Using, misusing, and improving online machine learning-based meta-analysis of neuroimaging published data: A perspective on NeuroQuery

Abstract

Online, text-based meta-analysis tools for large databases represent a new digital advance for medical, health, and neuroscience research, among other fields. NeuroQuery is an instance of such a tool for neuroimaging research; it employs supervised machine learning to draw from over 13,000 publications and perform a meta-synthesis, generating predictive fMRI scans based on keyword combinations. Although NeuroQuery is a sophisticated tool, a lack of understanding of how it practically works and its limitations may lead to flawed results and conclusions, undermining its potential value. We review potential risks and limitations, including algorithm limitations, potential biases in the database, and user misinterpretation. Simulating the perspective of an end user, we present an example of unreliable but possible metanalysis results on autistic spectrum disorder (ASD). We then report an analysis of the underlying query from a sophisticated user perspective. Using the same examples, we illustrate possible improvements for the use of NeuroQuery and identify how this tool may be valuable in the context of emerging machine-learning meta-analytical approaches. Although a thorough understanding of NeuroQuery is helpful, we conclude that understanding its limitations plays a more critical role in ensuring validity and reliability of its use. While NeuroQuery is currently not appropriate for rigorous scientific analysis, it could be useful for hypothesis development, preliminary fMRI data mining, exploratory and supplemental analysis as well as literature survey.