Name: concurrent eeg and functional mri recording and integration analysis for dynamic cortical activity imaging
Uploaded: 2018-06-30T12:30:00.000+00:00
Duration: 11 min 28 s
Description: Watch this Scientific Journal Video about Concurrent EEG and Functional MRI Recording and Integration Analysis for Dynamic Cortical Activity Imaging 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 process.</li>
	<li>Prepare the EEG cap and check impedance outside the MRI-scanner room.
	<ol>
		<li>Place an appropriately sized, passive, MRI-compatible EEG cap onto the subject&#39;s head. Position the electrodes as per the 10&#8211;20 international labeling system10.</li>
		<li>On the EEG recording software, check the impedance of the ground and reference electrodes. To do this, click on the &#39;impedance&#39; tab and select the electrode type on the software user-interface (see Figure 1). 
		NOTE: Exact instructions here are specific to the software used herein (see Table of Materials), and may need to be adapted to other systems.</li>
		<li>For each electrode, use a syringe to inject electrolyte gel into the electrode, then use a cotton swab to spread the gel to ensure skin-electrode contact.
		<ol>
			<li>As the impedance decreases, continue to monitor the values using the appropriate software (adjust the impedance scale as necessary, depending on the setup) to monitor impedance level properly (see Figure 1). Continue until all electrodes reach impedance levels below 10 k&#937; to ensure a high-quality signal. 
			NOTE: Per the materials listed and utilized here, it is considered unsafe to have any electrode with an impedance level above 50 k&#937; in MR-environment11. This may change depending on the design of the chosen cap and MRI settings, so please consult with the equipment manufacturer and MRI technologists to ensure the safety of the experimental setup.</li>
		</ol>
		</li>
	</ol>
	</li>
	<li>Simultaneous EEG/fMRI hardware setup.
	<ol>
		<li>Once the EEG cap preparation is done, have the subject moved to the MR-scanner with the hardware setup described in Figure 2. 
		NOTE: Some details of the figure may change, depending on the system in use.</li>
		<li>Set up the experimental paradigm display. Use a monitor located in the observation room, behind the glass window facing the front of the MR-scanner (see Figure 2). Use a head coil viewing mirror to allow subjects to view the monitor screen without moving their head or eyes while lying down.</li>
		<li>Display a sample image on the computer screen to ensure that subjects can comfortably view the screen, and that the paradigm will display properly. Make any necessary hardware or software adjustments.</li>
	</ol>
	</li>
	<li>Experimental paradigm (see Figure 3).
	<ol>
		<li>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.</li>
		<li>Start recording the EEG data (see Figure 4).</li>
		<li>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.
		<ol>
			<li>When using the set-up described here, first click &#34;Run&#34; in the presentation software and enter the subject number and trial number. The paradigm will initiate upon confirming these settings. 
			NOTE: The paradigm employed here consisted of 10 trials in which an emotionally motivated motor response was evoked by means of visual stimulus. For each trial, subjects were asked to first rest for 50 s watching a green screen, after which the image of an unpleasant (images corresponding to surprise, anger, or disgust) or not-unpleasant (images corresponding to happiness or neutrality) face12 was presented for 10 s. Five images from each category were presented in a randomized order, and subjects were asked to squeeze a ball upon identifying a face as unpleasant, and hold the squeeze until it disappeared.</li>
			<li>Use a Gradient-recalled Echo Planar Imaging (GR-EPI) sequence for fMRI recording (recommended); customize to suit the equipment and paradigm. 
