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<?xml version="1.0" encoding="UTF-8"?><!DOCTYPE article PUBLIC "-//NLM//DTD JATS (Z39.96) Journal Archiving and Interchange DTD v1.1d1 20130915//EN" "JATS-archivearticle1.dtd"><article article-type="research-article" dtd-version="1.1d1" xmlns:xlink="http://www.w3.org/1999/xlink"><front><journal-meta><journal-id journal-id-type="nlm-ta">eLife</journal-id><journal-id journal-id-type="hwp">elife</journal-id><journal-id journal-id-type="publisher-id">eLife</journal-id><journal-title-group><journal-title>eLife</journal-title></journal-title-group><issn publication-format="electronic">2050-084X</issn><publisher><publisher-name>eLife Sciences Publications, Ltd</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type="publisher-id">00231</article-id><article-id pub-id-type="doi">10.7554/eLife.00231</article-id><article-categories><subj-group subj-group-type="display-channel"><subject>Research article</subject></subj-group><subj-group subj-group-type="heading"><subject>Neuroscience</subject></subj-group></article-categories><title-group><article-title>Quantification of gait parameters in freely walking wild type and sensory deprived <italic>Drosophila melanogaster</italic></article-title></title-group><contrib-group><contrib contrib-type="author" id="author-2337"><name><surname>Mendes</surname><given-names>César S</given-names></name><xref ref-type="aff" rid="aff1"/><xref ref-type="other" rid="par-1"/><xref ref-type="other" rid="par-2"/><xref ref-type="other" rid="par-3"/><xref ref-type="other" rid="par-4"/><xref ref-type="other" rid="par-5"/><xref ref-type="fn" rid="conf1"/><xref ref-type="fn" rid="con1"/></contrib><contrib contrib-type="author" id="author-2338"><name><surname>Bartos</surname><given-names>Imre</given-names></name><xref ref-type="aff" rid="aff2"/><xref ref-type="fn" rid="conf1"/><xref ref-type="fn" rid="con2"/></contrib><contrib contrib-type="author" id="author-2339"><name><surname>Akay</surname><given-names>Turgay</given-names></name><xref ref-type="aff" rid="aff3"/><xref ref-type="other" rid="par-3"/><xref ref-type="fn" rid="conf1"/><xref ref-type="fn" rid="con3"/></contrib><contrib contrib-type="author" id="author-2340"><name><surname>Márka</surname><given-names>Szabolcs</given-names></name><xref ref-type="aff" rid="aff2"/><xref ref-type="fn" rid="conf1"/><xref ref-type="fn" rid="con4"/></contrib><contrib contrib-type="author" corresp="yes" id="author-2200"><name><surname>Mann</surname><given-names>Richard S</given-names></name><xref ref-type="aff" rid="aff1"/><xref ref-type="corresp" rid="cor1">*</xref><xref ref-type="other" rid="par-1"/><xref ref-type="other" rid="par-2"/><xref ref-type="other" rid="par-3"/><xref ref-type="other" rid="par-5"/><xref ref-type="fn" rid="conf1"/><xref ref-type="fn" rid="con5"/></contrib><aff id="aff1"><institution content-type="dept">Department of Biochemistry and Molecular Biophysics</institution>, <institution>Columbia University</institution>, <addr-line><named-content content-type="city">New York</named-content></addr-line>, <country>USA</country></aff><aff id="aff2"><institution content-type="dept">Department of Physics</institution>, <institution>Columbia University</institution>, <addr-line><named-content content-type="city">New York</named-content></addr-line>, <country>USA</country></aff><aff id="aff3"><institution content-type="dept">Department of Neurological Surgery</institution>, <institution>Columbia University</institution>, <addr-line><named-content content-type="city">New York</named-content></addr-line>, <country>USA</country></aff></contrib-group><contrib-group content-type="section"><contrib contrib-type="editor"><name><surname>Calabrese</surname><given-names>Ron</given-names></name><role>Reviewing editor</role><aff><institution>Emory University</institution>, <country>United States</country></aff></contrib></contrib-group><author-notes><corresp id="cor1"><label>*</label>For correspondence: <email>rsm10@columbia.edu</email></corresp></author-notes><pub-date date-type="pub" publication-format="electronic"><day>08</day><month>01</month><year>2013</year></pub-date><pub-date pub-type="collection"><year>2013</year></pub-date><volume>2</volume><elocation-id>e00231</elocation-id><history><date date-type="received"><day>10</day><month>09</month><year>2012</year></date><date date-type="accepted"><day>26</day><month>11</month><year>2012</year></date></history><permissions><copyright-statement>© 2013, Mendes et al</copyright-statement><copyright-year>2013</copyright-year><copyright-holder>Mendes et al</copyright-holder><license xlink:href="http://creativecommons.org/licenses/by/3.0/"><license-p>This article is distributed under the terms of the <ext-link ext-link-type="uri" xlink:href="http://creativecommons.org/licenses/by/3.0/">Creative Commons Attribution License</ext-link>, which permits unrestricted use and redistribution provided that the original author and source are credited.</license-p></license></permissions><self-uri content-type="pdf" xlink:href="elife00231.pdf"/><related-article ext-link-type="doi" id="ra1" related-article-type="commentary" xlink:href="10.7554/eLife.00450"/><abstract><object-id pub-id-type="doi">10.7554/eLife.00231.001</object-id><p>Coordinated walking in vertebrates and multi-legged invertebrates such as <italic>Drosophila melanogaster</italic> requires a complex neural network coupled to sensory feedback. An understanding of this network will benefit from systems such as <italic>Drosophila</italic> that have the ability to genetically manipulate neural activities. However, the fly's small size makes it challenging to analyze walking in this system. In order to overcome this limitation, we developed an optical method coupled with high-speed imaging that allows the tracking and quantification of gait parameters in freely walking flies with high temporal and spatial resolution. Using this method, we present a comprehensive description of many locomotion parameters, such as gait, tarsal positioning, and intersegmental and left-right coordination for wild type fruit flies. Surprisingly, we find that inactivation of sensory neurons in the fly's legs, to block proprioceptive feedback, led to deficient step precision, but interleg coordination and the ability to execute a tripod gait were unaffected.</p><p><bold>DOI:</bold> <ext-link ext-link-type="doi" xlink:href="10.7554/eLife.00231.001">http://dx.doi.org/10.7554/eLife.00231.001</ext-link></p></abstract><abstract abstract-type="executive-summary"><object-id pub-id-type="doi">10.7554/eLife.00231.002</object-id><title>eLife digest</title><p>Most animals need to be able to move to survive. Animals without limbs, such as snakes, move by generating by wave-like contractions along their bodies, whereas limbed animals, such as vertebrates and arthropods, walk by coordinating the movements of multi-jointed arms and legs. Locomotion in limbed animals involves bending each joint within each arm or leg in a coordinated manner, while also ensuring that the movements of all the limbs are coordinated with each other. In bipeds such as humans, for example, it is critical that one leg is in the stance phase when the other leg is in the swing phase. The rules that govern the coordination of limbs also depend on the gait, so the rules for walking are not the same as the rules for running.</p><p>The nervous systems of bipeds and other animals that walk solve these problems by using complex neural circuits that coordinate the firing of the relevant motor neurons. Two general mechanisms are used to coordinate the firing of motor neurons. In one mechanism, local interneurons within the central nervous system coordinate motor neuron activities: in vertebrates these interneurons are found in the spinal cord. A second mechanism, termed proprioception, relies on sensory neurons that report the load and joint angles from the arms and legs back to the central nervous system, and thereby influence the firing of the motor neurons. Remarkably, both of these mechanisms, and also the types of neurons that comprise motor neuron circuits, are conserved from arthropods to vertebrates.</p><p>Mendes et al. describe a new approach that can be used to analyze how the fruit fly, <italic>D. melanogaster</italic>, walks on surfaces. They use a combination of an optical touch sensor and high-speed video imaging to follow the body of the fly as it walks, and also to record when and where it places each of its six feet on the surface as it moves. Then, using a software package called FlyWalker, they are able to extract a large of number of parameters that can be used to describe locomotion in adult fruit flies with high temporal and spatial resolution. Many of these parameters have never been measured or studied before.</p><p>Mendes et al. show that fruit flies do not display the abrupt transitions in gait that are typically observed in vertebrates. However, they do modify their neural circuits depending on their speed: indeed it appears that flies use subtly different neural circuitry for walking at slow, medium and fast speeds. Moreover, when genetic methods are used to block sensory feedback, the fly is still able to walk, albeit with reduced coordination and precision. Further, the data suggest that proprioception is less important when flies walk faster compared to when they walk more slowly. The next step in this research will be to combine this new method for analyzing locomotion in flies with the wide range of genetic tools that are available for the study of <italic>Drosophila</italic>: this will allow researchers to explore in greater detail the components of the motor neuron circuitry and their role in coordinated walking.</p><p><bold>DOI:</bold> <ext-link ext-link-type="doi" xlink:href="10.7554/eLife.00231.002">http://dx.doi.org/10.7554/eLife.00231.002</ext-link></p></abstract><kwd-group kwd-group-type="author-keywords"><title>Author keywords</title><kwd>walking behavior</kwd><kwd>coordination</kwd><kwd>proprioception</kwd><kwd>sensory feedback</kwd><kwd>gait analysis</kwd><kwd>motor neuron</kwd></kwd-group><kwd-group kwd-group-type="research-organism"><title>Research organism</title><kwd>D. melanogaster</kwd></kwd-group><funding-group><award-group id="par-1"><funding-source><institution-wrap><institution>National Institutes of Health</institution></institution-wrap></funding-source><award-id>R01NS070644</award-id><principal-award-recipient><name><surname>Mendes</surname><given-names>César S</given-names></name><name><surname>Mann</surname><given-names>Richard S</given-names></name></principal-award-recipient></award-group><award-group id="par-2"><funding-source><institution-wrap><institution>Project ALS</institution></institution-wrap></funding-source><principal-award-recipient><name><surname>Mendes</surname><given-names>César S</given-names></name><name><surname>Mann</surname><given-names>Richard S</given-names></name></principal-award-recipient></award-group><award-group id="par-3"><funding-source><institution-wrap><institution>Motor Neuron Center, Columbia University</institution></institution-wrap></funding-source><principal-award-recipient><name><surname>Mendes</surname><given-names>César S</given-names></name><name><surname>Akay</surname><given-names>Turgay</given-names></name><name><surname>Mann</surname><given-names>Richard S</given-names></name></principal-award-recipient></award-group><award-group id="par-4"><funding-source><institution-wrap><institution>Fundação para a Ciência e a Tecnologia, Portugal</institution></institution-wrap></funding-source><principal-award-recipient><name><surname>Mendes</surname><given-names>César S</given-names></name></principal-award-recipient></award-group><award-group id="par-5"><funding-source><institution-wrap><institution>Ellison Medical Foundation</institution></institution-wrap></funding-source><principal-award-recipient><name><surname>Mendes</surname><given-names>César S</given-names></name><name><surname>Mann</surname><given-names>Richard S</given-names></name></principal-award-recipient></award-group><funding-statement>The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.</funding-statement></funding-group><custom-meta-group><custom-meta><meta-name>elife-xml-version</meta-name><meta-value>2</meta-value></custom-meta><custom-meta specific-use="meta-only"><meta-name>Author impact statement</meta-name><meta-value>Novel imaging experiments suggest that fruit flies modify their neural circuitry for walking at slow, medium and fast speeds, and that proprioception is not essential for coordinated walking.</meta-value></custom-meta></custom-meta-group></article-meta></front><body><sec id="s1" sec-type="intro"><title>Introduction</title><p>Ever since life became terrestrial ∼360 million years ago, animals have developed increasingly sophisticated methods to navigate their environments (<xref ref-type="bibr" rid="bib21">Dickinson et al., 2000</xref>). Locomotion is essential for animals to escape from predators, find mates, and search for food. One of the most common forms of terrestrial locomotion depends on the movement of multi-jointed legs. For this to occur, animal nervous systems face two main computational challenges. First, multiple leg joints must move rhythmically and in a precisely coordinated fashion to allow the stereotyped movements that occurs during the swing and stance phases of each step cycle. Second, these movements must be coordinated between legs, which number four in a typical tetrapod and six in a hexapod. Both challenges are met in part by interactions between central pattern generators (CPGs), neural networks within the central nervous system that have the capacity to generate rhythmic outputs (<xref ref-type="bibr" rid="bib37">MacKay-Lyons, 2002</xref>). When used for walking, individual CPGs result in the rhythmic and alternating activity of motor neurons that control the flexion and extension of single leg joints (<xref ref-type="bibr" rid="bib5">Bässler, 1977</xref>; <xref ref-type="bibr" rid="bib51">Strauss, 2002</xref>; <xref ref-type="bibr" rid="bib8">Borgmann et al., 2009</xref>; <xref ref-type="bibr" rid="bib14">Büschges et al., 2011</xref>). Leg movement coordination is also assisted by proprioceptive sensory inputs that report the load and position of leg joints (<xref ref-type="bibr" rid="bib5">Bässler, 1977</xref>; <xref ref-type="bibr" rid="bib8">Borgmann et al., 2009</xref>). Other sensory modalities, such as visual, olfactory and gravitational, also modulate the activity of locomotor CPGs to allow animals to readily change their motor behavior in response to their environment (<xref ref-type="bibr" rid="bib22">Frye, 2010</xref>).