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.
<ol>
	<li>Record intracranial EEG data during the cognitive experiment at the target electrodes.
	<ol>
		<li>Implant depth electrodes using the stereotactic method12.</li>
		<li>Use subdural platinum electrodes (diameter: 2.3 mm) and depth platinum electrodes (diameter: 0.8 mm) to simultaneously measure cortical and subcortical activity, respectively.</li>
		<li>Place reference electrodes on the surface of the skull of the midline dorsal frontal region, with the contacts of the electrodes facing away from the skull to avoid referential activation12.</li>
		<li>Amplify data, filter online (band pass: 0.5&#8211;300 Hz), and sample at 1,000 Hz.</li>
		<li>To record and statistically remove artifacts associated with eye movements, additionally record electrooculograms. Select the target electrodes based on theoretical interests. In addition, use individual MRI and computed tomography data to check the electrode locations.</li>
	</ol>
	</li>
	<li>Sample and preprocess trial intracranial EEG data (Figure 1). 
	NOTE: The analytical protocols can be applied to various types of data without any restriction to specific data-length or preprocessing methods.
	<ol>
		<li>Here, sample data during 3,000 ms (pre-stimulus: 1,000 ms; post-stimulus: 2,000 ms) for each trial.</li>
		<li>Because participants here showed abnormally high amplitude activity in some trials, possibly related to epilepsy, exclude these outlier trials using predefined thresholds (&#62;800 &#956;V and &#62;5 SD). Other preprocessing steps, including visual inspection and independent component analysis, may be required depending on the experimental objectives and conditions.</li>
	</ol>
	</li>
	<li>Convert the EEG system native format to a MATLAB-based SPM format (cf. SPM12 Manual 12.1 and 12.2). 
	NOTE: The most EEG data formats can be directly imported into SPM software by selecting Conversion in SPM Batch Editor interface and specifying all required input parameters. Another possible way is to use an example script &#34;spm_eeg_convert_arbitrary_data.m&#34; in the man/example_scripts subdirectory of the SPM program directory. This script provides a convenient way to convert an ASCII file or MAT file that can be exported by many EEG system with SPM format.</li>
</ol>
2. Time-frequency SPM Analysis
<ol>
	<li>Set up SPM12 (http://www.fil.ion.ucl.ac.uk/spm/) and use the M/EEG analytical menu13 (Figure 2).</li>
	<li>Perform the time&#8211;frequency SPM analysis2 by selecting &#34;Time-frequency analysis&#34; in the SPM menu for the preprocessed intracranial EEG data of each trial using continuous wavelet decomposition with Morlet wavelets based on predefined parameters (Figure 3). 
	NOTE: Wavelet transforms reveal the temporal evolution of spectral components by convolving intracranial EEG data with wavelets of multiple frequencies14.
	<ol>
		<li>Here, conduct wavelet decomposition using seven-cycle Morlet wavelets for the entire epoch (-1,000&#8211;2,000 ms) and frequency range of 4&#8211;300 Hz.</li>
		<li>Determine the mother wavelet and number of cycles based on a previous study15. Note that the number of cycles in the wavelet controls the time&#8211;frequency resolutions and is recommended to be greater than 5 to ensure estimation stability13.</li>
		<li>Determine time and frequency ranges based on the research interest.</li>
	</ol>
	</li>
	<li>Crop the resultant time&#8211;frequency maps automatically by selecting &#34;Crop&#34; in the SPM menu to remove edge effects. Here, crop the time&#8211;frequency maps into -200&#8211;500 ms.</li>
	<li>Perform the data transformation (optional) and baseline correction by selecting &#34;Time-frequency rescale&#34; in the SPM menu for the time&#8211;frequency maps to visualize the event-related power changes better and improve the normality of the data. 
	NOTE: Here, the time&#8211;frequency maps were log-transformed and baseline (-200&#8211;0 ms)-corrected.</li>
	<li>Convert the time&#8211;frequency maps into two-dimensional (2D) images by selecting &#34;Convert2Images&#34; in the SPM menu.
	<ol>
