Name: concurrent eeg and functional mri recording and integration analysis for dynamic cortical activity imaging
Uploaded: 2018-06-30T12:30:00
Description: Watch this Scientific Journal Video about Concurrent EEG and Functional MRI Recording and Integration Analysis for Dynamic Cortical Activity Imaging 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
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	<li>Obtain informed consent from the participant. Explain to the participant the purpose and procedure of the study, as well as the important safety measures for the simultaneous EEG/fMRI data recording pr...

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	<li>Belanger, M., Allaman, I., Magistretti, P. J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Brain+energy+metabolism:+focus+on+astrocyte-neuron+metabolic+cooperation.">Brain energy metabolism: focus on astrocyte-neuron metabolic cooperation.</a> Cell Metab. 14 (6), 724-738 (2011).</li><li>Logothetis, N. K. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+underpinnings+of+the+BOLD+functional+magnetic+resonance+imaging+signal.">The underpinnings of the BOLD functional magnetic resonance imaging signal.</a> J Neurosci. 23 (10), 3963-3971 (2003).</li><li>Michel, C. M., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=EEG+source+imaging.">EEG source imaging.</a> Clin Neurophysiol. 115 (10), 2195-2222 (2004).</li><li>Ramon, C., Schimpf, P. H., Haueisen, J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Influence+of+head+models+on+EEG+simulations+and+inverse+source+localizations.">Influence of head models on EEG simulations and inverse source localizations.</a> Biomed Eng Online. 5, 10 (2006).</li><li>Mosher, J. C., Spencer, M. E., Leahy, R. M., Lewis, P. S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Error+bounds+for+EEG+and+MEG+dipole+source+localization.">Error bounds for EEG and MEG dipole source localization.</a> Electroencephalogr Clin Neurophysiol. 86 (5), 303-321 (1993).</li><li>Hamalainen, M. S., Ilmoniemi, R. J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Interpreting+magnetic+fields+of+the+brain:+minimum+norm+estimates.">Interpreting magnetic fields of the brain: minimum norm estimates.</a> Med Biol Eng Comput. 32 (1), 35-42 (1994).</li><li>Pascual-Marqui, R. D. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Review+of+methods+for+solving+the+EEG+inverse+problem.">Review of methods for solving the EEG inverse problem.</a> International journal of bioelectromagnetism. 1 (1), 75-86 (1999).</li><li>Rosa, M. J., Daunizeau, J., Friston, K. J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=EEG-fMRI+integration:+a+critical+review+of+biophysical+modeling+and+data+analysis+approaches.">EEG-fMRI integration: a critical review of biophysical modeling and data analysis approaches.</a> J Integr Neurosci. 9 (4), 453-476 (2010).</li><li>Nguyen, T., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=EEG+Source+Imaging+Guided+by+Spatiotemporal+Specific+fMRI:+Toward+an+Understanding+of+Dynamic+Cognitive+Processes.">EEG Source Imaging Guided by Spatiotemporal Specific fMRI: Toward an Understanding of Dynamic Cognitive Processes.</a> Neural Plast. 2016, 10 (2016).</li><li>Klem, G. H., Luders, H. O., Jasper, H. H., Elger, C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+ten-twenty+electrode+system+of+the+International+Federation.+The+International+Federation+of+Clinical+Neurophysiology.">The ten-twenty electrode system of the International Federation. The International Federation of Clinical Neurophysiology.</a> Electroencephalogr Clin Neurophysiol Suppl. 52, 3-6 (1999).</li><li>Mullinger, K. J., Castellone, P., Bowtell, R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Best+current+practice+for+obtaining+high+quality+EEG+data+during+simultaneous+FMRI.">Best current practice for obtaining high quality EEG data during simultaneous FMRI.