The authors would like to thank Moumita Chatterjee and Alice Liu for technical support, and the Bloomington Drosophila Stock Centre (Indiana, USA) for making available the Drosophila strains used in this work. This work was supported by the Institute of Molecular and Cell Biology, Singapore; the Bioinformatics Institute, Singapore; the Agency for Science Technology and Research Joint Council Organization (grant number 15302FG149 to SA and LC); the Clinical Research Flagship Programme (Parkinson&#8217;s Disease) administered by the Singapore Ministry of Health&#8217;s National Medical Research Council (grant number NMRC/TCR/013-NNI/2014 to SA), the University of Alberta (startup grant to LC), and the Natural Sciences and Engineering Research Council of Canada (NSERC) Discovery Grant (grant number RGPIN-2019-04575 to LC).

1. System setup
<ol>
	<li>Ensure that the recording station has a high-speed camera and a stage over it to hold the arena chamber (Figure 1). Adjust the camera to record at a minimum of 250 frames per second (fps), with an accordingly fast shutter speed (in this case, recording is performed at 1000 fps with a shutter speed of 1 ms). 
	NOTE: Check that the video is suitable for tracking by ensuring that there is minimal or no motion blur of moving legs in all frames. If the moving leg is so blurred that a human annotator cannot track it, then the camera recording speed and/or shutter speed must be increased.</li>
	<li>Place the infrared LED lights at the top of the stage with a diffuser (translucent sheet) between the camera and sample (Figure 1A,B).</li>
	<li>Make the recording chamber by cutting a 1.6 mm thick acrylic sheet. In this experiment, use a field-of-view of 11 mm x 11 mm. Place the chamber between two glass slides (Figure 1C).</li>
</ol>
2. Preparation of flies for recording
<ol>
	<li>Transfer the flies to be recorded into a new food vial 24 h prior to recording. 
	NOTE: Do not use CO2 (usually used to anesthetize the flies upon first collection) on the flies less than 24 h before recording.</li>
	<li>About 40 min before recording, transfer the flies into empty vials and keep on ice for 5-7&#160; min.&#160;</li>
	<li>In the meantime, wipe the arena and clear glass slides with water and a wipe. 
	NOTE: Do not use ethanol to clean the chambers and the slides.</li>
	<li>Prepare the recording chamber. Fix one of the microscopic glass slides below the chamber with tape.</li>
	<li>When the flies have been anesthetized on ice, transfer one fly in each chamber using a clean brush. 
	NOTE: Both male and female flies can be used in this setup, and, as far as possible, flies of both sexes should be analyzed to avoid sex-specific bias.</li>
	<li>Secure the chamber with another microscopic glass slide with tape (Figure 1C).</li>
	<li>Keep the chambered flies at room temperature for 15 - 20 min for acclimatization.</li>
</ol>
3. Generation of videos for FLLIT analysis
NOTE: This step is specific to the video camera used. In this case, a commercially available video camera is used (see Table of Materials).
<ol>
	<li>Turn on the power source. Wait for the green LED for power and the orange LED for the ethernet interface connection to stabilize. Switch on the power for the infrared LED. Ensure that the voltage remains at 12.5 V.</li>
	<li>Open the Viewer application on the connected computer system.</li>
	<li>Change the recording frame rate to 1000 fps. Set the shutter speed to 1/1000 s (1 ms).</li>
	<li>Place the chamber with the fly on the recording arena and select the LIVE button. Ensure that the camera is focused on the leg tips when the fly is walking upright on the floor of the chamber; the leg tips should be in sharp focus.</li>
	<li>Click Record (Figure 2).</li>
	<li>Record the fly walking, ensuring that:&#160; 
	The fly walked in a relatively straight trajectory without touching the edge of the arena. 
	The fly walked at least three strides per leg. 
	The fly does not pause during the walk. 
	The distance walked is equivalent to at least one body length.&#160; 
	 
