Improved neuroimaging meta analyses with modern text based processing

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The scientific community relies heavily on meta-analyses to establish trustworthy results in a rapidly growing body of literature. Individual studies are subject to biases, reporting limitations, or lack of power, all of which are difficult to assess. Meta-analyses are key to reach consensus and extract solid information from the literature in life sciences and neuroscience. As the number of scientific publications is rapidly increasing, false positive findings are increasing as well. Because neuroimaging experiments are costly, the number of participants included in studies often provides limited power. Current meta-analysis tools -such as Neurosynth or NeuroQuery- are able to automatically process a large (~14,000) corpus of neuroimaging publications from which the brain activity coordinates have been extracted, and provide a way to compute brain maps associated with the publication labelled terms. These tools do not account for the methods used in the manuscript, even though it has been demonstrated that analysis pipelines have a major impact on neuroimaging results. Advanced methods in text analysis and natural language processing should not only permit better bibliometrics but also improved automatized analysis of the literature. We propose to explore ways to improve meta-analyses in neuroimaging with advanced text mining analysis of the literature. Our first task is to automatically extract labels or feature vector representations of both the neuroimaging domains and the data processing and analysis methods for each publication in a large corpus. Once articles have been annotated with labels for neuroimaging methods, we propose to analyse the impact of a given method by comparing meta-analysis results from articles using or not this specific method (e.g., cluster size test, specific type of movement correction for functional MRI analysis). We will also study the temporal evolution of results with the year of publication and assess if there is a link with the emergence of new methods, and propose correction strategies for those neuroimaging methods that introduce bias and/or variance in meta-analyses. Finally, we will use the meta-analysis results to robustify and improve analysis of new studies with limited sample sizes, and employ a very large neuroimaging dataset to validate the use of meta-analytic maps as priors to regularize statistical and prediction results.