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Neurophysiological patterns may distinguish which youth are at risk for the well-documented increase in internalizing symptoms during adolescence. Adolescents with internalizing problems exhibit altered resting-state functional connectivity (RSFC) of brain regions involved in socio-affective processing. Whether connectivity-based biotypes differentiate adolescents’ levels of internalizing problems remains unknown.
Sixty-eight adolescents (37 females) reported on their internalizing problems at ages 14, 16, and 18 years. A resting-state functional neuroimaging scan was collected at age 16. Time-series data of 15 internalizing-relevant brain regions were entered into the Subgroup-Group Iterative Multi-Model Estimation program to identify subgroups based on RSFC maps. Associations between internalizing problems and connectivity-based biotypes were tested with regression analyses.
Two connectivity-based biotypes were found: a Diffusely-connected biotype (N = 46), with long-range fronto-parietal paths, and a Hyper-connected biotype (N = 22), with paths between subcortical and medial frontal areas (e.g. affective and default-mode network regions). Higher levels of past (age 14) internalizing problems predicted a greater likelihood of belonging to the Hyper-connected biotype at age 16. The Hyper-connected biotype showed higher levels of concurrent problems (age 16) and future (age 18) internalizing problems.
Differential patterns of RSFC among socio-affective brain regions were predicted by earlier internalizing problems and predicted future internalizing problems in adolescence. Measuring connectivity-based biotypes in adolescence may offer insight into which youth face an elevated risk for internalizing disorders during this critical developmental period.
Over the past twenty years, several taxonomies of personality and psychopathology have been developed. More recently, many studies have compared dimensional models of personality pathology to categorical diagnoses of personality disorders. Altogether, this proliferation of research suggests the value of articulating the desirable properties of a good taxonomic system. Here, the authors extend basic research in cognitive science on the limitations of representational capacity, which suggests that humans need to compress complex clinical presentations to make good judgments. With this in mind, the authors propose that information compression and information fidelity are two principles that are essential to good taxonomy. The principle of information compression is that taxonomies should prune the complexities of a detailed clinical presentation to focus on important sources of covariation. The principle of information fidelity is that a good taxonomy should maintain essential features that reasonably approximate the structure of an individual or the population. They conclude with the claim that the overarching goal of taxonomic science in classifying personality pathology is to provide clinicians and researchers with empirically based informative priors that help to bias thinking toward useful clinical distinctions.
This chapter discusses mathematical models of learning in neural circuits with a focus on reinforcement learning. Formal models of learning provide insights into how we adapt to a complex, changing environment, and how this adaptation may break down in psychopathology. Computational clinical neuroscience is motivated to use mathematical models of decision processes to bridge between brain and behavior, with a particular focus on understanding individual differences in decision making. The chapter reviews the basics of model specification, model inversion (parameter estimation), and model-based approaches to understanding individual differences in health and disease. It illustrates how models can be specified based on theory and empirical observations, how they can be fitted to human behavior, and how model-predicted signals from neural recordings can be decoded. A functional MRI (fMRI) study of social cooperation is used to illustrate the application of reinforcement learning (RL) to test hypotheses about neural underpinnings of human social behavior.
The scientific discipline of clinical psychology has witnessed paradigm changes in the prevailing conceptualization of psychopathology and in the rigor of experimental methods to test psychosocial treatments. In parallel, neuroscience approaches to mental illness have become increasingly prominent and technologies to measure psychological constructs over time and across contexts are becoming ubiquitous in psychological research. Altogether, these changes have pushed clinical scientists to incorporate novel research methodologies and analytic approaches. Modern studies of clinical phenomena are often theoretically integrative and assess constructs across levels of measurement, ranging from the molecular to the behavioral. These shifts are fundamental, and necessitate changes in the way modern clinical psychologists design studies, collect data, and draw scientific conclusions. This book is intended to serve as a guide for the next generation of clinical psychologists, who will benefit from greater training in statistics, study design, developmental psychopathology, and multimethod approaches.
This book integrates philosophy of science, data acquisition methods, and statistical modeling techniques to present readers with a forward-thinking perspective on clinical science. It reviews modern research practices in clinical psychology that support the goals of psychological science, study designs that promote good research, and quantitative methods that can test specific scientific questions. It covers new themes in research including intensive longitudinal designs, neurobiology, developmental psychopathology, and advanced computational methods such as machine learning. Core chapters examine significant statistical topics, for example missing data, causality, meta-analysis, latent variable analysis, and dyadic data analysis. A balanced overview of observational and experimental designs is also supplied, including preclinical research and intervention science. This is a foundational resource that supports the methodological training of the current and future generations of clinical psychological scientists.
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