Abstract
Previous work investigated a range of spatio-temporal constraints for fMRI data analysis to provide robust detection of neural activation. We present a mixture-based method for the spatio-temporal modelling of fMRI data. This approach assumes that fMRI time series are generated by a probabilistic superposition of a small set of spatio-temporal prototypes (mixture components). Each prototype comprises a temporal model that explains fMRI signals on a single voxel and the model's “region of influence” through a spatial prior over the voxel space. As the key ingredient of our temporal model, the Hidden Process Model (HPM) framework proposed in Hutchinson et al. (2009) is adopted to infer the overlapping cognitive processes triggered by stimuli. Unlike the original HPM framework, we use a parametric model of Haemodynamic Response Function (HRF) so that biological constraints are naturally incorporated in the HRF estimation. The spatial priors are defined in terms of a parameterised distribution. Thus, the total number of parameters in the model does not depend on the number of voxels. The resulting model provides a conceptually principled and computationally efficient approach to identify spatio-temporal patterns of neural activation from fMRI data, in contrast to most conventional approaches in the literature focusing on the detection of spatial patterns. We first verify the proposed model in a controlled experimental setting using synthetic data. The model is further validated on real fMRI data obtained from a rapid event-related visual recognition experiment (Mayhew et al., 2012). Our model enables us to evaluate in a principled manner the variability of neural activations within individual regions of interest (ROIs). The results strongly suggest that, compared with occipitotemporal regions, the frontal ones are less homogeneous, requiring two HPM prototypes per region. Despite the rapid event-related experimental design, the model is capable of disentangling the perceptual judgement and motor response processes that are both activated in the frontal ROIs. Spatio-temporal heterogeneity in the frontal regions seems to be associated with diverse dynamic localizations of the two hidden processes in different subregions of frontal ROIs.
Highlights
Since the first report of the Blood Oxygen Level-Dependent (BOLD) effect in humans, fMRI has been established as a powerful tool to non-invasively study the link between cognitive processes and the haemodynamic (BOLD) response that indirectly reflects evoked neuronal activity (Ogawa et al, 1990)
Shen et al / NeuroImage 84 (2014) 657–671 the former approach, the latter one is computationally more efficient and yields more interpretable models. In this contribution we propose a new method for spatio-temporal modelling of fMRI data that advances the latter approach in four crucial aspects: 1. Previously, the localized temporal prototypes have mostly been General Linear Models (GLMs) (Friston et al, 1995), which could be relatively simple
Spatio-temporal behaviour of fMRI time series is summarized by a small number of prototypical temporal patterns
Summary
Since the first report of the Blood Oxygen Level-Dependent (BOLD) effect in humans, fMRI has been established as a powerful tool to non-invasively study the link between cognitive processes and the haemodynamic (BOLD) response that indirectly reflects evoked neuronal activity (Ogawa et al, 1990). Because of the limitation in sampling resolution and signal-to-noise ratio, statistical analysis of fMRI data plays an important role in revealing this relationship (Friston, 2005; Lindquist, 2008). The primary aim of fMRI data analysis is the detection of activated brain areas in response to given stimulus types. This is intrinsically related to estimation of the underlying temporal dynamics, usually referred to as characterisation of the Haemodynamic Response Function (HRF). Due to low sampling resolution and poor signal-to-noise ratio, an accurate estimation of HRF shapes is only available from a group of voxels eliciting signal fluctuations correlated with the paradigm, usually referred to as region of interest (ROI). Only spatio-temporal modelling of fMRI data can account for the relationship between a stimulus (or cognitive task) and the cortical response measured with fMRI (Derado et al, 2010; Gossl et al, 2001; Penny et al, 2005; Woolrich et al, 2004b)
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