Abstract:
Aphasia is an acquired language disorder with a diverse set of symptoms that can affect virtually any linguistic modality across both the comprehension and production of spoken language. Partial recovery of language function after injury is common but typically incomplete. Rehabilitation strategies focus on behavioral training to induce plasticity in underlying neural circuits to maximize linguistic recovery. Understanding the different neural circuits underlying diverse language functions is a key to developing more effective treatment strategies. This chapter discusses a systems identification analytic approach to the study of linguistic neural representation. The focus of this framework is a quantitative, model-based characterization of speech and language neural representations that can be used to decode, or predict, speech representations from measured brain activity. Recent results of this approach are discussed in the context of applications to understanding the neural basis of aphasia symptoms and the potential to optimize plasticity during the rehabilitation process.