Grant: CIHR Fellowship

One percent of the population is affected by stuttering. Treatment programs for people who stutter rely heavily on speech sequence learning and are known to fail. It is widely thought that the basal ganglia loop, a brain circuit involved in sequence learning, has a role in the cause of stuttering. The brain mechanisms underlying speech sequence learning in people who stutter are not understood. The main goal of our research project is to clarify these mechanisms. To accomplish this goal we will create a computer model of stuttering that accounts for the brain and behavioral breakdowns of the disorder. We will build upon a well established computer model of the brain's role in normal speech learning known as DIVA (Directions Into Velocities of Articulators). Each one of the components in the DIVA model represents a brain structure. It is possible to alter the interaction of the components in the DIVA model to induce stuttering like output. The basal ganglia loop is made up of many brain structures which are represented in the DIVA model. We will use the model to predict how these structures interact, or fail to interact, before and after a speech syllable sequence learning task under normal and stuttering conditions. The predictions are output in the form of a hypothesized map of functional magnetic resonance imaging (fMRI) brain activation. We will then test our predictions with human participants who do and do not stutter. We will use fMRI to measure the involvement of the basal ganglia loop brain structures before and after the same speech syllable sequence learning task used to make our predictions. Our results will inform us about the involvement of the basal ganglia loop mechanism in the speech sequence learning breakdowns experienced by people who stutter. As speech sequence learning is heavily used in stuttering treatment, our results will inform future research for the maximization of treatment effectiveness and maintenance of speech sequencing skills.