The rise of large-scale imaging studies in psychiatry

GigaScience

Jessica A Turner¹ 

[1] Department of Psychology and Neuroscience Institute, Georgia State University, P.O .Box 5010, Atlanta, GA 30302, USA

关键词: Data mining;    Clinical imaging;    Data sharing;    Neuroinformatics;   

Others  :  1118574
DOI  :  10.1186/2047-217X-3-29

 received in 2014-08-20, accepted in 2014-11-07,  发布年份 2014

【 摘 要 】

From the initial arguments over whether 12 to 20 subjects were sufficient for an fMRI study, sample sizes in psychiatric neuroimaging studies have expanded into the tens of thousands. These large-scale imaging studies fall into several categories, each of which has specific advantages and challenges. The different study types can be grouped based on their level of control: meta-analyses, at one extreme of the spectrum, control nothing about the imaging protocol or subject selection criteria in the datasets they include, On the other hand, planned multi-site mega studies pour intense efforts into strictly having the same protocols. However, there are several other combinations possible, each of which is best used to address certain questions. The growing investment of all these studies is delivering on the promises of neuroimaging for psychiatry, and holds incredible potential for impact at the level of the individual patient. However, to realize this potential requires both standardized data-sharing efforts, so that there is more staying power in the datasets for re-use and new applications, as well as training the next generation of neuropsychiatric researchers in “Big Data” techniques in addition to traditional experimental methods. The increased access to thousands of datasets along with the needed informatics demands a new emphasis on integrative scientific methods.

【 授权许可】

   
2014 Turner; licensee BioMed Central Ltd.

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