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>
2. EEG data recording
<ol>
	<li>Ask subjects to sit on a comfortable chair in a silent, temperature-controlled room, where the EEG data was recorded. Collect the 20-channel EEG data using the ANT EEG system in this protocol.</li>
	<li>Put the cap on the subjects&#39; head.
	<ol>
		<li>In this study, use a cap with medium size, since it was suitable for most adolescent or male subjects. For children, please measure the head circumference of each subject, and determine the cap with suitable size.</li>
		<li>Place the electrode Cz at approximately 50% of the distance between inion and nasion and 50% of the distance between the left and right inter-aural indentations. Place the reference electrodes on the left and right mastoid bone, respectively. Place the other scalp electrodes at the standard locations according to the international 10-20 system. 
		NOTE: An electrode system and EEG amplifier with 20 channels is sufficient for EEG microstate analysis and omega complexity analysis.</li>
	</ol>
	</li>
	<li>Fill all the electrodes with conductive gel by inserting a blunt needle through the electrodes. Use the conductive gel to lower the impedance. Keep all electrode impedances lower than 10 kiloohms (k&#937;).
	<ol>
		<li>During this period, provide some entertainment for the subjects (e.g., show a short film). If a dry electrode or a saline electrode is used, skip the step of injecting conductive gel.</li>
	</ol>
	</li>
	<li>Instruct the subjects to relax with eyes closed during the recording, which lasts 5 min. Use the digitization software to digitize and record the EEG signal. Use a sampling rate of at least 250 samples/s. Use an online filter broader than 0.1 and 80 Hz. 
	NOTE: Although most commercial available electrode-amplifier systems use an active electrode system, which could improve the signal to noise ratio of EEG signal, do not place this system close to any electrical devices during EEG recording.</li>
</ol>
3. EEG Data Preprocessing
NOTE: EEG data could be preprocessed using various open source or commercial software. The instructions provided below are specific for EEGLAB. This is only one out of many available options to preprocess EEG data.
<ol>
	<li>Import the raw EEG data to the EEG software (e.g., EEGLAB) (File | Import data | Using EEGLAB functions and plugins). 
	NOTE: The raw EEG data recorded from various electrode-amplifier systems could be recognized by EEGLAB, such as EGI, ANT, Brain Vision recorder, and Neuroscan.</li>
	<li>Load the channel location file into the EEG software (Edit | Channel locations). Although the EEG data and the channel names have been imported to EEGLAB, import a channel location file into the EEG software in order to obtain the spatial locations of these electrodes.</li>
	<li>Remove the reference electrodes (Edit | Select data| Select data in channel range). In the option of &#34;Select data in channel range&#34; of the pop-up dialogue box, select only the recording electrodes and do not select the reference electrodes so that the reference electrodes can be removed. The data recorded in reference electrodes is not &#39;true&#39; brain signal, since these two electrodes are placed on the left and right mastoid bone respectively.</li>
	<li>Band pass filter the EEG data between 0.5 and 80 Hz (Tools | Filter the data | Basic FIR filter [new, default]). In the pop-up dialogue box, choose 5 for the &#34;Lower edge of the frequency pass band (Hz)&#34;, and choose 80 for the &#34;Higher edge of the frequency pass band (Hz)&#34;. Then click the button of &#34;Ok&#34;.</li>
	<li>Remove the power line noise with a notch filter between 49 and 51 Hz (Tools | Filter the data | Basic FIR filter [new, default]). In the pop-up dialogue box, choose 49 for the &#34;Lower edge of the frequency pass band (Hz)&#34;, and choose 51 for the &#34;Higher edge of the frequency pass band (Hz)&#34;, and select the option of &#34;Notch filter the data instead of pass band&#34;. Then click the button of &#34;Ok&#34;.</li>
	<li>Correct the data portions contaminated by eye movements, electromyography (EMG) or any other non-physiological artifacts using the Blind Source Separation (BSS) algorithm15. For eye movements, click on Tools | Artifact removal using AAR 1.3 | EOG removal | Using BSS; for EMG, Tools | Artifact removal using AAR 1.3 | EMG removal using BSS.</li>
	<li>Segment the pre-processed continuous EEG data into epochs, with epoch length of 2 s. To do this, write &#39;EEG = eeg_regepochs(EEG, &#39;recurrence&#39;, 2, &#39;limits&#39;,[0 2],&#39;rmbase&#39; ,NaN); pop_saveset(EEG)&#39;, then hit the Enter key of the keyboard. A window will pop up that allows the saving of the segmented EEG data.</li>
	<li>Import the segmented EEG data to the EEG software (File | Load existing dataset).</li>
	<li>Reject EEG epochs with amplitude values exceeding &#177; 80 &#181;V at any electrode (Tools | Reject data epochs | Reject data [all methods]).</li>
	<li>Save the preprocessed EEG data (File | Save current dataset as).</li>
