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. J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Integration+of+olfactory+and+auditory+cues+eliciting+parental+behavior.">Integration of olfactory and auditory cues eliciting parental behavior.</a> J Neuroendocrin. 35 (7), e13307 (2023).</li></ol>

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

Universidad de Cartagena UDEC

Research

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JoVE Journal

Behavior

552 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

Brain Imaging Investigation of the Neural Correlates of Observing Virtual Social Interactions

The ability to gauge social interactions is crucial in the assessment of others&#x2019; intentions. Factors such as facial expressions and body language affect our decisions in personal and professional life alike 1. These "friend or foe" judgements are often based on first impressions, which in turn may affect our decisions to "approach or avoid". Previous studies investigating the neural correlates of social cognition tended to use static facial stimuli 2. Here, we illustrate an experimental design in which whole-body animated characters were used in conjunction with functional magnetic resonance imaging (fMRI) recordings. Fifteen participants were presented with short movie-clips of guest-host interactions in a business setting, while fMRI data were recorded; at the end of each movie, participants also provided ratings of the host behaviour. This design mimics more closely real-life situations, and hence may contribute to better understanding of the neural mechanisms of social interactions in healthy behaviour, and to gaining insight into possible causes of deficits in social behaviour in such clinical conditions as social anxiety and autism 3.

This article demonstrates an experimental design in which whole-body animated characters are used in conjunction with functional magnetic resonance imaging (fMRI) to investigate the neural correlates of observing virtual social interactions.

The ability to gauge social interactions is crucial in the assessment of others&#x2019; intentions. Factors such as facial expressions and body language ...

Neuroscience

Brain Imaging Investigation of the Neural Correlates of Emotional Autobiographical Recollection

Recollection of emotional autobiographical memories (AMs) is important to healthy cognitive and affective functioning 1 - remembering positive AMs is associated with increased personal well-being and self-esteem 2, whereas remembering and ruminating on negative AMs may lead to affective disorders 3. Although significant progress has been made in understanding the brain mechanisms underlying AM retrieval in general (reviewed in 4, 5), less is known about the effect of emotion on the subjective re-experience of AMs and the associated neural correlates. This is in part due to the fact that, unlike the investigations of the emotion effect on memory for laboratory-based microevents (reviewed in 6, 7-9), often times AM studies do not have a clear focus on the emotional aspects of remembering personal events (but see 10). Here, we present a protocol that allows investigation of the neural correlates of recollecting emotional AMs using functional magnetic resonance imaging (fMRI). Cues for these memories are collected prior to scanning by means of an autobiographical memory questionnaire (AMQ), therefore allowing for proper selection of emotional AMs based on their phenomenological properties (i.e., intensity, vividness, personal significance). This protocol can be used in healthy and clinical populations alike.

We present a protocol that allows investigation of the neural correlates of recollecting emotional autobiographical memories, using functional magnetic resonance imaging. This protocol can be used with both healthy and clinical participants.

Recollection of emotional autobiographical memories (AMs) is important to healthy cognitive and affective functioning 1 - remembering positive AMs is ...

Brain Imaging Investigation of the Neural Correlates of Emotion Regulation

The ability to control/regulate emotions is an important coping mechanism in the face of emotionally stressful situations. Although significant progress has been made in understanding conscious/deliberate emotion regulation (ER), less is known about non-conscious/automatic ER and the associated neural correlates. This is in part due to the problems inherent in the unitary concepts of automatic and conscious processing1. Here, we present a protocol that allows investigation of the neural correlates of both deliberate and automatic ER using functional magnetic resonance imaging (fMRI). This protocol allows new avenues of inquiry into various aspects of ER. For instance, the experimental design allows manipulation of the goal to regulate emotion (conscious vs. non-conscious), as well as the intensity of the emotional challenge (high vs. low). Moreover, it allows investigation of both immediate (emotion perception) and long-term effects (emotional memory) of ER strategies on emotion processing. Therefore, this protocol may contribute to better understanding of the neural mechanisms of emotion regulation in healthy behaviour, and to gaining insight into possible causes of deficits in depression and anxiety disorders in which emotion dysregulation is often among the core debilitating features.

We present a protocol that allows investigation of the neural correlates of deliberate and automatic emotion regulation, using functional magnetic resonance imaging. This protocol can be used in healthy participants, both young and older, as well as in clinical patients.

The ability to control/regulate emotions is an important coping mechanism in the face of emotionally stressful situations. Although significant progress ...

Brain Imaging Investigation of the Impairing Effect of Emotion on Cognition

Emotions can impact cognition by exerting both enhancing (e.g., better memory for emotional events) and impairing (e.g., increased emotional distractibility) effects (reviewed in 1). Complementing our recent protocol 2 describing a method that allows investigation of the neural correlates of the memory-enhancing effect of emotion (see also 1, 3-5), here we present a protocol that allows investigation of the neural correlates of the detrimental impact of emotion on cognition. The main feature of this method is that it allows identification of reciprocal modulations between activity in a ventral neural system, involved in 'hot' emotion processing (HotEmo system), and a dorsal system, involved in higher-level 'cold' cognitive/executive processing (ColdEx system), which are linked to cognitive performance and to individual variations in behavior (reviewed in 1). Since its initial introduction 6, this design has proven particularly versatile and influential in the elucidation of various aspects concerning the neural correlates of the detrimental impact of emotional distraction on cognition, with a focus on working memory (WM), and of coping with such distraction 7,11, in both healthy 8-11 and clinical participants 12-14.

