This work was supported by the National Natural Science Foundation of China (61977008) and the Young Top Notch Talents of Ten Thousand Talent Program.

The human experiment protocol was approved by the Institutional Review Board and Ethics Committee of the State Key Laboratory of Cognitive Neuroscience and Learning at Beijing Normal University. All participants gave written informed consent before the experiment began.
1. The simulation experiment
<ol>
	<li>Generate two time series of signals that correlate with each other, with one signal having autocorrelation at a 4 s time lag. Set the correlation coefficient of r between the two signals to 0.4.</li>
	<li>Furthermore, generate two time series of signals without any correlation but with autocorrelation in one signal.</li>
	<li>Calculate values of traditional 4 s time-lagged INS with Equation 2 based on the generated signals with or without correlation, which can be named time-lagged INSWTC with autocorrelation and time-lagged baseline INSWTC with autocorrelation. 
	NOTE: Here, the traditional time-lagged WTC is expressed by the following equation (Equation 2)25 
	<img alt="Equation 2" src="/files/ftp_upload/62927/62927eq02.jpg" /> 
	where, C denotes the continuous wavelet transform operator at different scales i and time points t. S denotes the smoothing operator. * denotes the complex conjugate operator. W and M indicate two individual time series of signals.</li>
	<li>Remove autocorrelation from the generated signals. Then, calculate the values of traditional 4 s time-lagged INSWTC with Equation 2 based on the generated signals with or without correlation, which can be named time-lagged INSWTC without autocorrelation and time-lagged baseline INSWTC without autocorrelation.</li>
	<li>Calculate the values of 4 s time-lagged pWTC with Equation 3 based on the generated signals with or without correlation, named time-lagged INSpWTC and time-lagged baseline INSpWTC. 
	NOTE: The pWTC can be calculated based on the following equation (Equation 3)23 
	<img alt="Equation 3" src="/files/ftp_upload/62927/62927eq03.jpg" /> 
	where, WTC (Wt, Mt+n) is the traditional time-lagged WTC. WTC (Mt, Mt+n)&#160;is the autocorrelated WTC of one individual. WTC (Wt, Mt) is the time-aligned WTC. * is the complex conjugate operator.</li>
	<li>Repeat the above procedures 1000 times.</li>
	<li>After subtracting the baseline INS, compare the results of time-lagged INSWTC with autocorrelation, time-lagged INSWTC without autocorrelation, and time-lagged INSpWTC using the analyses of variance (ANOVA) method. 
	&#8203;NOTE: Here, it is expected that the time-lagged INSWTC with autocorrelation will be significantly higher than the time-lagged INSWTC without autocorrelation and the time-lagged INSpWTC, and no significant difference is expected between the time-lagged INSWTC without autocorrelation and the time-lagged INSpWTC.</li>
</ol>
2. The empirical experiment
<ol>
	<li>Participants and procedure
	<ol>
		<li>Recruit appropriate participants. 
		NOTE: In this study, twenty-two pairs of close opposite-sex friends (mean age of women = 20.95, standard deviation (SD) = 1.86; mean age of men = 20.50, SD = 1.74) were recruited through advertising from undergraduates of universities in Beijing. All participants were right-handed and had normal or corrected-to-normal vision. Furthermore, no participants had any language, neurological, or psychiatric disorders.</li>
		<li>Ask each pair of participants to sit face-to-face during the experiment. Ask them to communicate freely on a supportive topic in one session and on a conflict topic in the other session. 
		NOTE: The topics were used to induce the intended positive or negative emotional valence. Each communication session lasted 10 min, and the order of topics was counterbalanced.</li>
		<li>Ask the participants to report about the supportive and the conflict topics as a standard set-up rule. Ask each partner to rate the positive or negative valence level that might have been induced on a definite point scale. Then, rank the reported topics according to the rating. 
		NOTE: In this work, the topics were selected with the following three steps. First, for the supportive topics, each participant was required to report 1-3 personal issues related to what she/he wanted to improve in her/his life. Each participant was required to report 1-3 cases that had induced or would induce conflict between them or that might endanger their relationship for the conflict topics. Second, each partner was required to rate the level of positive or negative valence each topic might induce on a 7-point scale (1 = not at all, and 7 = very much). Third, the reported topics were ranked according to the rating. The first two topics in the list of supportive topics and conflict topics were selected.</li>
