The neural architecture of pragmatic processing

项目总结

PROJECT SUMMARY Functional magnetic resonance imaging (fMRI) has been invaluable for illuminating the brain’s functional architecture. It has been especially important for cognitive abilities where animal models have limited utility, like language. However, the field of human cognitive neuroscience has been struggling in that many findings are not replicable, suffer from statistical flaws, or are difficult to compare across studies due to the use of divergent analytic approaches. Many have now recognized the need for more robust, replicable, and meaningful science. We here propose the development and dissemination of a powerful tool that can improve the field’s ability to establish a robust and cumulative research enterprise. Leveraging the data collected in our lab over the last ten years (>800 neurotypical participants across >1,200 scanning sessions), we propose to develop and make publicly available probabilistic functional atlases for four brain networks critical for high-level cognition: the language-selective network (which supports language processing; Fedorenko et al., 2010, 2011), the domain-general Multiple Demand (MD) network (which supports executive functions like cognitive control; Duncan, 2010), the Default Mode network (DMN) (which supports internally-directed cognition and construction of situation models; Buckner & DiNicola, 2019), and the Theory of Mind network (which supports general social inference; Saxe & Kanwisher, 2003). These atlases will be created based on large numbers of individual activation maps for well-established and extensively validated ‘localizer’ tasks targeting these networks (700+ participants for the first three networks, and ~150 participants for the ToM network) and can be used to estimate the probability that any given location in the common brain space belongs to a particular functional network. In Aim 1, we will develop these probabilistic atlases. To do so, we will aggregate all the relevant data for each of the localizer tasks, preprocess it through a uniform pipeline across two most commonly used software packages (SPM, Friston, 1997; and FreeSurfer, Dale et al., 1999), and overlay the individual activation maps in the relevant volume and surface spaces. We will additionally extract a set of key individual-level neural markers, so that their distributions can be used normatively for comparisons with other populations. In Aim 2, we will make the atlases (and constituent individual activation maps and neural markers) publicly available. To do so, we will create a robust and interactive web-based platform for the dissemination of the atlases. The proposed project is a critical step to bridge two fundamentally different and currently disjoint analytic traditions in functional brain imaging—group-averaging approaches and functional localization in individual brains—by providing common reference frames: probabilistic functional atlases based on well-established and widely used localizers for four high-level brain networks. The ability to more straightforwardly compare findings across diverse studies is bound to lead to more rigorous and transparent science thus improving our understanding of human communication and related abilities.