Already the hallmark of genetic data and also of neurobiological data in animals (e.g., the Allen Brain Atlas for the mouse), the idea of mining fMRI data has been around for over a decade (Van Horn and Gazzaniga, 2002) but has
come into its own only very recently (Yarkoni et al., 2011). With the launch of several large-scale funding efforts, such as the NIMH-funded “Human Connectome Project,” the Allen Institute for Brain Science’s “Project Mindscope,” the European “Blue Brain/ Human Brain” project, and the “BRAINS” project just recently announced by president Obama, there is no question that the next few years will see a massive ballooning of data, together with tools to mine it. Although to some extent these resources can be used simply as one component Selleck MDV3100 in the pipeline of an experiment,
they also can be the data to be studied in their own right, revealing new patterns. This then brings us to our final future direction: computational neuroscience that selleck compound combines measures of brain function and behavior with sophisticated mathematical models. There are several advantages to building concepts based on computational models, including precision, parametric quantification, and easy expandability. But one feature stands out in particular: such models may be unique in their applicability across a very wide range of levels of analysis, from cells to brain systems to behavior. Although model-based fMRI has been quite widely adopted in studies of learning and decision making, to date, relatively few have directly applied it to social neuroscience. One early example studied learning behavior in a strategic game and fit the fMRI data to computational models; the best fitting model showed not only that participants were tracking opponents’ actions (as a poorer-performing model showed) but also that the participants out understood that their opponents were tracking them (Hampton et al., 2008). The ability to link distinct computational components of a model to distinct neural regions
offers tremendous promise for understanding more precisely what it is that these brain regions contribute (Behrens et al., 2009 and Dunne and O’Doherty, 2013). Other studies have used computational models to identify neural correlates of tracking the quality of other peoples’ advice (Behrens et al., 2008 and Boorman et al., 2013) or applied the approach to understanding dysfunction in psychiatric illness (Montague et al., 2012). The computational approach to social neuroscience questions, although brand-new, is a growing subfield with substantial activity and promise for the future. Social neuroscience faces perennial themes of prediction and causality: fMRI, as is well known, is a purely correlational method.