	</ol>
	</li>
	<li>fNIRS data collection
	<ol>
		<li>Use 26-channel fNIRS topography system (see Table of Materials) to collect fNIRS data. 
		NOTE: Two customized optode probes set covered the bilateral frontal, temporal, and parietal cortices (Figure 1B).</li>
		<li>Precisely, ask each participant to wear a cap with two customized probe sets (see Table of Materials).</li>
		<li>Align the nasion, inion, and ear mastoids with Fpz, Opz, T7, and T8, which are typical landmarks of 10-20 international system26.</li>
		<li>Align channel (CH) 11 to T3 and CH25 to T4 following the international 10-20 system for the two probe sets27,28.</li>
		<li>Validate the anatomical locations of probe sets by scanning magnetic resonance imaging (MRI) data from a typical participant with a high-resolution T1-weighted magnetization-prepared rapid gradient-echo sequence (TR = 2530 ms; TE = 3.39 ms; flip angle = 7&#176;; slice thickness = 1.3 mm; voxel size = 1.3 x 1 x 1.3 mm).</li>
		<li>Use Statistical Parametric Mapping 12 (SPM12) to normalize the image to standard Montreal imaging institute coordinate (MNI coordinate) space29. Then, use the NIRS_SPM toolbox (see Table of Materials) to project the MNI coordinates of the probes to the automated anatomical labeling (AAL) template.</li>
		<li>Collect the optical density data of near-infrared light at three wavelengths (780, 805, and 830 nm) at a sampling rate of 55.6 Hz (equipment default parameters).</li>
		<li>Test the signal quality by using fNIRS topography system built-in equipment software (see Table of Materials).</li>
		<li>Begin signal recording. 
		NOTE: Some published protocols have demonstrated how to collect fNIRS signals with various equipment and systems30,31,32.</li>
	</ol>
	</li>
	<li>fNIRS data preprocessing
	<ol>
		<li>Export the data files from the equipment. 
		NOTE: In the current experiment, the built-in software automatically converted all-optical density data into oxyhemoglobin (HbO) concentration changes based on the modified Beer-Lambert law.</li>
		<li>Remove the first and last 15 s of data for each session to avoid transient responses.</li>
		<li>Use the MATLAB decimate built-in function to downsample the data from 55.6 Hz to 11.1 Hz. 
		NOTE: The power spectrum patterns between 55.6 Hz and 11.1 Hz are quite similar (Supplementary Figure 1).</li>
		<li>Use the built-in MATLAB application function (Homer3, see Table of Materials) with appropriate filtering function to apply the discrete wavelet transform filter method to correct motion artifacts.</li>
		<li>Use the MATLAB pca built-in function to remove global physiological noise. Remove the top 80% of the variance from the signals.</li>
		<li>Remove physiological noise based on the previous studies33. Precisely, remove frequency bands of each signal above 0.7 Hz to avoid aliasing of high-frequency physiological noise (e.g., cardiac activity).</li>
		<li>Then, remove frequency bands of each signal below 0.01 Hz to filter out very-low-frequency fluctuations.</li>
		<li>Finally, remove frequency bands of each signal within 0.15-0.3 Hz to exclude the potential impact of respiratory activity.</li>
	</ol>
	</li>
	<li>First-level fNIRS Data Processing
	<ol>
		<li>First, calculate INS using traditional WTC (INSWTC). 
		NOTE: Here, a women-led time-lagged pattern of INSWTC was predicted to occur between the brain activity of women and that of men because previous studies have suggested different roles of women and men during a conversation34,35. The traditional WTC calculated this pattern of INSWTC by shifting the brain activity of men backward relative to that of women (see Equation 2).</li>
		<li>Calculate the women-led 2 s-lagged INSWTC value after removing the initial 2 s of data from women and the last 2 s of data from men with Equation 2. Similarly, after removing the initial 2 s of data from men and the last 2 s of data from women, calculate the men-led 2 s-lagged INSWTC value with Equation 4. 
		NOTE: Here, the wcoherence function, which is a built-in function of the wavelet toolbox of MATLAB, was used (see Table of Materials).</li>
		<li>Repeat this procedure with different time lags n, i.e., n = 2 s, 4 s, 6 s, 8 s across all potential CH pairs (e.g., CH2 in women and CH10 in men, 676 pairs in total). Additionally, calculate the strength of men-led time-lagged INSWTC the same way (Equation 4). 
