References of "Maus, Bärbel"
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See detailOptimale experimentele studies voor functionele MRI
Maus, Bärbel ULg; van Breukelen, G. J. P.

in STAtOR (2012), 13(1), 23-26

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See detailInference and comparison of different genetic stratification techniques
Maus, Bärbel ULg; Génin, Emmanuelle; Mahachie John, Jestinah ULg et al

Conference (2012)

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See detailClustering of Crohn’s disease patients: Identification of sub-phenotypes and population stratification
Maus, Bärbel ULg; Génin, Emmanuelle; Mahachie John, Jestinah ULg et al

in Genetic Epidemiology (2012), 36(7), 729

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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 detailSpecific IgE against Staphylococcus aureus enterotoxins: An independent risk factor for asthma
Bachert, C.; Van Steen, Kristel ULg; Zhang, N. et al

in Journal of Allergy and Clinical Immunology (The) (2012), 130(2), 376-3818

Background: The role of IgE in patients with severe asthma is not fully understood. Objective: We sought to investigate whether IgE to Staphylococcus aureus enterotoxins might be relevant to disease ... [more ▼]

Background: The role of IgE in patients with severe asthma is not fully understood. Objective: We sought to investigate whether IgE to Staphylococcus aureus enterotoxins might be relevant to disease severity in adult asthmatic patients. Methods: Specific IgE antibody concentrations in serum against enterotoxins, grass pollen (GP), and house dust mite allergens and total IgE levels were measured in adult cohorts of 69 control subjects, 152 patients with nonsevere asthma, and 166 patients with severe asthma. Severe asthma was defined as inadequately controlled disease despite high-dose inhaled corticosteroids plus at least 2 other controller therapies, including oral steroids. Results: Enterotoxin IgE positivity was significantly greater in patients with severe asthma (59.6%) than in healthy control subjects (13%, P < .001). Twenty-one percent of patients with severe asthma with enterotoxin IgE were considered nonatopic. Logistic regression analyses demonstrated significantly increased risks for enterotoxin IgE-positive subjects to have any asthma (OR, 7.25; 95% CI, 2.7-19.1) or severe asthma (OR, 11.09; 95% CI, 4.1-29.6) versus enterotoxin IgE-negative subjects. The presence of GP or house dust mite IgE antibodies was not associated with either significantly increased risk for asthma or severity. Oral steroid use and hospitalizations were significantly increased in patients with enterotoxin IgE and nonatopic asthma. GP IgE was associated with a higher FEV 1 percent predicted value, and enterotoxin IgE was associated with a lower FEV 1 percent predicted value. Conclusions: Staphylococcal enterotoxin IgE antibodies, but not IgE against inhalant allergens, are risk factors for asthma severity. We hypothesize that the presence of enterotoxin IgE in serum indicates the involvement of staphylococcal superantigens in the pathophysiology of patients with severe asthma. © 2012 American Academy of Allergy, Asthma & Immunology. [less ▲]

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See detailChallenges and opportunities in genome-wide environmental interaction (GWEI) studies.
Aschard, Hugues; Lutz, Sharon; Maus, Bärbel ULg et al

in Human Genetics (2012), 131

The interest in performing gene-environment interaction studies has seen a significant increase with the increase of advanced molecular genetics techniques. Practically, it became possible to investigate ... [more ▼]

The interest in performing gene-environment interaction studies has seen a significant increase with the increase of advanced molecular genetics techniques. Practically, it became possible to investigate the role of environmental factors in disease risk and hence to investigate their role as genetic effect modifiers. The understanding that genetics is important in the uptake and metabolism of toxic substances is an example of how genetic profiles can modify important environmental risk factors to disease. Several rationales exist to set up gene-environment interaction studies and the technical challenges related to these studies-when the number of environmental or genetic risk factors is relatively small-has been described before. In the post-genomic era, it is now possible to study thousands of genes and their interaction with the environment. This brings along a whole range of new challenges and opportunities. Despite a continuing effort in developing efficient methods and optimal bioinformatics infrastructures to deal with the available wealth of data, the challenge remains how to best present and analyze genome-wide environmental interaction (GWEI) studies involving multiple genetic and environmental factors. Since GWEIs are performed at the intersection of statistical genetics, bioinformatics and epidemiology, usually similar problems need to be dealt with as for genome-wide association gene-gene interaction studies. However, additional complexities need to be considered which are typical for large-scale epidemiological studies, but are also related to "joining" two heterogeneous types of data in explaining complex disease trait variation or for prediction purposes. [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 experimental designs for funtional magnetic resonance imaging
Maus, Bärbel ULg

Doctoral thesis (2011)

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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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See detailOptimization of blocked designs in fMRI studies
Maus, Bärbel ULg; van Breukelen, G. J. P.; Goebel, R. et al

in Psychometrika (2010), 75(2), 373390

Blocked designs in functional magnetic resonance imaging (fMRI) are useful to localize functional brain areas. A blocked design consists of different blocks of trials of the same stimulus type and is ... [more ▼]

Blocked designs in functional magnetic resonance imaging (fMRI) are useful to localize functional brain areas. A blocked design consists of different blocks of trials of the same stimulus type and is characterized by three factors: the length of blocks, i.e., number of trials per blocks, the ordering of task and rest blocks, and the time between trials within one block. Optimal design theory was applied to find the optimal combination of these three design factors. Furthermore, different error structures were used within a general linear model for the analysis of fMRI data, and the maximin criterion was applied to find designs which are robust against misspecification of model parameters. [less ▲]

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See detailOptimization of blocked designs in fMRI studies
Maus, Bärbel ULg; Van Breukelen, G.J.P; Goebel, R. et al

in NeuroImage (2009), 47(Supplement 1), 125

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See detailOptimisation of blocked designs in fMRI studies
Maus, Bärbel ULg; van Breukelen, G. J. P.; Goebel, R. et al

Poster (2009)

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See detailOptimisation of blocked designs in fMRI studies
Maus, Bärbel ULg; van Breukelen, G. J. P.; Goebel, R. et al

Poster (2008)

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See detailImpact of geometry and viewing angle on classification accuracy of 2D based analysis of dysmorphic faces
Vollmar, Tobias; Maus, Bärbel ULg; Wurtz, R. P. et al

in European Journal of Medical Genetics (2008), 51(1), 44-53

Digital image analysis of faces has been demonstrated to be effective in a small number of syndromes. In this paper we investigate several aspects that help bringing these methods closer to clinical ... [more ▼]

Digital image analysis of faces has been demonstrated to be effective in a small number of syndromes. In this paper we investigate several aspects that help bringing these methods closer to clinical application. First, we investigate the impact of increasing the number of syndromes from 10 to 14 as compared to an earlier study. Second, we include a side-view pose into the analysis and third, we scrutinize the effect of geometry information. Picture analysis uses a Gabor wavelet transform, standardization of landmark coordinates and subsequent statistical analysis. We can demonstrate that classification accuracy drops from 76% for 10 syndromes to 70% for 14 syndromes for frontal images. Including side-views achieves an accuracy of 76% again. Geometry performs excellently with 85% for combined poses. Combination of wavelets and geometry for both poses increases accuracy to 93%. In conclusion, a larger number of syndromes can be handled effectively by means of image analysis. [less ▲]

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