Name: Network Analysis of the Default Mode Network Using Functional Connectivity MRI in Temporal Lobe Epilepsy (Video) | JoVE Journal
Uploaded: 2014-08-05T14:00:00.000+00:00
Duration: 12 min 9 s
Description: Learn seed-based functional connectivity MRI to analyze the Default Mode Network in Temporal Lobe Epilepsy patients versus healthy controls. This method reveals altered brain connectivity patterns using resting-state fMRI data.

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5 minutes

Funding for this research was provided by&#160;The Epilepsy Foundation of America, Baylor College of Medicine Computational and Integrative Biomedical Research Center (CIBR) Seed Grant Awards (ZH);&#160;NIH-NINDS K23 Grant NS044936 (JMS); and&#160;The Leff Family Foundation (JMS).&#160;Data acquisition was assisted by: Elizabeth Pierce (UCLA).

1. Subjects
<ol>
	<li>The study population of 36 subjects includes 3 groups: right TLE (n=11), left TLE (n=12), and healthy controls (n=13). Obtain written informed consent from all subjects. The study follows the guidelines of the University of California, Los Angeles (UCLA) Institutional Review Board.</li>
	<li>The epilepsy subject groups should be patients who are candidates for anterior temporal lobe resection as determined by video-EEG monitoring, brain MRI, PET imaging, and neuropsychological testing. Patients should continue their usual medications during the fMRI scan and should not be scanned immediately following a seizure. Make sure that all subjects have normal brain MRIs and are free from neurologic illness (other than epilepsy in the patient groups) or are using neurologic medications.</li>
</ol>
2. Imaging
<ol>
	<li>Use a 3 Tesla MRI system for imaging. Obtain axial slices for functional images using an echo planar imaging (EPI) sequence and for anatomic images using a spoiled gradient recalled (SPGR) sequence.</li>
	<li>Perform functional imaging using the following parameters: TR = 2,000 msec, TE = 30 msec, FOV = 210 mm, matrix = 64 x&#160;64, slice thickness 4 mm, 34 slices. Use the following parameters for high-resolution structural imaging: TR = 20 msec, TE = 3 msec, FOV = 256 mm, matrix = 256 x&#160;256, slice thickness 1 mm, 160 slices.</li>
	<li>Each imaging session should last 20 min. Ask participants to relax with eyes closed. No special auditory input is required.</li>
</ol>
3. Preprocessing of BOLD Data
<ol>
	<li>Preprocess the fMRI data using FSL (fMRIB Software Library) software version 4.1.6 (Oxford, UK, www.fmrib.ox.ac.uk/fsl)13,14. Preprocessing steps should include the following: Use FSL MCFLIRT to remove head movement artifact15. Use FSL BET to remove nonbrain tissue16 with BET option -F for BOLD files. This helps one run further analysis steps on the brain tissue alone.</li>
	<li>In FEAT, run a minimally processed analysis with registration. Select &#8220;First-level analysis&#8221; and change &#8220;Full analysis&#8221; to &#8220;Pre-stats&#8221; from the top two buttons.
	<ol>
		<li>Under Pre-stats tab, uncheck &#8220;BET brain extraction&#8221; and select &#8220;None&#8221; for &#8220;Motion correction&#8221; (as these were already done above). Register the functional (BOLD) images to the anatomical (SPGR) images, and then to a standard (MNI) image. This results in the generation of transformation matrices, which are used later during analysis to warp the seeds selected in standard space into the subject&#8217;s brain space.</li>
	</ol>
	</li>
	<li>Use the generated transformation matrix (named &#8220;standard2example_func.mat&#8221;) and transform CSF and white matter ROIs into individual BOLD space.
	<ol>
		<li>Extract the time series from the CSF and white matter ROIs using the fslmeants command, using the ROI in individual subject space as a mask. Normalize the extracted time series using the software &#8220;R&#8221;. These time series are used as regressors in the GLM later to remove the corresponding artifactual signals from the analysis.</li>
	</ol>
	</li>
	<li>The next step is removal of subject motion related artifacts. For regression of the motion parameters, set the following within FSL FEAT before running it.
	<ol>
		<li>Within the data tab, use motion-corrected and brain-extracted file as inputs, set the TR value to correspond to your dataset. Set high-pass filtering using a 100 sec filter. The high pass filtering will remove signals of no interest, which are of very low frequency. A low pass filter to remove high frequency signals will be applied later in step 4.1.</li>
		<li>Within the Pre-stats tab, choose &#34;None&#34; under &#34;Motion correction&#34; as it was already done. Uncheck &#34;BET brain extraction&#34; as it was already done. Perform spatial smoothing using a 5 mm full-width half-maximum (FWHM).</li>