		<img alt="Equation 4" src="/files/ftp_upload/62927/62927eq04.jpg" /></li>
		<li>Second, calculate INS using pWTC (INSpWTC). 
		NOTE: pWTC was calculated based on Equation 3. The calculation of INSpWTC was repeated with different time lags n, i.e., n = 2 s, 4 s, 6 s, 8 s across all potential channel pairs (e.g., CH2 in women and CH10 in men, 676 pairs in total). Additionally, the strength of the men-led time-lagged INSpWTC was calculated the same way (Equation 5). 
		<img alt="Equation 5" src="/files/ftp_upload/62927/62927eq05.jpg" /></li>
		<li>Generate time-lagged time series of fNIRS signals at different time lags.</li>
		<li>Calculate the values of the time-lagged WTC at different time lags.</li>
		<li>Generate autocorrelated time series of fNIRS signals at different time lags. To calculate the 2 s-autocorrelated value for men, remove the first 2 s of data from the men and the last 2 s data from the men.</li>
		<li>Calculate the autocorrelated WTC values at different time lags.</li>
		<li>Generate time-aligned time series of fNIRS signals at different time lags. To calculate the 2 s time-aligned WTC, remove the first 2 s of data from the men and the women&#39;s first 2 s of data.</li>
		<li>Calculate the time-aligned WTC values.</li>
		<li>Enter time-aligned WTC, time-lagged WTC, and autocorrelated WTC values at different time lag into Equation 3 and Equation 5-the equation of pWTC, generating INSpWTC.</li>
		<li>Finally, calculate INS using the GC method (INSGC). 
		NOTE: To further validate the pWTC method and evaluate its advantages and disadvantages, GC-based INS was calculated using the GC method (INSGC).</li>
		<li>Based on the pWTC result, bandpass filters the HbO signal of each individual at the SMC (i.e., 0.4-0.6 Hz, see Representative Results).</li>
		<li>Conduct a GC test (Econometric toolbox, MATLAB) within each dyad in the supportive and conflict topics separately. 
		NOTE: Four groups of F-values are obtained for INSGC: (1) from women to men on the supportive topic (W2M_supp); (2) from men to women on the supportive topic (M2W_supp); (3) from women to men on the conflict topic (W2M_conf); and (4) from men to women on the conflict topic (M2 W_conf). The F-values are used to index the INSGC.</li>
	</ol>
	</li>
	<li>Second-level fNIRS data processing
	<ol>
		<li>Transform INS with Fisher-z transformation, and then average INS at the temporal dimension. 
		NOTE: Here, Fisher-z transformation was conducted using a custom MATLAB script with Equation 636: 
		<img alt="Equation 6" src="/files/ftp_upload/62927/62927eq06.jpg" /> 
		where, r is the value of the WTC or pWTC, and z is the Fisher-z transformed value of the WTC or pWTC.</li>
		<li>For the averaged INS at each time lag, conduct a paired two-sample t-test (supportive vs. conflict) on each CH pair across the frequency range. Then, identify all significant frequency clusters (P &#60; 0.05).</li>
		<li>Conduct a cluster-based permutation test to establish a threshold for the results.
		<ol>
			<li>Reassign dyadic relationships by randomly assigning the participants to new two-member pairs, i.e., the participants of a dyad that had never communicated with one another. Recalculate the INS at each time lag, perform paired t-tests again in the new sample, and identify significant frequency clusters again.</li>
			<li>Select the cluster with the largest summed t-value. Repeat the above procedures 1000 times to generate a null distribution of the maximum false-positive t-values. 
			NOTE: The distribution is served as the chance level. The familywise error rate (FWER) is controlled at q = 0.05, which means that only the top 5% of the null distribution of the false-positive t-values exceeds the threshold (R*).</li>
			<li>Compare the summed t-value of each identified frequency cluster in the original sample with the null distribution to obtain significant statistical results.</li>
		</ol>
		</li>
		<li>Conduct a context (supportive, conflict) x direction (women to men, men to women) analysis of variance (ANOVA) to test the difference in INS direction between different conditions (i.e., topics) (p &#60; 0.05).</li>
		<li>Conduct a paired two-sample two-tailed t-test between the results of WTC (Wt, Mt + n) and WTC (Mt, Mt + n) to test the potential impact of autocorrelation on INS. 
		NOTE: The INS of WTC (Mt, Mt + n) reflects autocorrelation.</li>
	</ol>
	</li>
</ol>

