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Search Results: 1 - 10 of 63 matches for " Lourens Waldorp "
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Robust and Unbiased Variance of GLM Coefficients for Misspecified Autocorrelation and Hemodynamic Response Models in fMRI
Lourens Waldorp
International Journal of Biomedical Imaging , 2009, DOI: 10.1155/2009/723912
Abstract: As a consequence of misspecification of the hemodynamic response and noise variance models, tests on general linear model coefficients are not valid. Robust estimation of the variance of the general linear model (GLM) coefficients in fMRI time series is therefore essential. In this paper an alternative method to estimate the variance of the GLM coefficients accurately is suggested and compared to other methods. The alternative, referred to as the sandwich, is based primarily on the fact that the time series are obtained from multiple exchangeable stimulus presentations. The analytic results show that the sandwich is unbiased. Using this result, it is possible to obtain an exact statistic which keeps the 5% false positive rate. Extensive Monte Carlo simulations show that the sandwich is robust against misspeci cation of the autocorrelations and of the hemodynamic response model. The sandwich is seen to be in many circumstances robust, computationally efficient, and flexible with respect to correlation structures across the brain. In contrast, the smoothing approach can be robust to a certain extent but only with specific knowledge of the circumstances for the smoothing parameter.
Structure estimation for mixed graphical models in high-dimensional data
Jonas M. B. Haslbeck,Lourens J. Waldorp
Statistics , 2015,
Abstract: Undirected graphical models are a key component in the analysis of complex observational data in a large variety of disciplines. In many of these applications one is interested in estimating the undirected graphical model underlying a distribution over variables with different domains. Despite the pervasive need for such an estimation method, to date there is no such method that models all variables on their proper domain. We close this methodological gap by combining a new class of mixed graphical models with a structure estimation approach based on generalized covariance matrices. We report the performance of our methods using simulations, illustrate the method with a dataset on Autism Spectrum Disorder (ASD) and provide an implementation as an R-package.
mgm: Structure Estimation for Mixed Graphical Models in high-dimensional Data
Jonas M. B. Haslbeck,Lourens J. Waldorp
Statistics , 2015,
Abstract: We present the R-package mgm for the estimation of mixed graphical models underlying multivariate probability distributions over variables with different domains in high-dimensional data. Our method goes beyond existing methods in that it is the first general method to combine variables with categorical, count-measure and continuous domain, while modeling all variables on their proper domain, which avoids possible loss of information due to transformations. In addition to the presenting the estimation function, we provide a function to sample from pairwise mixed distributions and apply our method to a medical dataset.
The Sensory Consequences of Speaking: Parametric Neural Cancellation during Speech in Auditory Cortex
Ingrid K. Christoffels,Vincent van de Ven,Lourens J. Waldorp,Elia Formisano,Niels O. Schiller
PLOS ONE , 2012, DOI: 10.1371/journal.pone.0018307
Abstract: When we speak, we provide ourselves with auditory speech input. Efficient monitoring of speech is often hypothesized to depend on matching the predicted sensory consequences from internal motor commands (forward model) with actual sensory feedback. In this paper we tested the forward model hypothesis using functional Magnetic Resonance Imaging. We administered an overt picture naming task in which we parametrically reduced the quality of verbal feedback by noise masking. Presentation of the same auditory input in the absence of overt speech served as listening control condition. Our results suggest that a match between predicted and actual sensory feedback results in inhibition of cancellation of auditory activity because speaking with normal unmasked feedback reduced activity in the auditory cortex compared to listening control conditions. Moreover, during self-generated speech, activation in auditory cortex increased as the feedback quality of the self-generated speech decreased. We conclude that during speaking early auditory cortex is involved in matching external signals with an internally generated model or prediction of sensory consequences, the locus of which may reside in auditory or higher order brain areas. Matching at early auditory cortex may provide a very sensitive monitoring mechanism that highlights speech production errors at very early levels of processing and may efficiently determine the self-agency of speech input.
The Small World of Psychopathology
Denny Borsboom, Angélique O. J. Cramer, Verena D. Schmittmann, Sacha Epskamp, Lourens J. Waldorp
PLOS ONE , 2011, DOI: 10.1371/journal.pone.0027407
Abstract: Background Mental disorders are highly comorbid: people having one disorder are likely to have another as well. We explain empirical comorbidity patterns based on a network model of psychiatric symptoms, derived from an analysis of symptom overlap in the Diagnostic and Statistical Manual of Mental Disorders-IV (DSM-IV). Principal Findings We show that a) half of the symptoms in the DSM-IV network are connected, b) the architecture of these connections conforms to a small world structure, featuring a high degree of clustering but a short average path length, and c) distances between disorders in this structure predict empirical comorbidity rates. Network simulations of Major Depressive Episode and Generalized Anxiety Disorder show that the model faithfully reproduces empirical population statistics for these disorders. Conclusions In the network model, mental disorders are inherently complex. This explains the limited successes of genetic, neuroscientific, and etiological approaches to unravel their causes. We outline a psychosystems approach to investigate the structure and dynamics of mental disorders.
