Name: concurrent eeg and functional mri recording and integration analysis for dynamic cortical activity imaging
Uploaded: 2018-06-30T12:30:00
Description: Watch this Scientific Journal Video about Concurrent EEG and Functional MRI Recording and Integration Analysis for Dynamic Cortical Activity Imaging Video at JoVE.com

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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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>
		<tr height="40">
			<td height="40" style="height:40px;">POLGUI</td>
			<td style="width:117px;"></td>
			<td></td>
			<td>MATLAB software for digitization</td>
		</tr>
		<tr height="40">
			<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>
		</tr>
		<tr height="40">
			<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 Video at JoVE.com

Concurrent EEG and Functional MRI Recording and Integration Analysis for Dynamic Cortical Activity Imaging Video (Video) | JoVE

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.

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 ...

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