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 available for research purposes upon request (<a href="http://www.macbrain.org/resources.htm">http://www.macbrain.org/resources.htm</a>).
<ol>
	<li>Transfer the RGB pictures to grey scale to reduce differences between stimuli. See Table 1. Note: These commands require the Image Processing Toolbox (<a href="http://www.mathworks.co.uk/products/image/">http://www.mathworks.co.uk/products/image/</a>). Free alternatives may be found through the File Exchange (<a href="https://www.mathworks.co.uk/matlabcentral/fileexchange">https://www.mathworks.co.uk/matlabcentral/fileexchange</a>).</li>
	<li>Use experimental control software to implement the experiment with precise timing for stimulus presentation triggers using a series of commands (see Table 1 for an example).</li>
</ol>
2. Data Acquisition
<ol>
	<li>Ensure that the child is comfortable with the testing environment. Allow younger children to sit on the lap of their caregiver or in a comfortable child&#8217;s seat. Let the child see and feel the sensor net before applying it to the child&#8217;s head.&#160; If there is an extra net, have the parent also try&#160;one on, or place one on a doll or stuffed teddy.&#160;</li>
	<li>Measure the maximum head circumference to select the correct net size for the child. Use a measuring tape and hold it to the nasion. Then measure around the head around the maximum circumference (~1 cm above the inion). Note: keep a record of the measured head circumference and the sensor net used for later analysis26. It helps if the parents head is also measured to make children more comfortable with the situation.</li>
	<li>Identify the vertex of the head at the intersection of the mid distance between nasion and inion and left and right periauricular point. Mark this point with a china pen to ensure that the vertex channel is correctly positioned when applying the net.</li>
	<li>Apply the sensor net and make sure that key channels are aligned with the anatomical landmarks (nasion, inion, vertex and left/right mastoids). Note: For the most accurate results, the position of the channels on the head can be digitally acquired using special digitization equipment. Researchers wishing to acquire the sensor position should refer to the appropriate hardware and software manuals. Alternatively, electrode maps that assume standard placement of electrodes along anatomical landmarks can be used. These maps can be warped to age appropriate head models as described in the analysis section.</li>
	<li>Ensure that channels have good contact with the scalp by positioning the sensors individually; gently twist each sensor from side to side to move hair out of the way.</li>
	<li>Measure channel gains and channel impedances. Click &#8220;Start&#8221; to begin the recording in NetStation EEG recording software and start gain and impedance measurement. If measurement does not automatically start, use the &#8220;Calibrate Amplifier&#8221; and the &#8220;Measure Net Impedances&#8221; button.</li>
	<li>Check the recording software for channels with impedances higher than 50 k&#937; which will appear red. Apply additional electrolyte solution with a pipette to lower channel impedances. Check the EEG display for channels that show high frequency activity despite low impedance or noticeably less activity than surrounding channels (flat line channels). These channels may have loose contact with the scalp and require adjustment.</li>
	<li>In order to keep children comfortable during the EEG preparation, allow the child to listen to music, watch an age appropriate cartoon or distract them using another experimenter, e.g., blowing soap bubbles for toddlers.</li>
</ol>
3. Analysis
<ol>
	<li>Preprocessing
	<ol>
		<li>Digitally filter the data with a high pass filter with a cut off at 0.1 Hz to remove channel drifts27 (Table 1).</li>
		<li>For ERP analysis, apply a low pass filter with a cut off at 30 Hz27 (Table 1).</li>
		<li>Epoch the continuous data according to the trigger codes set during recording. For most experiments, use a baseline of 200 msec prior to stimulus onset and a post stimulus interval of 600 msec to cover the time interval of interest (Table 1).</li>
		<li>Remove epochs that contain movement or blink artifacts: mark channels with a peak to peak amplitude higher than 150 mV - adjust this threshold depending on the participant group and data quality. For consistency, use the same threshold for all participants in one study. If a channel is above this threshold in more than 30% of the epochs, remove the channel (channel activity may be interpolated from surrounding channels, if these contain acceptable data). If more than 20% of the channels are marked as bad in an epoch, remove the epoch. If more than 20% of channels are rejected by the algorithm or less than 50% of epochs are retained, consider removing the dataset from further analysis (Table 1). 
		Note: The percentage thresholds for epoch and channel rejection are ballpark figures that remove a sufficient amount of noise in our experience. The amount of artifact in the recording is likely to be different using other participant groups, experimental paradigms or EEG systems. The experimenters may want to adjust the percentage thresholds and check if they are satisfied with the artifact rejection. Alternatively, experimenters can reject trials that contain artifact through visual inspection.&#160;</li>
		<li>Re reference to average reference by subtracting the mean activity across channels from each channel (Table 1). Note: The vertex electrode is typically used as the recording reference in NetStation.</li>
	</ol>
	</li>
	<li>Artifact Correction Using Independent Component Analysis
	<ol>
		<li>Import the data into the FASTER toolbox28 and run the automatic artifact rejection algorithm on the data (Table 1).</li>
		<li>Use the Graphical User Interface (GUI) for FASTER; to open the GUI, type FASTER_GUI into the command line.</li>
		<li>Deselect the filtering options in the filtering menu as the data has already been filtered before the epoching.</li>
		<li>Specify the number of channels: 126 EEG channels with 2 electrooculagram (EOG) channels.</li>
		<li>Enter the markers used for epoching the data as strings in a cell array. For the presented case enter: {&#8216;face&#8217;,&#8216;scra&#8217;} for the face and scrambled face conditions.</li>
		<li>Select the channels for the independent component analysis (ICA). Typically select all recording channels, incl. external non EEG channels.</li>
		<li>Specify the input and output folder in the right pane of the GUI.</li>
		<li>Select the appropriate channel file for the recordings. Note: Channel files for most EEG system can either be downloaded from the manufacturer or can be downloaded from the EEGLAB website.</li>
		<li>Click RUN to start FASTER processing. Depending on the length of the recordings and the number of files, this processing can take several hours.</li>
		<li>Visually inspect the recordings, independent component maps and ERPs after the processing.</li>
	</ol>
	</li>
	<li>Channel Level Analysis of Event-related Potentials Data
	<ol>
		<li>Combine several channels to form a virtual channel with better signal to noise ratio (Table 1). Note: The selection of channels should be based on previous reports in the literature or a priori hypotheses. Selecting channels that show the highest amplitude within a given time window is not advised29.</li>
		<li>Obtain measures like peak amplitude, mean amplitude and peak latency to characterize the waveform and perform statistical tests (Table 1).</li>
	</ol>
	</li>
	<li>Create Boundary Element Models (BEM)
	<ol>
		<li>Segment the anatomical MRI scan with FreeSurfer. Note: For the most accurate results, base the boundary element model on individual MRI scans for each participant. If this is not feasible, average MRI templates that match the participants&#8217; age as closely as possible should be used. Please note that BEMs cannot be used for children under 24 months. Boundary element models assume that each shell (brain, skull, skin) consists of a closed shell. However, in young children the fontanelles in the skull are not closed, which violates the closed shell assumption.
		<ol>
			<li>In order to install FreeSurfer software, first download it from the FreeSurfer website (<a href="http://freesurfer.net/fswiki/DownloadAndInstall">http://freesurfer.net/fswiki/DownloadAndInstall</a>). Next, set up the shell environment include FreeSurfer; for .bashrc, include the following commands in the .bashrc file:
