Name: cortical source analysis of high-density eeg recordings in children
Uploaded: 2014-06-30T15:00:00
Description: Watch this Scientific Journal Video about Cortical Source Analysis of High-Density EEG Recordings in Children at JoVE.com

We want to thank Prof. John Richards, University of South Carolina, for granting us access to the Developmental MRI database and helpful discussions. We would also like to thank our funders Great Ormond Street Children&#8217;s Charity, UCL Impact &#38; Grand Challenges.

1. Designing EEG &#38; Event related potential experiments for children
Note: A simple experiment was designed for the purposes of this article that may be used to investigate face processing in young children. The following section will describe the experiment and explain how to implement it using MATLAB&#160;R2012b and Psychtoolbox V3.0.1123,24.&#160; Pictures taken from the NimStim set of emotional facial expression25 were used for this example. This stimulus set is avai...

<ol>
	<li>Michel, C. M., Murray, M. M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Towards+the+utilization+of+EEG+as+a+brain+imaging+tool.">Towards the utilization of EEG as a brain imaging tool.</a> NeuroImage. 61 (2), 371-385 (2012).</li><li>Brodbeck, V., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=EEG+microstates+of+wakefulness+and+NREM+sleep.">EEG microstates of wakefulness and NREM sleep.</a> NeuroImage. 62 (3), 2129-2139 (2012).</li><li>Sanchez, C. E., Richards, J. E., Almli, C. 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The present article describes the recording and analysis of high density EEG for reconstruction of cortical generators using boundary element models based on age appropriate average MRI templates and depth weighted minimum norm estimation in a standard ERP paradigm suitable for children. In this paradigm, pictures of faces and scrambled faces are presented. Different authors used this paradigm to investigate the development of face processing mechanisms over development35. On the channel level, more negative d...

