This work was supported in part by NIH DK082644 and the University of Houston.

The protocol presented here was designed and performed in accordance with all guidelines for ethical human research as set forth by the respective Institutional Review Boards of the University of Houston and the Houston Methodist Research Institute.
1. Simultaneous EEG/fMRI Recording
<ol>
	<li>Obtain informed consent from the participant. Explain to the participant the purpose and procedure of the study, as well as the important safety measures for the simultaneous EEG/fMRI data recording pr...

<ol>
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M., 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+source+imaging.">EEG source imaging.</a> Clin Neurophysiol. 115 (10), 2195-2222 (2004).</li><li>Ramon, C., Schimpf, P. H., Haueisen, J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Influence+of+head+models+on+EEG+simulations+and+inverse+source+localizations.">Influence of head models on EEG simulations and inverse source localizations.</a> Biomed Eng Online. 5, 10 (2006).</li><li>Mosher, J. C., Spencer, M. E., Leahy, R. M., Lewis, P. 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V. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> Pictures of Facial Affect. , (1976).</li><li>Dale, A. M., Fischl, B., Sereno, M. I. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Cortical+surface-based+analysis.+I.+Segmentation+and+surface+reconstruction.">Cortical surface-based analysis. I. Segmentation and surface reconstruction.</a> Neuroimage. 9 (2), 179-194 (1999).</li><li>Fischl, B., Sereno, M. I., Dale, A. M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Cortical+surface-based+analysis.+II:+Inflation,+flattening,+and+a+surface-based+coordinate+system.">Cortical surface-based analysis. II: Inflation, flattening, and a surface-based coordinate system.</a> Neuroimage. 9 (2), 195-207 (1999).</li><li>Gramfort, 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=MNE+software+for+processing+MEG+and+EEG+data.">MNE software for processing MEG and EEG data.</a> Neuroimage. 86, 446-460 (2014).</li><li>Hagler, D. J., Saygin, A. P., Sereno, M. I. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Smoothing+and+cluster+thresholding+for+cortical+surface-based+group+analysis+of+fMRI+data.">Smoothing and cluster thresholding for cortical surface-based group analysis of fMRI data.</a> Neuroimage. 33 (4), 1093-1103 (2006).</li><li>Jenkinson, M., Beckmann, C. F., Behrens, T. E., Woolrich, M. W., Smith, S. 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A., Nichols, T. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Thresholding+of+statistical+maps+in+functional+neuroimaging+using+the+false+discovery+rate.">Thresholding of statistical maps in functional neuroimaging using the false discovery rate.</a> Neuroimage. 15 (4), 870-878 (2002).</li><li>Klein, A., Tourville, J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=101+labeled+brain+images+and+a+consistent+human+cortical+labeling+protocol.">101 labeled brain images and a consistent human cortical labeling protocol.</a> Front Neurosci. 6, 171 (2012).</li><li>Fischl, B., 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=Automatically+parcellating+the+human+cerebral+cortex.">Automatically parcellating the human cerebral cortex.</a> Cereb Cortex. 14 (1), 11-22 (2004).</li><li>Delorme, A., Makeig, S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=EEGLAB:+an+open+source+toolbox+for+analysis+of+single-trial+EEG+dynamics+including+independent+component+analysis.">EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis.</a> J Neurosci Methods. 134 (1), 9-21 (2004).</li><li>Iannetti, G. 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=Simultaneous+recording+of+laser-evoked+brain+potentials+and+continuous,+high-field+functional+magnetic+resonance+imaging+in+humans.">Simultaneous recording of laser-evoked brain potentials and continuous, high-field functional magnetic resonance imaging in humans.</a> Neuroimage. 28 (3), 708-719 (2005).</li><li>Niazy, R. K., Beckmann, C. F., Iannetti, G. D., Brady, J. M., Smith, S. M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Removal+of+FMRI+environment+artifacts+from+EEG+data+using+optimal+basis+sets.">Removal of FMRI environment artifacts from EEG data using optimal basis sets.</a> Neuroimage. 28 (3), 720-737 (2005).</li></ol>

We have shown here the necessary steps to use the spatiotemporal fMRI constrained source analysis method for EEG/fMRI integration analysis. EEG and fMRI have become well established as the fundamental methods for non-invasively imaging brain activity, though they face difficulty in their respective spatial and temporal resolutions. While methods have been developed to capitalize on the favorable properties of each, current fMRI-constrained EEG source localization methods frequently rely upon simple fMRI constraints, whic...

Electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) can be viewed as neuroimaging modalities with complementary features. FMRI captures brain activity with large temporal scale, as hemodynamic signals indirectly measure the underlying neuronal activity with a poor temporal resolution (on the order of seconds)1,2. In contrast, EEG directly measures the dynamic electrophysiological activity of the brain with a very high temporal resolution (millisecond level), but poor spatial resolution3,4. These properties have led to multimodal approaches designed to optimize the favorable aspects of each individual method5. Simultaneous use of EEG and fMRI allows for the excellent temporal resolution of EEG to be combined with the high spatial accuracy of fMRI to overcome the limitations associated with unimodal fMRI or EEG.
Methods for EEG and fMRI integration begin with fMRI-informed EEG source localization6,7. This technique utilizes fMRI-derived spatial information to improve EEG source localization, however, one drawback is the potential spatial bias caused by the application of fMRI as a &#34;hard-constraint&#34; &#8212; fMRI-derived spatial information is considered an absolute truth. This poses two large issues that must be reconciled6-8. First, it must be considered that the use of a static map of Blood Oxygen Level Dependent (BOLD) contrasts may inadvertently strengthen any erroneous activity that falls within it, while damping true activity outside of it. Second, crosstalk from sources occurring outside of the BOLD activation map may influence the presentation of true activity within the results or cause erroneous activity. Despite this, the use of the high spatial resolution of fMRI to provide prior spatial knowledge remains a favorable solution5, as the modeling of the EEG inverse problem can be constrained both in the anatomical and functional senses.
In this paper, we demonstrate a spatiotemporal fMRI-constrained EEG source imaging approach that addresses the issue of temporal mismatch between EEG and fMRI by calculating the optimal subset of fMRI priors based on a hierarchical Bayesian model9. FMRI-priors are computed in a data-driven manner from particular windows of interest in the EEG data, leading to time-variant fMRI constraints. The proposed approach utilizes the high temporal resolution of EEG to compute a current density mapping of the cortical activity, informed by the high spatial resolution of fMRI in a time-variant, spatially selective manner that accurately images dynamic neural activity.