		<li>Smooth using a Gaussian kernel with a predefined full-width half-maximum (FWHM) value to compensate for inter-subject variability and conform to the assumptions of the random field theory used in the statistical inference2,13. 
		NOTE: Here, the time&#8211;frequency maps were smoothed with a Gaussian kernel of FWHM of 96 ms in the time domain and 12 Hz in the frequency domain based on a previous study2.</li>
	</ol>
	</li>
	<li>Enter the 2D images into the general linear model by selecting &#34;Specify 1st-level&#34; in the SPM menu.</li>
	<li>Estimate the general linear model by selecting &#34;Model estimation&#34; in the SPM menu.</li>
	<li>Perform statistical inferences on the time&#8211;frequency SPM{T} data based on the random field theory2 by selecting &#34;Results&#34; in the SPM menu. Detect significantly activated time&#8211;frequency clusters with predefined thresholds (possibly corrected for multiple comparisons). 
	NOTE: Here, the extent threshold of p &#60;0.05, which was FWE-corrected for multiple comparisons, with a height threshold of p &#60;0.001 (uncorrected) was used.</li>
</ol>
3. DCM of Induced Responses
<ol>
	<li>Set up SPM12 (http://www.fil.ion.ucl.ac.uk/spm/) and use the M/EEG analytical menu13 (Figure 4).
	<ol>
		<li>Start DCM analysis by clicking &#34;DCM&#34; button in the SPM menu. Activate DCM for induced responses by selecting &#34;IND&#34; in the list box. Import the preprocessed intracranial EEG data by clicking &#34;new data&#34; in the DCM for M/EEG menu.</li>
	</ol>
	</li>
	<li>Specify time window of interest, conditions of interest, contrasts for the selected conditions (this define the modulation inputs later used in network specification), frequency window of interest, and the number of the wavelet cycles in the DCM for M/EEG menu (Figure 5).
	<ol>
		<li>Use five-cycle Morlet wavelets (4&#8211;100 Hz in 1-Hz steps) and set the time window to 1&#8211;500 ms.</li>
		<li>Determine the wavelet cycle in accordance with the default setting. Note that the software recommendation is the value greater than 5 to ensure estimation stability13. The time&#8211;frequency ranges were determined based on our research interest. Note that a time window with an additional &#177; 512 ms was automatically used during computation to remove edge effects.</li>
	</ol>
	</li>
	<li>Based on the DCM framework4,5, define the (1) driving inputs, which represent the sensory inputs on neural states; (2) intrinsic connections, which embody the baseline connectivity among neural states and self-connections; and (3) modulatory effects on intrinsic connections via experimental manipulations for null and hypothesized models. Also define the type of modulation as linear (within-frequency) or nonlinear (between-frequency).
	<ol>
		<li>Specify intrinsic (linear and nonlinear) connections, driving inputs, and modulation inputs in the DCM for M/EEG menu.</li>
		<li>Modify the default settings of some related parameters (e.g., prior stimulus onset time and duration) if necessary. Estimate the models by selecting &#34;invert DCM&#34; in the DCM for M/EEG menu. After that select Save results as img&#160;to save frequency-frequency modulatory coupling parameter images.</li>
	</ol>
	</li>
	<li>Conduct a random-effects Bayesian model selection (BMS) analysis17 by clicking &#34;BMS&#34; in the DCM for M/EEG menu to identify the optimal network model. Use the model expected probabilities and/or exceedance probabilities as evaluation measures.</li>
	<li>Make inferences regarding the cross-frequency patterns of the modulatory connections using the winning model parameters by using the SPM menu (see Step 2).
	<ol>
		<li>Smooth the modulatory coupling parameter images by selecting &#34;Convert2Images&#34; in the SPM menu.</li>
		<li>Perform general linear model analyses by selecting &#34;Specify 2nd-level&#34; in the SPM menu.</li>
		<li>Calculate the 2D SPM{T} values by selecting &#34;Results&#34; in the SPM menu. 
		NOTE: Here, the FWHM was set at 8 Hz based on a previous study4. Significant values were exploratorily identified using a height threshold of p &#60;0.05 (uncorrected).</li>
	</ol>
	</li>
</ol>

