Sharing of MRI dyslexia datasets

One of the great things about blogging is that it allows for communication to proceed far more rapidly than would be possible through conventional academic publications. In previous posts I’ve made a plea for MRI researchers to share data so that claims about the neurobiology of conditions such as dyslexia and autism can be replicated. After my last blogpost, I was contacted by Mark Eckert from the Medical University of South Carolina, one of the pioneers of MRI studies of dyslexia (e.g. Eckert et al, 2005). He tells me that a data-sharing project on dyslexia is already underway and asked if I would be able to share this information with my followers. I am of course delighted to do so! Here is some background from Mark:

The structural neuroimaging literature on dyslexia and other complex disorders is filled with inconsistent results.  Meta-analysis provides a mechanism for identifying results that are common across studies, but direct analysis of the same datasets provides greater power, methodological consistency, and new analysis opportunities that include taking advantage of the behavioral and neural heterogeneity that is often problematic in small samples.  For those reasons, there is a growing interest in sharing data.  Prospective multi-site studies are ideal because the same data collection and quality control procedures can be used across sites.  These studies tend to be very expensive, however.  Retrospective studies take advantage of existing datasets that are housed in dusty hard drives, but are limited by methodological inconsistencies across sites.  A new NIH supported project, directed by Mark Eckert, uses dyslexia as a model to address the challenges facing retrospective multi-site studies.  Methods are being developed in this project to address subject privacy, behavioral heterogeneity in dyslexia and control samples, missing data, and the underestimation of the variance in datasets when pooling data across different research sites.  His research group is collecting existing neuroimaging datasets and aims to have more than 2000 pediatric and adult cases from reading disability studies.  One long term goal of this project is to make available much of the data collected for this study so that scientists can ask new questions, apply new methods to the data, and develop new collaborations with other scientists who have complementary expertise and interests in reading disability.  There are incentives for research groups to contribute data. For example, contributors will be included in a Dyslexia Data Consortium that will be included in the list of authors for manuscripts stemming from this project.  If you are interested in learning more about the study and/or would like to contribute data, please contact Mark Eckert at dyslexia @ musc.edu.

Eckert MA, Leonard CM, Wilke M, Eckert M, Richards T, Richards A, & Berninger V (2005). Anatomical signatures of dyslexia in children: unique information from manual and voxel based morphometry brain measures. Cortex; a journal devoted to the study of the nervous system and behavior, 41 (3), 304-15 PMID: 15871596