We thank all the members of the Yoshizawa lab including N. Cetraro, N. Simon, C. Valdez, C. Macapac, J. Choi, L. Lu, J. Nguyen, S. Podhorzer, H. Hernandes, J. Fong, J. Kato, and I. Lord for fish care on the experimental fish used in this manuscript. We also thank A. Keene lab members including P. Masek to train MY to assemble IR CCD camera. Lastly, we would like to thank the Media Lab - College of Social Sciences - School of Communications at the University of Hawai&#39;i M&#257;noa for their invaluable help with making the video, especially B. Smith, J. Lam, and S. White. This work was supported by Hawaiian Community Foundation (16CON-78919 and 18CON-90818) and National Institute of Health NIGMS (P20GM125508) grants to MY.

All procedures are performed following the guidelines described in &#34;Principles of Laboratory Animal Care&#34; (National Institute of Health publication no. 85-23, revised 1985) and the approved by University of Hawai&#39;i at Manoa Institutional Animal Care and Use Committee animal protocol 17-2560-3.
1. Vibration attraction behavior (VAB) assay ( &#8804; 10 min for entire recording procedure)
NOTE: Use an infrared sensitive camera or build an infrared camera by modifying a USB webcam. To modify a USB webcam, see a detailed description presented by the Keene Lab in this cavefish issue at JoVE (From this A. mexicanus issue), or a brief description in the Supplementary Materials.
<ol>
	<li>Recording setup
	<ol>
		<li>To ensure that the camera remains in position, still, and at the proper focal length from the subject(s) being recorded, build a black box frame out of polyvinyl chloride (PVC) pipes, measuring 120 cm H x 45 cm L x 90 cm W.</li>
		<li>After construction of the frame, cover it with a plastic blackout curtain such as the one intended for hydroponic agriculture.</li>
		<li>On top of the frame, put a black acrylic board with a window for the infrared camera at the center measuring the same diameter as the C-mounted adjustable zoom lens. Inside this box, place the VAB assay equipment (Figure 1).</li>
	</ol>
	</li>
	<li>Vibration apparatus 
	NOTE: Vibrations are produced using a small function generator.
	<ol>
		<li>For the following methods, tune vibrations to an amplitude of 0.15 mm and a frequency of 40 Hz, which is the frequency that elicits a maximum response of attraction5,16.</li>
		<li>Connect the function generator to a horizontal facing speaker.</li>
		<li>Attach a 7.5 mm diameter glass rod 14 cm in length to the dust cover on the face of the speaker by using hot-glue or a gasket adhesive.</li>
		<li>Perpendicular to this rod and facing downward, attach another 7.5 mm diameter glass rod 4 cm in length (Figure 1).</li>
	</ol>
	</li>
	<li>Behavioral assay
	<ol>
		<li>Acclimate an experimental A. mexicanus for 4 days in a cylindrical assay chamber filled with conditioned water (pH between 6.8 - 7.0, conductivity approx. 700 &#181;S, temperature approx. 22 &#730;C) with a 12/12 L/D cycle. Check whether fish have acclimated by observing their latency to forage. Longer latency than in their home tank indicates more acclimation time is needed. Throughout acclimation, feed once a day with live Artemia nauplii.</li>
		<li>The day prior to the day of the assay (after 3 days of acclimation), replace water in the assay chamber with fresh conditioned water.</li>
		<li>On the day of the assay (after 4 days of acclimation), deprive experimental fish of food until after the assay is complete. Satiation will change their response to vibrations.</li>
		<li>Set the recording parameters in the VirtualDub freeware17: 15 frames/s, codec: x264vfw, recording duration: 3 min 30 s.</li>
		<li>Prepare the vibration-emitting apparatus (see step 1.2) by tuning to 40 Hz. See Figure 1 for the explanation of apparatus. Rinse the vibrating glass rod with deionized water to remove any water-soluble chemicals.</li>
		<li>Working in the dark, place the assay cylinder on the recording stage illuminated by an infrared backlight in the black box and allow fish to acclimate for 3 min.
		<ol>
			<li>After the 3 min acclimation, record 3 min 30 s of video. At the onset of the recording, insert the vibrating glass rod into the water column (approx. 0.5 cm depth).</li>
			<li>Avoid making any noise or vibrations while positioning the vibrating glass rod in the water as the fish can sense even the most minor disturbances.</li>
			<li>Finish this procedure within 30 s of starting the video recording to ensure that more than 3 min of the behavior is recorded.</li>
		</ol>
		</li>
		<li>Monitor the video while recording to ensure that no errors occur during this stage.</li>
		<li>After finishing the recording, remove the vibrating glass rod from the cylindrical assay chamber and remove the assay chamber from the recording stage. Repeat from 1.3.5 for the next fish.</li>
	</ol>
	</li>
	<li>Video analysis 
	NOTE: Converting the codec into a format that ImageJ can load only works on Windows operating system18 (Table 1).
	<ol>
		<li>Convert the compressed avi video into a readable format for ImageJ and set analysis parameters.
		<ol>
			<li>Install AviSynth_260.exe&#160;(https://sourceforge.net/projects/avisynth2/), pfmap build 178 (http://pismotec.com/pfm/ap/), and avfs ver1.0.0.5 or ver1.0.0.6 (https://sourceforge.net/projects/avf/). Note that this method is program/version sensitive. The provided website links will guide to the proper versions (Table 1).</li>
			<li>Run batch file by double-clicking avs_creater.bat (supplemental file). Right click on the avs video file to be analyzed (select from the avs files created by avs_creater.bat).</li>
			<li>As video analysis using the Tracker plugin in ImageJ requires loading of the ImageJ macro (supplemental file Macro_VAB_moko.txt), load the macro by drag-and-drop into the GUI shell of ImageJ. This macro will enable certain hot keys for the following analysis.</li>
			<li>In the working directory, create a new folder entitled &#34;Process_ImageJ&#34;.</li>
			<li>Right click on the .avs file to be analyzed (select from the avs files created by avs_creater.bat). Select the Quick mount option. After the avs file is mounted as an external drive, open the avi file in ImageJ (the avi file has name ending with &#34;.avi&#34;).</li>
