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>

<ol>
	<li>Biswal, B. B., Yetkin, F. Z., Haughton, V. M., Hyde, J. S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Functional+connectivity+in+the+motor+cortex+of+resting+human+brain+using+echo-planar+MRI.">Functional connectivity in the motor cortex of resting human brain using echo-planar MRI.</a> Magn. Reson. Med. 34, 537-541 (1995).</li><li>Fox, M. D., Greicius, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Clinical+applications+of+resting+state+functional+connectivity.">Clinical applications of resting state functional connectivity.</a> Front. Syst. Neurosci. 4, 1-13 (2010).</li><li>Laufs, H., 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=Electroencephalographic+signatures+of+attentional+and+cognitive+default+modes+in+spontaneous+brain+activity+fluctuations+at+rest.">Electroencephalographic signatures of attentional and cognitive default modes in spontaneous brain activity fluctuations at rest.</a> Proc. Natl. Acad. Sci. U.S.A. 100, 11053-11058 (1073).</li><li>Shmuel, A., Leopold, D. A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Neuronal+correlates+of+spontaneous+fluctuations+in+fMRI+signals+in+monkey+visual+cortex:+Implications+for+functional+connectivity+at+rest.">Neuronal correlates of spontaneous fluctuations in fMRI signals in monkey visual cortex: Implications for functional connectivity at rest.</a> Hum. Brain Mapp. 29, 751-761 (2008).</li><li>Margulies, D. S., 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=Resting+developments:+a+review+of+fMRI+post-processing+methodologies+for+spontaneous+brain+activity.">Resting developments: a review of fMRI post-processing methodologies for spontaneous brain activity.</a> Magn. Mater. Phys. Biol. 23, 289-307 (2010).</li><li>Biswal, B. 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=Toward+discovery+science+of+human+brain+function.">Toward discovery science of human brain function.</a> Proc. Natl. Acad. Sci. U.S.A. 107, 4734-4739 (2010).</li><li>Haneef, Z., Lenartowicz, A., Yeh, H. J., Engel, J., Stern, J. M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Effect+of+lateralized+temporal+lobe+epilepsy+on+the+default+mode+network.">Effect of lateralized temporal lobe epilepsy on the default mode network.</a> Epilepsy Behav. 25, 350-357 (2012).</li><li>Pillai, J. J., Williams, H. T., Faro, S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Functional+imaging+in+temporal+lobe+epilepsy.">Functional imaging in temporal lobe epilepsy.</a> Semin. Ultrasound. CT MR. 28, 437-450 (2007).</li><li>Blumenfeld, H., 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=Positive+and+negative+network+correlations+in+temporal+lobe+epilepsy.">Positive and negative network correlations in temporal lobe epilepsy.</a> Cereb. Cortex. 14, 892-902 (2004).</li><li>Dupont, P., 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=Dynamic+perfusion+patterns+in+temporal+lobe+epilepsy.">Dynamic perfusion patterns in temporal lobe epilepsy.</a> Eur. J. Nucl. Med. Imaging. 36, 823-830 (2009).</li><li>Fransson, P., Marrelec, G. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+precuneus/posterior+cingulate+cortex+plays+a+pivotal+role+in+the+default+mode+network:+Evidence+from+a+partial+correlation+network+analysis.">The precuneus/posterior cingulate cortex plays a pivotal role in the default mode network: Evidence from a partial correlation network analysis.</a> Neuroimage. 42, 1178-1184 (2008).</li><li>Buckner, R. L., Andrews-Hanna, J. R., Schacter, D. L. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+brain's+default+network.">The brain's default network.</a> Ann. N. Y. Acad. Sci. 1124, 1-38 (2008).</li><li>Woolrich, M. W., Ripley, B. D., Brady, 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=Temporal+autocorrelation+in+univariate+linear+modeling+of+FMRI+data.">Temporal autocorrelation in univariate linear modeling of FMRI data.</a> Neuroimage. 14, 1370-1386 (2001).</li><li>Forman, S. 