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Reducing State Anxiety Using Working Memory Maintenance
Published on: July 19, 2017
Using Event-Related Potentials and Startle to Evaluate Time Course in Anxiety and Depression
Heide Klumpp1, Stewart A Shankman1
1Department of Psychiatry, University of Illinois at Chicago, Chicago, Illinois; Department of Psychology, University of Illinois at Chicago, Chicago, Illinois.
Abstract:
The National Institute of Mental Health's Research Domain Criteria initiative is a research framework designed toward understanding psychopathology as abnormalities of dimensional neurobehavioral constructs rather than in terms of DSM-defined categories. Research Domain Criteria constructs within the negative valence domain are particularly relevant for understanding anxiety and depressive disorders, which are pervasive, debilitating, and characterized by negative processing bias. One important direction for Research Domain Criteria research is investigating processes and parameters related to the time course (or chronometry) of negative valenced constructs. Two reliable methods for assessing chronometry are event-related potentials (ERPs) and startle blink. In this qualitative review, we examine ERP and startle studies of individuals with anxiety or depression or individuals vulnerable to affective disorders. The aim of the review is to highlight how these methods can inform the role of chronometry in the spectrum of anxiety and depression. ERP studies examining different chronometry facets of negative valenced responses have shown that transdiagnostic groups of individuals with internalizing psychopathologies exhibit abnormalities at early stages of processing. Startle reactivity studies have robustly differentiated fear-based disorders (e.g., panic disorder, social phobia) from other anxiety disorders (e.g., generalized anxiety disorder) and have also shown that different internalizing phenotypes exhibit different patterns of habituation. Findings lend support to the value of ERP and startle measures in identifying groups that cut across conventional classification systems. We also highlight methodological issues that can aid in the validity and reproducibility of ERP and startle findings and, ultimately, in the goal of developing more precise models of anxiety and depression.
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