Name: statistical modelling of cortical connectivity using non-invasive electroencephalograms
Uploaded: 2019-11-01T13:30:00
Description: Watch this Scientific Journal Video about Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms at JoVE.com

The publication of this manuscript has been partially supported by the SFI FutureNeruro-Funded Investigator grant to DT.

The following experimental protocol is in accordance with all local, national, and international ethics guidelines for human research. The data used to test the protocol have been acquired with authorization of the Ethical Committee of region Tuscany-protocol 2018SMIA112 SI-RE.
NOTE: The scripts used for implementing the analyses described are available at https://github.com/conorkeogh/NetworkAnalysis.
1. Raw Data Collection
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	<li>Prep...

<ol>
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B., Barnett, L. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Granger+causality+analysis+in+neuroscience+and+neuroimaging.">Granger causality analysis in neuroscience and neuroimaging.</a> Journal of Neuroscience. 35, 3293-3297 (2015).</li><li>Hesse, W., M&#246;ller, E., Arnold, M., Schack, B. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+use+of+time-variant+EEG+Granger+causality+for+inspecting+directed+interdependencies+of+neural+assemblies.">The use of time-variant EEG Granger causality for inspecting directed interdependencies of neural assemblies.</a> Journal of Neuroscience Methods. 124, 27-44 (2003).</li><li>Nunez, P. 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=EEG+coherency.+I:+Statistics,+reference+electrode,+volume+conduction,+Laplacians,+cortical+imaging,+and+interpretation+at+multiple+scales.">EEG coherency. I: Statistics, reference electrode, volume conduction, Laplacians, cortical imaging, and interpretation at multiple scales.</a> Electroencephalography and Clinical Neurophysiology. 103, 499-515 (1997).</li><li>Nunez, P. 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The described method allows the derivation of statistical maps of cortical network dynamics from non-invasive EEG data. This allows the investigation of phenomena not readily apparent on examination of simple time series data through assessment of how the recorded regions are interacting with each other, rather than evaluating what is happening in each individual location in isolation. This can reveal important insights into disease pathology18.
The essential aspect of ...