			<ol>
				<li>Export FREESURFER_HOME=/Applications/freesurfer/</li>
				<li>Source $FREESURFER_HOME/FreeSurferEnv.sh 
				Note: These commands assume that the FreeSurfer folder is in the Applications folder on a Unix system. There are more details on how to setup FreeSurfer with alternative shell environments, e.g. csh/tcsh, or operating systems on the FreeSurfer website (<a href="http://freesurfer.net/fswiki/DownloadAndInstall">http://freesurfer.net/fswiki/DownloadAndInstall</a>).</li>
			</ol>
			</li>
			<li>Next, define the Subject directory, i.e. the folder that the output will be written to using the following command:
			<ol>
				<li>export SUBJECTS_DIR=/&#8230;/BEMs/ 
				Note: The results may be written to any folder on the system.</li>
			</ol>
			</li>
			<li>Next, change the working directory to the folder that contains the MRI file for the Boundary Element Model using the following command:
			<ol>
				<li>cd /Users/joebathelt/Neurodevelopmental_MRI_database/Children/Brain/ 
				Note: Any folder on the system may be specified using the syntax of the cd command. This is an example of the primary researchers file structure.</li>
			</ol>
			</li>
			<li>Finally, start the reconstruction using the following commands:
			<ol>
				<li>recon-all -i &#60;mri_file&#62; -subjid &#60;subject_id&#62;</li>
				<li>recon-all -all -subjid &#60;subject_id&#62; 
				Note: &#60;mri_file&#62; needs to be replaced with the filename of the desired MRI scans in the current directory. &#60;subject_id&#62; can be replaced with any name. FreeSurfer will create a folder with this name in the subject directory. Depending on the system used, the last commands may require some time to run.</li>
			</ol>
			</li>
		</ol>
		</li>
		<li>Check the FreeSurfer segmentation for incorrect segmentation, e.g. overlapping spheres, anatomically unlikely compartments etc. by importing the segments into BrainStorm and use the display tools in the GUI:
		<ol>
			<li>In BrainStorm, select the anatomy pane. Import the segmented MRI by right clicking on the subject and selecting &#8220;Import Anatomy Folder&#8221;. Ensure the folder with the FreeSurfer output is selected. Inspect the segmentation visually by right clicking and selecting &#8220;Display&#8221;. Note: Alternatively, FreeSurfer commands can be used. A detailed description can be found on the FreeSurfer website: <a href="http://surfer.nmr.mgh.harvard.edu/fswiki/RecommendedReconstruction">http://surfer.nmr.mgh.harvard.edu/fswiki/RecommendedReconstruction</a>. If region of interest analysis based on anatomical parcellation is desired, the FreeSurfer functions <a href="https://surfer.nmr.mgh.harvard.edu/fswiki/mris_ca_label?action=fullsearch&amp;context=180&amp;value=linkto%3A%22mris_ca_label%22">mris_ca_label</a>, <a href="https://surfer.nmr.mgh.harvard.edu/fswiki/mri_annotation2label?action=fullsearch&amp;context=180&amp;value=linkto%3A%22mri_annotation2label%22">mri_annotation2label</a> and <a href="https://surfer.nmr.mgh.harvard.edu/fswiki/mri_mergelabels?action=fullsearch&amp;context=180&amp;value=linkto%3A%22mri_mergelabels%22">mri_mergelabels</a> can be used. Refer to the FreeSurfer publications and online help pages for more detailed information.</li>
		</ol>
		</li>
	</ol>
	</li>
	<li>Estimate the Source Activity in BrainStorm
	<ol>
		<li>Start BrainStorm by typing &#8220;brainstorm&#8221; in the command window.</li>
		<li>Create a new protocol by selecting New Protocol from the File menu.</li>
		<li>Add a new subject to the protocol by selecting New Subject from the File menu.</li>
		<li>Import EEG data for the participant by right-clicking on the subject and selecting &#8220;Import MEG/EEG&#8221;.</li>
		<li>Import a channel file by right clicking and choosing &#8220;Import channel file&#8221;. Note:&#160;The channel file needs to be aligned to the MRI for source reconstruction. BrainStorm uses a system of 4 anatomical reference points that the user needs to mark in the MRI. Please refer to the BrainStorm tutorials for more information (<a href="http://neuroimage.usc.edu/brainstorm/CoordinateSystems">http://neuroimage.usc.edu/brainstorm/CoordinateSystems</a>). The standard position as defined by a channel file for a certain EEG system or, ideally, the head positions that were digitized prior to the EEG recording can be used.</li>
		<li>Check that the BEM and the channels align as expected: Right click on the channel file for the subject and navigate to &#8220;MRI registration&#8221; and &#8220;Check&#8221;. Note: If the spheres within the model are overlapping or if the channels are within the BEM, the source reconstruction will produce incorrect results. Adjust the alignment by using the &#8220;Edit&#8221; option in the &#8220;MRI segmentation&#8221; menu.</li>
		<li>Calculate the noise covariance matrix from the baseline of each epoch by right clicking on the participant and choosing &#8220;Noise Covariance Matrix&#8221; and &#8220;Calculate from Recording&#8221;. Note: The authors of the BrainStorm toolbox recommend using a diagonal noise covariance matrix for short recordings (~ less time points than channels) and a full one for longer recordings. Please refer to the BrainStorm Source Estimation tutorial for more information: <a href="http://neuroimage.usc.edu/brainstorm/Tutorials/TutSourceEstimation">http://neuroimage.usc.edu/brainstorm/Tutorials/TutSourceEstimation</a> .</li>
		<li>Calculate the Source Model by right clicking on the subject and selecting &#8220;Compute source model&#8221;.</li>
		<li>Calculate the inverse solution using depth weighted Minimum Norm Estimation by right clicking on the subject and selecting &#8220;Compute Source&#8221; and &#8220;Minimum norm estimation&#8221;. Note: Other options (dSPM, sLORETA) are available. Each option has different advantages and drawbacks. The algorithm should be selected based on a priori considerations and previous reports in the literature. Further, some algorithms are better in resolving focal activation in certain areas, whereas others are more suitable for wide-spread activation. MNE was used for this study based on previous reports in the literature16. For consistency, the same algorithm for inverse solution should be used for all participants in one study. Researchers may also want to compare how robust findings are to the application of different inverse solution algorithms.&#160;</li>
		<li>Repeat Section 3 for all participants in the study. Note: Either use the graphical batching interface or scripts to repeat processing steps for participants. See the BrainStorm documentation for instructions (<a href="http://neuroimage.usc.edu/brainstorm/Tutorials/TutRawScript">http://neuroimage.usc.edu/brainstorm/Tutorials/TutRawScript</a>).</li>
		<li>Average source activity over trials per participants by dragging the recordings to the Process Menu and selecting &#8220;Average&#8221; and &#8220;By condition (subject average)&#8221;.</li>
		<li>Contrast the condition by selecting the &#8220;Processes 2&#8221; and dragging each condition in one window. Then, select &#8220;Test&#8221; and &#8220;Student&#8217;s t-test&#8221; or &#8220;Student&#8217;s t-test (paired)&#8221; depending on design. To perform multiple comparisons, set amplitude and area thresholds in the display of the resulting statistical map in the &#8220;Stat&#8221; menu. Note: Alternatively, the activation maps can be exported to SPM (<a href="http://www.fil.ion.ucl.ac.uk/spm/">http://www.fil.ion.ucl.ac.uk/spm/</a>) for more in-depth statistical analysis (<a href="http://neuroimage.usc.edu/brainstorm/ExportSpm12%20HYPERLINK%20%22http:/neuroimage.usc.edu/brainstorm/ExportSpm12%22)">http://neuroimage.usc.edu/brainstorm/ExportSpm12 HYPERLINK &#34;http://neuroimage.usc.edu/brainstorm/ExportSpm12&#34;)</a>.</li>
		<li>Calculate the event-related response for a region of interest. For parcellation based ROIs, load the FreeSurfer parcellation by right clicking on the cortex surface in the anatomy menu and select &#8220;Import labels&#8221;. Navigate the corresponding file and load it. Now, select a ROI in the &#8220;Scout&#8221; pane of the functional data menu.</li>
		<li>Obtain the ROI event related activity by dragging files to the Process 1 window and select &#8220;Extract Scout Time Series&#8221; from the Sources menu. Note: Several ROIs can be selected simultaneously and the ROI time series can be exported for further plotting and analysis by right-clicking on the scout time series data and selecting &#8220;Export to Matlab&#8221;.</li>
	</ol>
	</li>
</ol>