</ol>
4. EEG Microstate Analysis
NOTE: A modified version of the classical K-means clustering algorithm is used for microstate class analysis16, which contains a bottom-up procedure and an up-bottom procedure. In the bottom-up procedure, the group-level microstate classes are identified using the spatial correlation as a clustering criterion. Then in the up-bottom procedure, each topographical map of each subject in each group is assigned to the EEG microstate class with maximum spatial correlation. For resting-state EEG microstate analysis, the polarity of topographical maps is commonly disregarded. EEG microstate class analysis could be done using various open source softwares, such as CARTOOL, sLORETA, EMMA and MapWin. The instructions provided below are specific for the EEGLAB plugin for Microstates. This EEGLAB plugin could be downloaded from https://sccn.ucsd.edu/wiki/EEGLAB_Extensions_and_plug-ins.
<ol>
	<li>For each subject, load the preprocessed EEG data (File | Load existing dataset), convert reference channels to common average reference (Tools | Re-reference), and band pass filter the EEG data between 2 and 20 Hz (Tools | Filter the data | Basic FIR filter [new, default]).</li>
	<li>Identify the four microstate maps in each subject (Tools | Microstates | identify microstate maps). In the pop-up dialogue box, choose 3 for the &#34;Min number of classes&#34;, choose 6 for the &#34;Max number of classes&#34;, choose 50 for the &#34;number of restarts&#34;, choose &#34;Max number of maps to use&#34;, and select the options of &#34;GFP peak only&#34; and &#34;No polarity&#34;. Then click the button of &#34;Ok&#34;.</li>
	<li>Save the EEG data of each subject after identifying its own microstate maps (File | Save current dataset as).</li>
	<li>Import the EEG datasets of all the subjects saved in the last step at once (File | Load existing dataset).</li>
	<li>Identify the group-level microstate maps (Tools | Microstates | Average microstate maps across datasets). In the pop-up dialogue box, select the datasets of all subjects in the option &#34;Choose sets for averaging&#34;. In the option &#34;Name of mean&#34;, give a name for the group-level microstate maps. The default name is &#34;GrandMean&#34;. Then click the button of &#34;Ok&#34;. This will create a new data set named as &#34;GrandMean&#34;, which stores the group-level microstate maps.</li>
	<li>Manually sort the order of four group-level microstate maps according to their classic order (Plot | Edit microstate maps). In the pop-up, select &#34;More&#34;, and then the number of maps shown becomes four. Select &#34;Man. sort&#34;. In the pop-up dialogue box, enter the new order of four group-level microstate maps. Then click &#34;Close&#34;.</li>
	<li>Sort the order of the four microstate maps of each subject (Tools | Microstates | Sort individual microstate maps according to mean).</li>
	<li>Save the microstate parameters of each subject (Tools | Microstates | Quantify microstates in dataset [mean template maps]), which will invoke two pop-up dialogue boxes sequentially.
	<ol>
		<li>In the first dialogue box, select the data sets of all subjects. In the second dialogue box, select &#34;4 Classes&#34; for option &#34;Number of Classes&#34;, select the options of &#34;Fitting only on GFP peaks&#34; and &#34;Remove potentially truncated microstates&#34;, choose 30 for the &#34;Label smoothing window (ms)&#34; and choose 1 for the &#34;Non smoothness penalty&#34;. Then click &#34;Ok&#34;. A csv file which stores the microstate parameters will be saved on the computer.</li>
	</ol>
	</li>
</ol>
5. Omega Complexity Analysis
<ol>
	<li>Save the EEG data of each epoch and each subject in ASCII or txt format using customized script.An example of the customized script for step 5.1 could be found in the Supplementary Materials. 
	NOTE: If the global omega complexity is computed, the EEG data of all the scalp electrodes are needed to export in ASCII or txt format. If the regional omega complexity is computed, export only the EEG data of electrodes in that scalp region. For example, in order to compute anterior omega complexity, export only the EEG data of electrodes in anterior region (i.e., Fp1, Fp2, F7, F3, Fz, F4 and F8); in order to compute posterior omega complexity, export only the EEG data of electrodes in posterior region (i.e., T5, T6, P3, P4, Pz, O1 and O2).</li>
	<li>Compute the omega complexity of all discrete frequencies using the sLORETA software17 (Utilities | Global connectivity). This software is available at http://www.uzh.ch/keyinst/loreta.htm.</li>
	<li>Compute the omega complexity of each frequency band using customized script26. In our case, compute the omega complexity of the following eight frequency bands as the mean value within each frequency limit, which are delta (0.5-3.5 Hz), theta (4-7.5 Hz), alpha1 (8-10 Hz), alpha2 (10.5-13.5 Hz), beta1 (14-18 Hz), beta2 (18.5-30 Hz), gamma1 (30.5-48 Hz), and gamma2 (52-80 Hz)4. An example of the customized script for step 5.3 could be found in the Supplementary Materials.</li>
</ol>