This method aims to produce statistical maps of cortical networks based on non-invasive electrode recordings using a clinically viable setup, to allow for investigation of nervous system pathology, the impact of novel treatments, and the development of novel electrophysiological biomarkers.
EEG offers great potential for the investigation of nervous system function and disease1,2. This technology is inexpensive, readily available in research and clinical settings, and generally well tolerated. The simple, non-invasive nature of recordings make clinical use straightforward, and the existing framework of clinical EEG departments allows for easy access to the technology for clinicians.
From a technical perspective, EEG offers excellent time domain resolution3. This is of great importance when investigating nervous system function due to the rapid timescales of nervous system interactions and network dynamics. While imaging methods such as functional MRI offer greater spatial resolution and easily interpretable images, they are far more limited in their ability to interrogate nervous system function on the fine time scales offered by electrophysiological recordings4,5,6.
There is a growing need for the ability to interrogate nervous system function to inform diagnosis, treatment, and prognostication of nervous system diseases. The role of cortical network dynamics in nervous system pathology is increasingly recognized7. Many pathologies of the nervous system produce no macroscopic structural lesions visible with traditional imaging, but the abnormalities produced at the network level may be apparent with appropriate functional analysis methods.
Unfortunately, current EEG analysis methods are greatly limited in this regard. Traditional methods involve the analysis of simple time series data from individual electrodes. These signals represent the summation of field potentials in large cortical areas3,8. Analysis of data from individual channels in isolation using either visual inspection or simple statistical methods limits the usefulness of these recordings to detecting gross electrophysiological abnormalities in discrete, individual locations. With the increasing recognition of the importance of network-level effects to nervous system function and pathology, these simple analysis methods are clearly deficient in that they will fail to detect subtle relationships between signals, representing abnormalities in how cortical areas are interacting with one another at the network level.
A method of deriving statistical maps of cortical network connectivity from low-dimensional electrode recordings is demonstrated. This method allows investigation of the dynamics of interactions between varying brain regions in a way that is not possible with traditional analysis techniques, as well as visualization of these network interactions. This opens the possibility for non-invasive investigation of network level effects at high time domain resolutions in ways not previously possible. This method is based on the derivation of measures of interelectrode coherence9,10. These measures allow the investigation of how two recorded regions are interacting by evaluating the statistical relationships between the recordings of these areas11. By assessing how each recorded area interacts with every other recorded area, a statistical map of electrophysiological networks within the recorded areas can be made. This allows for the discovery of functional relationships that are not apparent on evaluation of individual channel data in isolation.
The focus of this manuscript is on the use of coherence on neural time series. Currently, there are a number of techniques for investigating the relationships between time series data that can be applied to channels in a pairwise fashion to derive models of cortical connectivity. Some methods, such as the related partial directed coherence12,13, aim to infer the direction of influence of the pair of signals investigated in order to better characterize the structure of the underlying networks, while other methods, such as Granger causality14,15, attempt to infer functional relationships through the ability of one signal to predict the data in another. Methods such as these can be applied in similar ways to generate high-dimensional models of cortical networks. However, the advantages of coherence as a means of investigating relationships between neural signals lies in its lack of assumptions. It is possible to investigate statistical relationships between recordings at two sites without making statements about the functional basis of these relationships and to build up a model of cortical connectivity based purely on statistical relationships with minimal assumptions about the cortical networks generating these signals.
Due to the purely mathematical nature of these measures, the relationship between the coherence measures of electrode recordings at the scalp and the underlying neural activity is complex16,17. While these methods allow the derivation of statistical constructs describing relationships between the electrode recordings for comparison, making direct causal inferences about the activity of the specific underlying neural populations is not straightforward3,8,16,17. These approaches allow for comparison of the network-level activity between groups to identify potentially useful biomarkers but are limited in terms of drawing specific conclusions regarding the relationship of these markers to specific neural mechanisms. This is due to the large number of confounding factors influencing the recorded activity3, as well as issues with estimating the specific cortical source of electrical signals recorded at the level of the scalp8. Rather, these approaches can produce statistical models of activity that can be interrogated and compared between groups to determine that differences exist at the network level18 and can be leveraged to produce novel biomarkers based on these constructs. However, these methods alone have a limited capacity to relate the differences seen to specific mechanisms and neural activities due to the complexity of the underlying system.
The use of network measures such as coherence is well established in systems neuroscience16,17. The full potential of these approaches for modelling and investigating cortical function has been limited by a lack of exploitation of these high-dimensional data structures. This work demonstrates that it is possible to apply these measures to EEG channels in a pairwise fashion in order to map data onto a high-dimensional feature space based purely on the statistical relationships between the electrical activity in cortical regions. It also demonstrates that, using modern statistical techniques, it is possible to use the generated models of cortical function to investigate these models without losing the information gained in the modelling process.
This method is potentially valuable in expanding the scope of applications of existing EEG technologies, improving the ability to derive useful functional measures without requiring adaptations to existing recording equipment18,19. By improving the ability to model cortical function and interrogate these models, the questions that can be investigated using EEG data are expanded. This further opens the possibility of greater integration of functional and structural evaluations for investigation of neurological disease20,21. This approach, using technology that is already widely available clinically, would allow investigation of cortical pathologies with both high temporal and spatial resolution.

<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>Electrode cap</td>
			<td>ElectroCap International</td>
			<td></td>
			<td>Or any suitable cap</td>
		</tr>
		<tr>
			<td>Conductive gel</td>
			<td>SignaGel</td>
			<td></td>
			<td>Or any suitable gel</td>
		</tr>
		<tr>
			<td>Pin-type electrodes</td>
			<td>BioSemi</td>
			<td></td>
			<td>Or any suitable electrode</td>
		</tr>
		<tr>
			<td>BioSemi Active Two recording system</td>
			<td>BioSemi</td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>ActiView recording environment</td>
			<td>BioSemi</td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>MATLAB software</td>
			<td>Mathworks</td>
			<td></td>
			<td></td>
		</tr>
	</tbody>
</table>

statistical modelling of cortical connectivity using non-invasive electroencephalograms

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

Measurements of the spectral power will produce n measures for each frequency band measured, where n is the number of channels recorded. These measures will be in decibels for the overall power. Measures of power within individual frequency bands should be expressed as relative power (i.e., the proportion of overall power represented by power within that band) to allow accurate comparisons between groups and conditions.
An example of visualiza...