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A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Evaluation+of+different+cortical+source+localization+methods+using+simulated+and+experimental+EEG+data.">Evaluation of different cortical source localization methods using simulated and experimental EEG data.</a> NeuroImage. 25 (2), 369-382 (2005).</li><li>Tandonnet, C., Burle, B., Hasbroucq, T., Vidal, F. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Spatial+enhancement+of+EEG+traces+by+surface+Laplacian+estimation:+Comparison+between+local+and+global+methods.">Spatial enhancement of EEG traces by surface Laplacian estimation: Comparison between local and global methods.</a> Clinical Neurophysiology. 116 (1), 18-24 (2005).</li><li>Tenke, C. E., Kayser, J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Generator+localization+by+current+source+density+(CSD):+implications+of+volume+conduction+and+field+closure+at+intracranial+and+scalp+resolutions.">Generator localization by current source density (CSD): implications of volume conduction and field closure at intracranial and scalp resolutions.</a> Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology. 123 (12), 2328-2345 (2012).</li><li>Brainard, D. H. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+psychophysics+toolbox.">The psychophysics toolbox.</a> Spatial vision. , (1997).</li><li>Kleiner, M., Brainard, D., Pelli, D., Ingling, A., Murray, R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=What's+new+in+Psychtoolbox-3.">What's new in Psychtoolbox-3.</a> Perception. , (2007).</li><li>Tottenham, N., 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=The+NimStim+set+of+facial+expressions:+judgments+from+untrained+research+participants.">The NimStim set of facial expressions: judgments from untrained research participants.</a> Psychiatry Research. 168 (3), 242-249 (2009).</li><li>Chaste, P., 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=Adjusting+head+circumference+for+covariates+in+autism:+Clinical+correlates+of+a+highly+heritable+continuous+trait.">Adjusting head circumference for covariates in autism: Clinical correlates of a highly heritable continuous trait.</a> Biological Psychiatry. 74 (8), 576-584 (2013).</li><li>Delorme, A., 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=EEGLAB,+SIFT,+NFT,+BCILAB,+and+ERICA:+New+tools+for+advanced+EEG+processing.">EEGLAB, SIFT, NFT, BCILAB, and ERICA: New tools for advanced EEG processing.</a> Computational Intelligence and Neuroscience. 2011, 130714 (2011).</li><li>Nolan, H., Whelan, R., Reilly, R. B. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=FASTER:+Fully+Automated+Statistical+Thresholding+for+EEG+artifact+Rejection.">FASTER: Fully Automated Statistical Thresholding for EEG artifact Rejection.</a> Journal of Neuroscience Methods. 192 (1), 152-162 (2010).</li><li>Kilner, J. M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Bias+in+a+common+EEG+and+MEG+statistical+analysis+and+how+to+avoid+it.">Bias in a common EEG and MEG statistical analysis and how to avoid it.</a> Clinical Neurophysiology. , (2013).</li><li>DeBoer, T., Scott, L. S., Nelson, C. A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=12+ERPs+in+Developmental+Populations.">12 ERPs in Developmental Populations.</a> Event-related Potentials: A. , (2005).</li><li>Sanchez, C. E., Richards, J. E., Almli, C. R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Neurodevelopmental+MRI+brain+templates+for+children+from+2+weeks+to+4+years+of+age.">Neurodevelopmental MRI brain templates for children from 2 weeks to 4 years of age.</a> Developmental Psychobiology. 54 (1), 77-91 (2011).</li><li>Tadel, F., Baillet, S., Mosher, J. C., Pantazis, D., Leahy, R. M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Brainstorm:+A+user-friendly+application+for+MEG/EEG+analysis.">Brainstorm: A user-friendly application for MEG/EEG analysis.</a> Computational Intelligence and Neuroscience. 2011, 879716 (2011).</li><li>Haan, M., Johnson, M. H., Halit, H. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Development+of+face-sensitive+event-related+potentials+during+infancy:+a+review.">Development of face-sensitive event-related potentials during infancy: a review.</a> International Journal of Psychophysiology. 51, 45-58 (2003).</li><li>Earp, B. D., Everett, J. A. C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Is+the+N170+face+specific?+Controversy,+context,+and+theory.">Is the N170 face specific? Controversy, context, and theory.</a> Neuropsychological Trends. 13 (1), 7-26 (2013).</li><li>Taylor, M. J., McCarthy, G., Saliba, E., Degiovanni, E. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=ERP+evidence+of+developmental+changes+in+processing+of+faces.">ERP evidence of developmental changes in processing of faces.</a> Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology. 110 (5), 910-915 (1999).</li><li>Ment, L. R., 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=Longitudinal+brain+volume+changes+in+preterm+and+term+control+subjects+during+late+childhood+and+adolescence.">Longitudinal brain volume changes in preterm and term control subjects during late childhood and adolescence.</a> PEDIATRICS. 123 (2), 503-511 (2009).</li><li>Courchesne, E., 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=Impairment+in+shifting+attention+in+autistic+and+cerebellar+patients.">Impairment in shifting attention in autistic and cerebellar patients.</a> Behavioral Neuroscience. 108 (5), 848-865 (1994).</li><li>Litvak, 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+and+MEG+data+analysis+in+SPM8.">EEG and MEG data analysis in SPM8.</a> Computational Intelligence and Neuroscience. (2011), (2011).</li><li>Daunizeau, J., David, O., Stephan, K. E. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Dynamic+causal+modelling:+A+critical+review+of+the+biophysical+and+statistical+foundations.">Dynamic causal modelling: A critical review of the biophysical and statistical foundations.</a> NeuroImage. 58 (2), 312-322 (2011).</li></ol>