The authors declare no competing financial interests.

In hyperscanning studies, it is usually essential to describe the directional and temporal patterns of information flow between individuals. Most previous fNIRS hyperscanning studies have used traditional WTC25 to infer these characteristics by calculating the time-lagged INS. However, as one of the intrinsic features of the fNIRS signal20,21, the autocorrelation effect might confound the time-lagged INS. To address this issue, in the prot...

Social interaction is of vital importance for human beings1,2. For understanding the dual-brain neurocognitive mechanism of social interaction, the hyperscanning approach has recently been extensively used, showing that the patterns of interpersonal neural synchronization (INS) can well characterize the social interaction process3,4,5,6,7,8,9,10,11,12,13,14. Among recent studies, an interesting finding is that the role difference of individuals in a dyad may lead to a time-lagged pattern of INS, i.e., INS occurs when the brain activity of one individual lags behind that of another individual by seconds, such as that from listeners to speakers5,9, from leaders to followers4, from teachers to students8, from mothers to children13,15, and from women to men in a romantic couple6. Most importantly, there is a good correspondence between the interval of the time-lagged INS and that of social interaction behaviors, such as between teachers questioning and students answering8 or between parenting behaviors of mothers and compliance behaviors of children15. Thus, time-lagged INS may reflect a directional information flow from one individual to another, as proposed in a recent hierarchical model for interpersonal verbal communication16.
Previously, the time-lagged INS was mainly calculated on the functional near-infrared spectroscopy (fNIRS) signal because of its relatively high spatial resolution, sound anatomical localization, and exceptionally high tolerance of motion artifacts17 when studying naturalistic social interactions. Moreover, to precisely characterize the correspondence between the neural time lag and the behavioral time lag during social interaction, it is essential to obtain the INS strength for each time lag (e.g., from no time lag to a time lag of 10 s). For this purpose, previously, the wavelet transform coherence (WTC) procedure was extensively applied after shifting the brain signal of one individual forward or backward relative to that of another individual5,6,18. When using this traditional WTC procedure for fNIRS signals, there is a potential challenge because the observed time-lagged INS may be confounded by the autocorrelation effect of the fNIRS signal for an individual19,20,21. For example, during a dyadic social interaction process, the signal of participant A at time point t may be synchronized with that of participant B at the same time point. Meanwhile, the signal of participant A at time point t may be synchronized with that of participant A at a later time point t+1 because of the autocorrelation effect. Therefore, a spurious time-lagged INS may occur between the signal of participant A at time point t and that of participant B at time point t+1.
Mihanovi&#263; and his colleagues22 first introduced a method termed partial wavelet transform coherence (pWTC), and then applied it in marine science23,24. The original purpose of this method was to control the exogenous confounding noise when estimating the coherence of two signals. Here, to address the autocorrelation issue in the fNIRS hyperscanning data, the pWTC method was extended to calculate time-lagged INS on the fNIRS signal. Precisely, a time-lagged INS (and a directional information flow) from participant A to participant B can be calculated using the equation below (Equation 1)23.
<img alt="Equation 1" src="/files/ftp_upload/62927/62927eq01.jpg" />
Here, it is assumed that there are two signals, A and B, from participants A and B, respectively. The occurrence of signal B always precedes that of signal A with a time lag of n, where WTC (At, Bt+n) is the traditional time-lagged WTC. WTC (At, At+n) is the autocorrelated WTC in participant A. WTC (At, Bt) is the time-aligned WTC at time point t between participant A and B. * is the complex conjugate operator (Figure 1A).
<img alt="Figure 1" class="xfigimg" src="/files/ftp_upload/62927/62927fig01.jpg" /> 
Figure 1: Overview of pWTC. (A) The logic of the pWTC. There are two signals A and B, within a dyad. The occurrence of A always follows that of B with a lag n. A gray box is a wavelet window at a certain time point t or t+n. Based on the pWTC equation (represented in the figure), three WTCs need to be calculated: the time-lagged WTC of At+n and Bt; the autocorrelated WTC in participant A of At and At+n; and the time-aligned WTC at timepoint t, At and Bt. (B)The layout of optode probe sets. CH11 was placed at T3, and CH25 was placed at T4 following the international 10-20 system27,28. <a href="https://www.jove.com/files/ftp_upload/62927/62927fig01large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
This protocol first introduced a simulation experiment to demonstrate how well the pWTC resolves the autocorrelation challenge. Then, it explained how to conduct pWTC in a step-by-step way based on an empirical experiment of naturalistic social interactions. Here, a communication context was used to introduce the method. This is because, previously, the time-lagged INS was usually calculated in a naturalistic communication context3,4,6,8,13,15,18. Additionally, a comparison between the pWTC and the traditional WTC and validation with the Granger causality (GC) test were also conducted.

