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Data released on October 31, 2016

Facilitating big data meta-analyses for clinical neuroimaging through ENIGMA wrapper scripts

Anglin, J; Borich, M; Jahanshad, N; Kan, E; Liew, S; Thompson, P (2016): Facilitating big data meta-analyses for clinical neuroimaging through ENIGMA wrapper scripts GigaScience Database. RIS BibTeX Text

A vast number of clinical disorders may involve changes in brain structure that are correlated with cognitive function and behavior (e.g., depression, schizophrenia, stroke, etc.). Reliably understanding the relationship between specific brain structures and relevant behaviors in worldwide clinical populations could dramatically improve healthcare decisions around the world. For instance, if a reliable relationship between brain structure after stroke and functional motor ability was established, brain imaging could be used to predict prognosis/recovery potential for individual patients. However, high heterogeneity in clinical populations in both individual neuroanatomy and behavioral outcomes make it difficult to develop accurate models of these potentially subtle relationships.
Large neuroimaging studies (n>10,000) would provide unprecedented power to successfully relate clinical neuroanatomy changes with behavioral measures. While these sample sizes might be difficult for any one individual to collect, the ENIGMA Center for WorldwideMedicine, Imaging, and Genomics has successfully pioneered meta- and mega-analytic methods to accomplish this task. ENIGMA brings together a global alliance of over 500 international researchers from over 35 countries to pool together neuroimaging data on different disease states in hopes of discovering critical brain-behavior relationships Individual investigators with relevant data run ENIGMA analysis protocols on their own data and send back an output folder containing the analysis results to be combined with data from other sites for a meta-analysis. In this way, large sample sizes can be acquired without the hassle of large-scale data transfers or actual neuroimaging data sharing.
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Related datasets:

doi:10.5524/100231 IsPartOf doi:10.5524/100215

Additional information:

Neuroscience, Imaging

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