Real-time fMRI Neurofeedback Based Stratification of Default Network Regulation

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DESCRIPTION (provided by applicant): The default network (DN) is a distributed pattern of brain regions associated with spontaneous cognition, internalized thought and emotional regulation that are consistently deactivated during the performance of goal- driven cognitive tasks. Failure to appropriately activate or deactivate the DN during performing of cognitive tasks is increasingly being implicated in psychiatric illness, with little specificity regarding disorderor attention to symptom domain. Further challenges arise from limitations of task-based and resting state functional MRI imaging approaches that have left the field with little insight into te nature of DN dysregulation (i.e. inability to modulate DN activity as opposed to the tendency to do so) in the various disorders. Consistent with the Research Domain Criteria Project (R-DoC), the proposed work capitalizes on recent innovations in real-time fMRI (RT-fMRI) based neurofeedback to provide a dimensional profile of DN regulation that can be linked to cognitive and psychiatric phenotyping profiles, as well as underlying brain architecture. Specifically, we propose a multi-faceted imaging study that characterizes DN regulation using a combination of neurofeedback RT-fMRI, to assess an individual's ability to modulate the DN, and task-based fMRI activation and deactivation (i.e., the self-referential processing task and the multi-source interference, respectively) to assess an individuals tendency to modulate the DN. Consistent with the "agnostic" approach promoted by R- DoC, we focus on a community-ascertained sample of 180 adults (ages: 25-40 years old), using minimally restrictive psychiatric exclusion criteria. The comprehensive phenotyping protocol established by the Nathan Kline Institute Rockland Sample (NKI-RS) will be used to characterize a range of psychiatric and cognitive domains. Successful completion of the proposed work will serve to: 1) Establish the relationship between DN modulation capacity as measured by RT-fMRI and DN modulation tendency as measured by task-related DN activation and deactivations, 2) link multidimensional imaging-based DN modulation and phenotypic profiles, and 3) link multidimensional DN modulation profiles to the brain's functional and structural architecture, as assessed by resting state fMRI and diffusion tensor imaging.