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=Improved+assessment+of+significant+activation+in+functional+magnetic+resonance+imaging+(fMRI):+use+of+a+cluster+size+threshold.">Improved assessment of significant activation in functional magnetic resonance imaging (fMRI): use of a cluster size threshold.</a> Magn. Reson. Med. 33, 636-647 (1995).</li><li>Jenkinson, M., Bannister, P., Brady, M., Smith, S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Improved+optimization+for+the+robust+and+accurate+linear+registration+and+motion+correction+of+brain+images.">Improved optimization for the robust and accurate linear registration and motion correction of brain images.</a> Neuroimage. 17, 825-841 (2002).</li><li>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=Fast+robust+automated+brain+extraction.">Fast robust automated brain extraction.</a> Hum. Brain Mapp. 17, 143-155 (2002).</li><li>Raichle, M. E., 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=A+default+mode+of+brain+function.">A default mode of brain function.</a> Proc. Natl. Acad. Sci. U.S.A. 98, 676-682 (2001).</li><li>Uddin, L. Q., Kelly, A. M., Biswal, B. B., Castellanos, F. X., Milham, M. P. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Functional+connectivity+of+default+mode+network+components:+correlation.">Functional connectivity of default mode network components: correlation.</a> Hum. Brain Mapp. 30, 625-637 (2009).</li><li>Singh, K. D., Fawcett, I. P. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Transient+and+linearly+graded+deactivation+of+the+human+default-mode+network+by+a+visual+detection+task.">Transient and linearly graded deactivation of the human default-mode network by a visual detection task.</a> Neuroimage. 41, 100-112 (2008).</li><li>Worsley, K. J., Evans, A., Marrett, S., Neelin, P. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+three-dimensional+statistical+analysis+for+CBF+activation+studies+in+human+brain.">A three-dimensional statistical analysis for CBF activation studies in human brain.</a> J. Cereb. Blood Flow Metab. 12, 900-918 (1992).</li><li>Spencer, S. S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Neural+networks+in+human+epilepsy:+evidence+of+and+implications+for+treatment.">Neural networks in human epilepsy: evidence of and implications for treatment.</a> Epilepsia. 43, 219-227 (2002).</li><li>Greicius, M. D., Srivastava, G., Reiss, A. L., Menon, V. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Default-mode+network+activity+distinguishes+Alzheimer's+disease+from+healthy+aging:+evidence+from+functional+MRI.">Default-mode network activity distinguishes Alzheimer's disease from healthy aging: evidence from functional MRI.</a> Proc. Natl. Acad. Sci. U.S.A. 101, 4637-4642 (2004).</li><li>Kennedy, D. P., Redcay, E., Courchesne, E. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Failing+to+deactivate:+resting+functional+abnormalities+in+autism.">Failing to deactivate: resting functional abnormalities in autism.</a> Proc. Natl. Acad. Sci. U.S.A. 103, 8275-8280 (2006).</li><li>Garrity, A. G., 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=Aberrant+&amp;#34;default+mode&amp;#34;+functional+connectivity+in+schizophrenia.">Aberrant &amp;#34;default mode&amp;#34; functional connectivity in schizophrenia.</a> Am. J. Psychiatry. 164, 450-457 (2007).</li><li>Mannell, M. V., 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=Resting+state+and+task-induced+deactivation:+A+methodological+comparison+in+patients+with+schizophrenia+and+healthy+controls.">Resting state and task-induced deactivation: A methodological comparison in patients with schizophrenia and healthy controls.</a> Hum. Brain Mapp. 31, 424-437 (2010).</li><li>Jones, 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=Age-related+changes+in+the+default+mode+network+are+more+advanced+in+Alzheimer+disease.">Age-related changes in the default mode network are more advanced in Alzheimer disease.</a> Neurology. 77, 1524-1531 (2011).</li><li>Kobayashi, Y., Amaral, D. G. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Macaque+monkey+retrosplenial+cortex+II.+Cortical+afferents.">Macaque monkey retrosplenial cortex II. Cortical afferents.