We would like to thank the Marlin and Kahn labs for supporting this research. We would also like to thank Dr. Kevin Cury for his insightful discussion surrounding our videography data. Research reported in this publication was supported by the Eunice Kennedy Shriver National Institute Of Child Health &#38; Human Development of the National Institutes of Health under Award Number F31HD114466 (BRM), the Howard Hughes Medical Institute (BJM), the UNCF E.E. Just Fellowship CU20-1071 (BJM), the BBRF NARSAD Young Investigator Grant 30380 (BJM), and The Whitehall Foundation (BJM). The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

All animal experiments were performed according to the protocols approved by the Columbia University Institutional Animal Care and Use Committee (IACUC), and all methods were carried out in accordance with relevant guidelines, regulations, and recommendations. The details of the equipment and software used are listed in the Table of Materials.
1. Software
<ol>
	<li>Download MATLAB from the Mathworks website. 
	NOTE: The code that comprises this analysis pipeline is executable solely through MATLAB and was specifically written to be compatible with MATLAB 2023a.</li>
	<li>This pipeline requires the Computer Vision Toolbox and the Image Processing Toolbox within MATLAB. Add the toolboxes to MATLAB add-ons by clicking on Manage Add-Ons, Get Add-Ons, and then search for and add them. 
	NOTE: Five MATLAB scripts are provided as supplementary files, titled as follows: script1_videocoreg.m (Supplementary Coding File 1), script2_optflow_roiselect.m (Supplementary Coding File 2), script3_optflow_analysis.m (Supplementary Coding File 3), script4_optflow_pca.m (Supplementary Coding File 4), and script5_optflow_pca_analysis.m (Supplementary Coding File 5). Two example videos (Video 1 and Video 2) and their corresponding event timing files (Supplementary File 1 and Supplementary File 2) are also provided. It is recommended to first run through the pipeline with the example data. 
	CAUTION: Beware of any comments in the code that include an exclamation mark, as these are areas that require action before execution. For example, there are comments such as &#34;edit the code block below!&#34; to highlight where edits must be made to fit file structures, experiment details, or analysis needs. These choices are set by default to fit the two provided example videos but will need adjustments once one starts to run the pipeline with their own data.</li>
	<li>To run each section of each script, click on Run and Advance on the Editor menu.</li>
</ol>
2. Video coregistration
NOTE: The first provided script in this analysis pipeline is script1_videocoreg.m (Supplementary Coding File 1), which spatially aligns all videos to each other via coregistration. The inputs for this script are the raw videos, and the main outputs are the transformed videos.
<ol>
	<li>Edit Sections 1, 2, 3, 4, 5, and 6 of the script where indicated to fit data structures.</li>
	<li>Run Section 1.</li>
	<li>Choose three&#160;points in each video to label, as in Figure 1A. These three&#160;points will be used to coregister all frames to each other. Run Section 2 of the script to pull up the first frame of each video&#160;and use the mouse to click once on the first two points, then double-click on the third point. After the last video&#39;s points have been selected, press Enter.</li>
	<li>Run Section 3 and Section 4.</li>
	<li>Set the flag aligned to 0 and run Section 5. Then, set the flag aligned to 1 and run Section 5 again.</li>
	<li>Run Section 6 and compare the resulting side-by-side figure to Figure 1B. Any aberrations observed in the non-aligned average image on the left should be lessened in the aligned average image on the right.</li>
</ol>
3. Optical flow estimation: Full FOV
NOTE: In order to assess movement through the scan videos, optical flow needs to be estimated for each transformed video. This can be done for the full field-of-view (FOV) of each video with the provided script script2_optflow_roiselect.m (Supplementary Coding File 2). The inputs for this script are the transformed videos, and the outputs are 3D matrices indicating the optical flow magnitude of each pixel across time for each video. Figure 2A shows an example frame with optical flow vectors overlaid in blue, where each vector&#39;s length represents the relative optical flow magnitude of that pixel. Saving the outputs as videos was found to be more efficient than saving them as 3D matrices.
<ol>
	<li>Edit Section 1 of the script where indicated to fit data structures.</li>
	<li>Run Section 1.</li>
</ol>
4. ROI selection
NOTE: The following analysis steps are made much more manageable in terms of computational load and data storage needs by selecting a region of interest (ROI) within the full FOV on which to focus the analysis. The ROI can be selected according to the experiment and behaviors of interest or through a more data-driven approach. For the representative results presented here, the standard deviation of the optical flow magnitude of each pixel in the full FOV was calculated with the script script2_optflow_roiselect.m, Section 2 (Supplementary Coding File 2). The inputs for this part of the script are the outputs of Section 1, and the main output is a visualization of the standard deviation of the optical flow magnitude across all pixels across all videos. In the representative results presented, the mirror FOV showed a relatively high standard deviation of optical flow, which guided the demonstrated choice of ROI.
<ol>
	<li>Edit Section 2 of the script where indicated to fit data structures.</li>