Watch this Scientific Journal Video about Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms at JoVE.com

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms

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

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

This protocol is significant as it allows the investigation of cortical networks by modeling how regions interact with one another to reveal differences not evident with standard analysis techniques. The main advantage of this technique is that it allows us to investigate network functions using widely available equipment so we can obtain non-invasive electrical recordings without the need for specialized materials. This technique allows the non-invasive investigation of neuropsychiatric diseases by examining network structures facilitating the development of novel diagnostic methods and therapeutic biomarkers.This method has a broad range of applications within the clinical neurosciences particularly as the role of network function in disease becomes increasingly relevant. For data collection, attach the electrode cap to the patient's head taking care to ensure a correct alignment. Inject conductive gel into each of the electrode ports beginning at the scalp and slowly withdrawing to the cap surface to establish electrical contact with the scalp and to improve the signal-to-noise ratio.Then use a predetermined electrode montage based on the 10-20 system to attach the electrodes to the electrode cap and secure the appropriate ground electrodes. To set up the EEG, connect all of the electrodes to an electrophysiological recording system and link the recording system with an appropriate digital recording environment. Examine all of the recording channels to ensure that the offset is within an appropriate range and to avoid excessive channel noise.The algorithm will produce results regardless of the data quality so the recordings should be performed under strict data quality conditions and should be analyzed prior to their use. Then instruct the patient that recording has started and to avoid all unnecessary movements before conducting a short test recording to verify appropriate recording quality. At the end of the analysis, load the EEG data and any additional script libraries as necessary into a suitable data analysis environment.Discard the first and last five minutes of each recording to reduce the contamination of any movement artifacts and split the data into epochs based on task or if it is a resting state recording predetermined duration. To prepare the data, correct the baseline of the recordings by subtracting the mean of all of the channels from the recordings to avoid the impact of any baseline wandering during prolonged recordings. Rereference all of the channels to an appropriate reference.Then digitally filter all of the channels to isolate the frequencies of interest. To calculate the overall power spectra of the data, perform a Fourier transform of each channel being analyzed across the whole frequency range to be assessed. To assess the activity of individual frequency bands, isolate the theta band at four to eight hertz, the alpha band at eight to 12 hertz, the beta band at 12 to 30 hertz, the delta band at 0.5 to four hertz, and the gamma band at greater than 30 hertz.To evaluate the interactions between the first electrode pair, derive a measure of inter-electrode coherence. To assess the coherence, map the measurements of the inter-electrode coherence to be visualized onto a two-dimensional data structure where each column is an electrode location, each row is an electrode location, and each cell is the coherence between the corresponding electrode pair and map the coherence values to between zero and one colors. Then export a color map visualizing the inter-electrode coherence between each electrode pair within the frequency limits used.To visualize higher order interactions between cortical areas and to map out network dynamics, calculate how each electrode pair coherence measure covaries with those of every other unique electrode pair across the overall spectrum and within specific bands. Then map these covariance measures to colors and export a color map visualizing the network dynamics within and across frequency bands. To perform a dimensionality reduction, derive measures for comparison between the groups that represent the overall network dynamics within the statistical models generated using the principle component analysis.Instruct a covariance matrix for the pair-wise coherence measures to allow visualization of the high level network relationships and decompose the covariance matrix into eigenvectors and corresponding eigenvalues to allow identification of the axis within the model feature space that contain the greatest variance without being bounded by the existing measures. Rank the eigenvectors by their corresponding eigenvalues to identify those accounting for the greatest proportion of variance within the model. Then compare the first principle components derived from the network models.To select a functional region of interest, isolate the coherence data within the frequency bands of interest. Perform a principle component analysis to derive measures of overall network activity within the bands of interest. Then compare the measures between the groups to evaluate the network differences at specific oscillatory frequencies.To perform unsupervised learning using a distance metric such as Euclidean distance, compute the measures of distance between subjects within the space defined by the network model. Then use a clustering algorithm such as k-nearest neighbors to identify the groups within the data based on the model parameters. The spectral power can be visualized interpolated across the scalp allowing a limited estimation of the source of activity.Each of the inter-electrode electrode measure indicates the extent to which the activity in one area changes depending on the activity in another area allowing for differences in the direction of the interaction and time lag. Higher values of inter-electrode coherence suggest interactions between areas from which it is apparent that the recorded areas are communicating with each other. By measuring the interactions between every unique electrode pair, a statistical map of how the recorded channels are interacting can be constructed allowing investigation of how the areas are communicating rather than focusing on individual areas of isolation.The visualization of higher order network dynamics facilitates the recognition of the kinds of interactions being compared by a principle component analysis or a classifier-based technique to evaluate how the coherence measurements at one electrode pair relate to changes in coherence at another pair. For example, here we can visualize differences evident in the network mapping between two subjects with different clinical phenotypes of a neuropsychiatric disorder affecting cortical function where there were no statistically significant differences using standard analysis methods. Following the derivation of network measures using this procedure, machine learning techniques can be employed to leverage the data-rich models produced to allow more sophisticated diagnostic and prognostic analyses.This technique has allowed the investigation of disease subtypes in Rett syndrome, a pediatric neuropsychiatric disease, as well as the prediction of responses to novel treatments and epilepsy status.