			NOTE: The sequence used herein included: Echo Time (TE) = 35 ms; Repetition Time (TR) = 1,500 ms; Slice Thickness = 5 mm; Flip Angle = 90 &#176;; Pixel Spacing: 2.75 mm x 2.75 mm. It may be necessary to use an MRI sequence that lasts slightly longer than the display of the paradigm itself, to ensure that the full paradigm is recorded without clipping.</li>
		</ol>
		</li>
	</ol>
	</li>
</ol>
2. Structural MRI Data Analysis and Forward Model Generation
<ol>
	<li>Apply full segmentation and reconstruction of various surfaces from the subject&#39;s T1-weighted anatomical MRI volume using the Freesurfer image analysis suite13,14. 
	NOTE: A folder containing all segmentation outputs will be generated by Freesurfer.</li>
	<li>Generate a subject-specific 3-layer Boundary Element Method (BEM) geometrical model following the instructions provided at (https://martinos.org/mne/dev/manual/appendix/bem_model.html)15 Use the available Graphical User Interface (GUI) to ensure that there is no overlap in the layers.
	<ol>
		<li>Open the Freeview Application. Click &#34;File&#34; &#62;&#62; &#34;Load Surface&#34;. Navigate to the Subjects directory in the Freesurfer folder. Open the &#34;BEM&#34; folder. Open the &#34;Watershed&#34; folder. Load the four files found here (&#39;outer_skin_surface&#39;, &#39;outer_skull_surface&#39;, &#39;brain_surface&#39;, and &#39;inner_skull_surface&#39;).</li>
		<li>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.
		<ol>
			<li>Load the original MRI data in the Freeview application by clicking &#34;File&#34; &#62;&#62; &#34;Load Volume&#34;. Navigate to the subject folder and open the &#34;mri&#34; folder. Then click on the &#34;orig&#34; directory and open the structural MRI data found there (should be in.mgz or.nii format). Click &#34;OK&#34;.</li>
			<li>View the greyscale image of the head. Look at the different layers of grey and black around the brain. Ensure that these layers do not have any gaps or irregularities. Use the &#34;Color Picker&#34; tool and select a voxel from the layer to be corrected.</li>
			<li>Switch to the &#34;Freehand Voxel Edit&#34;, and click to draw on the image. Use this to fill in any defects in the MRI image. Perform correction for all layers and MRI slices, where defects occur. 
			NOTE: the &#34;Polywire&#34; and &#34;Livewire&#34; voxel editing tools may also be used in place of the &#34;Freehand&#34;.</li>
		</ol>
		</li>
	</ol>
	</li>
	<li>Generate the source space based on the geometry of the pial surface.</li>
	<li>Perform subject-specific EEG sensor alignment (e.g., translation and rotation) to the MRI space using the Freesurfer head model overlay (Figure 5). Save the transformation.
	<ol>
		<li>Open the MNE_analyze application. Click on &#34;File&#34; &#62;&#62; &#34;Load surface&#34;. Navigate to the folder containing the subject data and load the pial surface.</li>
		<li>Click &#34;File&#34; &#62;&#62; &#34;Load Digitizer Data and select the EEG file of interest (should contain digitizer data). Click &#34;View&#34; &#62;&#62; &#34;Show Viewer&#34;. Once the viewer GUI appears, click &#34;Options&#34; and make sure that the &#34;Scalp&#34; and &#34;Digitizer data&#34; options are chosen. Electrodes here are shown in red, with fiducial points in yellow.</li>
		<li>On the main window (not the viewer), select &#34;Adjust&#34; &#62;&#62; &#34;Coordinate alignment&#34;. Using the &#39;Coordinate alignment GUI&#39;, use the arrow and L/R buttons to shift and rotate the EEG electrodes in the viewer. Adjust as much as necessary. Once the alignment is done, click &#34;Save&#8230;&#34; at the bottom of the &#39;Coordinate Alignment GUI&#39; to save the alignment. 
		NOTE: Typically, an even distribution of electrodes throughout the scalp with good fiducial alignment is required.</li>
	</ol>
	</li>
