Name: microstate and omega complexity analyses of the resting-state electroencephalography
Uploaded: 2018-06-15T12:30:00
Description: Watch this Scientific Journal Video about Microstate and Omega Complexity Analyses of the Resting-state Electroencephalography at JoVE.com

This article was supported by the National Natural Science Foundation of China (31671141).

This protocol was approved by the local ethical committee. All the participants and their parents signed an informed consent form for this experiment.
1. Subjects
<ol>
	<li>Only include 15 healthy male adolescent subjects, whose age ranges from 14 to 22 years (mean &#177; standard deviation: 18.3 &#177; 2.8 years). 
	NOTE: The current protocol to analyze the microstate and omega complexity has been developed for healthy subjects, but is not restricted to this group only.</li>
</ol>
<p c...

<ol>
	<li>Khanna, A., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Microstates+in+resting-state+EEG:+current+status+and+future+directions.">Microstates in resting-state EEG: current status and future directions.</a> Neurosci Biobehav Rev. 49, 105-113 (2015).</li><li>Chen, J. L., Ros, T., Gruzelier, J. H. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Dynamic+changes+of+ICA-derived+EEG+functional+connectivity+in+the+resting+state.">Dynamic changes of ICA-derived EEG functional connectivity in the resting state.</a> Hum Brain Mapp. 34 (4), 852-868 (2013).</li><li>Imperatori, 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=Aberrant+EEG+functional+connectivity+and+EEG+power+spectra+in+resting+state+post-traumatic+stress+disorder:+a+sLORETA+study.">Aberrant EEG functional connectivity and EEG power spectra in resting state post-traumatic stress disorder: a sLORETA study.</a> Biol Psychol. 102, 10-17 (2014).</li><li>Gao, F., 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+Resting-State+EEG+Microstate+Parameters+and+Enhanced+Spatial+Complexity+in+Male+Adolescent+Patients+with+Mild+Spastic+Diplegia.">Altered Resting-State EEG Microstate Parameters and Enhanced Spatial Complexity in Male Adolescent Patients with Mild Spastic Diplegia.</a> Brain Topogr. 30 (2), 233-244 (2017).</li><li>Seitzman, B. A., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Cognitive+manipulation+of+brain+electric+microstates.">Cognitive manipulation of brain electric microstates.</a> Neuroimage. 146, 533-543 (2017).</li><li>Lehmann, D., Michel, C. M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=EEG-defined+functional+microstates+as+basic+building+blocks+of+mental+processes.">EEG-defined functional microstates as basic building blocks of mental processes.</a> Clin Neurophysiol. 122 (6), 1073-1074 (2011).</li><li>Koenig, T., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Millisecond+by+millisecond,+year+by+year:+normative+EEG+microstates+and+developmental+stages.">Millisecond by millisecond, year by year: normative EEG microstates and developmental stages.</a> Neuroimage. 16 (1), 41-48 (2002).</li><li>Britz, J., Van De Ville, D., Michel, C. M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=BOLD+correlates+of+EEG+topography+reveal+rapid+resting-state+network+dynamics.">BOLD correlates of EEG topography reveal rapid resting-state network dynamics.</a> Neuroimage. 52 (4), 1162-1170 (2010).</li><li>Musso, F., 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=Spontaneous+brain+activity+and+EEG+microstates.+A+novel+EEG/fMRI+analysis+approach+to+explore+resting-state+networks.">Spontaneous brain activity and EEG microstates. A novel EEG/fMRI analysis approach to explore resting-state networks.</a> Neuroimage. 52 (4), 1149-1161 (2010).</li><li>Strelets, 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=Chronic+schizophrenics+with+positive+symptomatology+have+shortened+EEG+microstate+durations.">Chronic schizophrenics with positive symptomatology have shortened EEG microstate durations.</a> Clin Neurophysiol. 114 (11), 2043-2051 (2003).</li><li>Kikuchi, M., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=EEG+microstate+analysis+in+drug-naive+patients+with+panic+disorder.">EEG microstate analysis in drug-naive patients with panic disorder.</a> PLoS One. 6 (7), e22912 (2011).</li><li>Santarnecchi, 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=EEG+Microstate+Correlates+of+Fluid+Intelligence+and+Response+to+Cognitive+Training.">EEG Microstate Correlates of Fluid Intelligence and Response to Cognitive Training.</a> Brain Topogr. , (2017).</li><li>Schlegel, F., 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+microstates+during+resting+represent+personality+differences.">EEG microstates during resting represent personality differences.</a> Brain Topogr. 25 (1), 20-26 (2012).