The lack of a universally established vocabulary or ontology to describe mental processes and disorders is a strong impediment to meta-analysis ( Poldrack and Yarkoni, 2016). But closely related terms can lead to markedly different meta-analyses ( Figure 1). To automate the selection of studies, the common solution is to rely on terms present in publications. In what follows, we focus on automated meta-analysis.
#Comprehensive meta analysis 3 manual
However, manual meta-analyses are not scalable, and the corresponding degrees of freedom are difficult to control statistically. In principle, studies can be manually annotated as carefully as one likes. Choosing which studies to include in a meta-analysis can be challenging. However, they can only address neuroscience concepts that are easy to define.
Existing meta-analysis methods focus on identifying effects reported consistently across the literature, to distinguish true discoveries from noise and artifacts. Automating CBMA methods across the literature, as in NeuroSynth ( Yarkoni et al., 2011), enables large-scale analyses of brain-imaging studies, giving excellent statistical power. Coordinate-Based Meta-Analysis (CBMA) methods ( Laird et al., 2005 Wager et al., 2007 Eickhoff et al., 2009) assess the consistency of results across studies, comparing the observed spatial density of reported brain stereotactic coordinates to the null hypothesis of a uniform distribution. Meta-analyses can give objective views of the field, to ground a review article or a discussion of new results.
In addition, such a task is fundamentally difficult due to the many different aspects of behavior, as well as the diversity of the protocols used to probe them. There are too many studies to manually collect and aggregate their findings. But compiling an answer to a specific question from this impressive number of results is a daunting task. Finding consistent trends in the knowledge acquired across these studies is crucial, as individual studies by themselves seldom have enough statistical power to establish fully trustworthy results ( Button et al., 2013 Poldrack et al., 2017). IntroductionĮach year, thousands of brain-imaging studies explore the links between brain and behavior: more than 6000 publications a year contain the term ‘neuroimaging’ on PubMed. The resulting meta-analytic tool,, can ground hypothesis generation and data-analysis priors on a comprehensive view of published findings on the brain. We capture the relationships and neural correlates of 7547 neuroscience terms across 13 459 neuroimaging publications. This approach handles text of arbitrary length and terms that are too rare for standard meta-analysis. Our multivariate model predicts the spatial distribution of neurological observations, given text describing an experiment, cognitive process, or disease.
We propose a new paradigm, focusing on prediction rather than inference. Thus, large-scale meta-analyses only tackle single terms that occur frequently. Existing meta-analysis methods perform statistical tests on sets of publications associated with a particular concept. The variety of human neuroscience concepts and terminology poses a fundamental challenge to relating brain imaging results across the scientific literature. Reaching a global view of brain organization requires assembling evidence on widely different mental processes and mechanisms.
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