			<li>To set the scale of the distance measurement, select the diameter of the assay chamber by drawing a straight line across the chamber using the Straight-line selection tool, then click Analyze &#62; Set scale function. For example, input 9.4 cm if using a cylindrical dish with a 9.4 cm inner diameter. Check the radio box of Global in order to standardize the scale across all of the following video analyses.</li>
		</ol>
		</li>
		<li>Convert to binary stack and run analysis.
		<ol>
			<li>Copy the assay chamber area by using the oval selection tool and then right click and select Image &#62; Duplicate. At this time, specify the range of frames to keep for further analysis, e.g., keep the first 2,700 frames after the vibrating rod entered the water (at 15 fps this is exactly 3 minutes of video).</li>
			<li>Clear the outside of the assay chamber and convert to a binary image by hitting the hot key 7 on the number bar of the keyboard.</li>
			<li>After the background clears and a prompt appears, add a black dot at the center to indicate the position of the vibrating glass rod by using the oval selection tool already set to black with the fill function. Click OK and a prompt will appear to move on to the threshold adjustment.</li>
			<li>Set the threshold to make a binary (all black and white) image of the fish. Adjust the threshold so that the fish can be seen in entire video clips, and then select Apply.</li>
			<li>Run the &#34;Tracker&#34; plugin by hitting the hot key 8 on the number bar. Set the minimum pixel size to 100 when prompted and hit OK, generating the distance between the vibrating rod and the fish per frame for all 3 min of the binary video.</li>
			<li>Adjust the mis-tracking generated by noise in video. To do so, check the Results window to identify the frames that return the object number 3 or higher-indicating extra objects in those frames (e.g., particles in the water or the shade of the transparent arm of the rod) in addition to the &#34;rod&#34; and &#34;fish&#34; in the frame. Remove any extra objects using the paintbrush tool.</li>
			<li>Hit the hot key 9 on the number bar to export a binary stack of images of the entire video (in case it is necessary to reanalyze) and an .xls file with coordinates and distance data (supplementary files CF01.xls, Threshold_CF01.tif and, Trac_CF01.tif). Hot key 9 will also close all files associated with the current video. Repeat steps 1.4.2.1 through 1.4.2.6 for all replicates.</li>
			<li>Run the macro script (supplementary file JoVE_2cmVAB_template_15fps.xlsm) to consolidate multiple Tracker result files (.xls) into one spreadsheet and count the number and duration of approaches into a 1.5 cm area from the rod. Approaches not lasting at least 0.5 s will not be counted. Change the parameters of distance and time counted as an approach according to particular questions of interest.</li>
		</ol>
		</li>
		<li>Release the PC disk-space after finishing all analyses. Remove mounted files to free up disk-space - avi.avi and .avi.avs files (extensions generated by the software)-by running a batch file multiunmountdel.bat in the same folder where avs_creater.bat was run in the section 1.4.1.2.</li>
	</ol>
	</li>
</ol>
2. Sleep and hyperactivity assay (24 h recording)
<ol>
	<li>Behavioral assay
	<ol>
		<li>Acclimate five experimental fish for 4 days or more in each chamber of a custom-designed 10 L acrylic recording aquarium (45.9 cm x 17.8 cm x 17.8 cm; length x width x depth, respectively) filled with conditioned water (see step 1.3.1).
		<ol>
			<li>Separate each individual chamber with black acrylic boards making chambers equal in size, measuring 88.9 mm &#215; 177.8 mm &#215; 177.8 mm (Figure 2). Be sure to cover each tank to prevent fish from jumping between chambers.</li>
			<li>Set the programmable power timer to automatically turn on white LED light for 12 h, and off for 12 h every day during acclimation period (for example, set the light on at 7 A.M. and off at 7 P.M.). This will entrain the circadian rhythm of fish (if it is susceptible to entrainment).</li>
			<li>Use opaque, white acrylic boards of similar dimension to the 10 L tank as diffusers to pass white and infrared light through in order to provide diffuse light with even intensity across all tanks.</li>
			<li>Throughout acclimation, feed once a day with live Artemia nauplii and provide aeration through sponge filters in each aquarium. 
			NOTE: Ensure fish are fed at consistent times (i.e., 1x per day at 9:00 A.M.) as feeding time can also affect entrainment of circadian rhythms19.</li>
			<li>Check whether fish have become acclimated by observing their latency to forage. A longer latency than in their home tank indicates more acclimation time is needed.</li>
		</ol>
		</li>
		<li>The day prior to the day of the assay (3 days or more of the acclimation), replace water in the assay chamber with freshly conditioned water (see step 1.3.1).</li>
		<li>Set the recording parameter in the VirtualDub software17: 15 frames/s, codec: x264vfw, recording duration: 86,400 s (24 h).</li>
		<li>Turn on the infrared backlight behind the recording stage (see Figure 2). By observing the VirtualDub live image on screen, adjust the position of each aquarium to make them face the USB camera.</li>
		<li>On the day of recording, feed each fish with live Artemia nauplii, remove all sponge filters, and turn on the infrared backlight.</li>
		<li>Start 24 h recording in the morning (for example, the start time is 9 A.M. and the finish time is 9 A.M. the following day). Start capturing the video and secure the location to avoid a disturbance. Periodically check that the recording is running.</li>
		<li>After 24 h, make sure that the video saved correctly. Transfer the video to the PC workstation to track and analyze the fish&#39;s behavior.</li>
	</ol>
	</li>
	<li>Video analysis
	<ol>
		<li>First, check the video quality by looking at the lighting. Check if there is one fish in each section, and if there are any foreign movements that may cause mis-tracking.</li>
