Connectivity Analysis of Human Cortical Networks for Mapping and Decoding

During cued speech and movement, information propagates from primary sensory areas, through association areas, to primary and supplementary motor and language areas. Traditionally, the neural activity in each region has been probed independently at the level of individual neurons or small neuronal populations. This approach suffers from a lack of information about how different neuronal populations interact to relay information through the cortical network, actuate movement or speech, and monitor feedback. Recently, however, advances in computational capacity have permitted the modeling of dynamic neural propagation with high spatial and temporal resolution. These dynamic network models may contain new information about processing that can inform cortical mapping for clinical and scientific purposes, or improve the ability to decode movement or speech from neural signals. Such decoded movement or speech may be used as a control signal for brain machine interfaces. This dissertation explores the utility of time-varying neural connectivity features for mapping the neural correlates of movement and speech, and decoding movement and speech from neural signals. All neural data are recorded from human subjects ii