</p><p>An understanding of locomotion requires the identification of the neurons that comprise locomotor neural circuits. Given the complexity of these circuits, insects provide an attractive model to achieve this goal due to their relative simplicity, approachable physiology and availability of genetic tools. Moreover, adult insects share with vertebrates the same general principles of locomotion (<xref ref-type="bibr" rid="bib40">Pearson, 1993</xref>). Nevertheless, despite many anatomical similarities, it is less clear how similar the neural circuitries underlying locomotion are in vertebrates and invertebrates. Groundbreaking studies in cockroaches, locusts and stick insects have identified many components and fundamental rules that regulate the walking apparatus, such as the definition of multiple gaits (<xref ref-type="bibr" rid="bib27">Graham et al., 1985</xref>; <xref ref-type="bibr" rid="bib12">Burrows, 1992</xref>; <xref ref-type="bibr" rid="bib63">Zill et al., 2004</xref>; <xref ref-type="bibr" rid="bib43">Ritzmann and Büschges, 2007</xref>; <xref ref-type="bibr" rid="bib13">Büschges et al., 2008</xref>). However, unlike in vertebrates, where gait transitions are discontinuous, it is less clear in which situations insects use distinct gaits and how they transition between them. Although theoretical considerations suggested the possibility that gait transitions may be more gradual than they are in vertebrates, this question has not been fully resolved (<xref ref-type="bibr" rid="bib27">Graham et al., 1985</xref>). Walking speeds in cockroaches, for example, cluster into two groups, but both rely on the tripod gait (<xref ref-type="bibr" rid="bib7">Bender et al., 2011</xref>). The stick insect tends to use a tetrapod gait at slower speeds and a tripod gait at faster speeds (<xref ref-type="bibr" rid="bib27">Graham et al., 1985</xref>). <italic>Drosophila</italic>, on the other hand, are reported to primarily use the tripod gait (<xref ref-type="bibr" rid="bib52">Strauss and Heisenberg, 1990</xref>). Similar questions arise with respect to the role of sensory feedback in coordinated walking. Although feedback is thought to be critical for coordination between legs (<xref ref-type="bibr" rid="bib5">Bässler, 1977</xref>; <xref ref-type="bibr" rid="bib8">Borgmann et al., 2009</xref>) most studies address this question using electrophysiology readouts with tethered animals. In addition, with the exception of surgical ablation experiments (<xref ref-type="bibr" rid="bib56">Usherwood et al., 1968</xref>; <xref ref-type="bibr" rid="bib18">Cruse et al., 1984</xref>), it has been difficult to dissect the contribution of individual sensory modalities. Gaining further insights into these questions may benefit by the use of systems that use freely walking animals with access to genetic tools to identify and manipulate the individual components of locomotor circuits.</p><p>The fruit fly, <italic>D. melanogaster</italic>, is a powerful genetic model with a large collection of mutants and an increasingly sophisticated genetic toolkit (<xref ref-type="bibr" rid="bib41">Pfeiffer et al., 2008</xref>; <xref ref-type="bibr" rid="bib57">Venken et al., 2011</xref>). However, despite the availability of powerful genetic tools, there is a lack of quantitative and robust methods to analyze the consequences of manipulating locomotor circuits in the fruit fly. Consequently, many studies focus on relatively low-resolution locomotor assays such as monitoring the average speed of a population, walking trajectories, or the ability to fulfill simple motor tasks such as climbing a vertical surface (<xref ref-type="bibr" rid="bib23">Ganetzky and Flanagan, 1978</xref>; <xref ref-type="bibr" rid="bib9">Branson et al., 2009</xref>; <xref ref-type="bibr" rid="bib47">Slawson et al., 2009</xref>; <xref ref-type="bibr" rid="bib44">Robie et al., 2010</xref>). To obtain kinematic data, researchers have relied on the manual frame-by-frame analysis of videos (e.g. <xref ref-type="bibr" rid="bib52">Strauss and Heisenberg, 1990</xref>, <xref ref-type="bibr" rid="bib53">1993</xref>; <xref ref-type="bibr" rid="bib61">Wosnitza et al., 2012</xref>). The lack of a high-resolution and accessible assay to monitor fruit fly walking has greatly limited the use of this model system to study locomotion.</p><p>In order to quantitatively analyze locomotion in <italic>Drosophila</italic>, we developed an optical system to monitor walking that uses frustrated Total Internal Reflection (fTIR) coupled with high-speed video imaging. Our fTIR-based method is similar in principle to that used for analyzing the gaits of larger animals (e.g. CatWalk (<xref ref-type="bibr" rid="bib58">Vrinten and Hamers, 2003</xref>); <ext-link ext-link-type="uri" xlink:href="http://www.noldus.com/animal-behavior-research/products/catwalk/">http://www.noldus.com/animal-behavior-research/products/catwalk/</ext-link>), but differs significantly due to the small size and rapid walking speed of fruit flies. Using this approach, we are able to track the position of each footprint relative to the body at high spatial and temporal resolution as an untethered fly walks freely on a flat surface. Custom analysis software allows the extraction of many parameters of fly walking behavior, including gait, coordination between legs, and footprint positions. Using this method, we comprehensively characterized the walking behavior of wild type animals. Unlike walking in vertebrates, our data demonstrate that flies do not abruptly switch from one gait to another, but instead rely on a continuum of gait patterns that correlate with walking speed. However, several readouts suggest that flies may use distinct neural programs at slow, medium, and fast walking speeds. Genetic manipulations to specifically disrupt sensory feedback from the legs show that blocking proprioception results in altered step parameters and reduced walking precision, especially at slower speeds, but does not interfere with the ability of flies to execute a tripod gait. Together, these data reveal the underlying parameters of wild type walking in <italic>Drosophila</italic> and show that proprioceptive sensory feedback is important, but not absolutely required, for coordinated locomotion.</p></sec><sec id="s2" sec-type="results"><title>Results</title><sec id="s2-1"><title>High resolution tracking of gait parameters</title><p>The analysis of locomotion in large animals, notably mammals, often relies on the placement of visual marks in strategic positions, usually joints that can be readily detected and tracked (<xref ref-type="bibr" rid="bib1">Akay et al., 2006</xref>). However, in smaller insects such as <italic>Drosophila</italic>, such a strategy becomes not only technically challenging but is also likely to disturb walking behavior and generate artifacts. Such challenges have precluded a more detailed examination of the components that comprise the fly's walking behavior. To overcome these obstacles, and to measure the biomechanical features underlying walking in <italic>Drosophila</italic> we turned to an optical effect known as <italic>frustrated Total Internal Reflection</italic> (fTIR) (<xref ref-type="bibr" rid="bib62">Zhu et al., 1986</xref>). Total Internal Reflection occurs when light traveling through a medium—in this case optical glass—hits an interface with another medium with a lower refractive index, such as air. If the angle of incidence is above the so-called critical angle (as compared to the normal of the surface), defined by Snell's Law (<xref ref-type="bibr" rid="bib33">Katz, 2002</xref>), the light is no longer refracted but is internally reflected. For a glass-air interface this corresponds to ∼43°. However, if a denser material, such as the tarsus of an insect leg, contacts the surface of the glass, then the locally ‘frustrated’ total internal reflection will scatter the light, which can be recorded by a high-speed video camera (<xref ref-type="fig" rid="fig1">Figure 1</xref> and <xref ref-type="fig" rid="fig1s1">Figure 1—figure supplement 1</xref>) (<xref ref-type="bibr" rid="bib54">Sumriddetchkajorn and Amarit, 2006</xref>). A sample video of the unprocessed fTIR effect can be seen in <xref ref-type="other" rid="video1">Video 1</xref>.<fig-group><fig id="fig1" position="float"><object-id pub-id-type="doi">10.7554/eLife.00231.003</object-id><label>Figure 1.</label><caption><title>fTIR apparatus and FlyWalker software.</title><p>(<bold>A</bold>). Schematic of the fTIR optical effect. LED light sources are located at the edges of an optical glass and light propagates within the glass via internal reflection. Tarsal contacts lead to light scattering detected by a high-speed camera. See <xref ref-type="fig" rid="fig1s1">Figure 1—figure supplement 1</xref> for more details. (<bold>B</bold>). Single frame of a fTIR video. The fTIR effect can be seen for three legs in stance phase (yellow arrows). Background light partially illuminates the fly's body (orange dashed ellipse; the center of the body is indicated by an orange cross). (<bold>C</bold>). Image generated by the FlyWalker software. The fly's footprints and body center are tracked throughout the video. Present footprints are identified and labeled (yellow arrows). The fly body and trajectory are visualized by a blue line (white arrow). Past footprints can also be recorded (red arrows). A scale bar can be introduced. <italic>Step length</italic> is defined as the distance between two consecutive footprints (green arrows).</p><p><bold>DOI:</bold> <ext-link ext-link-type="doi" xlink:href="10.7554/eLife.00231.003">http://dx.doi.org/10.7554/eLife.00231.003</ext-link></p></caption><graphic xlink:href="elife00231f001"/></fig><fig id="fig1s1" position="float" specific-use="child-fig"><object-id pub-id-type="doi">10.7554/eLife.00231.004</object-id><label>Figure 1—figure supplement 1.</label><caption><title>Additional information on fTIR apparatus.</title><p>(<bold>A</bold>). Detailed schematic of the fTIR apparatus. Red dashed line shows position of the cross-section image in panel (<bold>B</bold>). (<bold>B</bold>) Cross-section of schematic at red dashed line in panel (<bold>A</bold>). (<bold>C</bold>) Photograph of the fTIR apparatus.</p><p><bold>DOI:</bold> <ext-link ext-link-type="doi" xlink:href="10.7554/eLife.00231.004">http://dx.doi.org/10.7554/eLife.00231.004</ext-link></p></caption><graphic xlink:href="elife00231fs001"/></fig><fig id="fig1s2" position="float" specific-use="child-fig"><object-id pub-id-type="doi">10.7554/eLife.00231.005</object-id><label>Figure 1—figure supplement 2.</label><caption><title>FlyWalker program.</title><p>(<bold>A</bold>). Screenshot of the FlyWalker graphical user interface used to edit fTIR videos. (<bold>B</bold>). Screenshot of the FlyWalker parameters window.</p><p><bold>DOI:</bold> <ext-link ext-link-type="doi" xlink:href="10.7554/eLife.00231.005">http://dx.doi.org/10.7554/eLife.00231.005</ext-link></p></caption><graphic xlink:href="elife00231fs002"/></fig></fig-group><media content-type="glencoe play-in-place height-250 width-310" id="video1" mime-subtype="avi" mimetype="video" xlink:href="elife00231v001.avi"><object-id pub-id-type="doi">10.7554/eLife.00231.006</object-id><label>Video 1.</label><caption><p>Unprocessed fTIR video.</p><p><bold>DOI:</bold> <ext-link ext-link-type="doi" xlink:href="10.7554/eLife.00231.006">http://dx.doi.org/10.7554/eLife.00231.006</ext-link></p></caption></media></p><p>To automate the tracking of the footprints and fly body revealed by the fTIR method, we created a program called FlyWalker that tracks and outputs several user-defined parameters (<xref ref-type="fig" rid="fig1s2">Figure 1—figure supplement 2</xref>). The program, which is freely available for download at <ext-link ext-link-type="uri" xlink:href="http://biooptics.markalab.org/FlyWalker">http://biooptics.markalab.org/FlyWalker</ext-link>, evaluates the fTIR signals in each video and generates a set of graphs that describe the walking behavior (<xref ref-type="other" rid="video2">Video 2</xref> shows a FlyWalker-processed video; see ‘Materials and methods’ for a list of parameters, their definitions and a list of graphs generated by FlyWalker). In addition, the program also creates a spreadsheet that contains the full data set. From this, the user can analyze, compile and compare several samples.<media content-type="glencoe play-in-place height-250 width-310" id="video2" mime-subtype="avi" mimetype="video" xlink:href="elife00231v002.avi"><object-id pub-id-type="doi">10.7554/eLife.00231.007</object-id><label>Video 2.</label><caption><p>Processed video by FlyWalker.</p><p><bold>DOI:</bold> <ext-link ext-link-type="doi" xlink:href="10.7554/eLife.00231.007">http://dx.doi.org/10.7554/eLife.00231.007</ext-link></p></caption></media></p></sec><sec id="s2-2"><title>Step parameters</title><p>As a proof-of-principle, we examined the walking behavior of freely walking upright wild-type adult flies on a flat horizontal surface. The distance recorded was on average 1.27 cm, a distance covered by the camera without compromising the fTIR signal. We collected 71 videos of animals that walked in a straight manner without any stops. <italic>Average speeds</italic> of each fly varied between 7.2 and 44.7 mm/s with 28 mm/s as the most representative speed (<xref ref-type="fig" rid="fig2">Figure 2A</xref>), similar to previously reported values (<xref ref-type="bibr" rid="bib44">Robie et al., 2010</xref>). Interestingly, speeds ∼20 mm/s were underrepresented in this data set (see ‘Materials and methods’), reminiscent of underrepresented speeds at gait transitions in humans or ponies (<xref ref-type="bibr" rid="bib28">Hoyt and Taylor, 1981</xref>).<fig-group><fig id="fig2" position="float"><object-id pub-id-type="doi">10.7554/eLife.00231.008</object-id><label>Figure 2.</label><caption><title>General walking parameters.</title><p>(<bold>A</bold>). Speed histogram of 71 videos recorded for wild type flies, with 2 mm/s bins. Average speeds vary between 7.2 and 44.7 mm/s, with 28 mm/s the most represented speed. Speeds at ∼20 mm/s are underrepresented in this dataset. (<bold>B</bold>). Durations of <italic>stance</italic> (blue) and <italic>swing</italic> (red) <italic>phases</italic> as a function of speed. <italic>Swing phase</italic> duration remains mostly constant while stance phase duration is inversely proportional to speed. Graphical fits for <italic>swing</italic> and <italic>stance phases</italic> versus speed are represented in red and blue lines, respectively. (<bold>C</bold>). <italic>Step period</italic> is inversely proportional to average speed. The blue line is a graphical fit. (<bold>D</bold>). Duration of the <italic>metachronal lag</italic> as a function of the hindleg <italic>period</italic> for slow (blue) and fast (red) flies. <italic>metachronal lag</italic> closely matches hindleg <italic>period</italic> for fast flies (<sub>H</sub>Lag<sub>F</sub> ≈ Period). Regression line for the slow walking flies: <sub>H</sub>Lag<sub>F</sub> = Period × 0.505 + 27.627. A total of 220 metachronal waves were used, error bars correspond the standard error of the mean. (<bold>E</bold>),(<bold>F</bold>). <italic>Step length</italic> (<bold>E</bold>) and <italic>swing speed</italic> (<bold>F</bold>) increase linearly with speed. A graphical fit is shown by the blue lines. Error bars in (<bold>B</bold>–<bold>F</bold>) correspond to standard error of the mean.</p><p><bold>DOI:</bold> <ext-link ext-link-type="doi" xlink:href="10.7554/eLife.00231.008">http://dx.doi.org/10.7554/eLife.00231.008</ext-link></p></caption><graphic xlink:href="elife00231f002"/></fig><fig id="fig2s1" position="float" specific-use="child-fig"><object-id pub-id-type="doi">10.7554/eLife.00231.009</object-id><label>Figure 2—figure supplement 1.</label><caption><title>Gait definitions.</title><p>For each leg, swing phases are represented in black and stance phases are represented in white (from top to bottom: right hind (RH); right mid (RM); right front (RF); left hind (LH); left middle (LM); left front (LF)). <italic>Step period</italic> is the time for one complete cycle (initiation of consecutive stance phases) and is divided into <italic>stance duration</italic> and <italic>swing duration</italic>. The <italic>metachronal lag</italic> is defined as the time between the start of sequential swing phases for the hind and forelegs on the ipsilateral side (red arrows).</p><p><bold>DOI:</bold> <ext-link ext-link-type="doi" xlink:href="10.7554/eLife.00231.009">http://dx.doi.org/10.7554/eLife.00231.009</ext-link></p></caption><graphic xlink:href="elife00231fs003"/></fig><fig id="fig2s2" position="float" specific-use="child-fig"><object-id pub-id-type="doi">10.7554/eLife.00231.010</object-id><label>Figure 2—figure supplement 2.