President Barack Obama described the human brain as the next frontier of scientific discovery with high importance for health and economy3&#160;(http://www.whitehouse.gov/share/brain-initiative). However,&#160;like any other field in the natural sciences, neuroscience depends on advances in methodologies and analysis techniques for progress. Two commonly used non invasive tools in studies about brain function in humans are magnetic resonance imaging (MRI) and electroencephalography (EEG). These tool exploit different physical properties and provide different insights into brain function with unique advantages and disadvantages. MRI uses the magnetic properties of water molecules within magnetic fields to obtain images of living tissues. The subject needs to be placed in a magnet with high field strength. The participant&#39;s movement is restricted during this procedures and the participant has to tolerate noise caused by rapid changes in the magnetic field. In addition to structural images, MRI also provides the possibility to measure changes in blood oxygenation to investigate brain function (fMRI). In summary, MRI offers relatively high spatial resolution of up to 0.5 mm3 with modern high fields scanners and optimized parameters4. In contrast, the temporal resolution of fMRI is limited to the slow kinetics of the BOLD response, which only indirectly reflects the high temporal dynamics of neural activity5,6.
On the other hand, EEG measures changes in electrical activity caused by the activity of neurons through electrodes placed on the scalp. Recent advances in EEG technology allow quick and easy application of the sensors for short term or long term and stationary as well as ambulatory recordings. Because EEG is less restrictive, it is also the method of choice for certain participant populations that do not tolerate the MRI environment well like pediatric and certain geriatric and psychiatric populations. The properties of EEG show an inverse pattern to those of MRI: the temporal resolution is very high with millisecond precision, but the spatial resolution is limited. Electrical currents pass through different tissues between their generator and the EEG electrodes on the surface of the scalp. This leads to mixing and spatial smearing of source activity known as the volume conduction effect. Therefore, activity measured by the electrodes on the surface of the scalp reflects activity from multiple sources that might be distant to the position of the electrode on the head1,7.
Much work in recent years has been dedicated to the merging of MRI and EEG in order to take advantage of their respective strengths. One line of work is dedicated to the simultaneous acquisition of EEG and MRI in functional studies. Another approach is to use the spatial information provided by structural MRI to take account of the volume conduction effect through biophysical modelling. The use of structural information for source reconstruction of EEG recordings is particularly useful for studies involving a pediatric population. The investigation of the development of brain function is central to understanding how complex cognitive skills are built on top of simple precursors8.
These investigations help to highlight changes in the neural substrates and response properties that correlate with changes in behavioral performance. However, the investigation of brain function and cognition during development also poses specific challenges. Particularly, the opportunity for functional MRI studies is limited as young children and infants either have to be asleep or sedated to obtain MRI data without movement artifacts and negative impact on participant wellbeing. Further, EEG is perceived as less risky and invasive by parents, which makes the recruitment of research participants easier. Therefore, EEG is the method of choice for many investigations of brain function in young children. Methodological advances in EEG systems allow the application of high density electrode arrays with 128 or more channels within minutes. Ease of application and wearing comfort are sufficient to even allow EEG recording in the youngest infants. However, often researchers are not only interested in the temporal dynamics of responses to particular stimuli, but would also like to compare the neural substrates that mediate the responses.
A prevailing assumption in channel level ERP analysis comparing different age groups is that the same neural substrates respond, but that the timing or response amplitude varies across ages9. Similar scalp topography is often used as an indicator of similar underlying neural activity. However, many different source configurations can lead to similar scalp topographies10. By applying source estimation, this uncertainty can be reduced and quantified. The independence of observations is critical for network accounts of brain function: if the sources are mixed, correlations will be biased towards higher local connectivity. Source reconstruction can be applied to reduce this bias11. Alternatively, differences in timing and phase can be used for connectivity analysis, but these mathematical models require assumptions that are hard to evaluate in non simulated data12. In summary, source estimation provides additional information to channel level EEG and ERP analysis based on knowledge about anatomy and biophysical properties of tissue.
Different algorithms have been devised to find solutions to the inverse problem. These algorithms fall broadly into two categories: parametric and non parametric13. Parametric models assume one or multiple dipoles that may vary in location, orientation and strength. In contrast, non parametric models contain a large number of dipoles with fixed location and orientation. In these models, the scalp electrical activity is explained as a combination of activations in the fixed dipoles10,13,14. Non parametric, distributed source models can be based on knowledge about anatomy and conductivity in different media. Boundary Element Models incorporate conductivity values for the main tissues of the head with different shells for the brain, cerebro spinal fluid, and skull. This is based on the assumption that conductivity is mostly constant within each compartment, but that marked changes occur at the boundary of different compartments. Finite element models are based on further segmentation of MR scans into grey and white matter so that conductivity values can be assigned to each voxel15.
In practical terms, non parametric models are particularly useful for source reconstruction in complex cognitive tasks, in which the number of areas involved may not be known10. Boundary element models are most widely used in the current literature, probably because the more accurate Finite Element Models pose comparably high computational demands. Further, there is considerable inter individual variability in grey and white matter so that FEMs should be based on individual MRI scans.
Non parametric models require a second step for matching the scalp measured activity to the predictions of the forward model. Again, different approaches with different advantages and drawbacks have been discussed in the literature (see Michel et al. 2004 for an overview). The most widely used algorithms are based on minimum norm estimation (MNE), which matches the scalp measured activity to a current distribution in the forward model with the lowest overall intensity16. MNE is biased towards weak and superficial sources. Depth weighted MNE algorithms try to reduce the surface bias by introducing weighting matrices based on mathematical assumptions10. The widely used LORETA approach is also based on weighted MNE, but additionally minimizes the Laplacian of sources, which leads to smoother solutions17,18. LORETA has been found to perform best for single sources in simulation studies19,20. However, LORETA may lead to over smoothing of solutions. Depth weighted MNE is preferable when the sources are unknown or multiple sources are likely to be present13,16. Comparing the results of different algorithms to evaluate the influence of different model assumptions is recommended.
In summary, source reconstruction through modelling methods has been limited for children until recently. This is because most EEG analysis software relies on head models based on adult anatomy that substantially limits the accuracy of source solutions in children2,8. The cheap access to computational power and the provision of user friendly software for source reconstruction make it possible to overcome these limitations. Applying source estimation to the EEG provides two important advantages over analysis based on channel level observations alone: improved spatial resolution and independence of observations.
Source estimation may not be informative in some cases: good coverage of the head is required to distinguish sources. High density systems with 128 or more electrodes are recommended10,15; a sparser coverage will act as a spatial filter leading to more wide spread source activation or false negative results10. Furthermore, source reconstruction based on the method described in this article has only been reported for cortical generators. Therefore, it is less suitable for testing hypotheses about subcortical substrates or cortical subcortical interactions. Lastly, source analysis should be based on detailed prior hypotheses about the cortical substrates, taking the existing literature from other imaging modalities into account. Spatial filtering techniques may also be used to improve the spatial resolution of the EEG signal by reducing spatial mixing on the scalp level. Alternative methods to reduce the influence of volume conduction effects without head modelling are used, e.g., Laplacian filtering21 or Current Source Density analysis22. However, these methods do not provide more information about neural generators as volume conduction effects are not only restricted to sensors in close spatial proximity1.
In the following sections, the article describes how experiments for the investigation of brain and cognitive function in children from 2 years of age are designed at the London Baby Lab. Next, EEG data acquisition with high&#160;density low impedance systems with children is discussed. Then, EEG preprocessing and analysis on the channel level is presented. Lastly, the article focuses on the processing of structural MRI data for cortical source reconstruction and analysis of source level signals.