	<li>Generate the forward model by providing the subject-specific BEM model, the source space, and the EEG sensor transformation using MNE software15.</li>
</ol>
3. Functional MRI Data Analysis
<ol>
	<li>Perform first-level (individual subject) fMRI statistical analysis using the General Linear Model (GLM) method to acquire BOLD activation maps for the tasks of interest. Correct for multiple comparisons as necessary16, using the cluster-based approach that is built into the Freesurfer group-analysis pipeline.</li>
	<li>Perform group-level analysis on all subjects, if desired, to acquire the BOLD activation map for all subjects in standard space (MNI or Talairach). 
	NOTE: The University of Oxford Centre for Functional MRI of the Brain (FMRIB) Software Library (FSL)17 and Analysis of Functional Neuroimages (AFNI)18 packages both allow for the analysis of fMRI data on the same surfaces generated by Freesurfer, making them convenient for subsequent analysis.</li>
	<li>Use the tksurfer visualization tool to perform region-of-interest (ROI) identification by loading the fMRI activation map (both individual-level and group-level), and setting the desired FDR-corrected threshold19 (p &#60;0.05 is used here). 
	&#8203;NOTE: The ROIs identified from individual-level activation maps will serve as the subject-specific fMRI-derived spatial priors for subsequent source localization.
	<ol>
		<li>Using the fMRI activation map on the gray matter layer, extract surface patches using a connected-labeling algorithm. 
		NOTE: Dulmage-Mendelsohn decomposition was used in this example.</li>
		<li>Further sub-divide the patches based on the labeling of a predefined brain atlas, so that any patch of activity covering more than one region is split. 
		NOTE: The atlas used here was the DKT40 atlas20 (available from Freesurfer)21. Atlases can be specialized or chosen, based on experimental preferences.</li>
	</ol>
	</li>
	<li>Project the acquired group-level ROIs (which are currently in standard space) back to the individual source spaces of each subject. After performing the individual subject&#39;s structural MRI segmentation (step 2.1), the coordinate transformations between the subject and standard space are provided in the lh.sphere.reg and rh.sphere.reg files, found in the &#34;surf&#34; folder of the subject&#39;s Freesurfer output folder. 
	NOTE: All subjects will thereby share the same set of ROIs, but in their own specific model. See Figure 6 for examples of the fMRI results and resultant ROIs.</li>
</ol>
4. EEG Data Analysis
NOTE: Details in this section may be specific to the software used (See Table of Materials for more details). Please refer to the appropriate documentation if using different software packages.
<ol>
	<li>Perform scanner gradient artifacts correction through template subtraction. For this, click on the &#34;MR Correction&#34; button in the &#34;Special Signal Processing&#34; menu, and select appropriate parameters in the EEG analysis software GUI (see Figure 7). Input the appropriate parameters to the chosen scanner sequence and experimental design. 
	NOTE: Primary parameters include: repetition time (TR) for the MRI scan, scan type (interleaved or continuous), MRI volume markers (or gradient detection method and gradient trigger), channels for correction, and artifact template.</li>
	<li>Remove cardioballistic artifacts through template subtraction. For this, click on the &#34;CB Correction&#34; button in the &#34;Special Signal Processing&#34; menu, and select appropriate parameters in the analysis software GUI. 
	NOTE: Parameters necessary here include minimum and maximum heart rate, artifact template, ECG channel, template correlation, and channels for correction.</li>
	<li>Apply filtration. Select the button for IIR filtration at the top of the analysis GUI, under &#34;Data Filtration&#34;. For example, apply high-pass at 0.05 Hz, low-pass at 40 Hz, and a notch-filter at the power-line frequency (60 Hz), with a roll-off of 48 dB/Hz. 