Publication costs for this article were sponsored by Electrical&#160;Geodesics,&#160;Inc.

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><tbody><tr><td>High-density EEG sensor net (128 or 256 channels)</td><td>HydroCel Geodesic Sensor Net 128</td><td>Electrical Geodesic Inc., Oregon, US</td><td></td></tr><tr><td>EEG high impendance amplifier</td><td>NetAmps 200</td><td>Electrical Geodesic Inc., Oregon, US</td><td></td></tr><tr><td>Data Acquisition Computer&amp;nbsp;</td><td>PowerMac G4</td><td>Apple Inc, California, US</td><td></td></tr><tr><td>Stimulus Presentation Computer</td><td>Optiplex 745</td><td>Dell Computers Inc., Texas, US</td><td></td></tr><tr><td>Stimulus Presentation Software</td><td>MATLAB R2012b with PsychToolBox</td><td>Brainard &lt;em&gt;et al.&lt;/em&gt; 1997</td><td></td></tr><tr><td>EEG recording software</td><td>NetStation 4.5.1</td><td>Electrical Geodesic Inc., Oregon, US</td><td></td></tr><tr><td>EEG analysis software</td><td>MATLAB R2012b</td><td>The Mathworks Inc.</td><td></td></tr><tr><td></td><td>EEGLAB</td><td>Delorme et &lt;em&gt;al.&lt;/em&gt; 2004</td><td></td></tr><tr><td></td><td>BrainStorm</td><td>Sylvain &lt;em&gt;et al.&lt;/em&gt; 2001</td><td></td></tr><tr><td>MRI processing software</td><td>FreeSurfer</td><td>Fischl &lt;em&gt;et al.&lt;/em&gt; 2004</td><td></td></tr><tr><td></td><td>OpenMEEG</td><td>Gramfort &lt;em&gt;et. al.&lt;/em&gt; 2010</td><td></td></tr><tr><td colspan="4">Delorme, A., &amp;amp; Makeig, S. EEGLAB: an open source toolbox for analysis of single trial EEG dynamics including independent component analysis. &lt;em&gt;Journal of Neuroscience Methods&lt;/em&gt;, &lt;strong&gt;134&lt;/strong&gt;(1), 9&amp;ndash;21, (2004).</td></tr><tr><td colspan="4">Sylvain, B., John, C., Dimitrios, P., &amp;amp; Richard, M. Brainstorm: A User Friendly Application for MEG/EEG Analysis. &lt;em&gt;Computational Intelligence and Neuroscience&lt;/em&gt;, 1&amp;ndash;13, (2011).</td></tr><tr><td colspan="4">Fischl, B. &lt;em&gt;et al.&lt;/em&gt; Automatically parcellating the human cerebral cortex. &lt;em&gt;Cerebral Cortex&lt;/em&gt;, &lt;strong&gt;14&lt;/strong&gt;(1), 11&amp;ndash;22, (2004).</td></tr><tr><td colspan="4">Gramfort, A., Papadopoulo, T., Olivi, E., &amp;amp; Clerc, M. OpenMEEG: opensource software for quasistatic bioelectromagnetics. &lt;em&gt;BioMedical Engineering OnLine&lt;/em&gt;, &lt;strong&gt;9&lt;/strong&gt;(1), 45, (2010).</td></tr><tr><td colspan="4">Brainard, D. H. The psychophysics toolbox. &lt;em&gt;Spatial vision&lt;/em&gt;, (1997).</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 ...

