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.

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			<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 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

Labeling hESCs and hMSCs with Iron Oxide Nanoparticles for Non-Invasive in vivo Tracking with MR Imaging

In recent years, stem cell research has led to a better understanding of developmental biology, various diseases and its potential impact on regenerative medicine. A non-invasive method to monitor the transplanted stem cells repeatedly in vivo would greatly enhance our ability to understand the mechanisms that control stem cell death and identify trophic factors and signaling pathways that improve stem cell engraftment. MR imaging has been proven to be an effective tool for the in vivo depiction of stem cells with near microscopic anatomical resolution. In order to detect stem cells with MR, the cells have to be labeled with cell specific MR contrast agents. For this purpose, iron oxide nanoparticles, such as superparamagnetic iron oxide particles (SPIO), are applied, because of their high sensitivity for cell detection and their excellent biocompatibility. SPIO particles are composed of an iron oxide core and a dextran, carboxydextran or starch coat, and function by creating local field inhomogeneities, that cause a decreased signal on T2-weighted MR images. This presentation will demonstrate techniques for labeling of stem cells with clinically applicable MR contrast agents for subsequent non-invasive in vivo tracking of the labeled cells with MR imaging.

For the evaluation of new stem cell therapies it is important to non-invasively track the injected cells in vivo. This video will show you how to label human mesenchymal and embryonic stem cells with iron oxide based contrast agents in vivo for subsequent MR imaging in vivo.

In recent years, stem cell research has led to a better understanding of developmental biology, various diseases and its potential impact on regenerative ...

Labeling Stem Cells with Fluorescent Dyes for non-invasive Detection with Optical Imaging

Optical imaging (OI) is an easy, fast and inexpensive tool for in vivo monitoring of new stem cell based therapies. The technique is based on ex vivo labeling of stem cells with a fluorescent dye, subsequent intravenous injection of the labeled cells and visualization of their accumulation in specific target organs or pathologies. The presented technique demonstrates how we label human mesenchymal stem cells (hMSC) by simple incubation with the lipophilic fluorescent dye DiD (C67H103CIN2O3S) and how we label human embryonic stem cells (hESC) with the FDA approved fluorescent dye Indocyanine Green (ICG). The uptake mechanism is via adherence and diffusion of the lypophilic dye across the phospholipid cell membrane bilayer. The labeling efficiency is usually improved if the cells are incubated with the dye in serum-free media as opposed to incubation in serum-containing media. Furthermore, the addition of the transfection agent Protamine Sulfate significantly improves contrast agent uptake.

This video shows techniques for labeling of human embryonic stem cells and mesenchymal stem cells with fluorescent dyes. This technique can be used for an in vivo tracking of transplanted stem cells with optical imaging and for histopathological correlations with fluorescence microscopy.

Optical imaging (OI) is an easy, fast and inexpensive tool for in vivo monitoring of new stem cell based therapies. The technique is based on ex vivo ...

Functional Imaging with Reinforcement, Eyetracking, and Physiological Monitoring

We use functional brain imaging (fMRI) to study neural circuits that underlie decision-making.  To understand how outcomes affect decision processes, simple perceptual tasks are combined with appetitive and aversive reinforcement.  However, the use of reinforcers such as juice and airpuffs can create challenges for fMRI.  Reinforcer delivery can cause head movement, which creates artifacts in the fMRI signal.  Reinforcement can also lead to changes in heart rate and respiration that are mediated by autonomic pathways.  Changes in heart rate and respiration can directly affect the fMRI (BOLD) signal in the brain and can be confounded with signal changes that are due to neural activity.  In this presentation, we demonstrate methods for administering reinforcers in a controlled manner, for stabilizing the head, and for measuring pulse and respiration.

This presentation demonstrates the use of fMRI to study neural circuits that underlie decision-making. Simple perceptual tasks are combined with appetitive and aversive reinforcements to investigate how outcomes affect decision processes.

We use functional brain imaging (fMRI) to study neural circuits that underlie decision-making.  To understand how outcomes affect decision processes, ...