		<li>Within the stats tab, regress the 6 motion parameters and their temporal derivatives. Select &#8220;None&#8221; for convolution and check &#8220;Apply temporal filtering.&#8221; Use the output of FSL MCFLIRT to get text files of movement parameters, which can be then input into the FEAT analysis model to regress these in a General Linear Model (GLM)</li>
		<li>Also add the CSF and white matter signals that were extracted and normalized in previous steps to the GLM. Select &#8220;None&#8221; for convolution, add temporal derivative, and uncheck &#8220;Apply temporal filtering&#8221;.</li>
	</ol>
	</li>
</ol>
4. Statistical Methods
<ol>
	<li>The residuals from preprocessing described above should be used for seed-based correlation. These residuals should be first passed through a low-pass filter of 0.1 Hz, then demeaned by subtracting the mean, dividing by the standard deviation, and then scaled by adding 100. Seeds should be defined with a diameter of 6 mm in the standard MNI space using MRICron software.</li>
	<li>The posterior and the anterior seeds should correspond to the following coordinates: (1) Rsp/ PCUN region (x=2, y=-60, z=36) and (2) ventromedial prefrontal cortex (vmPFC; x=3, y=60, z=-1). These seed locations have been defined within healthy controls and are transformed to the subject space in the next step17-19.
	<ol>
		<li>The seeds should subsequently be transformed to each subject&#39;s individual functional brain space from the standard MNI space. For this, use the transformation matrix generated above (named &#8220;standard2example_func.mat&#8221;) to transform the seed from standard (MNI) space to the individual functional (BOLD) space.</li>
		<li>Use the fslmeants command to extract the time series from the previously demeaned and scaled residual, using the seed in the individual subject space as a mask. Normalize the extracted time series using the software &#8220;R&#8221;.</li>
	</ol>
	</li>
	<li>Partial correlations between the seed voxels and all other brain voxels should be calculated separately for each subject for each run. For this, within the FSL FEAT GUI, select &#8220;First-level analysis&#8221;, and then &#8220;Stats + Post-stats&#8221;. Within the Data tab, the previously demeaned and scaled residual should be used as input into FEAT.</li>
	<li>Set the High pass filter cutoff to 10,000, as the residual is already high passed at 100 sec. Within the Stats tab, unselect &#34;Use FILM prewhitening&#34;, and use the previously extracted and normalized seed time series in the GLM. Within the Post-stats tab, set the desired Z-stat threshold to a value of 2.0.</li>
	<li>Prior to running group analysis combining runs within subjects, a Fisher&#39;s Z transform should be performed on the COPE&#160;(Contrast of Parameter Estimates)&#160;file generated from the previously run correlation analysis (step 4.3). Copy registration data from the &#8220;reg&#8221; directory of the FEAT analysis done in step 3.1 into the correlation run of step 4.3.&#160;</li>
	<li>Run a higher-level analysis by combining runs within each subject. For this, within the FSL FEAT GUI, select &#8220;Higher-level analysis&#8221;, and then &#8220;Stats + Post-stats&#8221;. Within the &#8220;Data&#8221; tab, choose &#34;Inputs are lower-level FEAT directories&#34; and enter subject&#39;s runs from step 4.4. Within the &#8220;Stats&#8221; tab, choose &#8220;Mixed effects: Simple OLS&#8221;. Set up model as mean effect; enter value of 1 for each of the subject&#39;s run.</li>
	<li>To combine data over runs between subjects, an Ordinary Least Squares (OLS) simple mixed effects analysis should be used. For this, within the FSL FEAT GUI, choose&#8221;Higher-level analysis&#8221;, and &#8220;Stats + Post-stats&#8221;. Within the Data tab, choose &#34;Inputs are lower-level FEAT directories&#34; and enter subjects&#39; combined runs from step 4.5.</li>
	<li>Within the Stats tab, choose &#8220;Mixed effects: Simple OLS&#8221;&#39; Set up a&#160;model as 3 groups; enter value of 1 for the group each subject belongs, 0 otherwise. Group analysis should be done on each voxel using a one-way ANOVA with three levels which corresponded to the three groups (right TLE, left TLE, and healthy controls).</li>
	<li>To threshold the Z statistic images use cluster forming threshold of Z &#62; 2.0 and corrected cluster significant threshold of p=0.05&#160;20. To obtain correct z-values on the correlation map, a reverse Fisher&#39;s Z transform should be performed on the results.</li>
	<li>The following specific contrasts should be compared (1) right TLE &#62; controls; (2) left TLE &#62; controls; (3) right TLE &#62; left TLE; (4) left TLE &#62; right TLE; (5) control &#62; right TLE; (6) control &#62; left TLE; (7) TLE (combined right and left) &#62; control; and (7) control &#62; TLE (combined right and left).</li>
</ol>

