A1191
Title: A Bayesian semi-parametric model for functional near-infrared spectroscopy data
Authors: Timothy Johnson - University of Michigan (United States) [presenting]
Abstract: Functional near-infrared spectroscopy (fNIRS) is a relatively new neuroimaging technique. It is a low-cost, portable, and non-invasive method to monitor brain activity. Similar to fMRI, it measures changes in the level of blood oxygen in the brain. Its time resolution is much finer than fMRI. However, its spatial resolution is much courser-similar to EEG or MEG. fNIRS is finding widespread use on young children who cannot remain still in the MRI magnet, and it can be used in situations where fMRI is contraindicated---such as with patients who have cochlear implants. A fully Bayesian semi-parametric model is proposed to analyze fNIRS data. Two defining features delineating my model from standard methods are using a time-varying autoregression component to handle the temporal correlation and B-splines bases to model low-frequency drift and motion artefacts. Simulation studies show that this Bayesian model easily handles motion artefacts and results in better statistical properties than the most widely used model, referred to as the AR-IRLS model. Then the model is fitted to two real datasets.
