Expanding a Neural Model of Speech Using Virtual Lesions and Connectivity Studies

University Of Texas Hlth Science Center, San Antonio TX

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Abstract

DESCRIPTION (provided by applicant): The long-range goal of this research program is the development of a system-level model of the human speech-motor system (SMS) that encompasses normal and disordered speech motor control. The goal of the current proposal is to test portions of the Directions into Velocities of Articulators (DIVA) a model of speech production that has both behavioral and neural components. In particular, we will the feedforward subsystem that includes primary mouth motor cortex (MC), ventral premotor cortex (PMv), and the supplementary motor area (SMA). This proposal will adopt a two stage strategy to test the DIVA model. The DIVA model hypothesizes that disruption of different areas will have differential effects on speech production. We will test this hypothesis by recording speech behavior while creating transient 'virtual'lesions in these cortical areas by using image guided robotic transcranial magnetic stimulation (irTMS) (Aim 1). The DIVA also makes specific predictions regarding the connections of these regions, and this will be verified by measuring the inter-regional connectivity of these regions by task independent connectivity mapping using TMS-PET (Aim 2). Data from this project will be used for R01 grants that will (1) explore other neural regions involved in the feedforward subsystem as well as the regions included in the feedback subsystem;and (2) develop models of motor speech disorders (e.g., stuttering, apraxia of speech, hyperkinetic dysarthria, and ataxic dysarthria) using the DIVA model. PUBLIC HEALTH RELEVANCE The long-range goal of our research program is the development of a system-level neural model of the human speech-motor system that integrates lesion and functional imaging data. Such a model will eventually account for normal and disordered speech motor control. A detailed mapping of the normal speech motor network will allow for predictive models of speech disorders that have a strong potential in driving the clinical enterprise by improving diagnosis and assisting in the rational design of behavioral treatments, identifying the reorganization the speech-motor system has undergone in speech disorders, and assist in understanding the mechanism of action of behavioral therapies.