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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, 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.
Monitoring behavioral responses during neural recordings is essential for 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 u....
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
To demonstrate the potential of this analysis pipeline, behavioral videos of head-fixed female mice — specifically mothers and virgins — were acquired while the mice were presented with auditory stimuli during functional magnetic resonance imaging (fMRI) scans. The stimuli consisted of pup calls and pure tones presented passively, thus without any instructed behavioral outputs. The pup calls were recordings of ultrasonic vocalizations emitted by 6-day-old mouse pups temporarily isolated from their nest. These.......
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 resp.......
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 & 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 ....
Name | Company | Catalog Number | Comments |
MATLAB Computer Vision Toolbox | MathWorks | https://www.mathworks.com/products/computer-vision.html | |
MATLAB Image Processing Toolbox | MathWorks | https://www.mathworks.com/products/image-processing.html | |
MATLAB software | MathWorks | https://www.mathworks.com/products/matlab.html | |
MR-compatible camera “12M-i” with integrated LED light | MRC Systems GmbH | 12M-i | https://www.mrc-systems.de/downloads/en/mri-compatible-cameras/manual_mrcam_12m-i.pdf |
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