		<li>Prepare the mask to avoid mis-tracking outside of the aquarium. Make two masks: one for &#39;even&#39; and one for &#39;odd&#39; fish, based on their sequence order in the tanks.</li>
		<li>Make two folders named &#34;odd&#34; and &#34;even&#34; for the masks described above. Move the tracking parameter file of SwisTrack in each of these folders.</li>
		<li>Open the tracking parameter file of SwisTrack tracking software (supplementary file Tracking_odd.swistrack or Tracking_even.swistrack). Specify the path to the video file and mask file, then save and exit out of the tracking parameter file. Adjust blob number and maximum pixels parameters in &#34;Blob detection&#34; and &#34;Nearest neighbor Tracking&#34; components, respectively, according to the experiments.</li>
		<li>Double-click to run a script of win-automation software which will automatically open SwisTrack software (supplementary file swistrack_1.exe, swistrack_2.exe, swistrack_3.exe or swistrack_4.exe- these are all the same executable files), which aids in updating the adaptive background subtraction in SwisTrack.</li>
		<li>Open Tracking_odd.swistrack or Tracking_even.swistrack in SwisTrack software to load the tracking parameter file. After loading the parameters, press the run button to start tracking.</li>
		<li>Within the initial 9,000 frames (600 s, i.e., first 10 min of the recorded video), check whether fish tracking is working by looking at the adaptive background subtraction, binary mask, and nearest neighbor tracking in the component list of SwisTrack (see accompanying video). Then select Adaptive background subtraction in the Component list.</li>
		<li>Hit the R button on the keyboard to resume win-automation and leave the PC to track. Tracking will take 5 - 7 h per 24 h video for a desktop with 4-CPU cores and 8 GB of memory. According to needs, run multiple SwisTrack processes (including odd and even arenas of a single video file) up to the number of cores in the CPU. For example, 4-cores can handle 4 videos at once.</li>
		<li>During this tracking, avoid using this PC for other purposes because win-automation program automatically moves the mouse pointer. The initial 9,000 frames will be discarded in the following procedure.</li>
		<li>Allocate 3 Perl script files (1.fillupGaps2.pl, 2.Calc_fish_id_moko_robust, and 3.pl, 3.Sleep_summary_4cm_movingWindow.pl) to the folder containing the tracking files generated by SwisTrack in the &#39;even&#39; and &#39;odd&#39; folders (see step 2.2.3).</li>
		<li>Clip one frame of the video from the video file using VirtualDub and import this clip as a photo into ImageJ. Select the length of the aquarium (45.9 cm) in ImageJ and calculate pixel/cm ratio. Write the pixel/cm ratio in 1.fillGaps2.pl in a text editor program and save.</li>
		<li>Launch CygWin program, a Unix emulator. Locate the SwisTrack folder that contains the 3 Perl scripts by using cd on the command line.</li>
		<li>Run the Perl script by typing Perl 1.fillGaps.pl. These three Perl scripts will assign each tracking file to a unique chamber of the aquarium and analyze the sleep duration and swimming distance while the fish was awake. It will take 1-2 h to finish the analysis.</li>
		<li>Assess the text file named Summary_Sleep.txt to determine if the number of frames dropped from the analysis is acceptably low; missing fewer than 15% of frames is considered acceptable.</li>
		<li>Copy and paste the analyzed results from Summary_Sleep.txt to a spreadsheet with the macro (supplementary file Sleep_12hr12hr_TEMPLATE.xlsm).</li>
		<li>Run the macro to extract the summary data of tracking files.</li>
	</ol>
	</li>
</ol>
3. DASPMI or DASPEI staining of mechanosensory neuromasts
NOTE: DASPMI and DASPEI staining is light-sensitive and should be done in dark conditions. Following protocol is for both DASPMI and DASPEI by using DASPMI as an example.
<ol>
	<li>Staining protocol
	<ol>
		<li>For a total of 1 L of staining stock solution (25 &#181;g/mL), add 0.025 g of DASPEI or DASPMI crystals to 1 L of dH2O and let it dissolve overnight. Keep solution stored at 4 &#176;C and protected from light.</li>
		<li>Immerse the fish in 2.5 &#181;g/mL DASPMI or DASPEI dissolved in conditioned water (see step 1.3.1) for 45 min in a dark environment at 22 &#176;C.</li>
		<li>After 45 min, remove fish from the DASPMI or DASPEI solution and anesthetize by immersion in an ice-bath of conditioned water with 66.7 &#181;g/mL of buffered-ethyl 3-aminobenzoate methane sulfonate salt (MS222).</li>
		<li>Mount fish in a Petri dish plate and photograph under a fluorescent microscope. Take z-stack images and save as .tif files for the following analysis.</li>
	</ol>
	</li>
	<li>Image analysis using ImageJ
	<ol>
		<li>Inside the folder containing .tif files, paste a template of the ImageJ macro file (Neuromast_ImageJ.txt) and create a new folder entitled &#34;Process_ImageJ&#34;. In the ImageJ macro file, set the path to the current directory.</li>
		<li>Launch ImageJ and open the macro by dragging the macro file into the GUI or by clicking File &#62; Open and selecting the macro file.</li>
		<li>Run the macro by clicking Macros &#62; Run Macro. The macro will then automatically open a picture file to be analyzed. If the picture file does not open, click Macro &#62; File pick up.</li>
		<li>For Neuromast quantification, select the region of interest using Polygon Tool.</li>
		<li>Hit the hot key 5 to duplicate region of interest.</li>
		<li>Use the Paint Tool to remove or add dots for extra or missing neuromast from the previous image and then hit 6. After hitting 6, two new windows will appear: scheme of numbered neuromasts dots and a table with total neuromasts quantified.</li>
		<li>Hit 7 to save both files: one file is stored as a .tif image file and the other is saved as an .xls file. After these files are stored, a new picture file will open for analysis.</li>
		<li>Consolidate the neuromast counts of each fish into one spreadsheet by running the macro script (SN_Number_Diameter.xlsm).</li>
	</ol>
	</li>
</ol>