</label><caption><title>Gait parameters by segment.</title><p>(<bold>A</bold>)–(<bold>D</bold>). Each column corresponds to the legs in a particular segment. Graphical fits are represented by blue lines. (<bold>A</bold>) <italic>Stance duration</italic>. (<bold>B</bold>) <italic>Swing duration</italic>. (<bold>C</bold>) <italic>Step length</italic>. (<bold>D</bold>) <italic>Swing speed</italic>.</p><p><bold>DOI:</bold> <ext-link ext-link-type="doi" xlink:href="10.7554/eLife.00231.010">http://dx.doi.org/10.7554/eLife.00231.010</ext-link></p></caption><graphic xlink:href="elife00231fs004"/></fig></fig-group></p><p>To initially describe the walking behavior of wild type animals, we plotted several step parameters (<xref ref-type="fig" rid="fig2">Figure 2B—F</xref> and <xref ref-type="fig" rid="fig2s1">Figure 2—figure supplement 1</xref>). As previously noted in several insect species (<xref ref-type="bibr" rid="bib60">Wilson, 1966</xref>; <xref ref-type="bibr" rid="bib26">Graham, 1972</xref>; <xref ref-type="bibr" rid="bib52">Strauss and Heisenberg, 1990</xref>) as speed increases, <italic>stance phase duration</italic> becomes shorter while <italic>swing phase duration</italic> remains largely constant; at the fastest speeds the durations of both swing and stance phases equalize (<xref ref-type="fig" rid="fig2">Figure 2B</xref>). Consequently, <italic>step period</italic> varies inversely with <italic>average speed</italic> up to approximately 30 mm/s, when <italic>step period</italic> reaches a steady state of about ∼60 ms, corresponding to a frequency of 16 cycles per s (<xref ref-type="fig" rid="fig2">Figure 2C</xref>).</p><p>Next we examined the relationship between step pattern and period for faster and slower walking animals. For this, we plotted the <italic>metachronal lag</italic>, (i.e., the time between sequential swing onsets from hind to forelegs on an ipsilateral side), as a function of hindleg <italic>period</italic> (<xref ref-type="bibr" rid="bib26">Graham, 1972</xref>) (<xref ref-type="fig" rid="fig2">Figure 2D</xref>, <xref ref-type="fig" rid="fig2s1">Figure 2—figure supplement 1</xref>). We compared data extracted from slow animals (<20 mm/s) with those from fast animals (>34 mm/s). These speed groups were chosen and consistently used in this and all subsequent analyses because several of our readouts exhibit discontinuous or non-linear behavior at ∼20 and ∼34 mm/s (see <xref ref-type="fig" rid="fig2 fig3 fig5">Figures 2A; 3C and 5B</xref> for examples). Data extracted from the fast set reveal that hindleg <italic>period</italic> equals the <italic>metachronal lag</italic> (<sub>H</sub>Lag<sub>F</sub> ≈ P<sub>H</sub>), typical of the tripod gait (also called Gait I; (<xref ref-type="bibr" rid="bib26">Graham, 1972</xref>)). In contrast, flies walking slower than 20 mm/s showed a <italic>period</italic>-dependent <italic>metachronal lag</italic> with a regression curve of (<sub>H</sub>Lag<sub>F</sub> = 0.505* P<sub>H</sub> + 27.6), typical of the tetrapod gait (also called Gait II; (<xref ref-type="bibr" rid="bib26">Graham, 1972</xref>)), see also ‘Gait parameters’, below) (<xref ref-type="fig" rid="fig2">Figure 2D</xref>). We also find that <italic>step length</italic> increased almost linearly with speed (<xref ref-type="fig" rid="fig2">Figure 2E</xref>). As a consequence, <italic>swing speed</italic> also follows this trend (<xref ref-type="fig" rid="fig2">Figure 2F</xref>).<fig-group><fig id="fig3" position="float"><object-id pub-id-type="doi">10.7554/eLife.00231.011</object-id><label>Figure 3.</label><caption><title>Spatial parameters.</title><p>(<bold>A</bold>). <italic>Stance traces</italic>. Representative plot of an animal walking at 28.82 mm/s. Traces are generated by the position of the stance phase footprints relative to the body center (set at 0.0,0.0). For each leg, stance onset corresponds to the <italic>Anterior Extreme Position</italic> (<italic>AEP</italic>) while stance offset is termed <italic>Posterior Extreme Position</italic> (<italic>PEP</italic>). (<bold>B</bold>). Method to quantify the <italic>stance linearity index</italic>. For each stance trace (brown), a smoothed trace is generated (using data from every five frames; yellow trace), and the average of the difference between these two lines (orange arrows) corresponds to the <italic>stance linearity index</italic>. (<bold>C</bold>). <italic>Stance linearity</italic> as a function of speed. Each data point corresponds to the average of all traces for all six legs for a single video greater than ∼34 mm/s, <italic>stance linearity</italic> becomes constant (red box). (<bold>D</bold>). Method to quantify <italic>footprint clustering</italic>. For each set of <italic>AEP</italic>/<italic>PEP</italic> footprints (red circles), an average ±STD xy point is created (blue cross). The <italic>footprint clustering</italic> value is calculated as the vector sum of the two STD values (orange arrow). (<bold>E</bold>). Quantification of <italic>footprint clustering</italic>. Data were grouped into slow (<20 mm/s), medium (between 20 and 34 mm/s) and fast (>34 mm/s) speeds. Boxplots represent the median as the middle line, with the lower and upper edges of the boxes representing the 25% and 75% quartiles, respectively; the whiskers represent the range of the full data set. Values are normalized for body size. Asterisks indicate the significance of the decrease in <italic>footprint clustering</italic> between the <italic>AEP</italic> and the <italic>PEP</italic> (using the paired parametric t test in the case of the slow speed group and the Wilcoxon non-parametric test for the remaining groups, **p<0.005, ***p<0.001). A comparison between the three speed groups also displays statistical significance (Kruskal–Wallis-ANOVA, p values of 0.0015 and 0.0014 for <italic>AEP</italic> and <italic>PEP</italic>, respectively). Dunn's <italic>post hoc</italic> significance tests show: slow <italic>AEP</italic> vs medium <italic>AEP</italic>, not significant (NS); medium <italic>AEP</italic> vs fast <italic>AEP</italic>, **; slow <italic>AEP</italic> vs fast <italic>AEP</italic>, **; slow <italic>PEP</italic> vs medium <italic>PEP</italic>, NS; medium <italic>PEP</italic> vs fast <italic>PEP</italic>, NS; slow <italic>PEP</italic> vs fast <italic>PEP</italic>, ***. (<bold>F</bold>). Footprint position relative to the body center. Data were pooled as in the previous panel. <italic>AEP</italic> and <italic>PEP</italic> values for each leg are represented on the left and right sections of the plot, respectively. Values are normalized for body size.</p><p><bold>DOI:</bold> <ext-link ext-link-type="doi" xlink:href="10.7554/eLife.00231.011">http://dx.doi.org/10.7554/eLife.00231.011</ext-link></p></caption><graphic xlink:href="elife00231f003"/></fig><fig id="fig3s1" position="float" specific-use="child-fig"><object-id pub-id-type="doi">10.7554/eLife.00231.012</object-id><label>Figure 3—figure supplement 1.</label><caption><title>Representative examples of stance traces and corresponding stance linearity values.</title><p>A less linear stance trace (e.g., (<bold>A</bold>′) compared to (<bold>A</bold>)) corresponds a higher <italic>stance linearity</italic> value.</p><p><bold>DOI:</bold> <ext-link ext-link-type="doi" xlink:href="10.7554/eLife.00231.012">http://dx.doi.org/10.7554/eLife.00231.012</ext-link></p></caption><graphic xlink:href="elife00231fs005"/></fig><fig id="fig3s2" position="float" specific-use="child-fig"><object-id pub-id-type="doi">10.7554/eLife.00231.013</object-id><label>Figure 3—figure supplement 2.</label><caption><title>AEP and PEP clustering values for all segments as a function of speed.</title><p>Linear trend lines for <italic>AEP</italic> and <italic>PEP</italic> versus average speed are shown as blue and red lines, respectively.</p><p><bold>DOI:</bold> <ext-link ext-link-type="doi" xlink:href="10.7554/eLife.00231.013">http://dx.doi.org/10.7554/eLife.00231.013</ext-link></p></caption><graphic xlink:href="elife00231fs006"/></fig><fig id="fig3s3" position="float" specific-use="child-fig"><object-id pub-id-type="doi">10.7554/eLife.00231.014</object-id><label>Figure 3—figure supplement 3.</label><caption><title>AEP and PEP clustering values for each segment as a function of speed.</title><p>(<bold>A</bold>). Linear trend lines for <italic>AEP</italic> and <italic>PEP</italic> are represented as blue and red lines, respectively. (<bold>A</bold>) forelegs; (<bold>A</bold>′) midlegs; (<bold>A</bold>″) hindlegs. (<bold>B</bold>). Boxplots of the data presented in (<bold>A</bold>). Data were grouped into slow (<20 mm/s), medium (between 20 and 34 mm/s) and fast (>34 mm/s) animals. Boxplots represent the median as the middle line, with the lower and upper edges of the boxes representing the 25% and 75% quartiles, respectively, and the whiskers representing the range of the full data set. Values are normalized for body size. Asterisks indicate statistical significance between <italic>AEP</italic> and <italic>PEP</italic>. (Data analyzed by the Wilcoxon or paired-t test the, **p<0.005, ***p<0.001). Values are normalized for body size.</p><p><bold>DOI:</bold> <ext-link ext-link-type="doi" xlink:href="10.7554/eLife.00231.014">http://dx.doi.org/10.7554/eLife.00231.014</ext-link></p></caption><graphic xlink:href="elife00231fs007"/></fig></fig-group></p><p>The FlyWalker program also extracts data for each individual leg for each of the three thoracic segments (<xref ref-type="fig" rid="fig2s2">Figure 2—figure supplement 2</xref>). Although legs in different segments show very similar trends for most parameters, some distinctions can be made. For example, as <italic>average speed</italic> increases there is a small but noticeable decrease in <italic>swing phase duration</italic> in the forelegs when compared to other segments. In addition, compared to other leg segments, foreleg <italic>swing speed</italic> varies more linearly with <italic>average speed</italic>, as seen by a larger R value (<xref ref-type="fig" rid="fig2s2">Figure 2—figure supplement 2</xref>). These observations support the hypothesis of a leading role for forelegs in forward locomotion suggested by experiments in the stick insect (<xref ref-type="bibr" rid="bib3">Akay et al., 2007</xref>; <xref ref-type="bibr" rid="bib6">Bässler and Büschges, 1998</xref>).</p></sec><sec id="s2-3"><title>Spatial parameters</title><p>In addition to following the six footprints, FlyWalker also monitors the position of the fly body. From these data we can reconstruct the stance positions relative to the center of the body (<xref ref-type="fig" rid="fig3">Figure 3A</xref>). In order to account for variations in body size, these plots are normalized to body length. Stance traces are generated as each leg contacts the glass. The onset of these traces is termed the <italic>Anterior Extreme Position</italic> (<italic>AEP</italic>), which corresponds to the position where the leg first contacts the glass after touchdown at the end protraction (<xref ref-type="bibr" rid="bib15">Cruse, 1976</xref>). The position at the end of the stance phase, just before the tarsi enter swing phase, corresponds to the <italic>Posterior Extreme Position</italic> (<italic>PEP</italic>) (<xref ref-type="bibr" rid="bib15">Cruse, 1976</xref>). <italic>Stance traces</italic> can be compared by their straightness, which is a measure of how much wobble there is in body position relative to each footprint, which are stationary. This parameter, the <italic>stance linearity index</italic>, is calculated by computing the average difference between an actual stance trace and a smoothed version of the trace (<xref ref-type="fig" rid="fig3">Figure 3B</xref>, <xref ref-type="fig" rid="fig3s1">Figure 3—figure supplement 1</xref>). Measuring this parameter, faster animals have straighter stance traces (lower <italic>stance linearity indexes</italic>) compared to slower animals. This trend is seen up to approximately 34 mm/s, after which this value stabilizes (<xref ref-type="fig" rid="fig3">Figure 3C</xref>).</p><p>Another informative parameter corresponds to the clustering of the <italic>AEP</italic>s and <italic>PEP</italic>s for each leg in a single video. This parameter—termed <italic>footprint clustering</italic>—corresponds to the vector sum of standard deviations (STDs) from the mean for all <italic>AEP</italic>s or <italic>PEP</italic>s calculated for each leg (<xref ref-type="fig" rid="fig3">Figure 3D</xref>). For example, a small <italic>footprint clustering</italic> value for the foreleg <italic>AEP</italic> means that the <italic>AEP</italic> coordinates (relative to the fly's body) were similar for all of the foreleg steps in a video. Using this parameter, we asked if there was a relationship between <italic>footprint clustering</italic> and <italic>average speed</italic>. For the reasons given above, we binned the speed values into three groups: <20 mm/s; between 20 and 34 mm/s; and >34 mm/s. For both <italic>AEP</italic> and <italic>PEP</italic>, the footprint clustering values were smaller for faster flies (<xref ref-type="fig" rid="fig3">Figure 3E</xref> and <xref ref-type="fig" rid="fig3s2 fig3s3">Figure 3—figure supplements 2 and 3</xref>), suggesting that animals have more spatially restricted steps as they increase their speed. We also found that <italic>AEP</italic> clustering values are generally smaller than <italic>PEP</italic> clustering values (<xref ref-type="fig" rid="fig3">Figure 3E</xref>), suggesting a tighter motor control at touchdown (<italic>AEP</italic>) compared to stance offset (<italic>PEP</italic>). Pooling the data for all legs, <italic>AEP</italic> clustering was smaller than <italic>PEP</italic> clustering in 61 out of 71 (86%) videos. This difference is largest for the forelegs and is very small for the hindlegs (<xref ref-type="fig" rid="fig3s3">Figure 3—figure supplement 3</xref>).</p><p>Knowing that flies increase their speed by making longer strides (<xref ref-type="fig" rid="fig2">Figure 2E</xref>), we next asked if there is a relationship between <italic>average speed</italic> and <italic>AEP</italic> or <italic>PEP</italic>. To address this question we plotted <italic>AEP</italic> and <italic>PEP</italic> for each leg for the three speed groups (<xref ref-type="fig" rid="fig3">Figure 3F</xref>). At faster speeds, for all legs, <italic>AEP</italic> coordinates are shifted anteriorly, while <italic>PEP</italic> coordinates are shifted posteriorly. Interestingly, at faster speeds midleg <italic>AEP</italic> and <italic>PEP</italic> are also shifted laterally, perhaps to increase stability at higher speeds. In contrast, the hindlegs are positioned closer to the body at higher speeds, perhaps to allow for a stronger power stroke.</p></sec><sec id="s2-4"><title>Gait parameters</title><p>Previous work described insect gaits as either tripod or tetrapod, depending on the speed and body load (<xref ref-type="bibr" rid="bib26">Graham, 1972</xref>). The <italic>tripod gait</italic> is characterized by three legs in stance phase and three legs in swing phase at any one time (<xref ref-type="fig" rid="fig4s1">Figure 4—figure supplement 1</xref>). Each group of three legs is composed of the fore and hind legs on one side and the midleg on the contralateral side. In contrast, in an idealized <italic>tetrapod gait</italic> only two legs are in swing phase while the remaining four legs are in stance phase (<xref ref-type="fig" rid="fig4s1">Figure 4—figure supplement 1</xref>). The two legs in swing phase are on contralateral sides and are offset by one segment. We also observed many noncanonical stance combinations that do not fit either of the idealized gaits, which we analyze below (<xref ref-type="fig" rid="fig4s1">Figure 4—figure supplement 1</xref> and <xref ref-type="table" rid="tbl1">Table 1</xref>).