Richard Cameron Craddock, Ph.D. - US grants

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.

ABSTRACT The BRAIN Initiative is designed to leverage sophisticated neuromodulation, electrophysiological recording, and macroscale neuroimaging techniques in human and non-human animal models in order to develop a multilevel understanding of human brain function. However, the necessary tools for organizing, processing and analyzing neuroimaging data generated through these efforts are not widely available as coherent and easy-to- use software packages. Gaps are particularly apparent for nonhuman data (i.e., monkey, rodent), as most of the existing processing and analytic software packages are specifically designed for human imaging. Methods have been proposed for addressing the challenges inherent to the processing of nonhuman data (e.g., brain extraction, tissue segmentation, spatial normalization, brain parcellation, temporal denoising); to date, these have not been readily integrated into an easy-to-use, robust, and reproducible analysis package. Similarly, many of the sophisticated machine learning and modeling methods developed for neuroimaging analyses are inaccessible to most researchers because they have not been integrated into easy-to-use pipeline software. As a result, translational and comparative neuroimaging researchers patch together neuroinformatics pipelines that use various combinations of disparate software packages and in-house code. We propose to extend the Configurable Pipeline for the Analysis of Connectomes (C-PAC) open-source software to provide robust and reproducible pipelines for functional and structural MRI data. We will integrate the various disparate image processing and analysis methods used to handle the challenges of nonhuman imaging data, into a single, open source, configurable, easy-to-use end-to-end analysis pipeline package that is accessible locally or via the cloud. The end product will not only improve the quality, transparency and reproducibility of nonhuman translational and comparative imaging, but also enable new avenues of scientific inquiry through our inclusion of methods that are yet to be applied to nonhuman imaging data (e.g., gradient- based cortical parcellation methods, hyperalignment). Specific aims of the proposed work include to: 1) Integrate neuroimaging processing and analysis methods optimized for BRAIN Initiative data, 2) Implement strategies for carrying out comparative studies of human and non-human populations, and 3) Extend C-PAC to include cutting-edge analytical strategies for identifying mechanisms of brain function. All development will occur ?in the open? using GitHub and other collaborative tools to maximally involve participation in the C-PAC project. Annual hackathons will be held to collaborate with investigators from BRAIN Initiative awards and other neuroinformatics development projects to integrate their tools with C-PAC. Hands-on training will be held to train investigators on optimal use of the newly developed tools.