Dissection of Hippocampal Dentate Gyrus from Adult Mouse

The hippocampus is one of the most widely studied areas in the brain because of its important functional role in memory processing and learning, its remarkable neuronal cell plasticity, and its involvement in epilepsy, neurodegenerative diseases, and psychiatric disorders. The hippocampus is composed of distinct regions; the dentate gyrus, which comprises mainly granule neurons, and Ammon's horn, which comprises mainly pyramidal neurons, and the two regions are connected by both anatomic and functional circuits. Many different mRNAs and proteins are selectively expressed in the dentate gyrus, and the dentate gyrus is a site of adult neurogenesis; that is, new neurons are continually generated in the adult dentate gyrus. To investigate mRNA and protein expression specific to the dentate gyrus, laser capture microdissection is often used. This method has some limitations, however, such as the need for special apparatuses and complicated handling procedures. In this video-recorded protocol, we demonstrate a dissection technique for removing the dentate gyrus from adult mouse under a stereomicroscope. Dentate gyrus samples prepared using this technique are suitable for any assay, including transcriptomic, proteomic, and cell biology analyses. We confirmed that the dissected tissue is dentate gyrus by conducting real-time PCR of dentate gyrus-specific genes, tryptophan 2,3-dioxygenase (TDO2) and desmoplakin (Dsp), and Ammon's horn enriched genes, Meis-related gene 1b (Mrg1b) and TYRO3 protein tyrosine kinase 3 (Tyro3). The mRNA expressions of TDO2 and Dsp in the dentate gyrus samples were detected at obviously higher levels, whereas Mrg1b and Tyro3 were lower levels, than those in the Ammon's horn samples. To demonstrate the advantage of this method, we performed DNA microarray analysis using samples of whole hippocampus and dentate gyrus. The mRNA expression of TDO2 and Dsp, which are expressed selectively in the dentate gyrus, in the whole hippocampus of alpha-CaMKII+/- mice, exhibited 0.037 and 0.10-fold changes compared to that of wild-type mice, respectively. In the isolated dentate gyrus, however, these expressions exhibited 0.011 and 0.021-fold changes compared to that of wild-type mice, demonstrating that gene expression changes in dentate gyrus can be detected with greater sensitivity. Taken together, this convenient and accurate dissection technique can be reliably used for studies focused on the dentate gyrus.

A dissection technique for removal of the dentate gyrus from adult mouse under a stereomicroscope was demonstrated in this video-recorded protocol.

The hippocampus is one of the most widely studied areas in the brain because of its important functional role in memory processing and learning, its ...

Robotics and Dynamic Image Analysis for Studies of Gene Expression in Plant Tissues

Gene expression in plant tissues is typically studied by destructive extraction of compounds from plant tissues for in vitro analyses. The methods presented here utilize the green fluorescent protein (gfp) gene for continual monitoring of gene expression in the same pieces of tissues, over time. The gfp gene was placed under regulatory control of different promoters and introduced into lima bean cotyledonary tissues via particle bombardment. Cotyledons were then placed on a robotic image collection system, which consisted of a fluorescence dissecting microscope with a digital camera and a 2-dimensional robotics platform custom-designed to allow secure attachment of culture dishes. Images were collected from cotyledonary tissues every hour for 100 hours to generate expression profiles for each promoter. Each collected series of 100 images was first subjected to manual image alignment using ImageReady to make certain that GFP-expressing foci were consistently retained within selected fields of analysis. Specific regions of the series measuring 300 x 400 pixels, were then selected for further analysis to provide GFP Intensity measurements using ImageJ software. Batch images were separated into the red, green and blue channels and GFP-expressing areas were identified using the threshold feature of ImageJ. After subtracting the background fluorescence (subtraction of gray values of non-expressing pixels from every pixel) in the respective red and green channels, GFP intensity was calculated by multiplying the mean grayscale value per pixel by the total number of GFP-expressing pixels in each channel, and then adding those values for both the red and green channels. GFP Intensity values were collected for all 100 time points to yield expression profiles. Variations in GFP expression profiles resulted from differences in factors such as promoter strength, presence of a silencing suppressor, or nature of the promoter. In addition to quantification of GFP intensity, the image series were also used to generate time-lapse animations using ImageReady. Time-lapse animations revealed that the clear majority of cells displayed a relatively rapid increase in GFP expression, followed by a slow decline. Some cells occasionally displayed a sudden loss of fluorescence, which may be associated with rapid cell death. Apparent transport of GFP across the membrane and cell wall to adjacent cells was also observed. Time lapse animations provided additional information that could not otherwise be obtained using GFP Intensity profiles or single time point image collections.

We report a method for introduction, tracking and quantitative analysis of GFP expression in plant cells. This method utilizes a custom-designed robotics system for semi-continuous image collection from large numbers of samples, over time. We also demonstrate the use of ImageJ and ImageReady for analysis of image series.

Gene expression in plant tissues is typically studied by destructive extraction of compounds from plant tissues for in vitro analyses. The methods ...

