CANDIShare: A Resource for Pediatric Neuroimaging Data

Steven Hodge (Department of Psychiatry, University of Massachusetts Medical School, Worcester, MA, USA), David Kennedy (Department of Psychiatry, University of Massachusetts Medical School, Worcester, MA, USA), Christian Haselgrove (Department of Psychiatry, University of Massachusetts Medical School, Worcester, MA, USA), Jean Frazier (Department of Psychiatry, University of Massachusetts Medical School, Worcester, MA, USA)

There are numerous psychiatric disorders that can plague the development of children. Each of these disorders manifests as a distinct pattern of clinical, behavioral, etiological, neuroanatomic and neurofunctional characteristics that challenge the management of the individual patient, as well as the development of successful intervention and prevention strategies. In the area of neuroimaging, a substantial number of studies have been performed to date; and while much has been learned from this investment, this represents only the tip-of-the-iceberg of the information that can be gleaned from the data. Unfortunately, most of this additional, untapped information resource is lost due to ineffective use of the principles of data sharing and integration.

It is of fundamental importance to facilitate interactive sharing of data amongst neuroscience investigators, in general, and within the child psychiatry community in particular. This project seeks to apply existing data sharing mechanisms and develop domain-specific sharing resources that connect researchers in child psychiatry.

Using the existing resources of the eXtensible Neuroimaging Archive Toolkit (XNAT) and the Internet Brain Segmentation Repository (IBSR) we are making a large set of MR image and anatomic analysis data available to the general neuroinformatics community. These data include: a) Image data - including structural and diffusion imaging at 1.5 and 3.0 Tesla, where each subject includes a comprehensive set of clinical, demographic and behavioral measures; b) results for general segmentation (subdivision of the imaged brain in terms of gross neuroanatomic subdivisions of gray, white and CSF tissue classes) and parcellation (regional compartmentalization of cortex and white matter); and c) the creation and dissemination of static and dynamic probabilistic atlases from specific subsets of these data for use in other segmentation and analysis frameworks.

The dataset to be released has been collected over the past 10 years by the investigators at the Child and Adolescent Neurodevelopment Initiative (CANDI), now at the University of Massachusetts Medical Center. This is one of the largest collections of neuroimaging studies in child psychiatry. These data include 263 subjects, span the ages of 3-21 years, and include normative subjects (70) as well as children with ADHD (31), bipolar disorder (130) and childhood onset schizophrenia (32). 150 of these subjects have complete general segmentation, and 123 of these cases also have complete parcellation.

The CANDI Neuroimaging Access Point was created in May, 2010 ( The current data release contains structural brain images of 29 healthy controls, 35 subjects with Bipolar Disorder, 19 subject with Bipolar Disorder and psychosis, and 20 subjects on the schizophrenia spectrum. As of April 2011, there have been 269 downloads of this set of data. Human expert anatomic segmentation results are also available.

This release of information is dramatically greater than merely making the images available: each image is associated with substantial analytic results, many of which have been utilized in the preparation of various publications and comparisons. Moreover, these data will be most effectively shared with the research community when shared in a way that preserves the linkages between the images, the resultant analytic data and meta-data, and its relationships to other public sources of related information. In short, this represents a knowledge management environment that will facilitate traversal of these data and linkages.

Preferred presentation format: Poster
Topic: Neuroimaging

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