	NOTE: Having the background subtracted cleanly is critical for accurate segmentation. The automated background subtraction algorithm employed by FLLIT requires that the imaged fly moves at least one body length in distance.</li>
	<li>Click Rec Done to stop recording (Figure 2).</li>
	<li>Crop the video to ensure that the recording encompasses only a straight walk of the fly (as described in Step 3.6).</li>
	<li>Click Save (Figure 2). Save the files in &#8216;.mraw&#8217; or &#8216;.tiff&#8217; format in the respective folders. 
	NOTE: &#8216;.mraw&#8217; format gives greater flexibility to change the file name (if required) and for storage of videos compared to &#8216;.tiff&#8217; file format.</li>
</ol>
4. Installation of FLLIT program
NOTE: Up-to-date instructions can be found at: https://github.com/BII-wushuang/FLLIT/blob/master/Compiled/Readme.pdf
<ol>
	<li>Download FLLIT on any operating system
	<ol>
		<li>Download FLLIT from the following Github link: https://github.com/BII-wushuang/FLLIT/archive/master.zip. Extract the contents of the zip file.</li>
		<li>Download sample datasets from the following Google Drive link: https://bit.ly/2EibvNY. Create a folder Data under FLLIT-master/Compiled and place dataset folders in this Data directory.</li>
	</ol>
	</li>
	<li>Install FLLIT In Ubuntu
	<ol>
		<li>Navigate to the FLLIT/Compiled directory.</li>
		<li>Right-click and select Open in Terminal.</li>
		<li>Issue the following command to download and install the MATLAB runtime libraries to $HOME/MCR: 
		bash MCR_2016a.sh</li>
		<li>After installation of the MATLAB runtime libraries has been completed, issue the following command to ensure that executable rights are accorded to FLLIT: 
		chmod +x FLLIT</li>
		<li>Open a terminal in the FLLIT/Compiled directory and issue the following command to execute FLLIT: 
		bash run_FLLIT.sh $HOME/MCR/v901</li>
	</ol>
	</li>
	<li>Install FLLIT in Windows
	<ol>
		<li>For 7 and 10th Home Edition, install Docker Toolbox at: 
		(https://github.com/docker/toolbox/releases/download/v19.03.1/DockerToolbox-19.03.1.exe).</li>
		<li>For Windows 10 Pro or Enterprise Edition, install Docker Desktop for Windows at: (https://download.docker.com/win/stable/Docker\%20Desktop\%20Installer.exe).</li>
		<li>To enable execution of GUI applications in a Docker container on Windows, first install VcXSrV (https://sourceforge.net/projects/vcxsrv). On starting VcXsrv, configure the settings as in Figure S1. 
		NOTE: Make sure Docker and VcXsrv are running before starting FLLIT.</li>
		<li>Double click FLLIT.bat to run FLLIT. 
		NOTE: When executing for the first time, it will take some time to pull the Docker image from Docker Hub.</li>
	</ol>
	</li>
	<li>Install FLLIT in MacOS
	<ol>
		<li>Download Docker Desktop for MacOS at https://download.docker.com/mac/stable/Docker.dmg</li>
		<li>Install socat by opening a terminal and issuing the following command: 
		brew install socat</li>
		<li>Start socat with: 
		socat TCP-LISTEN:6000,reuseaddr,fork UNIX-CLIENT:\&#34;$DISPLAY\&#34; &#38; disown</li>
		<li>Install XQuartz (https://www.xquartz.org) to enable execution of GUI applications in a Docker container on MacOS. Start XQuartz and change the preferences by checking the Allow connections from network clients in the Security tab as shown in Figure S2. 
		NOTE: Make sure that Docker, socat and XQuartz are all running before starting FLLIT.&#160;</li>
		<li>Open a terminal in the FLLIT/Compiled directory and execute FLLIT with the following command: 
		bash FLLIT_Mac.sh 
		NOTE: When executing for the first time, it will take some time to pull the Docker image from Docker Hub.</li>
	</ol>
	</li>
</ol>
5. Running FLLIT for automated leg tracking
<ol>
	<li>Segmentation
	<ol>
		<li>Convert the video into individual TIFF files and copy into the FLLIT data folder.</li>
		<li>Run FLLIT (In Ubuntu, right click to open FLLIT in Terminal).</li>
		<li>Select the folder containing the frame-by-frame TIFF images of the video to be tracked and click the Add button.</li>
		<li>In the pop-up window choose 0 for carrying out leg segmentation only, or 1 to include leg tracking with leg segmentation.</li>
		<li>Click Done to initiate segmentation and tracking of the selected video.&#160;</li>
	</ol>
	</li>
	<li>Tracking
	<ol>
		<li>To check the accuracy of tracking and carry out error corrections (if any), click on Select Data Folder. Select the folder to be tracked and click Open.</li>
		<li>Click on View Tracking. 
		NOTE: Check that Viewer Mode remains selected while viewing the tracked leg positions. Otherwise, any previous corrections made will be over-ridden.</li>
		<li>Check labeling for all the legs in the first frame 
		NOTE: Since the leg labels are placed according to the position on the screen, if the fly is walking upright, the fly&#8217;s RIGHT side is labelled as L1 (fore-leg), L2 (mid-leg), L3 (hind-leg) and&#160;the fly&#8217;s LEFT side is labelled as R1 (fore-leg), R2 (mid-leg), R3 (hind-leg), respectively (Figure 3). If the fly is walking&#160;upside, down the leg labels will be correctly annotated.</li>
		<li>If a leg is wrongly labeled and a correction is required, click Pause viewing, followed by Adjust Prediction (Figure 3).</li>
		<li>From the right panel headed Leg to Adjust, select the leg that requires correction.&#160;</li>
		<li>Double click on the correct position for this leg in the image window, click on Save, and then Exit. To go to the previous frame or subsequent frame, click Pause viewing followed by the forward and backward &#60;I and I&#62; buttons, respectively (Figure 3).</li>
		<li>To correct any incorrectly tracked videos, open the Data folder of the video to be retracked and select Manually Initiate Tracking.</li>
		<li>Click the Tracking button, which will then change its label to Initial.</li>
		<li>Click Adjust Prediction and correct the leg labels by double-clicking each leg tip and then assigning it with the correct leg label. Click Save and Exit.</li>
		<li>Click on Resume to initiate tracking.</li>
	</ol>
	</li>
	<li>Data processing and video generation
	<ol>
		<li>Click on Data Process. In the popup window, type in the number of&#160;frames per second (fps) at which the videos were recorded (e.g., 1,000 fps).&#160;</li>
		<li>Use the following equation to calculate the actual field of view of the captured video so that gait parameters can be measured in millimeters: 
		 
		<img alt="Equation 1" src="/files/ftp_upload/61012/61012eq1.jpg" /> 
		 
		NOTE: For example, if the actual chamber size is 7 mm, the width of the image frame is 137 mm, the width of the chamber in the image frame on the computer screen is 81 mm, and the width of the field of view was 11.83 mm (Figure S3).</li>
		<li>To see the tracking results, go to the Tracking folder under the Results folder.</li>
		<li>To generate a video of the tracked fly, select Make video. The video will be saved in the same Results folder as that of the original video data analyzed. 
		NOTE: The Start&#160;(first) and End (last) frames of the video can be selected.</li>
	</ol>
	</li>
	<li>Normalization to body length of each fly. 
	NOTE: As each fly may be slightly different in size, certain gait parameters should be normalized to the body length of each fly to facilitate comparisons (e.g., stride length may be longer in larger flies, and shorter in smaller flies).
	<ol>
		<li>Open three still images from the video of each fly (usually first, middle and last frames) using image software.</li>
		<li>Magnify each image frame to 800% and label the anterior-most pixel of the head and posterior-most pixel of the abdomen at the midline using a bright color (e.g., yellow).</li>
		<li>Open the labeled images in ImageJ.</li>
		<li>Use Set Scale to input the scale accordingly: Set Distance in pixels: 512; Known distance: Actual Field of view (mm) as measured in step 5.3.2.; Unit of length: mm.</li>
		<li>Draw a straight line between the labelled head and abdomen tip pixels to obtain the body length.</li>
		<li>Open Set Scale again to obtain the value in a known distance, which is the body length in mm.</li>
		<li>Take an average of the length determined in each of the three images to obtain the average body size in mm.</li>
	</ol>
	</li>
</ol>

