Name: analyzing neural activity and connectivity using intracranial eeg data with spm software
Uploaded: 2018-10-30T13:30:00
Description: Watch this Scientific Journal Video about Analyzing Neural Activity and Connectivity Using Intracranial EEG Data with SPM Software at JoVE.com

This study was supported by funds from the Benesse Corporation, Japan Society for the Promotion of Science (JSPS) Funding Program for Next Generation World-Leading Researchers (LZ008), the Organization for Promoting Research in Neurodevelopmental Disorders, and the JSPS KAKENHI (15K04185; 18K03174).

Our study was approved by the local institutional ethics committee.
1. Basic Information
NOTE: The analytical protocols can be applied to various types of data without any restrictions as to specific participants, electrodes, reference methods, or electrode locations. In our example, we tested six patients suffering from pharmacologically intractable focal epilepsy. We tested patients who had no epileptic foci in the regions of interest.
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	<li>Record intracran...

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The analytical protocols for intracranial EEG data using the SPM software introduced herein have several advantages compared with functional MRI. First, the protocols can depict neural activation at a high temporal resolution. Therefore, the results indicate whether the cognitive correlates of neural activation are implemented at early or late stages of processing. In our example, the face effect was identified during the very early stages (i.e., 110 ms) of visual processing. In addition, the comparison of the t...

Measuring neural activity and connectivity associated with cognitive functions at high spatial and temporal resolutions is one of the primary goals of cognitive neuroscience. However, accomplishing this goal is not easy. One popular method used to record neural activity is functional magnetic resonance imaging (MRI). Although functional MRI offers several advantages, such as a high spatial resolution at the millimeter level and non-invasive recording, a clear disadvantage of functional MRI is its low temporal resolution. In addition, functional MRI measures blood-oxygen-level-dependent signals, which only indirectly reflect electric neural activity. Popular electrophysiological methods, including electroencephalography (EEG) and magnetoencephalography (MEG), have high temporal resolutions at the millisecond level. However, they have relatively low spatial resolutions, because they record electric/magnetic signals at the scalp and must solve difficult inverse problems to depict brain activity.
Intracranial EEG can directly record electrical neural activity at high temporal (millisecond) and spatial (centimeter) resolutions1. This measure can provide valuable opportunities to understand neural activity and connectivity, although it has clear limitations (e.g., measurable regions are restricted to clinical criteria). Several intracranial EEG studies have applied traditional averaging analysis to depict neural activity. Although averaging analysis can sensitively detect time-locked and low-frequency band activation, it cannot detect non-phase-locked and/or high-frequency (e.g., gamma band) activation. In addition, functional neural coupling has not been analyzed in depth in the literature on intracranial EEG recordings. Several new techniques have been recently developed to depict neural activity and intra- and inter-regional connectivity in functional MRI and EEG/MEG recordings, which can be applied to analyze intracranial EEG data.
Here, we introduce analytical protocols that we have recently applied to analyze intracranial EEG data using the Statistical Parametric Mapping (SPM) software. First, to reveal when, and at which frequency, the brain regions could be activated, we performed time-frequency SPM analysis2. This analysis decomposes the time and frequency domains simultaneously using a continuous wavelet transform and appropriately corrects the family-wise error (FWE) rate in the time-frequency maps using the random field theory. Second, to reveal how brain regions communicate, we applied dynamic causal modeling (DCM) of induced responses4. DCM enables the investigation of effective connectivity (i.e., the causal and directional influences among brain regions5). Although DCM was originally proposed as a tool for analyzing functional MRI data5, DCM of induced responses has been extended to analyze the time-varying power spectra of electrophysiological signals4. This analysis allows the depiction of both intra- and inter-regional neural connectivity. Several neurophysiological studies have suggested that local intra-regional computations and long-range inter-regional communication mainly use gamma- and theta-band oscillations, respectively, and their interactions (e.g., entrainments) can be reflected by theta-gamma cross-frequency coupling3,6,7,8. This report focuses on the data analytical protocol; for an overview of background information9,10 and recording protocols11 of intracranial EEG, please refer to the literature.