	<li>Run Section 2 and use the resulting image to see where optical flow fluctuates most dramatically in the analyzed videos. An example of this image is shown in Figure 2B.</li>
</ol>
5. Optical flow quantification: ROI
NOTE: Section 3 of the script script2_optflow_roiselect.m (Supplementary Coding File 2) allows the user to select an ROI with a drawing tool and save the boundary coordinates. The inputs for this part of the script are the outputs of Section 1, and the outputs are 1D vectors indicating the average optical flow magnitude of the ROI over time for each video.
<ol>
	<li>Edit Section 3 of the script where indicated to fit data structures and analysis needs. 
	NOTE: There are a few options regarding how to proceed with ROI selection. Choose one option:
	<ol>
		<li>Option 1: Analyze the full video FOV.
		<ol>
			<li>Set the flag selectROI to 0&#160;and set provideROI to 0.</li>
		</ol>
		</li>
		<li>Option 2: Analyze a new ROI.
		<ol>
			<li>Set the flag selectROI to 1&#160;and set provideROI to 0. Then edit newCoordsName.</li>
		</ol>
		</li>
		<li>Option 3: Analyze a previously drawn ROI. 
		NOTE: Only select this option if one has previously run this code and created an ROI.
		<ol>
			<li>Set the flag selectROI&#160;to 0&#160;and set provideROI to 1. Then, edit inputCoords to provide a predetermined set of coordinates.</li>
		</ol>
		</li>
	</ol>
	</li>
	<li>Run Section 3.</li>
</ol>
6. Optical flow magnitude comparison
NOTE: The provided script script3_optflow_analysis.m (Supplementary Coding File 3) requires the most specialization to fit the user&#39;s data. The main inputs are the 1D vectors indicating the average optical flow magnitude of the ROI over time for each video, and when combined appropriately with event onsets and group/condition information, the main outputs are statistical comparisons that can be tailored to analysis interests.
<ol>
	<li>Edit Sections 1-3 of the script to fit data structures and analysis needs.</li>
	<li>Set the analysis options flags zsc, blrm, blzsc, demeanPerTrial, and applyLPfilter to 0 or 1 as desired in Section 1. In the representative results presented, zsc, blrm, and applyLPfilter were set to 1, and all other options were set to 0.</li>
	<li>Set LPfilter to a number representing the desired lowpass filter in hertz (Hz). In the representative results presented, a 5 Hz lowpass filter was applied, as behavioral responses were not expected to fluctuate at a rate faster than 5 Hz.</li>
	<li>Run Sections 1-3. Section 3 should produce plots for group average optical flow time series, group average cumulative optical flow, and summary cumulative optical flow, including plots such as those shown in Figure 3. Section 3 can be run for any combination of group/condition comparisons.</li>
</ol>
7. Optical flow PCA
NOTE: Building upon the estimation of optical flow magnitude, the outputs of script2_optflow_roiselect.m (Supplementary Coding File 2) also contain information about the spatial distribution of optical flow frame-by-frame per video. In order to reduce the dimensionality of this spatial information, the provided script script4_optflow_pca.m (Supplementary Coding File 4)&#160;performs principal component analysis (PCA) on the estimated optical flow. The main inputs are the 3D optical flow matrices, previously saved as videos for each behavioral video, and the main output is one .mat file per video containing PC and explained variance information.
<ol>
	<li>Edit Section 1 of the script to fit data structures and analysis needs.</li>
	<li>Set the analysis options flags stimnum, dsfactor, and fnfactor as desired in Section 1. In the representative results presented, each stimulus type was analyzed separately, and the default value of 1 maintained for dsfactor and fnfactor, resulting in no downsampling in space or time.</li>
	<li>Run Section 1.</li>
</ol>
8. Optical flow PCA comparison
NOTE: The provided script script5_optflow_pca_analysis.m (Supplementary Coding File 5) will carry out exploratory comparisons of PCA results across groups. The main inputs are the stimulus-specific PC and explained variance information obtained from script4_optflow_pca.m (Supplementary Coding File 4), and the main outputs are statistically thresholded heat maps depicting loadings of the first PC, though other PCs can also be analyzed with this script.
<ol>
	<li>Edit Section 1 of the script to fit data structures and analysis.</li>
	<li>Run Section 1 and Section 2.</li>
	<li>Edit Section 3 inputs, specifically the group and myTitle variables, to reflect the group analysis desired, as well as threshT. threshT represents the T-statistic threshold above which results will be considered significant, which can be calculated based on publicly-available T-statistic to p-value tables and the degrees of freedom.</li>
	<li>Run Section 3 for each group of interest. The code should produce a group summary statistic map, such as those shown in the left and center panels of Figure 4. Adjust the range of caxis, and thus the color bar limits, as desired for visualization.</li>
	<li>Edit Section 4 inputs, specifically the mag and myTitle variables to reflect the group comparison desired, as well as threshT. threshT represents the T-statistic threshold above which results will be considered significant, which can be calculated based on publicly-available T-statistic to p-value tables and the degrees of freedom.</li>
	<li>Run Section 4 for each group comparison of interest. The code should produce a group comparison statistic map,&#160;as shown in the right panel of Figure 4. Adjust the range of caxis, and thus the color bar limits, as desired for visualization.</li>
</ol>

