|
Abstract:
|
The goal of this talk is to present a general class of models for changes in brain connectivity during resting state or task. Our proposed models is a switching-regime vector autoregressive model (S-VAR) on the latent factors. This model captures dynamic changes in brain states (or brain connectivity) as the participant is exposed to different types of audio-visual and cognitive stimuli or during resting state. The experiment setting promotes a continuous interaction between the stimuli, and consequently, the states. In our model, those transitions between states are modeled by probabilities that are allowed to depend on the combinations of previously present stimuli. As part of this project, one contribution is a computer toolbox which implements seamless integration between model estimation and visualization of results. This work is in collaboration with Chee-Ming Ting (KAUST) and Marco Pinto.
|
