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>

<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. R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Age-specific+MRI+templates+for+pediatric+neuroimaging.">Age-specific MRI templates for pediatric neuroimaging.</a> Developmental Neuropsychology. 37 (5), 379-399 (2012).</li><li>Umutlu, L., Ladd, M. E., Forsting, M., Lauenstein, T. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=7+Tesla+MR+Imaging:+Opportunities+and+Challenges.">7 Tesla MR Imaging: Opportunities and Challenges.</a> RoFo : Fortschritte auf dem Gebiete der Rontgenstrahlen und der Nuklearmedizin. 186 (2), 121-129 (2014).</li><li>Logothetis, N. K. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Bold+claims+for+optogenetics.">Bold claims for optogenetics.</a> Nature. 468 (7323), (2010).</li><li>Logothetis, N. K. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=What+we+can+do+and+what+we+cannot+do+with+fMRI.">What we can do and what we cannot do with fMRI.</a> Nature. 453 (7197), 869-878 (2008).</li><li>Roche-Labarbe, 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=High-resolution+electroencephalography+and+source+localization+in+neonates.">High-resolution electroencephalography and source localization in neonates.</a> Human Brain Mapping. 29 (2), 167-176 (2008).</li><li>Johnson, M. H. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Interactive+Specialization:+A+domain-general+framework+for+human+functional+brain+development.">Interactive Specialization: A domain-general framework for human functional brain development.</a> Developmental cognitive neuroscience. 1, 7-21 (2010).</li><li>Nelson, C. A., McCleery, J. P. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Use+of+Event-Related+Potentials+in+the+Study+of+Typical+and+Atypical+Development.">Use of Event-Related Potentials in the Study of Typical and Atypical Development.</a> Journal of the American Academy of Child &amp; Adolescent Psychiatry. 47 (11), (2008).</li><li>Michel, C. M., Murray, M. M., Lantz, G., Gonzalez, S., Spinelli, L., Grave de Peralta, R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=EEG+source+imaging.">EEG source imaging.</a> Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology. 115 (10), 2195-2222 (2004).</li><li>Bathelt, J., O'Reilly, H., Clayden, J. D., Cross, J. H., de Haan, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Functional+brain+network+organisation+of+children+between+2+and+5years+derived+from+reconstructed+activity+of+cortical+sources+of+high-density+EEG+recordings.">Functional brain network organisation of children between 2 and 5years derived from reconstructed activity of cortical sources of high-density EEG recordings.</a> NeuroImage. 82, 595-604 (2013).</li><li>David, O., Cosmelli, D., Friston, K. J. <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+measures+of+functional+connectivity+using+a+neural+mass+model.">Evaluation of different measures of functional connectivity using a neural mass model.</a> NeuroImage. 21 (2), 659-673 (2004).</li><li>Grech, 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=Review+on+solving+the+inverse+problem+in+EEG+source+analysis.">Review on solving the inverse problem in EEG source analysis.</a> Journal of NeuroEngineering and Rehabilitation. 5 (1), 25 (2008).</li><li>Wendel, K., 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/MEG+source+imaging:+methods,+challenges,+and+open+issues.">EEG/MEG source imaging: methods, challenges, and open issues.</a> Computational Intelligence and Neuroscience. 2009, 13 (2009).</li><li>Richards, J. E. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Localizing+cortical+sources+of+event-related+potentials+in+infants'+covert+orienting.">Localizing cortical sources of event-related potentials in infants' covert orienting.</a> Developmental Science. 8 (3), 255-278 (2005).</li><li>Hauk, O. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Keep+it+simple:+a+case+for+using+classical+minimum+norm+estimation+in+the+analysis+of+EEG+and+MEG+data.">Keep it simple: a case for using classical minimum norm estimation in the analysis of EEG and MEG data.</a> NeuroImage. 21 (4), 1612-1621 (2004).</li><li>Pascual-Marqui, R. D., 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=Low+resolution+brain+electromagnetic+tomography+(LORETA)+functional+imaging+in+acute,+neuroleptic-naive,+first-episode,+productive+schizophrenia.">Low resolution brain electromagnetic tomography (LORETA) functional imaging in acute, neuroleptic-naive, first-episode, productive schizophrenia.</a> Psychiatry Research. 90 (3), 169-179 (1999).</li><li>Pascual-Marqui, R. D. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Standardized+low-resolution+brain+electromagnetic+tomography+(sLORETA):+technical+details.+Methods+and+findings+in+experimental+and+clinical+pharmacology.">Standardized low-resolution brain electromagnetic tomography (sLORETA): technical details. Methods and findings in experimental and clinical pharmacology.</a> 24 Suppl D. , 5-12 (2002).</li><li>Phillips, C., Rugg, M. D., Friston, K. J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Systematic+regularization+of+linear+inverse+solutions+of+the+EEG+source+localization+problem.">Systematic regularization of linear inverse solutions of the EEG source localization problem.</a> NeuroImage. 17 (1), 287-301 (2002).</li><li>Yao, J., Dewald, J. P. 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...