Pipeline for Analyzing Behavioral Responses to External Cues in MRI Environments

<ol>
	<li>Musall, S., Kaufman, M. T., Juavinett, A. L., Gluf, S., Churchland, A. K. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Single-trial+neural+dynamics+are+dominated+by+richly+varied+movements.">Single-trial neural dynamics are dominated by richly varied movements.</a> Nat Neurosci. 22 (10), 1677-1686 (2019).</li><li>Dolensek, N., Gehrlach, D. A., Klein, A. S., Gogolla, N. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Facial+expressions+of+emotion+states+and+their+neuronal+correlates+in+mice.">Facial expressions of emotion states and their neuronal correlates in mice.</a> Science. 368 (6486), 89-94 (2020).</li><li>Kepecs, A., Uchida, N., Mainen, Z. F. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+sniff+as+a+unit+of+olfactory+processing.">The sniff as a unit of olfactory processing.</a> Chemical Senses. 31 (2), 167-179 (2006).</li><li>Shusterman, R., Smear, M. C., Koulakov, A. A., Rinberg, D. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Precise+olfactory+responses+tile+the+sniff+cycle.">Precise olfactory responses tile the sniff cycle.</a> Nat Neurosci. 14 (8), 1039-1044 (2011).</li><li>Han, Z., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Awake+and+behaving+mouse+fMRI+during+Go/No-Go+task.">Awake and behaving mouse fMRI during Go/No-Go task.</a> NeuroImage. 188, 733-742 (2019).</li><li>Mathis, A., Mamidanna, P., Cury, K. M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=DeepLabCut:+Markerless+pose+estimation+of+user-defined+body+parts+with+deep+learning.">DeepLabCut: Markerless pose estimation of user-defined body parts with deep learning.</a> Nature Neurosci. 21 (9), 1281-1289 (2018).</li><li>Krakauer, J. W., Ghazanfar, A. A., Gomez-Marin, A., MacIver, M. A., Poeppel, D. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Neuroscience+needs+behavior:+Correcting+a+reductionist+bias.">Neuroscience needs behavior: Correcting a reductionist bias.</a> Neuron. 93 (3), 480-490 (2017).</li><li>Wiltschko, A. B., Tsukahara, T., Zeine, A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Revealing+the+structure+of+pharmacobehavioral+space+through+motion+sequencing.">Revealing the structure of pharmacobehavioral space through motion sequencing.</a> Nat Neurosci. 23 (11), 1433-1443 (2020).</li><li>Mo&#235;ne, O. L., Larsson, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+new+tool+for+quantifying+mouse+facial+expressions.">A new tool for quantifying mouse facial expressions.</a> eNeuro. 10 (2), (2023).</li><li>Paasonen, J., Stenroos, P., Laakso, H. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Whole-brain+studies+of+spontaneous+behavior+in+head-fixed+rats+enabled+by+zero+echo+time+MB-SWIFT+fMRI.">Whole-brain studies of spontaneous behavior in head-fixed rats enabled by zero echo time MB-SWIFT fMRI.</a> NeuroImage. 250, 118924 (2022).</li><li>Ehret, G. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Infant+rodent+ultrasounds:+A+Gate+to+the+understanding+of+sound+communication.">Infant rodent ultrasounds: A Gate to the understanding of sound communication.</a> Behav Genet. 35 (1), 19-29 (2005).</li><li>Marlin, B. J., Mitre, M., D'amour, J. A., Chao, M. V., Froemke, R. C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Oxytocin+enables+maternal+behavior+by+balancing+cortical+inhibition.">Oxytocin enables maternal behavior by balancing cortical inhibition.</a> Nature. 520 (7548), 499-504 (2015).</li><li>. Optical Flow Available from: <a target="_blank" href="https://www.mathworks.com/discovery/optical-flow.html">https://www.mathworks.com/discovery/optical-flow.html</a> (2024)</li><li>Horn, B. K. P., Schunck, B. G. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Determining+optical+flow.">Determining optical flow.</a> Artif Intell. 17 (1), 185-203 (1981).</li><li>Szeliski, R., Szeliski, R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Dense+motion+estimation.">Dense motion estimation.</a> Computer Vision: Algorithms and Applications. , 335-374 (2011).</li><li>Beauchemin, S. S., Barron, J. L. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+computation+of+optical+flow.">The computation of optical flow.</a> ACM Comput Surv. 27 (3), 433-466 (1995).</li><li>McRae, B. R., Andreu, V., Marlin, B. 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The authors have nothing to disclose.