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The authors have nothing to disclose.

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...

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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 ...

analyzing-neural-activity-connectivity-using-intracranial-eeg-data

Research

JoVE Journal

Neuroscience

9.4K Views.  Kyoto University. Deze methode kan helpen bij het beantwoorden van belangrijke vragen in de cognitieve neurowetenschappen veld. Het belangrijkste voordeel van deze techniek is dat het neurale activiteit en connectiviteit op hoog-ruimtelijke temporele evoluties kan analyseren. Als u wilt beginnen met de analyse van intracraniale EEG-gegevens, stelt u SPM12 in en selecteert u het ANALYTISCH MEG EEG-menu.Voer eerst tijdfrequentieanalyse uit voor de vooraf verwerkte intracraniale EEG-gegevens van elke proef...

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

Cortical Source Analysis of High-Density EEG Recordings in Children

EEG is traditionally described as a neuroimaging technique with high temporal and low spatial resolution. Recent advances in biophysical modelling and signal processing make it possible to exploit information from other imaging modalities like structural MRI that provide high spatial resolution to overcome this constraint1. This is especially useful for investigations that require high resolution in the temporal as well as spatial domain. In addition, due to the easy application and low cost of EEG recordings, EEG is often the method of choice when working with populations, such as young children, that do not tolerate functional MRI scans well. However, in order to investigate which neural substrates are involved, anatomical information from structural MRI is still needed. Most EEG analysis packages work with standard head models that are based on adult anatomy. The accuracy of these models when used for children is limited2, because the composition and spatial configuration of head tissues changes dramatically over development3.&#160;
In the present paper, we provide an overview of our recent work in utilizing head models based on individual structural MRI scans or age specific head models to reconstruct the cortical generators of high density EEG. This article describes how&#160;EEG recordings are acquired, processed, and analyzed with pediatric populations at the London Baby Lab, including laboratory setup, task design, EEG preprocessing, MRI processing, and EEG channel level and source analysis.&#160;

In recent years, there has been increasing interest in estimating the cortical sources of scalp measured electrical activity for cognitive neuroscience experiments. This article describes how high density EEG is acquired and how recordings are processed for cortical source estimation in children from the age of 2 years at the London Baby Lab.

EEG is traditionally described as a neuroimaging technique with high temporal and low spatial resolution. Recent advances in biophysical modelling and ...

Behavior

Utilizing Electroencephalography Measurements for Comparison of Task-Specific Neural Efficiencies: Spatial Intelligence Tasks

Spatial intelligence is often linked to success in engineering education and engineering professions. The use of electroencephalography enables comparative calculation of individuals&#39; neural efficiency as they perform successive tasks requiring spatial ability to derive solutions. Neural efficiency here is defined as having less beta activation, and therefore expending fewer neural resources, to perform a task in comparison to other groups or other tasks. For inter-task comparisons of tasks with similar durations, these measurements may enable a comparison of task type difficulty. For intra-participant and inter-participant comparisons, these measurements provide potential insight into the participant&#39;s level of spatial ability and different engineering problem solving tasks. Performance on the selected tasks can be analyzed and correlated with beta activities. This work presents a detailed research protocol studying the neural efficiency of students engaged in the solving of typical spatial ability and Statics problems. Students completed problems specific to the Mental Cutting Test (MCT), Purdue Spatial Visualization test of Rotations (PSVT:R), and Statics. While engaged in solving these problems, participants&#39; brain waves were measured with EEG allowing data to be collected regarding alpha and beta brain wave activation and use. The work looks to correlate functional performance on pure spatial tasks with spatially intensive engineering tasks to identify the pathways to successful performance in engineering and the resulting improvements in engineering education that may follow.

This manuscript describes an approach to measure neural activity of humans while solving spatially focused engineering problems. The electroencephalogram methodology helps interpret beta brain wave measurements in terms of neural efficiency, with the aim of ultimately enabling comparisons of task performance both between problem types and between participants.

Spatial intelligence is often linked to success in engineering education and engineering professions. The use of electroencephalography enables ...

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 ...

Extracting Visual Evoked Potentials from EEG Data Recorded During fMRI-guided Transcranial Magnetic Stimulation