	NOTE: After the application of a low-pass filter at a cutoff frequency of 40 Hz, the 60 Hz notch-filter is not strictly necessary, but is employed as a safeguard against any residual power-line frequencies that may have survived due to the roll-off at filter edges.</li>
	<li>Perform ocular artifact correction, on top of the analysis GUI: select &#34;Transformation&#34; &#62;&#62; &#34;Artifact Rejection/Reduction&#34; &#62;&#62; &#34;Ocular Correction ICA&#34;.</li>
	<li>Segment the EEG data into epochs based on the specified pre- and post-stimulus time, with respect to the event timing markers. To do this, select, &#34;Transformation&#34; &#62;&#62; &#34;Segment Analysis Functions&#34; &#62;&#62; &#34;Segmentation&#34;, then choose the marker of interest and the time segment of interest. 
	&#8203;NOTE: Segmentation lengths should be chosen to suit the paradigm and expected brain activity of interest.</li>
	<li>Perform manual or semi-automatic artifact rejection: select &#34;Transformation&#34; &#62;&#62; &#34;Artifact Rejection/Reduction&#34; &#62;&#62; &#34;Artifact Rejection&#34;. When prompted, define criteria for artifacts within the three tabs of the GUI and proceed as instructed on the GUI.
	<ol>
		<li>In the &#39;Inspection Method&#39; tab, select choose &#34;automatically&#34;, &#34;semi-automatically&#34;, or &#34;manually select artifacts&#34; (semi-automatic mode is recommended). Then select &#34;mark&#34; or &#34;remove artifacts&#34;, and specify if the corrections are for a single channel.</li>
		<li>In the &#39;Channel Selection&#39; tab, select the channels which will be corrected for artifacts.</li>
		<li>In the &#39;Criteria&#39; tab, select the basis by which artifacts will be identified. Make selections here to fit experimental needs. Click &#34;OK&#34; after selecting criteria, and artifacts will be identified and/or rejected in accordance with the selections.</li>
	</ol>
	</li>
	<li>Perform baseline correction and trial averaging (if applicable).
	<ol>
		<li>To perform baseline correction: select &#34;Transformation&#34; &#62;&#62; &#34;Segment Analysis Functions&#34; &#62;&#62; &#34;Baseline Correction&#34;. To average the segmented data: select &#34;Transformation&#34; &#62;&#62; &#34;Segment Analysis Functions&#34; &#62;&#62; &#34;Average&#34;.</li>
	</ol>
	</li>
</ol>
5. Spatiotemporal fMRI Constraints &#8212; EG Source Imaging
<ol>
	<li>Define window size and window overlapping size (the default setting calls for a 40 ms window size with 50% (20 ms) overlap).</li>
	<li>Select the subject-specific ROIs set (obtained in step 3) as the spatial prior set. For each EEG segment, the algorithm will then estimate a set of weights for the subset of spatial priors that maximizes the model evidence, and computes the source covariance matrix accordingly.</li>
	<li>Using the resulting source covariance matrix, perform source localization for the EEG segment being analyzed, yielding the source current density results.</li>
	<li>Perform steps 5.2 and 5.3 for all EEG segments and, if necessary, summarize the current density results for all time segments into one complete current density time-course by averaging the overlapping portion. 
	&#8203;NOTE: This step will result in a current density time-course of cortical activity at every source point defined in step 2.3 (this number is typically on the order of several thousands) (Figure 8).</li>
	<li>Extract the representative current density time-course at each of the ROIs.
	<ol>
		<li>Select the preferred method for summarizing the time-courses from the multiple source points within an ROI into a single signal time-course: average, first eigenvariate, etc.</li>
	</ol>
	</li>
	<li>Repeat steps 5.1 to 5.5 for all subjects.</li>
</ol>