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<li>Zhang, Z., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Altered+spontaneous+neuronal+activity+of+the+default-mode+network+in+mesial+temporal+lobe+epilepsy%5BTitle%5D)+AND+%22Brain+Res%22%5BJournal%5D)">Altered spontaneous neuronal activity of the default-mode network in mesial temporal lobe epilepsy.</a> Brain Res. 1323, 152-160 (2010).</li>
<li>Horovitz, S. G., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Decoupling+of+the+brain%27s+default+mode+network+during+deep+sleep%5BTitle%5D)+AND+%22Proc.+Natl.+Acad.+Sci.+U.S.A%22%5BJournal%5D)">Decoupling of the brain's default mode network during deep sleep.</a> Proc. Natl. Acad. Sci. U.S.A. 106, 11376-11381 (2009).</li>
<li>Deshpande, G., Kerssens, C., Sebel, P. S., Hu, X. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Altered+local+coherence+in+the+default+mode+network+due+to+sevoflurane+anesthesia%5BTitle%5D)+AND+%22Brain+Res%22%5BJournal%5D)">Altered local coherence in the default mode network due to sevoflurane anesthesia.</a> Brain Res. 1318, 110-121 (2010).</li>
</ol>

Dr. Engel is funded by NIH Grants P01 NS02808, R01 NS33310, and U01 NS42372, has patents WO 2009/123734A1, and WO 2009/123735A1, receives royalties from MedLink, Wolters Kluwer, Blackwell, and Elsevier, and has received honoraria from Medtronics, Wolters Kluwer, and Best Doctors. Dr. Stern has served as a paid consultant for UCB and Lundbeck. Dr. Stern is an editor of MedLink Neurology, and has received royalties from Wolters Kluwer and from McGraw-Hill. The remaining authors have no disclosures or&#160;conflicts of interest to declare.

Epilepsy is thought to be a network disease, and abnormalities of the involved networks are present during seizures and in the interictal state21. Task-based fMRI has been used to analyze abnormalities of the language and memory networks in TLE8. FcMRI has inherent advantages in studying the DMN12 as it is a network mainly active in the resting state. The DMN is a network of brain regions that has been found to be active in awake individuals who are left undisturbed and are engaged in spo...

Functional Connectivity MRI (fcMRI) is a relatively recent analytic approach to fMRI data that quantifies the relationship between different brain regions based on the similarity of their blood oxygenation level dependent (BOLD) signal time series &#8211; this is called &#8220;functional&#8221; connectivity, and is distinguishable from anatomical connectivity that describes the existence of physical connections between regions (e.g.,&#160;white matter fibers). In a special application of this approach, the time series are collected when the participant is not engaged in a task&#160;or is in the so-called &#8220;resting state&#8221;.
Although first described in 19951, there has been immense interest in fcMRI resulting in approximately 1,000 publications related to the technique in 2012. fcMRI has intrinsic benefits over task-based fMRI in (1) that there is no specific task to be performed, (2) subject cooperation is not necessary, (3) datasets can be used to query several different networks, (4) better signal to noise ratio is present likely due to differences in cerebral energetics involved, and (5) circumvention of task-related confounds2. As a proof of its concept, fcMRI changes have been shown to correspond with changes in EEG3 and local field potentials4 in the brain.
Techniques of fcMRI analysis include ROI/ seed-based techniques, independent component analysis (ICA), graph theory analysis, Granger causality analysis, local methods (amplitude of low frequency fluctuations, regional homogeneity analysis), and others5. No single technique has yet demonstrated clear superiority over another, although&#160;the most popular methods are seed-based and ICA methods6. Seed-based fcMRI correlates temporal fluctuations in BOLD signal from a preselected part of the putative network under study termed the &#8220;seed&#8221; or &#8220;region of interest (ROI)&#8221; to all other parts of the brain. Areas of the brain showing BOLD signal correlating to the seed area are thought to demarcate parts of the involved network. In contrast, ICA uses a model-free data-driven analysis to extract spatio-temporally correlated brain areas (Independent Components, ICs) by analyzing the hemodynamic signal characteristics of the whole brain5.
In the current manuscript, a description of methods used in a previously published study of resting state seed-based connectivity analysis of the DMN in TLE is presented7. TLE is the most common form of adult epilepsy. In addition to seizures, TLE causes dysfunction of multiple brain networks including memory, behavior, thought, and sensory function8. The DMN is constituted by cerebral regions subserving conscious, resting-state cognition. The DMN has been reported to be involved in seizures associated with reduced consciousness9,10. Additionally, the hippocampus is the key structure involved in TLE and has been thought to be component of the DMN. However, the connectivity of the PCC to the hippocampal formation is weaker than with other DMN components, such as medial prefrontal and inferior parietal cortices. This suggests that the hippocampus is either a subnetwork of the DMN&#160;or an interacting network11,12. These commonalities between TLE and DMN raise the possibility that DMN functional connectivity is altered in TLE. This analysis compares the DMN of subjects with TLE to healthy controls to gain insight into the involvement of DMN in TLE. The connectivity of seeds placed in the chief hubs of the DMN - the anterior and posterior hub regions were analyzed12. Seeds were placed in the posterior hub consisting of the retrosplenium/precuneus (Rsp/PCUN) as well as the anterior hub consisting of the ventromedial prefrontal cortex (vmPFC) in patients having TLE and in healthy controls to identify the posterior and anterior subnetworks of the DMN.

