Abstract
A major improvement in MRI techniques has led to an exponential increase in data acquisition and, consequently, in the number of published articles reporting brain impairments and cognitive deficits underlying a disorder. Meta-analysis offers a means of synthesizing the available literature, testing existing models in the light of scientific advances, and revealing unexpected information. However, article selection, author specialization and top-down hypotheses can mask some results and introduce bias into interpretations. LinkRdata is a platform for automated, data-driven, meta-analytical methods suitable for processing large numbers of MRI articles, that can reduce selection and interpretation biases, thereby allowing scientists to review neurocognitive correlates of disorders in relation to their own corpus of articles. To validate our method, we applied it to fMRI studies of post-traumatic stress disorder. Results confirmed LinkRdata's power to uncover findings hidden by the top-down hypothesis approach.
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