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The authors have nothing to disclose.

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

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Research

JoVE Journal

Biology

11.6K Views.  University of Houston. 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.

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

Functional Mapping with Simultaneous MEG and EEG

We use magnetoencephalography (MEG) and electroencephalography (EEG) to locate and determine the temporal evolution in brain areas involved in the processing of simple sensory stimuli. We will use somatosensory stimuli to locate the hand somatosensory areas, auditory stimuli to locate the auditory cortices, visual stimuli in four quadrants of the visual field to locate the early visual areas. These type of experiments are used for functional mapping in epileptic and brain tumor patients to locate eloquent cortices. In basic neuroscience similar experimental protocols are used to study the orchestration of cortical activity. The acquisition protocol includes quality assurance procedures, subject preparation for the combined MEG/EEG study, and acquisition of evoked-response data with somatosensory, auditory, and visual stimuli. We also demonstrate analysis of the data using the equivalent current dipole model and cortically-constrained minimum-norm estimates. Anatomical MRI data are employed in the analysis for visualization and for deriving boundaries of tissue boundaries for forward modeling and cortical location and orientation constraints for the minimum-norm estimates.

We use magnetoencephalography (MEG) and electroencephalography (EEG) to map brain areas involved in the processing of simple sensory stimuli.

We use magnetoencephalography (MEG) and electroencephalography (EEG) to locate and determine the temporal evolution in brain areas involved in the ...

Neuroscience

Best Current Practice for Obtaining High Quality EEG Data During Simultaneous fMRI

Simultaneous EEG-fMRI allows the excellent temporal resolution of EEG to be combined with the high spatial accuracy of fMRI. The data from these two modalities can be combined in a number of ways, but all rely on the acquisition of high quality EEG and fMRI data. EEG data acquired during simultaneous fMRI are affected by several artifacts, including the gradient artefact (due to the changing magnetic field gradients required for fMRI), the pulse artefact (linked to the cardiac cycle) and movement artifacts (resulting from movements in the strong magnetic field of the scanner, and muscle activity). Post-processing methods for successfully correcting the gradient and pulse artifacts require a number of criteria to be satisfied during data acquisition. Minimizing head motion during EEG-fMRI is also imperative for limiting the generation of artifacts.
Interactions between the radio frequency (RF) pulses required for MRI and the EEG hardware may occur and can cause heating. This is only a significant risk if safety guidelines are not satisfied. Hardware design and set-up, as well as careful selection of which MR sequences are run with the EEG hardware present must therefore be considered.
The above issues highlight the importance of the choice of the experimental protocol employed when performing a simultaneous EEG-fMRI experiment. Based on previous research we describe an optimal experimental set-up. This provides high quality EEG data during simultaneous fMRI when using commercial EEG and fMRI systems, with safety risks to the subject minimized. We demonstrate this set-up in an EEG-fMRI experiment using a simple visual stimulus. However, much more complex stimuli can be used. Here we show the EEG-fMRI set-up using a Brain Products GmbH (Gilching, Germany) MRplus, 32 channel EEG system in conjunction with a Philips Achieva (Best, Netherlands) 3T MR scanner, although many of the techniques are transferable to other systems.

Simultaneous electroencephalography (EEG) and functional Magnetic Resonance imaging (fMRI) is a powerful neuroimaging tool. However, the inside of an MRI scanner forms a difficult environment for EEG data recording and safety must be considered whenever operating EEG equipment inside a scanner. Here, we present an optimised EEG-fMRI data acquisition protocol.

Simultaneous  EEG-fMRI allows the excellent temporal resolution of EEG to be combined with the  high spatial accuracy of fMRI. The data  from these two ...

Behavior

Simultaneous fMRI and Electrophysiology in the Rodent Brain

To examine the neural basis of the blood oxygenation level dependent (BOLD) magnetic resonance imaging (MRI) signal, we have developed a rodent model in which functional MRI data and in vivo intracortical recording can be performed simultaneously. The combination of MRI and electrical recording is technically challenging because the electrodes used for recording distort the MRI images and the MRI acquisition induces noise in the electrical recording. To minimize the mutual interference of the two modalities, glass microelectrodes were used rather than metal and a noise removal algorithm was implemented for the electrophysiology data. In our studies, two microelectrodes were separately implanted in bilateral primary somatosensory cortices (SI) of the rat and fixed in place. One coronal slice covering the electrode tips was selected for functional MRI. Electrode shafts and fixation positions were not included in the image slice to avoid imaging artifacts. The removed scalp was replaced with toothpaste to reduce susceptibility mismatch and prevent Gibbs ringing artifacts in the images. The artifact structure induced in the electrical recordings by the rapidly-switching magnetic fields during image acquisition was characterized by averaging all cycles of scans for each run. The noise structure during imaging was then subtracted from original recordings. The denoised time courses were then used for further analysis in combination with the fMRI data. As an example, the simultaneous acquisition was used to determine the relationship between spontaneous fMRI BOLD signals and band-limited intracortical electrical activity. Simultaneous fMRI and electrophysiological recording in the rodent will provide a platform for many exciting applications in neuroscience in addition to elucidating the relationship between the fMRI BOLD signal and neuronal activity.