<ol>
	<li>Keene, A. C., Yoshizawa, M., McGaugh, S. E. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> Biology and Evolution of the Mexican Cavefish. Biology and Evolution of the Mexican Cavefish. , (2015).</li><li>Mitchell, R. W., Russell, W. H., Elliott, W. 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> Mexican eyeless characin fishes, genus Astyanax: Environment, distribution, and evolution.Special publications the museum Texas Tech University. (12), (1977).</li><li>Wilkens, H. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Evolution+and+genetics+of+epigean+and+cave+Astyanax-fasciatus+(Characidae,+Pisces)+-+Support+for+the+neutral+mutation+theory.">Evolution and genetics of epigean and cave Astyanax-fasciatus (Characidae, Pisces) - Support for the neutral mutation theory.</a> Evolutionary Biology. 23, 271-367 (1988).</li><li>Fumey, J., Hinaux, H., Noirot, C., Thermes, C., R&#233;taux, S., Casane, D. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Evidence+for+late+Pleistocene+origin+of+Astyanax+mexicanus+cavefish.">Evidence for late Pleistocene origin of Astyanax mexicanus cavefish.</a> BMC Evolutionary Biology. 18 (1), 1-19 (2018).</li><li>Yoshizawa, M., Gori&#269;ki, S., Soares, D., Jeffery, W. R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Evolution+of+a+behavioral+shift+mediated+by+superficial+neuromasts+helps+cavefish+find+food+in+darkness.">Evolution of a behavioral shift mediated by superficial neuromasts helps cavefish find food in darkness.</a> Current Biology. 20 (18), 1631-1636 (2010).</li><li>Moran, D., Softley, R., Warrant, E. J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Eyeless+Mexican+cavefish+save+energy+by+eliminating+the+circadian+rhythm+in+metabolism.">Eyeless Mexican cavefish save energy by eliminating the circadian rhythm in metabolism.</a> PloS One. 9 (9), e107877 (2014).</li><li>Moran, D., Softley, R., Warrant, E. J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+energetic+cost+of+vision+and+the+evolution+of+eyeless+Mexican+cavefish.">The energetic cost of vision and the evolution of eyeless Mexican cavefish.</a> Science Advances. 1 (8), e1500363 (2015).</li><li>Yoshizawa, 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=The+Evolution+of+a+Series+of+Behavioral+Traits+is+associated+with+Autism-Risk+Genes+in+Cavefish.">The Evolution of a Series of Behavioral Traits is associated with Autism-Risk Genes in Cavefish.</a> BMC Evolutionary Biology. 18 (1), 89 (2018).</li><li>Riddle, M. R., 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=Insulin+resistance+in+cavefish+as+an+adaptation+to+a+nutrient-limited+environment.">Insulin resistance in cavefish as an adaptation to a nutrient-limited environment.</a> Nature. 555 (7698), 647-651 (2018).</li><li>Protas, M. E., 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=Genetic+analysis+of+cavefish+reveals+molecular+convergence+in+the+evolution+of+albinism.">Genetic analysis of cavefish reveals molecular convergence in the evolution of albinism.</a> Nature Genetics. 38 (1), 107-111 (2006).</li><li>Yoshizawa, 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=Distinct+genetic+architecture+underlies+the+emergence+of+sleep+loss+and+prey-seeking+behavior+in+the+Mexican+cavefish.">Distinct genetic architecture underlies the emergence of sleep loss and prey-seeking behavior in the Mexican cavefish.</a> BMC Biology. 13 (1), 15 (2015).</li><li>Dubou&#233;, E. R., Keene, A. C., Borowsky, R. L. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Evolutionary+convergence+on+sleep+loss+in+cavefish+populations.">Evolutionary convergence on sleep loss in cavefish populations.</a> Current Biology. 21 (8), 671-676 (2011).</li><li>Fernandes, V. F. L., Macaspac, C., Lu, L., Yoshizawa, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Evolution+of+the+developmental+plasticity+and+a+coupling+between+left+mechanosensory+neuromasts+and+an+adaptive+foraging+behavior.">Evolution of the developmental plasticity and a coupling between left mechanosensory neuromasts and an adaptive foraging behavior.</a> Developmental Biology. 441 (2), 262-271 (2018).</li><li>P&#233;rez-Escudero, A., Vicente-Page, J., Hinz, R. C., Arganda, S., de Polavieja, G. G. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=idTracker:+tracking+individuals+in+a+group+by+automatic+identification+of+unmarked+animals.">idTracker: tracking individuals in a group by automatic identification of unmarked animals.</a> Nature Methods. 11, 743 (2014).</li><li>Branson, K., Robie, A. A., Bender, J., Perona, P., Dickinson, M. H. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=High-throughput+ethomics+in+large+groups+of+Drosophila.">High-throughput ethomics in large groups of Drosophila.</a> Nature Methods. 6 (6), 451-457 (2009).</li><li>Yoshizawa, M., Jeffery, W. R., Van Netten, S. M., McHenry, M. J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+sensitivity+of+lateral+line+receptors+and+their+role+in+the+behavior+of+Mexican+blind+cavefish+(Astyanax+mexicanus).">The sensitivity of lateral line receptors and their role in the behavior of Mexican blind cavefish (Astyanax mexicanus).</a> Journal of Experimental Biology. 217 (6), (2014).</li><li>Lee, A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> Virtualdub. , (2014).</li><li>Schneider, C. A., Rasband, W. S., Eliceiri, K. W. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=NIH+Image+to+ImageJ:+25+years+of+image+analysis.">NIH Image to ImageJ: 25 years of image analysis.</a> Nature Methods. 9 (7), 671-675 (2012).</li><li>Cavallari, N., 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+blind+circadian+clock+in+cavefish+reveals+that+opsins+mediate+peripheral+clock+photoreception.">A blind circadian clock in cavefish reveals that opsins mediate peripheral clock photoreception.</a> PLoS Biology. 9 (9), e1001142 (2011).</li><li>Swimmer, B., Lang, H. H. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> Surface Wave Discrimination between Prey and Nonprey by the Back Swimmer Notonecta glauca L. (Hemiptera , Heteroptera ). 6 (3), 233-246 (1980).</li><li>Montgomery, J. C., Macdonald, J. A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> Sensory Tuning of Lateral Line Receptors in Antarctic Fish to the Movements of Planktonic Prey. 235 (4785), 195-196 (1987).</li><li>Prober, D. A., Rihel, J., Onah, A. A., Sung, R. J., Schier, A. F. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Hypocretin/orexin+overexpression+induces+an+insomnia-like+phenotype+in+zebrafish.">Hypocretin/orexin overexpression induces an insomnia-like phenotype in zebrafish.</a> The Journal of Neuroscience. 26 (51), 13400-13410 (2006).</li><li>Zhdanova, I. V., Wang, S. Y., Leclair, O. U., Danilova, N. P. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Melatonin+promotes+sleep-like+state+in+zebrafish.">Melatonin promotes sleep-like state in zebrafish.</a> Brain Research. 903 (1-2), 263-268 (2001).</li><li>Nussbaum-Krammer, C. I., Neto, M. F., Brielmann, R. M., Pedersen, J. S., Morimoto, R. I. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Investigating+the+Spreading+and+Toxicity+of+Prion-like+Proteins+Using+the+Metazoan+Model+Organism+C.+elegans.">Investigating the Spreading and Toxicity of Prion-like Proteins Using the Metazoan Model Organism C. elegans.</a> Journal of Visualized Experiments. (95), e52321 (2015).</li><li>Rasband, W. S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> Object Tracker. , (2000).</li><li>Ferreira, T., Rasband, W. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Create+Shortcuts.">Create Shortcuts.</a> ImageJ User Guide. , (2012).</li><li>Lochmatter, T., Roduit, P., Cianci, C., Correll, N., Jacot, J., Martinoli, A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> SwisTrack. , (2008).</li></ol>

