Elsevier

NeuroImage

Volume 62, Issue 2, 15 August 2012, Pages 1157-1162
NeuroImage

Review
The development of event-related fMRI designs

https://doi.org/10.1016/j.neuroimage.2011.10.008Get rights and content

Abstract

The diversity of experimental designs that can be used with functional magnetic resonance imaging (fMRI) has played a key role in its widespread application to studies of human cognition. This flexibility is possible because the fMRI response to external stimuli is remarkably well approximated as the response of a linear time variant system. The experimental demonstration of this property provided the foundation for the development of event-related designs, in which the fMRI response is modeled as the linear summation of the hemodynamic response to discrete events. Building upon prior work from the fields of engineering, neuroscience, and statistics, researchers in the field have created a rich collection of event-related designs and developed a rigorous theoretical framework for characterizing and optimizing the performance of designs. Ongoing challenges include the optimization of designs in the presence of experimental constraints and the development of more time-efficient optimization algorithms.

Introduction

Event-related designs are now a standard part of the fMRI experimental repertoire. The paper by Scott Huettel in this issue provides an excellent discussion of the importance of event-related designs for cognitive neuroscience. In this paper, I will review the technical development of designs for event-related fMRI. In the spirit of this special issue of NeuroImage, in addition to describing the overall arc of the technical development of experimental designs for fMRI, I will also provide a bit of personal perspective on some of the challenges faced and insights gained along the path.

One can trace the birth of event-related fMRI design to the work of Blamire et al. (1992), which appeared in the same year that the first papers demonstrating human fMRI were published. This work showed that one could measure the fMRI response to brief stimuli that were widely spaced and provided an early example of what is now commonly referred to as the hemodynamic response function. It was also the first example of a slow event related design in which the wide spacing between stimuli allowed the experimenter to isolate the responses to individual stimuli. Such designs have been used to measure the brain response to isolated events (McCarthy et al., 1997), and have also been a standard tool for basic studies of the dynamics of the hemodynamic response (Behzadi and Liu, 2006, Huettel et al., 2001).

One of the key advances needed for the progression to more complex designs was the introduction of the convolution model for fMRI analysis by Friston et al. (1994). This framework was subsequently extended to the analysis of slow event related fMRI experiments (Josephs et al., 1997). A key assumption of these studies was that the fMRI signal could be modeled as the output of a linear time invariant system. The seminal work of Boynton et al. (1996) provided the field with the experimental data showing that the assumption of linear time variance was reasonable. Additional support for this assumption was subsequently provided by Dale and Buckner (1997).

Section snippets

Selective averaging and rapid presentation rates

The demonstration of linearity gave researchers in the field the confidence to explore fast event related designs in which the hemodynamic responses from different stimuli could overlap. The initial step was taken by Dale and Buckner (1997), who demonstrated the ability to obtain robust activation maps when using left and right-hemifield visual checkerboard stimulus presented in a random order and spaced as little as 2 s apart. Adopting a key concept from prior work on selective averaging of

Optimizing design performance

The promising results obtained with randomized event-related designs led researchers in the field to ask what type of design was optimal. Was it a design with randomized rapid presentation of events or a periodic single trial design in which the spacing between stimuli was fixed and on the order of the length of the hemodynamic response? How did these event-related designs compare to the more traditional blocked design? In 1999, three related efforts used the framework of the general linear

Extension to multiple trial types and the importance of m-sequences

Once the fundamental trade-off between detection power and estimation efficiency had been demonstrated for experimental designs containing one trial type plus a null event (e.g. a simple design with visual flicker as the trial type and a fixation cross as null event), the next logical step was to see if this trade-off held for designs with multiple trial types. Although preliminary simulations indicated that the trade-off observed for designs with multiple trial types was similar to that found

Modeling assumptions with basis functions

A deeper understanding of the trade-offs inherent in fMRI design can be gained by revisiting the work of Friston et al. (1999), who pointed out that the use of basis functions allows us to flexibly describe our assumptions about the shape of the hemodynamic response. If we assume that the shape is completely known, then there is just one basis function that is equal to the assumed shape. The main drawback to making this assumption is that the shape of the hemodynamic response can vary greatly

The geometry of fMRI designs

As the mathematics underlying fMRI design can sometimes be a bit daunting, I have found it helpful over the years to use geometric arguments to understand the basic principles. To develop a geometric picture, we start with the general linear model y = Xh + n where y is the observed fMRI time series, X is the design matrix that contains the stimulus timing information, h is the hemodynamic response function, and n represents additive noise. For any given level of additive noise, the optimal design

Spectral interpretation

Because the spread of the singular values is related to the shape of the power spectrum of the design (Haykin, 1996), there is a corresponding spectral interpretation of fMRI designs (see lefthand column of Fig. 2). A design with a dominant singular value, such as a block design, will have a dominant peak in its power spectrum which allows it to concentrate most of its energy at a single frequency. On the other hand, a design with equally spread eigenvalues corresponds to a flat power spectrum.

Genetic algorithms

The mathematical framework that has been developed for fMRI design tell us what levels of performance are achievable, but except for a few limiting cases (such as m-sequences and block designs), can only provide general guidance on how to generate additional designs (e.g. clustered m-sequences). These designs can achieve various trade-offs between efficiency and power, but are not guaranteed to find optimal points within the large search space. In addition, the basic framework does not provide

The future of fMRI design

Although the basic trade-offs involved in fMRI design are now generally well known by the community, the actual design of experiments has not necessarily benefited from the latest technical developments in the field. Many researchers still use routines provided in open source fMRI software packages to randomly generate and evaluate designs, even though m-sequences or genetic algorithms would provide better performance in many cases. Of course, even a random search is preferred to arbitrarily

Acknowledgments

This work was supported in part by NIH grants , .

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