Neural indicators of error processing in generalized anxiety disorder, obsessive-compulsive disorder, and major depressive disorder

• Published in: Journal of abnormal psychology (1965) Vol. 124; no. 1; p. 172
• Main Authors: Weinberg, Anna, Kotov, Roman, Proudfit, Greg H
• Format: Journal Article
• Language: English
• Published: United States 01.02.2015
• Subjects: Adult; Analysis of Variance; Anxiety Disorders - physiopathology; Brain Mapping; Case-Control Studies; Depressive Disorder, Major - physiopathology; Electroencephalography - methods; Evoked Potentials - physiology; Female; Gyrus Cinguli - physiology; Humans; Male; Middle Aged; Obsessive-Compulsive Disorder - physiopathology; Phenotype; Reaction Time - physiology
• ISSN: 1939-1846
• DOI: 10.1037/abn0000019

The ability to detect and respond to errors is critical to successful adaptation to a changing environment, and variation in error-related brain activity has been linked to psychopathology. The error-related negativity (ERN), an event-related potential component, represents a unique neural response to errors and is generated in the anterior cingulate cortex (ACC). In the present study, we measured the ERN in a sample of individuals with Generalized Anxiety Disorder (GAD), Obsessive Compulsive Disorder (OCD), Major Depressive Disorder (MDD), or some combination of the 3. Also included were 56 healthy control participants. Consistent with previous research, a diagnosis of GAD, only in the absence of a comorbid diagnosis of depression, was characterized by a larger ERN than controls. No such enhancement was evident in the depressed group, or the comorbid group, suggesting comorbid depression may eliminate the relationship between the ERN and GAD. Across all groups, symptoms of checking were associated with a larger ERN, whereas symptoms of psychomotor retardation were associated with a smaller ERN. The results of the present study indicate that interactions among transdiagnostic dimensions will likely need to be considered in the creation of neurobiologically informed classification schemes.