The authors have nothing to disclose.

These presented methods are easy-to-access but can be complicated to perform due to the nature of its freeware origins. Therefore, it is highly recommended to perform trial assays and analyses before any actual experimentation.
The rate of data generation can be rapid once the experimental and analytical framework are established. Once established, it is possible to record two fish in 7 min for the VAB assay, 30 fish in 24 h for the activity/sleep assay, and one fish in 2.5 to 3 min for neurom...

The Mexican tetra, Astyanax mexicanus (Teleostei: Characidae), is unique among fishes for having two radically distinct alternative morphs - a sighted, surface-dwelling morph and a blind, cave-dwelling morph comprised of several distinct populations1. Although different in morphology and physiology, they are still interfertile2,3. These interfertile morphs appear to have evolved rapidly (~20,000 years)4, which makes them an ideal model system for the study of rapid adaptation. Cavefish are known to have a suite of divergent morphological and behavioral traits including increased density of taste buds, increased number of mechanosensors, foraging behavior tuned to a particular frequency of a vibrating stimulus, hyperactivity, and sleeplessness. Many of these behaviors likely evolved simultaneously, some of which have been suggested to be advantageous in the darkness of caves for foraging5 and conserving energy in dark and food-sparse environments6,7.
In many evolutionary model systems, it is difficult to acquire integrated knowledge on how animal morphology and behavior change in response to the environment because most species are distributed across a continuous gradient in complex environments. However, the stark contrast between the cave and surface morph Astyanax that evolved in highly contrasting environments delineated by a sharp ecotone has led to Astyanax emerging as an excellent model to understand animal evolution. This makes it possible to more easily link genes and developmental processes with adaptive traits and selection in the environment. Furthermore, recent biomedical investigations of these traits in Astyanax has shown that these traits may parallel human symptoms8,9,10. For example, loss of sociality and sleep, and gain of hyperactivity, repetitive behavior, and cortisol level are similar to what is observed in humans with autism spectrum disorder8.
To address the complex co-evolution of many behaviors and morphological traits, it is advantageous to assay many of them to highlight underlying genetic and molecular pathways. Presented herein are methods for characterizing the degree of cave-type behavioral phenotypes of surface, cave, and hybrid morphs of Astyanax. The focal behaviors analyzed to characterize phenotype are cave-adapted foraging behavior (vibration attraction behavior, referred to henceforth as VAB), and hyperactivity/sleep duration11,12. Also presented is an imaging method for the sensory system associated with VAB13. Recently, many open-source tracking software for running behavioral assays have become available14,15. These work very well for short videos, less than 10 minutes long. However, it becomes problematic if the video is longer because of intense computation/tracking time. Capable commercially available software can be expensive. The methods presented mainly use freeware and therefore are considered cost-effective and high-throughput methods. Also included are representative results based on these methods.

<table border="0" cellpadding="0" cellspacing="0" style="width:600px;" width="600">
	<tbody>
		<tr height="62">
			<td height="62" style="height:62px;width:221px;">4-Di-1-ASP (4-(4-(dimethylaminostyryl)-1-methylpyridinium iodide)</td>
			<td style="width:85px;">MilliporeSigma</td>
			<td style="width:123px;">D3418</td>
			<td style="width:171px;"></td>
		</tr>
		<tr height="62">
			<td height="62" style="height:62px;width:221px;">880 nm wave length black light</td>
			<td style="width:85px;">Advanced Illumination</td>
			<td style="width:123px;">BL41192-880</td>
			<td style="width:171px;"></td>
		</tr>
		<tr height="42">
			<td height="42" style="height:42px;width:221px;">avfs</td>
			<td style="width:85px;">freeware</td>
			<td style="width:123px;">Version 1.0.0.6</td>
			<td style="width:171px;">http://turtlewar.org/avfs/</td>
		</tr>
		<tr height="42">
			<td height="42" style="height:42px;width:221px;">Avisynth</td>
			<td style="width:85px;">freeware</td>
			<td style="width:123px;">Version 2.6.0</td>
			<td style="width:171px;">http://avisynth.nl/index.php/Main_Page</td>
		</tr>
		<tr height="42">
			<td height="42" style="height:42px;width:221px;">Cygwin</td>
			<td style="width:85px;">freeware</td>
			<td style="width:123px;">Version 2.11.0</td>
			<td style="width:171px;">https://www.cygwin.com/</td>
		</tr>
		<tr height="42">
			<td height="42" style="height:42px;width:221px;">Cylindrical assay chamber (Pyrex 325 ml glass dish)</td>
			<td style="width:85px;">Corning</td>
			<td style="width:123px;">3140-100</td>
			<td style="width:171px;">10 cm diameter 5 cm high</td>
		</tr>
		<tr height="187">
			<td height="187" style="height:187px;width:221px;">Ethovision XT</td>
			<td style="width:85px;">Noldus Information&#160; Technology, Wageningen, The Netherlands</td>
			<td style="width:123px;">Version 14</td>
			<td style="width:171px;">https://www.noldus.com/animal-behavior-research/products/ethovision-xt</td>
		</tr>
		<tr height="83">
			<td height="83" style="height:83px;width:221px;">Fish Aquarium Cylinder Soft Sponge Stone Water Filter, Black</td>
			<td style="width:85px;">Jardin (through Amazon.com)</td>
			<td style="width:123px;">NA</td>
			<td style="width:171px;">Sponge filter for Sleep/hyperactivity recording system</td>
		</tr>
		<tr height="62">
			<td height="62" style="height:62px;width:221px;">Grade A Brine shrimp eggs</td>
			<td style="width:85px;">Brine shrimp direct</td>
			<td style="width:123px;">BSEA16Z</td>
			<td style="width:171px;"></td>
		</tr>
		<tr height="42">
			<td height="42" style="height:42px;width:221px;">ImageJ</td>
			<td style="width:85px;">freeware</td>
			<td style="width:123px;">Version 1.52e</td>
			<td style="width:171px;">https://imagej.nih.gov/ij/</td>
		</tr>
		<tr height="83">
			<td height="83" style="height:83px;width:221px;">macro 1.8/12.5-75mm C-mount zoom lens</td>
			<td style="width:85px;">Toyo</td>
			<td style="width:123px;">NA</td>
			<td style="width:171px;">Attach to USB webcam by using c-mount, which is printed in 3-D printer</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:221px;">Neutral Regulator</td>
			<td style="width:85px;">Seachem</td>
			<td style="width:123px;">NA</td>
			<td style="width:171px;"></td>
		</tr>
		<tr height="62">
			<td height="62" style="height:62px;width:221px;">Optical cast plastic IR long-pass filter</td>
			<td style="width:85px;">Edmund optics</td>
			<td style="width:123px;">43-948</td>
			<td style="width:171px;">Cut into a small piece to fit in the CCD of USB webcam</td>
		</tr>
		<tr height="83">
			<td height="83" style="height:83px;width:221px;">pfmap</td>
			<td style="width:85px;">freeware</td>
			<td style="width:123px;">Build 178</td>
			<td style="width:171px;">http://pismotec.com/download/ (at &#8220;Download Archive&#8221; link at the bottom)</td>
		</tr>
		<tr height="42">
			<td height="42" style="height:42px;width:221px;">Reef Crystals Reef Salt</td>
			<td style="width:85px;">Instant Ocean</td>
			<td style="width:123px;">RC15-10</td>
			<td style="width:171px;"></td>
		</tr>
		<tr height="42">
			<td height="42" style="height:42px;width:221px;">SwisTrack</td>
			<td style="width:85px;">freeware</td>
			<td style="width:123px;">Version 4</td>
			<td style="width:171px;">https://en.wikibooks.org/wiki/SwisTrack</td>
		</tr>
		<tr height="42">
			<td height="42" style="height:42px;width:221px;">USB webcam (LifeCam Studio 1080p HD Webcam)</td>
			<td style="width:85px;">Microsoft</td>
			<td style="width:123px;">Q2F-00013</td>
			<td style="width:171px;">Cut 2-2.5 cm of the front</td>
		</tr>
		<tr height="83">
			<td height="83" style="height:83px;width:221px;">WinAutomation</td>
			<td style="width:85px;">freeware</td>
			<td style="width:123px;">Version 8</td>
			<td style="width:171px;">https://www.winautomation.com/ (free stand-alone app for this procedure)</td>
		</tr>
		<tr height="42">
			<td height="42" style="height:42px;width:221px;">Windows operating system</td>
			<td style="width:85px;">Microsoft</td>
			<td style="width:123px;">7, 8 or 10</td>
			<td style="width:171px;">https://www.microsoft.com/en-us/windows</td>
		</tr>
		<tr height="42">
			<td height="42" style="height:42px;width:221px;">x264vfw</td>
			<td style="width:85px;">freeware</td>
			<td style="width:123px;">NA</td>
			<td style="width:171px;">https://sourceforge.net/projects/x264vfw/</td>
		</tr>
	</tbody>
</table>