Rapid Analysis and Exploration of Fluorescence Microscopy Images

Despite rapid advances in high-throughput microscopy, quantitative image-based assays still pose significant challenges. While a variety of specialized image analysis tools are available, most traditional image-analysis-based workflows have steep learning curves (for fine tuning of analysis parameters) and result in long turnaround times between imaging and analysis. In particular, cell segmentation, the process of identifying individual cells in an image, is a major bottleneck in this regard.
Here we present an alternate, cell-segmentation-free workflow based on PhenoRipper, an open-source software platform designed for the rapid analysis and exploration of microscopy images. The pipeline presented here is optimized for immunofluorescence microscopy images of cell cultures and requires minimal user intervention. Within half an hour, PhenoRipper can analyze data from a typical 96-well experiment and generate image profiles. Users can then visually explore their data, perform quality control on their experiment, ensure response to perturbations and check reproducibility of replicates. This facilitates a rapid feedback cycle between analysis and experiment, which is crucial during assay optimization. This protocol is useful not just as a first pass analysis for quality control, but also may be used as an end-to-end solution, especially for screening. The workflow described here scales to large data sets such as those generated by high-throughput screens, and has been shown to group experimental conditions by phenotype accurately over a wide range of biological systems. The PhenoBrowser interface provides an intuitive framework to explore the phenotypic space and relate image properties to biological annotations. Taken together, the protocol described here will lower the barriers to adopting quantitative analysis of image based screens.

Here we describe a workflow for rapidly analyzing and exploring collections of fluorescence microscopy images using PhenoRipper, a recently developed image-analysis platform.

Despite rapid advances in high-throughput microscopy, quantitative image-based assays still pose significant challenges. While a variety of specialized ...

Functional Reconstitution and Channel Activity Measurements of Purified Wildtype and Mutant CFTR Protein

The Cystic Fibrosis Transmembrane Conductance Regulator (CFTR) is a unique channel-forming member of the ATP Binding Cassette (ABC) superfamily of transporters. The phosphorylation and nucleotide dependent chloride channel activity of CFTR has been frequently studied in whole cell systems and as single channels in excised membrane patches. Many Cystic Fibrosis-causing mutations have been shown to alter this activity. While a small number of purification protocols have been published, a fast reconstitution method that retains channel activity and a suitable method for studying population channel activity in a purified system have been lacking. Here rapid methods are described for purification and functional reconstitution of the full-length CFTR protein into proteoliposomes of defined lipid composition that retains activity as a regulated halide channel. This reconstitution method together with a novel flux-based assay of channel activity is a suitable system for studying the population channel properties of wild type CFTR and the disease-causing mutants F508del- and G551D-CFTR. Specifically, the method has utility in studying the direct effects of phosphorylation, nucleotides and small molecules such as potentiators and inhibitors on CFTR channel activity. The methods are also amenable to the study of other membrane channels/transporters for anionic substrates.

Described here is a rapid and effective procedure for functional reconstitution of purified wild-type and mutant CFTR protein that preserves activity for this chloride channel, which is defective in Cystic Fibrosis. Iodide efflux from reconstituted proteoliposomes mediated by CFTR allows studies of channel activity and the effects of small molecules.

The Cystic Fibrosis Transmembrane Conductance Regulator (CFTR) is a unique channel-forming member of the ATP Binding Cassette (ABC) superfamily of ...

Implementation of a Coherent Anti-Stokes Raman Scattering (CARS) System on a Ti:Sapphire and OPO Laser Based Standard Laser Scanning Microscope

Laser scanning microscopes combining a femtosecond Ti:sapphire laser and an optical parametric oscillator (OPO) to duplicate the laser line have become available for biologists. These systems are primarily designed for multi-channel two-photon fluorescence microscopy. However, without any modification, complementary non-linear optical microscopy such as second-harmonic generation (SHG) or third harmonic generation (THG) can also be performed with this set-up, allowing label-free imaging of structured molecules or aqueous medium-lipid interfaces. These techniques are well suited for in-vivo observation, but are limited in chemical specificity. Chemically selective imaging can be obtained from inherent vibration signals based on Raman scattering. Confocal Raman microscopy provides 3D spatial resolution, but it requires high average power and long acquisition time. To overcome these difficulties, recent advances in laser technology have permitted the development of nonlinear optical vibrational microscopy, in particular coherent anti-Stokes Raman scattering (CARS). CARS microscopy has therefore emerged as a powerful tool for biological and live cell imaging, by chemically mapping lipids (via C-H stretch vibration), water (via O-H stretch vibrations), proteins or DNA. In this work, we describe the implementation of the CARS technique on a standard OPO-coupled multiphoton laser scanning microscope. It is based on the in-time synchronization of the two laser lines by adjusting the length of one of the laser beam path. We present a step-by-step implementation of this technique on an existing multiphoton system. A basic background in experimental optics is helpful and the presented system does not require expensive supplementary equipment. We also illustrate CARS imaging obtained on myelin sheaths of sciatic nerve of rodent, and we show that this imaging can be performed simultaneously with other nonlinear optical imaging, such as standard two-photon fluorescence technique and second-harmonic generation.