<fig-group><fig id="fig4" position="float"><object-id pub-id-type="doi">10.7554/eLife.00231.015</object-id><label>Figure 4.</label><caption><title>Gait parameters.</title><p>(<bold>A</bold>),(<bold>B</bold>). Upper panels show the step pattern for representative videos of animals walking at 44.7 (<bold>A</bold>) and at 14.6 mm/s (<bold>B</bold>). For each leg swing phases are represented in black (from top to bottom: right hind (RH); right mid (RM); right front (RF); left hind (LH); left middle (LM); left front (LF)). (<bold>A</bold>′),(<bold>B</bold>′) show the <italic>instantaneous speed</italic> for the same video. Thick and thin lines correspond to integration times of 25 or 12.5 ms, respectively. In faster animals (<bold>A</bold>), peak speeds are observed halfway through the stance phase (red dashed line), while minimum speeds are observed during gait transitions (blue dashed line). (<bold>A</bold>″),(<bold>B</bold>″), for each frame the corresponding gait was color coded as follows: green (tripod), blue (tetrapod), and gray (non-canonical). Red brackets indicate the eight frame windows used to generate the gait index plots (<bold>A</bold>‴),(<bold>B</bold>‴). Tripod = +1; tetrapod = −1; and noncanonical = 0. (<bold>C</bold>). Quantification of the <italic>average gait index</italic> for three speed groups. For each video the average gait index was calculated for all frames (p<0.0001 for Kruskal–Wallis-ANOVA test. Dunn's <italic>post hoc</italic> significance test: **p<0.005, ***p<0.001). (<bold>D</bold>),(<bold>E</bold>). Tripod (<bold>D</bold>) and tetrapod (<bold>E</bold>) indexes as a function of speed. Graphical fits are represented in blue. Data for points labeled (<bold>A</bold>) and (<bold>B</bold>) are shown in panels (<bold>A</bold>) and (<bold>B</bold>), respectively.</p><p><bold>DOI:</bold> <ext-link ext-link-type="doi" xlink:href="10.7554/eLife.00231.015">http://dx.doi.org/10.7554/eLife.00231.015</ext-link></p></caption><graphic xlink:href="elife00231f004"/></fig><fig id="fig4s1" position="float" specific-use="child-fig"><object-id pub-id-type="doi">10.7554/eLife.00231.016</object-id><label>Figure 4—figure supplement 1.</label><caption><title>Gait features.</title><p>(<bold>A</bold>)–(<bold>C</bold>). Walking gaits and leg combinations. Each leg can be either in a swing or stance phase, represented by white or black circles, respectively. Underneath each combination the corresponding numerical code (‘1’ and ‘0’ correspond to stance or swing, respectively. Digit order: left foreleg; left midleg; left hindleg; right foreleg; right midleg; right hindleg. (<bold>A</bold>) Tripod gait combinations. (<bold>B</bold>) Tetrapod gait combinations. Tetrapod gait can be either right or left handed depending on which side is swinging more anteriorly. (<bold>C</bold>) Representative noncanonical combinations, which are not represented by tripod or tetrapod configurations.</p><p><bold>DOI:</bold> <ext-link ext-link-type="doi" xlink:href="10.7554/eLife.00231.016">http://dx.doi.org/10.7554/eLife.00231.016</ext-link></p></caption><graphic xlink:href="elife00231fs010"/></fig><fig id="fig4s2" position="float" specific-use="child-fig"><object-id pub-id-type="doi">10.7554/eLife.00231.017</object-id><label>Figure 4—figure supplement 2.</label><caption><title>Gait index plot.</title><p>(<bold>A</bold>). <italic>Average gait index</italic> as a function of speed. Each point corresponds to the average of all frames in a video (<xref ref-type="fig" rid="fig4">Figure 4A‴,B‴</xref>). (<bold>B</bold>). <italic>Instantaneous speed</italic>, <italic>gait map</italic> and <italic>gait index</italic> plots for data points labeled in (<bold>A</bold>) by I., II., and III. are shown. Data for points labeled A* and B* are shown in <xref ref-type="fig" rid="fig4">Figure 4A,B</xref>, respectively.</p><p><bold>DOI:</bold> <ext-link ext-link-type="doi" xlink:href="10.7554/eLife.00231.017">http://dx.doi.org/10.7554/eLife.00231.017</ext-link></p></caption><graphic xlink:href="elife00231fs011"/></fig></fig-group></p><p>For each video, a step pattern can be generated while simultaneously plotting the <italic>instantaneous speed</italic> and gait characteristics with high temporal resolution (<xref ref-type="fig" rid="fig4">Figure 4A,B</xref>). Notably, the <italic>instantaneous speed</italic> plot has a wave-like appearance (<xref ref-type="fig" rid="fig4">Figure 4A′,B′</xref>), particularly in high speed animals, with maxima and minima that can differ up to 30 mm/s. Consistent with the observations of Graham in the first instar stick insect (<xref ref-type="bibr" rid="bib26">Graham, 1972</xref>), peak speeds are observed midway through each stance phase when the retracting tripod reaches its maximum motor output. Conversely, minimum speeds are observed at the transition between phases, when the stance switches to a different set of legs. The difference between the maxima and minima in these <italic>instantaneous speed</italic> plots decreases at slower speeds (<xref ref-type="fig" rid="fig4">Figure 4B</xref>), suggesting that power is more evenly distributed throughout each period at slower speeds.</p><p>For each frame in a video we also classify whether the fly is in a tripod, tetrapod, or noncanonical stance. The resulting <italic>gait map</italic> graphically illustrates the gaits used over time (yellow for tripod, blue for tetrapod, and grey for noncanonical) (<xref ref-type="fig" rid="fig4">Figure 4A″,B″</xref>). Visual inspection of the full data set shows that flies walk preferentially using the tripod gait (data not shown) (<xref ref-type="bibr" rid="bib52">Strauss and Heisenberg, 1990</xref>; <xref ref-type="bibr" rid="bib61">Wosnitza et al., 2012</xref>), but also underscores that as flies decrease their speed they increasingly use tetrapod and noncanonical combinations. To quantify these <italic>gait maps</italic> we plot the <italic>gait index.</italic> To calculate the <italic>gait index</italic> each frame is assigned a value (+1 for tripod, −1 for tetrapod and 0 for noncanonical) and these values are averaged for a sliding window of <italic>n</italic> frames; empirically, we find that <italic>n</italic> = 8 is most effective at distinguishing flies primarily using the tetrapod and tripod gaits (<xref ref-type="fig" rid="fig4">Figure 4A‴,B‴</xref> and <xref ref-type="fig" rid="fig4s2">Figure 4—figure supplement 2</xref>). The average value for an entire video corresponds to the <italic>average gait index</italic>. We calculated the <italic>average gait index</italic> for all 71 videos in our data set and binned the results using the same three speed groups used previously (<xref ref-type="fig" rid="fig4">Figure 4C</xref>). Each group (slow, medium, and fast) had statistically distinct <italic>average gait indexes</italic>: slower animals had more negative values while faster animals had increasingly positive values, reflecting their increasing use of the tripod gait.</p><p>As a complementary method to quantify differences in gait, we analyzed our fTIR data to determine the fraction of time flies spend using either idealized tripod or tetrapod configurations, and if these fractions depend on <italic>average speed</italic> (<xref ref-type="fig" rid="fig4">Figure 4D,E</xref>). We define the <italic>tripod</italic> or <italic>tetrapod indexes</italic> as the fraction of frames in a video in which a leg combination matches one of these two gaits. As expected, faster flies spend a larger fraction of time in a tripod configuration, which presumably allows flies to maximize leg thrust (<xref ref-type="fig" rid="fig4">Figure 4D</xref>). The inverse relationship is seen for the tetrapod configuration, where slower animals display the highest proportion of this gait (<xref ref-type="fig" rid="fig4">Figure 4E</xref>).</p><p>Two additional conclusions can be derived from these data. First, there is not an abrupt transition between the tripod and tetrapod gaits at a particular speed. Instead, the proportion of time a fly spends using each gait varies gradually according to <italic>average speed</italic>. Second, the idealized tripod and tetrapod combinations only account for a subset of all stance configurations present in our data set. For example, the fastest flies in our data set had <italic>tripod</italic> and <italic>tetrapod indexes</italic> of ∼70% and <10%, respectively. Noncanonical leg configurations in which none or only one leg is in swing phase account for 48% to 21% of all combinations, depending on the <italic>average speed</italic> (<xref ref-type="table" rid="tbl1">Table 1</xref>). Inspection of these videos reveals that these noncanonical combinations generally occur at the transitions between stances (data not shown and <xref ref-type="fig" rid="fig4">Figure 4A″</xref>). Thus, slower animals spend less time using the tripod gait in part because they use the tetrapod gait more, but also because they spend a larger fraction of time in gait transitions, where noncanonical leg configurations play a dominant role. In addition, at slow speeds occasional short sequences of pentapod stances (≤3 steps) fit the so-called ‘<italic>wave gait</italic>’ in which individual legs swing in a wave-like pattern from front to back (<xref ref-type="bibr" rid="bib61">Wosnitza et al., 2012</xref>).<table-wrap id="tbl1" position="float"><object-id pub-id-type="doi">10.7554/eLife.00231.018</object-id><label>Table 1.</label><caption><p>Gait combinations in three speed classes<xref ref-type="table-fn" rid="tblfn1">a</xref></p><p><bold>DOI:</bold> <ext-link ext-link-type="doi" xlink:href="10.7554/eLife.00231.018">http://dx.doi.org/10.7554/eLife.00231.018</ext-link></p></caption><table frame="hsides" rules="groups"><thead><tr><td colspan="2">Slow [≤19.9 mm/s]</td><td colspan="2">Medium [20–33.9 mm/s]</td><td colspan="2">Fast [≥34 mm/s]</td></tr></thead><tbody><tr><td>Tripod</td><td>31.37</td><td>Tripod</td><td>51.63</td><td>Tripod</td><td>64.98</td></tr><tr><td>Tetrapod</td><td>25.45</td><td>Tetrapod</td><td>15.90</td><td>Tetrapod</td><td>7.26</td></tr><tr><td><italic>Total</italic></td><td><italic>56.82</italic></td><td><italic>Total</italic></td><td><italic>67.54</italic></td><td><italic>Total</italic></td><td><italic>72.24</italic></td></tr><tr><td colspan="2"><italic>Additional combinations</italic></td><td colspan="2"><italic>Additional combinations</italic></td><td colspan="2"><italic>Additional combinations</italic></td></tr><tr><td>111110</td><td>8.27</td><td>111111</td><td>4.76</td><td>111011</td><td>4.89</td></tr><tr><td>110111</td><td>7.60</td><td>111110</td><td>4.61</td><td>111111</td><td>4.25</td></tr><tr><td>111111</td><td>6.38</td><td>011111</td><td>4.30</td><td>011111</td><td>4.07</td></tr><tr><td>111011</td><td>5.62</td><td>111011</td><td>4.26</td><td>010111</td><td>3.36</td></tr><tr><td>011111</td><td>5.03</td><td>010111</td><td>3.84</td><td>111010</td><td>2.80</td></tr><tr><td>010111</td><td>2.47</td><td>110111</td><td>3.79</td><td>110111</td><td>2.32</td></tr><tr><td>111010</td><td>2.45</td><td>111010</td><td>2.91</td><td>111110</td><td>1.29</td></tr><tr><td>101111</td><td>1.94</td><td>101111</td><td>0.71</td><td>011011</td><td>1.25</td></tr><tr><td>111101</td><td>1.69</td><td>011011</td><td>0.52</td><td>011010</td><td>0.69</td></tr><tr><td>Pentapod</td><td>30.15</td><td>Pentapod</td><td>14.37</td><td>Pentapod</td><td>13.33</td></tr><tr><td>3 gaits combined<xref ref-type="table-fn" rid="tblfn2">b</xref></td><td>86.97</td><td>3 gaits combined<xref ref-type="table-fn" rid="tblfn2">b</xref></td><td>81.91</td><td>3 gaits combined<xref ref-type="table-fn" rid="tblfn2">b</xref></td><td>85.57</td></tr><tr><td><italic>Total</italic><xref ref-type="table-fn" rid="tblfn3">c</xref></td><td><italic>98.3</italic></td><td><italic>Total</italic><xref ref-type="table-fn" rid="tblfn3">c</xref></td><td><italic>97.2</italic></td><td><italic>Total</italic><xref ref-type="table-fn" rid="tblfn3">c</xref></td><td><italic>97.15</italic></td></tr></tbody></table><table-wrap-foot><fn id="tblfn1"><label>a</label><p>Values are expressed as percentage (%). Leg order in combination: LF LM LH RF RM RH. 1, footprint present; 0, footprint is absent.</p></fn><fn id="tblfn2"><label>b</label><p>The sum of tripod, tetrapod, and pentapod gaits.</p></fn><fn id="tblfn3"><label>c</label><p>The sum of all gait patterns listed in the table.</p></fn></table-wrap-foot></table-wrap></p></sec><sec id="s2-5"><title>Coordination parameters</title><p>Typically for hexapods, leg touchdown occurs close to where the immediately anterior ipsilateral leg made contact, a behavior termed <italic>follow-the-leader</italic> (<xref ref-type="bibr" rid="bib20">Dean and Wendler, 1983</xref>; <xref ref-type="bibr" rid="bib18">Cruse et al., 1984</xref>; <xref ref-type="bibr" rid="bib48">Song and Choi, 1989</xref>; <xref ref-type="bibr" rid="bib11">Brunn and Dean, 1994</xref>; <xref ref-type="bibr" rid="bib49">Sponberg and Full, 2008</xref>). This behavior depends on sensory feedback and specialized intersegmental interneurons (<xref ref-type="bibr" rid="bib11">Brunn and Dean, 1994</xref>). One possible advantage of this behavior is to ensure that the animal places its legs on safe ground, particularly on rough terrain. As a consequence, mid and hindleg footprints fall close to where the foreleg was placed (<xref ref-type="fig" rid="fig1">Figure 1C</xref>). We quantified this tendency by measuring the <italic>footprint alignment</italic> parameter, which corresponds to the mean standard deviation of the projection of the fore, mid, and hind footprints along the body's displacement axis (<xref ref-type="fig" rid="fig5">Figure 5A</xref>). Accordingly, footprints that are more aligned have a smaller <italic>footprint alignment</italic> value (<xref ref-type="fig" rid="fig5s1">Figure 5—figure supplement 1</xref>). Interestingly, plotting <italic>footprint alignment</italic> as a function of speed revealed a non-linear relationship in which the data points cluster into three groups (<xref ref-type="fig" rid="fig5">Figure 5B</xref>). At slow speeds (<20 mm/s), <italic>footprint alignment</italic> values were relatively large while at fast speeds (>34 mm/s) these values were much smaller, suggesting that <italic>footprint alignment</italic> is highly constrained. Notably, at intermediate speeds (between 20 and 34 mm/s), there was no correlation between speed and <italic>footprint alignment</italic>. The more aligned footprints observed in faster flies could assist the maintenance of static stability during tripod transitions (<xref ref-type="bibr" rid="bib48">Song and Choi, 1989</xref>). In support of this possibility, the <italic>tripod index</italic> increases with lower <italic>footprint alignment</italic> values (data not shown). The abrupt difference seen in alignment values between slow, intermediate, and fast flies suggests that the motor circuits controlling walking in these three speed groups may differ. Interestingly, one of the transitions in <italic>footprint alignment</italic> values occurs at speeds (∼20 mm/s) that are underrepresented in our data set, consistent with the existence of a possible gait transition at this speed.</p><fig-group><fig id="fig5" position="float"><object-id pub-id-type="doi">10.7554/eLife.00231.019</object-id><label>Figure 5.</label><caption><title>Coordination parameters.