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.

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			<td>MRI machine</td>
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			<td>Linux workstation with image analysis software installed</td>
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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 ...

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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

Generation of Subcutaneous and Intrahepatic Human Hepatocellular Carcinoma Xenografts in Immunodeficient Mice

In vivo experimental models of hepatocellular carcinoma (HCC) that recapitulate the human disease provide a valuable platform for research into disease pathophysiology and for the preclinical evaluation of novel therapies. We present a variety of methods to generate subcutaneous or orthotopic human HCC xenografts in immunodeficient mice that could be utilized in a variety of research applications. With a focus on the use of primary tumor tissue from patients undergoing surgical resection as a starting point, we describe the preparation of cell suspensions or tumor fragments for xenografting. We describe specific techniques to xenograft these tissues i) subcutaneously; or ii) intrahepatically, either by direct implantation of tumor cells or fragments into the liver, or indirectly by injection of cells into the mouse spleen. We also describe the use of partial resection of the native mouse liver at the time of xenografting as a strategy to induce a state of active liver regeneration in the recipient mouse that may facilitate the intrahepatic engraftment of primary human tumor cells. The expected results of these techniques are illustrated. The protocols described have been validated using primary human HCC samples and xenografts, which typically perform less robustly than the well-established human HCC cell lines that are widely used and frequently cited in the literature. In comparison with cell lines, we discuss factors which may contribute to the relatively low chance of primary HCC engraftment in xenotransplantation models and comment on technical issues that may influence the kinetics of xenograft growth. We also suggest methods that should be applied to ensure that xenografts obtained accurately resemble parent HCC tissues.

Human tumor xenografts in immunodeficient mice are valuable tools to study cancer biology. Specific protocols to generate subcutaneous and intrahepatic xenografts from human hepatocellular carcinoma cells or tumor fragments are described. Liver regeneration induced by partial hepatectomy in recipient mice is presented as a strategy to facilitate intrahepatic engraftment.

In  vivo experimental models of hepatocellular carcinoma (HCC) that recapitulate  the human disease provide a valuable platform for research into disease  ...

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 ...

In Vivo and Ex Vivo Approaches to Study Ovarian Cancer Metastatic Colonization of Milky Spot Structures in Peritoneal Adipose

High-grade serous ovarian cancer (HGSC), the cause of widespread peritoneal metastases, continues to have an extremely poor prognosis; fewer than 30% of women are alive 5 years after diagnosis. The omentum is a preferred site of HGSC metastasis formation. Despite the clinical importance of this microenvironment, the contribution of omental adipose tissue to ovarian cancer progression remains understudied. Omental adipose is unusual in that it contains structures known as milky spots, which are comprised of B, T, and NK cells, macrophages, and progenitor cells surrounding dense nests of vasculature. Milky spots play a key role in the physiologic functions of the omentum, which are required for peritoneal homeostasis. We have shown that milky spots also promote ovarian cancer metastatic colonization of peritoneal adipose, a key step in the development of peritoneal metastases. Here we describe the approaches we developed to evaluate and quantify milky spots in peritoneal adipose and study their functional contribution to ovarian cancer cell metastatic colonization of omental tissues both in vivo and ex vivo. These approaches are generalizable to additional mouse models and cell lines, thus enabling the study of ovarian cancer metastasis formation from initial localization of cells to milky spot structures to the development of widespread peritoneal metastases.

In Vivo and Ex Vivo Approaches to Study Ovarian Cancer Metastatic Colonization of Milky Spot Structures in Peritoneal Adipose

We outline a protocol that implements both in vivo and ex vivo approaches to study ovarian cancer colonization of peritoneal adipose tissues, particularly the omentum. Furthermore, we present a protocol to quantitate and analyze immune cell-structures in the omentum known as milky spots, which promote metastases of peritoneal adipose.

High-grade serous ovarian cancer (HGSC), the cause of widespread peritoneal metastases, continues to have an extremely poor prognosis; fewer than 30% of ...