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Neuroscience

Using Informational Connectivity to Measure the Synchronous Emergence of fMRI Multi-voxel Information Across Time

It is now appreciated that condition-relevant information can be present within distributed patterns of functional magnetic resonance imaging (fMRI) brain activity, even for conditions with similar levels of univariate activation. Multi-voxel pattern (MVP) analysis has been used to decode this information with great success. FMRI investigators also often seek to understand how brain regions interact in interconnected networks, and use functional connectivity (FC) to identify regions that have correlated responses over time. Just as univariate analyses can be insensitive to information in MVPs, FC may not fully characterize the brain networks that process conditions with characteristic MVP signatures. The method described here, informational connectivity (IC), can identify regions with correlated changes in MVP-discriminability across time, revealing connectivity that is not accessible to FC. The method can be exploratory, using searchlights to identify seed-connected areas, or planned, between pre-selected regions-of-interest. The results can elucidate networks of regions that process MVP-related conditions, can breakdown MVPA searchlight maps into separate networks, or can be compared across tasks and patient groups.

Informational connectivity measures the correspondence between time courses of multi-voxel information across different brain regions. Multi-voxel pattern discriminability time series are extracted from regions and compared, revealing networks that are not identified in a typical functional connectivity approach.

It is now appreciated that condition-relevant information can be present within distributed patterns of functional magnetic resonance imaging (fMRI) brain ...

Non-invasive Parenchymal, Vascular and Metabolic High-frequency Ultrasound and Photoacoustic Rat Deep Brain Imaging

Photoacoustics and high frequency ultrasound stands out as powerful tools for neurobiological applications enabling high-resolution imaging on the central nervous system of small animals. However, transdermal and transcranial neuroimaging is frequently affected by low sensitivity, image aberrations and loss of space resolution, requiring scalp or even skull removal before imaging. To overcome this challenge, a new protocol is presented to gain significant insights in brain hemodynamics by photoacoustic and high-frequency ultrasounds imaging with the animal skin and skull intact. The procedure relies on the passage of ultrasound (US) waves and laser directly through the fissures that are naturally present on the animal cranium. By juxtaposing the imaging transducer device exactly in correspondence to these selected areas where the skull has a reduced thickness or is totally absent, one can acquire high quality deep images and explore internal brain regions that are usually difficult to anatomically or functionally describe without an invasive approach. By applying this experimental procedure, significant data can be collected in both sonic and optoacoustic modalities, enabling to image the parenchymal and the vascular anatomy far below the head surface. Deep brain features such as parenchymal convolutions and fissures separating the lobes were clearly visible. Moreover, the configuration of large and small blood vessels was imaged at several millimeters of depth, and precise information were collected about blood fluxes, vascular stream velocities and the hemoglobin chemical state. This repertoire of data could be crucial in several research contests, ranging from brain vascular disease studies to experimental techniques involving the systemic administration of exogenous chemicals or other objects endowed with imaging contrast enhancement properties. In conclusion, thanks to the presented protocol, the US and PA techniques become an attractive noninvasive performance-competitive means for cortical and internal brain imaging, retaining a significant potential in many neurologic fields.

The present work describes a new protocol to perform non-invasive high-frequency ultrasound and photoacoustic based imaging on rat brain, to efficiently visualize deep subcortical regions and their vascular patterns by directing signals on skull foramina naturally present on animal cranium.

Photoacoustics and high frequency ultrasound stands out as powerful tools for neurobiological applications enabling high-resolution imaging on the central ...