</a> J. Comp. Neurol. 466, 48-79 (2003).</li><li>Dupont, P., 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=Dynamic+perfusion+patterns+in+temporal+lobe+epilepsy.">Dynamic perfusion patterns in temporal lobe epilepsy.</a> Eur. J. Nuclear Med. Mol. Imaging. 36, 823-830 (2009).</li><li>Laufs, H., 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=Temporal+lobe+interictal+epileptic+discharges+affect+cerebral+activity+in+&#8220;default+mode&#8221;+brain+regions.">Temporal lobe interictal epileptic discharges affect cerebral activity in &#8220;default mode&#8221; brain regions.</a> Hum. Brain Mapp. 28, 1023-1032 (2007).</li><li>Morgan, V. L., Gore, J. C., Abou-Khalil, B. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Functional+epileptic+network+in+left+mesial+temporal+lobe+epilepsy+detected+using+resting+fMRI.">Functional epileptic network in left mesial temporal lobe epilepsy detected using resting fMRI.</a> Epilepsy Res. 88, 168-178 (2010).</li><li>Gotman, J., 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=Generalized+epileptic+discharges+show+thalamocortical+activation+and+suspension+of+the+default+state+of+the+brain.">Generalized epileptic discharges show thalamocortical activation and suspension of the default state of the brain.</a> Proc. Natl. Acad. Sci. U.S.A. 102, 15236-15240 (2005).</li><li>Hamandi, K., 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-fMRI+of+idiopathic+and+secondarily+generalized+epilepsies.">EEG-fMRI of idiopathic and secondarily generalized epilepsies.</a> Neuroimage. 31, 1700-1710 (2006).</li><li>Pittau, F., Grova, C., Moeller, F., Dubeau, F., Gotman, J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Patterns+of+altered+functional+connectivity+in+mesial+temporal+lobe+epilepsy.">Patterns of altered functional connectivity in mesial temporal lobe epilepsy.</a> Epilepsia. 53, 1013-1023 (2012).</li><li>Liao, W., 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=Default+mode+network+abnormalities+in+mesial+temporal+lobe+epilepsy:+a+study+combining+fMRI+and+DTI.">Default mode network abnormalities in mesial temporal lobe epilepsy: a study combining fMRI and DTI.</a> Hum. Brain Mapp. 32, 883-895 (2011).</li><li>Pereira, F. R., 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=Asymmetrical+hippocampal+connectivity+in+mesial+temporal+lobe+epilepsy:+evidence+from+resting+state+fMRI.">Asymmetrical hippocampal connectivity in mesial temporal lobe epilepsy: evidence from resting state fMRI.</a> BMC Neurosci. 11, 1-13 (2010).</li><li>Dupont, S., 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=Bilateral+hemispheric+alteration+of+memory+processes+in+right+medial+temporal+lobe+epilepsy.">Bilateral hemispheric alteration of memory processes in right medial temporal lobe epilepsy.</a> J. Neurol. Neurosurg. Psychiatr. 73, 478-485 (2002).</li><li>Vlooswijk, M. C., 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=Functional+MRI+in+chronic+epilepsy:+associations+with+cognitive+impairment.">Functional MRI in chronic epilepsy: associations with cognitive impairment.</a> Lancet Neurol. 9, 1018-1027 (2010).</li><li>McCormick, C., Quraan, M., Cohn, M., Valiante, T. A., McAndrews, M. P. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Default+mode+network+connectivity+indicates+episodic+memory+capacity+in+mesial+temporal+lobe+epilepsy.">Default mode network connectivity indicates episodic memory capacity in mesial temporal lobe epilepsy.</a> Epilepsia. 54, (2013).</li><li>Zhang, Z., 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=Altered+spontaneous+neuronal+activity+of+the+default-mode+network+in+mesial+temporal+lobe+epilepsy.">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/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Decoupling+of+the+brain's+default+mode+network+during+deep+sleep.">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/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Altered+local+coherence+in+the+default+mode+network+due+to+sevoflurane+anesthesia.">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