We have developed a method for simultaneous functional magnetic resonance imaging and electrophysiological recording in the rodent brain, providing a platform for the investigation of the relationship between neural activity and the blood oxygenation level dependent (BOLD) MRI signal.

To examine the neural basis of the blood oxygenation level dependent (BOLD) magnetic resonance imaging (MRI) signal, we have developed a rodent model in ...

Simultaneous Transcranial Alternating Current Stimulation and Functional Magnetic Resonance Imaging

Transcranial alternating current stimulation (tACS) is a promising tool for noninvasive investigation of brain oscillations. TACS employs frequency-specific stimulation of the human brain through current applied to the scalp with surface electrodes. Most current knowledge of the technique is based on behavioral studies; thus, combining the method with brain imaging holds potential to better understand the mechanisms of tACS. Because of electrical and susceptibility artifacts, combining tACS with brain imaging can be challenging, however, one brain imaging technique that is well suited to be applied simultaneously with tACS is functional magnetic resonance imaging (fMRI). In our lab, we have successfully combined tACS with simultaneous fMRI measurements to show that tACS effects are state, current, and frequency dependent, and that modulation of brain activity is not limited to the area directly below the electrodes. This article describes a safe and reliable setup for applying tACS simultaneously with visual task fMRI studies, which can lend to understanding oscillatory brain function as well as the effects of tACS on the brain.

Transcranial alternating current stimulation (tACS) is a promising tool for noninvasive investigation of brain oscillations, though its effects are not completely understood. This article describes a safe and reliable setup for applying tACS simultaneously with functional magnetic resonance imaging, which can increase understanding oscillatory brain function and effects of tACS.

Transcranial alternating current stimulation (tACS) is a promising tool for noninvasive investigation of brain oscillations. TACS employs ...

Mapping Cortical Dynamics Using Simultaneous MEG/EEG and Anatomically-constrained Minimum-norm Estimates: an Auditory Attention Example

Magneto- and electroencephalography (MEG/EEG) are neuroimaging techniques that provide a high temporal resolution particularly suitable to investigate the cortical networks involved in dynamical perceptual and cognitive tasks, such as attending to different sounds in a cocktail party. Many past studies have employed data recorded at the sensor level only, i.e., the magnetic fields or the electric potentials recorded outside and on the scalp, and have usually focused on activity that is time-locked to the stimulus presentation. This type of event-related field / potential analysis is particularly useful when there are only a small number of distinct dipolar patterns that can be isolated and identified in space and time. Alternatively, by utilizing anatomical information, these distinct field patterns can be localized as current sources on the cortex. However, for a more sustained response that may not be time-locked to a specific stimulus (e.g., in preparation for listening to one of the two simultaneously presented spoken digits based on the cued auditory feature) or may be distributed across multiple spatial locations unknown a priori, the recruitment of a distributed cortical network may not be adequately captured by using a limited number of focal sources.
Here, we describe a procedure that employs individual anatomical MRI data to establish a relationship between the sensor information and the dipole activation on the cortex through the use of minimum-norm estimates (MNE). This inverse imaging approach provides us a tool for distributed source analysis. For illustrative purposes, we will describe all procedures using FreeSurfer and MNE software, both freely available. We will summarize the MRI sequences and analysis steps required to produce a forward model that enables us to relate the expected field pattern caused by the dipoles distributed on the cortex onto the M/EEG sensors. Next, we will step through the necessary processes that facilitate us in denoising the sensor data from environmental and physiological contaminants. We will then outline the procedure for combining and mapping MEG/EEG sensor data onto the cortical space, thereby producing a family of time-series of cortical dipole activation on the brain surface (or "brain movies") related to each experimental condition. Finally, we will highlight a few statistical techniques that enable us to make scientific inference across a subject population (i.e., perform group-level analysis) based on a common cortical coordinate space.

