Annual Review of Clinical Psychology

Assessment in clinical psychology typically relies on global retrospective self-reports collected at research or clinic visits, which are limited by recall bias and are not well suited to address how behavior changes over time and across contexts. Ecological momentary assessment (EMA) involves repeated sampling of subjects’ current behaviors and experiences in real time, in subjects’ natural environments. EMA aims to minimize recall bias, maximize ecological validity, and allow study of microprocesses that influence behavior in real-world contexts. EMA studies assess particular events in subjects’ lives or assess subjects at periodic intervals, often by random time sampling, using technologies ranging from written diaries and telephones to electronic diaries and physiological sensors. We discuss the rationale for EMA, EMA designs, methodological and practical issues, and comparisons of EMA and recall data. EMA holds unique promise to advance the science and practice of clinical psychology by shedding light on the dynamics of behavior in real-world settings.

Trong các phương pháp tiếp cận mạng về tâm thần học, các rối loạn phát sinh từ sự tương tác nguyên nhân giữa các triệu chứng (ví dụ, lo âu → mất ngủ → mệt mỏi), có thể liên quan đến các vòng phản hồi (ví dụ, một người có thể lạm dụng chất kích thích để quên đi những vấn đề phát sinh do lạm dụng chất này). Bài đánh giá hiện tại xem xét các phương pháp phù hợp để xác định các mạng triệu chứng và thảo luận về các kỹ thuật phân tích mạng có thể được sử dụng để trích xuất thông tin có giá trị lâm sàng và khoa học từ các mạng đó (ví dụ, triệu chứng nào là trung tâm nhất trong mạng lưới của một người). Các tác giả cũng chỉ ra cách các kỹ thuật phân tích mạng có thể được sử dụng để xây dựng các mô hình mô phỏng bắt chước động lực triệu chứng. Các phương pháp tiếp cận mạng giải thích một cách tự nhiên sự thành công hạn chế của các chiến lược nghiên cứu truyền thống, vốn thường dựa trên ý tưởng rằng các triệu chứng là biểu hiện của một yếu tố chung tiềm ẩn nào đó, đồng thời cung cấp các phương pháp thay thế hứa hẹn. Thêm vào đó, các kỹ thuật này có thể mở ra khả năng hướng dẫn và đánh giá các can thiệp điều trị.

There has been enormous progress in psychotherapy research. This has culminated in recognition of several treatments that have strong evidence in their behalf. Even so, after decades of psychotherapy research, we cannot provide an evidence-based explanation for how or why even our most well studied interventions produce change, that is, the mechanism(s) through which treatments operate. This chapter presents central requirements for demonstrating mediators and mechanisms of change and reviews current data-analytic and designs approaches and why they fall short of meeting these requirements. The role of the therapeutic alliance in psychotherapy and cognitive changes in cognitive therapy for depression are highlighted to illustrate key issues. Promising lines of work to identify mediators and mechanisms, ways of bringing to bear multiple types of evidence, recommendations to make progress in understanding how therapy works, and conceptual and research challenges in evaluating mediators and mechanisms are also presented.

Group-based trajectory models are increasingly being applied in clinical research to map the developmental course of symptoms and assess heterogeneity in response to clinical interventions. In this review, we provide a nontechnical overview of group-based trajectory and growth mixture modeling alongside a sampling of how these models have been applied in clinical research. We discuss the challenges associated with the application of both types of group-based models and propose a set of preliminary guidelines for applied researchers to follow when reporting model results. Future directions in group-based modeling applications are discussed, including the use of trajectory models to facilitate causal inference when random assignment to treatment condition is not possible.

A review of recent research on cognitive processing indicates that biases in attention, memory, and interpretation, as well as repetitive negative thoughts, are common across emotional disorders, although they vary in form according to type of disorder. Current cognitive models emphasize specific forms of biased processing, such as variations in the focus of attention or habitual interpretative styles that contribute to the risk of developing particular disorders. As well as predicting risk of emotional disorders, new studies haveprovided evidence of a causal relationship between processing bias and vulnerability. Beyond merely demonstrating the existence of biased processing, research is thus beginning to explore the cognitive causes of emotional vulnerability, and their modification.

Motivational interviewing (MI) is a client-centered, directive therapeutic style to enhance readiness for change by helping clients explore and resolve ambivalence. An evolution of Rogers's person-centered counseling approach, MI elicits the client's own motivations for change. The rapidly growing evidence base for MI is summarized in a new meta-analysis of 72 clinical trials spanning a range of target problems. The average short-term between-group effect size of MI was 0.77, decreasing to 0.30 at follow-ups to one year. Observed effect sizes of MI were larger with ethnic minority populations, and when the practice of MI was not manual-guided. The highly variable effectiveness of MI across providers, populations, target problems, and settings suggests a need to understand and specify how MI exerts its effects. Progress toward a theory of MI is described, as is research on how clinicians develop proficiency in this method.

Neuropsychiatric disorders are associated with abnormal function of the default mode network (DMN), a distributed network of brain regions more active during rest than during performance of many attention-demanding tasks and characterized by a high degree of functional connectivity (i.e., temporal correlations between brain regions). Functional magnetic resonance imaging studies have revealed that the DMN in the healthy brain is associated with stimulus-independent thought and self-reflection and that greater suppression of the DMN is associated with better performance on attention-demanding tasks. In schizophrenia and depression, the DMN is often found to be hyperactivated and hyperconnected. In schizophrenia this may relate to overly intensive self-reference and impairments in attention and working memory. In depression, DMN hyperactivity may be related to negative rumination. These findings are considered in terms of what is known about psychological functions supported by the DMN, and alteration of the DMN in other neuropsychiatric disorders.

Coping, defined as action-oriented and intrapsychic efforts to manage the demands created by stressful events, is coming to be recognized both for its significant impact on stress-related mental and physical health outcomes and for its intervention potential. We review coping resources that aid in this process, including individual differences in optimism, mastery, self-esteem, and social support, and examine appraisal and coping processes, especially those marked by approach or avoidance. We address the origins of coping resources and processes in genes, early life experience, and gene-environment interactions, and address neural underpinnings of coping that may shed light on evaluating coping interventions. We conclude by outlining possible intervention strategies for improving coping processes.

Comorbidity has presented a persistent puzzle for psychopathology research. We review recent literature indicating that the puzzle of comorbidity is being solved by research fitting explicit quantitative models to data on comorbidity. We present a meta-analysis of a liability spectrum model of comorbidity, in which specific mental disorders are understood as manifestations of latent liability factors that explain comorbidity by virtue of their impact on multiple disorders. Nosological, structural, etiological, and psychological aspects of this liability spectrum approach to understanding comorbidity are discussed.

Brain graphs provide a relatively simple and increasingly popular way of modeling the human brain connectome, using graph theory to abstractly define a nervous system as a set of nodes (denoting anatomical regions or recording electrodes) and interconnecting edges (denoting structural or functional connections). Topological and geometrical properties of these graphs can be measured and compared to random graphs and to graphs derived from other neuroscience data or other (nonneural) complex systems. Both structural and functional human brain graphs have consistently demonstrated key topological properties such as small-worldness, modularity, and heterogeneous degree distributions. Brain graphs are also physically embedded so as to nearly minimize wiring cost, a key geometric property. Here we offer a conceptual review and methodological guide to graphical analysis of human neuroimaging data, with an emphasis on some of the key assumptions, issues, and trade-offs facing the investigator.