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Journal ArticleDOI

Establishing correlations of scalp field maps with other experimental variables using covariance analysis and resampling methods.

01 Jun 2008-Clinical Neurophysiology (Elsevier)-Vol. 119, Iss: 6, pp 1262-1270
TL;DR: Covariance mapping combined with bootstrapping methods has high statistical power and yields unique and directly interpretable results in EEG/MEG scalp data analysis.
About: This article is published in Clinical Neurophysiology.The article was published on 2008-06-01 and is currently open access. It has received 45 citations till now. The article focuses on the topics: Covariance & Resampling.

Summary (2 min read)

1. Introduction

  • Technical improvements have made it possible that in EEG/MEG and ERP/ERF experiments, the number of sensors on the scalp could be substantially increased.
  • In many studies, the statistical approaches chosen to analyze multi-channel scalp field data do not take the relations between sensors properly into account and disregard the physical basis of the signals to be analyzed.
  • The additional information obtained with higher spatial sampling is thus often poorly exploited by the statistics applied.
  • In terms of statistics of EEG/MEG and ERP/ERF data, this implies that the basic entity for analysis should be the scalp electric field.
  • By repeating this randomization many times, one can therefore obtain a good estimate of the distribution of the total map difference under the null hypothesis.

2. Methods

  • Bold symbols denote a column vector or matrix and non-bold symbols, a scalar magnitude.
  • When inter-subject covariance is modeled, 1980) .
  • In the Fisher transformed distribution of r * , the so-called studentized bootstrap confidence intervals are constructed from the bootstrap replicates (see Davison and Hinkley, 1997 for details).
  • The TANOVA is thus similar to the ANOVA in the sense that it used categorical independent variables, while the present method is similar to the ANCOVA by using one continuous independent variable.

3. Simulations

  • The random map was multiplied by the random external variable to obtain simulated data that are compatible with the model outlined in formula (4).
  • Using these noisy random simulated data and the random external variable, the p-value was computed for each simulation run.
  • For each step of the SNR ratio, 500 simulations were computed, each with 500 randomization runs.
  • The graphs of the mean p-values and the mean r 2 -values against the SNR are shown in Fig. 1 .
  • The figure indicates that with 12 subjects and 19 channels of EEG, significant (p < .05) effects can be detected at SNRs above about 10%.

4. Example

  • Seventy-four channel ERPs were collected in 10 English-speaking exchange students to Switzerland while reading single-German words.
  • The ERPs were collected after the students had spent about 3 months in Switzerland and had already acquired some proficiency in German.
  • The study was approved by the Canton of Bern's ethical committee.
  • The topography of the map series was correlated to a combined score of both language tests.
  • The covariance map at the most significant time point (448 ms post stimulus) is shown, together with a distributed inverse solution of that map (Fig. 2 ).

5. Discussion

  • The presented methodology provides an extension of the currently available methods for multi-channel randomization statistics of EEG and MEG topographies.
  • By limiting the number of external variables to one, no rotation of the covariance matrix is necessary, and the resulting covariance map remains directly interpretable in terms of intracerebral sources associated with the external variable.
  • By basing the analysis on a vector of scalp potentials that is scaled for each subject (Eq. 5), it is furthermore assumed that across subjects a common vector of scalp potentials corresponds to the same distribution and orientation of current density.
  • Since ERP averaging has produced an abundant mass of meaningful results and often served as the basis for the computation of convincing inverse solutions, the authors think that practically, this assumption is very plausible.

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Citations
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Journal ArticleDOI
TL;DR: The aim of Ragu is to maximize statistical power while minimizing the need for a-priori choices of models and parameters (like inverse models or sensors of interest) that interact with and bias statistics.
Abstract: We present a program (Ragu; Randomization Graphical User interface) for statistical analyses of multichannel event-related EEG and MEG experiments. Based on measures of scalp field differences including all sensors, and using powerful, assumption-free randomization statistics, the program yields robust, physiologically meaningful conclusions based on the entire, untransformed, and unbiased set of measurements. Ragu accommodates up to two within-subject factors and one between-subject factor with multiple levels each. Significance is computed as function of time and can be controlled for type II errors with overall analyses. Results are displayed in an intuitive visual interface that allows further exploration of the findings. A sample analysis of an ERP experiment illustrates the different possibilities offered by Ragu. The aim of Ragu is tomaximize statistical power while minimizing the need for a-priori choices of models and parameters (like inverse models or sensors of interest) that interact with and bias statistics.

280 citations


Cites background or methods from "Establishing correlations of scalp ..."

  • ...The suggested quantifier and the suggested statistical testing rely on previously reviewed and published papers [2, 4, 5] and are briefly explained below....

    [...]