We use magneto- and electroencephalography (MEG/EEG), combined with anatomical information captured by magnetic resonance imaging (MRI), to map the dynamics of the cortical network associated with auditory attention.

Magneto- and electroencephalography (MEG/EEG) are neuroimaging techniques that provide a high temporal resolution particularly suitable to investigate the ...

Extracting Visual Evoked Potentials from EEG Data Recorded During fMRI-guided Transcranial Magnetic Stimulation

Transcranial Magnetic Stimulation (TMS) is an effective method for establishing a causal link between a cortical area and cognitive/neurophysiological effects. Specifically, by creating a transient interference with the normal activity of a target region and measuring changes in an electrophysiological signal, we can establish a causal link between the stimulated brain area or network and the electrophysiological signal that we record. If target brain areas are functionally defined with prior fMRI scan, TMS could be used to link the fMRI activations with evoked potentials recorded. However, conducting such experiments presents significant technical challenges given the high amplitude artifacts introduced into the EEG signal by the magnetic pulse, and the difficulty to successfully target areas that were functionally defined by fMRI. Here we describe a methodology for combining these three common tools: TMS, EEG, and fMRI. We explain how to guide the stimulator&#39;s coil to the desired target area using anatomical or functional MRI data, how to record EEG during concurrent TMS, how to design an ERP study suitable for EEG-TMS combination and how to extract reliable ERP from the recorded data. We will provide representative results from a previously published study, in which fMRI-guided TMS was used concurrently with EEG to show that the face-selective N1 and the body-selective N1 component of the ERP are associated with distinct neural networks in extrastriate cortex. This method allows us to combine the high spatial resolution of fMRI with the high temporal resolution of TMS and EEG and therefore obtain a comprehensive understanding of the neural basis of various cognitive processes.

This paper describes a method for collecting and analyzing electroencephalography (EEG) data during concurrent transcranial magnetic stimulation (TMS) guided by activations revealed with functional magnetic resonance imaging (fMRI). A method for TMS artifact removal and extraction of event related potentials is described as well as considerations in paradigm design and experimental setup.

Transcranial Magnetic Stimulation (TMS) is an effective method for establishing a causal link between a cortical area and cognitive/neurophysiological ...

A Multimodal Imaging- and Stimulation-based Method of Evaluating Connectivity-related Brain Excitability in Patients with Epilepsy

Resting-state functional connectivity MRI (rs-fcMRI) is a technique that identifies connectivity between different brain regions based on correlations over time in the blood-oxygenation level dependent signal. rs-fcMRI has been applied extensively to identify abnormalities in brain connectivity in different neurologic and psychiatric diseases. However, the relationship among rs-fcMRI connectivity abnormalities, brain electrophysiology and disease state is unknown, in part because the causal significance of alterations in functional connectivity in disease pathophysiology has not been established. Transcranial Magnetic Stimulation (TMS) is a technique that uses electromagnetic induction to noninvasively produce focal changes in cortical activity. When combined with electroencephalography (EEG), TMS can be used to assess the brain's response to external perturbations. Here we provide a protocol for combining rs-fcMRI, TMS and EEG to assess the physiologic significance of alterations in functional connectivity in patients with neuropsychiatric disease. We provide representative results from a previously published study in which rs-fcMRI was used to identify regions with abnormal connectivity in patients with epilepsy due to a malformation of cortical development, periventricular nodular heterotopia (PNH). Stimulation in patients with epilepsy resulted in abnormal TMS-evoked EEG activity relative to stimulation of the same sites in matched healthy control patients, with an abnormal increase in the late component of the TMS-evoked potential, consistent with cortical hyperexcitability. This abnormality was specific to regions with abnormal resting-state functional connectivity. Electrical source analysis in a subject with previously recorded seizures demonstrated that the origin of the abnormal TMS-evoked activity co-localized with the seizure-onset zone, suggesting the presence of an epileptogenic circuit. These results demonstrate how rs-fcMRI, TMS and EEG can be utilized together to identify and understand the physiological significance of abnormal brain connectivity in human diseases.