<table><tbody><tr><td>MRI machine</td><td></td><td></td><td></td></tr><tr><td>Linux workstation with image analysis software installed</td><td></td><td></td><td></td></tr></tbody></table>

network analysis of the default mode network using functional connectivity mri in temporal lobe epilepsy

Network Analysis of the Default Mode Network Using Functional Connectivity MRI in Temporal Lobe Epilepsy

Functional connectivity MRI (fcMRI) is an fMRI method that examines the connectivity of different brain areas based on the correlation of BOLD signal fluctuations over time. Temporal Lobe Epilepsy (TLE) is the most common type of adult epilepsy and involves multiple brain networks. The default mode network (DMN) is involved in conscious, resting state cognition and is thought to be affected in TLE where seizures cause impairment of consciousness. The DMN in epilepsy was examined using seed based fcMRI. The anterior and posterior hubs of the DMN were used as seeds in this analysis. The results show a disconnection between the anterior and posterior hubs of the DMN in TLE during the basal state. In addition, increased DMN connectivity to other brain regions in left TLE along with decreased connectivity in right TLE is revealed. The analysis demonstrates how seed-based fcMRI can be used to probe cerebral networks in brain disorders such as TLE.

Figure 1 shows the DMN revealed with connectivity from a posterior seed (Rsp/ PCUN, red-yellow colors) and an anterior seed (vmPFC, blue-green colors) and compares the networks found in the different subject groups (Figures 1A-C) and between each other, namely healthy controls compared to all patients with TLE (Figures 1D and 1E), and then healthy controls compared separately to left TLE (Figures 1F and 1G) and right TLE...

The Default Mode Network (DMN) in Temporal Lobe Epilepsy (TLE) is analyzed in the resting state of the brain using seed-based functional connectivity MRI (fcMRI).

Functional connectivity MRI (fcMRI) is an fMRI method that examines the connectivity of different brain areas based on the correlation of BOLD signal ...