cortical-source-analysis-of-high-density-eeg-recordings-in-children

Research

JoVE Journal

Behavior

21.3K Views.  UCL Institute of Child Health.  De laatste jaren is er toenemende belangstelling voor het schatten van de corticale bronnen hoofdhuid gemeten elektrische activiteit tegen cognitieve neurologie experimenten geweest. Dit artikel beschrijft hoe een hoge dichtheid EEG wordt verworven en hoe opnames worden verwerkt voor corticale bron schatting bij kinderen vanaf de leeftijd van 2 jaar op de London baby Lab. 

Video: Cortical Source Analysis of High-Density EEG Recordings in Children

Infant Auditory Processing and Event-related Brain Oscillations

Rapid auditory processing and acoustic change detection abilities play a critical role in allowing human infants to efficiently process the fine spectral and temporal changes that are characteristic of human language. These abilities lay the foundation for effective language acquisition; allowing infants to hone in on the sounds of their native language. Invasive procedures in animals and scalp-recorded potentials from human adults suggest that simultaneous, rhythmic activity (oscillations) between and within brain regions are fundamental to sensory development; determining the resolution with which incoming stimuli are parsed. At this time, little is known about oscillatory dynamics in human infant development. However, animal neurophysiology and adult EEG data provide the basis for a strong hypothesis that rapid auditory processing in infants is mediated by oscillatory synchrony in discrete frequency bands. In order to investigate this, 128-channel, high-density EEG responses of 4-month old infants to frequency change in tone pairs, presented in two rate conditions (Rapid: 70 msec ISI and Control: 300 msec ISI) were examined. To determine the frequency band and magnitude of activity, auditory evoked response averages were first co-registered with age-appropriate brain templates. Next, the principal components of the response were identified and localized using a two-dipole model of brain activity. Single-trial analysis of oscillatory power showed a robust index of frequency change processing in bursts of Theta band (3 - 8 Hz) activity in both right and left auditory cortices, with left activation more prominent in the Rapid condition. These methods have produced data that are not only some of the first reported evoked oscillations analyses in infants, but are also, importantly, the product of a well-established method of recording and analyzing clean, meticulously collected, infant EEG and ERPs. In this article, we describe our method for infant EEG net application, recording, dynamic brain response analysis, and representative results.

High-density electroencephalography (dEEG) is being used increasingly to study brain development and plasticity in the early years of life. Here we present an application of sophisticated analysis techniques that builds on traditional EEG recording to understand the oscillatory dynamics of rapid auditory processing in the infant brain.