Resting-state functional-connectivity MRI has identified abnormalities in patients with a wide range of neuropsychiatric disorders, including epilepsy due to malformations of cortical development. Transcranial Magnetic Stimulation in combination with EEG can demonstrate that patients with epilepsy have cortical hyperexcitability in regions with abnormal connectivity.

Resting-state functional connectivity MRI (rs-fcMRI) is a technique that identifies connectivity between different brain regions based on correlations ...

Medicine

Analyzing Neural Activity and Connectivity Using Intracranial EEG Data with SPM Software

Measuring neural activity and connectivity associated with cognitive functions at high spatial and temporal resolutions is an important goal in cognitive neuroscience. Intracranial electroencephalography (EEG) can directly record electrical neural activity and has the unique potential to accomplish this goal. Traditionally, averaging analysis has been applied to analyze intracranial EEG data; however, several new techniques are available for depicting neural activity and intra- and inter-regional connectivity. Here, we introduce two analytical protocols we recently applied to analyze intracranial EEG data using the Statistical Parametric Mapping (SPM) software: time-frequency SPM analysis for neural activity and dynamic causal modeling of induced responses for intra- and inter-regional connectivity. We report our analysis of intracranial EEG data during the observation of faces as representative results. The results revealed that the inferior occipital gyrus (IOG) showed gamma-band activity at very early stages (110 ms) in response to faces, and both the IOG and amygdala showed rapid intra- and inter-regional connectivity using various types of oscillations. These analytical protocols have the potential to identify the neural mechanisms underlying cognitive functions with high spatial and temporal profiles.

We present two analytical protocols that can be used to analyze intracranial electroencephalography data using the Statistical Parametric Mapping (SPM) software: time-frequency statistical parametric mapping analysis for neural activity, and dynamic causal modeling of induced responses for intra- and inter-regional connectivity.

Measuring neural activity and connectivity associated with cognitive functions at high spatial and temporal resolutions is an important goal in cognitive ...

Equipment Setup and Artifact Removal for Simultaneous Electroencephalogram and Functional Magnetic Resonance Imaging for Clinical Review in Epilepsy

Simultaneous electroencephalogram and functional magnetic resonance imaging (EEG-fMRI) is a unique combined technique that provides synergy in the understanding and localization of seizure onset in epilepsy. However, reported experimental protocols for EEG-fMRI recordings fail to address details about conducting such procedures on epilepsy patients. In addition, these protocols are limited solely to research settings. To fill the gap between patient monitoring in an epilepsy monitoring unit (EMU) and conducting research with an epilepsy patient, we introduce a unique EEG-fMRI recording protocol of epilepsy during the interictal period. The use of an MR conditional electrode set, which can also be used in the EMU for a simultaneous scalp EEG and video recording, allows an easy transition of EEG recordings from the EMU to the scanning room for concurrent EEG-fMRI recordings. Details on the recording procedures using this specific MR conditional electrode set are provided. In addition, the study explains step-by-step EEG processing procedures to remove the imaging artifacts, which can then be used for clinical review. This experimental protocol promotes an amendment to the conventional EEG-fMRI recording for enhanced applicability in both clinical (i.e., EMU) and research settings. Furthermore, this protocol provides the potential to expand this modality to postictal EEG-fMRI recordings in the clinical setting.

This article details simultaneous electroencephalogram and functional magnetic resonance imaging (EEG-fMRI) recording procedures that can be used in both clinical and research settings. EEG processing procedures to remove imaging artifacts for clinical review are also included. This study focuses on the example of epilepsy during the interictal period.

Simultaneous electroencephalogram and functional magnetic resonance imaging (EEG-fMRI) is a unique combined technique that provides synergy in the ...

Bioengineering

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms

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

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

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