The Functional Magnetic Resonance Imaging Data Center (fMRIDC): the challenges and rewards of large-scale databasing of neuroimaging studies.

The Functional Magnetic Resonance Imaging Data Center (fMRIDC) (http://www.fmridc.org) was established in the Autumn of 1999 with the objective of creating a mechanism by which members of the neuroscientific community may more easily share functional neuroimaging data. Examples in other sciences offer proof of the usefulness and benefit that sharing data provides through encouraging growth and development in those fields. By building a publicly accessible repository of raw data from peer-reviewed studies, the Data Center hopes to create a similarly successful environment for the neurosciences. In this article, we discuss the continuum of data-sharing efforts and provide an overview of the scientific and practical difficulties inherent in managing various fMRI data-sharing approaches. Next, we detail the organization, design and foundation of the fMRIDC, ranging from its current capabilities to the issues involved in the submitting and requesting of data. We discuss how a publicly accessible database enables other fields to develop relevant tools that can aid in the growth of understanding of cognitive processes. Information retrieval and meta-analytic techniques can be used to search, sort and categorize study information with a view towards subjecting study data to secondary 'meta-' and 'mega-analyses'. In addition, we detail the technical and policy challenges that have had to be addressed in the formation of the Data Center. Among others, these include: human subject confidentiality issues; ensuring investigator's rights; heterogeneous data description and organization; development of search tools; and data transfer issues. We conclude with comments concerning the future of the fMRIDC effort, its role in promoting the sharing of neuroscientific data, and how this may alter the manner in which studies are published.

Multiresolution elastic image registration.

We have developed a multiscale algorithm for elastic (or molded) alignment of images. There is a wide array of medical applications of elastic (as opposed to strictly rigid) alignment: Subtraction of previous images from current ones to identify changes is perhaps the most obvious. We present preliminary results of this molding technique on a variety of images, and conclude with some closing remarks about this and future directions and goals of this work.

Elastic image registration using correlations.

We have developed a multiscale algorithm for elastic registration of images. Rigid registration has many applications but it is often limited by distortions in the images. For example, different views of the same object produce distortions. Common examples of slightly different views producing a distortion can be found in medical imaging, such as matching a current mammogram or chest radiograph with one from a previous year, and in remote sensing, such as matching images taken from different satellite positions. We have developed two methods of elastic registration. Both are multiscale but one used an iterative minimization of the local error and the other uses a windowed correlation. We present preliminary results of the elastic registration method used on windowed correlations.