Il presidente Barack Obama ha descritto il cervello umano come la prossima frontiera della scoperta scientifica con l&#39;alta importanza per la salute e l&#39;economia 3 (http://www.whitehouse.gov/share/brain-initiative). Tuttavia, come ogni altro campo nelle scienze naturali, neuroscienze dipende progressi nelle metodologie e tecniche di analisi per il progresso. Due strumenti invasivi non comunemente utilizzati in studi sulla funzione del cervello negli esseri umani sono la risonanza magnetica (MRI) ed elettroencefalografia (EEG). Queste strumento di sfruttare diverse proprietà fisiche e fornire diversi spunti in funzione del cervello, con vantaggi e svantaggi. RM utilizza le proprietà magnetiche delle molecole di acqua all&#39;interno di campi magnetici per ottenere immagini di tessuti viventi. Il soggetto deve essere posto in un magnete con elevata intensità di campo. Il movimento del partecipante è limitato durante queste procedure e il partecipante deve tollerare il rumore causato dai rapidi cambiamenti del magneticocampo. Oltre alle immagini strutturali, RM fornisce anche la possibilità di misurare le variazioni di ossigenazione del sangue per studiare la funzione del cervello (fMRI). In sintesi, MRI offre relativamente elevata risoluzione spaziale fino a 0,5 mm 3 con moderni campi ad alta scanner e parametri ottimizzati 4. In contrasto, la risoluzione temporale della fMRI è limitata alla cinetica lenta della risposta BOLD, che riflette solo indirettamente l&#39;alta dinamica temporale di attività neurale 5,6. D&#39;altra parte, EEG misura le variazioni nell&#39;attività elettrica causata dall&#39;attività dei neuroni attraverso elettrodi posti sul cuoio capelluto. I recenti progressi nella tecnologia EEG permette un&#39;applicazione rapida e semplice dei sensori per breve o lungo termine e fissi così come le registrazioni ambulatoriali. Perché EEG è meno restrittiva, è anche il metodo di scelta per alcune popolazioni partecipanti che non tollerano l&#39;ambiente MRI bene come pediatrica e certopopolazioni geriatriche e psichiatriche. Le proprietà di EEG mostrano un pattern inverso a quelli della MRI: la risoluzione temporale è molto alta con precisione micrometrica, ma la risoluzione spaziale è limitata. Correnti elettriche passano attraverso diversi tessuti tra loro generatore e gli elettrodi EEG sulla superficie del cuoio capelluto. Questo porta a miscelazione e sbavature spaziale dell&#39;attività sorgente noto come effetto di conduzione volume. Pertanto l&#39;attività misurata dagli elettrodi sulla superficie del cuoio capelluto riflette l&#39;attività da molteplici fonti che potrebbero essere lontana alla posizione dell&#39;elettrodo sulla testa 1,7. Molto lavoro negli ultimi anni è stata dedicata alla fusione di MRI e EEG, al fine di sfruttare i loro rispettivi punti di forza. Una linea di lavoro è dedicata alla acquisizione simultanea di EEG e RM in studi funzionali. Un altro approccio è quello di utilizzare le informazioni spaziali fornite dalla MRI strutturale per tener conto del volume conduction effetto attraverso la modellazione biofisica. L&#39;uso delle informazioni strutturali per la ricostruzione fonte di registrazioni EEG è particolarmente utile per studi condotti su un pediatrico. L&#39;indagine dello sviluppo della funzione cerebrale è fondamentale per capire come le abilità cognitive complesse sono costruite sulla cima di precursori semplici 8. Queste indagini aiutano a evidenziare cambiamenti nei substrati neurali e le proprietà di risposta che correlano con i cambiamenti nelle prestazioni comportamentali. Tuttavia, l&#39;indagine delle funzioni cerebrali e cognitive durante lo sviluppo pone anche sfide specifiche. In particolare, la possibilità per gli studi di risonanza magnetica funzionale è limitata come i bambini piccoli ei neonati devono o essere addormentato o sedato per ottenere i dati di risonanza magnetica senza artefatti da movimento e l&#39;impatto negativo sul benessere dei partecipanti. Inoltre, EEG è percepita come meno rischioso e invasivo da parte dei genitori, che rende il reclutamento dei partecipanti alla ricerca più facile. Therefore, EEG è il metodo di scelta per molte indagini della funzione cerebrale nei bambini piccoli. Progressi metodologici nei sistemi EEG permettono l&#39;applicazione di schiere di elettrodi ad alta densità con 128 o più canali in pochi minuti. Facilità di applicazione e comfort sono sufficienti per consentire anche la registrazione EEG nei neonati più giovani. Tuttavia, spesso i ricercatori non sono solo interessati alle dinamiche temporali delle risposte a particolari stimoli, ma vorrebbe anche di confrontare i substrati neurali che mediano le risposte. Una ipotesi prevalente nel livello del canale di analisi ERP confronto diverse fasce di età è che gli stessi substrati neurali rispondono, ma che il tempo o l&#39;ampiezza di risposta varia a seconda dei secoli 9. Simile cuoio capelluto topografia viene spesso utilizzato come indicatore di simile attività neurale sottostante. Tuttavia, molte configurazioni di base diversi possono portare a simili topografie cuoio capelluto 10. Applicando la stima di origine, questo Uncertainty può essere ridotto e quantificato. L&#39;indipendenza delle osservazioni è fondamentale per gli account di rete della funzione cerebrale: se le fonti sono mescolati, le correlazioni saranno sbilanciata verso una maggiore connettività locale. Fonte ricostruzione può essere applicata per ridurre questa distorsione 11. In alternativa, sfasamenti temporali e di fase possono essere utilizzati per l&#39;analisi connettività, ma questi modelli matematici richiedono ipotesi che sono difficili da valutare in dati non simulati 12. In sintesi, la stima fonte fornisce informazioni