The presented behavioral analysis pipeline allows for the extraction of valuable information from videos of animals exhibiting uninstructed behaviors in response to passively presented stimuli. These representative behavioral videos were acquired in conjunction with whole-brain functional magnetic resonance imaging (fMRI) data, for which multiple constraints were overcome to capture behavioral responses in the MRI environment. Employing the method of optical flow estimation, differences in how mothers versus virgins respond to pup calls&#8212;but not pure tones&#8212;were revealed, supporting the initial hypothesis13,14,15,16. Taking advantage of dimensionality reduction of the optical flow information, this finding was extended to an exploratory analysis of the spatial distribution of these behaviors.
This analysis pipeline is widely amenable to various experimental paradigms. It is completely agnostic to the type of neural data acquired and does not require excessive computational power or specialized equipment. The main experimental requirement is that videography data be recorded with the relevant timestamps of stimulus presentation onsets. Furthermore, by providing the MATLAB scripts used to perform the demonstrated analyses, the implementation of this pipeline should be fairly straightforward, even for those with minimal MATLAB experience. The general steps, as described in the protocol, consist of video coregistration, optical flow estimation, region-of-interest (ROI) selection, ROI-specific optical flow quantification, optical flow magnitude comparison, principal component analysis (PCA), and PCA comparison. At each step, the pipeline can be modified to fit experimental and analytical needs, as stated in the protocol and highlighted&#160;with&#160;comments throughout the code.
There are several limitations to keep in mind when employing the presented video analysis pipeline. First, the design of the behavioral apparatus should be carefully considered. If it is unclear what behavioral responses can be expected, it may benefit future analyses to have as many viewpoints of the animal as possible. Therefore, installing one or multiple mirrors to simultaneously capture the animal&#8217;s frontal and profile views is recommended, as shown in the example video frames in Figure 1A. Second, it is recommended that the illumination provided for video acquisition is bright enough to see all relevant features of the field-of-view (FOV), but not so bright as to reach saturation. In Figure 1A, it can be observed that the frontal view of the mouse nose is at a point of near saturation, making it difficult to capture any fluctuations in brightness that would indicate movement. This may have contributed to the lack of optical flow variation observed in this part of the FOV, as shown in Figure 2B. Fortunately, the profile view of the nose did not face this problem, allowing for the analysis of the mirror ROI. Additionally, the possibility of saturation should be considered if visual stimuli are involved in the experiment. In the validation dataset experiment, a light cue used to signal the delivery of a water reward introduced temporary saturation in the acquired videos, posing a temporary artifact in the optical flow estimation. This artifact can be observed in Figure 5A, where the two sharp peaks in the optical flow traces for both reward conditions align exactly with when the light cue was turned on and off. This artifact rendered these short time windows difficult to analyze with the presented pipeline, and had it not been present, the results presented in Figure 5B may have been even more significant. Constant scene illumination is a necessary assumption for most optical flow algorithms14,16. Therefore, this video analysis pipeline is not recommended for experiments in which the illumination of the environment changes drastically, both within and between trials. Third and finally, the sufficient habituation of animals to the recording environment is a key component of any experiment that will take advantage of this pipeline. If not properly habituated, animals may show significant stress responses, washing out any subtle stimulus-driven effects that could otherwise be parsed out via video analysis.
Moving forward, this behavioral analysis pipeline can be employed to facilitate the coordinated evaluation of simultaneously recorded behavioral and neural responses. As shown, quantitatively assessing uninstructed behaviors allows for the evaluation of the salience of stimuli in a complex recording environment, as well as testing for different behavioral responses across experimental groups and conditions. Further analyses can be conducted to explore individual differences or trial-by-trial trends in behavioral responses such that the corresponding neural activity can be examined. Additionally, incorporating PCA to obtain spatial information about stimulus-evoked movement can be used to further investigate whether specific types of behavioral sequences observed during stimulus presentation differ across groups and conditions. This analysis pipeline can be easily extended to various neural recording methods, providing versatile behavioral monitoring in constrained environments. By investigating how external stimuli are represented in both the brain and behavior, researchers are primed to obtain a more comprehensive understanding of how the perception of stimuli affects internal states and their external manifestations.

Monitoring behavioral responses during neural recordings is essential for&#160;understanding stimulus-evoked coordinated activity across the brain. In the event that animals are not predicted to respond to stimuli in a specific and goal-oriented manner, observing uninstructed behaviors can offer insights into how external cues inform their internal states1,2. Recent advancements in neuroimaging tools, such as wide-field calcium imaging and functional magnetic resonance imaging (fMRI), have enabled neuroscientists to expand investigations beyond singular brain regions. However, to achieve a more comprehensive understanding of such high-dimensional neural data, the ability to evaluate the behavioral outputs of these complex patterns must also advance accordingly.
State-of-the-art methods for characterizing task-instructed behaviors are widely used in neuroscience research, including temperature and pressure sensors to detect sniffs3,4, light beams to detect licks5, and markerless pose estimation to track predetermined body parts6. However, the data-driven evaluation of uninstructed behavioral patterns remains a challenge in the field7. While methods for data-driven behavioral analysis are advancing rapidly, existing methods typically require substantial computational power, specialized equipment, or a particularly clear view of the animal2,6,8,9.&#160;Presented here is a behavioral video analysis pipeline that is easily amenable to any videography data and allows for the extraction of valuable behavioral measures from videos acquired during passive stimulation.
This behavioral analysis pipeline was designed to be compatible with the coordinated acquisition of neural and video data in head-fixed animals exposed to a broad range of external stimuli and multiple recording environments, even those with considerable constraints. For example, the MRI environment poses specific challenges to behavioral monitoring, including a restricted field of view and limited availability of equipment without ferromagnetic components. These constraints render commonly employed methods incompatible, leaving measures such as head motion (i.e., how the brain moves between volume acquisitions) to be among the most accessible yet&#160;limited&#160;ways to assess bodily responses during scans10. By&#160;overcoming these limitations, this protocol facilitated the analysis of MR-compatible videography data, showing&#160;that female mice with&#160;different maternal experiences exhibit distinct behavioral responses to auditory stimuli. Mothers and virgins were presented with auditory stimuli during fMRI scans, including distressed pup vocalizations (&#34;pup calls&#34;) and pure tones. These stimuli were presented passively, thus without any instructed behavioral outputs. While mothers were predicted to show heightened responses to pup calls compared to virgins, there is little literature on&#160;how female mice respond to infant cues in a head-fixed environment. Therefore, there was no specific behavioral output to track a priori, making this experiment the perfect test for the proposed data-driven behavioral analyses. Employing methods such as optical flow estimation and dimensionality reduction, group differences in behavioral responses were detected, both in magnitude and spatial manifestation.
Moving forward, the coordinated evaluation of behavioral and neural responses can be leveraged to achieve a more comprehensive understanding of how external stimuli alter internal states and inform complex behavioral outputs. This understanding should extend to stimuli presented in the absence of imposed tasks and instructed behaviors. The analysis pipeline presented here can be extended to various neural recording techniques, providing versatile behavioral monitoring even in constrained environments.