  • ...In the literature, the procedure to compare groups and/or conditions has been called TANOVA (topographic analysis of variance); if a linear predictor is used, the proposed term is TANCOVA (topographic analysis of covariance)....

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  • ...If instead of a group/condition membership a predictor is available that is assumed to be linearly related to the activity of an unknown set of sources, the scalp field produced by this set of sources can be estimated using the so-called covariance maps βj [4]....

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  • ...By checking the “continuous/rank data” box in the betweensubject design dialog, the individual performance (learning rates in the present example) can be entered (Figure 4(b)), and the program will compute a TANCOVA....

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  • ...This approach is called TANCOVA and is also available in the program....

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Journal ArticleDOI
TL;DR: A simple and effective method to test whether an event consistently activates a set of brain electric sources across repeated measurements of event-related scalp field data, called topographic consistency test (TCT).
Abstract: We present a simple and effective method to test whether an event consistently activates a set of brain electric sources across repeated measurements of event-related scalp field data. These repeated measurements can be single trials, single subject ERPs, or ERPs from different studies. The method considers all sensors simultaneously, but can be applied separately to each time frame or frequency band of the data. This allows limiting the analysis to time periods and frequency bands where there is positive evidence of a consistent relation between the event and some brain electric sources. The test may therefore avoid false conclusions about the data resulting from an inadequate selection of the analysis window and bandpass filter, and permit the exploration of alternate hypotheses when group/condition differences are observed in evoked field data. The test will be called topographic consistency test (TCT). The statistical inference is based on simple randomization techniques. Apart form the methodological introduction, the paper contains a series of simulations testing the statistical power of the method as function of number of sensors and observations, a sample analysis of EEG potentials related to self-initiated finger movements, and Matlab source code to facilitate the implementation. Furthermore a series of measures to control for multiple testing are introduced and applied to the sample data.

167 citations


Cites methods from "Establishing correlations of scalp ..."

  • ...The TCT complements other global procedures for statistical testing of ERPs that are also based on randomization and bootstrapping (Galan et al. 1997; Greenblatt and Pflieger 2004; Karniski et al. 1994; Koenig et al. 2008; Lobaugh et al. 2001), but that are used to compare different conditions....

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Journal ArticleDOI
Kay Jann1, Mara Kottlow1, Thomas Dierks1, Chris Boesch1, Thomas Koenig1 
22 Sep 2010-PLOS ONE
TL;DR: The data supports the physiological and neuronal origin of the RSNs and substantiates the assumption that the standard EEG frequency bands and their topographies can be seen as electrophysiological signatures of underlying distributed neuronal networks.
Abstract: Background: fMRI Resting State Networks (RSNs) have gained importance in the present fMRI literature. Although their functional role is unquestioned and their physiological origin is nowadays widely accepted, little is known about their relationship to neuronal activity. The combined recording of EEG and fMRI allows the temporal correlation between fluctuations of the RSNs and the dynamics of EEG spectral amplitudes. So far, only relationships between several EEG frequency bands and some RSNs could be demonstrated, but no study accounted for the spatial distribution of frequency domain EEG. Methodology/Principal Findings: In the present study we report on the topographic association of EEG spectral fluctuations and RSN dynamics using EEG covariance mapping. All RSNs displayed significant covariance maps across a broad EEG frequency range. Cluster analysis of the found covariance maps revealed the common standard EEG frequency bands. We found significant differences between covariance maps of the different RSNs and these differences depended on the frequency band. Conclusions/Significance: Our data supports the physiological and neuronal origin of the RSNs and substantiates the assumption that the standard EEG frequency bands and their topographies can be seen as electrophysiological signatures of underlying distributed neuronal networks.

157 citations


Cites methods from "Establishing correlations of scalp ..."

  • ...While in previous studies the BOLD signal fluctuations in each voxel or of a whole RSN were explained by a single EEG feature (e.g. arbitrary single or few channels [15,16,22] or global features such as global field power [19] or global field synchronization [17]), we provide the relation of the variance of EEG spectral power at each electrode to the dynamics of different RSN using Covariance Mapping [23]....

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  • ...Covariance Mapping The Covariance Mapping for the ten selected RSNs revealed specific significant spatial distributions of the spectral scalp field across frequencies (Figure 1)....

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  • ...Combination: Covariance Mapping of the fMRI GCs (RSNs) and the EEG The covariance between the normalized individual datasets extracted from the EEG respectively fMRI were calculated similar to the approach presented in Koenig et al. [23]....

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  • ...arbitrary single or few channels [15,16,22] or global features such as global field power [19] or global field synchronization [17]), we provide the relation of the variance of EEG spectral power at each electrode to the dynamics of different RSN using Covariance Mapping [23]....