<table border="0" cellpadding="0" cellspacing="0" style="width:869px;" width="868">
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	<tbody>
		<tr height="40">
			<td height="40" style="height:40px;width:219px;">BrainAmp MR Plus</td>
			<td style="width:117px;">Brain Products</td>
			<td style="width:119px;"></td>
			<td style="width:415px;">Amplifiers for EEG recording, MR-compatible</td>
		</tr>
		<tr height="40">
			<td height="40" style="height:40px;">BrainAmp ExG MR&#160;</td>
			<td style="width:117px;">Brain Products</td>
			<td style="width:119px;"></td>
			<td>Amplifier for auxilary sensor (EMG), MR-compatible</td>
		</tr>
		<tr height="40">
			<td height="40" style="height:40px;">BrainAmp Power Pack</td>
			<td style="width:117px;">Brain Products</td>
			<td style="width:119px;"></td>
			<td>Provide power to amplifiers in the MR environment</td>
		</tr>
		<tr height="40">
			<td height="40" style="height:40px;">Ribbon Cables</td>
			<td style="width:117px;">Brain Products</td>
			<td style="width:119px;"></td>
			<td>Connects the Power Pack to Amplifiers</td>
		</tr>
		<tr height="40">
			<td height="40" style="height:40px;">SyncBox</td>
			<td style="width:117px;">Brain Products</td>
			<td style="width:119px;"></td>
			<td>Synchronize MR scanner clock with EEG amplifier clock</td>
		</tr>
		<tr height="40">
			<td height="40" style="height:40px;">BrainCap MR</td>
			<td style="width:117px;">Brain Products</td>
			<td style="width:119px;"></td>
			<td>Passive-electrode 64-channel EEG cap, MR-compatible</td>
		</tr>
		<tr height="40">
			<td height="40" style="height:40px;">BrainVision Recorder</td>
			<td style="width:117px;">Brain Products</td>
			<td style="width:119px;"></td>
			<td>EEG data recording software (steps 1.2-1.4.2)</td>
		</tr>
		<tr height="40">
			<td height="40" style="height:40px;">BrainVision Analyzer 2.0</td>
			<td style="width:117px;">Brain Products</td>
			<td style="width:119px;"></td>
			<td>EEG analysis software (steps 4.1-4.6)</td>
		</tr>
		<tr height="40">
			<td height="40" style="height:40px;">USB 2 Adapter (also known as BUA)</td>
			<td style="width:117px;">Brain Products</td>
			<td style="width:119px;"></td>
			<td>Interface between the amplifiers and data acquisition computer</td>
		</tr>
		<tr height="40">
			<td height="40" style="height:40px;">Fiber Optic Cables</td>
			<td style="width:117px;">Brain Products</td>
			<td style="width:119px;"></td>
			<td>Connects the EEG cap in the MR scanner to the Recording Computer</td>
		</tr>
		<tr height="40">
			<td height="40" style="height:40px;">SyncBox Scanner Interface</td>
			<td style="width:117px;">Brain Products</td>
			<td style="width:119px;"></td>
			<td>Synchronize MR scanner clock with EEG amplifier clock</td>
		</tr>
		<tr height="40">
			<td height="40" style="height:40px;">Trigger Cable</td>
			<td style="width:117px;">Brain Products</td>
			<td style="width:119px;"></td>
			<td>Used to send scanner/paradigm triggers to the recording computer</td>
		</tr>
		<tr height="40">
			<td height="40" style="height:40px;">ABRALYT HiCl EEG Electrode Gel</td>
			<td style="width:117px;">EasyCap</td>
			<td style="width:119px;"></td>
			<td>Abrasive EEG gel for passive electrode in MR environment</td>
		</tr>
		<tr height="40">
			<td height="40" style="height:40px;">Ingenia 3.0T MR system</td>
			<td style="width:117px;">Philips</td>
			<td style="width:119px;"></td>
			<td>3.0 T MRI system</td>
		</tr>
		<tr height="40">
			<td height="40" style="height:40px;">Patriot Digitizer</td>
			<td style="width:117px;">Polhemus</td>
			<td style="width:119px;"></td>
			<td>EEG channel location digitization&#160;</td>
		</tr>
		<tr height="40">
			<td height="40" style="height:40px;">MATLAB r2014a</td>
			<td style="width:117px;">MathWorks</td>
			<td></td>
			<td>Programming base for the DBTN algorithm (steps 3.3-3.4 and 5.1-5.7)</td>
		</tr>
		<tr height="40">
			<td height="40" style="height:40px;">Pictures of Facial Affect</td>
			<td style="width:117px;">Paul Eckman Group</td>
			<td></td>
			<td>A series of emotionally valent faces used as stimuli</td>
		</tr>
		<tr height="40">
			<td height="40" style="height:40px;">E-Prime 2.0</td>
			<td style="width:117px;">Psychology Software Tools, Inc</td>
			<td></td>
			<td>Presentation Software (step 1.4.3)</td>
		</tr>
		<tr height="40">
			<td height="40" style="height:40px;">Bipolar skin EMG electrode</td>
			<td style="width:117px;">Brain Products</td>
			<td></td>
			<td>Used to detect muscle activity.</td>
		</tr>
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			<td height="40" style="height:40px;">POLGUI</td>
			<td style="width:117px;"></td>
			<td></td>
			<td>MATLAB software for digitization</td>
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			<td height="40" style="height:40px;">Freesurfer</td>
			<td style="width:117px;"></td>
			<td></td>
			<td>Software used in steps 2.1-2.4, and steps 3.1-3.2</td>
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			<td height="40" style="height:40px;">MNE</td>
			<td style="width:117px;"></td>
			<td></td>
			<td>Software used in step 2.5</td>
		</tr>
	</tbody>
</table>

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.