</li><li>Wackermann, J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Beyond+mapping:+estimating+complexity+of+multichannel+EEG+recordings.">Beyond mapping: estimating complexity of multichannel EEG recordings.</a> Acta Neurobiol Exp (Wars). 56 (1), 197-208 (1996).</li><li>Jung, T. 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=Removing+electroencephalographic+artifacts+by+blind+source+separation.">Removing electroencephalographic artifacts by blind source separation.</a> Psychophysiology. 37 (2), 163-178 (2000).</li><li>Pascual-Marqui, R. D., Michel, C. M., Lehmann, D. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Segmentation+of+brain+electrical+activity+into+microstates:+model+estimation+and+validation.">Segmentation of brain electrical activity into microstates: model estimation and validation.</a> IEEE Trans Biomed Eng. 42 (7), 658-665 (1995).</li><li>Pascual-Marqui, R. D. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Standardized+low-resolution+brain+electromagnetic+tomography+(sLORETA):+technical+details.">Standardized low-resolution brain electromagnetic tomography (sLORETA): technical details.</a> Methods Find Exp Clin Pharmacol. 24, 5-12 (2002).</li><li>Hu, L., 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=The+primary+somatosensory+cortex+contributes+to+the+latest+part+of+the+cortical+response+elicited+by+nociceptive+somatosensory+stimuli+in+humans.">The primary somatosensory cortex contributes to the latest part of the cortical response elicited by nociceptive somatosensory stimuli in humans.</a> Neuroimage. 84, 383-393 (2014).</li><li>Murray, M. M., Brunet, D., Michel, C. M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Topographic+ERP+analyses:+a+step-by-step+tutorial+review.">Topographic ERP analyses: a step-by-step tutorial review.</a> Brain Topogr. 20 (4), 249-264 (2008).</li><li>Van de Ville, D., Britz, J., Michel, C. M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=EEG+microstate+sequences+in+healthy+humans+at+rest+reveal+scale-free+dynamics.">EEG microstate sequences in healthy humans at rest reveal scale-free dynamics.</a> Proc Natl Acad Sci U S A. 107 (42), 18179-18184 (2010).</li><li>Gonuguntla, V., Mallipeddi, R., Veluvolu, K. C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Identification+of+emotion+associated+brain+functional+network+with+phase+locking+value.">Identification of emotion associated brain functional network with phase locking value.</a> Conf Proc IEEE Eng Med Biol Soc. 2016, 4515-4518 (2016).</li><li>Wen, 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=Resting-state+EEG+coupling+analysis+of+amnestic+mild+cognitive+impairment+with+type+2+diabetes+mellitus+by+using+permutation+conditional+mutual+information.">Resting-state EEG coupling analysis of amnestic mild cognitive impairment with type 2 diabetes mellitus by using permutation conditional mutual information.</a> Clin Neurophysiol. 127 (1), 335-348 (2016).</li><li>Rosales, F., 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=An+efficient+implementation+of+the+synchronization+likelihood+algorithm+for+functional+connectivity.">An efficient implementation of the synchronization likelihood algorithm for functional connectivity.</a> Neuroinformatics. 13 (2), 245-258 (2015).</li><li>Jia, H., Peng, W., Hu, L. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+novel+approach+to+identify+time-frequency+oscillatory+features+in+electrocortical+signals.">A novel approach to identify time-frequency oscillatory features in electrocortical signals.</a> J Neurosci Methods. 253, 18-27 (2015).</li><li>Wackermann, J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Global+characterization+of+brain+electrical+activity+by+means+of+the+&#937;+complexity+production+rate.">Global characterization of brain electrical activity by means of the &#937; complexity production rate.</a> Brain Topogr. 18, 135 (2005).</li><li>Wackermann, J., Putz, P., Ga&#223;ler, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Unfolding+EEG+spatial+complexity+as+a+function+of+frequency.">Unfolding EEG spatial complexity as a function of frequency.</a> Brain Topogr. 16 (2), 124 (2003).</li></ol>

In this article, two kinds of EEG analytic methods (i.e., microstate analysis and omega complexity analysis), measuring the temporal complexity and spatial complexity of human brain respectively, were described in detail. There are several critical steps within the protocol that should be mentioned. Firstly, the EEG data must be cleaned before the computation of the microstate and omega complexity. Secondly, the EEG data should be remontaged against the average reference before the computation of the microstate and omega...