<table><tbody><tr><td>MATLAB Computer Vision Toolbox</td><td>MathWorks</td><td></td><td>https://www.mathworks.com/products/computer-vision.html</td></tr><tr><td>MATLAB Image Processing Toolbox</td><td>MathWorks</td><td></td><td>https://www.mathworks.com/products/image-processing.html</td></tr><tr><td>MATLAB software</td><td>MathWorks</td><td></td><td>https://www.mathworks.com/products/matlab.html</td></tr><tr><td>MR-compatible camera &amp;ldquo;12M-i&amp;rdquo; with integrated LED light</td><td>MRC Systems GmbH</td><td>12M-i</td><td>https://www.mrc-systems.de/downloads/en/mri-compatible-cameras/manual_mrcam_12m-i.pdf</td></tr></tbody></table>

profiling maternal behavior responses during whole-brain imaging

Recent advancements in whole-brain imaging tools have enabled neuroscientists to investigate how coordinated brain activity processes external cues, influencing internal state changes and eliciting behavioral responses. For example, functional magnetic resonance imaging (fMRI) is a noninvasive technique that allows for the measurement of whole-brain activity in awake, behaving mice using the blood oxygenation-level-dependent (BOLD) response. However, to fully understand BOLD responses evoked by external stimuli, it is crucial that experimenters also assess behavioral responses during scans. The MRI environment poses challenges to this goal, rendering commonly employed methods of behavioral monitoring incompatible. These challenges include (1) a restricted field of view and (2) the limited availability of equipment without ferromagnetic components. Presented here is a behavioral video analysis pipeline that overcomes these limitations by extracting valuable information from videos acquired within these environmental constraints, enabling the evaluation of behavior during the acquisition of whole-brain neural data. Employing methods such as optical flow estimation and dimensionality reduction, robust differences can be detected in behavioral responses to stimuli presented during fMRI scans. For example, representative results suggest that mouse pup vocalizations, but not pure tones, evoke significantly different behavioral responses in maternal versus virgin female mice. Moving forward, this behavioral analysis pipeline, initially tailored to overcome challenges in fMRI experiments, can be extended to various neural recording methods, providing versatile behavioral monitoring in constrained environments. The coordinated evaluation of behavioral and neural responses will offer a more comprehensive understanding of how the perception of stimuli leads to the coordination of complex behavioral outputs.

Profiling Maternal Behavior Responses During Whole-Brain Imaging

Presented here is a video analysis pipeline that overcomes challenges in behavioral monitoring within MRI environments, allowing for the detection of uninstructed behavioral responses to external cues. This analysis will facilitate a more comprehensive understanding of evoked internal state changes and brain-wide activity.

Recent advancements in whole-brain imaging tools have enabled neuroscientists to investigate how coordinated brain activity processes external cues, ...

profiling-maternal-behavior-responses-during-whole-brain-imaging

Research

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JoVE Journal

Behavior

561 Views.  Columbia University. Presented here is a video analysis pipeline that overcomes challenges in behavioral monitoring within MRI environments, allowing for the detection of uninstructed behavioral responses to external cues. This analysis will facilitate a more comprehensive understanding of evoked internal state changes and brain-wide activity.

Video: Profiling Maternal Behavior Responses During Whole-Brain Imaging

Best Current Practice for Obtaining High Quality EEG Data During Simultaneous fMRI

Simultaneous EEG-fMRI allows the excellent temporal resolution of EEG to be combined with the high spatial accuracy of fMRI. The data from these two modalities can be combined in a number of ways, but all rely on the acquisition of high quality EEG and fMRI data. EEG data acquired during simultaneous fMRI are affected by several artifacts, including the gradient artefact (due to the changing magnetic field gradients required for fMRI), the pulse artefact (linked to the cardiac cycle) and movement artifacts (resulting from movements in the strong magnetic field of the scanner, and muscle activity). Post-processing methods for successfully correcting the gradient and pulse artifacts require a number of criteria to be satisfied during data acquisition. Minimizing head motion during EEG-fMRI is also imperative for limiting the generation of artifacts.
Interactions between the radio frequency (RF) pulses required for MRI and the EEG hardware may occur and can cause heating. This is only a significant risk if safety guidelines are not satisfied. Hardware design and set-up, as well as careful selection of which MR sequences are run with the EEG hardware present must therefore be considered.
The above issues highlight the importance of the choice of the experimental protocol employed when performing a simultaneous EEG-fMRI experiment. Based on previous research we describe an optimal experimental set-up. This provides high quality EEG data during simultaneous fMRI when using commercial EEG and fMRI systems, with safety risks to the subject minimized. We demonstrate this set-up in an EEG-fMRI experiment using a simple visual stimulus. However, much more complex stimuli can be used. Here we show the EEG-fMRI set-up using a Brain Products GmbH (Gilching, Germany) MRplus, 32 channel EEG system in conjunction with a Philips Achieva (Best, Netherlands) 3T MR scanner, although many of the techniques are transferable to other systems.