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.

Using the protocol presented herein, we analyzed intracranial EEG data in response to faces18,19. We recorded data from six patients during the passive viewing of faces, mosaics, and houses in upright and inverted orientations. The contrasts of upright faces versus upright mosaics and upright faces versus upright houses revealed the face effect (i.e., face-specific brain activity relative to other object...

Watch this Scientific Journal Video about Analyzing Neural Activity and Connectivity Using Intracranial EEG Data with SPM Software at JoVE.com

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

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 ...

This method can help answer key questions in the cognitive neuroscience field. The main advantage of this technique is that it can analyze neural activity and connectivity on high-spatial temporal evolutions. To begin analysis of intracranial EEG data, set up SPM12 and select the MEG EEG analytical menu.First, perform time-frequency analysis for the preprocessed intracranial EEG data of each trial using continuous wavelet decomposition with Morlet wavelets based on predefined parameters. To reveal the temporal evolution of spectral components, conduct wavelet decomposition using seven-cycle Morlet wavelets for the entire epoch of 1, 000 to 2, 000 milliseconds using the frequency range of four to 300 hertz. Next, determine the mother wavelet and the number of cycles and note that the number of cycles controls the time-frequency resolutions and should be greater than five to ensure estimation stability.Determine time and frequency ranges. Then crop the resultant time frequency maps automatically to remove edge effects. Here, the maps are cropped at 200 to 500 milliseconds.Perform data transformation if desired and baseline correction by selecting time frequency rescale for the time frequency maps to better visualize event-related power changes and improve normality. Next, convert the time frequency maps into two-dimensional images. Also smooth using a Gaussian kernel with a predefined full-width half-maximum value to compensate for inter-subject variability and to conform to assumptions of random field theory.Now select specify second level in the SPM menu and enter the 2D images that will be analyzed. Then run the general linear model by selecting model estimation. Finally, select results to perform statistical inferences on the time-frequency data based on random field theory.Detect significantly activated time-frequency clusters with predefined thresholds such as those seen here. Start dynamic causal modeling analysis by selecting DCM in the SPM menu. Then choose the IND option and select new data to import the preprocessed intracranial EEG data.Next, use the MEG EEG menu to specify the time window of interest, frequency window of interest, number of wavelet cycles that will be used, conditions of interest, and the contrasts for the conditions. Set the time window to one to 500 milliseconds. Use five-cycle Morlet wavelets of four to 100 hertz in one hertz steps.Use the default setting for the wavelet cycle. Determine the time-frequency ranges based on research interest. Note that a time window with an additional 512 milliseconds can be automatically used during computation to remove edge effects.Based on the DCM framework, define the driving inputs which represent sensory inputs on neural states and the intrinsic connections which embody baseline connectivity among neural states. Also, define the modulatory effects on the intrinsic connections via experimental manipulations for null and hypothesized models. Define the type of modulation as linear or nonlinear.Now specify the intrinsic linear and nonlinear connections, driving inputs and modulation inputs. Modify the default settings of related parameters if necessary such as prior stimulus onset time and duration. Then choose invert DCM to estimate the models.After that, select save results as image to save frequency-frequency modulatory coupling parameter images. Next, conduct a random effects Bayesian Model Selection analysis by selecting BMS to identify the optimal network model. Use the model expected probabilities and accedence probabilities as evaluation measures.Then make inferences regarding the cross-frequency patterns of the modulatory connections using the winning model parameters. Now smooth the modulatory coupling parameter images by selecting convert to images. Then use specify second level to perform general linear model analysis.Finally, select results to calculate the 2D SPMT values. Here, the full-width half-maximum was set at eight hertz and significant values were exploratorily identified using an uncorrected height threshold of P less than 0.05. Time-frequency analyses were conducted to investigate the temporal and frequency profiles of Inferior Occipital Gyrus or IOG activity during the processing of phases.Here we see time-frequency maps of the right IOG activity for the upright phase and upright mosaic conditions. The SPMT data for upright phase versus upright mosaic are also shown. Functional network models are shown here.Eight possible combinations of modulatory input of upright phase versus upright mosaic onto connections between the IOG and amygdala and self-connection onto each region were investigated. Frequency-frequency modulatory coupling parameters and SPMT values for upright phase versus upright mosaic for the IOG versus amygdala and amygdala versus IOG modulation are shown here. The red-yellow areas indicate significant excitatory connectivity while the blue-cyan areas indicate inhibitory connectivity.After watching this video, you should have a good understanding on how to analyze intracranial EEG data for detecting neural activity and connectivity using the SPM software.