EEG source localization at the basic level involves the solving of the forward and inverse problem. The components required to build and solve the forward problem are shown in Figure 5C. Using a subject-specific T1 image, three layers &#8212; brain, skull, and skin&#160;&#8212; were segmented and meshed. These layers served as the inputs to generate the BEM model. Similarly, the subject&#39;s grey-matter layer was segmented from the structura...

Watch this Scientific Journal Video about Concurrent EEG and Functional MRI Recording and Integration Analysis for Dynamic Cortical Activity Imaging at JoVE.com

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

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.

Electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) are two of the fundamental noninvasive methods for identifying brain ...

This method can help answer key questions in the field of neuroscience, such as determining the temporal relationship between multiple brain regions or exploring the detailed neural pathway of complex cognitive processes. The main advantage of this technique is that it allows for the integration of multi-modal EEG and functional data in a spatial and a temporal specific manner to improve the resolution and accuracy of functional neuroimaging. The implications of this technique extends toward the understanding of epilepsy and neurocognitive disorders.Because the method improves on the spatial and temporal accuracy for noninvasive neuroimaging. Though this method can be used to provide insight to the visual motor and emotional processing systems, it can also be applied to other systems, such as sensorimotor integration, language, and memory processing. Visual demonstration of this method is critical, as the concurrent EEG and fMRI data recording steps can be difficult to learn.Also because the requirement for specialized facility, equipment, and signal processing methods. To begin this procedure, place an appropriately sized passive MRI-compatible EEG cap onto the subject's head. Position the electrodes as per the 10-20 international labeling system.In the EEG recording software, check the impedance of the ground and reference electrodes. To do this, click on the impedance tab and select the electrode type on the software user interface. Next, use the syringe to inject electrolyte get into each electrode.Then use a cotton swab to spread the gel to ensure skin-electrode contact. Once the EEG cap preparation is done, have the subject move to the MR scanner. Set up the experimental paradigm display on a monitor located in the observation room.Use a head coil viewing mirror to allow the subject to view the monitor screen without moving their head or eyes while lying down. Display a sample image on the computer screen to ensure that the subject can comfortably view the screen and that the paradigm will display properly. Make any necessary hardware or software adjustments.After that, instruct the subject to remain still and perform an initial T1 weighted anatomical MRI scan. If possible, use a field of view that reaches from the bottom of the cerebellum to the top of the head, including the skull and skin. Now, start recording the EEG data.Simultaneously click the appropriate buttons to begin the MRI recording and initiate the paradigm of interest on the presentation software. Check the EEG data recording to ensure signal quality, and if desired, appropriate markers are being recorded. To analyze the structural MRI data, use the available GUI to ensure that there is no overlap in the layers.Next, open the FreeView application. Click File, then Load Surface. Navigate to the subject's directory in the free surfer folder.Then, open the bem folder, followed by the watershed folder. Load the four files, outer skin surface, outer skull surface, brain surface, and inner skull surface. Subsequently, move the slice selection sliders and look for overlap in the yellow surface layers.If overlap does occur, double check the MRI data for anatomical defects or errors, and use the GUI drawing tools to clarify the layers. Afterward, load the original MRI data in the FreeView application by clicking File, then Load Volume. Navigate to the subject folder and open the mri folder.Then click on the original directory and open the structural mri data and click OK.Next, view the gray scale image of the head and look at the different layers of gray and black around the brain. Ensure that these layers do not have any gaps or irregularities. Use the color picker tool to select the Voxel from the layer to be corrected.Switch to the freehand voxel edit, and click to draw on the image. Use this procedure to fill in any defects in the MRI image. Perform correction for all layers and MRI slices where defects occur.Subsequently, perform subject-specific EEG sensor alignment to the MRI space using the free surfer head model overlay. Then, save the transformation. Open the MNE Analyze application.Click on File, then Load Surface. Navigate to the folder containing the subject data, and load the pial surface. Click File, then click Load digitizer data, and select the EEG file of interest.Click View and Show Viewer. Once the Viewer GUI appears, click Options and make sure that the scalp and digitizer data options are chosen. Electrodes here are shown in red with fiducial points in yellow.On the main window, select Adjust, then Coordinate alignment. Using the Coordinate alignment GUI, shift and rotate the EEG