<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>fNIRS topography system</td>
			<td>Shimadzu Corporation</td>
			<td>Shimadzu LABNIRS systen</td>
			<td>LABNIRS system contains 40 emitters and 40 detectors for fNIRS signals measurement. In this protocol we used these emitters and detectors created two customized 26-channels probe sets and attached to two caps accroding to 10-20 system. Further, LABNIRS system also contains built-in GUI softwares for data quality check, data convert and data export.</td>
		</tr>
		<tr>
			<td>MATLAB</td>
			<td>The MathWorks, Inc.</td>
			<td>MATLAB 2019a</td>
			<td>In this protocol, several toolboxs and functions bulit in MATLAB were used: 
			SPM12 toolbox was used to normalize the valided MRI data through its GUI. 
			NIRS_SPM toolbox was used to project the MNI coordinates of the probes to the AAL template through its GUI. 
			Homer3 toolbox was used to remove motion artifacts through its function hmrMotionCorrectWavelet with default parameters. 
			Wavelet toolbox was used to compute WTC and pWTC through its function wcoherence.</td>
		</tr>
		<tr>
			<td>MRI scanner</td>
			<td>Siemens Healthineers</td>
			<td>TRIO 3-Tesla scanner</td>
			<td>In this protocol, the MRI scanner was used to obtain MNI coordinates of each channel and optpde. Scan parameters are described in main text.</td>
		</tr>
		<tr>
			<td>customized caps</td>
			<td></td>
			<td></td>
			<td>In this protocol, we first marked two nylon caps with 10-20 system. Then, we made two 26-channels customized optode probes sets. Finally, we attached probes sets to caps aligned with landmarks.</td>
		</tr>
	</tbody>
</table>

measurement of the directional information flow in fnirs-hyperscanning data using the partial wavelet transform coherence method

Social interaction is of vital importance for human beings. While the hyperscanning approach has been extensively used to study interpersonal neural synchronization (INS) during social interactions, functional near-infrared spectroscopy (fNIRS) is one of the most popular techniques for hyperscanning naturalistic social interactions because of its relatively high spatial resolution, sound anatomical localization, and exceptionally high tolerance of motion artifacts. Previous fNIRS-based hyperscanning studies usually calculate a time-lagged INS using wavelet transform coherence (WTC) to describe the direction and temporal pattern of information flow between individuals. However, the results of this method might be confounded by the autocorrelation effect of the fNIRS signal of each individual. For addressing this issue, a method termed partial wavelet transform coherence (pWTC) was introduced, which aimed to remove the autocorrelation effect and maintain the high temporal-spectrum resolution of the fNIRS signal. In this study, a simulation experiment was performed first to show the effectiveness of the pWTC in removing the impact of autocorrelation on INS. Then, step-by-step guidance was offered on the operation of the pWTC based on the fNIRS dataset from a social interaction experiment. Additionally, a comparison between the pWTC method and the traditional WTC method and that between the pWTC method and the Granger causality (GC) method was drawn. The results showed that pWTC could be used to determine the INS difference between different experimental conditions and INS's directional and temporal pattern between individuals during naturalistic social interactions. Moreover, it provides better temporal and frequency resolution than the traditional WTC and better flexibility than the GC method. Thus, pWTC is a strong candidate for inferring the direction and temporal pattern of information flow between individuals during naturalistic social interactions.

Simulation results 
The results showed that the time-lagged INSWTC with autocorrelation was significantly higher than the time-lagged INSWTC without autocorrelation (t(1998) = 4.696, p &#60; 0.001) and time-lagged INSpWTC (t(1998) = 5.098, p &#60; 0.001). Additionally, there was no significant difference between time-lagged INSWTC without autocorrelation and INSpWTC (t(1998) = 1.573, p = 0.114, <stron...

Watch this Scientific Journal Video about Measurement of the Directional Information Flow in fNIRS-Hyperscanning Data using the Partial Wavelet Transform Coherence Method at JoVE.com

Measurement of the Directional Information Flow in fNIRS-Hyperscanning Data using the Partial Wavelet Transform Coherence Method

This protocol describes partial wavelet transform coherence (pWTC) for calculating the time-lagged pattern of interpersonal neural synchronization (INS) to infer the direction and temporal pattern of information flow during social interaction. The effectiveness of pWTC in removing the confounds of signal autocorrelation on INS was proved by two experiments.

Social interaction is of vital importance for human beings. While the hyperscanning approach has been extensively used to study interpersonal neural ...

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2.8K Views.  Beijing Normal University. This protocol describes partial wavelet transform coherence (pWTC) for calculating the time-lagged pattern of interpersonal neural synchronization (INS) to infer the direction and temporal pattern of information flow during social interaction. The effectiveness of pWTC in removing the confounds of signal autocorrelation on INS was proved by two experiments.

Video: Measurement of the Directional Information Flow in fNIRS-Hyperscanning Data using the Partial Wavelet Transform Coherence Method

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 ...