Transcranial Magnetic Stimulation (TMS) is an effective method for establishing a causal link between a cortical area and cognitive/neurophysiological effects. Specifically, by creating a transient interference with the normal activity of a target region and measuring changes in an electrophysiological signal, we can establish a causal link between the stimulated brain area or network and the electrophysiological signal that we record. If target brain areas are functionally defined with prior fMRI scan, TMS could be used to link the fMRI activations with evoked potentials recorded. However, conducting such experiments presents significant technical challenges given the high amplitude artifacts introduced into the EEG signal by the magnetic pulse, and the difficulty to successfully target areas that were functionally defined by fMRI. Here we describe a methodology for combining these three common tools: TMS, EEG, and fMRI. We explain how to guide the stimulator&#39;s coil to the desired target area using anatomical or functional MRI data, how to record EEG during concurrent TMS, how to design an ERP study suitable for EEG-TMS combination and how to extract reliable ERP from the recorded data. We will provide representative results from a previously published study, in which fMRI-guided TMS was used concurrently with EEG to show that the face-selective N1 and the body-selective N1 component of the ERP are associated with distinct neural networks in extrastriate cortex. This method allows us to combine the high spatial resolution of fMRI with the high temporal resolution of TMS and EEG and therefore obtain a comprehensive understanding of the neural basis of various cognitive processes.

This paper describes a method for collecting and analyzing electroencephalography (EEG) data during concurrent transcranial magnetic stimulation (TMS) guided by activations revealed with functional magnetic resonance imaging (fMRI). A method for TMS artifact removal and extraction of event related potentials is described as well as considerations in paradigm design and experimental setup.

Transcranial Magnetic Stimulation (TMS) is an effective method for establishing a causal link between a cortical area and cognitive/neurophysiological ...

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 ...

A Multimodal Imaging- and Stimulation-based Method of Evaluating Connectivity-related Brain Excitability in Patients with Epilepsy

Resting-state functional connectivity MRI (rs-fcMRI) is a technique that identifies connectivity between different brain regions based on correlations over time in the blood-oxygenation level dependent signal. rs-fcMRI has been applied extensively to identify abnormalities in brain connectivity in different neurologic and psychiatric diseases. However, the relationship among rs-fcMRI connectivity abnormalities, brain electrophysiology and disease state is unknown, in part because the causal significance of alterations in functional connectivity in disease pathophysiology has not been established. Transcranial Magnetic Stimulation (TMS) is a technique that uses electromagnetic induction to noninvasively produce focal changes in cortical activity. When combined with electroencephalography (EEG), TMS can be used to assess the brain's response to external perturbations. Here we provide a protocol for combining rs-fcMRI, TMS and EEG to assess the physiologic significance of alterations in functional connectivity in patients with neuropsychiatric disease. We provide representative results from a previously published study in which rs-fcMRI was used to identify regions with abnormal connectivity in patients with epilepsy due to a malformation of cortical development, periventricular nodular heterotopia (PNH). Stimulation in patients with epilepsy resulted in abnormal TMS-evoked EEG activity relative to stimulation of the same sites in matched healthy control patients, with an abnormal increase in the late component of the TMS-evoked potential, consistent with cortical hyperexcitability. This abnormality was specific to regions with abnormal resting-state functional connectivity. Electrical source analysis in a subject with previously recorded seizures demonstrated that the origin of the abnormal TMS-evoked activity co-localized with the seizure-onset zone, suggesting the presence of an epileptogenic circuit. These results demonstrate how rs-fcMRI, TMS and EEG can be utilized together to identify and understand the physiological significance of abnormal brain connectivity in human diseases.

Resting-state functional-connectivity MRI has identified abnormalities in patients with a wide range of neuropsychiatric disorders, including epilepsy due to malformations of cortical development. Transcranial Magnetic Stimulation in combination with EEG can demonstrate that patients with epilepsy have cortical hyperexcitability in regions with abnormal connectivity.

Resting-state functional connectivity MRI (rs-fcMRI) is a technique that identifies connectivity between different brain regions based on correlations ...

Medicine

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.

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, ...

Conducting Concurrent Electroencephalography and Functional Near-Infrared Spectroscopy Recordings with a Flanker Task

Concurrent EEG and fNIRS recordings offer an excellent opportunity to gain a full understanding of the neural mechanism of cognitive processing by inspecting the relationship between the neural and hemodynamic signals. EEG is an electrophysiological technology that can measure the rapid neuronal activity of the cortex, whereas fNIRS relies on the hemodynamic responses to infer brain activation. The combination of EEG and fNIRS neuroimaging techniques can identify more features and reveal more information associated with the functioning of the brain. In this protocol, fused EEG-fNIRS measurements were performed for concurrent recordings of evoked-electrical potentials and hemodynamic responses during a Flanker task. In addition, the critical steps for setting up the hardware and software system as well as the procedures for data acquisition and analysis were provided and discussed in detail. It is expected that the present protocol can pave a new avenue for improving the understanding of the neural mechanisms underlying various cognitive processes by using the EEG and fNIRS signals.

