothetic generalization methods that are based on a firm idiographic foundation. We argue that our proposal is available, viable, and can readily be integrated into existing research strategies. We further argue that adopting an individual dynamic network approach combined with experimental analyses will accelerate the study of treatment change processes, which is necessary as the field of evidence-based care moves toward a process-based model. We encourage future research to gather empirical evidence to examine this approach.

Exercise and emotion dynamics: An experience sampling study.

Though it has been widely demonstrated that regular exercise is associated with better emotional wellbeing, the nature of this association remains unclear. The present study explored the relationship between voluntary exercise and the temporal dynamics of daily emotions, and thus how voluntary exercise could be impacting emotional reactivity and recovery in naturalistic contexts. Seventy-six young adults participated simultaneously in this ecological momentary assessment study, and received 75 prompts over the course of 15 days. Emotional inertia (persistence of emotional states), emotional variability (intensity of emotional fluctuations), and emotional instability (tendency for emotional fluctuations) were considered. Past research has shown that low wellbeing tends to be associated with high inertia, variability, and instability. Each prompt included ratings of present emotions (anxiety, sadness, cheerfulness, contentment) and any recent physical activity. Greater average exercise time was significantly associated with less inertia (reduced autocorrelation) of anxiety. Exercise was not significantly associated with inertia of the other emotions, although results were in the same direction. Exercise habits were unrelated to emotional variability and instability. Results suggest that exercise may buffer against prolonged or persistent negative affective states and consequently could benefit a person's ability to self-regulate or recover from changes in the environment and internal emotional experiences, rather than simply reducing the frequency or intensity of anxious emotions. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

The role of the individual in the coming era of process-based therapy.

For decades the development of evidence-based therapy has been based on experimental tests of protocols designed to impact psychiatric syndromes. As this paradigm weakens, a more process-based therapy approach is rising in its place, focused on how to best target and change core biopsychosocial processes in specific situations for given goals with given clients. This is an inherently more idiographic question than has normally been at issue in evidence-based therapy over the last few decades. In this article we explore methods of assessment and analysis that can integrate idiographic and nomothetic approaches in a process-based era.