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&#160;1G) and righ...

Watch this Scientific Journal Video about Network Analysis of the Default Mode Network Using Functional Connectivity MRI in Temporal Lobe Epilepsy at JoVE.com

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

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 ...

network-analysis-default-mode-network-using-functional-connectivity

Research

JoVE Journal

Medicine

17.8K Views.  Baylor College of Medicine. 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).

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

Functional Mapping with Simultaneous MEG and EEG

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

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

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

Neuroscience

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

The default mode network is a group of brain regions that are active when an individual is not focused on the outside world and the brain is at "wakeful rest."1,2,3 It is thought the default mode network corresponds to self-referential or "internal mentation".2,3
It has been hypothesized that, in humans, activity within the default mode network is correlated with certain pathologies (for instance, hyper-activation has been linked to schizophrenia 4,5,6 and autism spectrum disorders 7 whilst hypo-activation of the network has been linked to Alzheimer's and other neurodegenerative diseases 8). As such, noninvasive modulation of this network may represent a potential therapeutic intervention for a number of neurological and psychiatric pathologies linked to abnormal network activation. One possible tool to effect this modulation is Transcranial Magnetic Stimulation: a non-invasive neurostimulatory and neuromodulatory technique that can transiently or lastingly modulate cortical excitability (either increasing or decreasing it) via the application of localized magnetic field pulses.9
In order to explore the default mode network's propensity towards and tolerance of modulation, we will be combining TMS (to the left inferior parietal lobe) with functional magnetic resonance imaging (fMRI). Through this article, we will examine the protocol and considerations necessary to successfully combine these two neuroscientific tools.

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.

The default mode network is a group of brain regions that are active when an individual is not focused on the outside world and the brain is at "wakeful ...

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

The study of complex computational systems is facilitated by network maps, such as circuit diagrams. Such mapping is particularly informative when studying the brain, as the functional role that a brain area fulfills may be largely defined by its connections to other brain areas. In this report, we describe a novel, non-invasive approach for relating brain structure and function using magnetic resonance imaging (MRI). This approach, a combination of structural imaging of long-range fiber connections and functional imaging data, is illustrated in two distinct cognitive domains, visual attention and face perception. Structural imaging is performed with diffusion-weighted imaging (DWI) and fiber tractography, which track the diffusion of water molecules along white-matter fiber tracts in the brain (Figure 1). By visualizing these fiber tracts, we are able to investigate the long-range connective architecture of the brain. The results compare favorably with one of the most widely-used techniques in DWI, diffusion tensor imaging (DTI). DTI is unable to resolve complex configurations of fiber tracts, limiting its utility for constructing detailed, anatomically-informed models of brain function. In contrast, our analyses reproduce known neuroanatomy with precision and accuracy. This advantage is partly due to data acquisition procedures: while many DTI protocols measure diffusion in a small number of directions (e.g., 6 or 12), we employ a diffusion spectrum imaging (DSI)1, 2 protocol which assesses diffusion in 257 directions and at a range of magnetic gradient strengths. Moreover, DSI data allow us to use more sophisticated methods for reconstructing acquired data. In two experiments (visual attention and face perception), tractography reveals that co-active areas of the human brain are anatomically connected, supporting extant hypotheses that they form functional networks. DWI allows us to create a "circuit diagram" and reproduce it on an individual-subject basis, for the purpose of monitoring task-relevant brain activity in networks of interest.

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.

The study of complex computational systems is facilitated by network maps, such as circuit diagrams. Such mapping is particularly informative when ...

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

Complementary structural and functional neuroimaging techniques used to examine the Default Mode Network (DMN) could potentially improve assessments of psychiatric illness severity and provide added validity to the clinical diagnostic process. Recent neuroimaging research suggests that DMN processes may be disrupted in a number of stress-related psychiatric illnesses, such as posttraumatic stress disorder (PTSD).
Although specific DMN functions remain under investigation, it is generally thought to be involved in introspection and self-processing. In healthy individuals it exhibits greatest activity during periods of rest, with less activity, observed as deactivation, during cognitive tasks, e.g., working memory. This network consists of the medial prefrontal cortex, posterior cingulate cortex/precuneus, lateral parietal cortices and medial temporal regions.
Multiple functional and structural imaging approaches have been developed to study the DMN. These have unprecedented potential to further the understanding of the function and dysfunction of this network. Functional approaches, such as the evaluation of resting state connectivity and task-induced deactivation, have excellent potential to identify targeted neurocognitive and neuroaffective (functional) diagnostic markers and may indicate illness severity and prognosis with increased accuracy or specificity. Structural approaches, such as evaluation of morphometry and connectivity, may provide unique markers of etiology and long-term outcomes. Combined, functional and structural methods provide strong multimodal, complementary and synergistic approaches to develop valid DMN-based imaging phenotypes in stress-related psychiatric conditions. This protocol aims to integrate these methods to investigate DMN structure and function in PTSD, relating findings to illness severity and relevant clinical factors.

