The integration of large-scale neural network modeling and functional brain imaging in speech motor control

Speech production demands a number of integrated processing stages. The system must encode the speech motor programs that command movement trajectories of the articulators and monitor transient spatiotemporal variations in auditory and somatosensory feedback. Early models of this system proposed that independent neural regions perform specialized speech processes. As technology advanced, neuroimaging data revealed that the dynamic sensorimotor processes of speech require a distributed set of interacting neural regions. The DIVA (Directions into Velocities of Articulators) neurocomputational model elaborates on early theories, integrating existing data and contemporary ideologies, to provide a mechanistic account of acoustic, kinematic, and functional magnetic resonance imaging (fMRI) data on speech acquisition and production. This large-scale neural network model is composed of several interconnected components whose cell activities and synaptic weight strengths are governed by differential equations. Cells in the model are associated with neuroanatomical substrates and have been mapped to locations in Montreal Neurological Institute stereotactic space, providing a means to compare simulated and empirical fMRI data. The DIVA model also provides a computational and neurophysiological framework within which to interpret and organize research on speech acquisition and production in fluent and dysfluent child and adult speakers. The purpose of this review article is to demonstrate how the DIVA model is used to motivate and guide functional imaging studies. We describe how model predictions are evaluated using voxel-based, region-of-interest-based parametric analyses and inter-regional effective connectivity modeling of fMRI data.