Electroencephalography (EEG) has been widely used to record electrical activity of the human brain both in clinical diagnosis and scientific research, since it is noninvasive, low-costed and has very high temporal resolution1. In order to study the EEG signals in resting state, researchers have developed many EEG techniques (e.g., power spectrum analysis, functional connectivity analysis)2,3. Of these, microstate analysis and omega complexity analysis could make good use of the spatial and temporal information inherent in EEG signals4.
Previous researches have shown that although the topographical distribution of EEG signals varies over time in eye-closed or eye-open resting state, the series of momentary maps show discontinuous changes of landscapes, i.e., periods of stability alternating with short transition periods between certain quasi-stable EEG topographies5. Microstates are defined as these episodes with quasi-stable EEG topographies, which last between 80 and 120 ms1. Since different electric potential landscapes must have been generated by different neural sources, these microstates may qualify as the basic blocks of mentation and can be considered as &#34;atoms of thought and emotion&#34;6. Using modern pattern classification algorithms, four resting EEG microstate classes have been consistently observed, which were labeled as class A, class B, class C and class D7. Moreover, researchers revealed that these four microstate classes of resting EEG data were closely associated with well-known functional systems observed in many resting-state fMRI (functional magnetic resonance imaging) studies8,9.Thus, the microstate analysis provided a novel approach to study the resting state networks (RSNs) of human brain. In addition, the average duration and the frequency of occurrence of each microstate class, the topographical shape of the four microstate maps are significantly influenced by some brain disorders4,10,11, and are associated with fluid intelligence12 and personality13.
In the other aspect, traditional functional connectivity of multi-channel EEG could only describe the functional connections between two scalp electrodes, thus failed to assess the global functional connectivity across scalp or within a certain brain region. The omega complexity, proposed by Wackermann (1996)14 and calculated through an approach combining principal component analysis (PCA) and Shannon entropy, has been used to quantify the broad-band global synchronization between spatially distributed brain regions. In order to assess the omega complexity of each frequency band, Fourier transform was commonly conducted as an initial step25.
The microstates and omega complexity can be used to reflect two closely linked concepts, i.e., the temporal complexity and spatial complexity4. Since the microstate classes represent certain mental operations in human brain, they can reflect the temporal structure of neuronal oscillations. Lower duration and higher occurrence rate per second must indicate higher temporal complexity. The omega complexity is positively related with the number of independent neural sources in brain, thus are commonly considered as an indicator of spatial complexity4.
The current article describes the protocol of EEG microstate analysis and omega complexity analysis in detail. The EEG microstate and omega complexity analyses offer the opportunity to measure the temporal and spatial complexities of brain activity respectively.

<table>
	<tbody>
		<tr>
			<td>ANT 20 channels EEG/ERP system</td>
			<td>ASA-Lab, ANT B.V., Netherlands</td>
			<td></td>
			<td>company web address: 
			http://www.ant-neuro.com/</td>
		</tr>
		<tr>
			<td>EEGLAB plugin for Microstates</td>
			<td>Thomas Koenig</td>
			<td></td>
			<td>https://sccn.ucsd.edu/wiki/EEGLAB_Extensions_and_plug-ins</td>
		</tr>
		<tr>
			<td>sLORETA</td>
			<td>Roberto D. Pascual-Marqui</td>
			<td></td>
			<td>http://www.uzh.ch/keyinst/loreta.htm</td>
		</tr>
		<tr>
			<td>MATLAB 2010a</td>
			<td>The MathWorks Inc.</td>
			<td></td>
			<td>company web address: 
			http://www.mathworks.com/</td>
		</tr>
		<tr>
			<td>eeglab</td>
			<td>Swartz Center for Computational Neuroscience, University of California, San Diego</td>
			<td></td>
			<td>https://sccn.ucsd.edu/eeglab/index.php</td>
		</tr>
	</tbody>
</table>

microstate and omega complexity analyses of the resting-state electroencephalography

Microstate and omega complexity are two reference-free electroencephalography (EEG) measures that can represent the temporal and spatial complexities of EEG data and have been widely used to investigate the neural mechanisms in some brain disorders. The goal of this article is to describe the protocol underlying EEG microstate and omega complexity analyses step by step. The main advantage of these two measures is that they could eliminate the reference-dependent problem inherent to traditional spectrum analysis. In addition, microstate analysis makes good use of high time resolution of resting-state EEG, and the four obtained microstate classes could match the corresponding resting-state networks respectively. The omega complexity characterizes the spatial complexity of the whole brain or specific brain regions, which has obvious advantage compared with traditional complexity measures focusing on the signal complexity in a single channel. These two EEG measures could complement each other to investigate the brain complexity from the temporal and spatial domain respectively.