The overall goal of the following experiment is to obtain statistical maps. Comparing the difference of resting state FMRI scans between healthy controls and patients with epilepsy. This is achieved by obtaining FMRI data in subjects with temporal lobe epilepsy, as well as healthy control subjects.As a second step, the FMRI is pre-processed, which prepares the data to be statistically analyzed. Next, the pre-processed FMRI data is analyzed in order to obtain statistically valid comparisons between the two groups. Results are obtained that show differences in brain networks among patients with epilepsy compared to brain networks of healthy control subjects.Based on statistical differences in brain network maps between these two groups, It's become increasingly apparent that even epilepsies that are manifest by focal seizures are emergent properties of diffuse network disturbances. The idea for this technique came after recognizing the importance of the DEF Favo network during seizures and wondering whether the default LO network is abnormal between seizures in temporal lobe epilepsy. The study population for this protocol should include three groups, right?Temporal lobe epilepsy patients, left temporal lobe epilepsy patients, as well as healthy controls. A total of about 35 subjects are recommended. The epilepsy subject groups should be patients who are diagnosed to have temporal lobe epilepsy, such as candidates for anterior temporal lobe resection as determined by video, EEG monitoring, PET imaging and neuropsychological testing assure that all subjects have normal brain MRIs and are free from neurologic illness other than epilepsy in the patient groups.Obtain IRB approval and written informed consent from all subjects prior to imaging and screen for MRI.Safety. Patients should continue their usual medications during the FMRI scan and should not be scanned immediately following a seizure. A three Tesla MRI system should be used for all imaging described in this protocol.Obtain axial slices for functional images using an echo planar imaging sequence and for anatomic images, use a spoiled gradient recalled sequence. Ask participants to relax and remain still with their eyes closed, and perform functional imaging using the parameters as seen here. Also use the following parameters for the S PGR T one weighted high resolution structural imaging.Each imaging session should last about 20 minutes. Begin by pre-processing the FMRI data using FSL software First. Use FSL MFL to remove head movement artifact.Then use the FSL brain extraction tool or BET to remove non brainin tissue with the option dash F for bold files. This allows for further analysis steps on the brain tissue alone. Next in feet, run a minimally processed analysis with registration.Select first level analysis and change full analysis to pre stats from the top two buttons. Then under pres stats tab, uncheck bet brain extraction, and select none for motion correction as these were already performed. Then register the functional images to the anatomical images, and then to a standard MNI image.This results in the generation of transformation matrices, which are used later during analysis to warp the seed selected in standard space into the subject's brain space. Next, use the generated transformation matrix named standard to example funk dot mat and transform CSF and white matter ROIs into the individual bold space. Then using the FSL mean TS command, extract the time series from the CSF and white matter ROIs.Using the ROI in individual subject space as a mask normalize the extracted time series using the software R.These time series will later be used as regressors in the general linear model to remove the corresponding artifactual signals from the analysis. The next step is removal of subject motion related artifacts. For regression of the motion parameters.Set the following within FSL feet before running it first within the data tab, use the motion corrected and brain extracted file as inputs and set the TR value to correspond to your dataset. Set high pass filtering using a 102nd filter, which will remove very low frequency signals of no interest. A lowpass filter to remove high frequency signals will be applied later within the pres stats tab.Choose none under motion correction and uncheck, but brain extraction. As these steps have been performed, perform spatial smoothing using a five millimeter full width half maximum. Then within the stats tab, regress the six motion parameters and their temporal derivatives.Select none for convolution and check apply temporal filtering. Use the output of F selmic flirt to get text files of movement parameters, which can then be input into the feet analysis to regress these in a general linear model. Also, add the CSF and white matter signals that were extracted and normalized in previous steps to the GLM.Select none for convolution. Add temporal derivative and uncheck apply temporal filtering. The residuals from pre-processing described above should be used for seed based correlation.These residuals should be first passed through a low PESS filter of 0.1 hertz, then demeaned by subtracting the mean, dividing by the standard deviation and then scaled by adding 100 seed should be defined with a diameter of six millimeters. In the standard MNI space. Using MRI crown software, the posterior and the anterior seeds should correspond to the coordinates as seen here.Note that these seed locations have been defined within healthy controls. The seeds should subsequently be transformed to each subject's individual functional brain space from the standard MNI space. For this, use the transformation matrix previously generated to transform the seed from standard m and i space to the individual functional space.Next, use the FSL mean Ts command to extract the time series from the previously demeaned and scaled residual. Using the seed in the individual subject space as a mask. Normalize the extracted time series using the software R partial correlations between the seed voxels and all of their brain voxels should be calculated separately for each subject for each run.For this, within the FSL feet interface, select first level analysis and then stats plus post stats within the data tab. The previously demeaned and scaled residual should be used as input. Set the high pass filter cutoff to 10, 000 as the residual is already high, passed at 100 seconds within the stats tab.Unselect use film pre whitening and use the previously extracted and normalized seed time series. In the GLM within the post stats tab, set the desired Z stat threshold to a value of 2.0 prior to running group analysis. Combining runs within subjects, a Fisher Z transformation should be performed on the contrast of parameter estimates.File generated from the correlation analysis, copy the registration data from the reg directory of the feet analysis into the correlation run. Run a higher level analysis by combining runs within each subject. First, select higher level analysis and then stats plus post stats.Then within the data tab, choose inputs are lower level feet directories and enter the subject's runs within the stats tab. Choose mixed effects. Simple OLS set up a model as the mean effect and enter a value of one for each of the subjects runs.To combine data overrun between subjects an ordinary least square simple mixed effects analysis should be used for this, choose higher level analysis and stats plus post stats within the data tab. Choose inputs are lower level feet directories and enter the subject's combined Runs within the stats tab, choose mixed effects. Simple OLS set up a model as three groups enter a value of one for the group.Each subject belongs to zero. Otherwise, group analysis should be done on each voxel using a one-way innova with three levels which correspond to the three groups to threshold. The Z statistic images use a cluster forming threshold of Z greater than 2.0 and a corrected cluster significant threshold of P equals 0.05 to obtain correct Z values on the correlation map.A reverse fisher Z transform should be performed on the results. Finally, use the following specific contrasts as seen on the screen here. This figure shows the default mode network revealed with connectivity from a posterior seed, including the retro splenium and precuneus in red yellow colors and in anterior seed, including the vent medial prefrontal cortex in blue green colors.The first row reveals the network for control subjects, the second row for left temporal lobe epilepsy, and the bottom row for right temporal lobe epilepsy. The following figures compare these networks between these three groups. Here we see the default mode networks revealed with an anterior and a posterior seed for combined right and left temporal lobe epilepsy compared to healthy controls.This figure shows the default mode networks revealed with the same seed points for left temporal lobe epilepsy only compared to healthy controls. While this figure shows the networks revealed for right temporal lobe epilepsy only compared to healthy controls, and finally here we see the default mode networks revealed with an anterior and a posterior seed for left temporal lobe epilepsy compared to right temporal lobe epilepsy. Studies of functional connectivity that include the entire brain are essential for understanding the fundamental mechanisms of epilepsy.We used a seed based technique in this experiment to assess connectivity to the default mode network. It'll be interesting to see how other techniques compare in their results when studying temporal lobe epilepsy.