</title><p>(<bold>A</bold>). Method to calculate <italic>footprint alignment</italic>. For each set of footprints, the projection points along the displacement axis (horizontal black arrow) are calculated. <italic>Footprint alignment</italic> corresponds to the standard deviation (STD) of the average point (orange arrowhead). (<bold>B</bold>). Quantification of <italic>footprint alignment</italic> versus speed. Values are color coded according to speed group: <20 mm/s in dark blue; between 20 and 34 mm/s in purple and >34 mm/s in turquoise. Values cluster into three main groups indicated by the dotted boxes (p<0.0001 for Kruskal–Wallis-ANOVA test. Dunn's post hoc significance test: *p<0.005, ***p<0.001). (<bold>C</bold>)–(<bold>E</bold>). Radial plots of contralateral leg phases comparing slow (blue triangles) and fast (red triangles) walking animals. Mean r vectors for slow and fast animals are represented by blue and red arrows, respectively. Inner circles indicate a Rayleigh p value of 0.05.</p><p><bold>DOI:</bold> <ext-link ext-link-type="doi" xlink:href="10.7554/eLife.00231.019">http://dx.doi.org/10.7554/eLife.00231.019</ext-link></p></caption><graphic xlink:href="elife00231f005"/></fig><fig id="fig5s1" position="float" specific-use="child-fig"><object-id pub-id-type="doi">10.7554/eLife.00231.020</object-id><label>Figure 5—figure supplement 1.</label><caption><title>Representative images of footprints with different footprint alignment values.</title><p><bold>DOI:</bold> <ext-link ext-link-type="doi" xlink:href="10.7554/eLife.00231.020">http://dx.doi.org/10.7554/eLife.00231.020</ext-link></p></caption><graphic xlink:href="elife00231fs008"/></fig><fig id="fig5s2" position="float" specific-use="child-fig"><object-id pub-id-type="doi">10.7554/eLife.00231.021</object-id><label>Figure 5—figure supplement 2.</label><caption><title>Radial plots of adjacent ipsilateral leg phases comparing slow and fast walking animals.</title><p>Individual data points for slow and fast flies are represented by blue circles and red triangles, respectively. Mean r vectors for slow and fast animals are represented by blue and red arrows, respectively. Inner circles indicate a Rayleigh p value of 0.05.</p><p><bold>DOI:</bold> <ext-link ext-link-type="doi" xlink:href="10.7554/eLife.00231.021">http://dx.doi.org/10.7554/eLife.00231.021</ext-link></p></caption><graphic xlink:href="elife00231fs009"/></fig></fig-group><p>Coordinated walking requires that CPGs interact with other CPGs. Such coordination is seen when examining the phase differences between the contralateral and ipsilateral legs of the same segment. For example, during a tripod gait, contralateral legs within the same segment maintain a consistent phase value of 0.5 ± 0.05 (<xref ref-type="bibr" rid="bib26">Graham, 1972</xref>). The FlyWalker software computes the phase values for contralateral legs within the same segment and between adjacent ipsilateral legs. We compared these values for the slow (<20 mm/s) and fast (>34 mm/s) groups of flies in our data set. In both groups, contralateral legs within the same segment display an average phase of approximately 0.5 (<xref ref-type="fig" rid="fig5">Figure 5C–E</xref>). However, slower animals display a higher degree of variability as seen by a shorter r vector in these radial plots. Examining hindlegs vs midlegs and midlegs vs forelegs revealed a decrease in the phase values for slower animals (<xref ref-type="fig" rid="fig5s2">Figure 5—figure supplement 2</xref>), consistent with an increased step period without a proportional increase in the step lag.</p></sec><sec id="s2-6"><title>Consequences of impairing leg sensory feedback</title><p>The mechanosensory system constantly reports the surface properties and the relative position of each of an animal's appendages (<xref ref-type="bibr" rid="bib5">Bässler, 1977</xref>). Moreover, sensory feedback is thought to trigger inter-leg coupling of local circuits to allow stable and well-coordinated gaits (<xref ref-type="bibr" rid="bib11">Brunn and Dean, 1994</xref>). Different types of proprioceptor organs, positioned in different regions of the legs, report different aspects of the posture and terrain. For example, the tibial campaniform sensilla (or sensilla campaniformia) are mostly responsible for measuring body load while the femoral chordotonal organ (ChO) is a stretch sensor, reporting joint angles as the animal walks (<xref ref-type="bibr" rid="bib46">Shanbhag et al., 1992</xref>; <xref ref-type="bibr" rid="bib63">Zill et al., 2004</xref>).</p><p>To quantify the effect of inactivating sensory neurons in the <italic>Drosophila</italic> leg, we tested both inactivation of a small subset of neurons and broader sensory inactivation within the legs. First, we tested the inactivation of the leg ChO. The <italic>nanchung</italic> (<italic>nan</italic>) gene encodes for a cation channel subunit and is expressed exclusively in the sensory cilia of chordotonal organs (<xref ref-type="bibr" rid="bib35">Kim et al., 2003</xref>) (<xref ref-type="fig" rid="fig6">Figure 6A</xref>). Loss of function alleles for <italic>nan</italic> display loss of sound perception, hygrosensation and negative gravitaxis defects, in addition to an ‘uncoordinated’ phenotype (<xref ref-type="bibr" rid="bib36">Liu et al., 2007</xref>; <xref ref-type="bibr" rid="bib32">Kamikouchi et al., 2009</xref>; <xref ref-type="bibr" rid="bib55">Sun et al., 2009</xref>). Second, we tested neuronal inactivation induced by the expression of tetanus toxin (TNT) in a large subset of sensory neurons in the legs (<xref ref-type="fig" rid="fig6">Figure 6B</xref>). To restrict TNT expression to leg sensory neurons we relied on a intersectional approach using a <italic>UAS-FRT-stop-FRT-TNT</italic> line (<xref ref-type="bibr" rid="bib50">Stockinger et al., 2005</xref>), the pan-sensory driver <italic>5-40-Gal4</italic> (<xref ref-type="bibr" rid="bib29">Hughes and Thomas, 2007</xref>), and a 567 base pair <italic>cis</italic>-regulatory enhancer fragment of <italic>dachshund</italic> (<italic>dac</italic>) to drive the FLP recombinase in the leg imaginal disc (<xref ref-type="bibr" rid="bib24">Giorgianni and Mann, 2011</xref>). For simplicity, we refer to this genotype as <italic>5-40</italic><sup><italic>Leg</italic></sup><italic>>TNT</italic>. <xref ref-type="other" rid="video3">Video 3</xref> shows an example of walking by these flies.<fig id="fig6" position="float"><object-id pub-id-type="doi">10.7554/eLife.00231.022</object-id><label>Figure 6.</label><caption><title>Effects of sensory deprivation on walking.</title><p>(<bold>A</bold>). Expression pattern driven by <italic>nanchung</italic>-Gal4. Genotype: <italic>F-Gal4, UAS-GFP</italic>. (<bold>B</bold>). GFP expression under combinatorial control of <italic>5-40-Gal4</italic> and <italic>dac</italic><sup><italic>RE</italic></sup><italic>-flp</italic>. Genotype: <italic>5-40-Gal4</italic>, <italic>dac</italic><sup><italic>RE</italic></sup><italic>-flp</italic>, <italic>UAS-FRT-stop-FRP-GFP</italic>. All classes of sensory neurons in the leg express GFP. tChO, tibia chordotonal organ; fChO, femur chordotonal organ; hp, hair plates; cs, campaniform sensilla; ms, mechanosensory brisles. Bar, 100 μm. (<bold>C</bold>). <italic>Average speed</italic>. Boxplots with the median as the middle line and the lower and upper edges of the boxes representing the 25% and 75% quartiles, respectively; the whiskers represent the range of the full data set. Statistical analysis with one-way-ANOVA (p<0.0001) followed by Tukey's <italic>post hoc</italic> test, ***p<0.001. (<bold>D</bold>). <italic>Tripod index</italic>. Lines represent graphical fits. See <xref ref-type="table" rid="tbl2">Table 2</xref> for statistical analysis. (<bold>E</bold>). Gait patterns, instantaneous speeds, and gait maps for representative wild type and sensory deprived animals walking at similar speeds. See <xref ref-type="fig" rid="fig2s1">Figure 2—figure supplement 1</xref> for details. Tripod gait properties remain unchanged.</p><p><bold>DOI:</bold> <ext-link ext-link-type="doi" xlink:href="10.7554/eLife.00231.022">http://dx.doi.org/10.7554/eLife.00231.022</ext-link></p></caption><graphic xlink:href="elife00231f006"/></fig><media content-type="glencoe play-in-place height-250 width-310" id="video3" mime-subtype="avi" mimetype="video" xlink:href="elife00231v003.avi"><object-id pub-id-type="doi">10.7554/eLife.00231.023</object-id><label>Video 3.</label><caption><p>Processed video of <italic>5-40</italic><sup><italic>Leg</italic></sup><italic>>TNT</italic> fly.</p><p><bold>DOI:</bold> <ext-link ext-link-type="doi" xlink:href="10.7554/eLife.00231.023">http://dx.doi.org/10.7554/eLife.00231.023</ext-link></p></caption></media></p><p>Flies of both genotypes (<italic>nan</italic><sup><italic>36a</italic></sup> and <italic>5-40</italic><sup><italic>Leg</italic></sup><italic>>TNT</italic>) walked slower than wild type flies but, remarkably, maintained a typical tripod gait (<xref ref-type="fig" rid="fig6">Figure 6C,D</xref>). These flies also exhibited normal left-right and intersegmental coordination (<xref ref-type="fig" rid="fig6">Figure 6E</xref>). Strikingly, however, stance traces for <italic>nan</italic><sup><italic>36a</italic></sup> and <italic>5-40</italic><sup><italic>Leg</italic></sup><italic>>TNT</italic> flies highlight several locomotion defects (<xref ref-type="fig" rid="fig7">Figure 7A</xref>), which were quantified (<xref ref-type="fig" rid="fig7">Figure 7</xref> and <xref ref-type="table" rid="tbl2">Table 2</xref>). We observed an increase in the <italic>step length</italic> (<xref ref-type="fig" rid="fig7">Figure 7B</xref>); a more wobbly body placement (reflected by an increase in <italic>stance linearity</italic>, <xref ref-type="fig" rid="fig7">Figure 7C</xref>); and a larger variability in <italic>footprint clustering</italic> (<xref ref-type="fig" rid="fig7">Figure7D</xref>). In addition, both <italic>AEP</italic> and <italic>PEP</italic> were altered, in part because of a longer stride, and midleg placement was farther from the body (<xref ref-type="fig" rid="fig7">Figure 7A</xref> and data not shown). Both <italic>swing</italic> and <italic>stance duration</italic> increased (<xref ref-type="fig" rid="fig7">Figure 7E,F</xref>) resulting in a longer <italic>period</italic>. The increase in <italic>step length</italic> reflects both overreaching (more anterior AEPs) and delayed swing onset (more posterior PEPs). However, <italic>swing speeds</italic> were minimally affected (<xref ref-type="fig" rid="fig7">Figure 7G</xref>), suggesting that the motor neurons, themselves, were not compromised in proprioception deficient flies. <italic>Footprint alignment</italic> values also decreased in flies deprived of sensory feedback (<xref ref-type="fig" rid="fig7">Figure 7H</xref>). This phenotype was partially a consequence of a longer stride but also because protraction was often completed when direct contact was made with the next anterior leg. Importantly, although both <italic>nan</italic><sup><italic>36a</italic></sup> and <italic>5-40</italic><sup><italic>Leg</italic></sup><italic>>TNT</italic> affect sensory structures in the antennae, only an increase in <italic>swing duration</italic> was observed upon surgical removal of the antenna, arguing that all other phenotypes are a result of knocking out sensory feedback specifically from the legs (data not shown).<fig id="fig7" position="float"><object-id pub-id-type="doi">10.7554/eLife.00231.024</object-id><label>Figure 7.</label><caption><title>Quantification of gait parameters in sensory deprived animals.</title><p>(<bold>A</bold>). <italic>Stance traces</italic> of three representative animals walking at a similar speed. For simplicity, only left stance traces are shown. Traces for sensory deprived genotypes display a longer step length; higher jitter and a more variable AEP and PEP. (See panels (<bold>B</bold>)–(<bold>D</bold>) and (<bold>H</bold>) for quantification). In (<bold>B</bold>),(<bold>C</bold>) and (<bold>E</bold>)–(<bold>G</bold>), colored lines represent graphical fits. See <xref ref-type="table" rid="tbl2">Table 2</xref> for statistical analysis of (<bold>B</bold>),(<bold>C</bold>),(<bold>E</bold>),(<bold>F</bold>) and (<bold>G</bold>). (<bold>B</bold>). <italic>Step length</italic>. Sensory deprived animals display an increased step length. (<bold>C</bold>). <italic>Stance linearity</italic>. Sensory deprived animals display a more jittery movement. (<bold>D</bold>). AEP and PEP footprint clustering. Wild type data correspond to the speed range of <italic>nan</italic><sup><italic>36a</italic></sup> and <italic>5-40</italic><sup><italic>Leg</italic></sup><italic>>TNT</italic> (12.6 to 27.7 mm/s). In (<bold>D</bold>),(<bold>H</bold>), boxplots represent the median as the middle line and the lower and upper edges of the boxes representing the 25% and 75% quartiles, respectively; the whiskers represent the range of the full data set. Statistical analysis with Kruskal–Wallis-ANOVA (p<0.0001) followed by Dunn's <italic>post hoc</italic> test, ***p<0.001. (<bold>E</bold>). Swing duration. (<bold>F</bold>). Stance duration. (<bold>G</bold>). Swing speed. (<bold>H</bold>). <italic>Footprint alignment</italic>. Slow-walking sensory deprived animals display more aligned footprints compared to wild type flies. Data were grouped into slow (<20 mm/s) and medium speeds (between 20 and 34 mm/s). Kruskal–Wallis-ANOVA test: p=0.0003 and NS for the slow and medium speed groups, respectively. Asterisks indicate the significance of the decrease in <italic>footprint clustering</italic> between genotypes. (Data analyzed by the Dunn's <italic>post hoc</italic> significance test, **p<0.005).</p><p><bold>DOI:</bold> <ext-link ext-link-type="doi" xlink:href="10.7554/eLife.00231.024">http://dx.doi.org/10.7554/eLife.00231.024</ext-link></p></caption><graphic xlink:href="elife00231f007"/></fig><table-wrap id="tbl2" position="float"><object-id pub-id-type="doi">10.7554/eLife.00231.025</object-id><label>Table 2.