A Neuroinformatics Framework For Linking Genetic and Neuroimaging Data

• ¹ Harvard University, Center for Brain Science, United States

A Neuroinformatics Framework For Linking Genetic and Neuroimaging Data Timothy M. O’Keefe1, Victor I. Petrov2, Randy L. Buckner1,2,3 and Gabriele R. Fariello1,2 1. Harvard University, Center for Brain Science 2. Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital 3. Howard Hughes Medical Institute An informatics challenge has emerged at the intersection of genetics and human brain imaging research. The cause of this challenge is situated in that each modality is data intensive and requires significant processing to derive useful information. Discovering links between modalities necessitates a system for capturing and vetting data, deriving measures, and linking derived data types. In large-scale projects, there exists a further need to collect and share data between institutions and computational environments. To address these challenges, we have developed a neuroinformatics framework. The framework offers a system that is capable of automatically processing acquired data and provides access to the raw, quality control, computationally derived, and summary data. The framework is built upon a custom installation of the eXtensible Neuroimaging Archive Toolkit (XNAT; Marcus et al. 2007), advancements in MRI data acquisition methods, automated processing pipelines, online cognitive, personality and behavioral assessments, and programmatic APIs for data retrieval and subsequent processing. To illustrate the utility of this approach, the framework has been utilized over the past two years to power the Brain Genomics Superstruct Project as an installation known as “GSPCentral”. GSPCentral has since succeeded in capturing neuroimaging (e.g., fMRI, anatomical), genetics, cognitive (e.g., WAIS III, WMS III), behavioral (e.g., STAI-T, POMS), and derived imaging data (e.g., Morphometry, Functional Connectivity) for approximately 3000 human participants acquired across 20 investigators and 5 matched MRI scanners. This large sample has already enabled researchers to explore aspects of neuroanatomy, behavior, and cognition including revealing the relations between brain structure and personality traits (e.g., anxiety), exposing the organization of large-scale networks, and quantifying the hemispheric asymmetry of functional networks. The challenges of the approach will be discussed as well as how these derived brain and behavioral measures are being linked to genetic information.