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Journal ArticleDOI
TL;DR: Although CT, MRI, and TCD were determined to be the most useful modalities in the clinical setting, no single imaging modality proved sufficient for all patients due to the heterogeneity of TBI; all imaging modalities reviewed demonstrated the potential to emerge as part of future clinical care.
Abstract: The incidence of traumatic brain injury (TBI) in the United States was 3.5 million cases in 2009, according to the Centers for Disease Control and Prevention. It is a contributing factor in 30.5% of injury-related deaths among civilians. Additionally, since 2000, more than 260,000 service members were diagnosed with TBI, with the vast majority classified as mild or concussive (76%). The objective assessment of TBI via imaging is a critical research gap, both in the military and civilian communities. In 2011, the Department of Defense (DoD) prepared a congressional report summarizing the effectiveness of seven neuroimaging modalities (computed tomography [CT], magnetic resonance imaging [MRI], transcranial Doppler [TCD], positron emission tomography, single photon emission computed tomography, electrophysiologic techniques [magnetoencephalography and electroencephalography], and functional near-infrared spectroscopy) to assess the spectrum of TBI from concussion to coma. For this report, neuroimag...

152 citations

Journal ArticleDOI
TL;DR: A randomization-based procedure that works without assigning grand-mean microstate prototypes to individual data, and shows an increased robustness to noise, and a higher sensitivity for more subtle effects of microstate timing, is proposed.
Abstract: Dynamic changes in ERP topographies can be conveniently analyzed by means of microstates, the so-called "atoms of thoughts", that represent brief periods of quasi-stable synchronized network activation. Comparing temporal microstate features such as on- and offset or duration between groups and conditions therefore allows a precise assessment of the timing of cognitive processes. So far, this has been achieved by assigning the individual time-varying ERP maps to spatially defined microstate templates obtained from clustering the grand mean data into predetermined numbers of topographies (microstate prototypes). Features obtained from these individual assignments were then statistically compared. This has the problem that the individual noise dilutes the match between individual topographies and templates leading to lower statistical power. We therefore propose a randomization-based procedure that works without assigning grand-mean microstate prototypes to individual data. In addition, we propose a new criterion to select the optimal number of microstate prototypes based on cross-validation across subjects. After a formal introduction, the method is applied to a sample data set of an N400 experiment and to simulated data with varying signal-to-noise ratios, and the results are compared to existing methods. In a first comparison with previously employed statistical procedures, the new method showed an increased robustness to noise, and a higher sensitivity for more subtle effects of microstate timing. We conclude that the proposed method is well-suited for the assessment of timing differences in cognitive processes. The increased statistical power allows identifying more subtle effects, which is particularly important in small and scarce patient populations.

118 citations


Cites background or methods from "Establishing correlations of scalp ..."

  • ...Sample Data Analysis and Simulations The sample data and analysis are based on an experiment that has previously been used to demonstrate statistical procedures of the analysis of ERPs (Koenig et al. 2008, 2011)....

    [...]

  • ...…data consist of ERPs recorded in 16 healthy young English-speaking exchange students that spent a year in the German-speaking part of Switzerland and that participated in a larger study on the neurobiology of training-related changes of the language system (Koenig et al. 2008; Stein et al. 2006)....

    [...]

  • ...These data consist of ERPs recorded in 16 healthy young English-speaking exchange students that spent a year in the German-speaking part of Switzerland and that participated in a larger study on the neurobiology of training-related changes of the language system (Koenig et al. 2008; Stein et al. 2006)....

    [...]

References
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TL;DR: A method is proposed to determine components of evoked scalp potentials, in terms of times of occurrence (latency) and location on the scalp (topography), suggesting a stable localization of the generating process in depth.

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"Establishing correlations of scalp ..." refers background or methods in this paper

  • ...In order to obtain a global (across electrodes) measure of the size of the estimator b, we calculated the Global Field Power measure (GFP, Lehmann and Skrandies, 1980) using the following equation: d ¼ GFP bð Þ ¼…...

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TL;DR: Partial least squares serves as an important extension by extracting new information from imaging data that is not accessible through other currently used univariate and multivariate image analysis tools.

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"Establishing correlations of scalp ..." refers methods in this paper

  • ...…significance using resampling methods, (b) illustrate the utility of such a method using foreign-language evoked potentials in subjects with varying language proficiency and (c) relate the method to other methods such as partial least squares (PLS, see McIntosh et al., 1996; Lobaugh et al., 2001)....

