Structure-function correspondence in the cortical organization of language

Abstract Characterizing the organization of the language system in individual brains has long been a challenge in both research and clinical practice. Advances in fMRI-based localization of functionally specialized brain regions have revealed that the language areas in individual brains are highly focal and reliably selective in their unique response to language. However, cortical areas are also defined by their unique structural characteristics, and these are not well understood for language areas. To address this knowledge gap, the proposed work will use a structure–function predictive framework to accurately localize language areas in individual brains. This approach will test the hypothesis that cortical language areas are defined by distinct structural features. Aim 1 and Aim 2 both use local architectural features and long-range connectivity profiles to predict the location of a fMRI-defined cortical language area. In Aim 1, an interpretable regression model is used to identify the structural features supporting language computations in the specialized cortical areas. With Aim 2, we instead use a more complex model to localize language areas with very high accuracy, supporting the existence of structure–function correspondence while also providing a useful localization tool with far-reaching applications. Aim 1 and Aim 2 reflect distinct approaches to understanding structure–function correspondence. In Aim 2, the model is capable of extracting complex feature representations and combining information across levels of granularity, which impedes interpretation of the important structural features but supports high accuracy classification. Lastly, in Aim 3, the predictive models are applied to individuals with typical or impaired language skills to understand how cortical arealization relates to interindividual differences in the capacity for language. We hypothesize that accuracy of the structure–function predictions will be significantly associated with language test scores, suggesting a relationship between cortical arealization and behavior. This work will contribute to research and clinical work by (i) providing structural evidence supporting the existence of cortical language areas, (ii) developing new methods for localizing the language network, and (iii) advancing our understanding of the relationship between cortical arealization and language competence.