electrodes in the viewer with the arrow and left right buttons. Adjust as much as necessary.Once the alignment is done, click Save at the bottom of the Coordinate alignment GUI to save the alignment. For EEG data analysis, perform scanner gradient artifacts correction through template subtraction by clicking on the MR correction button in the special signal processing menu, and selecting appropriate parameters in the EEG analysis software GUI. To remove cardioballistic artifacts through template subtraction, click on the CB correction button in the special signal processing menu and select appropriate parameters in the analysis software GUI.Then, apply filtration under data filtration. Select the button for IIR filtration at the top of the analysis GUI. For example, apply high pass at 0.05 Hertz, low pass at 40 Hertz, and a notch filter at the power line frequency with a roll off of 48 decibels per Hertz.To perform ocular artifact correction, select Transformation, then Artifact rejection reduction, followed by ocular correction ICA. Next, segment the EEG data into epics based on the specified pre-and post-stimulus time with respect to the event timing markers by selecting Transformation, then segment analysis functions, followed by segmentation. Afterward, choose the marker of interest and the time segment of interest.Subsequently, perform manual or semi-automatic artifact rejection by selecting Transformation then Artifact rejection reduction in Artifact rejection. When prompted, define criteria for artifacts within the three tabs of the GUI and proceed as instructed. In the Inspection Method tab, choose automatically, semi-automatically, or manually select artifacts.Then select mark or remove artifacts and specify if the corrections are for a single channel. Next in the channel selection tab, select the channels which will be corrected for artifacts. In the Criteria tab, select the basis by which artifacts will be identified.Click OK after selecting criteria, and artifacts will be identified and/or rejected in accordance with the selections. To perform baseline correction, select Transformation then Segment analysis functions and baseline correction. And to average the segmented data, select Transformation, then segment analysis functions, and then average.First, the EEG epic of interest is divided into smaller time windows with pre-defined window and overlap sizes. Meanwhile, the fMRI activation map is subdivided into multiple regions of interest to be used as spatial priors for EEG source analysis. During each time window, an appropriate set of spatial priors will be selected for source localization.Combining these will yield the final time course of cortical activity for the entire EEG epic of interest. Here, reconstructed brain activity is shown comparing the new spatio-temporally specific fMRI constrained EEG source imaging on top, with the commonly used time invariant fMRI constraint source imaging on the bottom. Reconstructed brain activity is shown at several time points for one representative subject undergoing the visual motor activation paradigm.Localization results from the new method were more focal and aligned better with the expected brain regions. Here, the robustness of the method is tested by applying the method with different window sizes. Notice that the modest window sizes are fairly stable, while large window sizes show disparate results.Once mastered, the concurrent EEG and fMRI data recording session can be done in about three hours, and data analysis can be performed in two to three days if they are done properly. While attempting this procedure, it's important to remember to check your clock synchronization and triggers while recording. During the analysis, you should also pay attention to the parameters necessary to capture your activity of interest.Following this procedure, measures like EEG and functional MRI data analysis all-brain connectivity analysis can be performed in order to investigate the underlying connectivity structure or activity in the brain during cognitive tasks. After this development, this technique paved the way for researchers in the field of functional neuroimaging to explore the highly dynamic brain activity involved in complex cognitive processing and to study the detailed changes in neural pathways associated with neurological disorders. After watching this video, you should have a good understanding of how to conduct concurrent EEG and fMRI recording and to perform basics of spatial temporal fMRI constrained EEG socioanalysis.In order to reconstruct brain activity with high spatial temporal resolution and accuracy. Don't forget that working with participants and equipment in the MRI environment requires constant awareness about safety concerns. No unspecified metal objects should be brought into the MRI room, and all electrode impedances should be kept under 50 coulombs.

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11.5K vistas.  University of Houston. This method can help answer key questions in the field of neuroscience, such as determining the temporal relationship between multiple brain regions or exploring the detailed neural pathway of complex cognitive processes. The main advantage of this technique is that it allows for the integration of multi-modal EEG and functional data in a spatial and a temporal specific manner to improve the resolution and accuracy of functional neuroimaging. The implications of this technique extends toward ...