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Research

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Neuroscience

Functional Mapping with Simultaneous MEG and EEG

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

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

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

Establishing Intracranial Brain Tumor Xenografts With Subsequent Analysis of Tumor Growth and Response to Therapy using Bioluminescence Imaging

Transplantation models using human brain tumor cells have served an essential function in neuro-oncology research for many years. In the past, the most commonly used procedure for human tumor xenograft establishment consisted of the collection of cells from culture flasks, followed by the subcutaneous injection of the collected cells in immunocompromised mice. Whereas this approach still sees frequent use in many laboratories, there has been a significant shift in emphasis over the past decade towards orthotopic xenograft establishment, which, in the instance of brain tumors, requires tumor cell injection into appropriate neuroanatomical structures. Because intracranial xenograft establishment eliminates the ability to monitor tumor growth through direct measurement, such as by use of calipers, the shift in emphasis towards orthotopic brain tumor xenograft models has necessitated the utilization of non-invasive imaging for assessing tumor burden in host animals. Of the currently available imaging methods, bioluminescence monitoring is generally considered to offer the best combination of sensitivity, expediency, and cost. Here, we will demonstrate procedures for orthotopic brain tumor establishment, and for monitoring tumor growth and response to treatment when testing experimental therapies.

Luciferase-modified human brain tumor xenografts can be established intracranially in athymic mice, with subsequent monitoring of tumor growth and response to therapy using bioluminescence imaging. In combination with survival analysis, bioluminescence monitoring is an essential research tool for pre-clinical testing of therapies being considered for treating brain tumors.

Transplantation models using human brain tumor cells have served an essential function in neuro-oncology research for many years. In the past, the most ...

High-density EEG Recordings of the Freely Moving Mice using Polyimide-based Microelectrode

Electroencephalogram (EEG) indicates the averaged electrical activity of the neuronal populations on a large-scale level. It is widely utilized as a noninvasive brain monitoring tool in cognitive neuroscience as well as a diagnostic tool for epilepsy and sleep disorders in neurology. However, the underlying mechanism of EEG rhythm generation is still under the veil. Recently introduced polyimide-based microelectrode (PBM-array) for high resolution mouse EEG1 is one of the trials to answer the neurophysiological questions on EEG signals based on a rich genetic resource that the mouse model contains for the analysis of complex EEG generation process. This application of nanofabricated PBM-array to mouse skull is an efficient tool for collecting large-scale brain activity of transgenic mice and accommodates to identify the neural correlates to certain EEG rhythms in conjunction with behavior. However its ultra-thin thickness and bifurcated structure cause a trouble in handling and implantation of PBM-array. In the presented video, the preparation and surgery steps for the implantation of PBM-array on a mouse skull are described step by step. Handling and surgery tips to help researchers succeed in implantation are also provided.

In this article, we described the surgery procedure and handling tips for implantation of ultra-thin polyimide-based microelectrode array (PBM-array) on the mouse skull for acquisition of high-density encephalography (EEG) in a mouse model.

Electroencephalogram (EEG) indicates the averaged electrical activity of the neuronal populations on a large-scale level. It is widely utilized as a ...