aggiuntive a livello del canale EEG e analisi ERP basata sulla conoscenza dell&#39;anatomia e della proprietà biofisiche del tessuto. Diversi algoritmi sono stati ideati per trovare soluzioni al problema inverso. Questi algoritmi rientrano in generale in due categorie: parametrici e non parametrici 13. Modelli parametrici assumono uno o più dipoli che possono variare in posizione, l&#39;orientamento e la forza. Al contrario, non modelli parametrici contena gran numero di dipoli con postazione fissa e orientamento. In questi modelli, l&#39;attività elettrica del cuoio capelluto è spiegata come una combinazione di attivazioni in dipoli fissi 10,13,14. Non parametrica, modelli di origine distribuiti possono essere basati sulle conoscenze di anatomia e conducibilità in diversi media. Modelli Boundary Element incorporano valori di conducibilità per i principali tessuti della testa con diversi proiettili per il cervello, il liquido cerebro-spinale, e del cranio. Questo si basa sul presupposto che la conducibilità è pressoché costante all&#39;interno di ciascun compartimento, ma che si verificano notevoli cambiamenti al confine di diversi scomparti. Modelli ad elementi finiti sono basati su un ulteriore segmentazione di esami di risonanza magnetica nella materia grigia e bianca in modo che i valori di conducibilità possono essere assegnati a ciascun voxel 15. In termini pratici, modelli non parametrici sono particolarmente utili per la ricostruzione fonte in compiti cognitivi complessi, in cui il numero delle aree interessate possononon essere conosciuto 10. Modelli ad elementi di contorno sono più utilizzati nella letteratura corrente, probabilmente perché i più accurati modelli agli elementi finiti pongono relativamente elevate esigenze computazionali. Inoltre, vi è una notevole variabilità individuale tra nella materia grigia e bianca in modo che FEM dovrebbe basarsi sui singoli scansioni MRI. Modelli non parametrici richiedono una seconda fase per la corrispondenza del cuoio capelluto attività misurata alle previsioni del modello in avanti. Ancora una volta, approcci differenti con differenti vantaggi e svantaggi sono stati discussi in letteratura (vedi Michel et al. 2004 per una panoramica). Gli algoritmi più utilizzati sono basate sulla stima norma minima (MNE), che corrisponde al cuoio capelluto attività misurata ad una distribuzione di corrente nel modello in avanti con l&#39;intensità globale più basso 16. MNE è sbilanciata verso le fonti deboli e superficiali. Profondità algoritmi MNE ponderati cercano di ridurre il bias superficie con l&#39;introduzione di pesimatrici basate su ipotesi matematiche 10. L&#39;approccio LORETA ampiamente utilizzato si basa anche sul pesata MNE, ma riduce inoltre il laplaciano di fonti, che porta a soluzioni uniformi 17,18. LORETA è stata trovata a svolgere meglio per singole sorgenti in studi di simulazione 19,20. Tuttavia, LORETA può comportare oltre lisciatura di soluzioni. Profondità ponderata MNE è preferibile quando le fonti sono sconosciute o più fonti sono suscettibili di essere presente il 13, 16. Confrontando i risultati di diversi algoritmi per valutare l&#39;influenza delle diverse ipotesi di modello è raccomandato. In sintesi, la ricostruzione fonte attraverso metodi di modellazione è stato limitato per i bambini fino a poco tempo. Questo perché la maggior parte dei software di analisi EEG si basa su modelli di testa basato su adulto anatomia che limita notevolmente la precisione delle soluzioni origine nei bambini 2,8. L&#39;accesso a buon mercato per potenza di calcolo e la fornitura disoftware facile da usare per la ricostruzione di origine permettono di superare queste limitazioni. Applicando la stima sorgente alla EEG offre due importanti vantaggi rispetto analisi basate su osservazioni del livello del canale da solo: migliore risoluzione spaziale e l&#39;indipendenza delle osservazioni. Stima di origine non può essere informativo in alcuni casi: buona copertura della testa è necessaria per distinguere fonti. Sistemi ad alta densità con 128 o più elettrodi sono raccomandati 10,15; una copertura rada agirà come un filtro spaziale che porta alla più ampia attivazione fonte diffusione o falsi negativi 10. Inoltre, ricostruzione source basato sul metodo descritto in questo articolo è stato riportato solo per generatori corticali. Pertanto, è meno adatto per la verifica di ipotesi circa i substrati sottocorticali o interazioni subcorticali corticali. Infine, l&#39;analisi fonte dovrebbe essere basata su ipotesi precedenti dettagliate sui substrati corticali,prendendo la letteratura esistente da altre modalità di imaging in considerazione. Tecniche di filtraggio spaziale può essere utilizzato anche per migliorare la risoluzione spaziale del segnale EEG riducendo miscelazione spaziale a livello cuoio capelluto. Vengono utilizzati metodi alternativi per ridurre l&#39;influenza degli effetti di conduzione del volume senza modellazione testa, ad esempio, Laplacian filtraggio 21 o attuale analisi Sorgente di densità 22. Tuttavia, questi metodi non forniscono maggiori informazioni per generatori neurali come effetti di conduzione del volume non sono solo limitati a sensori in prossimità spaziale 1. Nelle sezioni seguenti, l&#39;articolo descrive come gli esperimenti per lo studio del cervello e della funzione cognitiva nei bambini dai 2 anni di età sono progettati al London Baby-Lab. Avanti, acquisizione dati EEG con i sistemi a bassa impedenza ad alta densità con bambini è discusso. Poi, EEG pre-elaborazione e analisi sul livello del canale è presentato. Lastly, l&#39;articolo si concentra sul trattamento dei dati MRI strutturali per la ricostruzione di origine corticale e l&#39;analisi dei segnali di livello sorgente.