EEG microstate
Grand mean normalized microstate maps are displayed in Figure 1. The electric potential landscapes of these four microstate classes identified here are very similar to those found in previous studies4.
The mean and standard deviation (SD) of microstate parameters of the healthy subjects were shown in <st...

Watch this Scientific Journal Video about Microstate and Omega Complexity Analyses of the Resting-state Electroencephalography at JoVE.com

Microstate and Omega Complexity Analyses of the Resting-state Electroencephalography

This article describes the protocol underlying electroencephalography (EEG) microstate analysis and omega complexity analysis, which are two reference-free EEG measures and highly valuable to explore the neural mechanisms of brain disorders.

Microstate and omega complexity are two reference-free electroencephalography (EEG) measures that can represent the temporal and spatial complexities of ...

This semester have answered 10 questions in the field of brain disorder, such as how to identify an efficient pair of markers to a code of bios in today's diagnosis. The main advantage of this technique is that it could emanate as a reference of the pending problem when you're adhering to traditional EEG analysis. The applications of this technique extend toward our understanding of resting-states networks of human brain.To begin this procedure, import the raw EEG data to the EEG lab software. Next, load the channel location file into the EEG lab software to obtain the spatial locations of these electrodes. To remove the reference electrodes, an option of Select data in channel range of the popup dialog box, select only the recording electrodes, and do not select the reference electrodes so that the reference electrodes can be removed.To band pass filter the EEG data between 0.5 and 80 hertz, in the popup dialog box, choose 0.5 for the lower edge of the frequency pass band hertz and choose 80 for the higher edge of the frequency pass band hertz. Then click Ok.To remove the powerline noise with a notch filter between 49 and 51 hertz, in the popup dialog box, choose 49 for the lower edge of the frequency pass band hertz and choose 51 for the higher edge of the frequency pass band hertz. Then select the option of Notch filter the data instead of pass band, and click Ok.To remove eye movements, click on Tools, then click Artifact removal using AAR 1.3, and EOG removal using BSS.To remove EMG, click Tools, then click Artifact removal using AAR 1.3 and EMG removal using BSS. Next, segment the preprocessed continuous EEG data into epochs with an epoch length of two seconds. A window will pop up that allows the saving of the segmented EEG data.Then import the segmented EEG data to the EEG lab software and reject EEG epochs with amplitude values exceeding plus or minus 80 microvolts at any electrode. Next, save the preprocessed EEG data. In this procedure for each subject, load the preprocessed EEG data, convert reference channels to common average reference, and band pass filter the EEG data between two and 20 hertz.Next, identify the four microstate maps in each subject. In the popup dialog box, choose three for the min number of classes, choose six for the max number of classes, choose 50 for the number of restarts, choose max number of maps to use, and select the options of GFP peak only, and No polarity. Then, click the Ok button.Subsequently, save the EEG data of each subject after identifying its own microstate maps. Import the EEG data sets of all subjects saved in the last step at once. Then identify the group level microstate maps.In the popup dialog box, select the data sets of all subjects in the option, Choose sets for averaging. In the option, Name of mean, give a name for the group level microstate maps, then click the button Ok.This will create a new data set named as GrandMean which stores the group level microstate maps. Manually sort the order of four group level microstate maps according to their classic order.In the popup, select More, and then the number of maps shown becomes four. After that, select Man sort. In the popup dialog box, enter the new order of four group level microstate maps and click Close.Following that, sort the order of the four microstate maps of each subject, save the microstate parameters for each subject which will invoke two popup dialog boxes sequentially. In the first dialog box, select the data sets of all subjects. In the second dialog box, select 4-Classes for option, Number of classes.Select the options of Fitting only on GFP peaks and Remove potentially truncated microstates. Next, choose 30 for the label smoothing window, ms, and choose One for the the Non-Smoothness penalty, then click Ok.A CSV file which stores submicrostate parameters will be saved on the computer. These images show that microstate Class A and B have a right frontal to left occipital orientation, and a left frontal to right occipital orientation respectively.Microstate Class C and D have symmetric topographies but prefrontal to occipital orientation, and front to central to occipital orientation were observed respectively. This table shows the mean and standard deviation of microstate parameters of the healthy subjects. Once mastered, this technique can be done in one hour if it is performed properly.While attempting this procedure, it's important to remember that the EEG data should be preprocessed carefully. Following this procedure, other methods like source localization can be performed in order to answer additional questions such as where these microstate maps come from. This technique paved a way for researchers in the field of brain science to expose disease as in human brain.