Implementation of a Coherent Anti-Stokes Raman Scattering CARS System on a Ti:Sapphire and OPO Laser Based Standard Laser Scanning Microscope

Coherent anti-Stokes Raman scattering (CARS) microscopy based on inherent vibration of molecule bonds permits label-free chemically selective live cell imaging. This work presents the implementation of a complementary microscopy technique on a standard multiphoton laser scanning microscope based on a femtosecond Ti:sapphire laser and an OPO laser.

Laser scanning microscopes combining a femtosecond Ti:sapphire laser and an optical parametric oscillator (OPO) to duplicate the laser line have become ...

Lens-free Video Microscopy for the Dynamic and Quantitative Analysis of Adherent Cell Culture

Here, we demonstrate that lens-free video microscopy enables us to simultaneously capture the kinetics of thousands of cells directly inside the incubator and that it is possible to monitor and quantify single cells along several cell cycles. We describe the full protocol used to monitor and quantify a HeLa cell culture for 2.7 days. First, cell culture acquisition is performed with a lens-free video microscope, and then the data is analyzed following a four-step process: multi-wavelength holographic reconstruction, cell-tracking, cell segmentation and cell division detection algorithms. As a result, we show that it is possible to gather a dataset featuring more than 10,000 cell cycle tracks and more than 2 x 106 cell morphological measurements.

Lens-free video microscopy enables us to monitor cell cultures directly inside the incubator. Here we describe the full protocol used to acquire and analyze a 2.7 day long acquisition of cultured HeLa cells, leading to a dataset of 2.2 x 106 measurements of individual cell morphology and 10584 cell cycle tracks.

Here, we demonstrate that lens-free video microscopy enables us to simultaneously capture the kinetics of thousands of cells directly inside the incubator ...

Oligomerization Dynamics of Cell Surface Receptors in Living Cells by Total Internal Reflection Fluorescence Microscopy Combined with Number and Brightness Analysis

Despite the importance and ubiquity of receptor oligomerization, few methods are applicable for detecting clustering events and measuring the degree of clustering. Here, we describe an imaging approach to determine the average oligomeric state of mEGFP-tagged-receptor homocomplexes in the membrane of living cells. The protocol is based on Total Internal Reflection Fluorescence (TIRF) microscopy combined with Number and Brightness (N&#38;B) analysis. N&#38;B is a method similar to fluorescence-correlation spectroscopy (FCS) and photon counting histogram (PCH), which are based on the statistical analysis of the fluctuations of the fluorescence intensity of fluorophores diffusing in and out of an illumination volume during an observation time. In particular, N&#38;B is a simplification of PCH to obtain information on the average number of proteins in oligomeric mixtures. The intensity fluctuation amplitudes are described by the molecular brightness of the fluorophore and the average number of fluorophores within the illumination volume. Thus, N&#38;B considers only the first and second moments of the amplitude distribution, namely, the mean intensity and the variance. This is, at the same time, the strength and the weakness of the method. Because only two moments are considered, N&#38;B cannot determine the molar fraction of unknown oligomers in a mixture, but it only estimates the average oligomerization state of the mixture. Nevertheless, it can be applied to relatively small time series (compared to other moment methods) of images of live cells on a pixel-by-pixel basis, simply by monitoring the time fluctuations of the fluorescence intensity. It reduces the effective time-per-pixel to a few microseconds, allowing acquisition in the time range of seconds to milliseconds, which is necessary for fast oligomerization kinetics. Finally, large cell areas as well as sub-cellular compartments can be explored.

We describe an imaging approach for the determination of the average oligomeric state of mEGFP-tagged-receptor oligomers induced by ligand binding in the plasma membrane of living cells. The protocol is based on Total Internal Reflection Fluorescence (TIRF) microscopy combined with Number and Brightness (N&#38;B) analysis.

Despite the importance and ubiquity of receptor oligomerization, few methods are applicable for detecting clustering events and measuring the degree of ...