Neuroinformatics Research

Current projects and developing areas of investigation include:

CANDIShare: Exposing The Deep Content Of The Publication: Knowledge Extraction For Neuroimaging In Child Psychiatry

Numerous psychiatric disorders can plague the development of children. Each of these disorders has a distinct pattern of clinical, behavioral, and anatomic characteristics that challenge the management of the individual patient, as well as the development of successful intervention and prevention strategies. Following upon the successful development of numerous data sharing resources and mandates, this endeavor seeks to develop the tools necessary to gain more knowledge from the publication, in order to enhance our ability to solve specific problems in child psychiatry. Successful execution of this program fosters bolder ‘discovery- mode’ data interrogation designed to capture the richness of the neuroimaging data landscape and provide better directed hypotheses for future study into diagnosis, prediction, and monitoring of therapeutic intervention.

MPI: Kennedy & Frazier

 

ReproNim: A Center for Reproducible Neuroimaging Computation

Over the last two decades, a vast computational infrastructure has emerged and transformed how information is collected and knowledge is gathered in neuroscience. Reuse of data and analysis methods has become a focal point in a growing concern about the reproducibility of many of today’s studies, particularly with respect to research in the areas of development and developmental disorders. ReproNim: A Center for Reproducible Neuroimaging Computation seeks to implement a shift in the way neuroimaging research is performed. Through the development of technology that supports a comprehensive set of data management, analysis, and utilization frameworks in support of both basic research and clinical activities, our overarching goal is to improve the reproducibility of neuroimaging science and to extend the value of our national investment in neuroimaging research. The overall center aims to

• Deliver a reproducible analysis system;
• Working with a community of collaborator and service users, deploy, test, and validate the reproducible analysis system with a wide variety of use cases ranging from software developers to applied scientists that support the archiving and reuse of raw data and the archival and reuse of derived results to promote reproducible clinical research (and its publication) in multiple different application areas; and
• Provide training and education to the community to foster continued use and development of the reproducible framework in neuroimaging research.

PI: Kennedy