Optical Recording of Suprathreshold Neural Activity with Single-cell and Single-spike Resolution

Signaling of information in the vertebrate central nervous system is often carried by populations of neurons rather than individual neurons. Also propagation of suprathreshold spiking activity involves populations of neurons. Empirical studies addressing cortical function directly thus require recordings from populations of neurons with high resolution. Here we describe an optical method and a deconvolution algorithm to record neural activity from up to 100 neurons with single-cell and single-spike resolution. This method relies on detection of the transient increases in intracellular somatic calcium concentration associated with suprathreshold electrical spikes (action potentials) in cortical neurons. High temporal resolution of the optical recordings is achieved by a fast random-access scanning technique using acousto-optical deflectors (AODs)1. Two-photon excitation of the calcium-sensitive dye results in high spatial resolution in opaque brain tissue2. Reconstruction of spikes from the fluorescence calcium recordings is achieved by a maximum-likelihood method. Simultaneous electrophysiological and optical recordings indicate that our method reliably detects spikes (&gt;97% spike detection efficiency), has a low rate of false positive spike detection (&lt; 0.003 spikes/sec), and a high temporal precision (about 3 msec) 3. This optical method of spike detection can be used to record neural activity in vitro and in anesthetized animals in vivo3,4.

Understanding the function of the vertebrate central nervous system requires recordings from many neurons because cortical function arises on the level of populations of neurons. Here we describe an optical method to record suprathreshold neural activity with single-cell and single-spike resolution, dithered random-access scanning. This method records somatic fluorescence calcium signals from up to 100 neurons with high temporal resolution. A maximum-likelihood algorithm deconvolves the underlying suprathreshold neural activity from the somatic fluorescence calcium signals. This method reliably detects spikes with high detection efficiency and a low rate of false positives and can be used to study neural populations in vitro and in vivo.

Signaling of information in the vertebrate central nervous system is often carried by populations of neurons rather than individual neurons. Also ...

Applications of EEG Neuroimaging Data: Event-related Potentials, Spectral Power, and Multiscale Entropy

When considering human neuroimaging data, an appreciation of signal variability represents a fundamental innovation in the way we think about brain signal. Typically, researchers represent the brain's response as the mean across repeated experimental trials and disregard signal fluctuations over time as "noise". However, it is becoming clear that brain signal variability conveys meaningful functional information about neural network dynamics. This article describes the novel method of multiscale entropy (MSE) for quantifying brain signal variability. MSE may be particularly informative of neural network dynamics because it shows timescale dependence and sensitivity to linear and nonlinear dynamics in the data.

Neuroimaging researchers typically consider the brain's response as the mean activity across repeated experimental trials and disregard signal variability over time as "noise". However, it is becoming clear that there is signal in that noise. This article describes the novel method of multiscale entropy for quantifying brain signal variability in the time domain.

When considering human neuroimaging data, an appreciation of signal variability represents a fundamental innovation in the way we think about brain ...

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 ...

Neural Activity Propagation in an Unfolded Hippocampal Preparation with a Penetrating Micro-electrode Array

This protocol describes a method for preparing a new in vitro flat hippocampus preparation combined with a micro-machined array to map neural activity in the hippocampus. The transverse hippocampal slice preparation is the most common tissue preparation to study hippocampus electrophysiology. A longitudinal hippocampal slice was also developed in order to investigate longitudinal connections in the hippocampus. The intact mouse hippocampus can also be maintained in vitro because its thickness allows adequate oxygen diffusion. However, these three preparations do not provide direct access to neural propagation since some of the tissue is either missing or folded. The unfolded intact hippocampus provides both transverse and longitudinal connections in a flat configuration for direct access to the tissue to analyze the full extent of signal propagation in the hippocampus in vitro. In order to effectively monitor the neural activity from the cell layer, a custom made penetrating micro-electrode array (PMEA) was fabricated and applied to the unfolded hippocampus. The PMEA with 64 electrodes of 200 &#181;m in height could record neural activity deep inside the mouse hippocampus. The unique combination of an unfolded hippocampal preparation and the PMEA provides a new in-vitro tool to study the speed and direction of propagation of neural activity in the two-dimensional CA1-CA3 regions of the hippocampus with a high signal to noise ratio.

We have developed an in vitro unfolded hippocampus which preserves CA1-CA3 array of neurons. Combined with the penetrating micro-electrode array, neural activity can be monitored in both the longitudinal and transverse orientations. This method provides advantages over hippocampal slice preparations as the propagation in the entire hippocampus can be recorded simultaneously.