Non-invasive Assessment of Changes in Corticomotoneuronal Transmission in Humans

The corticospinal pathway is the major pathway connecting the brain with the muscles and is therefore highly relevant for movement control and motor learning. There exists a number of noninvasive electrophysiological methods investigating the excitability and plasticity of this pathway. However, most methods are based on quantification of compound potentials and neglect that the corticospinal pathway consists of many different connections that are more or less direct. Here, we present a method that allows testing excitability of different fractions of the corticospinal transmission. This so called H-reflex conditioning technique allows one to assess excitability of the fastest (monosynaptic) and also polysynaptic corticospinal pathways. Furthermore, by using two different stimulation sites, the motor cortex and the cervicomedullary junction, it allows not only differentiation between cortical and spinal effects but also assessment of transmission at the corticomotoneural synapse. In this manuscript, we describe how this method can be used to assess corticomotoneural transmission after low-frequency repetitive transcranial magnetic stimulation, a method that was previously shown to reduce excitability of cortical cells. Here we demonstrate that not only cortical cells are affected by this repetitive stimulation but also transmission at the corticomotoneuronal synapse at the spinal level. This finding is important for the understanding of basic mechanisms and sites of neuroplasticity. Besides investigation of basic mechanisms, the H-reflex conditioning technique may be applied to test changes in corticospinal transmission following behavioral (e.g., training) or therapeutic interventions, pathology or aging and therefore allows a better understanding of neural processes that underlie movement control and motor learning.

The aim of the present study was to assess changes in transmission at the corticomotoneuronal synapses in humans after repetitive transcranial magnetic stimulation. For this purpose, an electrophysiological method is introduced that allows assessment of pathway specific corticospinal transmission, i.e. differentiation of fast, direct corticospinal pathways from polysynaptic connections.

The corticospinal pathway is the major pathway connecting the brain with the muscles and is therefore highly relevant for movement control and motor ...

Analyzing Dendritic Morphology in Columns and Layers

In many regions of the central nervous systems, such as the fly optic lobes and the vertebrate cortex, synaptic circuits are organized in layers and columns to facilitate brain wiring during development and information processing in developed animals. Postsynaptic neurons elaborate dendrites in type-specific patterns in specific layers to synapse with appropriate presynaptic terminals. The fly medulla neuropil is composed of 10 layers and about 750 columns; each column is innervated by dendrites of over 38 types of medulla neurons, which match with the axonal terminals of some 7 types of afferents in a type-specific fashion. This report details the procedures to image and analyze dendrites of medulla neurons. The workflow includes three sections: (i) the dual-view imaging section combines two confocal image stacks collected at orthogonal orientations into a high-resolution 3D image of dendrites; (ii) the dendrite tracing and registration section traces dendritic arbors in 3D and registers dendritic traces to the reference column array; (iii) the dendritic analysis section analyzes dendritic patterns with respect to columns and layers, including layer-specific termination and planar projection direction of dendritic arbors, and derives estimates of dendritic branching and termination frequencies. The protocols utilize custom plugins built on the open-source MIPAV (Medical Imaging Processing, Analysis, and Visualization) platform and custom toolboxes in the matrix laboratory language. Together, these protocols provide a complete workflow to analyze the dendritic routing of Drosophila medulla neurons in layers and columns, to identify cell types, and to determine defects in mutants.

Here, we show how to analyze dendritic routing of Drosophila medulla neurons in columns and layers. The workflow includes a dual-view imaging technique to improve the image quality and computational tools for tracing, registering dendritic arbors to the reference column array and for analyzing the dendritic structures in 3D space.

In many regions of the central nervous systems, such as the fly optic lobes and the vertebrate cortex, synaptic circuits are organized in layers and ...

Modelling Zika Virus Infection of the Developing Human Brain In Vitro Using Stem Cell Derived Cerebral Organoids

The recent emergence of Zika virus (ZIKV) in susceptible populations has led to an abrupt increase in microcephaly and other neurodevelopmental conditions in newborn infants. While mosquitos are the main route of viral transmission, it has also been shown to spread via sexual contact and vertical mother-to-fetus transmission. In this latter case of transmission, due to the unique viral tropism of ZIKV, the virus is believed to predominantly target the neural progenitor cells (NPCs) of the developing brain.
Here a method for modeling ZIKV infection, and the resulting microcephaly, that occur when human cerebral organoids are exposed to live ZIKV is described. The organoids display high levels of virus within their neural progenitor population, and exhibit severe cell death and microcephaly over time. This three-dimensional cerebral organoid model allows researchers to conduct species-matched experiments to observe and potentially intervene with ZIKV infection of the developing human brain. The model provides improved relevance over standard two-dimensional methods, and contains human-specific cellular architecture and protein expression that are not possible in animal models.

