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In This Article

  • Summary
  • Abstract
  • Introduction
  • Protocol
  • Representative Results
  • Discussion
  • Acknowledgements
  • Materials
  • References
  • Reprints and Permissions

Summary

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.

Abstract

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

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 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. 

Introduction

President Barack Obama described the human brain as the next frontier of scientific discovery with high importance for health and economy(http://www.whitehouse.gov/share/brain-initiative). However, 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 propert....

Protocol

1. Designing EEG & 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 R2012b and Psychtoolbox V3.0.1123,24.  Pictures taken from the NimStim set of emotional facial expression25 were used for this example. This stimulus set is avai.......

Representative Results

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.......

Discussion

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.......

Acknowledgements

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’s Charity, UCL Impact & Grand Challenges.

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Materials

NameCompanyCatalog NumberComments
High-density EEG sensor net (128 or 256 channels)HydroCel Geodesic Sensor Net 128Electrical Geodesic Inc., Oregon, US
EEG high impendance amplifierNetAmps 200Electrical Geodesic Inc., Oregon, US
Data Acquisition Computer PowerMac G4Apple Inc, California, US
Stimulus Presentation ComputerOptiplex 745Dell Computers Inc., Texas, US
Stimulus Presentation SoftwareMatlab R2012b with PsychToolBoxBrainard et al. 1997
EEG recording softwareNetStation 4.5.1Electrical Geodesic Inc., Oregon, US
EEG analysis softwareMatlab R2012bThe Mathworks Inc., 
EEGLABDelorme et al. 2004
BrainStormSylvain et al. 2001
MRI processing softwareFreeSurferFischl et al. 2004
OpenMEEGGramfort et al. 2010
References
Delorme, A., & 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), 9–21.
Sylvain, B., John, C., Dimitrios, P., & Richard, M. (2011). Brainstorm: A User-Friendly Application for MEG/EEG Analysis. Computational Intelligence and Neuroscience, 2011, 1–13.
Fischl, B., Van Der Kouwe, A., Destrieux, C., Halgren, E., Ségonne, F., Salat, D. H., et al. (2004). Automatically parcellating the human cerebral cortex. Cerebral Cortex, 14(1), 11–22.
Gramfort, A., Papadopoulo, T., Olivi, E., & Clerc, M. (2010). OpenMEEG: opensource software for quasistatic bioelectromagnetics. BioMedical Engineering OnLine, 9(1), 45. doi:10.1186/1475-925X-9-45
Brainard, D. H. (1997). The psychophysics toolbox. Spatial vision.

References

  1. Michel, C. M., Murray, M. M. Towards the utilization of EEG as a brain imaging tool. NeuroImage. 61 (2), 371-385 (2012).
  2. Brodbeck, V., et al. EEG microstates of wakefulness and NREM sleep. NeuroImage. 62 (....

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EEGCortical Source AnalysisHigh density EEGChildrenStructural MRIHead ModelsTemporal ResolutionSpatial ResolutionPediatric PopulationsLondon Baby Lab

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