<ol>
	<li>Wu, S., 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=Fully+automated+leg+tracking+of+Drosophila+neurodegeneration+models+reveals+distinct+conserved+movement+signatures.">Fully automated leg tracking of Drosophila neurodegeneration models reveals distinct conserved movement signatures.</a> PLoS Biology. 17 (6), 3000346 (2019).</li><li>Mathis, A., 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=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 Neuroscience. 19, 1281-1289 (2018).</li><li>Pereira, T. D., 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=Fast+animal+pose+estimation+using+deep+neural+networks.">Fast animal pose estimation using deep neural networks.</a> Nature Methods. 16 (1), 117-125 (2019).</li><li>Machado, A. S., Darmohray, D. M., Fayad, J., Marques, H. G., Carey, M. R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+quantitative+framework+for+whole-body+coordination+reveals+specific+deficits+in+freely+walking+ataxic+mice.">A quantitative framework for whole-body coordination reveals specific deficits in freely walking ataxic mice.</a> eLife. 4, (2015).</li><li>Lu, B., Vogel, H. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Drosophila+models+of+neurodegenerative+diseases.">Drosophila models of neurodegenerative diseases.</a> Annual Reviews in Pathology. 4, 315-342 (2009).</li><li>McGurk, L., Berson, A., Bonini, N. M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Drosophila+as+an+In+Vivo+Model+for+Human+Neurodegenerative+Disease.">Drosophila as an In Vivo Model for Human Neurodegenerative Disease.</a> Genetics. 201 (2), 377-402 (2015).</li><li>Dionne, H., Hibbard, K., Cavallaro, A., Kao, J. -. C., Rubin, G. M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Genetic+reagents+for+making+split-GAL4+lines+in+Drosophila.">Genetic reagents for making split-GAL4 lines in Drosophila.</a> bioRxiv. , (2017).</li><li>Cande, J., 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=Optogenetic+dissection+of+descending+behavioral+control+in+Drosophila.">Optogenetic dissection of descending behavioral control in Drosophila.</a> eLife. 7, (2018).</li><li>Nern, A., Pfeiffer, B. D., Rubin, G. M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Optimized+tools+for+multicolor+stochastic+labeling+reveal+diverse+stereotyped+cell+arrangements+in+the+fly+visual+system.">Optimized tools for multicolor stochastic labeling reveal diverse stereotyped cell arrangements in the fly visual system.</a> Proceedings of the National Academy of Sciences of the United States of America. 112 (22), 2967-2976 (2015).</li><li>Xie, T., 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=A+Genetic+Toolkit+for+Dissecting+Dopamine+Circuit+Function+in+Drosophila.">A Genetic Toolkit for Dissecting Dopamine Circuit Function in Drosophila.</a> Cell Reports. 23 (2), 652-665 (2018).</li><li>Warrick, J. M., 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=Ataxin-3+suppresses+polyglutamine+neurodegeneration+in+Drosophila+by+a+ubiquitin-associated+mechanism.">Ataxin-3 suppresses polyglutamine neurodegeneration in Drosophila by a ubiquitin-associated mechanism.</a> Molecular Cell. 18 (1), 37-48 (2005).</li><li>Ebersbach, G., 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=Comparative+analysis+of+gait+in+Parkinson's+disease,+cerebellar+ataxia+and+subcortical+arteriosclerotic+encephalopathy.">Comparative analysis of gait in Parkinson's disease, cerebellar ataxia and subcortical arteriosclerotic encephalopathy.</a> Brain. 122 (7), 1349-1355 (1999).</li><li>Stolze, H., 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=Typical+features+of+cerebellar+ataxic+gait.">Typical features of cerebellar ataxic gait.</a> Journal of Neurology, Neurosurgery and Psychiatry. 73 (3), 310-312 (2002).</li><li>Mendes, C. S., Bartos, I., Akay, T., Marka, S., Mann, R. S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Quantification+of+gait+parameters+in+freely+walking+wild+type+and+sensory+deprived+Drosophila+melanogaster.">Quantification of gait parameters in freely walking wild type and sensory deprived Drosophila melanogaster.</a> eLife. 2, 00231 (2013).</li><li>DeAngelis, B. D., Zavatone-Veth, J. A., Clark, D. A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+manifold+structure+of+limb+coordination+in+walking+Drosophila.">The manifold structure of limb coordination in walking Drosophila.</a> eLife. 8, (2019).</li><li>Gunel, S., 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=DeepFly3D,+a+deep+learning-based+approach+for+3D+limb+and+appendage+tracking+in+tethered,+adult+Drosophila.">DeepFly3D, a deep learning-based approach for 3D limb and appendage tracking in tethered, adult Drosophila.</a> eLife. 8, (2019).</li><li>Isakov, A., 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=Recovery+of+locomotion+after+injury+in+Drosophila+melanogaster+depends+on+proprioception.">Recovery of locomotion after injury in Drosophila melanogaster depends on proprioception.</a> The Journal of Experimental Biology. 219, 1760-1771 (2016).</li><li>Aw, S. S., Lim, I. K. H., Tang, M. X. M., Cohen, S. 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+Glio-Protective+Role+of+mir-263a+by+Tuning+Sensitivity+to+Glutamate.">A Glio-Protective Role of mir-263a by Tuning Sensitivity to Glutamate.</a> Cell Reports. 19 (9), 1783-1793 (2017).</li><li>Eberl, D. F., Duyk, G. M., Perrimon, N. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+genetic+screen+for+mutations+that+disrupt+an+auditory+response+in+Drosophila+melanogaster.">A genetic screen for mutations that disrupt an auditory response in Drosophila melanogaster.</a> Proceedings of the National Academy of Sciences of the United States of America. 94 (26), 14837-14842 (1997).</li></ol>

The authors have nothing to disclose.

In this manuscript, we describe in detail the steps involved in using FLLIT, an automated machine-learning program1, to analyze gait in freely walking Drosophila. After tracking and data analysis, FLLIT automatically generates raw data for the positional information of the body and leg claws, producing twenty body and gait features as well as a video of the tracked fly to enable gait visualization.
There are now a number of methods for leg movement tracking of Drosophila and other animals1,2,3,4,14,15,16, giving researchers a wide range of options depending on the goals of the experiment. Some of these are foot printing-based approaches, which are highly accurate but which report only claw contact points with the detection surface4,14. On the other hand, recent deep learning approaches2,3,16 are highly versatile, allowing analysis of behaviors that require tracking of leg joints and other body parts in any animal, with the caveat that the algorithms need to first be trained with user annotated datasets. A third type of approach uses morphology or image-contrast-based methods1,15,17 to find the outline of each leg to identify claw positions. In general, these methods deal poorly with behaviors where the legs cross over (e.g. during grooming). FLLIT combines the second and third approaches, using morphological parameters to train a boosting algorithm for leg segmentation. This allows FLLIT to bypass the laborious task of user annotation to generate the training dataset, while enhancing accuracy using machine learning. Future improvements to FLLIT will have to deal with instances where legs cross over, to allow for analysis of more complex behaviors.
FLLIT is robust to slight changes in illumination, recording resolution and frame speed1. However, frame speed of recorded videos should not fall below 250 fps, and FLLIT runs optimally for videos recorded at 1000 fps. If there is motion blur in the images, such that it is challenging for a human annotator to identify the leg position, FLLIT will not be able to accurately identify leg tips in those frames. In the light of this, it is essential that the camera be focused sharply on the leg tips. To prevent segmentation artifacts, the arena should be cleaned thoroughly, and should not be moved during the recording. For accurate background subtraction and clean segmentation, the fly should move at least one body length during the recording, without pausing. After automatic segmentation and tracking the labeling of the all legs must be checked. If the fly gait is not tracked or tracked wrongly, the file should be retracked manually using the Manually Initiate Tracking option (step 5.2.7 &#8211; 5.2.10).
Neurodegenerative diseases and movement disorders are increasingly prevalent in our aging societies. Fly models of neurodegeneration have been studied for more than 2 decades, during which advances have been made regarding the molecular and cellular aspects of disease pathophysiology. However, specific behavioral consequences of disease have been technically difficult to assess. For example, while reports of trembling movements in the fly have been made18,19, these had not been quantitatively studied until recently1. The climbing assay has been a useful and quantitative, yet relatively coarse measure6. This technical deficit has similarly hampered high-resolution movement analysis in other animal models. The advent of new tools for behavioral analysis, hence, has promise to rejuvenate the field of movement disorders to enable researchers to study how molecular and cellular mechanisms of neuromuscular diseases lead to specific behavioral outcomes in animal models. In this paper and in our previous work1, we showed using FLLIT that fly models of SCA3 exhibit a hyperkinetic ataxic gait, while PD fly models exhibit a hypokinetic rigid gait, recapitulating movement hallmarks of the respective human diseases1. Gait analysis also enabled us to identify distinct neuronal populations underlying specific movement dysfunctions. Going forward, detailed movement analysis, combined with the powerful imaging and functional tools available in the fly, will allow us to gain novel insight into mechanisms of locomotor dysfunction, illuminating our understanding of neurodegenerative diseases with respect to circuit mechanisms.
FLLIT should be widely applicable to study gait in other small arthropods, as it was previously demonstrated to be highly accurate for tracking spider leg movements1. While we focus here on the use of detailed movement phenotyping for quantifying pathogenic gait and its underlying circuitry, advances in movement tracking have already revolutionized, and will have continuing impact on, the understanding of normal walking coordination and gait and its underlying circuits, especially in myriad different branches of the evolutionary tree.&#160;