</label><caption><p>Multiple regression models for wild type versus sensory-deprived flies<xref ref-type="table-fn" rid="tblfn4">a</xref></p><p><bold>DOI:</bold> <ext-link ext-link-type="doi" xlink:href="10.7554/eLife.00231.025">http://dx.doi.org/10.7554/eLife.00231.025</ext-link></p></caption><table frame="hsides" rules="groups"><thead><tr><td colspan="20">Wild type vs <italic>nan</italic><sup><italic>36a</italic></sup></td></tr><tr><td rowspan="2"/><td colspan="3">Step length (<xref ref-type="fig" rid="fig7">Figure 7B</xref>)</td><td colspan="3">Stance linearity (<xref ref-type="fig" rid="fig7">Figure 7C</xref>)</td><td colspan="4">Swing duration (<xref ref-type="fig" rid="fig7">Figure 7E</xref>)</td><td colspan="3">Swing speed (<xref ref-type="fig" rid="fig7">Figure 7G</xref>)</td><td colspan="3">Stance duration (<xref ref-type="fig" rid="fig7">Figure 7F</xref>)</td><td colspan="3">Tripod index (<xref ref-type="fig" rid="fig6">Figure 6D</xref>)</td></tr><tr><td>Coef</td><td>SE</td><td>p-value</td><td>Coef</td><td>SE</td><td>p-value</td><td>Coef</td><td colspan="2">SE</td><td>p-value</td><td>Coef</td><td>SE</td><td>p-value</td><td>Coef</td><td>SE</td><td>p-value</td><td>Coef</td><td>SE</td><td>p-value</td></tr></thead><tbody><tr><td>WT y intercept</td><td>746.7</td><td>93.8</td><td>0.0</td><td>291.6</td><td>32.6</td><td>0.0</td><td>3.6 × 10<sup>−2</sup></td><td colspan="2">1.9 × 10<sup>−3</sup></td><td>0.0</td><td>19.1</td><td>2.1</td><td>0.0</td><td>2.3 × 10<sup>−1</sup></td><td>1.2 × 10<sup>−2</sup></td><td>0.0</td><td>−9.2 × 10<sup>−4</sup></td><td>4.2 × 10<sup>−2</sup></td><td>0.982</td></tr><tr><td><italic>Δ y intercept</italic></td><td><italic>587.4</italic></td><td><italic>270.5</italic></td><td><italic><underline>0.0</underline></italic></td><td><italic>349.8</italic></td><td><italic>96.4</italic></td><td><italic><underline>0.0</underline></italic></td><td><italic>2.2 × 10</italic><sup><italic>−2</italic></sup></td><td colspan="2"><italic>5.2 × 10</italic><sup><italic>−3</italic></sup></td><td><italic><underline>0.0</underline></italic></td><td><italic>−4.5</italic></td><td><italic>5.9</italic></td><td><bold><italic>0.451</italic></bold></td><td><italic>1.9 × 10</italic><sup><italic>−1</italic></sup></td><td><italic>3.4 × 10</italic><sup><italic>−2</italic></sup></td><td><italic><underline>0.0</underline></italic></td><td><italic>−4.1 × 10</italic><sup><italic>−1</italic></sup></td><td><italic>1.2 × 10</italic><sup><italic>−1</italic></sup></td><td><italic><underline>0.001</underline></italic></td></tr><tr><td>WT slope</td><td>47.2</td><td>3.3</td><td>0.0</td><td>−70.9</td><td>9.9</td><td>0.0</td><td>−1.3 × 10<sup>−4</sup></td><td colspan="2">6.4 × 10<sup>−5</sup></td><td>0.043</td><td>0.6</td><td>0.1</td><td>0.0</td><td>−5.4 × 10<sup>−2</sup></td><td>3.5 × 10<sup>−3</sup></td><td>0.0</td><td>1.8 × 10<sup>−2</sup></td><td>1.4 × 10<sup>−3</sup></td><td>0.0</td></tr><tr><td><italic>Δ Slope</italic></td><td><italic>−11.3</italic></td><td><italic>12.9</italic></td><td><bold><italic>0.4</italic></bold></td><td><italic>−89.1</italic></td><td><italic>32.1</italic></td><td><italic><underline>0.007</underline></italic></td><td><italic>−8.4 × 10</italic><sup><italic>−4</italic></sup></td><td colspan="2"><italic>2.5 × 10</italic><sup><italic>−4</italic></sup></td><td><italic><underline>0.001</underline></italic></td><td><italic>0.5</italic></td><td><italic>0.3</italic></td><td><bold><italic>0.115</italic></bold></td><td><italic>−1.6 × 10</italic><sup><italic>−2</italic></sup></td><td><italic>4.8 × 10</italic><sup><italic>−3</italic></sup></td><td><italic><underline>0.002</underline></italic></td><td><italic>1.6 × 10</italic><sup><italic>−2</italic></sup></td><td><italic>5.6 × 10</italic><sup><italic>−3</italic></sup></td><td><italic><underline>0.006</underline></italic></td></tr><tr><td colspan="20"><bold>Wild type versus <italic>5-40</italic><sup><italic>Leg</italic></sup><italic>>TNT</italic></bold></td></tr><tr><td>WT y intercept</td><td>746.7</td><td>93.8</td><td>0.0</td><td>291.6</td><td>34.0</td><td>0.0</td><td>3.6 × 10<sup>−2</sup></td><td>1.9 × 10<sup>−3</sup></td><td colspan="2"><0.001</td><td>19.1</td><td>2.1</td><td><0.001</td><td>2.3 × 10<sup>−1</sup></td><td>1.2 × 10<sup>−2</sup></td><td>0.0</td><td>−9.2 × 10<sup>−4</sup></td><td>4.3 × 10<sup>−2</sup></td><td>0.983</td></tr><tr><td><italic>Δ y intercept</italic></td><td><italic>622.8</italic></td><td><italic>295.3</italic></td><td><italic><underline>0.038</underline></italic></td><td><italic>242.2</italic></td><td><italic>115.7</italic></td><td><italic><underline>0.039</underline></italic></td><td><italic>1.7 × 10</italic><sup><italic>−2</italic></sup></td><td><italic>1.0 × 10</italic><sup><italic>−3</italic></sup></td><td colspan="2"><italic><underline><0.001</underline></italic></td><td><italic>10.5</italic></td><td><italic>6.4</italic></td><td><bold><italic>0.1</italic></bold></td><td><italic>1.6 × 10</italic><sup><italic>−1</italic></sup></td><td><italic>4.1 × 10</italic><sup><italic>−2</italic></sup></td><td><italic><underline>0.0</underline></italic></td><td><italic>−3.6 × 10</italic><sup><italic>−1</italic></sup></td><td><italic>1.4 × 10</italic><sup><italic>−1</italic></sup></td><td><italic><underline>0.009</underline></italic></td></tr><tr><td>WT slope</td><td>47.2</td><td>3.2</td><td>0.0</td><td>−70.9</td><td>10.3</td><td>0.0</td><td>−1.0 × 10<sup>−4</sup></td><td>6.0 × 10<sup>−5</sup></td><td colspan="2">0.04</td><td>0.7</td><td>0.1</td><td><0.001</td><td>−5.4 × 10<sup>−2</sup></td><td>3.7 × 10<sup>−3</sup></td><td>0.0</td><td>1.8 × 10<sup>−2</sup></td><td>1.5 × 10<sup>−3</sup></td><td>0.0</td></tr><tr><td><italic>Δ Slope</italic></td><td><italic>11.4</italic></td><td><italic>15.6</italic></td><td><bold><italic>0.465</italic></bold></td><td><italic>−54.8</italic></td><td><italic>39.7</italic></td><td><bold><italic>0.17</italic></bold></td><td><italic>−2.4 × 10</italic><sup><italic>−5</italic></sup></td><td><italic>3.1 × 10</italic><sup><italic>−4</italic></sup></td><td colspan="2"><bold><italic>0.94</italic></bold></td><td><italic>−0.5</italic></td><td><italic>0.3</italic></td><td><bold><italic>0.12</italic></bold></td><td><italic>−4.4 × 10</italic><sup><italic>−2</italic></sup></td><td><italic>1.4 × 10</italic><sup><italic>−2</italic></sup></td><td><italic><underline>0.003</underline></italic></td><td><italic>1.6 × 10</italic><sup><italic>−2</italic></sup></td><td><italic>7.1 × 10</italic><sup><italic>−3</italic></sup></td><td><italic><underline>0.032</underline></italic></td></tr></tbody></table><table-wrap-foot><fn id="tblfn4"><label>a</label><p>Coef stands for the estimated regression coefficient, SE represents its standard error. In this model we log transformed average speed for parameters that were non-linear with respect to speed (<italic>step linearity</italic> and <italic>stance duration</italic>). <italic>WT y intercept</italic> indicates the y intercept for wild type. <italic>Δ y intercept</italic> indicates the difference in the y intercept between the experimental condition and wild type. <italic>WT slope</italic> reports the slopes of the wild type regression lines. p-values >0.05 are in bold italics; those <0.05 are in italics underlined. If <italic>Δ slope</italic> is >0.05 (bold italics) the regression curves are considered non-interacting (∼parallel). If <italic>Δ y intercept</italic> is <0.05 (italics underlined), the parameter is considered different from WT.</p></fn></table-wrap-foot></table-wrap></p><p>Interestingly, several of the differences we observed in flies deprived of sensory feedback were more pronounced in animals that walked more slowly compared to faster flies (e.g. <italic>stance duration</italic> and <italic>footprint alignment</italic>; <xref ref-type="fig" rid="fig7">Figure 7F,H</xref>). The <italic>tripod index</italic> also decreased in slower flies (<xref ref-type="fig" rid="fig6">Figure 6D</xref>). It is also noteworthy that several of the trends observed in sensory-deprived flies (such as <italic>AEP</italic>, <italic>PEP</italic>, and <italic>footprint alignment</italic>) are the same as those observed in faster wild type flies. In addition, sensory deprived flies position their midlegs further from their bodies, similar to the trend seen in fast flies (<xref ref-type="fig" rid="fig3 fig7">Figures 3F and 7A</xref>). These observations are consistent with the idea that flies walking at slow, medium, and fast speeds use distinct neural programs, and that flies walking at fast speeds are less dependent on sensory feedback.</p></sec></sec><sec id="s3" sec-type="discussion"><title>Discussion</title><p>Research using insects has contributed greatly to the field of locomotion and sensory feedback (<xref ref-type="bibr" rid="bib16">Cruse, 1990</xref>; <xref ref-type="bibr" rid="bib19">Cruse et al., 2007</xref>; <xref ref-type="bibr" rid="bib43">Ritzmann and Büschges, 2007</xref>; <xref ref-type="bibr" rid="bib49">Sponberg and Full, 2008</xref>). Emerging and established tools have set the fruit fly as a valuable genetic system for studying many behaviors such as olfaction and courtship. Improved expression tools (<xref ref-type="bibr" rid="bib41">Pfeiffer et al., 2008</xref>; <xref ref-type="bibr" rid="bib30">Jenett et al., 2012</xref>; <xref ref-type="bibr" rid="bib31">Jory et al., 2012</xref>; <xref ref-type="bibr" rid="bib38">Manning et al., 2012</xref>), transgenic neuromodulators (<xref ref-type="bibr" rid="bib42">Pulver et al., 2009</xref>) and imaging of neuronal activity (<xref ref-type="bibr" rid="bib45">Seelig et al., 2010</xref>), continue to improve the <italic>Drosophila</italic> toolkit. However, despite the growing collection of tools to disturb the circuit that regulates waking, <italic>Drosophila</italic> neurogenetics lacks a reliable method to measure the outcome of these manipulations. Here we fill this gap in the toolkit by describing an approach that unambiguously detects with high temporal and spatial resolution the kinematic behavior of freely walking fruit flies on a flat surface. A powerful and downloadable FlyWalker software tool allows the tracking of both the fly body and legs when they contact the ground as the animal moves forward.</p><p>Using these tools, we analyzed the behavior of wild-type animals during straight walking on a horizontal plane, extending previous analyses (<xref ref-type="bibr" rid="bib52">Strauss and Heisenberg, 1990</xref>, <xref ref-type="bibr" rid="bib53">1993</xref>; <xref ref-type="bibr" rid="bib61">Wosnitza et al., 2012</xref>). In addition to corroborating earlier findings, the high degree of temporal and spatial resolution, coupled to the simultaneous tracking of the body allowed us to define several additional parameters, particularly those that address the spatial aspects of locomotion. Several observations also suggest the existence of previously unknown transitions that occur at ∼20 mm/s and ∼34 mm/s. For one, we find that flies walking at 20 mm/s are underrepresented in our data set. In addition, some parameters, such as <italic>stance linearity</italic> and <italic>footprint alignment</italic>, show striking differences in flies walking slower and faster than these speeds. These transitions do not, however, represent abrupt transitions in gait because the tripod gait was observed at all speeds, at gradually diminishing frequencies in slower flies. This scenario contrasts with abrupt gait transitions observed in many vertebrates, for example horses, where each gait is only observed in a defined range of speeds (<xref ref-type="bibr" rid="bib28">Hoyt and Taylor, 1981</xref>). Nevertheless, our data suggest that in flies modifications of a common neural circuit may regulate walking at these different speeds (<xref ref-type="bibr" rid="bib59">Watson et al., 2002</xref>; <xref ref-type="bibr" rid="bib49">Sponberg and Full, 2008</xref>). Because each leg muscle is targeted by multiple motor neurons (<xref ref-type="bibr" rid="bib4">Baek and Mann, 2009</xref>), it is feasible that different subsets of MNs play a more or less dominant role at different speeds.</p><sec id="s3-1"><title>Walking by proprioceptive-deficient flies</title><p>In larger insects such as the stick insect and locust, the role of sensory feedback in coordinated walking has been addressed by local surgical ablations coupled to electrophysiological measurements (<xref ref-type="bibr" rid="bib56">Usherwood et al., 1968</xref>; <xref ref-type="bibr" rid="bib18">Cruse et al., 1984</xref>; <xref ref-type="bibr" rid="bib2">Akay et al., 2001</xref>; <xref ref-type="bibr" rid="bib8">Borgmann et al., 2009</xref>). For example, using single leg preparations of the stick insect, it was found that for mid and foreleg steps to be out of phase (as they are in wild type walking), stimulation of sensory afferents in the midleg was required; without this stimulation, the CPGs for these legs fired in phase (<xref ref-type="bibr" rid="bib8">Borgmann et al., 2009</xref>). These, as well as additional observations (<xref ref-type="bibr" rid="bib13">Büschges et al., 2008</xref>), argue that sensory feedback from legs in stance phase is required for interleg coordination.</p><p>The use of <italic>Drosophila</italic> brings an additional set of tools to address these questions. In one set of experiments, we took advantage of a mutation that specifically disrupts the function of the ChOs while a second set of experiments used a combinatorial misexpression approach to inactivate the majority of sensory neurons in the leg, including the ChO, hair plates, campaniform sensilla and mechanosensory bristles. In both sets of experiments, gait parameters and interleg swing phases were largely normal when sensory feedback was impaired. These results suggest that in the fly interleg coordination is not dominated by sensory feedback. Instead, they suggest that communication between CPGs, perhaps by local interneurons, may be sufficient for coordination, a suggestion that is supported by recent studies in vertebrates ((<xref ref-type="bibr" rid="bib34">Kiehn, 2011</xref>) for review). In insects, interneurons have been implicated in interjoint coordination within individual legs and in footfall placement, but their role in interleg coordination has not been established (<xref ref-type="bibr" rid="bib11">Brunn and Dean, 1994</xref>; <xref ref-type="bibr" rid="bib10">Brunn, 1998</xref>; <xref ref-type="bibr" rid="bib39">Matheson, 2002</xref>). The different requirements for sensory feedback observed here compared to electrophysiology studies in the stick insect may in part be because in our experiments leg sensory neurons were inactivated in all six legs (in the case of <italic>5-40</italic><sup><italic>leg</italic></sup><italic>>TNT</italic>) or all ChOs in the animal (in the case of the <italic>nan</italic> mutant). We speculate that when all six legs are equally impaired, the flies resort to a CPG and interneuron-dominated system, which is sufficient to execute the tripod gait. Consistent with this notion, although interleg coordination was impaired upon amputation of a single hindleg in <italic>Drosophila</italic>, the flies were able to recover a partial tripod gait (<xref ref-type="bibr" rid="bib61">Wosnitza et al., 2012</xref>).</p><p>The data derived from sensory-deprived flies also supports the idea that flies may be less dependent on sensory feedback when they walk fast because, for several parameters, the defects were significantly more pronounced in slow flies. This observation is consistent with the idea that when flies walk fast, they use a largely sensory-independent CPG-based system where individual CPGs communicate by local interneurons to achieve coordination. In contrast, in slower flies, sensory feedback would be invoked to allow animals to negotiate more complex terrains. These speculations are supported by our observations that several parameters measured here, most prominently <italic>footprint clustering</italic>, appear distinct at slow, medium, and fast walking speeds. Thus, although the tripod gait is used at all speeds, distinct neural programs may come into play that are more or less dependent on sensory feedback, depending on the speed.</p></sec></sec><sec id="s4" sec-type="conclusions"><title>Conclusions</title><p>The fTIR method and FlyWalker software provides a robust suite of tools for analyzing walking in <italic>Drosophila</italic>. Using this approach, we present many parameters that comprehensively describe walking by wild type flies. Our initial analysis of sensory-deprived flies reveal that proprioception, at least from the legs, is largely dispensable for coordinated walking and the tripod gait. In the future, additional expression tools will allow the targeting of other subsets of sensory and interneurons and the ability to carry out gain-of-function experiments. Moreover, the fTIR apparatus can be used with other arthropods as long as they, like <italic>Drosophila</italic>, possess adhesive structures on their tarsi (<xref ref-type="bibr" rid="bib25">Gorb et al., 2007</xref>). However, although footprints and body position can be readily identified with this method, our current setup does not provide the ability to follow individual leg joints, which can be done in larger insects such as the stick insect (<xref ref-type="bibr" rid="bib17">Cruse and Bartling, 1995</xref>). Nevertheless, the set of parameters measured here underscore the complex mechanisms and circuits regulating hexapod locomotion. Our approach, in combination with the growing collection of genetic tools available in <italic>Drosophila</italic> should open many additional opportunities to unravel the mechanism of locomotion in animals and improve bio-inspired machines.