Causal Modeling: Methods and Their Application to Speech and Language

With the advent of non-invasive functional neuroimaging methods in the late 1970s, localization theories of language—based on brain lesion studies—have long given way to distributed models of language, implicating a network of sequential and parallel functional connections. This renders the processes within the speech and language network well suited to effective connectivity analysis using causal modeling approaches. Despite the large number of studies examining various components of the language system, the relationship between these processes and the directionality of causal influences between the brain regions mediating these processes remain far less understood. The chapter presents select studies that have used causal modeling to investigate the neural basis of speech and language networks in healthy controls and clinical populations drawing on measures of directed functional connectivity and effective connectivity like Granger Causality and DCM. Applications to novel data from magnetoencephalography illustrate the usefulness of this approach to disorders of higher level cognition.