Biomedical subjects

Hilde M Huizenga

Publications and source records attributed to Hilde M Huizenga.

8 recordsLinked to original sources

PubMed2006

Hypothesis testing in distributed source models for EEG and MEG data.

Hypothesis testing in distributed source models for the electro- or magnetoencephalogram is generally performed for each voxel separately. Derived from the analysis of functional magnetic resonance imaging data, such a statistical parametric map (SPM) ignores the spatial smoothing in hypothesis testing with distributed source models. For example, when intending to test a single voxel, actually an entire region of voxels is tested simultaneously. Because there are more parameters than observations, typically constraints are employed to arrive at a solution which spatially smooths the solution. If ignored, it can be concluded from the hypothesis test that there is activity at some location where there is none. In addition, an SPM on distributed source models gives the illusion of very high resolution. As an alternative, a multivariate approach is suggested in which a region of interest is tested that is spatially smooth. In simulations with MEG and EEG it is shown that clear hypothesis testing in distributed source models is possible, provided that there is high correspondence between what is intended to be tested and what is actually tested. The approach is also illustrated by an application to data from an experiment measuring visual evoked fields when presenting checkerboard patterns.

Brain↗

PubMed2006

A dynamical model of general intelligence: the positive manifold of intelligence by mutualism.

Scores on cognitive tasks used in intelligence tests correlate positively with each other, that is, they display a positive manifold of correlations. The positive manifold is often explained by positing a dominant latent variable, the g factor, associated with a single quantitative cognitive or biological process or capacity. In this article, a new explanation of the positive manifold based on a dynamical model is proposed, in which reciprocal causation or mutualism plays a central role. It is shown that the positive manifold emerges purely by positive beneficial interactions between cognitive processes during development. A single underlying g factor plays no role in the model. The model offers explanations of important findings in intelligence research, such as the hierarchical factor structure of intelligence, the low predictability of intelligence from early childhood performance, the integration/differentiation effect, the increase in heritability of g, and the Jensen effect, and is consistent with current explanations of the Flynn effect.

Humans↗

PubMed2005

Model selection in spatio-temporal electromagnetic source analysis.

Several methods [model selection procedures (MSPs)] to determine the number of sources in electroencephalogram (EEG) and magnetoencphalogram (MEG) data have previously been investigated in an instantaneous analysis. In this paper, these MSPs are extended to a spatio-temporal analysis if possible. It is seen that the residual variance (RV) tends to overestimate the number of sources. The Akaike information criterion (AIC) and the Wald test on amplitudes (WA) and the Wald test on locations (WL) have the highest probabilities of selecting the correct number of sources. The WA has the advantage that it offers the opportunity to test which source is active at which time sample.

Algorithms↗

PubMed2004

The coupled dipole model: an integrated model for multiple MEG/EEG data sets.

Often MEG/EEG is measured in a few slightly different conditions to investigate the functionality of the human brain. This kind of data sets show similarities, though are different for each condition. When solving the inverse problem (IP), performing the source localization, one encounters the problem that this IP is ill-posed: constraints are necessary to solve and stabilize the solution to the IP. Moreover, a substantial amount of data is needed to avoid a signal to noise ratio (SNR) that is too poor for source localizations. In the case of similar conditions, this common information can be exploited by analyzing the data sets simultaneously. The here proposed coupled dipole model (CDM) provides an integrated method in which these similarities between conditions are used to solve and stabilize the inverse problem. The coupled dipole model is applicable when data sets contain common sources or common source time functions. The coupled dipole model uses a set of common sources and a set of common source time functions (STFs) to model all conditions in one single model. The data of each condition are mathematically described as a linear combination of these common spatial and common temporal components. This linear combination is specified in a coupling matrix for each data set. The coupled dipole model was applied in two simulation studies and in one experimental study. The simulations show that the errors in the estimated spatial and temporal parameters decrease compared to the standard separate analyses. A decrease in position error of a factor of 10 was shown for the localization of two nearby sources. In the experimental application, the coupled dipole model was shown to be necessary to obtain a plausible solution in at least 3 of 15 conditions investigated. Moreover, using the CDM, a direct comparison between parameters in different conditions is possible, whereas in separate models, the scaling of the amplitude parameters varies in general from data set to data set.