</p></sec><sec id="s5" sec-type="materials|methods"><title>Materials and methods</title><sec id="s5-1"><title>Fly strains</title><p>Oregon R flies were reared on standard cornmeal food at 25°C. <italic>nan-Gal4</italic> (<italic>F-Gal4</italic>) was obtained from the Bloomington Stock Center. <italic>5-40-Gal4</italic> was a gift from Cynthia Hughes (<xref ref-type="bibr" rid="bib29">Hughes and Thomas, 2007</xref>), <italic>UAS-FRT-stop-FRT-TNT</italic> was from Barry Dickson (<xref ref-type="bibr" rid="bib50">Stockinger et al., 2005</xref>) and <italic>nan</italic><sup><italic>36a</italic></sup> was kindly provided by Marco Gallio. <italic>dac</italic><sup><italic>RE</italic></sup><italic>-FLP</italic> was generated by TOPO cloning the Ring Enhancer (<italic>RE</italic>) fragment from the regulatory region of <italic>dac</italic> (<xref ref-type="bibr" rid="bib24">Giorgianni and Mann, 2011</xref>) into an entry clone followed by a Gateway reaction into a FLP destination plasmid. Transgenic lines were generated by standard procedures in a <italic>yw</italic> background. All experiments were carried out with 1 to 7 day old animals at room temperature. To select the correct genotype, flies were anesthetized on a cold plate and allowed to recover at least 24 h. Before imaging, flies were kept in clean glass vials for ∼15 min.</p></sec><sec id="s5-2"><title>fTIR apparatus</title><p>Five Neutral White (4100K) LEDs from Luxeonstar (Brantford, Ontario, Canada), Pre-Mounted on a 10 mm Square Base (230 lm @ 700mA) were wired in series and glued to a 60 mm CPU heat-sink and fan (StarTech.com Lockbourne, Ohio). Three LED/heat-sink sets were wired in parallel and clamped to the edges of a 150 mm × 150 mm × 6.5 mm Borofloat Glass (Advanced Optics, Pewaukee, WI). The edges of the glass were scribed and broken so they would be clear, but not polished. The fourth edge of the glass was fixed to a filter mount and the whole system was attached to a bench plate by steel mounting posts (Edmund optics, Barrington, NJ). The LEDs were powered by a variable power supply (Tekpower, Montclair, CA). In order to minimize deflected light, the edges of the glass and the LEDs are covered with black ink or black cardboard. Consequently, most of the light from the LEDs enters the glass below the critical angle. To recycle light that reaches the edges of the optical glass, the remaining edges of the glass are covered with aluminum foil. Movies were acquired using a Photron (San Diego, CA) Fastcam MC2 camera using a 55 mm Computar (Commack, NY) telecentric lens, which has very little optical distortion. By measuring 1 mm intervals at multiple positions within the field of view we determined the optical distortion to be less than 4%. The lens aperture (f/2.8) was maximized in order to increase light sensitivity and minimize depth of field.</p><p>If the glass–air interface becomes disrupted by the footprints of a fruit fly, the light becomes deflected and is detected by the high-speed video camera. Consequently, when a leg is in stance phase it is detected by fTIR and the absence of signal indicates a swing phase. In addition, a small amount of reflected light illuminates the fly's body (<xref ref-type="fig" rid="fig1s1">Figure 1—figure supplement 1</xref>). In the center of the fTIR apparatus, a plexiglass tunnel limits the mobility of the fly and increases the chance it will move in a straight line (<xref ref-type="fig" rid="fig1s1">Figure 1—figure supplement 1</xref>). This tunnel is slightly separated from the glass by a nylon string in order to minimize the contact between the plexiglass and optical glass, which would produce a fTIR signal. In order to trigger an optomotor response, two optical fibers connected to a UV light source are present at both ends of the tunnel (<xref ref-type="fig" rid="fig1s1">Figure 1—figure supplement 1</xref>). Images are detected within a ∼1.2 cm<sup>2</sup> area by a high-speed camera at a rate of 250 frames per second at f/2.8. This allows sufficient light to be detected per frame while permitting a high temporal resolution of 4 milliseconds per frame.</p><p>For the fly tunnel, a rectangle measuring 7 by 40 mm was cut out of a transparent plexiglass plate measuring 4.76 × 46 × 23 mm. At both ends, a 1 mm diameter hole was made in order to accommodate an optical fiber. As a cover for the tunnel, a second piece plexiglass of equal size was glued on top. A 5 mm hole was drilled on the cover approximately 1 cm from the edge in order to insert the flies into the chamber. In order to raise the chamber slightly, two small holes 1 mm apart, were drilled at each of the four corners of the chamber. A nylon wire—0.48 mm diameter—was inserted through the two holes to create a loop that functions as spacers to prevent the chamber from contacting the optical glass. Finally, the chamber was painted in black on the outside and the interior walls coated with Fluon AD-1 (www.entosupplies.com.au) mixed with black India ink in order to encourage walking on the optical glass (<xref ref-type="fig" rid="fig1s1">Figure 1—figure supplement 1</xref>).</p></sec><sec id="s5-3"><title>FlyWalker software</title><p>FlyWalker was created in MATLAB, and analyzes the sequence of images from the videos by registering the position of the body and each footprint. Once the image sequence is loaded into the FlyWalker interface (<xref ref-type="fig" rid="fig1s2">Figure 1—figure supplement 2</xref>), its analysis becomes a two-step process. First, body and footprints are tracked automatically by the software based on sudden increases of brightness within preset thresholds, as well as information from preceding frames. Any fTIR signal present in frames prior to the appearance of the fly (due for example to dust) is subtracted from all subsequent frames to minimize false positive signals. Sequential frame comparison sets the displacement axis, which helps with the identification of each of the six footprints, for example the left and right fore (LF, RF), left and right mid (LM, RM), and left and right hind (LH, RH) legs. Second, the software allows the user to manually correct any mislabeled or missed footprints. A sample video of the processed fTIR effect can be seen in <xref ref-type="other" rid="video2">Video 2</xref>. For each frame, the user can reject a mislabeled footprint or add a footprint that is visible, but was not included because it fell below the preset threshold. The script is optimized to minimize the time spent editing each video. A setup window allows the user to define auto-tracking parameters and which features to show on-screen (<xref ref-type="fig" rid="fig1s2">Figure 1—figure supplement 2</xref>). For example, depending on the user-defined settings, the body trace and/or past footprints can be visualized (<xref ref-type="fig" rid="fig1">Figure 1C</xref>). Importantly, through the use of a calibration reticle, the user can input the pixel/μm ratio, which allows the program to introduce a scale bar (<xref ref-type="fig" rid="fig1">Figure 1C</xref>), calculate distances and speeds.</p></sec><sec id="s5-4"><title>Parameters quantified by FlyWalker</title><p>Speed (instantaneous and average)</p><p>Frequency</p><p>Period</p><p>Metachronal lag</p><p>Swing speed (average and for individual steps)</p><p>Step length (average and for individual steps)</p><p>Swing time (average and for individual steps)</p><p>Stance time (average and for individual steps)</p><p>Footprint alignment (average and for individual footprint clusters)</p><p>Anterior Extreme Position (AEP)</p><p>Posterior Extreme Position (PEP)</p><p>Footprint clustering (AEP and PEP)</p><p>Stance trace (average and for individual segments)</p><p>Body trace</p><p>Tripod index</p><p>Tetrapod index</p><p>Gait index</p><p>Step period (LH:RH; LM:RM; LF:RF; LH:LM; LM:LF; RH:RM; RM:RF)</p></sec><sec id="s5-5"><title>List of files generated by FlyWalker</title><p>Angle between footprint and displacement axis vs time</p><p>Footprint distance to body center vs time</p><p>Footprint parallel distance to body center vs time</p><p>Footprint perpendicular distance to body center vs time</p><p>Instantaneous speed vs time</p><p>Gait vs time</p><p>Combined gait and instantaneous speed vs time</p><p>Anterior extreme position plus stance trace</p><p>Posterior extreme position plus stance trace</p><p>Geometric combinations generated by footprints</p><p>Step size vs time</p><p>Gait maps (fixed and automatic time scale)</p><p>Gait index vs time (fixed and automatic time scale)</p><p>Step velocity; instantaneous speed; color code and gait index plots combined vs time (fixed and automatic time scale).</p><p>Summary plots including combined gait and instantaneous speed and anterior extreme position plus stance trace; plus values for average speed, tripod index, tetrapod index, stance trace and average step distance.</p><p>Excel file with all parameters</p><p>Image sequence from the tracking program.</p></sec><sec id="s5-6"><title>Statistical analyses</title><p>For most plots, each data point comes from a single video, except for phase and metachronal lag plots where each video generated multiple data points. Because some parameters changed with speed we fit multiple regression models (<xref ref-type="table" rid="tbl2">Table 2</xref>). The interaction term corresponds to the slope variation (<italic>Δ slope</italic>). When the relationship between the parameter and the average speed was non linear (stance linearity and stance duration), we transformed the average speed by its natural log.</p><p>Because of the different behaviors of the slow, medium, and fast speed groups, some parameters were compared between individual speed groups. In these cases, the data were presented as box and whisker plots. For independent observations, comparisons between speed groups (<xref ref-type="fig" rid="fig3 fig4">Figures 3E and 4C</xref>) or between mutant groups (<xref ref-type="fig" rid="fig6 fig7">Figures 6C and 7D,H</xref>) were done using Kruskal–Wallis test followed by Dunn's <italic>post hoc</italic> test (for non-normal distributions) or one-way-ANOVA followed by Tukey's <italic>post hoc</italic> tests (for normal distributions). For paired observations (AEP vs PEP in <xref ref-type="fig" rid="fig3">Figure 3E</xref> and <xref ref-type="fig" rid="fig3s3">Figure 3—figure supplement 3</xref>) with a normal distribution, a paired t-test was used. Otherwise a Wilcoxon signed rank test was used (GraphPad Prism, San Diego, CA).</p><p>The least square regression line and the R correlation coefficient are indicated in all scatter plots.</p><p>Although <italic>average speeds</italic> of ∼20 mm/sec are underrepresented in our collection of wild type videos, the histogram shown in <xref ref-type="fig" rid="fig2">Figure 2A</xref> is not statistically different from a normal distribution (Shapiro-Wilk test; <italic>p=0.31</italic>). However, a normal probability plot shows non-linearity of the data at <20 mm/s and greater than ∼35 mm/s (data not shown), suggesting that the distribution has significant non-Gaussian outliers in the tails.</p></sec><sec id="s5-7"><title>Parameter definitions</title><p>Terms and definitions for parameters used in this paper. § indicates definitions described previously by <xref ref-type="bibr" rid="bib52">Strauss and Heisenberg 1990</xref> (and references within).</p><sec id="s5-7-1"><title>Period<sup>§</sup></title><p>Time taken to complete one leg cycle consisting of one swing and one stance phase.</p></sec><sec id="s5-7-2"><title>Step length<sup>§</sup></title><p>Distance between two successive footprints of the same leg. No normalization in relation of the direction of propagation.</p></sec><sec id="s5-7-3"><title>Stance linearity index</title><p>Average difference between the stance traces generated by each leg during stance phase and a 5-point smoothed line.</p></sec><sec id="s5-7-4"><title>Anterior Extreme Position (AEP) (<xref ref-type="bibr" rid="bib15">Cruse, 1976</xref>)</title><p>Position where the leg first contacts the glass after touchdown at the end of swing phase (or protraction).</p></sec><sec id="s5-7-5"><title>Posterior Extreme Position (PEP) (<xref ref-type="bibr" rid="bib15">Cruse, 1976</xref>)</title><p>Position at the end of the stance phase, just before the tarsi enter swing phase.</p></sec><sec id="s5-7-6"><title>Footprint clustering</title><p>Standard deviation from the average position for all <italic>AEP</italic>s or <italic>PEP</italic>s.</p></sec><sec id="s5-7-7"><title><sub>X</sub>Lag<sub>Y</sub><sup>§</sup></title><p>Time interval between the beginning of swing phase in leg x and immediate following swing phase onset of another leg y.</p></sec><sec id="s5-7-8"><title>Metachronal Lag<sup>§</sup></title><p>Time interval between the beginning of swing phase in hindleg and immediate following swing phase onset of the ipsilateral foreleg (<sub><italic>H</italic></sub><italic>Lag</italic><sub><italic>F</italic></sub>).</p></sec><sec id="s5-7-9"><title>Phase<sup>§</sup></title><p>Lag between two legs divided by the period of the first.</p></sec><sec id="s5-7-10"><title>Tripod index</title><p>Percentage of frames in a video that display leg combinations defined by the tripod gait.</p></sec><sec id="s5-7-11"><title>Tetrapod index</title><p>Percentage of frames in a video that display leg combinations defined by the tetrapod gait.</p></sec><sec id="s5-7-12"><title>Gait index</title><p>Average value for a window of frames where tripod configurations = +1 value; tetrapod = −1; and noncanonical = 0.</p></sec><sec id="s5-7-13"><title>Footprint alignment</title><p>Standard deviation from the average point of adjacent ipsilateral footprints projected onto the displacement axis. Each set of ipsilateral footprints includes one foreleg, one midleg and one hindleg.</p></sec></sec></sec></body><back><ack id="ack"><title>Acknowledgements</title><p>We thank the Bloomington Stock Center, Barry Dickson, Marco Gallio, Cynthia Hughes and Matt Giorgianni for reagents. We also thank Ioannis Kymissis for initial advice on this project, Charles Zuker and members of the Mann laboratory for encouragement, suggestions and comments on the manuscript. We also thank the Irving Institute (<ext-link ext-link-type="uri" xlink:href="http://irvinginstitute.columbia.edu/resources/biostatistics.html">http://irvinginstitute.columbia.edu/resources/biostatistics.html</ext-link>; Grant Number UL1 RR024156) and in particular Jimmy Duong and Arthur Palmer for assistance with the statistical analysis.</p></ack><sec sec-type="additional-information"><title>Additional information</title><fn-group content-type="competing-interest"><title>Competing interests</title><fn fn-type="conflict" id="conf1"><p>The authors have declared that no competing interests exist</p></fn></fn-group><fn-group content-type="author-contribution"><title>Author contributions</title><fn fn-type="con" id="con1"><p>CSM, Conception and design, Acquisition of data, Analysis and interpretation of data, Drafting or revising the article</p></fn><fn fn-type="con" id="con2"><p>IB, Conception and design, Analysis and interpretation of data, Drafting or revising the article</p></fn><fn fn-type="con" id="con3"><p>TA, Conception and design, Analysis and interpretation of data, Drafting or revising the article</p></fn><fn fn-type="con" id="con4"><p>SM, Conception and design, Drafting or revising the article</p></fn><fn fn-type="con" id="con5"><p>RSM, Conception and design, Analysis and interpretation of data, Drafting or revising the article</p></fn></fn-group></sec><ref-list><title>References</title><ref id="bib1"><element-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Akay</surname><given-names>T</given-names></name><name><surname>Acharya</surname><given-names>HJ</given-names></name><name><surname>Fouad</surname><given-names>K</given-names></name><name><surname>Pearson</surname><given-names>KG</given-names></name></person-group><year>2006</year><article-title>Behavioral and electromyographic characterization of mice lacking EphA4 receptors</article-title><source>J 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An edited version of the letter sent to the authors after peer review is shown, indicating the substantive concerns or comments; minor concerns are not usually shown. Reviewers have the opportunity to discuss the decision before the letter is sent (see <ext-link ext-link-type="uri" xlink:href="http://www.elifesciences.org/the-journal/review-process">review process</ext-link>). Similarly, the author response typically shows only responses to the major concerns raised by the reviewers.