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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 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. Furthermore, fNIRS measures the concentration of both oxygenated and deoxygenated hemoglobin, both of which may be of scientific interest. We propose a fully Bayesian time-varying autoregressive model to analyze fNIRS data within the multivariate DLM framework. 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 processes vary with time. Via simulation studies, we show that this model naturally handles motion artifacts and gives good statistical properties. The model is then applied to a fNIRS data set.