Modelling Zika Virus Infection of the Developing Human Brain In Vitro Using Stem Cell Derived Cerebral Organoids

This protocol describes a technique used to model Zika virus infection of the developing human brain. Using wildtype or engineered stem cell lines, researchers may use this technique to uncover the various mechanisms or treatments that may affect early brain infection and resulting microcephaly in Zika virus-infected embryos.

The recent emergence of Zika virus (ZIKV) in susceptible populations has led to an abrupt increase in microcephaly and other neurodevelopmental conditions ...

SwarmSight: Real-time Tracking of Insect Antenna Movements and Proboscis Extension Reflex Using a Common Preparation and Conventional Hardware

Many scientifically and agriculturally important insects use antennae to detect the presence of volatile chemical compounds and extend their proboscis during feeding. The ability to rapidly obtain high-resolution measurements of natural antenna and proboscis movements and assess how they change in response to chemical, developmental, and genetic manipulations can aid the understanding of insect behavior. By extending our previous work on assessing aggregate insect swarm or animal group movements from natural and laboratory videos using the video analysis software SwarmSight, we developed a novel, free, and open-source software module, SwarmSight Appendage Tracking (SwarmSight.org) for frame-by-frame tracking of insect antenna and proboscis positions from conventional web camera videos using conventional computers. The software processes frames about 120 times faster than humans, performs at better than human accuracy, and, using 30 frames per second (fps) videos, can capture antennal dynamics up to 15 Hz. The software was used to track the antennal response of honey bees to two odors and found significant mean antennal retractions away from the odor source about 1 s after odor presentation. We observed antenna position density heat map cluster formation and cluster and mean angle dependence on odor concentration.

This protocol describes steps for using the novel software, SwarmSight, for frame-by-frame tracking of insect antenna and proboscis positions from conventional web camera videos using conventional computers. The free, open-source software processes frames about 120 times faster than humans and performs at better than human accuracy.

Many scientifically and agriculturally important insects use antennae to detect the presence of volatile chemical compounds and extend their proboscis ...

Visual Evoked Potential Recordings in Mice Using a Dry Non-invasive Multi-channel Scalp EEG Sensor

For scalp EEG research environments with laboratory mice, we designed a dry-type 16 channel EEG sensor which is non-invasive, deformable, and re-usable because of the plunger-spring-barrel structural facet and mechanical strengths resulting from metal materials. The whole process for acquiring the VEP responses in vivo from a mouse consists of four steps: (1) sensor assembly, (2) animal preparation, (3) VEP measurement, and (4) signal processing. This paper presents representative measurements of VEP responses from multiple mice with a submicro-voltage signal resolution and sub-hundred millisecond temporal resolution. Although the proposed method is safer and more convenient compared to other previously reported animal EEG acquiring methods, there are remaining issues including how to enhance the signal-to-noise ratio and how to apply this technique with freely moving animals. The proposed method utilizes easily available resources and shows a repetitive VEP response with a satisfactory signal quality. Therefore, this method could be utilized for longitudinal experimental studies and reliable translational research exploiting non-invasive paradigms.

We designed a dry-type 16 channel EEG sensor which is non-invasive, deformable, and re-usable. This paper describes the whole process from manufacturing the proposed EEG electrode to signal processing of visual evoked potential (VEP) signals measured on a mouse scalp using a dry non-invasive multi-channel EEG sensor.

For scalp EEG research environments with laboratory mice, we designed a dry-type 16 channel EEG sensor which is non-invasive, deformable, and re-usable ...