Quadruple Immunostaining of the Olfactory Bulb for Visualization of Olfactory Sensory Axon Molecular Identity Codes

The mouse olfactory system is often used to study mechanisms of neural circuit formation&#12288;because of its simple anatomical structure. An Olfactory Sensory Neuron (OSN) is a bipolar cell with a single dendrite and a single unbranched axon. An OSN expresses only one Olfactory Receptor (OR) gene, OSNs expressing a given type of OR converge their axons to a few sets of invariant glomeruli in the Olfactory Bulb (OB). A remarkable feature of OSN projection is that the expressed ORs play instructive roles in axonal projection. ORs regulate the expression of multiple axon-sorting molecules and generate the combinatorial molecular code of axon-sorting molecules at the OSN axon termini. Thus, to understand the molecular mechanisms of OR-specific axon guidance mechanisms, it is vital to characterize their expression profiles at the OSN axon termini within the same glomerulus. The aim of this article was to introduce methods for collecting as many glomeruli as possible on a single OB section and for performing immunostaining using multiple antibodies. This would allow the comparison and analysis of the expression patterns of axon-sorting molecules without staining variation between OB sections.

Olfactory sensory neurons express a wide variety of axon-sorting molecules to establish proper neural circuitry. This protocol describes an immunohistochemical staining method to visualize combinatorial expressions of axon-sorting molecules at the axon termini of olfactory sensory neurons.

The mouse olfactory system is often used to study mechanisms of neural circuit formation&#12288;because of its simple anatomical structure. An Olfactory ...

Ocular Kinematics Measured by In Vitro Stimulation of the Cranial Nerves in the Turtle

After animals are euthanized, their tissues begin to die. Turtles offer an advantage because of a longer survival time of their tissues, especially when compared to warm-blooded vertebrates. Because of this, in vitro experiments in turtles can be performed for extended periods of time to investigate the neural signals and control of their target actions. Using an isolated head preparation, we measured the kinematics of eye movements in turtles, and their modulation by electrical signals carried by cranial nerves. After the brain was removed from the skull, leaving the cranial nerves intact, the dissected head was placed in a gimbal to calibrate eye movements. Glass electrodes were attached to cranial nerves (oculomotor, trochlear, and abducens) and stimulated with currents to evoke eye movements. We monitored eye movements with an infrared video tracking system and quantified rotations of the eyes. Current pulses with a range of amplitudes, frequencies, and train durations were used to observe effects on responses. Because the preparation is separated from the brain, the efferent pathway going to muscle targets can be examined in isolation to investigate neural signaling in the absence of centrally processed sensory information.

Ocular Kinematics Measured by In Vitro Stimulation of the Cranial Nerves in the Turtle

This protocol describes how to use an in vitro isolated turtle head preparation to measure the kinematics of their eye movements. After removal of the brain from the cranium, cranial nerves can be stimulated with currents to quantify rotations of the eye and changes in pupil sizes.

After animals are euthanized, their tissues begin to die. Turtles offer an advantage because of a longer survival time of their tissues, especially when ...

Simultaneous Recordings of Cortical Local Field Potentials, Electrocardiogram, Electromyogram, and Breathing Rhythm from a Freely Moving Rat

Monitoring the physiological dynamics of the brain and peripheral tissues is necessary for addressing a number of questions about how the brain controls body functions and internal organ rhythms when animals are exposed to emotional challenges and changes in their living environments. In general experiments, signals from different organs, such as the brain and the heart, are recorded by independent recording systems that require multiple recording devices and different procedures for processing the data files. This study describes a new method that can simultaneously monitor electrical biosignals, including tens of local field potentials in multiple brain regions, electrocardiograms that represent the cardiac rhythm, electromyograms that represent awake/sleep-related muscle contraction, and breathing signals, in a freely moving rat. The recording configuration of this method is based on a conventional micro-drive array for cortical local field potential recordings in which tens of electrodes are accommodated, and the signals obtained from these electrodes are integrated into a single electrical board mounted on the animal's head. Here, this recording system was improved so that signals from the peripheral organs are also transferred to an electrical interface board. In a single surgery, electrodes are first separately implanted into the appropriate body parts and the target brain areas. The open ends of all of these electrodes are then soldered to individual channels of the electrical board above the animal's head so that all of the signals can be integrated into the single electrical board. Connecting this board to a recording device allows for the collection of all of the signals into a single device, which reduces experimental costs and simplifies data processing, because all data can be handled in the same data file. This technique will aid the understanding of the neurophysiological correlates of the associations between central and peripheral organs.

This study introduces a method for the simultaneous recording of local field potentials in the brain, electrocardiograms, electromyograms, and breathing signals of a freely moving rat. This technique, which reduces experimental costs and simplifies data analysis, will contribute to the understanding of the interactions between the brain and peripheral organs.