arf3DS4: An Integrated Framework for Localization and Connectivity Analysis of fMRI Data
Wouter D. Weeda,Frank de Vos,Lourens J. Waldorp,Raoul Grasman
Journal of Statistical Software , 2011,
Abstract: In standard fMRI analysis all voxels are tested in a massive univariate approach, that is, each voxel is tested independently. This requires stringent corrections for multiple comparisons to control the number of false positive tests (i.e., marking voxels as active while they are actually not). As a result, fMRI analyses may suffer from low power to detect activation, especially in studies with high levels of noise in the data, for example developmental or single-subject studies. Activated region fitting (ARF) yields a solution by modeling fMRI data by multiple Gaussian shaped regions. ARF only requires a small number of parameters and therefore has increased power to detect activation. If required, the estimated regions can be directly used as regions of interest in a functional connectivity analysis. ARF is implemented in the R package arf3DS4. In this paper ARF and its implementation are described and illustrated with an example.
qgraph: Network Visualizations of Relationships in Psychometric Data
Sacha Epskamp,Angelique O. J. Cramer,Lourens J. Waldorp,Verena D. Schmittmann
Journal of Statistical Software , 2012,
Abstract: We present the qgraph package for R, which provides an interface to visualize data through network modeling techniques. For instance, a correlation matrix can be represented as a network in which each variable is a node and each correlation an edge; by varying the width of the edges according to the magnitude of the correlation, the structure of the correlation matrix can be visualized. A wide variety of matrices that are used in statistics can be represented in this fashion, for example matrices that contain (implied) covariances, factor loadings, regression parameters and p values. qgraph can also be used as a psychometric tool, as it performs exploratory and confirmatory factor analysis, using sem and lavaan; the output of these packages is automatically visualized in qgraph, which may aid the interpretation of results. In this article, we introduce qgraph by applying the package functions to data from the NEO-PI-R, a widely used personality questionnaire.
A Fast and Reliable Method for Simultaneous Waveform, Amplitude and Latency Estimation of Single-Trial EEG/MEG Data
Wouter D. Weeda, Raoul P. P. P. Grasman, Lourens J. Waldorp, Maria C. van de Laar, Maurits W. van der Molen, Hilde M. Huizenga
PLOS ONE , 2012, DOI: 10.1371/journal.pone.0038292
Abstract: The amplitude and latency of single-trial EEG/MEG signals may provide valuable information concerning human brain functioning. In this article we propose a new method to reliably estimate single-trial amplitude and latency of EEG/MEG signals. The advantages of the method are fourfold. First, no a-priori specified template function is required. Second, the method allows for multiple signals that may vary independently in amplitude and/or latency. Third, the method is less sensitive to noise as it models data with a parsimonious set of basis functions. Finally, the method is very fast since it is based on an iterative linear least squares algorithm. A simulation study shows that the method yields reliable estimates under different levels of latency variation and signal-to-noise ratio?s. Furthermore, it shows that the existence of multiple signals can be correctly determined. An application to empirical data from a choice reaction time study indicates that the method describes these data accurately.
Hidden Multiplicity in Multiway ANOVA: Prevalence and Remedies
Angelique O. J. Cramer,Don van Ravenzwaaij,Dora Matzke,Helen Steingroever,Ruud Wetzels,Raoul P. P. P. Grasman,Lourens J. Waldorp,Eric-Jan Wagenmakers
Statistics , 2014,
Abstract: Many psychologists do not realize that exploratory use of the popular multiway analysis of variance (ANOVA) harbors a multiple comparison problem. In the case of two factors, three separate null hypotheses are subject to test (i.e., two main effects and one interaction). Consequently, the probability of at least one Type I error (if all null hypotheses are true) is 14% rather than 5% if the three tests are independent. We explain the multiple comparison problem and demonstrate that researchers almost never correct for it. To mitigate the problem, we describe four remedies: the omnibus F test, the control of familywise error rate, the control of false discovery rate, and the preregistration of hypotheses.
Resensie: "Poppekas"
A Lourens
Tydskrif vir letterkunde , 2013,
Abstract: Poppekas. Deborah Steinmair. Pretoria: LAPA Uitgewers, 2012. 249 pp. ISBN: 978-0-7993-5520-8.
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