In the last few decades, neurodegenerative diseases and movement disorders have grown more prevalent in our aging populations. Although our understanding of many neurodegenerative diseases has advanced at the molecular and cellular level, fundamental features of the affected neuronal circuitry underlying disease remain poorly understood. Recently developed behavioral tracking tools1,2,3,4 now allow us to study movement abnormalities in animal disease models in order to identify molecular, cellular and circuit dysregulation underlying disease.
Molecular pathways involved in many neurodegenerative diseases are conserved in the fruit fly&#160;Drosophila&#160;melanogaster, and Drosophila disease models have helped to elucidate fundamental mechanisms underlying neurodegeneration5,6. We recently showed that fly models of Parkinson&#8217;s Disease (PD) and Spinocerebellar ataxia 3 (SCA3) exhibit distinct, conserved gait signatures that resemble those of the respective human diseases1, demonstrating that the fly model can be used to understand circuit mechanisms underlying movement dysfunction in specific movement disorders. The rich and continually growing arsenal of tools in the fly model for targeted manipulation and visualization of neurons at the single gene and single cell level7,8,9,10 makes the fly an ideal model one to probe the relationship between disease pathways, neuronal circuitry and behavioral phenotypic manifestation in vivo. To enable precise, automated insect gait analysis, we recently developed a machine learning method, Feature&#160;Learning-based&#160;LImb segmentation and Tracking (FLLIT)1.
FLLIT consists of a fully automated multi-stage algorithm that first segments the leg pixels, which are subsequently used to locate and track the corresponding leg claws. FLLIT employs a boosting algorithm for segmentation, in contrast to deep learning algorithms used in recent work2,3. There are some similarities with convolutional neural networks in that for both frameworks, feature extraction is done automatically through learning convolutional kernels. The first step in FLLIT involves using morphological operations (edge and skeletonization) to automatically generate positive (pixels on the legs) and negative (background or pixels on the fly body) training samples with high confidence. Hence, FLLIT is fully automated and does not require user annotated training samples. Using the above training samples, a classifier is then trained in the framework of a boosting algorithm. An ensemble of weak classifiers is iteratively learnt, with each consisting a set of convolutional kernels for feature extraction and a decision tree. The final learnt classifier is then used for leg segmentation and is able to better discern difficult regions/hard samples better than morphological operations, producing an overall much more accurate segmentation for tracking1. From the segmented legs, we locate the tips and track them using the Hungarian algorithm: by matching tips across frames such that the sum of the distance moved by each tip is minimized. FLLIT can handle occlusion cases by remembering the last seen location (in fly centered coordinates) so that a leg tip is recovered once it is no longer under occlusion.
We previously showed that FLLIT can automatically and accurately track leg movements and analyze gait in an unmarked, freely moving fly or spider from high-speed video1; FLLIT should hence be broadly applicable for arthropod leg tracking. By extracting machine learning training sets using morphological parameters, FLLIT automatically trains itself to segment and track insect legs without the need for laborious manual annotation, which is required for most deep learning methods. FLLIT is hence fully automated. After leg segmentation and tracking, FLLIT automatically produces raw tracked body and leg positions in every video frame, 20 gait parameters, 5 plots and a tracked video for gait analysis and visualization of gait movements. This protocol provides a step-by-step guide to using FLLIT.

<table border="0" cellpadding="0" cellspacing="0" style="width:1025px;" width="1025">
	<colgroup>
		<col />
		<col />
		<col span="2" />
	</colgroup>
	<tbody>
		<tr height="40">
			<td height="40" style="height:40px;width:215px;">Acrylic Sheets</td>
			<td style="width:123px;">Dama</td>
			<td style="width:344px;">1.6 mm thickness, Clear sheets</td>
			<td style="width:344px;">Singapore, Singapore</td>
		</tr>
		<tr height="40">
			<td height="40" style="height:40px;width:215px;">Clear Glass slides</td>
			<td style="width:123px;">Biomedia</td>
			<td style="width:344px;">BMH 880101</td>
			<td style="width:344px;">Singapore, Singapore</td>
		</tr>
		<tr height="40">
			<td height="40" style="height:40px;width:215px;">High speed camera</td>
			<td style="width:123px;">Photron</td>
			<td style="width:344px;">Fastcam MC2.1</td>
			<td style="width:344px;">Tokyo, Japan. A shutter speed of 1 msec or faster is ideal to reduce motion blur of captured images</td>
		</tr>
		<tr height="40">
			<td height="40" style="height:40px;width:215px;">Infra Red LED</td>
			<td style="width:123px;">Any - Generic from hardware store</td>
			<td style="width:344px;">940nm Infrared Light Board</td>
			<td style="width:344px;">Singapore, Singapore</td>
		</tr>
		<tr height="40">
			<td height="40" style="height:40px;width:215px;">Kimwipe</td>
			<td style="width:123px;">Kimberly Clark</td>
			<td style="width:344px;">34155-01LS</td>
			<td style="width:344px;">Irving, Texas, USA</td>
		</tr>
	</tbody>
</table>

fully automated leg tracking in freely moving insects using feature learning leg segmentation and tracking (fllit)

The Drosophila model has been invaluable for the study of neurological function and for understanding the molecular and cellular mechanisms that underlie neurodegeneration. While fly techniques for the manipulation and study of neuronal subsets have grown increasingly sophisticated, the richness of the resultant behavioral phenotypes has not been captured at a similar detail. To be able to study subtle fly leg movements for comparison amongst mutants requires the ability to automatically measure and quantify high-speed and rapid leg movements. Hence, we developed a machine-learning algorithm for automated leg claw tracking in freely walking flies, Feature Learning-based Limb segmentation and Tracking (FLLIT). Unlike most deep learning methods, FLLIT is fully automated and generates its own training sets without a need for user annotation, using morphological parameters built into the learning algorithm. This article describes an in depth protocol for carrying out gait analysis using FLLIT. It details the procedures for camera setup, arena construction, video recording, leg segmentation and leg claw tracking. It also gives an overview of the data produced by FLLIT, which includes raw tracked body and leg positions in every video frame, 20 gait parameters, 5 plots and a tracked video. To demonstrate the use of FLLIT, we quantify relevant diseased gait parameters in a fly model of Spinocerebellar ataxia 3.