Rapid auditory processing and acoustic change detection abilities play a critical role in allowing human infants to efficiently process the fine spectral ...

Investigating Social Cognition in Infants and Adults Using Dense Array Electroencephalography (dEEG)

Dense array electroencephalography (dEEG), which provides a non-invasive window for measuring brain activity and a temporal resolution unsurpassed by any other current brain imaging technology1,2, is being used increasingly in the study of social cognitive functioning in infants and adults. While dEEG is enabling researchers to examine brain activity patterns with unprecedented levels of sensitivity, conventional EEG recording systems continue to face certain limitations, including 1) poor spatial resolution and source localization3,4,2) the physical discomfort for test subjects of enduring the individual application of numerous electrodes to the surface of the scalp, and 3) the complexity for researchers of learning to use multiple software packages to collect and process data. Here we present an overview of an established methodology that represents a significant improvement on conventional methodologies for studying EEG in infants and adults. Although several analytical software techniques can be used to establish indirect indices of source localization to improve the spatial resolution of dEEG, the HydroCel Geodesic Sensor Net (HCGSN) by Electrical Geodesics, Inc. (EGI), a dense sensory array that maintains equal distances among adjacent recording electrodes on all surfaces of the scalp, further enhances spatial resolution4,5,6 compared to standard dEEG systems. The sponge-based HCGSN can be applied rapidly and without scalp abrasion, making it ideal for use with adults7,8, children9,10,11, and infants12, in both research and clinical4,5,6,13,14,15 settings. This feature allows for considerable cost and time savings by decreasing the average net application time compared to other dEEG systems. Moreover, the HCGSN includes unified, seamless software applications for all phases of data, greatly simplifying the collection, processing, and analysis of dEEG data.
The HCGSN features a low-profile electrode pedestal, which, when filled with electrolyte solution, creates a sealed microenvironment and an electrode-scalp interface. In all Geodesic dEEG systems, EEG sensors detect changes in voltage originating from the participant's scalp, along with a small amount of electrical noise originating from the room environment. Electrical signals from all sensors of the Geodesic sensor net are received simultaneously by the amplifier, where they are automatically processed, packaged, and sent to the data-acquisition computer (DAC). Once received by the DAC, scalp electrical activity can be isolated from artifacts for analysis using the filtering and artifact detection tools included in the EGI software. Typically, the HCGSN can be used continuously for only up to two hours because the electrolyte solution dries out over time, gradually decreasing the quality of the scalp-electrode interface.
In the Parent-Infant Research Lab at the University of Toronto, we are using dEEG to study social cognitive processes including memory, emotion, goals, intentionality, anticipation, and executive functioning in both adult and infant participants.

Investigating Social Cognition in Infants and Adults Using Dense Array Electroencephalography dEEG

Dense array electroencephalography is being used increasingly to study social cognitive functions in infants and adults. Here we present an established methodology that represents a significant improvement on conventional methodologies for studying EEG in infants and adults.

Dense array electroencephalography (dEEG), which provides a non-invasive window for measuring brain activity and a temporal resolution unsurpassed by any ...

Neuroscience

Investigating the Function of Deep Cortical and Subcortical Structures Using Stereotactic Electroencephalography: Lessons from the Anterior Cingulate Cortex

Stereotactic Electroencephalography (SEEG) is a technique used to localize seizure foci in patients with medically intractable epilepsy. This procedure involves the chronic placement of multiple depth electrodes into regions of the brain typically inaccessible via subdural grid electrode placement. SEEG thus provides a unique opportunity to investigate brain function. In this paper we demonstrate how SEEG can be used to investigate the role of the dorsal anterior cingulate cortex (dACC) in cognitive control. We include a description of the SEEG procedure, demonstrating the surgical placement of the electrodes. We describe the components and process required to record local field potential (LFP) data from consenting subjects while they are engaged in a behavioral task. In the example provided, subjects play a cognitive interference task, and we demonstrate how signals are recorded and analyzed from electrodes in the dorsal anterior cingulate cortex, an area intimately involved in decision-making. We conclude with further suggestions of ways in which this method can be used for investigating human cognitive processes.

Stereotactic Electroencephalography (SEEG) is an operative technique used in epilepsy surgery to help localize seizure foci. It also affords a unique opportunity to investigate brain function. Here we describe how SEEG can be used to investigate cognitive processes in human subjects.

Stereotactic Electroencephalography (SEEG) is a technique used to localize seizure foci in patients with medically intractable epilepsy. This procedure ...

Brain Source Imaging in Preclinical Rat Models of Focal Epilepsy using High-Resolution EEG Recordings

Electroencephalogram (EEG) has been traditionally used to determine which brain regions are the most likely candidates for resection in patients with focal epilepsy. This methodology relies on the assumption that seizures originate from the same regions of the brain from which interictal epileptiform discharges (IEDs) emerge. Preclinical models are very useful to find correlates between IED locations and the actual regions underlying seizure initiation in focal epilepsy. Rats have been commonly used in preclinical studies of epilepsy1; hence, there exist a large variety of models for focal epilepsy in this particular species. However, it is challenging to record multichannel EEG and to perform brain source imaging in such a small animal. To overcome this issue, we combine a patented-technology to obtain 32-channel EEG recordings from rodents2 and an MRI probabilistic atlas for brain anatomical structures in Wistar rats to perform brain source imaging. In this video, we introduce the procedures to acquire multichannel EEG from Wistar rats with focal cortical dysplasia, and describe the steps both to define the volume conductor model from the MRI atlas and to uniquely determine the IEDs. Finally, we validate the whole methodology by obtaining brain source images of IEDs and compare them with those obtained at different time frames during the seizure onset.