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.

The results presented herein are representative examples of what can be acquired with the presented methods. Therefore, results can deviate slightly from the ones presented here for both cavefish and surface fish depending on the experimental conditions.
Vibration attraction behavior
Representative results for VAB can be found in F...

Watch this Scientific Journal Video about Behavioral Tracking and Neuromast Imaging of Mexican Cavefish at JoVE.com

Behavioral Tracking and Neuromast Imaging of Mexican Cavefish

行動追跡とメキシコの血脈の Neuromast イメージング

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 ...

behavioral-tracking-and-neuromast-imaging-of-mexican-cavefish

Research

JoVE Journal

Behavior

7.5K ビュー.  University of Hawai'i at Mānoa. 洞窟フィッシュは、進化と生物医学的プロセスを理解するために素晴らしい動物の壁画として育てています。私たちは現在、これらの形質を人間の症状の平行線として使用して生物医学的調査を行っています。ここでは、進化研究と生物医学的研究の両方に使用できる行動アッセイと分析システムのバッテリーを示します。アッセイ振動が引き付け、行動を起こし?...

ビデオ: Behavioral Tracking and Neuromast Imaging of Mexican Cavefish

Behavioral Phenotyping of Murine Disease Models with the Integrated Behavioral Station (INBEST)

Due to rapid advances in genetic engineering, small rodents have become the preferred subjects in many disciplines of biomedical research. In studies of chronic CNS disorders, there is an increasing demand for murine models with high validity at the behavioral level. However, multiple pathogenic mechanisms and complex functional deficits often impose challenges to reliably measure and interpret behavior of chronically sick mice. Therefore, the assessment of peripheral pathology and a behavioral profile at several time points using a battery of tests are required. Video-tracking, behavioral spectroscopy, and remote acquisition of physiological measures are emerging technologies that allow for comprehensive, accurate, and unbiased behavioral analysis in a home-base-like setting. This report describes a refined phenotyping protocol, which includes a custom-made monitoring apparatus (Integrated Behavioral Station, INBEST) that focuses on prolonged measurements of basic functional outputs, such as spontaneous activity, food/water intake and motivated behavior in a relatively stress-free environment. Technical and conceptual improvements in INBEST design may further promote reproducibility and standardization of behavioral studies.

Behavioral Phenotyping of Murine Disease Models with the Integrated Behavioral Station INBEST

Prolonged and comprehensive monitoring of mice in a home-cage environment provides a deeper understanding of aberrant behavior in murine models of brain diseases. This paper describes the Integrated Behavioral Station (INBEST) as the key component of contemporary behavioral analysis.

統合された行動ステーション（INBEST）を有するマウス疾患モデルの行動表現型

Due to rapid advances in genetic engineering, small rodents have become the preferred subjects in many disciplines of biomedical research. In studies of ...

行動学

Performing Behavioral Tasks in Subjects with Intracranial Electrodes

Patients having stereo-electroencephalography (SEEG) electrode, subdural grid or depth electrode implants have a multitude of electrodes implanted in different areas of their brain for the localization of their seizure focus and eloquent areas. After implantation, the patient must remain in the hospital until the pathological area of brain is found and possibly resected. During this time, these patients offer a unique opportunity to the research community because any number of behavioral paradigms can be performed to uncover the neural correlates that guide behavior. Here we present a method for recording brain activity from intracranial implants as subjects perform a behavioral task designed to assess decision-making and reward encoding. All electrophysiological data from the intracranial electrodes are recorded during the behavioral task, allowing for the examination of the many brain areas involved in a single function at time scales relevant to behavior. Moreover, and unlike animal studies, human patients can learn a wide variety of behavioral tasks quickly, allowing for the ability to perform more than one task in the same subject or for performing controls. Despite the many advantages of this technique for understanding human brain function, there are also methodological limitations that we discuss, including environmental factors, analgesic effects, time constraints and recordings from diseased tissue. This method may be easily implemented by any institution that performs intracranial assessments; providing the opportunity to directly examine human brain function during behavior.