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Research

JoVE Journal

Neuroscience

Morphometric Analyses of Retinal Sections

Morphometric analyses of retinal sections have been used in examining retinal diseases. For examples, neuronal cells were significantly lost in the retinal ganglion cell layer (RGCL) in rat models with N-methyl-D-aspartate (NMDA)&#x2013;induced excitotoxicity1, retinal ischemia-reperfusion injury2 and glaucoma3. Reduction of INL and inner plexiform layer (IPL) thicknesses were reversed with citicoline treatment in rats' eyes subjected to kainic acid-mediated glutamate excitotoxicity4. Alteration of RGC density and soma sizes were observed with different drug treatments in eyes with elevated intraocular pressure3,5,6. Therefore, having objective methods of analyzing the retinal morphometries may be of great significance in evaluating retinal pathologies and the effectiveness of therapeutic strategies.
The retinal structure is multi-layers and several different kinds of neurons exist in the retina. The morphometric parameters of retina such as cell number, cell size and thickness of different layers are more complex than the cell culture system. Early on, these parameters can be detected using other commercial imaging software. The values are normally of relative value, and changing to the precise value may need further accurate calculation. Also, the tracing of the cell size and morphology may not be accurate and sensitive enough for statistic analysis, especially in the chronic glaucoma model. The measurements used in this protocol provided a more precise and easy way. And the absolute length of the line and size of the cell can be reported directly and easy to be copied to other files. For example, we traced the margin of the inner and outer most nuclei in the INL and formed a line then using the software to draw a 90 degree angle to measure the thickness. While without the help of the software, the line maybe oblique and the changing of retinal thickness may not be repeatable among individual observers. In addition, the number and density of RGCs can also be quantified. This protocol successfully decreases the variability in quantitating features of the retina, increases the sensitivity in detecting minimal changes.
 	This video will demonstrate three types of morphometric analyses of the retinal sections. They include measuring the INL thickness, quantifying the number of RGCs and measuring the sizes of RGCs in absolute value. These three analyses are carried out with Stereo Investigator (MBF Bioscience &mdash; MicroBrightField, Inc.). The technique can offer a simple but scientific platform for morphometric analyses.

This video demonstrates three types of morphometric analyses of the retina, which include measuring the inner nuclear layer thickness, quantifying the number of retinal ganglion cells (RGCs) and measuring the sizes of RGCs. The technique can offer a simple but scientific platform for morphometric analyses.

Morphometric analyses of retinal sections have been used in examining retinal diseases. For examples, neuronal cells were significantly lost in the ...

Simultaneous Electroencephalography, Real-time Measurement of Lactate Concentration and Optogenetic Manipulation of Neuronal Activity in the Rodent Cerebral Cortex

Although the brain represents less than 5% of the body by mass, it utilizes approximately one quarter of the glucose used by the body at rest1. The function of non rapid eye movement sleep (NREMS), the largest portion of sleep by time, is uncertain. However, one salient feature of NREMS is a significant reduction in the rate of cerebral glucose utilization relative to wakefulness2-4. This and other findings have led to the widely held belief that sleep serves a function related to cerebral metabolism. Yet, the mechanisms underlying the reduction in cerebral glucose metabolism during NREMS remain to be elucidated.
	One phenomenon associated with NREMS that might impact cerebral metabolic rate is the occurrence of slow waves, oscillations at frequencies less than 4 Hz, in the electroencephalogram5,6. These slow waves detected at the level of the skull or cerebral cortical surface reflect the oscillations of underlying neurons between a depolarized/up state and a hyperpolarized/down state7. During the down state, cells do not undergo action potentials for intervals of up to several hundred milliseconds. Restoration of ionic concentration gradients subsequent to action potentials represents a significant metabolic load on the cell8; absence of action potentials during down states associated with NREMS may contribute to reduced metabolism relative to wake.
	Two technical challenges had to be addressed in order for this hypothetical relationship to be tested. First, it was necessary to measure cerebral glycolytic metabolism with a temporal resolution reflective of the dynamics of the cerebral EEG (that is, over seconds rather than minutes). To do so, we measured the concentration of lactate, the product of aerobic glycolysis, and therefore a readout of the rate of glucose metabolism in the brains of mice. Lactate was measured using a lactate oxidase based real time sensor embedded in the frontal cortex. The sensing mechanism consists of a platinum-iridium electrode surrounded by a layer of lactate oxidase molecules. Metabolism of lactate by lactate oxidase produces hydrogen peroxide, which produces a current in the platinum-iridium electrode. So a ramping up of cerebral glycolysis provides an increase in the concentration of substrate for lactate oxidase, which then is reflected in increased current at the sensing electrode. It was additionally necessary to measure these variables while manipulating the excitability of the cerebral cortex, in order to isolate this variable from other facets of NREMS.
	We devised an experimental system for simultaneous measurement of neuronal activity via the elecetroencephalogram, measurement of glycolytic flux via a lactate biosensor, and manipulation of cerebral cortical neuronal activity via optogenetic activation of pyramidal neurons. We have utilized this system to document the relationship between sleep-related electroencephalographic waveforms and the moment-to-moment dynamics of lactate concentration in the cerebral cortex. The protocol may be useful for any individual interested in studying, in freely behaving rodents, the relationship between neuronal activity measured at the electroencephalographic level and cellular energetics within the brain.