The present protocol describes how to perform concurrent EEG and fNIRS recordings and how to inspect the relationship between the EEG and fNIRS data.&#160;

Concurrent EEG and fNIRS recordings offer an excellent opportunity to gain a full understanding of the neural mechanism of cognitive processing by ...

Qualitative and Comparative Cortical Activity Data Analyses from a Functional Near-Infrared Spectroscopy Experiment Applying Block Design

Neuroimaging studies play a pivotal role in the evaluation of pre- vs. post-interventional neurological conditions such as in rehabilitation and surgical treatment. Among the many neuroimaging technologies used to measure brain activity, functional near-infrared spectroscopy (fNIRS) enables the evaluation of dynamic cortical activities by measuring the local hemoglobin levels similar to functional magnetic resonance imaging (fMRI). Also, due to lesser physical restriction in fNIRS, multiple variants of sensorimotor tasks can be evaluated. Many laboratories have developed several methods for fNIRS data analysis; however, despite the fact that the general principles are the same, there is no universally standardized method. Here, we present the qualitative and comparative analytic methods of data obtained from a multi-channel fNIRS experiment using a block design. For qualitative analysis, we used a software for NIRS as a mass-univariate approach based on the generalized linear model. The NIRS-SPM analysis shows qualitative results for each session by visualizing the activated area during the task. In addition, the non-invasive three-dimensional digitizer can be used to estimate the fNIRS channel locations relative to the brain. To corroborate the NIRS-SPM findings, the amplitude of the changes in hemoglobin levels induced by the sensorimotor task can be statistically analyzed by comparing the data obtained from two different sessions (before and after intervention) of the same study subject using a multi-channel hierarchical mixed model. Our methods can be used to measure the pre- vs. post-intervention analysis in a variety of neurological disorders such as movement disorders, cerebrovascular diseases, and neuropsychiatric disorders.

We describe the analysis of continuous-wave functional near-infrared spectroscopy experiment using a block design with a sensorimotor task. To increase the reliability of the data analysis, we used the qualitative general linear model-based statistical parametric mapping and the comparative hierarchical mixed models for multi-channels.

Neuroimaging studies play a pivotal role in the evaluation of pre- vs. post-interventional neurological conditions such as in rehabilitation and surgical ...

A Visuospatial Planning Task Coupled with Eye-Tracker and Electroencephalogram Systems

The planning process, characterized by the capability to formulate an organized plan to reach a goal, is essential for human goal-directed behavior. Since planning is compromised in several neuropsychiatric disorders, the implementation of proper clinical and experimental tests to examine planning is critical. Due to the nature of the deployment of planning, in which several cognitive domains participate, the assessment of planning and the design of behavioral paradigms coupled with neuroimaging methods are current challenges in cognitive neuroscience. A planning task was evaluated in combination with an electroencephalogram (EEG) system and eye movement recordings in 27 healthy adult participants. Planning can be separated into two stages: a mental planning stage in which a sequence of steps is internally represented and an execution stage in which motor action is used to achieve a previously planned goal. Our protocol included a planning task and a control task. The planning task involved solving 36 maze trials, each representing a zoo map. The task had four periods: i) planning, where the subjects were instructed to plan a path to visit the locations of four animals according to a set of rules; ii) maintenance, where the subjects had to retain the planned path in their working memory; iii) execution, where the subjects used eye movements to trace the previously planned path as indicated by the eye-tracker system; and iv) response, where the subjects reported the order of the visited animals. The control task had a similar structure, but the cognitive planning component was removed by modifying the task goal. The spatial and temporal patterns of the EEG revealed that planning induces a gradual and lasting rise in frontal-midline theta activity (FM&#952;) over time. The source of this activity was identified within the prefrontal cortex by source analyses. Our results suggested that the experimental paradigm combining EEG and eye-tracker systems was optimal for evaluating cognitive planning.

The study of cognitive planning combining EEG and eye-tracking systems provides a multimodal approach to investigate the neural mechanisms that mediate cognitive control and goal-directed behavior in humans. Here, we describe a protocol for investigating the role of brain oscillations and eye movements in planning performance.

The planning process, characterized by the capability to formulate an organized plan to reach a goal, is essential for human goal-directed behavior. Since ...

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

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