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JoVE Journal

Medicine

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

Functional Mapping with Simultaneous MEG and EEG

In this article, we examine the methodology and considerations relevant to the combination of TMS and fMRI to examine the effects of brain stimulation on the default network.

Combining Transcranial Magnetic Stimulation and fMRI to Examine the Default Mode Network

We describe a novel approach for simultaneous analysis of brain function and structure using magnetic resonance imaging (MRI). We assess brain structure with high-resolution diffusion-weighted imaging and white-matter fiber tractography. Unlike standard structural MRI, these techniques allow us to directly relate anatomical connectivity to functional properties of brain networks.

Co-analysis of Brain Structure and Function using fMRI and Diffusion-weighted Imaging

Source: Shafi, M. M., et al. A Multimodal Imaging- and Stimulation-based Method of Evaluating Connectivity-related Brain Excitability in Patients with Epilepsy. J. Vis. Exp. (2016)The video demonstrates the procedure for using transcranial magnetic stimulation (TMS) and electroencephalography (EEG) to assess neuronal excitability in patients with epilepsy. It details the steps for aligning the patient’s head with a pre-recorded brain scan, positioning the TMS coil over a functionally connected...

Assessing Brain Hyperexcitability with Transcranial Magnetic Stimulation and Electroencephalography

This protocol describes the complementary neuroimaging techniques of resting state structural connectivity, task-induced deactivation, and structural connectivity analyses to examine the default network in post-traumatic stress disorder. The use of synergistic methods could potentially lead to improved diagnostics and assessments of severity, outcome, and other relevant clinical factors.

Developing Neuroimaging Phenotypes of the Default Mode Network in PTSD: Integrating the Resting State, Working Memory, and Structural Connectivity

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

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

This protocol describes a procedure to track the evolution of mesial network measures in temporal lobe epilepsy (TLE) patients. It is based on the combination of intracranial recordings with a novel numerical technique for data analysis. Specifically, we present a protocol for network analyses of foramen ovale recordings.

Network Analysis of Foramen Ovale Electrode Recordings in Drug-resistant Temporal Lobe Epilepsy Patients

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

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

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

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms

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

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

A novel protocol for personalized transcranial magnetic stimulation in combination with electroencephalography (TMS–EEG) mapping of cortical networks, informed by multiple magnetic resonance imaging (MRI) data modalities.

Neuroimaging-Guided TMS&#8211;EEG for Real-Time Cortical Network Mapping

Learn seed-based functional connectivity MRI to analyze the Default Mode Network in Temporal Lobe Epilepsy patients versus healthy controls. This method reveals altered brain connectivity patterns using resting-state fMRI data.

Network Analysis of the Default Mode Network Using Functional Connectivity MRI in Temporal Lobe Epilepsy

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