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

EEG è tradizionalmente descritto come una tecnica neuroimaging con elevata risoluzione spaziale e temporale basso. I recenti progressi nella modellazione biofisica e di elaborazione del segnale permettono di sfruttare informazioni provenienti da altre modalità di imaging come la risonanza magnetica strutturale che forniscono ad alta risoluzione spaziale per superare questo vincolo 1. Ciò è particolarmente utile per le indagini che richiedono alta risoluzione nel temporale e dominio spaziale. Inoltre, a causa dell&#39;applicazione facile e basso costo di registrazioni EEG, EEG è spesso il metodo di scelta quando si lavora con popolazioni, come i bambini, che non tollerano MRI funzionale analizza bene. Tuttavia, al fine di indagare quali substrati neurali sono coinvolte, informazioni anatomiche da MRI strutturale è ancora necessaria. La maggior parte dei pacchetti di analisi EEG lavorano con modelli standard di testa che si basano su adulto anatomia. L&#39;accuratezza di questi modelli quando usato nei bambini è limitata 2, perché il comPosition e la configurazione spaziale della testa tessuti cambia notevolmente nel corso dello sviluppo 3. Nel presente documento, forniamo una panoramica del nostro recente lavoro utilizzando modelli di testa basato su singole scansioni MRI strutturali o di età modelli di testa specifici per ricostruire i generatori corticali di EEG ad alta densità. Questo articolo descrive come le registrazioni EEG vengono acquisiti, elaborati e analizzati con popolazione pediatrica al London Baby-Lab, tra cui l&#39;installazione di laboratorio, progettazione compito, EEG pre-elaborazione, trasformazione risonanza magnetica, e il livello dei canali EEG e analisi sorgente.

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...

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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.

Corticale Fonte Analisi di alta densità EEG registrazioni nei bambini

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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21.2K Views.  UCL Institute of Child Health. 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.