Patients implanted with intracranial electrodes provide a unique opportunity to record neurological data from multiple areas of the brain while the patient performs behavioral tasks. Here, we present a method of recording from implanted patients that can be reproducible at other institutions with access to this patient population.

頭蓋内電極を有する被験者に行動タスクの実行

Patients having stereo-electroencephalography (SEEG) electrode, subdural grid or depth electrode implants have a multitude of electrodes implanted in ...

The Rodent Psychomotor Vigilance Test (rPVT): A Method for Assessing Neurobehavioral Performance in Rats and Mice

The human Psychomotor Vigilance Test (PVT) is a widely used procedure for measuring changes in fatigue and sustained attention. The present article describes a rodent version of the PVT&#8212;termed the &#34;rPVT&#34;&#8212;that measures similar aspects of attention (i.e., performance accuracy, motor speed, premature responding, and lapses in attention). Data are presented that demonstrate both the short- and long-term usefulness of the rPVT when employed with laboratory rats. Rats easily learn the rPVT, and learning to perform the basic procedure takes less than two weeks of training. Once acquired, rat performances in the rPVT show a high degree of similarity to these same performance measures in the human PVT, including similarities in, lapses in attention, reaction times, vigilance decrements across session time (i.e., the human &#34;time-on-task&#34; effects), and the response-stimulus interval (RSI) effect described for humans. Thus the rPVT can be an extremely valuable tool for assessing the effects of a wide range of variables on sustained attention quite similar to human PVT performances, and thus can be useful for developing novel treatments for neurobehavioral dysfunctions.

The Rodent Psychomotor Vigilance Test rPVT: A Method for Assessing Neurobehavioral Performance in Rats and Mice

A rat version of the human Psychomotor Vigilance Test (PVT) is described that measures aspects of attention similar to those measured with the human PVT, including aspects of human vigilance such as performance accuracy, motor speed, premature responding, and lapses in attention.

げっ歯類精神運動警戒試験（rPVT）：ラットとマウスの神経行動のパフォーマンスを評価するための方法

The human Psychomotor Vigilance Test (PVT) is a widely used procedure for measuring changes in fatigue and sustained attention. The present article ...

Basic Methods for the Study of Reproductive Ecology of Fish in Aquaria

Captive-rearing observations are valuable for revealing aspects of fish behavior and ecology when continuous field investigations are impossible. Here, a series of basic techniques are described to enable observations of the reproductive behavior of a wild-caught gobiid fish, as a model, kept in an aquarium. The method focuses on three steps: collection, transport, and observations of reproductive ecology of a substrate spawner. Essential aspects of live fish collection and transport are (1) preventing injury to the fish, and (2) careful acclimation to the aquarium. Preventing harm through injuries such as scratches or a sudden change of water pressure is imperative when collecting live fish, as any physical damage is likely to negatively affect the survival and later behavior of the fish. Careful acclimation to aquaria decreases the incidence death and mitigates the shock of transport. Observations during captive rearing include (1) the identification of individual fish and (2) monitoring spawned eggs without negative effects to the fish or eggs, thereby enabling detailed investigation of the study species' reproductive ecology. The subcutaneous injection of a visible implant elastomer (VIE) tag is a precise method for the subsequent identification of individual fish, and it can be used with a wide size range of fish, with minimal influence on their survival and behavior. If the study species is a substrate spawner that deposits adhesive eggs, an artificial nest site constructed from polyvinyl chloride (PVC) pipe with the addition of a removable waterproof sheet will facilitate counting and monitoring the eggs, lessening the investigator's influence on the nest-holding and egg-guarding behavior of the fish. Although this basic method entails techniques that are seldom mentioned in detail in research articles, they are fundamental for undertaking experiments that require the captive rearing of a wild fish.

A series of basic methods to enable the study of the reproductive ecology of fish kept in aquaria are described. These are useful protocols for collecting fish using SCUBA, transporting live fish, and observing the reproductive behavior of wild-caught fish kept in aquaria.

アクアリアにおける魚類の生殖生態に関する基礎的研究

Captive-rearing observations are valuable for revealing aspects of fish behavior and ecology when continuous field investigations are impossible. Here, a ...

A Wireless, Bidirectional Interface for In Vivo Recording and Stimulation of Neural Activity in Freely Behaving Rats

In vivo electrophysiology is a powerful technique to investigate the relationship between brain activity and behavior at a millisecond and micrometer scale. However, current methods mostly rely on tethered cable recordings or only use unidirectional systems, allowing either recording or stimulation of neural activity, but not at the same time or same target. Here, a new wireless, bidirectional device for simultaneous multichannel recording and stimulation of neural activity in freely behaving rats is described. The system operates through a single portable head stage that both transmits recorded activity and can be targeted in real-time for brain stimulation using a telemetry-based multichannel software. The head stage is equipped with a preamplifier and a rechargeable battery, allowing stable long-term recordings or stimulation for up to 1 h. Importantly, the head stage is compact, weighs 12 g (including battery) and thus has minimal impact on the animal&#180;s behavioral repertoire, making the method applicable to a broad set of behavioral tasks. Moreover, the method has the major advantage that the effect of brain stimulation on neural activity and behavior can be measured simultaneously, providing a tool to assess the causal relationships between specific brain activation patterns and behavior. This feature makes the method particularly valuable for the field of deep brain stimulation, allowing precise assessment, monitoring, and adjustment of stimulation parameters during long-term behavioral experiments. The applicability of the system has been validated using the inferior colliculus as a model structure.

A Wireless, Bidirectional Interface for In Vivo Recording and Stimulation of Neural Activity in Freely Behaving Rats

A wireless, bidirectional system for multi-channel neural recordings and stimulation in freely behaving rats is introduced. The system is light and compact, thus having minimal impact on the animal&#180;s behavioral repertoire. Moreover, this bidirectional system provides a sophisticated tool to assess causal relationships between brain activation patterns and behavior.

ワイヤレス、 In Vivo記録そして自由行動下ラットにおける神経活動の刺激のための双方向インタ フェース

In vivo electrophysiology is a powerful technique to investigate the relationship between brain activity and behavior at a millisecond and micrometer ...