A procedure is described for manipulating the activity of cerebral cortical pyramidal neurons optogenetically while the electroencephalogram, electromyogram, and cerebral lactate concentration are monitored. Experimental recordings are performed on cable-tethered mice while they undergo spontaneous sleep/wake cycles. Optogenetic equipment is assembled in our laboratory; recording equipment is commercially available.

Although the brain represents less than 5% of the body  by mass, it utilizes approximately one quarter of the glucose used by the body  at rest1. The ...

Optogenetic Perturbation of Neural Activity with Laser Illumination in Semi-intact Drosophila Larvae in Motion

Drosophila larval locomotion is a splendid model system in developmental and physiological neuroscience, by virtue of the genetic accessibility of the underlying neuronal components in the circuits1-6. Application of optogenetics7,8 in the larval neural circuit allows us to manipulate neuronal activity in spatially and temporally patterned ways9-13. Typically, specimens are broadly illuminated with a mercury lamp or LED, so specificity of the target neurons is controlled by binary gene expression systems such as the Gal4-UAS system14,15. In this work, to improve the spatial resolution to "sub-genetic resolution", we locally illuminated a subset of neurons in the ventral nerve cord using lasers implemented in a conventional confocal microscope. While monitoring the motion of the body wall of the semi-intact larvae, we interactively activated or inhibited neural activity with channelrhodopsin16,17 or halorhodopsin18-20, respectively. By spatially and temporally restricted illumination of the neural tissue, we can manipulate the activity of specific neurons in the circuit at a specific phase of behavior. This method is useful for studying the relationship between the activities of a local neural assembly in the ventral nerve cord and the spatiotemporal pattern of motor output.

Optogenetic Perturbation of Neural Activity with Laser Illumination in Semi-intact Drosophila Larvae in Motion

Here we describe a protocol for optogenetic manipulation of motoneuronal activity while monitoring changes in motor output (muscle contraction) in semi-intact Drosophila larvae using lasers within a conventional confocal microscope. This technique enables researchers to achieve local perturbation of neural activity in a few neuromeres to elucidate the dynamics of motor circuits.

Drosophila larval locomotion is a splendid model system in developmental and physiological neuroscience, by virtue of the genetic accessibility of the ...

Investigating the Function of Deep Cortical and Subcortical Structures Using Stereotactic Electroencephalography: Lessons from the Anterior Cingulate Cortex

Stereotactic Electroencephalography (SEEG) is a technique used to localize seizure foci in patients with medically intractable epilepsy. This procedure involves the chronic placement of multiple depth electrodes into regions of the brain typically inaccessible via subdural grid electrode placement. SEEG thus provides a unique opportunity to investigate brain function. In this paper we demonstrate how SEEG can be used to investigate the role of the dorsal anterior cingulate cortex (dACC) in cognitive control. We include a description of the SEEG procedure, demonstrating the surgical placement of the electrodes. We describe the components and process required to record local field potential (LFP) data from consenting subjects while they are engaged in a behavioral task. In the example provided, subjects play a cognitive interference task, and we demonstrate how signals are recorded and analyzed from electrodes in the dorsal anterior cingulate cortex, an area intimately involved in decision-making. We conclude with further suggestions of ways in which this method can be used for investigating human cognitive processes.

Stereotactic Electroencephalography (SEEG) is an operative technique used in epilepsy surgery to help localize seizure foci. It also affords a unique opportunity to investigate brain function. Here we describe how SEEG can be used to investigate cognitive processes in human subjects.

Stereotactic Electroencephalography (SEEG) is a technique used to localize seizure foci in patients with medically intractable epilepsy. This procedure ...

