Goals

The UW Integrated Brain Project is one project within the national Human Brain Project, a national multi-agency effort to develop informatics tools for managing the exploding amount of information that is accumulating about the human brain.

The objective of the UW Integrated Brain Project effort is to organize and integrate distributed functional information about the brain around the structural information framework that is the long term goal of our work. This application therefore extends the utility of the Digital Anatomist Project by using it to organize non-structural information.

The initial driving neuroscience problem we have been addressing is the management, visualization and analysis of cortical language mapping data. In recent years, advances in imaging technology such as PET and functional MRI have allowed researchers to observe areas of the cortex that are activated when the subject performs language tasks. These advances have greatly accelerated the amount of data available about human language, but have also emphasized the need to organize and integrate the sometimes contradictory sources of data, in order to develop theories about language organization. Our hypothesis (and that of most Brain Project researchers) is that neuroanatomy is the common substrate on which the diverse kinds of data can be integrated.

A result of our work is a set of software tools for generating a 3-D reconstruction of the patient's own brain from MRI, for mapping functional data to this reconstruction, for normalizing individual anatomy by warping to a canonical brain atlas and by annotating data with terms from an anatomy ontology, for managing individual lab data in local laboratory information systems, for integrating and querying data across separate data management systems, and for visualizing the integrated results.

These tools and methods are described in our current progress report, our online publications, and our demos and downloads page.

We are currently in the process of scaling up these tools and methods to larger numbers of data sources, and to different neuroscience applications.