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Free keywords:
functional magnetic resonance imaging, time varying activation and hemodynamic response function, varying coefficient model, panalized least squares estimation, event-related functional magnetic resonance imaging, auditory oddball
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. Hum Brain Mapp 36:731-743, 2015. (c) 2014
Wiley Periodicals, Inc.