Following leg segmentation, tracking and data processing, FLLIT automatically generates raw data for the positions of the body and each leg claw, 20 gait parameters, 5 plots and a tracked video (Table 1).
Here, we demonstrate these analyses using a fly model of Spinocerebellar ataxia 3 (SCA3). The pan-neuronal driver Elav-GAL4 was used to drive either the full-length wildtype human SCA3 with 27 glutamines in the polyQ tract (UAS-SCA3-flQ27), or a full-length mutant human SCA3 with 84 glutamines in the polyQ tract (UAS-SCA3-flQ84)11. SCA3 is typified by an ataxic gait with body veering, erratic foot placement and short, lurching steps12,13 (Table 2). To characterize gait of mutant SCA3 flies and investigate whether they display a similar gait to that of human patients, we analyzed relevant gait parameters generated by FLLIT, namely: Number of body turns, footprint regularity, leg domain overlap and sizes, and leg stride lengths (Table 2).
We found that SCA3-Q84 flies exhibited more turns (Figure 4A,A&#8217;), erratic foot placement as exhibited by low footprint regularity (enlarged standard deviations of the AEP14) (Figure 4B), increased leg domain overlap (Figure 4C-D), enlarged leg domains in length and area (Figure 4E,F), and decreased stride length (Figure 4G).
FLLIT also generates a video showing the tracked fly and legs in the arena-centered and body-centered views, body trajectory and heading direction, and vertical and lateral displacements of each leg (Figure 5). The tracked videos allow side-by-side comparison of leg movements in different flies. Representative videos of Elav-GAL4&#62;SCA3-flQ27 (Video 1) and Elav-GAL4&#62;SCA3-flQ84 (Video 2) flies demonstrate that compared to Elav-GAL4&#62;UAS-SCA3-flQ27 flies (Figure 5A), Elav-GAL4&#62;UAS-SCA3-flQ84 flies (Figure 5B) exhibit irregular, intersecting leg domains of different sizes, indicative of a lurching, ataxic gait.
<img alt="Figure 1" class="xfigimg" src="/files/ftp_upload/61012/61012fig1.jpg" /> 
Figure 1. Setup of the recording station and arena. Recordings from the (A) front and (B) side views. (C) An example of an arena used for making fly recordings for FLLIT tracking. <a href="https://www.jove.com/files/ftp_upload/61012/61012fig1large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 2" class="xfigimg" src="/files/ftp_upload/61012/61012fig2.jpg" /> 
Figure 2: View of the active window during fly gait recording using a dual head camera, which allows simultaneous recording of two flies. <a href="https://www.jove.com/files/ftp_upload/61012/61012fig2large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 3" class="xfigimg" src="/files/ftp_upload/61012/61012fig3.jpg" /> 
Figure 3: Active FLLIT window showing the button panel and labelled legs after segmentation and tracking. <a href="https://www.jove.com/files/ftp_upload/61012/61012fig3large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 4" class="xfigimg" src="/files/ftp_upload/61012/61012fig4.jpg" /> 
Figure 4: Representative FLLIT-generated data for relevant gait parameters of flies expressing wildtype (SCA3-flQ27) vs. mutant (SCA3-flQ84) SCA3. (A) Number of turns in the body trajectory. (B) Mid-leg footprint regularity normalized to body length. (C-C&#8217;) Traversed leg domains of each leg. (D) Domain overlap between legs. (E) Mid-leg domain length normalized to body length. (F) Mid-leg domain area normalized to body length2. (G) Mid-leg stride length normalized to body length. <a href="https://www.jove.com/files/ftp_upload/61012/61012fig4large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 5" class="xfigimg" src="/files/ftp_upload/61012/61012fig5.jpg" /> 
Figure 5: Snapshot of representative FLLIT-generated videos. (A) Elav-GAL4&#62;UAS-SCA3-flQ27 and (B) Elav-GAL4&#62;UAS-SCA3-flQ84 flies. <a href="https://www.jove.com/files/ftp_upload/61012/61012fig5large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Video 1" src="/files/ftp_upload/61012/61012video1.jpg" /> 
Video 1: Representative video of a fly expressing pan-neuronal wild-type human full-length SCA3 (Elav-GAL4&#62;UAS-SCA3-flQ27). <a href="https://www.jove.com/files/ftp_upload/61012/Video_1_Elav-GAL4_SCA3-flQ27_22d.mp4" target="_blank">Please click here to view this video. (Right-click to download.)</a>
<img alt="Video 2" src="/files/ftp_upload/61012/61012video2.jpg" /> 
Video 2: Representative video of a fly expressing pan-neuronal mutant human full-length SCA3 (Elav-GAL4&#62;UAS-SCA3-flQ84). <a href="https://www.jove.com/files/ftp_upload/61012/Video_2_Elav-GAL4_SCA3-flQ84_22d.mp4" target="_blank">Please click here to view this video. (Right-click to download.)</a>
<img alt="Supplemental Figure 1" src="/files/ftp_upload/61012/61012supfig1.jpg" /> 
Supplemental Figure 1: Configurations for VcXSrv. <a href="https://www.jove.com/files/ftp_upload/61012/61012supfig1large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Supplemental Figure 2" src="/files/ftp_upload/61012/61012supfig2.jpg" /> 
Supplemental Figure 2: Configuration for Xquartz. <a href="https://www.jove.com/files/ftp_upload/61012/61012supfig2large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Supplemental Figure 3" src="/files/ftp_upload/61012/61012supfig3.jpg" /> 
Supplemental Figure 3: Image labelled with the dimensions needed for calculating Field of view. <a href="https://www.jove.com/files/ftp_upload/61012/61012supfig3large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<table border="1" fo:keep-together.within-page="1">
	<tbody>
		<tr>
			<td>Category</td>
			<td>Parameters</td>
			<td>Description</td>
			<td>File/Plot (if applicable)</td>
		</tr>
		<tr>
			<td rowspan="4">Raw data</td>
			<td>Body position</td>
			<td>Positional coordinates of the body centroid in each frame</td>
			<td>First two columns of CoM.csv</td>
		</tr>
		<tr>
			<td>Body trajectory</td>
			<td>Angle of rotation of the body axis in degrees (relative to the y-axis)</td>
			<td>Third column of CoM.csv</td>
		</tr>
		<tr>
			<td>Arena-centred leg claw 
			positions</td>
			<td>Positional coordinates of each leg claw in each frame based on arena coordinates</td>
			<td>trajectory.csv</td>
		</tr>
		<tr>
			<td>Body-centred leg claw 
			positions</td>
			<td>Positional coordinates of each leg claw in each frame 
			based on arena coordinates</td>
			<td>norm_trajectory.csv</td>
		</tr>
		<tr>
			<td rowspan="3">Body motion</td>
			<td>Body length (mm)</td>
			<td>Length of the sample animal estimated in each frame (anterior-most position on head to posterior-most 
			position on the wings)</td>
			<td>bodylength.csv</td>
		</tr>
		<tr>
			<td>Instantaneous body velocity 
			(mm/s)</td>
			<td>Instantaneous velocity of the body (centroid) in the sample animal</td>
			<td>BodyVelocity.csv; BodyVelocity.pdf</td>
		</tr>
		<tr>
			<td>Turning points of the body 
			trajectory</td>
			<td>To locate the turning points, the trajectory is reduced to a piecewise-linear curve using the Dougl as&#226;ASPeucker 
			algorithm, following which a turning event is identified as involving an angle &#62; 50 deg between two neighbouring 
			linear segments constituting the simplified trajectory</td>
			<td>BodyTrajectory.pdf</td>
		</tr>
		<tr>
			<td rowspan="9">Individual stride parameters</td>
			<td>Stride duration (ms)</td>
			<td>The duration of a stride event</td>
			<td>StrideParameters.csv</td>
		</tr>
		<tr>
			<td>Stride period (ms)</td>
			<td>The duration from one stride event to the next</td>
			<td></td>
		</tr>
		<tr>
			<td>Stride displacement (mm)</td>
			<td>Displacement of the leg claw during a stride event</td>
			<td></td>
		</tr>
		<tr>
			<td>Stride path covered (mm)</td>
			<td>Total path covered by the leg claw during a stride event</td>
			<td></td>
		</tr>
		<tr>
			<td>Anterior extreme position 
			(mm)</td>
			<td>Landing position (relative to the body) of a leg claw at the end of a stride event</td>
			<td></td>
		</tr>
		<tr>
			<td>Posterior extreme position (mm)</td>
			<td>Take-off position (relative to the body) of a leg claw at the start of a stride event</td>
			<td></td>
		</tr>
		<tr>
			<td>Stride amplitude (mm)</td>
			<td>Displacement along the direction of motion for a stride event</td>
			<td></td>
		</tr>
		<tr>
			<td>Stance linearity (mm)</td>
			<td>Defined as the deviation of the stride path from a curve smoothed over (at 20ms intervals) the corresponding anterior 
			and posterior extreme positions of the stride</td>
			<td></td>
		</tr>
		<tr>
			<td>Stride stretch (mm)</td>
			<td>Distance of the leg claw position from the body centre in the middle of a stride event</td>
			<td></td>
		</tr>
		<tr>
			<td rowspan="10">Leg motion</td>
			<td>Leg speed (mm/s)</td>
			<td>The instantaneous speed of each leg</td>
			<td>LegSpeed.csv; Gait.pdf</td>
		</tr>
		<tr>
			<td>Gait index</td>
			<td>This measures the type of gait coordination exhibited by the (six-legged) sample animal during its motion. A gait 
			index of 1 corresponds to a tripod gait, _1 corresponds to a tetrapod gait while 0 constitutes an non-canonical gait. 
			In our implementation, the gait index is obtained by a moving average over a 120 ms window</td>
			<td>GaitIndex.csv; GaitIndex.pdf</td>
		</tr>
		<tr>
			<td>Movement percentage</td>
			<td>Percentage of the time that a leg is in motion</td>
			<td>LegParameters.csv</td>
		</tr>
		<tr>
			<td>Mean stride period (ms)</td>
			<td>Average duration from one stride event to the next</td>
			<td>LegParameters.csv</td>
		</tr>
		<tr>
			<td>Footprint regularity (mm)</td>
			<td>Measured as the standard deviations of the posterior and</td>
			<td>LegParameters.csv</td>
		</tr>
		<tr>
			<td></td>
			<td>anterior extreme positions of a leg</td>
			<td></td>
		</tr>
		<tr>
			<td>Leg trajectory domain area 
			(mm2)</td>
			<td>The area of the minimal convex hull that contains the entire leg trajectory in the body-centred frame of reference</td>
			<td>LegParameters.csv; LegDomain.pdf</td>
		</tr>
		<tr>
			<td>Length and width of the 
			leg trajectory domain (mm)</td>
			<td>Obtained via the maximum projected distance of the claw positions onto the major (domain length) and minor 
			(domain width) principal axes</td>
			<td>LegParameters.csv</td>
		</tr>
		<tr>
			<td>Leg domain intersection/overlap 
			(mm2)</td>
			<td>The intersection/overlap between each possible</td>
			<td>LegDomainOverlap.csv</td>
		</tr>
		<tr>
			<td>Stance width (mm)</td>
			<td>Average of the distance between the AEP and PEP of the left and middle legs</td>
			<td>StanceWidth.csv</td>
		</tr>
	</tbody>
</table>
Table 1: Gait parameters generated by FLLIT.
<table border="1" fo:keep-together.within-page="1">
	<tbody>
		<tr>
			<td></td>
			<td colspan="4">Gait feature</td>
		</tr>
		<tr>
			<td>Gait features of Spinocerebellar ataxia 3 (SCA3)</td>
			<td>Veering</td>
			<td>Erratic foot placement and leg crossing over</td>
			<td>Lurching steps</td>
			<td>Short strides</td>
		</tr>
		<tr>
			<td>Measurement Parameter</td>
			<td>Number of body turn events</td>
			<td>Footprint regularity</td>
			<td>Size of leg domains, degree of domain overlap</td>
			<td>Stride length</td>
		</tr>
		<tr>
			<td>FLLIT File</td>
			<td>BodyTrajectory.pdf</td>
			<td>LegParameters.csv</td>
			<td>LegDomainOverlap.csv</td>
			<td>StrideParameters.csv</td>
		</tr>
	</tbody>
</table>
Table 2: Table showing hallmark SCA3 gait features in human patients with their corresponding FLLIT parameters and output files.