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The authors have nothing to disclose.

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...

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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 ...

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10.1K Views.  Peking University People's Hospital. 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.

Video: Microstate and Omega Complexity Analyses of the Resting-state Electroencephalography

Investigating Social Cognition in Infants and Adults Using Dense Array Electroencephalography (dEEG)

Dense array electroencephalography (dEEG), which provides a non-invasive window for measuring brain activity and a temporal resolution unsurpassed by any other current brain imaging technology1,2, is being used increasingly in the study of social cognitive functioning in infants and adults. While dEEG is enabling researchers to examine brain activity patterns with unprecedented levels of sensitivity, conventional EEG recording systems continue to face certain limitations, including 1) poor spatial resolution and source localization3,4,2) the physical discomfort for test subjects of enduring the individual application of numerous electrodes to the surface of the scalp, and 3) the complexity for researchers of learning to use multiple software packages to collect and process data. Here we present an overview of an established methodology that represents a significant improvement on conventional methodologies for studying EEG in infants and adults. Although several analytical software techniques can be used to establish indirect indices of source localization to improve the spatial resolution of dEEG, the HydroCel Geodesic Sensor Net (HCGSN) by Electrical Geodesics, Inc. (EGI), a dense sensory array that maintains equal distances among adjacent recording electrodes on all surfaces of the scalp, further enhances spatial resolution4,5,6 compared to standard dEEG systems. The sponge-based HCGSN can be applied rapidly and without scalp abrasion, making it ideal for use with adults7,8, children9,10,11, and infants12, in both research and clinical4,5,6,13,14,15 settings. This feature allows for considerable cost and time savings by decreasing the average net application time compared to other dEEG systems. Moreover, the HCGSN includes unified, seamless software applications for all phases of data, greatly simplifying the collection, processing, and analysis of dEEG data.
The HCGSN features a low-profile electrode pedestal, which, when filled with electrolyte solution, creates a sealed microenvironment and an electrode-scalp interface. In all Geodesic dEEG systems, EEG sensors detect changes in voltage originating from the participant's scalp, along with a small amount of electrical noise originating from the room environment. Electrical signals from all sensors of the Geodesic sensor net are received simultaneously by the amplifier, where they are automatically processed, packaged, and sent to the data-acquisition computer (DAC). Once received by the DAC, scalp electrical activity can be isolated from artifacts for analysis using the filtering and artifact detection tools included in the EGI software. Typically, the HCGSN can be used continuously for only up to two hours because the electrolyte solution dries out over time, gradually decreasing the quality of the scalp-electrode interface.
In the Parent-Infant Research Lab at the University of Toronto, we are using dEEG to study social cognitive processes including memory, emotion, goals, intentionality, anticipation, and executive functioning in both adult and infant participants.

Investigating Social Cognition in Infants and Adults Using Dense Array Electroencephalography dEEG

Dense array electroencephalography is being used increasingly to study social cognitive functions in infants and adults. Here we present an established methodology that represents a significant improvement on conventional methodologies for studying EEG in infants and adults.

Dense array electroencephalography (dEEG), which provides a non-invasive window for measuring brain activity and a temporal resolution unsurpassed by any ...

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 ...

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 ...