<table border="0" cellpadding="0" cellspacing="0" width="1256">
	<tbody>
		<tr height="21">
			<td height="21" style="height:21px;width:473px;">High-density EEG sensor net (128 or 256 channels)</td>
			<td style="width:448px;">HydroCel Geodesic Sensor Net 128</td>
			<td style="width:247px;">Electrical Geodesic Inc., Oregon, US</td>
			<td style="width:88px;"></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;">EEG high impendance amplifier</td>
			<td>NetAmps 200</td>
			<td>Electrical Geodesic Inc., Oregon, US</td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;">Data Acquisition Computer&#160;</td>
			<td>PowerMac G4</td>
			<td>Apple Inc, California, US</td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;">Stimulus Presentation Computer</td>
			<td>Optiplex 745</td>
			<td>Dell Computers Inc., Texas, US</td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;">Stimulus Presentation Software</td>
			<td>Matlab R2012b with PsychToolBox</td>
			<td>Brainard et al. 1997</td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;">EEG recording software</td>
			<td>NetStation 4.5.1</td>
			<td>Electrical Geodesic Inc., Oregon, US</td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;">EEG analysis software</td>
			<td>Matlab R2012b</td>
			<td>The Mathworks Inc.,&#160;</td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;"></td>
			<td>EEGLAB</td>
			<td>Delorme et al. 2004</td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;"></td>
			<td>BrainStorm</td>
			<td>Sylvain et al. 2001</td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;">MRI processing software</td>
			<td>FreeSurfer</td>
			<td>Fischl et al. 2004</td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;"></td>
			<td>OpenMEEG</td>
			<td>Gramfort et al. 2010</td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;"></td>
			<td></td>
			<td></td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;"></td>
			<td></td>
			<td></td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;">References</td>
			<td></td>
			<td></td>
			<td></td>
		</tr>
		<tr height="21">
			<td colspan="4" height="21" style="height:21px;">Delorme, A., &#38; Makeig, S. (2004). EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis. Journal of Neuroscience Methods, 134(1), 9&#8211;21.</td>
		</tr>
		<tr height="21">
			<td colspan="3" height="21" style="height:21px;">Sylvain, B., John, C., Dimitrios, P., &#38; Richard, M. (2011). Brainstorm: A User-Friendly Application for MEG/EEG Analysis. Computational Intelligence and Neuroscience, 2011, 1&#8211;13.</td>
			<td></td>
		</tr>
		<tr height="21">
			<td colspan="3" height="21" style="height:21px;">Fischl, B., Van Der Kouwe, A., Destrieux, C., Halgren, E., S&#233;gonne, F., Salat, D. H., et al. (2004). Automatically parcellating the human cerebral cortex. Cerebral Cortex, 14(1), 11&#8211;22.</td>
			<td></td>
		</tr>
		<tr height="21">
			<td colspan="4" height="21" style="height:21px;">Gramfort, A., Papadopoulo, T., Olivi, E., &#38; Clerc, M. (2010). OpenMEEG: opensource software for quasistatic bioelectromagnetics. BioMedical Engineering OnLine, 9(1), 45. doi:10.1186/1475-925X-9-45</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;">Brainard, D. H. (1997). The psychophysics toolbox. Spatial vision.</td>
			<td></td>
			<td></td>
			<td></td>
		</tr>
	</tbody>
</table>

cortical source analysis of high-density eeg recordings in children

EEG is traditionally described as a neuroimaging technique with high temporal and low spatial resolution. Recent advances in biophysical modelling and signal processing make it possible to exploit information from other imaging modalities like structural MRI that provide high spatial resolution to overcome this constraint1. This is especially useful for investigations that require high resolution in the temporal as well as spatial domain. In addition, due to the easy application and low cost of EEG recordings, EEG is often the method of choice when working with populations, such as young children, that do not tolerate functional MRI scans well. However, in order to investigate which neural substrates are involved, anatomical information from structural MRI is still needed. Most EEG analysis packages work with standard head models that are based on adult anatomy. The accuracy of these models when used for children is limited2, because the composition and spatial configuration of head tissues changes dramatically over development3.&#160;
In the present paper, we provide an overview of our recent work in utilizing head models based on individual structural MRI scans or age specific head models to reconstruct the cortical generators of high density EEG. This article describes how&#160;EEG recordings are acquired, processed, and analyzed with pediatric populations at the London Baby Lab, including laboratory setup, task design, EEG preprocessing, MRI processing, and EEG channel level and source analysis.&#160;

Designing ERP experiments for infants and children is often challenging, because of their limited capacity to tolerate long repetitive experiments30. This problem is further aggravated when the experimenter plans to apply source reconstruction, because accurate source reconstruction will require a high signal to noise ratio1. Figure 1 displays an experimental protocol for the investigation of face processing mechanisms that can be used with very young children. The paradigm is adapt...

Watch this Scientific Journal Video about Cortical Source Analysis of High-Density EEG Recordings in Children at JoVE.com

Cortical Source Analysis of High-Density EEG Recordings in Children

In recent years, there has been increasing interest in estimating the cortical sources of scalp measured electrical activity for cognitive neuroscience experiments. This article describes how high density EEG is acquired and how recordings are processed for cortical source estimation in children from the age of 2 years at the London Baby Lab.

EEG is traditionally described as a neuroimaging technique with high temporal and low spatial resolution. Recent advances in biophysical modelling and ...

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