Watch this Scientific Journal Video about Fully Automated Leg Tracking in Freely Moving Insects using Feature Learning Leg Segmentation and Tracking FLLIT at JoVE.com

Fully Automated Leg Tracking in Freely Moving Insects using Feature Learning Leg Segmentation and Tracking FLLIT

We describe detailed protocols for using FLLIT, a fully automated machine learning method for leg claw movement tracking in freely moving Drosophila melanogaster and other insects. These protocols can be used to quantitatively measure subtle walking gait movements in wild type flies, mutant flies and fly models of neurodegeneration.

Fully Automated Leg Tracking in Freely Moving Insects using Feature Learning Leg Segmentation and Tracking (FLLIT)

The Drosophila model has been invaluable for the study of neurological function and for understanding the molecular and cellular mechanisms that underlie ...

fully-automated-leg-tracking-freely-moving-insects-using-feature

Research

JoVE Journal

Behavior

6.7K Views.  Agency for Science, Technology and Research. We describe detailed protocols for using FLLIT, a fully automated machine learning method for leg claw movement tracking in freely moving Drosophila melanogaster and other insects. These protocols can be used to quantitatively measure subtle walking gait movements in wild type flies, mutant flies and fly models of neurodegeneration.

Video: Fully Automated Leg Tracking in Freely Moving Insects using Feature Learning Leg Segmentation and Tracking FLLIT

Quantifying Learning in Young Infants: Tracking Leg Actions During a Discovery-learning Task

Task-specific actions emerge from spontaneous movement during infancy. It has been proposed that task-specific actions emerge through a discovery-learning process. Here a method is described in which 3-4 month old infants learn a task by discovery and their leg movements are captured to quantify the learning process. This discovery-learning task uses an infant activated mobile that rotates and plays music based on specified leg action of infants. Supine infants activate the mobile by moving their feet vertically across a virtual threshold. This paradigm is unique in that as infants independently discover that their leg actions activate the mobile, the infants&#8217; leg movements are tracked using a motion capture system allowing for the quantification of the learning process. Specifically, learning is quantified in terms of the duration of mobile activation, the position variance of the end effectors (feet) that activate the mobile, changes in hip-knee coordination patterns, and changes in hip and knee muscle torque. This information describes infant exploration and exploitation at the interplay of person and environmental constraints that support task-specific action. Subsequent research using this method can investigate how specific impairments of different populations of infants at risk for movement disorders influence the discovery-learning process for task-specific action.