Monitoring the physiological dynamics of the brain and peripheral tissues is necessary for addressing a number of questions about how the brain controls ...

A Volumetric Method for Quantification of Cerebral Vasospasm in a Murine Model of Subarachnoid Hemorrhage

Subarachnoid hemorrhage (SAH) is a subtype of hemorrhagic stroke. Cerebral vasospasm that occurs in the aftermath of the bleeding is an important factor determining patient outcome and is therefore frequently taken as a study endpoint. However, in small animal studies on SAH, quantification of cerebral vasospasm is a major challenge. Here, an ex vivo method is presented that allows quantification of volumes of entire vessel segments, which can be used as an objective measure to quantify cerebral vasospasm. In a first step, endovascular casting of the cerebral vasculature is performed using a radiopaque casting agent. Then, cross-sectional imaging data are acquired by micro computed tomography. The final step involves 3-dimensional reconstruction of the virtual vascular tree, followed by an algorithm to calculate center lines and volumes of the selected vessel segments. The method resulted in a highly accurate virtual reconstruction of the cerebrovascular tree shown by a diameter-based comparison of anatomical samples with their virtual reconstructions. Compared with vessel diameters alone, the vessel volumes highlight the differences between vasospastic and non-vasospastic vessels shown in a series of SAH and sham-operated mice.

The goal of this article is to present a method that allows a 3-dimensional reconstruction of the cerebrovascular tree in mice after micro computed tomography and determination of volumes of entire vessel segments that can be used to quantify cerebral vasospasm in murine models of subarachnoid hemorrhage.

Subarachnoid hemorrhage (SAH) is a subtype of hemorrhagic stroke. Cerebral vasospasm that occurs in the aftermath of the bleeding is an important factor ...

Reliable Acquisition of Electroencephalography Data during Simultaneous Electroencephalography and Functional MRI

Simultaneous electroencephalography (EEG) and functional magnetic resonance imaging (fMRI), EEG-fMRI, combines the complementary properties of scalp EEG (good temporal resolution) and fMRI (good spatial resolution) to measure neuronal activity during an electrographic event, through hemodynamic responses known as blood-oxygen-level-dependent (BOLD) changes. It is a non-invasive research tool that is utilized in neuroscience research and is highly beneficial to the clinical community, especially for the management of neurological diseases, provided that proper equipment and protocols are administered during data acquisition. Although recording EEG-fMRI is apparently straightforward, the correct preparation, especially in placing and securing the electrodes, is not only important for safety but is also critical in ensuring the reliability and analyzability of the EEG data obtained. This is also the most experience-demanding part of the preparation. To address these issues, a straightforward protocol that ensures data quality was developed. This article provides a step-by-step guide for acquiring reliable EEG data during EEG-fMRI using this protocol that utilizes readily available medical products. The presented protocol can be adapted to different applications of EEG-fMRI in research and clinical settings, and may be beneficial to both inexperienced and expert operators.

This article provides a straightforward protocol for acquiring good quality electroencephalography (EEG) data during simultaneous EEG and functional magnetic resonance imaging by utilizing readily available medical products.

Simultaneous electroencephalography (EEG) and functional magnetic resonance imaging (fMRI), EEG-fMRI, combines the complementary properties of scalp EEG ...

Inter-Brain Synchrony in Open-Ended Collaborative Learning: An fNIRS-Hyperscanning Study

fNIRS hyperscanning is widely used to detect the neurobiological underpinnings of social interaction. With this technique, researchers qualify the concurrent brain activity of two or more interactive individuals with a novel index called inter-brain synchrony (IBS) (i.e., phase and/or amplitude alignment of the neuronal or hemodynamic signals across time). A protocol for conducting fNIRS hyperscanning experiments on collaborative learning dyads in a naturalistic learning environment is presented here. Further, a pipeline of analyzing IBS of oxygenated hemoglobin (Oxy-Hb) signal is explained. Specifically, the experimental design, the process of NIRS data recording, data analysis methods, and future directions are all discussed. Overall, implementing a standardized fNIRS hyperscanning pipeline is a fundamental part of second-person neuroscience. Also, this is in line with the call for open-science to aid the reproducibility of research.

The protocol for conducting fNIRS hyperscanning experiments on collaborative learning dyads in a naturalistic learning environment is outlined. Further, a pipeline to analyze the Inter-Brain Synchrony (IBS) of oxygenated hemoglobin (Oxy-Hb) signals is presented.

fNIRS hyperscanning is widely used to detect the neurobiological underpinnings of social interaction. With this technique, researchers qualify the ...