This protocol describes a method for preparing a new in vitro flat hippocampus preparation combined with a micro-machined array to map neural activity in ...

Extracellular Recording of Neuronal Activity Combined with Microiontophoretic Application of Neuroactive Substances in Awake Mice

Differences in the activity of neurotransmitters and neuromodulators, and consequently different neural responses, can be found between anesthetized and awake animals. Therefore, methods allowing the manipulation of synaptic systems in awake animals are required in order to determine the contribution of synaptic inputs to neuronal processing unaffected by anesthetics. Here, we present methodology for the construction of electrodes to simultaneously record extracellular neural activity and release multiple neuroactive substances at the vicinity of the recording sites in awake mice. By combining these procedures, we performed microiontophoretic injections of gabazine to selectively block GABAA receptors in neurons of the inferior colliculus of head-restrained mice. Gabazine successfully modified neural response properties such as the frequency response area and stimulus-specific adaptation. Thus, we demonstrate that our methods are suitable for recording single-unit activity and for dissecting the role of specific neurotransmitter receptors in auditory processing.
The main limitation of the described procedure is the relatively short recording time (~3 hr), which is determined by the level of habituation of the animal to the recording sessions. On the other hand, multiple recording sessions can be performed in the same animal. The advantage of this technique over other experimental procedures used to manipulate the level of neurotransmission or neuromodulation (such as systemic injections or the use of optogenetic models), is that the drug effect is confined to the local synaptic inputs to the target neuron. In addition, the custom-manufacture of electrodes allows adjustment of specific parameters according to the neural structure and type of neuron of interest (such as the tip resistance for improving the signal-to-noise ratio of the recordings).

We present methods for the construction of electrodes to simultaneously record extracellular neural activity and release multiple neuroactive substances at the vicinity of the recording sites in awake mice. This technique allows the detailed analysis of putative local synaptic inputs to the neuron of interest.

Differences in the activity of neurotransmitters and neuromodulators, and consequently different neural responses, can be found between anesthetized and ...

Analyzing Synaptic Modulation of Drosophila melanogaster Photoreceptors after Exposure to Prolonged Light

The nervous system has the remarkable ability to adapt and respond to various stimuli. This neural adjustment is largely achieved through plasticity at the synaptic level. The Active Zone (AZ) is the region at the presynaptic membrane that mediates neurotransmitter release and is composed of a dense collection of scaffold proteins. AZs of Drosophila melanogaster (Drosophila) photoreceptors undergo molecular remodeling after prolonged exposure to natural ambient light. Thus the level of neuronal activity can rearrange the molecular composition of the AZ and contribute to the regulation of the functional output.
Starting from the light exposure set-up preparation to the immunohistochemistry, this protocol details how to quantify the number, the spatial distribution, and the delocalization level of synaptic molecules at AZs in Drosophila photoreceptors. Using image analysis software, clusters of the GFP-fused AZ component Bruchpilot were identified for each R8 photoreceptor (R8) axon terminal. Detected Bruchpilot spots were automatically assigned to individual R8 axons. To calculate the distribution of spot frequency along the axon, we implemented a customized software plugin. Each axon&#39;s start-point and end-point were manually defined and the position of each Bruchpilot spot was projected onto the connecting line between start and end-point. Besides the number of Bruchpilot clusters, we also quantified the delocalization level of Bruchpilot-GFP within the clusters. These measurements reflect in detail the spatially resolved synaptic dynamics in a single neuron under different environmental conditions to stimuli.

Analyzing Synaptic Modulation of Drosophila melanogaster Photoreceptors after Exposure to Prolonged Light

Here we show how to quantify the number and spatial distribution of synaptic active zones in Drosophila melanogaster&#160;photoreceptors, highlighted with a genetically encoded molecular marker, and their modulation after prolonged exposure to light.

The nervous system has the remarkable ability to adapt and respond to various stimuli. This neural adjustment is largely achieved through plasticity at ...

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 ...