A0583
Title: A time-varying AR, bivariate DLM 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 measure brain activity via the blood oxygen level-dependent signal. 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 closer, 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. Furthermore, fNIRS measures the concentration of both oxygenated and deoxygenated hemoglobin, both of which may be of scientific interest. A fully Bayesian time-varying autoregressive model is proposed to analyze fNIRS data within the multivariate DLM framework. Low-frequency drift is modelled with a variable B-spline model (both locations and number of knots are allowed to vary). Both the model error and the auto-regressive processes vary with time. Simulation studies show that this model naturally handles motion artifacts and has good statistical properties. The model is then applied to a fNIRS data set.
