References of "van Breukelen, Gerard J P"
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See detailOptimal design for nonlinear estimation of the hemodynamic response function.
Maus, Bärbel ULg; van Breukelen, Gerard J P; Goebel, Rainer et al

in Human Brain Mapping (2012), 33(6), 1253-1267

Subject-specific hemodynamic response functions (HRFs) have been recommended to capture variation in the form of the hemodynamic response between subjects (Aguirre et al., [1998]: Neuroimage 8:360–369 ... [more ▼]

Subject-specific hemodynamic response functions (HRFs) have been recommended to capture variation in the form of the hemodynamic response between subjects (Aguirre et al., [1998]: Neuroimage 8:360–369). The purpose of this article is to find optimal designs for estimation of subject-specific parameters for the double gamma HRF. As the double gamma function is a nonlinear function of its parameters, optimal design theory for nonlinear models is employed in this article. The double gamma function is linearized by a Taylor approximation and the maximin criterion is used to handle dependency of the D-optimal design on the expansion point of the Taylor approximation. A realistic range of double gamma HRF parameters is used for the expansion point of the Taylor approximation. Furthermore, a genetic algorithm (GA) (Kao et al., [2009]: Neuroimage 44:849–856) is applied to find locally optimal designs for the different expansion points and the maximin design chosen from the locally optimal designs is compared to maximin designs obtained by m-sequences, blocked designs, designs with constant interstimulus interval (ISI) and random event-related designs. The maximin design obtained by the GA is most efficient. Random event-related designs chosen from several generated designs and m-sequences have a high efficiency, while blocked designs and designs with a constant ISI have a low efficiency compared to the maximin GA design. [less ▲]

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See detailOptimal multi-subject fMRI experiments
Maus, Bärbel ULg; van Breukelen, Gerard J P; Goebel, R et al

Conference (2011)

Functional magnetic resonance imaging is a neuroimaging method which is used to study the human brain and its functional areas. In multi-subject fMRI experiments, data from several subjects is collected ... [more ▼]

Functional magnetic resonance imaging is a neuroimaging method which is used to study the human brain and its functional areas. In multi-subject fMRI experiments, data from several subjects is collected while these subjects perform each the same task of interest, e.g., passive viewing of houses presented on a screen, in the scanner. In my talk optimal designs for multi-subject fMRI experiments with fixed experimental budget are considered. The optimal combination of number of subjects and fMRI scanner time/imaging time per subject will be studied. Analytical and numerical results based on a linear mixed effects model with uncorrelated and correlated errors will be presented for common parameters of fMRI experiments. It will be shown how the optimal number of subjects and optimal scanner [less ▲]

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See detailOptimal and robust event-related designs for fMRI
Maus, Bärbel ULg; van Breukelen, Gerard J P; Goebel, R et al

Conference (2011)

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See detailOptimal design of multi-subject blocked fMRI experiments.
Maus, Bärbel ULg; van Breukelen, Gerard J P; Goebel, Rainer et al

in NeuroImage (2011), 56(3), 1338-1352

The design of a multi-subject fMRI experiment needs specification of the number of subjects and scanning time per subject. For example, for a blocked design with conditions A or B, fixed block length and ... [more ▼]

The design of a multi-subject fMRI experiment needs specification of the number of subjects and scanning time per subject. For example, for a blocked design with conditions A or B, fixed block length and block order ABN, where N denotes a null block, the optimal number of cycles of ABN and the optimal number of subjects have to be determined. This paper presents a method to determine the optimal number of subjects and optimal number of cycles for a blocked design based on the A-optimality criterion and a linear cost function by which the number of cycles and the number of subjects are restricted. Estimation of individual stimulus effects and estimation of contrasts between stimulus effects are both considered. The mixed-effects model is applied and analytical results for the A-optimal number of subjects and A-optimal number of cycles are obtained under the assumption of uncorrelated errors. For correlated errors with a first-order autoregressive (AR1) error structure, numerical results are presented. Our results show how the optimal number of cycles and subjects depend on the within- to between-subject variance ratio. Our method is a new approach to determine the optimal scanning time and optimal number of subjects for a multi-subject fMRI experiment. In contrast to previous results based on power analyses, the optimal number of cycles and subjects can be described analytically and costs are considered. [less ▲]

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See detailOptimal design for functional magnetic resonance imaging (fMRI) experiments based on linear models
Maus, Bärbel ULg; van Breukelen, Gerard J P; Goebel, R et al

Conference (2010)

In the first part of this presentation it will be shown how the general linear model is used to describe experimental functional magnetic resonance imaging (fMRI) data from one subject. Functional ... [more ▼]

In the first part of this presentation it will be shown how the general linear model is used to describe experimental functional magnetic resonance imaging (fMRI) data from one subject. Functional magnetic resonance imaging is a neuroimaging method which is used to study the human brain and its functional areas. Based on the general linear model, optimal designs for one-subject fMRI experiments can be obtained by application of the D- and A-optimality criterion. Because of the huge design space for fMRI experiments, a genetic algorithm (GA) is employed to find optimal designs for fMRI experiments based on a multi-objective design criterion. The second part of the presentation will focus on the application of mixed effects models in analysis of fMRI experiments from multiple subjects. Optimal designs for multi-subject experiments are considered and the optimal combination of number of subjects and fMRI scanner time/imaging time per subject will be studied with respect to a linear cost function. [less ▲]

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See detailRobustness of optimal design of fMRI experiments with application of a genetic algorithm.
Maus, Bärbel ULg; van Breukelen, Gerard J P; Goebel, Rainer et al

in NeuroImage (2010), 49(3), 2433-2443

In this paper we apply the genetic algorithm developed by Kao et al. (2009) to find designs which are robust against misspecification of the error autocorrelation. Two common optimality criteria, the A ... [more ▼]

In this paper we apply the genetic algorithm developed by Kao et al. (2009) to find designs which are robust against misspecification of the error autocorrelation. Two common optimality criteria, the A-optimality criterion and the D-optimality criterion, based upon a general linear model are employed to obtain locally optimal designs for a given value of the autocorrelation. The maximin criterion is then used to obtain designs which are robust against misspecification of the autocorrelation. Furthermore, robustness depending on the choice of optimality criterion is evaluated. We show analytically and empirically that the A- and D-optimality criterion will result in different optimal designs, e.g. with different stimulus frequencies. Optimal stimulus frequency for the A-optimality criterion has been derived by Liu et al. (2004) whereas we derive here the optimal stimulus frequency for the D-optimality criterion. Conclusions about the robustness of an optimal design against misspecification of model parameters and choice of optimality criterion are drawn based upon our results. [less ▲]

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