This video introduces the preparation, recording, and source analysis procedures of high-resolution EEG on sedated rats with a particular preclinical model of focal epilepsy under noninvasive conditions.

Electroencephalogram (EEG) has been traditionally used to determine which brain regions are the most likely candidates for resection in patients with ...

Medicine

High-density Electroencephalographic Acquisition in a Rodent Model Using Low-cost and Open-source Resources

Advanced electroencephalographic analysis techniques requiring high spatial resolution, including electrical source imaging and measures of network connectivity, are applicable to an expanding variety of questions in neuroscience. Performing these kinds of analyses in a rodent model requires higher electrode density than traditional screw electrodes can accomplish. While higher-density electroencephalographic montages for rodents exist, they are of limited availability to most researchers, are not robust enough for repeated experiments over an extended period of time, or are limited to use in anesthetized rodents.1-3 A proposed low-cost method for constructing a durable, high-count, transcranial electrode array, consisting of bilaterally implantable headpieces is investigated as a means to perform advanced electroencephalogram analyses in mice or rats.

Procedures for headpiece fabrication and surgical implantation necessary to produce high signal to noise, low-impedance electroencephalographic and electromyographic signals are presented. While the methodology is useful in both rats and mice, this manuscript focuses on the more challenging implementation for the smaller mouse skull. Freely moving mice are only tethered to cables via a common adapter during recording. One version of this electrode system that includes 26 electroencephalographic channels and 4 electromyographic channels is described below.

Instructions for the low-cost construction and surgical implantation of a chronic transcranial high-density electroencephalographic montage into mice are provided. Signal recording, extraction, and processing techniques are also described.

Advanced electroencephalographic analysis techniques requiring high spatial resolution, including electrical source imaging and measures of network ...

Interictal High Frequency Oscillations Detected with Simultaneous Magnetoencephalography and Electroencephalography as Biomarker of Pediatric Epilepsy

Crucial to the success of epilepsy surgery is the availability of a robust biomarker that identifies the Epileptogenic Zone (EZ). High Frequency Oscillations (HFOs) have emerged as potential presurgical biomarkers for the identification of the EZ in addition to Interictal Epileptiform Discharges (IEDs) and ictal activity. Although they are promising to localize the EZ, they are not yet suited for the diagnosis or monitoring of epilepsy in clinical practice. Primary barriers remain: the lack of a formal and global definition for HFOs; the consequent heterogeneity of methodological approaches used for their study; and the practical difficulties to detect and localize them noninvasively from scalp recordings. Here, we present a methodology for the recording, detection, and localization of interictal HFOs from pediatric patients with refractory epilepsy. We report representative data of HFOs detected noninvasively from interictal scalp EEG and MEG from two children undergoing surgery.
The underlying generators of HFOs were localized by solving the inverse problem and their localization was compared to the Seizure Onset Zone (SOZ) as this was defined by the epileptologists. For both patients, Interictal Epileptogenic Discharges (IEDs) and HFOs were localized with source imaging at concordant locations. For one patient, intracranial EEG (iEEG) data were also available. For this patient, we found that the HFOs localization was concordant between noninvasive and invasive methods. The comparison of iEEG with the results from scalp recordings served to validate these findings. To our best knowledge, this is the first study that presents the source localization of scalp HFOs from simultaneous EEG and MEG recordings comparing the results with invasive recordings. These findings suggest that HFOs can be reliably detected and localized noninvasively with scalp EEG and MEG. We conclude that the noninvasive localization of interictal HFOs could significantly improve the presurgical evaluation for pediatric patients with epilepsy.

High Frequency Oscillations (HFOs) have emerged as presurgical biomarkers for the identification of the epileptogenic zone in pediatric patients with medically refractory epilepsy. A methodology for the noninvasive recording, detection, and localization of HFOs with simultaneous scalp electroencephalography (EEG) and magnetoencephalography (MEG) is presented.

Crucial to the success of epilepsy surgery is the availability of a robust biomarker that identifies the Epileptogenic Zone (EZ). High Frequency ...

Application of an Amplitude-integrated EEG Monitor (Cerebral Function Monitor) to Neonates

Amplitude-integrated EEG (aEEG) is an easily accessible technique to monitor the electrocortical activity in preterm and term infants in neonatal intensive care units (NICUs). This method was first used to monitor newborns after asphyxia, providing information about future neurological outcomes. The aEEG is also helpful to select newborns who benefit from cooling. The aEEG monitoring of preterm infants is becoming more widespread, as various studies have shown that neurodevelopmental outcome is related to early aEEG tracings. Here, we demonstrate the application of the aEEG monitoring system and present typical patterns that depend upon gestational age and pathophysiological conditions. Furthermore, we mention pitfalls in the interpretation of the aEEG, as this method requires accurate fixation and localization of the electrodes. Additionally, the raw EEG can be used to detect neonatal seizures or to identify aEEG application problems. In conclusion, aEEG is a safe and generally well-tolerated method for the bedside monitoring of neonatal cerebral function; it can even provide information about long-term outcome.

Application of an Amplitude-integrated EEG Monitor Cerebral Function Monitor to Neonates

Here, we show how to apply amplitude-integrated electroencephalography to monitor cerebral function in neonates.

Amplitude-integrated EEG (aEEG) is an easily accessible technique to monitor the electrocortical activity in preterm and term infants in neonatal ...