Simultaneous electroencephalography (EEG) and functional Magnetic Resonance imaging (fMRI) is a powerful neuroimaging tool. However, the inside of an MRI scanner forms a difficult environment for EEG data recording and safety must be considered whenever operating EEG equipment inside a scanner. Here, we present an optimised EEG-fMRI data acquisition protocol.

Simultaneous  EEG-fMRI allows the excellent temporal resolution of EEG to be combined with the  high spatial accuracy of fMRI. The data  from these two ...

Simultaneous EEG Monitoring During Transcranial Direct Current Stimulation

Transcranial direct current stimulation (tDCS) is a technique that delivers weak electric currents through the scalp. This constant electric current induces shifts in neuronal membrane excitability, resulting in secondary changes in cortical activity. Although tDCS has most of its neuromodulatory effects on the underlying cortex, tDCS effects can also be observed in distant neural networks. Therefore, concomitant EEG monitoring of the effects of tDCS can provide valuable information on the mechanisms of tDCS. In addition, EEG findings can be an important surrogate marker for the effects of tDCS and thus can be used to optimize its parameters. This combined EEG-tDCS system can also be used for preventive treatment of neurological conditions characterized by abnormal peaks of cortical excitability, such as seizures. Such a system would be the basis of a non-invasive closed-loop device. In this article, we present a novel device that is capable of utilizing tDCS and EEG simultaneously. For that, we describe in a step-by-step fashion the main procedures of the application of this device using schematic figures, tables and video demonstrations. Additionally, we provide a literature review on clinical uses of tDCS and its cortical effects measured by EEG techniques.

Transcranial direct current stimulation (tDCS) is a non-invasive brain stimulation technique that has shown initial therapeutic effects in several neurological conditions. The main mechanism underlying these therapeutic effects is the modulation of cortical excitability. Therefore, online monitoring of cortical excitability would help guide stimulation parameters and optimize its therapeutic effects. In the present article we review the use of a novel device that combines simultaneous tDCS and EEG monitoring in real time.

Transcranial  direct current stimulation (tDCS) is a technique that delivers weak electric  currents through the scalp. This constant electric current ...

Meal Duration as a Measure of Orofacial Nociceptive Responses in Rodents

A lengthening in meal duration can be used to measure an increase in orofacial mechanical hyperalgesia having similarities to the guarding behavior of humans with orofacial pain. To measure meal duration unrestrained rats are continuously kept in sound attenuated, computerized feeding modules for days to weeks to record feeding behavior. These sound-attenuated chambers are equipped with chow pellet dispensers. The dispenser has a pellet trough with a photobeam placed at the bottom of the trough and when a rodent removes a pellet from the feeder trough this beam is no longer blocked, signaling the computer to drop another pellet. The computer records the date and time when the pellets were taken from the trough and from this data the experimenter can calculate the meal parameters. When calculating meal parameters a meal was defined based on previous work and was set at 10 min (in other words when the animal does not eat for 10 min that would be the end of the animal&#39;s meal) also the minimum meal size was set at 3 pellets. The meal duration, meal number, food intake, meal size and inter-meal interval can then be calculated by the software for any time period that the operator desires. Of the feeding parameters that can be calculated meal duration has been shown to be a continuous noninvasive biological marker of orofacial nociception in male rats and mice and female rats. Meal duration measurements are quantitative, require no training or animal manipulation, require cortical participation, and do not compete with other experimentally induced behaviors. These factors distinguish this assay from other operant or reflex methods for recording orofacial nociception.

A lengthening in meal duration represents orofacial nociceptive behavior in rodents similar to the guarding behavior of humans with orofacial pain. Eating is a behavior that requires no training or animal manipulation, requires cortical participation, and is not competing with other experimentally induced behaviors, distinguishing this assay from alternative reflex or operant measurements.

A lengthening in meal duration can be used to measure an increase in orofacial mechanical hyperalgesia having similarities to the guarding behavior of ...

Corticospinal Excitability Modulation During Action Observation

This study used the transcranial magnetic stimulation/motor evoked potential (TMS/MEP) technique to pinpoint when the automatic tendency to mirror someone else's action becomes anticipatory simulation of a complementary act. TMS was delivered to the left primary motor cortex corresponding to the hand to induce the highest level of MEP activity from the abductor digiti minimi (ADM; the muscle serving little finger abduction) as well as the first dorsal interosseus (FDI; the muscle serving index finger flexion/extension) muscles. A neuronavigation system was used to maintain the position of the TMS coil, and electromyographic (EMG) activity was recorded from the right ADM and FDI muscles. Producing original data with regard to motor resonance, the combined TMS/MEP technique has taken research on the perception-action coupling mechanism a step further. Specifically, it has answered the questions of how and when observing another person's actions produces motor facilitation in an onlooker's corresponding muscles and in what way corticospinal excitability is modulated in social contexts.

Single-pulse transcranial magnetic stimulation over the primary motor cortex, neuronavigation, and registration of electromyographic activity of hand muscles were used in this study to explore corticospinal excitability while participants were observing action sequences.