Analyzing Neural Activity and Connectivity Using Intracranial EEG Data with SPM Software

Measuring neural activity and connectivity associated with cognitive functions at high spatial and temporal resolutions is an important goal in cognitive neuroscience. Intracranial electroencephalography (EEG) can directly record electrical neural activity and has the unique potential to accomplish this goal. Traditionally, averaging analysis has been applied to analyze intracranial EEG data; however, several new techniques are available for depicting neural activity and intra- and inter-regional connectivity. Here, we introduce two analytical protocols we recently applied to analyze intracranial EEG data using the Statistical Parametric Mapping (SPM) software: time-frequency SPM analysis for neural activity and dynamic causal modeling of induced responses for intra- and inter-regional connectivity. We report our analysis of intracranial EEG data during the observation of faces as representative results. The results revealed that the inferior occipital gyrus (IOG) showed gamma-band activity at very early stages (110 ms) in response to faces, and both the IOG and amygdala showed rapid intra- and inter-regional connectivity using various types of oscillations. These analytical protocols have the potential to identify the neural mechanisms underlying cognitive functions with high spatial and temporal profiles.

We present two analytical protocols that can be used to analyze intracranial electroencephalography data using the Statistical Parametric Mapping (SPM) software: time-frequency statistical parametric mapping analysis for neural activity, and dynamic causal modeling of induced responses for intra- and inter-regional connectivity.

Measuring neural activity and connectivity associated with cognitive functions at high spatial and temporal resolutions is an important goal in cognitive ...

Non-invasive Strategies for Chronic Manipulation of DREADD-controlled Neuronal Activity

Chemogenetic strategies have emerged as reliable tools for remote control of neuronal activity. Among these, designer receptors exclusively activated by designer drugs (DREADDs) have become the most popular chemogenetic approach used in modern neuroscience. Most studies deliver the ligand clozapine-N-oxide (CNO) using a single intraperitoneal injection, which is suitable for the acute activation/inhibition of the targeted neuronal population. There are, however, only a few examples of strategies for chronic modulation of DREADD-controlled neurons, the majority of which rely on the use of delivery systems that require surgical intervention. Here, we expand on two non-invasive strategies for delivering the ligand CNO to chronically manipulate neural population in mice. CNO was administered either by using repetitive (daily) eye-drops, or chronically through the animal&#39;s drinking water. These non-invasive paradigms result in robust activation of the designer receptors that persisted throughout the CNO treatments. The methods described here offer alternatives for the chronic DREADD-mediated control of neuronal activity and may be useful for experiments designed to evaluate behavior in freely moving animals, focusing on less-invasive CNO delivery methods.

Here we describe two non-invasive methods to chronically control neuronal activity using chemogenetics in mice. Eye-drops were used to deliver clozapine-N-oxide (CNO) daily. We also describe two methods for prolonged administration of CNO in drinking water. These strategies for chronic neuronal control require minimal intervention reducing animals&#8217; stress.

Chemogenetic strategies have emerged as reliable tools for remote control of neuronal activity. Among these, designer receptors exclusively activated by ...

Non-Invasive Modulation and Robotic Mapping of Motor Cortex in the Developing Brain

Mapping the motor cortex with transcranial magnetic stimulation (TMS) has potential to interrogate motor cortex physiology and plasticity but carries unique challenges in children. Similarly, transcranial direct current stimulation (tDCS) can improve motor learning in adults but has only recently been applied to children. The use of tDCS and emerging techniques like high&#8211;definition tDCS (HD-tDCS) require special methodological considerations in the developing brain. Robotic TMS motor mapping may confer unique advantages for mapping, particularly in the developing brain. Here, we aim to provide a practical, standardized approach for two integrated methods capable of simultaneously exploring motor cortex modulation and motor maps in children. First, we describe a protocol for robotic TMS motor mapping. Individualized, MRI-navigated 12x12 grids centered on the motor cortex guide a robot to administer single-pulse TMS. Mean motor evoked potential (MEP) amplitudes per grid point are used to generate 3D motor maps of individual hand muscles with outcomes including map area, volume, and center of gravity. Tools to measure safety and tolerability of both methods are also included. Second, we describe the application of both tDCS and HD-tDCS to modulate the motor cortex and motor learning. An experimental training paradigm and sample results are described. These methods will advance the application of non-invasive brain stimulation in children.

We demonstrate protocols for the modulation (tDCS, HD-tDCS) and mapping (robotic TMS) of the motor cortex in children.

Mapping the motor cortex with transcranial magnetic stimulation (TMS) has potential to interrogate motor cortex physiology and plasticity but carries ...