MRI-guided dmPFC-rTMS as a Treatment for Treatment-resistant Major Depressive Disorder

Here we outline the protocol for magnetic resonance imaging (MRI) guided repetitive transcranial magnetic stimulation (rTMS) to the dorsal medial prefrontal cortex (dmPFC) in patients with major depressive disorder (MDD). Technicians used a neuronavigation system to process patient MRIs to generate a 3-dimensional head model. The head model was subsequently used to identify patient-specific stimulatory targets. The dmPFC was stimulated daily for 20 sessions. Stimulation intensity was titrated to address scalp pain associated with rTMS. Weekly assessments were conducted on the patients using the Hamilton Rating Scale for Depression (HamD17) and Beck Depression Index II (BDI-II). Treatment-resistant MDD patients achieved significant improvements on both HAMD and BDI-II. Of note, angled, double-cone coil rTMS at 120% resting motor threshold allows for optimal stimulation of deeper midline prefrontal regions, which results in a possible therapeutic application for MDD. One major limitation of the rTMS field is the heterogeneity of treatment parameters across studies, including duty cycle, number of pulses per session and intensity. Further work should be done to clarify the effect of stimulation parameters on outcome. Future dmPFC-rTMS work should include sham-controlled studies to confirm its clinical efficacy in MDD.

Here we outline the procedure for MRI-guided repetitive transcranial magnetic stimulation to the dorsomedial prefrontal cortex as an experimental treatment for major depressive disorder.

Here we outline the protocol for magnetic resonance imaging (MRI) guided repetitive transcranial magnetic stimulation (rTMS) to the dorsal medial ...

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 ...

Solubility of Hydrophobic Compounds in Aqueous Solution Using Combinations of Self-assembling Peptide and Amino Acid

Self-assembling peptides (SAPs) are promising vehicles for the delivery of hydrophobic therapeutics for clinical applications; their amphipathic properties allow them to dissolve hydrophobic compounds in the aqueous environment of the human body. However, self-assembling peptide solutions have poor blood compatibility (e.g., low osmolarity), hindering their clinical application through intravenous administrations. We have recently developed a generalized platform for hydrophobic drug delivery, which combines SAPs with amino acid solutions (SAP-AA) to enhance drug solubility and increase formulation osmolarity to reach the requirements for clinical uses. This formulation strategy was thoroughly tested in the context of three structurally different hydrophobic compounds &#8211; PP2, rottlerin, and curcumin &#8211; in order to demonstrate its versatility. Furthermore, we examined effects of changing formulation components by analyzing 6 different SAPs, 20 naturally existing amino acids at low and high concentrations, and two different co-solvents dimethyl sulfoxide (DMSO) and ethanol. Our strategy proved to be effective in optimizing components for a given hydrophobic drug, and therapeutic function of the formulated inhibitor, PP2, was observed both in vitro and in vivo. This manuscript outlines our generalized formulation method using SAP-AA combinations for hydrophobic compounds, and analysis of solubility as a first step towards potential use of these formulations in more functional studies. We include representative solubility results for formulation of the hydrophobic compound, curcumin, and discuss how our methodology serves as a platform for future biological studies and disease models.

This protocol describes a clinically-applicable means of dissolving hydrophobic compounds in an aqueous environment using combinations of self-assembling peptide and amino acid solutions. Our method resolves a major limitation of hydrophobic therapeutics, which lack safe, efficient means of solubility and delivery methods into clinical settings.

Self-assembling peptides (SAPs) are promising vehicles for the delivery of hydrophobic therapeutics for clinical applications; their amphipathic ...

The Pilocarpine Model of Temporal Lobe Epilepsy and EEG Monitoring Using Radiotelemetry System in Mice

Temporal lobe epilepsy (TLE) is a common neurological disorder in adulthood. For translational studies of chronic epilepsy, pilocarpine-induced status epilepticus (SE) is frequently selected to recapitulate spontaneous recurrent seizures (SRS). Here we present a protocol of SE induction by intraperitoneal (i.p.) injection of pilocarpine and monitoring of chronic recurring seizures in live animals using a wireless telemetry video and electroencephalogram (EEG) system. We demonstrated notable behavioral changes that need attention after pilocarpine injection and their correlation with hippocampal neuronal loss at 7 days and 6 weeks post-pilocarpine. We also describe the experimental procedures of electrode implantation for video and EEG recording, and analysis of the frequency and duration of chronic recurrent seizures. Finally, we discuss the possible reasons why the expected results are not achieved in each case. This provides a basic overview of modeling chronic epilepsy in mice and guidelines for troubleshooting. We believe this protocol can serve as a baseline for suitable models of chronic epilepsy and epileptogenesis.

This manuscript describes a method of inducing status epilepticus by systemic pilocarpine injection and of monitoring spontaneous recurrent seizures in live animals using a wireless telemetry video and electroencephalogram system. This protocol can be utilized for studying the pathophysiologic mechanisms of chronic epilepsy, epileptogenesis, and acute seizures.