A method is described in which 3-4 month old infants learn a task by discovery and their leg movements are captured to quantify the learning process.

Task-specific actions emerge from spontaneous movement during infancy. It has been proposed that task-specific actions emerge through a discovery-learning ...

A Method to Quantify Visual Information Processing in Children Using Eye Tracking

Visual problems that occur early in life can have major impact on a child's development. Without verbal communication and only based on observational methods, it is difficult to make a quantitative assessment of a child's visual problems. This limits accurate diagnostics in children under the age of 4 years and in children with intellectual disabilities. Here we describe a quantitative method that overcomes these problems. The method uses a remote eye tracker and a four choice preferential looking paradigm to measure eye movement responses to different visual stimuli. The child sits without head support in front of a monitor with integrated infrared cameras. In one of four monitor quadrants a visual stimulus is presented. Each stimulus has a specific visual modality with respect to the background, e.g., form, motion, contrast or color. From the reflexive eye movement responses to these specific visual modalities, output parameters such as reaction times, fixation accuracy and fixation duration are calculated to quantify a child's viewing behavior. With this approach, the quality of visual information processing can be assessed without the use of communication. By comparing results with reference values obtained in typically developing children from 0-12 years, the method provides a characterization of visual information processing in visually impaired children. The quantitative information provided by this method can be advantageous for the field of clinical visual assessment and rehabilitation in multiple ways. The parameter values provide a good basis to: (i) characterize early visual capacities and consequently to enable early interventions; (ii) compare risk groups and follow visual development over time; and (iii), construct an individual visual profile for each child.

A method is described to quantify the quality of visual information processing based on reflexive eye movements in response to specific visual modalities. Reaction times and fixation output parameters are used to characterize visual performance in children with and without visual impairments from 6 months of age.

Visual problems that occur early in life can have major impact on a child's development. Without verbal communication and only based on observational ...

Psychophysical Tracking Method to Measure Taste Preferences in Children and Adults

The Monell two-series, forced-choice, paired-comparison tracking method provides a reliable measure of sweet taste preferences from childhood to adulthood. The method, which is identical for children, adolescents, and adults, is of short duration (&#60; 15 min), does not rely on sustained attention or place demands on memory (which would yield spurious age differences), and minimizes the impact of language development, making this method amenable to the cognitive limitations of pediatric populations. In this whole-mouth tasting method, subjects are asked to taste (without swallowing) pairs of solutions of different sucrose concentrations and to point to the solution they prefer. Each subsequent pair contains the participant&#39;s preceding preferred concentration and an adjacent stimulus concentration. The procedure continues until the subject chooses either a given concentration of sucrose when paired with both a higher and a lower concentration, or the highest or lowest concentration two consecutive times. Subjects are prevented from reaching response criteria on the basis of first or second position bias by the two-series design of the method, which counterbalances the order of solution presentation within each pair between the series (the weaker concentration is presented first in Series 1, second in Series 2). The geometric mean of the two sucrose concentrations chosen in Series 1 and 2 is an estimate of the participant&#39;s most preferred level of sucrose. Sucrose preference as determined with this laboratory-based measure has been shown to be associated with preference for sugars in foods and beverages and with taste receptor genotype, family history of alcoholism, and race/ethnicity, as well as depressive symptomatology among pediatric populations. The method has real-world relevance and has been applied to determine most preferred level of other tastes (e.g., salt), making it a valuable psychophysical tool.

Sweet taste has powerful hedonic appeal among people of all ages, particularly children. Described herein is a reliable and valid method that can be used to determine the level of sweetness most preferred, making it a valuable psychophysical tool for scientists.

The Monell two-series, forced-choice, paired-comparison tracking method provides a reliable measure of sweet taste preferences from childhood to ...

Multi-layer Cortical Ca2+ Imaging in Freely Moving Mice with Prism Probes and Miniaturized Fluorescence Microscopy

In vivo circuit and cellular level functional imaging is a critical tool for understanding the brain in action. High resolution imaging of mouse cortical neurons with two-photon microscopy has provided unique insights into cortical structure, function and plasticity. However, these studies are limited to head fixed animals, greatly reducing the behavioral complexity available for study. In this paper, we describe a procedure for performing chronic fluorescence microscopy with cellular-resolution across multiple cortical layers in freely behaving mice. We used an integrated miniaturized fluorescence microscope paired with an implanted prism probe to simultaneously visualize and record the calcium dynamics of hundreds of neurons across multiple layers of the somatosensory cortex as the mouse engaged in a novel object exploration task, over several days. This technique can be adapted to other brain regions in different animal species for other behavioral paradigms.

Multi-layer Cortical Ca2+ Imaging in Freely Moving Mice with Prism Probes and Miniaturized Fluorescence Microscopy

Here, we present a procedure for performing large-scale Ca2+ imaging with cellular-resolution across multiple cortical layers in freely moving mice. Hundreds of active cells can be observed simultaneously using a miniature, head-mounted microscope coupled with an implanted prism probe.

In vivo circuit and cellular level functional imaging is a critical tool for understanding the brain in action. High resolution imaging of mouse cortical ...

Behavioral Tracking and Neuromast Imaging of Mexican Cavefish

Cave-dwelling animals have evolved a series of morphological and behavioral traits to adapt to their perpetually dark and food-sparse environments. Among these traits, foraging behavior is one of the useful windows into functional advantages of behavioral trait evolution. Presented herein are updated methods for analyzing vibration attraction behavior (VAB: an adaptive foraging behavior) and imaging of associated mechanosensors of cave-adapted tetra, Astyanax mexicanus. In addition, methods are presented for high-throughput tracking of a series of additional cavefish behaviors including hyperactivity and sleep-loss. Cavefish also show asociality, repetitive behavior and higher anxiety. Therefore, cavefish serve as an animal model for evolved behaviors. These methods use free-software and custom-made scripts that can be applied to other types of behavior. These methods provide practical and cost-effective alternatives to commercially available tracking software.

Here, we present methods for high-throughput study of a series of the Mexican cavefish behaviors and vital staining of a mechanosensory system. These methods use free-software and custom-made scripts, providing a practical and cost-effective method for the studies of behaviors.

Cave-dwelling animals have evolved a series of morphological and behavioral traits to adapt to their perpetually dark and food-sparse environments. Among ...