Assessment of Hippocampal Dendritic Complexity in Aged Mice Using the Golgi-Cox Method

Dendritic spines are the protuberances from the neuronal dendritic shafts that contain&#160; excitatory synapses. The morphological and branching variations of the neuronal dendrites within the hippocampus are implicated in cognition and memory formation. There are several approaches to Golgi staining, all of which have been useful for determining the morphological characteristics of dendritic arbors and produce a clear background. The present Golgi-Cox method, (a slight variation of the protocol that is provided with a commercial Golgi staining kit), was designed to assess how a relatively low dose of the chemotherapeutic drug 5-flurouracil (5-Fu) would affect dendritic morphology, the number of spines, and the complexity of arborization within the hippocampus. The 5-Fu significantly modulated the dendritic complexity and decreased the spine density throughout the hippocampus in a region-specific manner. The data presented show that the Golgi staining method effectively stained the mature neurons in the CA1, the CA3, and the dentate gyrus (DG) of the hippocampus. This protocol reports the details for each step so that other researchers can reliably stain tissue throughout the brain with high quality results and minimal troubleshooting.

Here we present a Golgi-Cox protocol in extensive detail. This reliable tissue stain method allows for a high-quality assessment of the cytoarchitecture in the hippocampus, and throughout the entire brain, with minimal troubleshooting.

Dendritic spines are the protuberances from the neuronal dendritic shafts that contain&#160; excitatory synapses. The morphological and branching ...

Concurrent Recording of Co-localized Electroencephalography and Local Field Potential in Rodent

Although electroencephalography (EEG) is widely used as a non-invasive technique for recording neural activities of the brain, our understanding of the neurogenesis of EEG is still very limited. Local field potentials (LFPs) recorded via a multi-laminar microelectrode can provide a more detailed account of simultaneous neural activity across different cortical layers in the neocortex, but the technique is invasive. Combining EEG and LFP measurements in a pre-clinical model can greatly enhance understanding of the neural mechanisms involved in the generation of EEG signals, and facilitate the derivation of a more realistic and biologically accurate mathematical model of EEG. A simple procedure for acquiring concurrent and co-localized EEG and multi-laminar LFP signals in the anesthetized rodent is presented here. We also investigated whether EEG signals were significantly affected by a burr hole drilled in the skull for the insertion of a microelectrode. Our results suggest that the burr hole has a negligible impact on EEG recordings.

This protocol describes a simple method for concurrent recording of co-localized electroencephalography (EEG) and multi-laminar local field potential in an anesthetized rat. A burr hole drilled in the skull for the insertion of a microelectrode is shown to produce negligible distortion of the EEG signal.

Although electroencephalography (EEG) is widely used as a non-invasive technique for recording neural activities of the brain, our understanding of the ...

Quantitative Analysis of Neuronal Dendritic Arborization Complexity in Drosophila

Dendrites are the branched projections of a neuron, and dendritic morphology reflects synaptic organization during the development of the nervous system. Drosophila larval neuronal dendritic arborization (da) is an ideal model for studying morphogenesis of neural dendrites and gene function in the development of nervous system. There are four classes of da neurons. Class IV is the most complex with a branching pattern that covers almost the entire area of the larval body wall. We have previously characterized the effect of silencing the Drosophila ortholog of SOX5 on class IV neuronal dendritic arborization complexity (NDAC) using four parameters: the length of dendrites, the surface area of dendrite coverage, the total number of branches, and the branching structure. This protocol presents the workflow of NDAC quantitative analysis, consisting of larval dissection, confocal microscopy, and image analysis procedures using ImageJ software. Further insight into da neuronal development and its underlying mechanisms will improve the understanding of neuronal function and provide clues about the fundamental causes of neurological and neurodevelopmental disorders.

Quantitative Analysis of Neuronal Dendritic Arborization Complexity in Drosophila

This protocol focuses on quantitative analysis of neuronal dendritic arborization complexity (NDAC) in Drosophila, which can be used for studies of dendritic morphogenesis.

Dendrites are the branched projections of a neuron, and dendritic morphology reflects synaptic organization during the development of the nervous system. ...