This study used the transcranial magnetic stimulation/motor evoked potential (TMS/MEP) technique to pinpoint when the automatic tendency to mirror someone ...

fMRI Validation of fNIRS Measurements During a Naturalistic Task

We present a method to compare brain activity recorded with near-infrared spectroscopy (fNIRS) in a dance video game task to that recorded in a reduced version of the task using fMRI (functional magnetic resonance imaging). Recently, it has been shown that fNIRS can accurately record functional brain activities equivalent to those concurrently recorded with functional magnetic resonance imaging for classic psychophysical tasks and simple finger tapping paradigms. However, an often quoted benefit of fNIRS is that the technique allows for studying neural mechanisms of complex, naturalistic behaviors that are not possible using the constrained environment of fMRI. Our goal was to extend the findings of previous studies that have shown high correlation between concurrently recorded fNIRS and fMRI signals to compare neural recordings obtained in fMRI procedures to those separately obtained in naturalistic fNIRS experiments. Specifically, we developed a modified version of the dance video game Dance Dance Revolution (DDR) to be compatible with both fMRI and fNIRS imaging procedures. In this methodology we explain the modifications to the software and hardware for compatibility with each technique as well as the scanning and calibration procedures used to obtain representative results. The results of the study show a task-related increase in oxyhemoglobin in both modalities and demonstrate that it is possible to replicate the findings of fMRI using fNIRS in a naturalistic task. This technique represents a methodology to compare fMRI imaging paradigms which utilize a reduced-world environment to fNIRS in closer approximation to naturalistic, full-body activities and behaviors. Further development of this technique may apply to neurodegenerative diseases, such as Parkinson&#8217;s disease, late states of dementia, or those with magnetic susceptibility which are contraindicated for fMRI scanning.

We present a method to compare functional brain activity recorded during a naturalistic task using fNIRS with activity recorded during fMRI.

We present a method to compare brain activity recorded with near-infrared spectroscopy (fNIRS) in a dance video game task to that recorded in a reduced ...

How to Study Placebo Responses in Motion Sickness with a Rotation Chair Paradigm in Healthy Participants

Placebo responses occur in every medical intervention when patients or participants expect to receive an effective treatment to relieve symptoms. However, underlying mechanisms of placebo responses are not fully understood. It has repeatedly been shown that placebo responses are associated with changes in neural activity but for many conditions it is unclear whether they also affect the target organ, such as the stomach in motion sickness. Therefore, we present a methodology for the multivariate assessment of placebo responses by subjective, behavioral and objective measures in motion sickness with a rotation chair paradigm. The physiological correlate of motion sickness is a shift in gastric myoelectrical activity towards tachygastria that can be recorded with electrogastrography. The presented study applied the so-called balanced placebo design (BPD) to investigate the effects of ginger compared to placebo and the effects of expectations by verbal information. However, the study revealed no significant main or interactional effects of ginger (as a drug) or information on outcome measures but showed interactions when sex of participants and experimenters are taken into considerations. We discuss limitations of the presented study and report modifications that were used in subsequent studies demonstrating placebo responses when rotation speed was lowered. In general, future placebo studies have to identify the appropriate target organ for the studied placebo responses and to apply the specific methods to assess the physiological correlates.

An experimental rotation chair paradigm is used to investigate whether a placebo intervention on motion sickness affects subjective outcome measures only or also affects behavioral and objective measures in a balanced placebo design.

Placebo responses occur in every medical intervention when patients or participants expect to receive an effective treatment to relieve symptoms. However, ...

Use of Galvanic Skin Responses, Salivary Biomarkers, and Self-reports to Assess Undergraduate Student Performance During a Laboratory Exam Activity

Typically, self-reports are used in educational research to assess student response and performance to a classroom activity. Yet, addition of biological and physiological measures such as salivary biomarkers and galvanic skin responses are rarely included, limiting the wealth of information that can be obtained to better understand student performance. A laboratory protocol to study undergraduate students&#39; responses to classroom events (e.g., exams) is presented. Participants were asked to complete a representative exam for their degree. Before and after the laboratory exam session, students completed an academic achievement emotions self-report and an interview that paralleled these questions when participants wore a galvanic skin sensor and salivary biomarkers were collected. Data collected from the three methods resulted in greater depth of information about students&#39; performance when compared to the self-report. The work can expand educational research capabilities through more comprehensive methods for obtaining nearer to real-time student responses to an examination activity.

Research on undergraduate students' academic achievement emotions have primarily relied on self-reports in laboratory settings. Studies rarely include bio-physiological measures to these self-reports. This protocol will describe a methodology that integrates self-reports with and bio-physiological measures to assess student response and performance during a laboratory examination activity.

Typically, self-reports are used in educational research to assess student response and performance to a classroom activity. Yet, addition of biological ...