Algorithms↗

PubMed2004

Frequency domain simultaneous source and source coherence estimation with an application to MEG.

Interactions between cortical areas are crucial for cognitive functioning. Methods currently in use to assess such interactions are not well suited for this task because they lack timing precision, localization precision, or both. We present a method for simultaneous estimation of source location and orientation parameters and cross-spectral parameters to overcome these problems. Different estimators are evaluated for their performance. From a simulation study, we conclude that the estimators derived from the maximum-likelihood procedure have good statistical properties with moderate sample sizes, whereas those obtained from the generalized least squares procedure are biased and give poor-quality standard errors. The method is illustrated with visually evoked field data, with inconclusive results.

Adult↗

PubMed2003

A mathematical approach to the temporal stationarity of background noise in MEG/EEG measurements.

The general spatiotemporal covariance matrix of the background noise in MEG/EEG signals is huge. To reduce the dimensionality of this matrix it is modeled as a Kronecker product of a spatial and a temporal covariance matrix. When the number of time samples is larger than, say, J = 500, the iterative Maximum Likelihood estimation of these two matrices is still too time-consuming to be useful on a routine basis. In this study we looked for methods to circumvent this computationally expensive procedure by using a parametric model with subject-dependent parameters. Such a model would additionally help with interpreting MEG/EEG signals. For the spatial covariance, models have been derived already and it has been shown that measured MEG/EEG signals can be understood spatially as random processes, generated by random dipoles. The temporal covariance, however, has not been modeled yet, therefore we studied the temporal covariance matrix in several subjects. For all subjects the temporal covariance shows an alpha oscillation and vanishes for large time lag. This gives rise to a temporal noise model consisting of two components: alpha activity and additional random noise. The alpha activity is modeled as randomly occurring waves with random phase and the covariance of the additional noise decreases exponentially with lag. This model requires only six parameters instead of 12 J(J + 1). Theoretically, this model is stationary but in practice the stationarity of the matrix is highly influenced by the baseline correction. It appears that very good agreement between the data and the parametric model can be obtained when the baseline correction window is taken into account properly. This finding implies that the background noise is in principle a stationary process and that nonstationarities are mainly caused by the nature of the preprocessing method. When analyzing events at a fixed sample after the stimulus (e.g., the SEF N20 response) one can take advantage of this nonstationarity by optimizing the baseline window to obtain a low noise variance at this particular sample.

Algorithms↗

PubMed2002

Spatiotemporal EEG/MEG source analysis based on a parametric noise covariance model.

A method is described to incorporate the spatiotemporal noise covariance matrix into a spatiotemporal source analysis. The essential feature is that the estimation problem is split into two parts. First, a model is fitted to the observed noise covariance matrix. This model is a Kronecker product of a spatial and a temporal matrix. The spatial matrix models the spatial covariances by a function dependent on sensor distance. The temporal matrix models the temporal covariances as lag dependent. In the second part, sources are estimated given this noise model, which can be done very efficiently due to the Kronecker formulation. An application to real electroencephalogram (EEG) data shows that the noise model fits the data very well. Simulation results show that the resulting source estimates are more precise than those obtained from a standard analysis neglecting the noise covariance. In addition, the estimated standard errors of the source parameter estimates are far more precise than those obtained from a standard analysis. Finally, the source parameter standard errors are used to investigate the effects of temporal sampling. It is shown that increasing the sampling by a factor x, decreases the standard errors of all source parameters with the square root of x.

Computer Simulation↗

PubMed2002

Model selection in electromagnetic source analysis with an application to VEFs.

In electromagnetic source analysis, it is necessary to determine how many sources are required to describe the electroencephalogram or magnetoencephalogram adequately. Model selection procedures (MSPs) or goodness of fit procedures give an estimate of the required number of sources. Existing and new MSPs are evaluated in different source and noise settings: two sources which are close or distant and noise which is uncorrelated or correlated. The commonly used MSP residual variance is seen to be ineffective, that is it often selects too many sources. Alternatives like the adjusted Hotelling's test, Bayes information criterion and the Wald test on source amplitudes are seen to be effective. The adjusted Hotelling's test is recommended if a conservative approach is taken and MSPs such as Bayes information criterion or the Wald test on source amplitudes are recommended if a more liberal approach is desirable. The MSPs are applied to empirical data (visual evoked fields).

Algorithms↗