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.

Complementary structural and functional neuroimaging techniques used to examine the Default Mode Network (DMN) could potentially improve assessments of ...

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

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

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

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

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

Approximately 30% of epilepsy patients are refractory to antiepileptic drugs. In these cases, surgery is the only alternative to eliminate/control seizures. However, a significant minority of patients continues to exhibit post-operative seizures, even in those cases in which the suspected source of seizures has been correctly localized and resected. The protocol presented here combines a clinical procedure routinely employed during the pre-operative evaluation of temporal lobe epilepsy (TLE) patients with a novel technique for network analysis. The method allows for the evaluation of the temporal evolution of mesial network parameters. The bilateral insertion of foramen ovale electrodes (FOE) into the ambient cistern simultaneously records electrocortical activity at several mesial areas in the temporal lobe. Furthermore, network methodology applied to the recorded time series tracks the temporal evolution of the mesial networks both interictally and during the seizures. In this way, the presented protocol offers a unique way to visualize and quantify measures that considers the relationships between several mesial areas instead of a single area.

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.

Approximately 30% of epilepsy patients are refractory to antiepileptic drugs. In these cases, surgery is the only alternative to eliminate/control ...

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

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

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

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

Bioengineering

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms

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

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

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

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.

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 ...

Biology

Integrating MEG and HD-EEG in Pediatric Epilepsy Localization

Electromagnetic Source Imaging in Presurgical Evaluation of Children with Drug-Resistant Epilepsy

For children with drug-resistant epilepsy (DRE), seizure freedom relies on the delineation and resection (or ablation/disconnection) of the epileptogenic zone (EZ) while preserving the eloquent brain areas. The development of a reliable and noninvasive localization method that provides clinically useful information for the localization of the EZ is, therefore, crucial to achieving successful surgical outcomes. Electric and magnetic source imaging (ESI and MSI) have been increasingly utilized in the presurgical evaluation of these patients showing promising findings in the delineation of epileptogenic as well as eloquent brain areas. Moreover, the combination of ESI and MSI into a single solution, namely electromagnetic source imaging (EMSI), performed on simultaneous high-density electroencephalography (HD-EEG) and magnetoencephalography (MEG) recordings has shown higher source localization accuracy than either modality alone. Despite these encouraging findings, such techniques are performed in only a few tertiary epilepsy centers, are rarely recorded simultaneously, and are underutilized in pediatric cohorts. This study illustrates the experimental setup for recording simultaneous MEG and HD-EEG data as well as the methodological framework for analyzing these data aiming to localize the irritative zone, the seizure onset zone, and eloquent brain areas in children with DRE. More specifically, the experimental setups are presented for (i) recording and localizing interictal and ictal epileptiform activity during sleep and (ii) recording visual-, motor-, auditory-, and somatosensory-evoked responses and mapping relevant eloquent brain areas (i.e., visual, motor, auditory, and somatosensory) during visuomotor task, as well as auditory and somatosensory stimulations. Detailed steps of the data analysis pipeline are further presented for performing EMSI as well as individual ESI and MSI using equivalent current dipole (ECD) and dynamic statistical parametric mapping (dSPM).

Author Spotlight: Advancing Pediatric Epilepsy Surgery in Children Through Novel Biomarkers and Enhanced Localization

Magnetoencephalography (MEG) and high-density electroencephalography (HD-EEG) are rarely recorded simultaneously, although they yield confirmatory and complementary information. Here, we illustrate the experimental setup for recording simultaneous MEG and HD-EEG and the methodology for analyzing these data aiming to localize epileptogenic and eloquent brain areas in children with drug-resistant epilepsy.

For children with drug-resistant epilepsy (DRE), seizure freedom relies on the delineation and resection (or ablation/disconnection) of the epileptogenic ...