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TL;DR: A discussion of the validity assumptions for both overall and sub-effect tests and a multivariate approach which allows exact analysis of such designs are offered and a modification of the univariate approach is also described.
Abstract: Violation of the validity assumptions of repeated measures analysis of variance continues to be a problem in psychophysiology. Such violation results in positive bias for those tests involving the repeated measures factor(s), Recently it has been shown that the tests of simple interactions and multiple comparisons are even more vulnerable to bias (Boik. 1981; Mitzel & Games, 1981). The present paper offers a discussion of the validity assumptions for both overall and sub-effect tests and describes a multivariate approach which allows exact analysis of such designs. A modification of the univariate approach is also described. Validity concerns for both approaches are much less problematic than those of the traditional approach.

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TL;DR: Novel reformulations of the basic EEG and MEG kernels that dispel the myth that EEG is inherently more complicated to calculate than MEG are presented and evidence that improvements over currently published BEM methods can be realized using alternative error-weighting methods is presented.
Abstract: A solution of the forward problem is an important component of any method for computing the spatio-temporal activity of the neural sources of magnetoencephalography (MEG) and electroencephalography (EEG) data. The forward problem involves computing the scalp potentials or external magnetic field at a finite set of sensor locations for a putative source configuration. We present a unified treatment of analytical and numerical solutions of the forward problem in a form suitable for use in inverse methods. This formulation is achieved through factorization of the lead field into the product of the moment of the elemental current dipole source with a "kernel matrix" that depends on the head geometry and source and sensor locations, and a "sensor matrix" that models sensor orientation and gradiometer effects in MEG and differential measurements in EEG. Using this formulation and a recently developed approximation formula for EEG, based on the "Berg parameters", we present novel reformulations of the basic EEG and MEG kernels that dispel the myth that EEG is inherently more complicated to calculate than MEG. We also present novel investigations of different boundary element methods (BEMs) and present evidence that improvements over currently published BEM methods can be realized using alternative error-weighting methods. Explicit expressions for the matrix kernels for MEG and EEG for spherical and realistic head geometries are included.

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"Establishing correlations of scalp ..." refers background in this paper

  • ...With several sources simultaneously active, the measured scalp field becomes the sum of the scalp fields produced by those sources (Mosher et al., 1999)....

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Journal ArticleDOI
TL;DR: A precise mathematical formulation of the model for evoked potential recordings is presented, where the microstates are represented as normalized vectors constituted by scalp electric potentials due to the underlying generators.
Abstract: A brain microstate is defined as a functional/physiological state of the brain during which specific neural computations are performed. It is characterized uniquely by a fixed spatial distribution of active neuronal generators with time varying intensity. Brain electrical activity is modeled as being composed of a time sequence of nonoverlapping microstates with variable duration. A precise mathematical formulation of the model for evoked potential recordings is presented, where the microstates are represented as normalized vectors constituted by scalp electric potentials due to the underlying generators. An algorithm is developed for estimating the microstates, based on a modified version of the classical k-means clustering method, in which cluster orientations are estimated, Consequently, each instantaneous multichannel evoked potential measurement is classified as belonging to some microstate, thus producing a natural segmentation of brain activity. Use is made of statistical image segmentation techniques for obtaining smooth continuous segments. Time varying intensities are estimated by projecting the measurements onto their corresponding microstates. A goodness of fit statistic for the model is presented. Finally, a method is introduced for estimating the number of microstates, based on nonparametric data-driven statistical resampling techniques. >

770 citations


"Establishing correlations of scalp ..." refers methods in this paper

  • ...In order to overcome the problem of multiple testing across time, one may employ procedures to identify ERP components (i.e. Pascual-Marqui et al., 1995; Michel et al., 2001) and average the data across time periods belonging to the same component before the statistics are computed....

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Frequently Asked Questions (5)
Q1. What contributions have the authors mentioned in the paper "Establishing correlations of scalp field maps with other experimental variables using covariance analysis and resampling methods" ?

The authors introduce a procedure to identify spatially extended scalp fields that correlate with some external, continuous measure ( reaction-time, performance, clinical status ) and to test their significance. The authors formally deduce that the channel-wise covariance of some experimental variable with scalp field data directly represents intracerebral sources associated with that variable. The authors furthermore show how the significance of such a representation can be tested with resampling techniques. The introduced methodology overcomes some of the ‘ traditional ’ statistical problems in EEG/MEG scalp data analysis. In a sample analysis of real data, the authors found that foreign-language evoked ERP data were significantly associated with foreign-language proficiency. 

Seventy-four channel ERPs were collected in 10 English-speaking exchange students to Switzerland while reading single-German words. 

The amplitudes of the estimated covariance map b depend on the variance of V, on the variance of X, and on the strength of the relation between V and X. 

In order to establish the statistical significance of such difference maps, one can either use the standard multivariate statistical approaches such as MANOVA (Vasey and Thayer, 1987). 

Increasing the number of subjects or electrodes improves the sensitivity of the method, and effects can be detected at lower SNRs.