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Previous neuroimaging research has identified a number of brain regions sensitive to different aspects of linguistic processing, but precise functional characterization of these regions has proven challenging. We hypothesize that clearer functional specificity may emerge if candidate language-sensitive regions are identified functionally within each subject individually, a method that has revealed striking functional specificity in visual cortex but that has rarely been applied to neuroimaging studies of language. This method enables pooling of data from corresponding functional regions across subjects rather than from corresponding locations in stereotaxic space (which may differ functionally because of the anatomical variability across subjects). However, it is far from obvious a priori that this method will work as it requires that multiple stringent conditions be met. Specifically, candidate language-sensitive brain regions must be identifiable functionally within individual subjects in a short scan, must be replicable within subjects and have clear correspondence across subjects, and must manifest key signatures of language processing (e.g., a higher response to sentences than nonword strings, whether visual or auditory). We show here that this method does indeed work: we identify 13 candidate language-sensitive regions that meet these criteria, each present in &gt;or=80% of subjects individually. The selectivity of these regions is stronger using our method than when standard group analyses are conducted on the same data, suggesting that the future application of this method may reveal clearer functional specificity than has been evident in prior neuroimaging research on language.

New Method for fMRI Investigations of Language: Defining ROIs Functionally in Individual Subjects