We present a protocol that allows investigation of the neural mechanisms mediating the detrimental impact of emotion on cognition, using functional magnetic resonance imaging. This protocol can be used with both healthy and clinical participants.

Emotions can impact cognition by exerting both enhancing (e.g., better memory for emotional events) and impairing (e.g., increased emotional ...

Correlating Behavioral Responses to fMRI Signals from Human Prefrontal Cortex: Examining Cognitive Processes Using Task Analysis

The aim of this methods paper is to describe how to implement a neuroimaging technique to examine complementary brain processes engaged by two similar tasks. Participants' behavior during task performance in an fMRI scanner can then be correlated to the brain activity using the blood-oxygen-level-dependent signal. We measure behavior to be able to sort correct trials, where the subject performed the task correctly and then be able to examine the brain signals related to correct performance. Conversely, if subjects do not perform the task correctly, and these trials are included in the same analysis with the correct trials we would introduce trials that were not only for correct performance. Thus, in many cases these errors can be used themselves to then correlate brain activity to them. We describe two complementary tasks that are used in our lab to examine the brain during suppression of an automatic responses: the stroop1 and anti-saccade tasks. The emotional stroop paradigm instructs participants to either report the superimposed emotional 'word' across the affective faces or the facial 'expressions' of the face stimuli1,2. When the word and the facial expression refer to different emotions, a conflict between what must be said and what is automatically read occurs. The participant has to resolve the conflict between two simultaneously competing processes of word reading and facial expression. Our urge to read out a word leads to strong 'stimulus-response (SR)' associations; hence inhibiting these strong SR's is difficult and participants are prone to making errors. Overcoming this conflict and directing attention away from the face or the word requires the subject to inhibit bottom up processes which typically directs attention to the more salient stimulus. Similarly, in the anti-saccade task3,4,5,6, where an instruction cue is used to direct only attention to a peripheral stimulus location but then the eye movement is made to the mirror opposite position. Yet again we measure behavior by recording the eye movements of participants which allows for the sorting of the behavioral responses into correct and error trials7 which then can be correlated to brain activity. Neuroimaging now allows researchers to measure different behaviors of correct and error trials that are indicative of different cognitive processes and pinpoint the different neural networks involved.

The goal of our research is to correlate behavior to brain activity. Accurate behavioral measures and imaging techniques allow us to elucidate brain-behavior relationships.

The aim of this methods paper is to describe how to implement a neuroimaging technique to examine complementary brain processes engaged by two similar ...

Deep Brain Stimulation with Simultaneous fMRI in Rodents

In order to visualize the global and downstream neuronal responses to deep brain stimulation (DBS) at various targets, we have developed a protocol for using blood oxygen level dependent (BOLD) functional magnetic resonance imaging (fMRI) to image rodents with simultaneous DBS. DBS fMRI presents a number of technical challenges, including accuracy of electrode implantation, MR artifacts created by the electrode, choice of anesthesia and paralytic to minimize any neuronal effects while simultaneously eliminating animal motion, and maintenance of physiological parameters, deviation from which can confound the BOLD signal.&#160;Our laboratory has developed a set of procedures that are capable of overcoming most of these possible issues. For electrical stimulation, a homemade tungsten bipolar microelectrode is used, inserted stereotactically at the stimulation site in the anesthetized subject. In preparation for imaging, rodents are fixed on a plastic headpiece and transferred to the magnet bore. For sedation and paralysis during scanning, a cocktail of dexmedetomidine and pancuronium is continuously infused, along with a minimal dose of isoflurane; this preparation minimizes the BOLD ceiling effect of volatile anesthetics. In this example experiment, stimulation of the subthalamic nucleus (STN) produces BOLD responses which are observed primarily in ipsilateral cortical regions, centered in motor cortex. Simultaneous DBS and fMRI allows the unambiguous modulation of neural circuits dependent on stimulation location and stimulation parameters, and permits observation of neuronal modulations free of regional bias. This technique may be used to explore the downstream effects of modulating neural circuitry at nearly any brain region, with implications for both experimental and clinical DBS.

This protocol describes a standard method for simultaneous functional magnetic resonance imaging and deep brain stimulation in the rodent. The combined use of these experimental tools allows for the exploration of global downstream activity in response to electrical stimulation at virtually any brain target.

In order to visualize the global and downstream neuronal responses to deep brain stimulation (DBS) at various targets, we have developed a protocol for ...