Temporal lobe epilepsy (TLE) is a common neurological disorder in adulthood. For translational studies of chronic epilepsy, pilocarpine-induced status ...

Whole-brain Segmentation and Change-point Analysis of Anatomical Brain MRI&#8212;Application in Premanifest Huntington's Disease

Recent advances in MRI offer a variety of useful markers to identify neurodegenerative diseases. In Huntington&#39;s disease (HD), regional brain atrophy begins many years prior to the motor onset (during the &#34;premanifest&#34; period), but the spatiotemporal pattern of regional atrophy across the brain has not been fully characterized. Here we demonstrate an online cloud-computing platform, &#34;MRICloud&#34;, which provides atlas-based whole-brain segmentation of T1-weighted images at multiple granularity levels, and thereby, enables us to access the regional features of brain anatomy. We then describe a regression model that detects statistically significant inflection points, at which regional brain atrophy starts to be noticeable, i.e. the &#34;change-point&#34;, with respect to a disease progression index. We used the CAG-age product (CAP) score to index the disease progression in HD patients. Change-point analysis of the volumetric measurements from the segmentation pipeline, therefore, provides important information of the order and pattern of structural atrophy across the brain. The paper illustrates the use of these techniques on T1-weighted MRI data of premanifest HD subjects from a large multicenter PREDICT-HD study. This design potentially has wide applications in a range of neurodegenerative diseases to investigate the dynamic changes of brain anatomy.

This paper describes a statistical model for volumetric MRI data analysis, which identifies the &#34;change-point&#34; when brain atrophy begins in premanifest Huntington&#39;s disease. Whole-brain mapping of the change-points is achieved based on brain volumes obtained using an atlas-based segmentation pipeline of T1-weighted images.

Recent advances in MRI offer a variety of useful markers to identify neurodegenerative diseases. In Huntington&#39;s disease (HD), regional brain atrophy ...

Harvest of Endothelial Cells from the Balloon Tips of Swan-Ganz Catheters after Right Heart Catheterization

A variety of pathologies lead to pulmonary hypertension (PH), which is defined as a mean pulmonary artery pressure exceeding 25 mmHg at rest. To further diagnose and manage PH, patients undergo repeated right heart catheterizations (RHC) wherein a Swan-Ganz catheter is advanced into a branch of the pulmonary artery and a balloon is inflated to wedge the catheter tip. This article illustrates a protocol whereby pulmonary artery endothelial cells (PAECs) may be harvested from the balloon tips of Swan-Ganz catheters after RHC, and purified with an anti- CD146 affinity column technique to purify putative PAECs. These cells might be used to provide an in situ snapshot of the biological state of the pulmonary vasculature endothelium to complement hemodynamic measurements obtained during RHC. Harvested and purified PAECs may be used for either cell culture or for subsequent analytical assays such as flow cytometery.

This protocol describes the removal and purification of pulmonary artery endothelial cells (PAECs) that adhere to the balloon tips of Swan-Ganz catheters while they are wedged in the pulmonary artery during right heart catheterization and remain adherent to the deflated balloon following removal from the patient's body.

A variety of pathologies lead to pulmonary hypertension (PH), which is defined as a mean pulmonary artery pressure exceeding 25 mmHg at rest. To further ...

Assessment of Static Graviceptive Perception in the Roll-Plane using the Subjective Visual Vertical Paradigm

Vestibular disorders are among the most common syndromes in medicine. In recent years, new vestibular diagnostic systems have been introduced that allow the examination of all semicircular canals in the clinical setting. Assessment methods of the otolithic system, which is responsible for the perception of linear acceleration and perception of gravity, are far less in clinical use. There are several experimental approaches for measuring the perception of gravity. The most frequently used method is the determination of the subjective visual vertical. This is usually measured with the head in an upright position. We present here an assessment method for testing otolith function in the roll plane. The subjective visual vertical is measured in the head upright position as well as with head inclination of &#177; 15&#176; and &#177; 30&#176; in the roll plane. This extended functional paradigm is an easy-to-perform clinical test of otolith function and ensures increased information content for the detection of impaired graviceptive perception.

The perception of gravity is commonly determined by the subjective visual vertical in the head upright position. The additional assessment at head tilts of &#177; 15&#176; and &#177; 30&#176; in the roll plane ensures increased information content for the detection of impaired graviceptive perception.

Vestibular disorders are among the most common syndromes in medicine. In recent years, new vestibular diagnostic systems have been introduced that allow ...