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Abstract:
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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. In this talk, I propose a fully Bayesian semi-parametric hierarchical model to analyze fNIRS data. The hemodynamic response function is modeled using a Bayesuab P-spline as well as the low frequency drift. We assume the residual time-series is a high-order AR process and adopt a spike-and-slab prior to shrink unnecessary AR parameters to zero. This also has the added benefit of automatically removing motion artifacts. Our current work is a model for a single time-series of fNIRS data. However, our model is easily adapted to handle the bivariate fNIRS time-series data at a single detector (oxygenated-and deoxygenated-hemoglobin). It can also easily be adapted to handle the spatial aspects of an array of detectors as well as a population-level analysis.
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