Statistical modeling of time‐dependent fMRI activation effects

Abstract

Functional magnetic resonance imaging (fMRI) activation detection within stimulus-based experimental paradigms is conventionally based on the assumption that activation effects remain constant over time. This assumption neglects the fact that the strength of activation may vary, for example, due to habituation processes or changing attention. Neither the functional form of time variation can be retrieved nor short-lasting effects can be detected by conventional methods. In this work, a new dynamic approach is proposed that allows to estimate time-varying effect profiles and hemodynamic response functions in event-related fMRI paradigms. To this end, we incorporate the time-varying coefficient methodology into the fMRI general regression framework. Inference is based on a voxelwise penalized least squares procedure. We assess the strength of activation and corresponding time variation on the basis of pointwise confidence intervals on a voxel level. Additionally, spatial clusters of effect curves are presented. Results of the analysis of an active oddball experiment show that activation effects deviating from a constant trend coexist with time-varying effects that exhibit different types of shapes, such as linear, (inversely) U-shaped or fluctuating forms. In a comparison to conventional approaches, like classical SPM, we observe that time-constant methods are rather insensitive to detect temporary effects, because these do not emerge when aggregated across the entire experiment. Hence, it is recommended to base activation detection analyses not merely on time-constant procedures but to include flexible time-varying effects that harbour valuable information on individual response patterns.

Bayesian models for functional magnetic resonance imaging data analysis

“…Information from EEG/MEG data has been incorporated in some of the modeling approaches for fMRI data that we have previously described. For example, Kalus et al extended the approach in Kalus et al by using EEG‐informed spatial priors in their Bayesian variable selection approach to detect brain activation. Specifically, they relate the prior activation probabilities to a latent predictor stage ζ = ( ζ 1 , …, ζ V ) T via a probit link p ( γ v = 1) = Φ ( ζ v ), with Φ the standard normal cdf and ζ v consisting of an intercept term and an EEG effect, that is where J v , v = 1, …, V is the continuous spatial EEG information and where 0, glob and flex indicate three types of predictors: predictor 0 contains a spatially‐varying intercept ς 0 = ( ς 0,1 , …, ς 0, V ) T , and corresponds to an fMRI activation detection scheme without incorporating EEG information; predictor glob contains a global EEG effect ς G in addition to the intercept; predictor flex contains a spatially‐varying EEG effect ς = ( ς 1 , …, ς V ) T .…”