</a> J Vis Exp. (76), (2013).</li><li>Ekman, P., Friesen, W. V. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> Pictures of Facial Affect. , (1976).</li><li>Dale, A. M., Fischl, B., Sereno, M. I. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Cortical+surface-based+analysis.+I.+Segmentation+and+surface+reconstruction.">Cortical surface-based analysis. I. Segmentation and surface reconstruction.</a> Neuroimage. 9 (2), 179-194 (1999).</li><li>Fischl, B., Sereno, M. I., Dale, A. M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Cortical+surface-based+analysis.+II:+Inflation,+flattening,+and+a+surface-based+coordinate+system.">Cortical surface-based analysis. II: Inflation, flattening, and a surface-based coordinate system.</a> Neuroimage. 9 (2), 195-207 (1999).</li><li>Gramfort, A., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=MNE+software+for+processing+MEG+and+EEG+data.">MNE software for processing MEG and EEG data.</a> Neuroimage. 86, 446-460 (2014).</li><li>Hagler, D. J., Saygin, A. P., Sereno, M. I. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Smoothing+and+cluster+thresholding+for+cortical+surface-based+group+analysis+of+fMRI+data.">Smoothing and cluster thresholding for cortical surface-based group analysis of fMRI data.</a> Neuroimage. 33 (4), 1093-1103 (2006).</li><li>Jenkinson, M., Beckmann, C. F., Behrens, T. E., Woolrich, M. W., Smith, S. M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Fsl.">Fsl.</a> Neuroimage. 62 (2), 782-790 (2012).</li><li>Cox, R. W. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=AFNI:+software+for+analysis+and+visualization+of+functional+magnetic+resonance+neuroimages.">AFNI: software for analysis and visualization of functional magnetic resonance neuroimages.</a> Comput Biomed Res. 29 (3), 162-173 (1996).</li><li>Genovese, C. R., Lazar, N. A., Nichols, T. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Thresholding+of+statistical+maps+in+functional+neuroimaging+using+the+false+discovery+rate.">Thresholding of statistical maps in functional neuroimaging using the false discovery rate.</a> Neuroimage. 15 (4), 870-878 (2002).</li><li>Klein, A., Tourville, J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=101+labeled+brain+images+and+a+consistent+human+cortical+labeling+protocol.">101 labeled brain images and a consistent human cortical labeling protocol.</a> Front Neurosci. 6, 171 (2012).</li><li>Fischl, B., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Automatically+parcellating+the+human+cerebral+cortex.">Automatically parcellating the human cerebral cortex.</a> Cereb Cortex. 14 (1), 11-22 (2004).</li><li>Delorme, A., Makeig, S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=EEGLAB:+an+open+source+toolbox+for+analysis+of+single-trial+EEG+dynamics+including+independent+component+analysis.">EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis.</a> J Neurosci Methods. 134 (1), 9-21 (2004).</li><li>Iannetti, G. D., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Simultaneous+recording+of+laser-evoked+brain+potentials+and+continuous,+high-field+functional+magnetic+resonance+imaging+in+humans.">Simultaneous recording of laser-evoked brain potentials and continuous, high-field functional magnetic resonance imaging in humans.</a> Neuroimage. 28 (3), 708-719 (2005).</li><li>Niazy, R. K., Beckmann, C. F., Iannetti, G. D., Brady, J. M., Smith, S. M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Removal+of+FMRI+environment+artifacts+from+EEG+data+using+optimal+basis+sets.">Removal of FMRI environment artifacts from EEG data using optimal basis sets.</a> Neuroimage. 28 (3), 720-737 (2005).</li></ol>

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

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

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

concurrent eeg and functional mri recording and integration analysis for dynamic cortical activity imaging

Electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) are two of the fundamental noninvasive methods for identifying brain activity. Multimodal methods have sought to combine the high temporal resolution of EEG with the spatial precision of fMRI, but the complexity of this approach is currently in need of improvement. The protocol presented here describes the recently developed spatiotemporal fMRI-constrained EEG source imaging method, which seeks to rectify source biases and improve EEG-fMRI source localization through the dynamic recruitment of fMRI sub-regions. The process begins with the collection of multimodal data from concurrent EEG and fMRI scans, the generation of 3D cortical models, and independent EEG and fMRI processing. The processed fMRI activation maps are then split into multiple priors, according to their location and surrounding area. These are taken as priors in a two-level hierarchical Bayesian algorithm for EEG source localization. For each window of interest (defined by the operator), specific segments of the fMRI activation map will be identified as active to optimize a parameter known as model evidence. These will be used as soft constraints on the identified cortical activity, increasing the specificity of the multimodal imaging method by reducing cross-talk and avoiding erroneous activity in other conditionally active fMRI regions. The method generates cortical maps of activity and time-courses, which may be taken as final results, or used as a basis for further analyses (analyses of correlation, causation, etc.) While the method is somewhat limited by its modalities (it will not find EEG-invisible sources), it is broadly compatible with most major processing software, and is suitable for most neuroimaging studies.

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

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

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

An EEG-fMRI multimodal imaging method, known as the spatiotemporal fMRI-constrained EEG source imaging method, is described here. The presented method employs conditionally-active fMRI sub-maps, or priors, to guide EEG source localization in a manner that improves spatial specificity and limits erroneous results.

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

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

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Research

JoVE Journal

Biology

Application of Light-cured Dental Adhesive Resin for Mounting Electrodes or Microdialysis Probes in Chronic Experiments

In chronic recording experiments, self-curing dental acrylic resins have been used as a mounting base of electrodes or microdialysis-probes. Since these acrylics do not bond to the bone, screws have been used as anchors. However, in small experimental animals like finches or mouse, their craniums are very fragile and can not successfully hold the anchors. In this report, we propose a new application of light-curing dental resins for mounting base of electrodes or microdialysis probes in chronic experiments. This material allows direct bonding to the cranium. Therefore, anchor screws are not required and surgical field can be reduced considerably. Past experiences show that the bonding effect maintains more than 2 months. Conventional resin's window of time when the materials are pliable and workable is a few minutes. However, the window of working time for these dental adhesives is significantly wider and adjustable.

In this report, we propose a new application of light-curing dental resins for mounting base of electrodes or microdialysis probes in chronic experiments. This material allows direct bonding to the cranium.

In chronic recording experiments, self-curing dental acrylic resins have been used as a mounting base of electrodes or microdialysis-probes. Since these ...

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 ...

Mutagenesis and Functional Analysis of Ion Channels Heterologously Expressed in Mammalian Cells

We will demonstrate how to study the functional effects of introducing a point mutation in an ion channel. We study G protein-gated inwardly rectifying potassium (referred to as GIRK) channels, which are important for regulating the excitability of neurons. There are four different mammalian GIRK channel subunits (GIRK1-GIRK4) - we focus on GIRK2 because it forms a homotetramer. Stimulation of different types of G protein-coupled receptors (GPCRs), such as the muscarinic receptor (M2R), leads to activation of GIRK channels. Alcohol also directly activates GIRK channels. We will show how to mutate one amino acid by specifically changing one or more nucleotides in the cDNA for the GIRK channel. This mutated cDNA sequence will be amplified in bacteria, purified, and the presence of the point mutation will be confirmed by DNA sequencing. The cDNAs for the mutated and wild-type GIRK channels will be transfected into human embryonic kidney HEK293T cells cultured in vitro. Lastly, whole-cell patch-clamp electrophysiology will be used to study the macroscopic potassium currents through the ectopically expressed wild-type or mutated GIRK channels. In this experiment, we will examine the effect of a L257W mutation in GIRK2 channels on M2R-dependent and alcohol-dependent activation.

We will demonstrate how to study the effect of a single point mutation on the function of an ion channel.

We will demonstrate how to study the functional effects of introducing a point mutation in an ion channel. We study G protein-gated inwardly rectifying ...

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 ...