A spatial mixture approach to inferring sub-ROI spatio-temporal patterns from rapid event-related fMRI data

Previous works investigated a range of spatio-temporal models for fMRI data analysis to provide robust determination of functional region-of-interest (ROI). We present a novel spatio-temporal fMRI model that is suitable for identifying a number of distinct temporal patterns and their spatial support in the voxel space. Accordingly, fMRI signals on a single voxel are modeled as a probabilistic superposition of those temporal patterns. The spatially varying influence of individual patterns is defined in terms of a parameterised function. The temporal pattern is characterised by both the underlying hemodynamic response function (HRF) and a time series of the individual stimulus-response magnitudes, which makes the proposed model particularly suitable for modeling rapid event-related fMRI data. Moreover, a parametric approach is adopted to represent the HRFs. The resulting methodology is conceptually principled and computationally efficient. We first verify the proposed model in a controlled experimental setting using synthetic data. The model is further applied to analyzing real fMRI data, with focus on functional homogeneity within individual ROIs.