Title: A time-varying AR coefficient model of 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 whom cannot remain still in the MRI magnet and it can be used in situations where fMRI is contraindicated---such as with patients whom have cochlear implants. We propose a fully Bayesian time-varying autoregressive model to analyze fNIRS data. The hemodynamic response function is modeled with the canonical HRF and the low frequency drift with a variable B-spline model (both locations and number of knots are allowed to vary). Both the model error and the auto-regressive process vary with time. Via a simulation studies, we show that this model naturally handles motion artifacts and gives good statistical properties. The model is then apply to a fNIRS study.