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

Dette arbejde blev delvist understøttet af NIH DK082644 og University of Houston.

Protokollen præsenteres her blev designet og udført i overensstemmelse med alle retningslinjer for etisk menneskelige forskning som sæt frem af de respektive institutionelle Review bestyrelserne for University of Houston og Houston Methodist Research Institute.
1. samtidig EEG/fMRI optagelse
<ol><li>Indhente informeret samtykke fra deltageren. Forklare deltager formålet og proceduren for undersøgelsen, samt de vigtige sikkerhedsforanstaltninger for de samtidige EEG/fMRI dataregistrering...

<ol>
	<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>

Vi har vist her de nødvendige skridt til at bruge metoden spatiotemporelle fMRI begrænset kilde analyse for EEG/fMRI integration analyse. EEG og fMRI har blevet veletableret som de grundlæggende metoder for ikke-invasivt imaging hjerneaktivitet, selvom de vanskeligheder i deres respektive rumlige og tidsmæssige opløsning. Mens metoder er blevet udviklet for at udnytte de gunstige egenskaber af hver, afhængige nuværende fMRI-begrænset EEG kilde lokalisering metoder ofte af enkle fMRI begrænsninger, som kan underg...

Electroencefalografi (EEG) og funktionel magnetisk resonans imaging (fMRI) kan ses som neuroimaging modaliteter med supplerende funktioner. FMRI opfanger hjerneaktivitet med store tidsmæssige omfang, som hæmodynamiske signaler indirekte måle den underliggende neuronal aktivitet med en dårlig tidsmæssige opløsning (om sekunder)1,2. Derimod måler EEG direkte den dynamiske elektrofysiologiske aktivitet af hjernen med en meget høj tidsmæssige opløsning (millisekund niveau), men dårlig rumlige opløsning3,4. Disse egenskaber har ført til multimodale tilgange designet til at optimere de gunstige aspekter af hver enkelt metode5. Samtidig brug af EEG og fMRI giver mulighed for den fremragende tidsmæssige opløsning af EEG skal kombineres med høj rumlig nøjagtighed af fMRI til at overvinde de begrænsninger i forbindelse med unimodale fMRI eller EEG.
Metoder til EEG og fMRI integration begynder med fMRI-orienteret EEG kilde lokalisering6,7. Denne teknik anvender fMRI-afledte geografisk information for at forbedre EEG kilde lokalisering, dog en ulempe er den potentielle geografiske skævhed forårsaget af anvendelse af fMRI som en "hård-begrænsningen" — fMRI-afledte geoinformation betragtes en absolut sandhed. Dette udgør to store problemer, der skal være afstemt6-8. Først, skal det overvejes, at brug af et statisk kort over blod ilt niveau afhængige (fed) kontraster uforvarende kan styrke eventuelle fejlagtige aktivitet, der falder inden for it, mens dæmpning ægte aktivitet uden for det. For det andet kan krydstale fra kilder forekommer uden for fed aktivering kort påvirke præsentationen af ægte aktivitet inden for resultaterne eller forårsage fejlagtige aktivitet. På trods af dette begrænset brugen af de høje metoders opløsningsevne fMRI til at give forudgående geografisk viden forbliver en gunstig løsning5, som modellering af EEG inverse problem kan være både i de anatomiske og funktionelle sanser.
I dette papir vise vi en spatiotemporelle fMRI-begrænset EEG source imaging tilgang, der omhandler spørgsmålet om tidsmæssige uoverensstemmelse mellem EEG og fMRI ved at beregne den optimale delmængde af fMRI priors baseret på en hierarkisk Bayesian model9. FMRI-priors er beregnet på en data-drevet måde fra bestemte windows af interesse i EEG data, fører til tid-variant fMRI begrænsninger. Den foreslåede tilgang udnytter den høje tidsmæssige opløsning af EEG til at beregne en strømtæthed kortlægning af de kortikale aktivitet, underrettet af fMRI høj rumlige opløsning i en tid-variant, rumligt selektiv måde, der præcist billeder dynamisk neurale aktivitet.

<table border="0" cellpadding="0" cellspacing="0" style="width:869px;" width="868">
	<colgroup>
		<col />
		<col />
		<col />
		<col />
	</colgroup>
	<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

Electroencefalografi (EEG) og funktionel magnetisk resonans imaging (fMRI) er to af de grundlæggende noninvasive metoder til identifikation af hjerneaktivitet. Multimodal metoder har forsøgt at kombinere de høje tidsmæssige opløsning af EEG med fMRI rumlige præcision, men kompleksiteten af denne tilgang er i øjeblikket behov for forbedringer. Protokollen præsenteres her beskriver de nyligt udviklede spatiotemporelle fMRI-begrænset EEG source imaging metode, som har til forsøger at afhjælpe kilde bias og forbedre EEG-fMRI kilde lokalisering gennem dynamiske ansættelse af fMRI subregioner. Processen begynder med indsamling af multimodale data fra samtidige EEG og fMRI scanninger, generation af 3D kortikale modeller, og uafhængige EEG og fMRI behandling. Forarbejdede fMRI aktivering kortene er så delt i flere priors, afhængigt af deres placering og omegn. Disse er taget som priors i en to-niveau hierarkisk Bayesiansk algoritme for EEG kilde lokalisering. For hvert vindue af interesse (defineret af operatøren), vil specifikke segmenter af fMRI aktivering kort blive identificeret som aktivt at optimere en parameter, kendt som model beviser. Disse vil blive brugt som bløde begrænsninger på de identificerede kortikale aktivitet, stiger specificiteten af den multimodale imaging metode ved at reducere cross-talk og undgå fejlagtige aktivitet i andre betinget aktive fMRI regioner. Metoden genererer kortikale kort af aktivitet og tiden-kurser, som kan tages som endelige resultater, eller bruges som grundlag for yderligere analyser (analyser af korrelation og årsagssammenhæng m.m.) mens metoden er noget begrænset af sin modaliteter (det ikke vil finde EEG-invisible kilder), det er kompatible med de fleste store forarbejdning software, og er velegnet til de fleste neuroimaging undersøgelser.

EEG kilde lokalisering på det grundlæggende niveau indebærer løse problemets fremad og inverse. De komponenter, der kræves til at opbygge og løse frem er vist i figur 5C. Ved hjælp af en emnespecifik T1 billede, tre lag — hjernen, kraniet og huden — var segmenteret og fintmaskede. Disse lag tjente som input til at generere BEM model. Ligeledes var fagets grå-sagen lag segmenteret fra strukturelle Mr og bruges til at konstruere den...

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

Samtidige EEG og funktionel MRI optagelse og Integration analyse for dynamisk kortikale aktivitet Imaging

En EEG-fMRI multimodale imaging metode, kendt som den spatiotemporelle fMRI-begrænset EEG source imaging metode, der beskrives her. Metoden præsenteres beskæftiger betinget-aktive fMRI underkort eller priors, til at guide EEG kilde lokalisering på en måde, der forbedrer rumlige specificitet og begrænser fejlagtige resultater.

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.

Research

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Biology

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