</p></boxed-text><p>Thank you for choosing to send your work entitled “Quantification of gait parameters in freely walking wild type and sensory deprived <italic>Drosophila melanogaster</italic>” for consideration at <italic>eLife</italic>. Your article has been evaluated by a Senior Editor and 2 reviewers, one of whom is a member of our Board of Reviewing Editors. The following individuals responsible for the peer review of your submission want to reveal their identity: Ronald L Calabrese, Reviewing Editor.</p><p>The Reviewing Editor and the other reviewer discussed their comments before we reached this decision, and the Reviewing Editor has assembled the following comments based on the two reports.</p><p>The authors develop very sophisticated behavioral technology and software for the analysis of insect walking focusing their experimental effort on the rich genetic system <italic>Drosophila</italic>. Their automated optical method for tracking insect footfalls and analyzing the kinematic data are powerful and impressive. The data acquired is extensive and is mined for many pertinent locomotor parameters including interlimb coordination and gait. Data is presented that gait and some other characteristics, such as stance linearity and footprint alignment, are different in flies walking slower and faster speeds. There are not discrete transitions in gait, however: the typical tripod gait occurs at all speeds, but with diminishing frequency in slower flies. The normative data set is extensive and of real value for future analysis in this important genetic system. The most interesting aspect of the work involves inactivating sensory neurons of the legs; one set of experiments uses a mutation that specifically disrupts the function of chrodotonal organs, while a second set of experiments uses a combinatorial misexpression approach to inactivate the majority of sensory neurons in the leg.</p><p>These experiments give a surprising result; sensory deficient flies walked slower than wild-type flies, but maintained a typical tripod gait with normal left–right and interlimb coordination. Stance traces showed some defects, however: step length increased with a more wobbly body placement and a larger variability in footprint clustering. The surprise here comes because in the well-studied stick insect system, sensory inactivation of a leg disrupts proper interlimb coordination. The walking of sensory-deprived flies suggests that flies may be less dependent on sensory feedback when they walk fast because defects were more pronounced in slow flies. Stick insects walk very slowly and rarely on flat surfaces.</p><p>The writing is generally succinct and lucid, and the paper is easy to follow and interesting. The figures contain clear data but do need improvement. The work is carefully done with much detailed analysis (which provides a rich database for future workers) but there are some concerns about the analysis. FlyWalker is a nice technical innovation but some recognition of similar systems for rodents (<ext-link ext-link-type="uri" xlink:href="http://www.noldus.com/animal-behavior-research/products/catwalk">CatWalk</ext-link>) should be made and an emphasis placed on the scaling to fly size. In addition to the technological innovation, the real scientific interest is the impetus to future work provided by the analysis of the sensory-deprived flies with FlyWalker. This manuscript can be accepted upon the completion of the revisions described below. The authors should give special attention to those revisions pertaining to the statistical and other analyses of the data.</p><p><bold>Specific revisions:</bold></p><p>1. While the writing is generally clear, there is concern that the Introduction presents a naïve view of how CPGs work and interact with descending inputs and sensory feedback. A primary interest of the manuscript is what it tells us about how sensory inputs interact with the locomotory CPG. Specifically, the opening paragraph of the manuscript should be reworked to reflect the more sophisticated view of CPGs put forth by the references cited so that this interest is properly showcased.</p><p>2. While this manuscript was under review, another manuscript concerning fly walking has been accepted for publication. Wosnitza A, Bockemühl T, Dübbert M, Scholz H, Büschges A. Inter-leg coordination in the control of walking speed in Drosophila. J Exp Biol. 2012 Oct 4. [Epub ahead of print] PubMed PMID: 23038731. This paper should be cited and the results contrasted; in particular a contrast should be made between leg amputation and sensory inactivation as a means of exploring how sensory inputs interact with the locomotory CPGs. In that manuscript all speed data are shown in body lengths so comparisons will be difficult but should nevertheless be made.</p><p>3. There were significant concerns about some of the data analysis.</p><p>a) The authors should more explicitly rationalize splitting the distribution of Figure 2A into three speed groups. (An external reviewer wrote “Although the data showing differences between fast and slow animals are fairly convincing I'd like to see a rationale for dividing the animals in 3 groups (why not 2 groups: above and below average; or 4 groups?). The way they group animals is going to affect their statistical analysis.”).</p><p>b) The distribution of Figure 2A does suggest a break at a speed of 20mm/s. Is there a statistical test that can verify this split in the distribution or can more animals at low speed be measured so that a clear break can be seen?</p><p>c) In the Materials and methods the authors state: “Because of the different behaviors of the slow, medium, and fast speed groups, some parameters were compared between individual speed groups. In these cases, the data were presented as box and whisker plots. When the data sets had a normal distribution, a Student's t-test was used. Otherwise a Mann-Whitney U-test was used.” There was general concern that these statistical methods were not adequate and that some form of ANOVA (or equivalent non-parametric test), followed by appropriate post-hoc tests, should be pursued.</p><p>d) The legend to Figure 2A states: “Speed histogram of 71 videos recorded for wild type flies, with 2 mm/s bins. Average speeds vary between 7.2 and 44.7 mm/s, with 28 mm/s the most represented speed. Although the data in this histogram fit a normal distribution, speeds of 20 mm/s are underrepresented. Additionally, both tails of the distribution curve have significant outliers (data not shown).” How were outliers determined and on what basis were they then excluded?</p><p>4. <italic>eLife</italic> allows authors to embed useful data into the text. Much of the supplemental data, including videos, can be handled in this way. The authors are encouraged to work with the editorial staff to ensure that the most fundamental supplemental data and the videos are properly integrated.</p></body></sub-article><sub-article article-type="reply" id="SA2"><front-stub><article-id pub-id-type="doi">10.7554/eLife.00231.027</article-id><title-group><article-title>Author response</article-title></title-group></front-stub><body><p><italic>1. While the writing is generally clear, there is concern that the Introduction presents a naïve view of how CPGs work and interact with descending inputs and sensory feedback. A primary interest of the manuscript is what it tells us about how sensory inputs interact with the locomotory CPG. Specifically, the opening paragraph of the manuscript should be reworked to reflect the more sophisticated view of CPGs put forth by the references cited so that this interest is properly showcased</italic>.</p><p>We have revised the first and second paragraphs accordingly.</p><p><italic>2. While this manuscript was under review, another manuscript concerning fly walking has been accepted for publication. Wosnitza A, Bockemühl T, Dübbert M, Scholz H, Büschges A. Inter-leg coordination in the control of walking speed in Drosophila. J Exp Biol. 2012 Oct 4. [Epub ahead of print] PubMed PMID: 23038731. This paper should be cited and the results contrasted; in particular a contrast should be made between leg amputation and sensory inactivation as a means of exploring how sensory inputs interact with the locomotory CPGs. In that manuscript all speed data are shown in body lengths so comparisons will be difficult but should nevertheless be made</italic>.</p><p>We now cite Wosnitza et al several times in the manuscript (in the Introduction and Discussion) and mention how their leg amputation data fit with our findings.</p><p><italic>3. There were significant concerns about some of the data analysis</italic>.</p><p><italic>a) The authors should more explicitly rationalize splitting the distribution of Figure 2A into three speed groups. (An external reviewer wrote “Although the data showing differences between fast and slow animals are fairly convincing I'd like to see a rationale for dividing the animals in 3 groups (why not 2 groups: above and below average; or 4 groups?). The way they group animals is going to affect their statistical analysis.”)</italic>.</p><p>There are several independent results in our paper that support the division of the data into three speed groups. Perhaps the most clear of these are the data presented in Figure 5B (footprint alignment vs speed), where the data naturally fall into three distinct groups (<20 mm/sec, >20 to <34 mm/sec, and >34 mm/sec). Second, there is a dip in the number of flies walking at average speed values of ∼20 mm/sec (Figure 2A). Third, the stance linearity data shown in Figure 3C argue that speeds >34 mm/sec behave differently than speeds < 34mm/sec. Fourth, normal probability plots are linear between ∼20 and ∼35 mm/sec, but show non-linearity above and below this range (see our response to comment 3d, below, to see this plot). Thus, the two speeds (∼20 and ∼34 mm/sec) that define our three speed groups were chosen due to non-linear or discontinuous behaviors of the data derived from four independent readouts.</p><p>Although the data supporting these three speed groups are in separate figures, we now explicitly describe this early in the paper when it is first raised, referring briefly to the later figures.</p><p><italic>b) The distribution of Figure 2A does suggest a break at a speed of 20mm/s. Is there a statistical test that can verify this split in the distribution or can more animals at low speed be measured so that a clear break can be seen</italic>?</p><p>We carried out multiple statistical tests to determine if the speed distributions shown in Figure 2A fit a normal distribution. In all cases, the tests (Kolmogorov-Smirnov, D’Agostino and Pearson, and Shapiro–Wilk) failed to support a significant deviation from a normal distribution, which is what we reported in the paper. However, as detailed below (in response to comment d), a normal probability plot revealed that the tails of the distribution have significant outliers. Notably, deviations from linearity in these plots occur at ∼20 and ∼34 mm/sec (see plot below).</p><p>Because of the nature of the speed distribution data, it is very difficult to obtain additional videos of flies walking at slow speeds – approximately 8 of 10 videos show average speeds >20 mm/sec – making it impractical to increase the numbers sufficiently to improve the statistical analysis.</p><p>In summary, although the full speed data set fits the strict definition of a normal distribution, speeds at ∼20 mm/sec are nevertheless underrepresented. A normal probability plot reveals that the data below and above 20 and 34 mm/sec, respectively, are not well modeled by a normal distribution. All of this is now better explained in the paper.</p><p><italic>c) In the Materials and methods the authors state: “Because of the different behaviors of the slow, medium, and fast speed groups, some parameters were compared between individual speed groups. In these cases, the data were presented as box and whisker plots. When the data sets had a normal distribution, a Student's t-test was used. Otherwise a Mann-Whitney U-test was used.” There was general concern that these statistical methods were not adequate and that some form of ANOVA (or equivalent non-parametric test), followed by appropriate post-hoc tests, should be pursued</italic>.</p><p>We revised our statistical analysis when comparing measurements between the three speed groups. For group comparisons that seem to have normal distributions, we used one-way-ANOVA followed by Tukey’s post hoc test for pairwise comparisons. For group comparisons where the distributions seemed non-normal, a Kruskal–Wallis test followed by Dunn’s post hoc tests for pairwise comparisons were used. Moreover, comparisons between AEP and PEP (within the same speed group) were revised taking into account that these two parameters are paired (i.e., the same video generated one AEP and PEP value). Depending on whether the groups had normal or non-normal distributions, we used the paired t-test or Wilcoxon signed rank test, respectively.</p><p>Accordingly, we updated the <italic>Statistical analysis</italic> section in the <italic>Materials and methods</italic>, the figures, and figure legends. Importantly, these changes do not alter our conclusions.</p><p><italic>d) The legend to Figure 2A states: “Speed histogram of 71 videos recorded for wild type flies, with 2 mm/s bins. Average speeds vary between 7.2 and 44.7 mm/s, with 28 mm/s the most represented speed. Although the data in this histogram fit a normal distribution, speeds of 20 mm/s are underrepresented. Additionally, both tails of the distribution curve have significant outliers (data not shown).” How were outliers determined and on what basis were they then excluded</italic>?</p><p>No data were excluded from this analysis.</p><p>The statement that “both tails of the distribution curve have significant outliers” was based on a normal probability plot. For a normal distribution, these plots should be linear. However, for the speed data shown in Figure 2A, the data are linear for speeds between 20 and ∼34 mm/sec, but then non-linear for speeds <20 mm/sec and >∼34 mm/sec. Interestingly, the transitions from linearity match very closely to the divisions between the three speed groups as defined above, providing additional support for these three speed groups. The normal distribution plot is shown below.<fig id="fig8" position="float"><graphic xlink:href="elife00231f008"/></fig></p><p>For simplicity, we have clarified the above points in the text, and refer to the normal probability plot as ‘data not shown’ in the <italic>Statistical analysis</italic> section in the <italic>Materials and methods</italic>.</p></body></sub-article></article>