How to Calculate and Validate Inter-brain Synchronization in a fNIRS Hyperscanning Study

The dynamics between coupled brains of individuals have been increasingly represented by inter-brain synchronization (IBS) when they coordinate with each other, mostly using simultaneous-recording signals of brains (namely hyperscanning) with fNIRS. In fNIRS hyperscanning studies, IBS has been commonly assessed through the wavelet transform coherence (WTC) method because of its advantage on expanding time series into time-frequency space where oscillations can be seen in a highly intuitive way. The observed IBS can be further validated via the permutation-based random pairing of the trial, partner, and condition. Here, a protocol is presented to describe how to obtain brain signals via fNIRS technology, calculate IBS through the WTC method, and validate IBS by permutation in a hyperscanning study. Further, we discuss the critical issues when using the above methods, including the choice of fNIRS signals, methods of data preprocessing, and optional parameters of computations.&#160;In summary, using the WTC method and permutation is a potentially standard pipeline for analyzing IBS in fNIRS hyperscanning studies, contributing to both the reproducibility and reliability of IBS.

The dynamics between coupled brains of individuals have been increasingly represented by inter-brain synchronization (IBS) when they coordinate with each ...

Mouse Retina OCT Fibergram Alignment with Confocal Images

Alignment of Visible-Light Optical Coherence Tomography Fibergrams with Confocal Images of the Same Mouse Retina

In recent years, in vivo retinal imaging, which provides non-invasive, real-time, and longitudinal information about biological systems and processes, has been increasingly applied to obtain an objective assessment of neural damage in eye diseases. Ex vivo confocal imaging of the same retina is often necessary to validate the in vivo findings especially in animal research. In this study, we demonstrated a method for aligning an ex vivo confocal image of the mouse retina with its in vivo images. A new clinical-ready imaging technology called visible light optical coherence tomography fibergraphy (vis-OCTF) was applied to acquire in vivo images of the mouse retina. We then performed the confocal imaging of the same retina as the &#34;gold standard&#34; to validate the in vivo vis-OCTF images. This study not only enables further investigation of the molecular and cellular mechanisms but also establishes a foundation for a sensitive and objective evaluation of neural damage in vivo.

Author Spotlight: Advancements in In Vivo and Ex Vivo Retinal Imaging for Improved Glaucoma Diagnosis and Treatment 

The present protocol outlines the steps for aligning in vivo visible-light optical coherence tomography fibergraphy (vis-OCTF) images with ex vivo confocal images of the same mouse retina for the purpose of verifying the observed retinal ganglion cell axon bundle morphology in the in vivo images.

In recent years, in vivo retinal imaging, which provides non-invasive, real-time, and longitudinal information about biological systems and processes, has ...

Revolutionizing fNIRS Studies&amp;#58; A Novel Approach to Neural Coupling

New Framework for Understanding Cross-Brain Coherence in Functional Near-Infrared Spectroscopy (fNIRS) Hyperscanning Studies

Despite the growing body of functional near-infrared spectroscopy (fNIRS) hyperscanning studies, the assessment of coupling between two neural signals using wavelet transform coherence (WTC) seems to ignore the directionality of the interaction. The field is currently lacking a framework that allows researchers to determine whether a high coherence value obtained using a WTC function reflects in-phase synchronization (i.e., neural activation is seen in both members of the dyad at the same time), lagged synchronization (i.e., neural activation is seen in one member of the dyad prior to the other member), or anti-phase synchronization (i.e., neural activation is increased in one member of the dyad and decreased in the other). To address this need, a complementary and more sensitive approach for analyzing the phase coherence of two neural signals is proposed in this work. The toolbox allows investigators to estimate the coupling directionality by classifying the phase angle values obtained using traditional WTC into in-phase synchronization, lagged synchronization, and anti-phase synchronization. The toolbox also allows researchers to assess how the dynamics of interactions develop and change throughout the task. Using this novel WTC approach and the toolbox will advance our understanding of complex social interactions through their uses in fNIRS hyperscanning studies.

Author Spotlight: Unlocking New Insights in fNIRS Studies -  A Novel Framework for Inter-Brain Synchrony Analysis 

Wavelet transform coherence (WTC) is a common methodology for assessing the coupling between signals that is used in functional near-infrared spectroscopy (fNIRS) hyperscanning studies. A toolbox for assessing the directionality of the signal interaction is presented in this work.

Despite the growing body of functional near-infrared spectroscopy (fNIRS) hyperscanning studies, the assessment of coupling between two neural signals ...