Combined Transcranial Magnetic Stimulation and Electroencephalography of the Dorsolateral Prefrontal Cortex

Transcranial magnetic stimulation (TMS) is a non-invasive method that produces neural excitation in the cortex by means of brief, time-varying magnetic field pulses. The initiation of cortical activation or its modulation depends on the background activation of the neurons of the cortical region activated, the characteristics of the coil, its position and its orientation with respect to the head. TMS combined with simultaneous electrocephalography (EEG) and neuronavigation (nTMS-EEG) allows for the assessment of cortico-cortical excitability and connectivity in almost all cortical areas in a reproducible manner. This advance makes nTMS-EEG a powerful tool that can accurately assess brain dynamics and neurophysiology in test-retest paradigms that are required for clinical trials. Limitations of this method include artifacts that cover the initial brain reactivity to stimulation. Thus, the process of removing artifacts may also extract valuable information. Moreover, the optimal parameters for dorsolateral prefrontal (DLPFC) stimulation are not fully known and current protocols utilize variations from the motor cortex (M1) stimulation paradigms. However, evolving nTMS-EEG designs hope to address these issues. The protocol presented here introduces some standard practices for assessing neurophysiological functioning from stimulation to the DLPFC that can be applied in patients with treatment resistant psychiatric disorders that receive treatment such as transcranial direct current stimulation (tDCS), repetitive transcranial magnetic stimulation (rTMS), magnetic seizure therapy (MST) or electroconvulsive therapy (ECT).

The protocol presented here is for TMS-EEG studies utilizing intracortical excitability test-retest design paradigms. The intent of the protocol is to produce reliable and reproducible cortical excitability measures for assessing neurophysiological functioning related to therapeutic interventions in the treatment of neuropsychiatric diseases such as major depression.

Transcranial magnetic stimulation (TMS) is a non-invasive method that produces neural excitation in the cortex by means of brief, time-varying magnetic ...

Collection of Frozen Rodent Brain Regions for Downstream Analyses

As our understanding of neurobiology has progressed, molecular analyses are often performed on small brain areas such as the medial prefrontal cortex (mPFC) or nucleus accumbens. The challenge in this work is to dissect the correct area while preserving the microenvironment to be examined. In this paper, we describe a simple, low-cost method using resources readily available in most labs. This method preserves nucleic acid and proteins by keeping the tissue frozen throughout the process. Brains are cut into 0.5&#8211;1.0 mm sections using a brain matrix and arranged on a frozen glass plate. Landmarks within each section are compared to a reference, such as the Allen Mouse Brain Atlas, and regions are dissected using a cold scalpel or biopsy punch. Tissue is then stored at -80 &#176;C until use. Through this process rat and mouse mPFC, nucleus accumbens, dorsal and ventral hippocampus and other regions have been successfully analyzed using qRT-PCR and Western assays. This method is limited to brain regions that can be identified by clear landmarks.

This procedure describes the collection of discrete frozen brain regions to obtain high-quality protein and RNA using inexpensive and commonly available tools.

As our understanding of neurobiology has progressed, molecular analyses are often performed on small brain areas such as the medial prefrontal cortex ...

Preparation of the Rat Vocal Fold for Neuromuscular Analyses

The purpose of this tutorial is to describe the preparation of the rat vocal fold for histochemical neuromuscular study. This protocol outlines procedures for rat laryngeal dissection, flash-freezing, and cryosectioning of the vocal folds. This study describes how to cryosection vocal folds in both longitudinal and cross-sectional planes. A novelty of this protocol is the laryngeal tracking during cryosectioning that ensures accurate identification of the intrinsic laryngeal muscles and reduces the chance of tissue loss. Figures demonstrate the progressive cryosectioning in both planes. Twenty-nine rat hemi-larynges were cryosectioned and tracked from the emergence of the thyroid cartilage to the appearance of the first section that included the full vocal fold. The full vocal fold was visualized for all animals in both planes. There was high variability in the distance from the appearance of the thyroid cartilage to the appearance of the full vocal fold in both planes. Weight was not correlated to depth of laryngeal landmarks, suggesting individual variability and other factors related to tissue preparation may be responsible for the high variability in the appearance of landmarks during sectioning. This study details a methodology and presents morphological data for preparing the rat vocal fold for histochemical neuromuscular investigation. Due to high individual variability, laryngeal landmarks should be closely tracked during cryosectioning to prevent oversectioning tissue and tissue loss. The use of a consistent methodology, including adequate tissue preparation and awareness of landmarks within the rat larynx, will assist with consistent results across studies and aid new researchers interested in using the rat vocal fold as a model to investigate laryngeal neuromuscular mechanisms.

This protocol describes methods used to prepare rat vocal folds for histochemical neuromuscular study.

The purpose of this tutorial is to describe the preparation of the rat vocal fold for histochemical neuromuscular study. This protocol outlines procedures ...