Simultaneous Eye Tracking and Single-Neuron Recordings in Human Epilepsy Patients

Intracranial recordings from patients with intractable epilepsy provide a unique opportunity to study the activity of individual human neurons during active behavior. An important tool for quantifying behavior is eye tracking, which is an indispensable tool for studying visual attention. However, eye tracking is challenging to use concurrently with invasive electrophysiology and this approach has consequently been little used. Here, we present a proven experimental protocol to conduct single-neuron recordings with simultaneous eye tracking in humans. We describe how the systems are connected and the optimal settings to record neurons and eye movements. To illustrate the utility of this method, we summarize results that were made possible by this setup. This data shows how using eye tracking in a memory-guided visual search task allowed us to describe a new class of neurons called target neurons, whose response was reflective of top-down attention to the current search target. Lastly, we discuss the significance and solutions to potential problems of this setup. Together, our protocol and results suggest that single-neuron recordings with simultaneous eye tracking in humans are an effective method to study human brain function. It provides a key missing link between animal neurophysiology and human cognitive neuroscience.

We describe a method to conduct single-neuron recordings with simultaneous eye tracking in humans. We demonstrate the utility of this method and illustrate how we used this approach to obtain neurons in the human medial temporal lobe that encode targets of a visual search.

Intracranial recordings from patients with intractable epilepsy provide a unique opportunity to study the activity of individual human neurons during ...

Exploring Infant Sensitivity to Visual Language using Eye Tracking and the Preferential Looking Paradigm

We discuss the use of the preferential looking paradigm in eye tracking studies in order to study how infants develop, understand, and attend to the world around them. Eye tracking is a safe and non-invasive way to collect gaze data from infants, and the preferential looking paradigm is simple to design and only requires the infant to be attending to the screen. By simultaneously showing two visual stimuli that differ in one dimension, we can assess whether infants show different looking behavior for either stimulus, thus demonstrating sensitivity to that difference. The challenges in such experimental approaches are that experiments must be kept brief (no more than 10 min) and be carefully controlled such that the two stimuli differ in only one way. The interpretation of null results must also be carefully considered. In this paper, we illustrate a successful example of an infant eye tracking study with a preferential looking paradigm to discover that 6-month-olds are sensitive to linguistic cues in a signed language despite having no prior exposure to signed language, suggesting that infants possess intrinsic or innate sensitivities to these cues.

Eye tracking studies using a preferential looking paradigm can be used to study infants&#39;&#160;emerging understanding of, and attention to, their external visual world.

We discuss the use of the preferential looking paradigm in eye tracking studies in order to study how infants develop, understand, and attend to the world ...

Optogenetic Manipulation of Neuronal Activity to Modulate Behavior in Freely Moving Mice

Optogenetic modulation of neuronal circuits in freely moving mice affects acute and long-term behavior. This method is able to perform manipulations of single neurons and region-specific transmitter release, up to whole neuronal circuitries in the central nervous system, and allows the direct measurement of behavioral outcomes. Neurons express optogenetic tools via an injection of viral vectors carrying the DNA of choice, such as Channelrhodopsin2 (ChR2). Light is brought into specific brain regions via chronic optical implants that terminate directly above the target region. After two weeks of recovery and proper tool-expression, mice can be repeatedly used for behavioral tests with optogenetic stimulation of the neurons of interest.
Optogenetic modulation has a high temporal and spatial resolution that can be accomplished with high cell specificity, compared to the commonly used methods such as chemical or electrical stimulation. The light does not harm neuronal tissue and can therefore be used for long-term experiments as well as for multiple behavioral experiments in one mouse. The possibilities of optogenetic tools are nearly unlimited and enable the activation or silencing of whole neurons, or even the manipulation of a specific receptor type by light.
The results of such behavioral experiments with integrated optogenetic stimulation directly visualizes changes in behavior caused by the manipulation. The behavior of the same animal without light stimulation as a baseline is a good control for induced changes. This allows a detailed overview of neuronal types or neurotransmitter systems involved in specific behaviors, such as anxiety. The plasticity of neuronal networks can also be investigated in great detail through long-term stimulation or behavioral observations after optical stimulation. Optogenetics will help to enlighten neuronal signaling in several kinds of neurological diseases.

With optogenetic manipulation of specific neuronal populations or brain regions, behavior can be modified with high temporal and spatial resolution in freely moving animals. By using different optogenetic tools in combination with chronically implanted optical fibers, a variety of neuronal modulations and behavioral testing can be performed.

Optogenetic modulation of neuronal circuits in freely moving mice affects acute and long-term behavior. This method is able to perform manipulations of ...

Tracking Rats in Operant Conditioning Chambers Using a Versatile Homemade Video Camera and DeepLabCut

Operant conditioning chambers are used to perform a wide range of behavioral tests in the field of neuroscience. The recorded data is typically based on the triggering of lever and nose-poke sensors present inside the chambers. While this provides a detailed view of when and how animals perform certain responses, it cannot be used to evaluate behaviors that do not trigger any sensors. As such, assessing how animals position themselves and move inside the chamber is rarely possible. To obtain this information, researchers generally have to record and analyze videos. Manufacturers of operant conditioning chambers can typically supply their customers with high-quality camera setups. However, these can be very costly and do not necessarily fit chambers from other manufacturers or other behavioral test setups. The current protocol describes how to build an inexpensive and versatile video camera using hobby electronics components. It further describes how to use the image analysis software package DeepLabCut to track the status of a strong light signal, as well as the position of a rat, in videos gathered from an operant conditioning chamber. The former is a great aid when selecting short segments of interest in videos that cover entire test sessions, and the latter enables analysis of parameters that cannot be obtained from the data logs produced by the operant chambers.

This protocol describes how to build a small and versatile video camera, and how to use videos obtained from it to train a neural network to track the position of an animal inside operant conditioning chambers. This is a valuable complement to standard analyses of data logs obtained from operant conditioning tests.

Operant conditioning chambers are used to perform a wide range of behavioral tests in the field of neuroscience. The recorded data is typically based on ...

Eye-tracking Technology and Data-mining Techniques used for a Behavioral Analysis of Adults engaged in Learning Processes

Behavioral analysis of adults engaged in learning tasks is a major challenge in the field of adult education. Nowadays, in a world of continuous technological changes and scientific advances, there is a need for life-long learning and education within both formal and non-formal educational environments. In response to this challenge, the use of eye-tracking technology and data-mining techniques, respectively, for supervised (mainly prediction) and unsupervised (specifically cluster analysis) learning, provide methods for the detection of forms of learning among users and/or the classification of their learning styles. In this study, a protocol is proposed for the study of learning styles among adults with and without previous knowledge at different ages (18 to 69-year-old) and at different points throughout the learning process (start and end). Statistical analysis-of-variance techniques mean that differences may be detected between the participants by type of learner and previous knowledge of the task. Likewise, the use of&#160;unsupervised learning clustering techniques throws light on similar forms of learning among the participants across different groups. All these data will facilitate personalized proposals from the teacher for the presentation of each task at different points in the chain of information processing. It will likewise be easier for the teacher to adapt teaching materials to the learning needs of each student or group of students with similar characteristics.

We present a protocol for a behavioral analysis of adults (ages 18 to 70-year-old) engaged in learning processes, undertaking tasks designed for Self-Regulated Learning (SRL). The participants, university teachers and students, and adults from the University of Experience, were monitored with eye-tracking devices and the data were analyzed with data-mining techniques.