Characterization of Brain Signatures to Add Precision to Self-Management Health Information Interventions. Issue 2 (March 2019) - Moore, Shirley M.; Musil, Carol M.; Jack, Anthony I.; Alder, Megan L.; Fresco, David M.; Webel, Allison; Wright, Kathy D.; Sattar, Abdus; Higgins, Patricia
Abstract : Background: Although many of the proposed mediating processes of self-management interventions are operationally defined as cognitive processes (e.g., acquiring and using information, self-efficacy, motivation, and decision-making), little is known about their underlying brain mechanisms. Brain biomarkers of how people process health information may be an important characteristic on which to individualize health information to optimize self-management of chronic conditions. Objectives: We describe a program of research addressing the identification of brain biomarkers that differentially predict responses to two types of health information (analytic focused and emotion focused) designed to support optimal self-management of chronic conditions. Methods: We pooled data from two pilot studies ( N = 52) that included functional magnetic resonance imaging during a specially designed, ecologically valid protocol to examine brain activation (task differentiation) associated with two large-scale neural networks—the Analytic Network and the Empathy Network—and the ventral medial prefrontal cortex while individuals responded to different types of health information (analytic and emotional). Results: Findings indicate that analytic information and emotional information are processed differently in the brain, and the magnitude of this differentiation in response to type of information varies from person to person. Activation in the a priori regions identified in response to both analytic and emotion information was confirmed. The feasibility of obtaining brain imaging data from persons with chronic conditions also is demonstrated. Discussion: An understanding of brain signatures related to information processing has potential to assist in the design of more individualized, effective self-management interventions.