An Automated T-maze Based Apparatus and Protocol for Analyzing Delay- and Effort-based Decision Making in  Free Moving Rodents

Many neurological and psychiatric patients demonstrate difficulties and/or deficits in decision making. Rodent models are helpful to produce a deeper understanding of the neurobiological causes underlying the decision-making problems. A cost-benefit based T-maze task is used for measuring decision making in which rodents choose between a high reward arm (HRA) and a low reward arm (LRA). There are two paradigms of the T-maze decision-making task, one in which the cost is a time delay and the other in which it is physical effort. Both paradigms require a tedious and labor-intensive management of experimental animals, multiple doors, pellet reward, and arm choice recordings. In the current work, we invented an apparatus based on traditional T-maze with full automation for pellet delivery, door management and choice recordings. This automated setup can be used for the evaluation of both delay- and effort-based decision making in rodents. With the protocol described here, our lab investigated the decision-making phenotypes of multiple genetically modified mice. In the representative data, we showed that the mice with ablated medial habenular showed aversions of both delay and effort and tended to choose the immediate and effortless reward. This protocol helps to decrease the variability caused by experimenter intervention and to enhance experiment efficiency. In addition, chronic silicon probe or microelectrode recording, fiber-optic imaging and/or manipulation of neural activity can be easily applied during the decision-making task using the setup described here.

This article introduces an automated T-maze apparatus that we invented, and a protocol based on this apparatus for analyzing delay-based decision making and effort-based decision making in free moving rodents.

自動化における遅延および努力ベース無料移動齧歯動物における意思決定を分析するための装置およびプロトコルのベース

Many neurological and psychiatric patients demonstrate difficulties and/or deficits in decision making. Rodent models are helpful to produce a deeper ...

Continuous Noninvasive Measuring of Crayfish Cardiac and Behavioral Activities

A crayfish is a pivotal aquatic organism that serves both as a practical biological model for behavioral and physiological studies of invertebrates and as a useful biological indicator of water quality. Even though crayfish cannot directly specify the substances that cause water quality deterioration, they can immediately (within a few seconds) warn humans of water quality deterioration via acute changes in their cardiac and behavioral activities.
In this study, we present a noninvasive method that is simple enough to be implemented under various conditions due to a combination of simplicity and reliability in one model.
This approach, in which the biological organisms are implemented into environmental evaluation processes, provides a reliable and timely alarm for warning of and preventing acute water deterioration in an ambient environment. Therefore, this noninvasive system based on crayfish physiological and ethological parameter recordings was investigated for the detection of changes in an aquatic environment. This system is now applied at a local brewery for controlling&#160;quality of the water used for beverage production, but it can be used at any water treatment and supply facility for continuous, real-time water quality evaluation and for regular laboratory investigations of crayfish cardiac physiology and behavior.

This article presents a noninvasive biomonitoring system for the continuous recording and analyses of crayfish cardiac and locomotor activities. This system consists of a near-infrared optical sensor, a video-tracking module, and software for evaluating crayfish heartbeats that reflects its physiological condition and characterizes crayfish behavior during heartbeat fluctuations.

ザリガニの心臓と行動活動の継続的な非侵襲的測定

A crayfish is a pivotal aquatic organism that serves both as a practical biological model for behavioral and physiological studies of invertebrates and as ...

Behavioral Approaches to Studying Innate Stress in Zebrafish

Responding appropriately to stressful stimuli is essential for survival of an organism. Extensive research has been done on a wide spectrum of stress-related diseases and psychiatric disorders, yet further studies into the genetic and neuronal regulation of stress are still required to develop better therapeutics. The zebrafish provides a powerful genetic model to investigate the neural underpinnings of stress, as there exists a large collection of mutant and transgenic lines. Moreover, pharmacology can easily be applied to zebrafish, as most drugs can be added directly to water. We describe here the use of the 'novel tank test' as a method to study innate stress responses in zebrafish, and demonstrate how potential anxiolytic drugs can be validated using the assay. The method can easily be coupled with zebrafish lines harboring genetic mutations, or those in which transgenic approaches for manipulating precise neural circuits are used. The assay can also be used in other fish models. Together, the described protocol should facilitate the adoption of this simple assay to other laboratories.

This manuscript describes a simple method to measure stress behaviorally in adult zebrafish. The approach takes advantage of the innate tendency that zebrafish prefer the bottom half of a tank when in a stressful state. We also describe methods for coupling the assay with pharmacology.

ゼブラフィッシュの先天性ストレスを研究する行動的アプローチ

Responding appropriately to stressful stimuli is essential for survival of an organism. Extensive research has been done on a wide spectrum of ...

Automated Measurements of Sleep and Locomotor Activity in Mexican Cavefish

Across phyla, sleep is characterized by highly conserved behavioral characteristics that include elevated arousal threshold, rebound following sleep deprivation, and consolidated periods of behavioral immobility. The Mexican cavefish, Astyanax mexicanus (A. mexicanus), is a model for studying trait evolution in response to environmental perturbation. A. mexicanus exist as in eyed surface-dwelling forms and multiple blind cave-dwelling populations that have robust morphological and behavioral differences. Sleep loss has occurred in multiple, independently-evolved cavefish populations. This protocol describes a methodology for quantifying sleep and locomotor activity in A. mexicanus cave and surface fish. A cost-effective video monitoring system allows for behavioral imaging of individually-housed larval or adult fish for periods of a week or longer. The system can be applied to fish aged 4 days post fertilization through adulthood. The approach can also be adapted for measuring the effects of social interactions on sleep by recording multiple fish in a single arena. Following behavioral recordings, data is analyzed using automated tracking software and sleep analysis is processed using customized scripts that quantify multiple sleep variables including duration, bout length, and bout number. This system can be applied to measure sleep, circadian behavior, and locomotor activity in almost any fish species including zebrafish and sticklebacks.

This protocol details the methodology for quantifying locomotor behavior and sleep in the Mexican cavefish. Previous analyses are extended to measure these behaviors in socially-housed fish. This system can be widely applied to study sleep and activity in other fish species.

睡眠とメキシコの血脈の自発運動の自動測定

Across phyla, sleep is characterized by highly conserved behavioral characteristics that include elevated arousal threshold, rebound following sleep ...

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.

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.

特徴学習脚のセグメンテーションとトラッキング(FLLIT)を用いて、自由に動く昆虫の完全自動脚追跡

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