Stimulus-specific Cortical Visual Evoked Potential Morphological Patterns

This paper presents a methodology for the recording and analysis of cortical visual evoked potentials (CVEPs) in response to various visual stimuli using 128-channel high-density electroencephalography (EEG). The specific aim of the described stimuli and analyses is to examine whether it is feasible to replicate previously reported CVEP morphological patterns elicited by an apparent motion stimulus, designed to simultaneously stimulate both ventral and dorsal central visual networks, using object and motion stimuli designed to separately stimulate ventral and dorsal visual cortical networks.&#160; Four visual paradigms are presented: 1. Randomized visual objects with consistent temporal presentation. 2. Randomized visual objects with inconsistent temporal presentation (or jitter).&#160; 3. Visual motion via a radial field of coherent central dot motion without jitter.&#160; 4. Visual motion via a radial field of coherent central dot motion with jitter.&#160; These four paradigms are presented in a pseudo-randomized order for each participant.&#160; Jitter is introduced in order to view how possible anticipatory-related effects may affect the morphology of the object-onset and motion-onset CVEP response.&#160; EEG data analyses are described in detail, including steps of data exportation from and importation to signal processing platforms, bad channel identification&#160;and removal, artifact rejection, averaging, and categorization of average CVEP morphological pattern type based upon latency ranges of component peaks. Representative data show that the methodological approach is indeed sensitive in eliciting differential object-onset and motion-onset CVEP morphological patterns and may, therefore, be useful in addressing the larger research aim. Given the high temporal resolution of EEG and the possible application of high-density EEG in source localization analyses, this protocol is ideal for the investigation of distinct CVEP morphological patterns and the underlying neural mechanisms which generate these differential responses.&#160; &#160; &#160;&#160;

In this paper, we present a protocol to investigate differential cortical visual evoked potential morphological patterns through stimulation of ventral and dorsal networks using high-density EEG. Visual object and motion stimulus paradigms, with and without temporal jitter, are described. Visual evoked potential morphological analyses are also outlined.&#160;&#160;

This paper presents a methodology for the recording and analysis of cortical visual evoked potentials (CVEPs) in response to various visual stimuli using ...

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms

Non-invasive electrophysiological recordings are useful for the evaluation of nervous system function. These techniques are inexpensive, fast, replicable, and less resource-intensive than imaging. Further, the functional data produced have excellent temporal resolution, which is not achievable with structural imaging.
Current applications of electroencephalograms (EEG) are limited by data processing methods. Standard analysis techniques using raw time series data at individual channels are very limited methods of interrogating nervous system activity. More detailed information about cortical function can be achieved by examining relationships between channels and deriving statistical models of how areas are interacting, allowing visualization of connectivity between networks.
This manuscript describes a method for deriving statistical models of cortical network activity by recording EEG in a standard manner, then examining the interelectrode coherence measures to assess relationships between the recorded areas. Higher order interactions can be further examined by assessing the covariance between the coherence pairs, producing high-dimensional &#34;maps&#34; of network interactions. These data constructs can be examined to assess cortical network function and its relationship to pathology in ways not achievable with traditional techniques.
This approach offers greater sensitivity to network level interactions than is achievable with raw time series analysis. It is, however, limited by the complexity of drawing specific mechanistic conclusions about the underlying neural populations and the high volumes of data generated, requiring more advanced statistical techniques for evaluation, including dimensionality reduction and classifier-based approaches.

Standard EEG analysis techniques offer limited insight into nervous system function. Deriving statistical models of cortical connectivity offers far greater ability to investigate underlying network dynamics. Improved functional assessment opens new possibilities for diagnosis, prognostication, and outcome prediction in nervous system diseases.

Non-invasive electrophysiological recordings are useful for the evaluation of nervous system function. These techniques are inexpensive, fast, replicable, ...

Concurrent EEG and Functional MRI Recording and Integration Analysis for Dynamic Cortical Activity Imaging

Electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) are two of the fundamental noninvasive methods for identifying brain activity. Multimodal methods have sought to combine the high temporal resolution of EEG with the spatial precision of fMRI, but the complexity of this approach is currently in need of improvement. The protocol presented here describes the recently developed spatiotemporal fMRI-constrained EEG source imaging method, which seeks to rectify source biases and improve EEG-fMRI source localization through the dynamic recruitment of fMRI sub-regions. The process begins with the collection of multimodal data from concurrent EEG and fMRI scans, the generation of 3D cortical models, and independent EEG and fMRI processing. The processed fMRI activation maps are then split into multiple priors, according to their location and surrounding area. These are taken as priors in a two-level hierarchical Bayesian algorithm for EEG source localization. For each window of interest (defined by the operator), specific segments of the fMRI activation map will be identified as active to optimize a parameter known as model evidence. These will be used as soft constraints on the identified cortical activity, increasing the specificity of the multimodal imaging method by reducing cross-talk and avoiding erroneous activity in other conditionally active fMRI regions. The method generates cortical maps of activity and time-courses, which may be taken as final results, or used as a basis for further analyses (analyses of correlation, causation, etc.) While the method is somewhat limited by its modalities (it will not find EEG-invisible sources), it is broadly compatible with most major processing software, and is suitable for most neuroimaging studies.

An EEG-fMRI multimodal imaging method, known as the spatiotemporal fMRI-constrained EEG source imaging method, is described here. The presented method employs conditionally-active fMRI sub-maps, or priors, to guide EEG source localization in a manner that improves spatial specificity and limits erroneous results.

Cortical Source Analysis of High-Density EEG Recordings in Children

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