Concurrent Electroencephalography Recording During Transcranial Alternating Current Stimulation (tACS)

Oscillatory brain activities are considered to reflect the basis of rhythmic changes in transmission efficacy across brain networks and are assumed to integrate cognitive neural processes. Transcranial alternating current stimulation (tACS) holds the promise to elucidate the causal link between specific frequencies of oscillatory brain activity and cognitive processes. Simultaneous electroencephalography (EEG) recording during tACS would offer an opportunity to directly explore immediate neurophysiological effects of tACS. However, it is not trivial to measure EEG signals during tACS, as tACS creates a huge artifact in EEG data. Here we explain how to set up concurrent tACS-EEG experiments. Two necessary considerations for successful EEG recording while applying tACS are highlighted. First, bridging of the tACS and EEG electrodes via leaking EEG gel immediately saturates the EEG amplifier. To avoid bridging via gel, the viscosity of the EEG gel is the most important parameter. The EEG gel must be viscous to avoid bridging, but at the same time sufficiently fluid to create contact between the tACS electrode and the scalp. Second, due to the large amplitude of the tACS artifact, it is important to consider using an EEG system with a high resolution analog-to-digital (A/D) converter. In particular, the magnitude of the tACS artifact can exceed 100 mV at the vicinity of a stimulation electrode when 1 mA tACS is applied. The resolution of the A/D converter is of importance to measure good quality EEG data from the vicinity of the stimulation site. By following these guidelines for the procedures and technical considerations, successful concurrent EEG recording during tACS will be realized.

Concurrent Electroencephalography Recording During Transcranial Alternating Current Stimulation tACS

In this article we explain how to set up a concurrent transcranial alternating current stimulation and EEG experiment.

Oscillatory brain activities are considered to reflect the basis of rhythmic changes in transmission efficacy across brain networks and are assumed to ...

Profiling Anti-Neu5Gc IgG in Human Sera with a Sialoglycan Microarray Assay

Cells are covered with a cloak of carbohydrate chains (glycans) that is commonly altered in cancer and that includes variations in sialic acid (Sia) expression. These are acidic sugars that have a 9-carbon backbone and that cap vertebrate glycans on cell surfaces. Two of the major Sia forms in mammals are N-acetylneuraminic acid (Neu5Ac) and its hydroxylated form, N-glycolylneuraminic acid (Neu5Gc). Humans cannot produce endogenous Neu5Gc due to the inactivation of the gene encoding cytidine 5&#39;monophosphate-Neu5Ac (CMP-Neu5Ac) hydroxylase (CMAH). Foreign Neu5Gc is acquired by human cells through the dietary consumption of red meat and dairy and subsequently appears on diverse glycans on the cell surface, accumulating mostly on carcinomas. Consequently, humans have circulating anti-Neu5Gc antibodies that play diverse roles in cancer and other chronic inflammation-mediated diseases and that are becoming potential diagnostic and therapeutic targets. Here, we describe a high-throughput sialoglycan microarray assay to assess such anti-Neu5Gc antibodies in the human sera. Neu5Gc-containing glycans and their matched pairs of controls (Neu5Ac-containing glycans), each with a core primary amine, are covalently linked to epoxy-coated glass slides. We exemplify the printing of 56 slides in a 16-well format using a specific nano-printer capable of generating up to 896 arrays per print. Each slide can be used to screen 16 different human sera samples for the evaluation of anti-Neu5Gc antibody specificity, intensity, and diversity. The protocol describes the complexity of this robust tool and provides a basic guideline for those aiming to investigate the response to Neu5Gc dietary carbohydrate antigen in diverse clinical samples in an array format.

A sialoglycan microarray assay can be used to evaluate anti-Neu5Gc antibodies in human sera, making it a potential high-throughput diagnostic assay for cancer and other chronic inflammation-mediated human diseases.

Cells are covered with a cloak of carbohydrate chains (glycans) that is commonly altered in cancer and that includes variations in sialic acid (Sia) ...

Multi-Modal Signals for Analyzing Pain Responses to Thermal and Electrical Stimuli

The assessment of pain relies mostly on methods that require a person to communicate. However, for people with cognitive and verbal impairments, existing methods are not sufficient as they lack reliability and validity. To approach this problem, recent research focuses on an objective pain assessment facilitated by parameters of responses derived from physiology, and video and audio signals. To develop reliable automated pain recognition systems, efforts have been made in creating multimodal databases in order to analyze pain and detect valid pain patterns. While the results are promising, they only focus on discriminating pain or pain intensities versus no pain. In order to advance this, research should also consider the quality and duration of pain as they provide additional valuable information for more advanced pain management. To complement existing databases and the analysis of pain regarding quality and length, this paper proposes a psychophysiological experiment to elicit, measure, and collect valid pain reactions. Participants are subjected to painful stimuli that differ in intensity (low, medium, and high), duration (5 s / 1 min), and modality (heat / electric pain) while audio, video (e.g., facial expressions, body gestures, facial skin temperature), and physiological signals (e.g., electrocardiogram [ECG], skin conductance level [SCL], facial electromyography [EMG], and EMG of M. trapezius) are being recorded. The study consists of a calibration phase to determine a subject&#8217;s individual pain range (from low to intolerable pain) and a stimulation phase in which pain stimuli, depending on the calibrated range, are applied. The obtained data may allow refining, improving, and evaluating automated recognition systems in terms of an objective pain assessment. For further development of such systems and to investigate pain reactions in more detail, additional pain modalities such as pressure, chemical, or cold pain should be included in future studies. Recorded data of this study will be released as the &#8220;X-ITE Pain Database&#8221;.

This article focuses on the experimental elicitation of pain through heat (thermal) and electrical stimulation while recording physiological, visual, and paralinguistic responses. It aims at collecting valid multimodal data for analyzing pain based on its intensity, quality, and duration.&#160;

The assessment of pain relies mostly on methods that require a person to communicate. However, for people with cognitive and verbal impairments, existing ...