Dynamic Inter-subject Functional Connectivity Reveals Moment-to-Moment Brain Network Configurations Driven by Continuous or Communication Paradigms

Task-based functional magnetic resonance imaging bears great potential to understand how our brain reacts to various types of stimulation; however, this is often achieved without considering the dynamic facet of functional processing, and analytical outputs typically account for merged influences of task-driven effects and underlying spontaneous fluctuations of brain activity. Here, we introduce a novel methodological pipeline that can go beyond these limitations: the use of a sliding-window analytical scheme permits tracking of functional changes over time, and through cross-subject correlational measurements, the approach can isolate purely stimulus-related effects. Thanks to a rigorous thresholding process, significant changes in inter-subject functional correlation can be extracted and analyzed.
On a set of healthy subjects who underwent naturalistic audio-visual stimulation, we demonstrate the usefulness of the approach by tying the unraveled functional reconfigurations to particular cues of the movie. We show how, through our method, one can capture either a temporal profile of brain activity (the evolution of a given connection), or focus on a spatial snapshot at a key time point. We provide a publicly available version of the whole pipeline, and describe its use and the influence of its key parameters step by step.

The goal of the described approach is to determine at what moments of the paradigm (temporal perspective), and between which regions (spatial perspective), significant reconfigurations in functional connectivity occur on functional magnetic resonance imaging recordings during which a time-locked stimulus is played.

Task-based functional magnetic resonance imaging bears great potential to understand how our brain reacts to various types of stimulation; however, this ...

Brain Morphology of Cannabis Users With or Without Psychosis: A Pilot MRI Study

Cannabis is the illicit drug most commonly used worldwide, and its consumption can both induce psychiatric symptoms in otherwise healthy subjects and unmask a florid psychotic picture in patients with a prior psychotic risk. Previous studies suggest that chronic and long-term cannabis exposure may exert significant negative effects in brain areas enriched with cannabinoid receptors. However, whether brain alterations determined by cannabis dependency will lead to a clinically significant phenotype or to a psychotic outbreak at some point of an abuser&#8217;s life remains unclear. The aim of this study was to investigate morphological brain differences between chronic cannabis users with cannabis-induced psychosis (CIP) and non-psychotic cannabis users (NPCU) without any psychiatric conditions and correlate brain deficits with selective socio-demographic, clinical and psychosocial variables.
3T magnetic resonance imaging (MRI) scans of 10 CIP patients and 12 NPCU were acquired. The type of drug, the frequency, and the duration, as well socio-demographic, clinical and psychosocial parameters of dependency were measured. CIP patients had extensive grey matter (GM) decreases in right superior frontal gyrus, right precentral, right superior temporal gyrus, insula bilaterally, right precuneus, right medial occipital gyrus, right fusiform gyrus, and left hippocampus in comparison to chronic cannabis users without psychosis. Finally, in CIP patients, the results showed a negative correlation between a domain of the Brief Psychiatric Rating Scale (BPRS), BPRS-Activity, and selective GM volumes. Overall, the results suggest that cannabis-induced psychosis is characterized by selective brain reductions that are not present in NPCU. Therefore, neuroimaging studies may provide a potential ground for identifying putative biomarkers associated with the risk of developing psychosis in cannabis users.

This is a 3T magnetic resonance imaging study aiming to investigate grey matter volume differences between cannabis-induced psychosis patients and non-psychotic chronic cannabis users.

Cannabis is the illicit drug most commonly used worldwide, and its consumption can both induce psychiatric symptoms in otherwise healthy subjects and ...

Revolutionizing fNIRS Studies&#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 ...

Concurrent EEG and Functional MRI Recording and Integration Analysis for Dynamic Cortical Activity Imaging

Electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) are two of the fundamental noninvasive methods for identifying brain activity. Multimodal methods have sought to combine the high temporal resolution of EEG with the spatial precision of fMRI, but the complexity of this approach is currently in need of improvement. The protocol presented here describes the recently developed spatiotemporal fMRI-constrained EEG source imaging method, which seeks to rectify source biases and improve EEG-fMRI source localization through the dynamic recruitment of fMRI sub-regions. The process begins with the collection of multimodal data from concurrent EEG and fMRI scans, the generation of 3D cortical models, and independent EEG and fMRI processing. The processed fMRI activation maps are then split into multiple priors, according to their location and surrounding area. These are taken as priors in a two-level hierarchical Bayesian algorithm for EEG source localization. For each window of interest (defined by the operator), specific segments of the fMRI activation map will be identified as active to optimize a parameter known as model evidence. These will be used as soft constraints on the identified cortical activity, increasing the specificity of the multimodal imaging method by reducing cross-talk and avoiding erroneous activity in other conditionally active fMRI regions. The method generates cortical maps of activity and time-courses, which may be taken as final results, or used as a basis for further analyses (analyses of correlation, causation, etc.) While the method is somewhat limited by its modalities (it will not find EEG-invisible sources), it is broadly compatible with most major processing software, and is suitable for most neuroimaging studies.

An EEG-fMRI multimodal imaging method, known as the spatiotemporal fMRI-constrained EEG source imaging method, is described here. The presented method employs conditionally-active fMRI sub-maps, or priors, to guide EEG source localization in a manner that improves spatial specificity and limits erroneous results.