We developed a head-fixed sensory-guided locomotion task compatible with high-throughput, three-dimensional behavioral tracking (Video 1). Using a custom running wheel (Warren and Hoffman, 2018) with a transparent floor and a mirror mounted inside at 45°, we reconstructed the body pose in three dimensions with a high-speed camera (Figure 1A–B). DeepLabCut software (Mathis and Warren, 2018; Mathis et al., 2018) was used to automatically track the paws and tail with high accuracy (Figure 1B; Materials and methods). We also developed custom neural network tools to determine when the paws and whiskers contacted the obstacle (the latter was determined using an additional high-speed camera focused on the whiskers) (Figure 1B–C, Figure 1—figure supplement 1F–K; Materials and methods).

Mice ran in a dark enclosure, receiving a water reward every 5.4 m. Their running intermittently activated an illuminated cylindrical obstacle ~30 cm away that moved towards them at a speed matching that of the wheel (Figure 1A; Figure 1—figure supplement 1A–B). Hence, this closed-loop setup simulates stepping over a stationary object. The obstacle was visible for several hundred milliseconds before it reached the mouse (0.67 ± 0.39 s depending on the speed of locomotion), such that mice had the opportunity to make visually driven preparatory gait modifications like those described in other species (Drew et al., 1996; Higuchi, 2013). To reduce the predictability of the obstacle’s position, on each trial we randomized the obstacle height (4–10 mm), the wheel distance necessary to engage the obstacle (1.8 ± 0.1 m), and the distance of the obstacle to the mouse when it started moving (0.31 ± 0.015 m).

Some mice stepped over the obstacle from the very first trial, whereas others tended to grasp it. We therefore adopted a training regimen to discourage grasping in which contact with the obstacle triggered a break of the wheel and a loud auditory stimulus (Materials and methods). After training (~2 weeks), mice successfully cleared the obstacle on a large fraction of trials irrespective of obstacle height (Figure 1D; success defined as <= 20 ms of paw contact with the obstacle in a trial).

Notably, mice maintained high running speeds even as they stepped over the obstacle (Figure 1E, Figure 1—figure supplement 1A; Video 2). In past research in humans (van Hedel et al., 2002), cats (Drew et al., 1996), rats (Sato et al., 2012; Setogawa et al., 2015), and mice (Asante et al., 2010), obstacle avoidance was studied at substantially slower speeds associated with walking gaits. Mice in our paradigm ran ~5 times faster than the mice in Asante et al., 2010 and ~30% faster than the cats in Drew et al., 1996. Clearing obstacles at high speeds may require behavioral strategies and neural mechanisms that are distinct from those studied previously.

We characterized the three-dimensional kinematics of all four paws as they cleared the obstacle. To allow direct comparison with past studies in freely moving rodents, we analyzed kinematics in ‘un-head-fixed’ coordinates by subtracting the displacement of the wheel from the positional measurements (Video 1). Kinematics thus represent locomotion as if mice were moving forward in space. Kinematic analysis revealed that mice ran mostly in a trot pattern, wherein diagonal pairs of limbs move together but are antiphase with the opposite pair (Figure 1H; Bellardita and Kiehn, 2015; Machado et al., 2015). Consistent with previous studies of obstacle avoidance in freely moving rodents (Aoki et al., 2012; Sato et al., 2012; Setogawa et al., 2015), this pattern was usually maintained during obstacle clearance, such that paws cleared the obstacle sequentially: leading forelimb (LF), trailing forelimb (TF), leading hindlimb (LH), and trailing hindlimb (TH) (Figure 1F–H).

Mice made large adjustments to the trajectories of all paws as they cleared the obstacle, lifting them 2–3 times higher (Figure 1—figure supplement 1D; p<10⁻¹⁰ for all paws) and extending them further (Figure 1—figure supplement 1E; LF, p<0.05; TF, p<0.001; LH, p<0.01; TH, p<0.01) than control steps (Figure 1G; control steps [dashed gray traces] are those that occurred prior to whisker contact with the obstacle). Furthermore, we found a clear relationship between obstacle height and paw height, with mice stepping higher to clear higher obstacles (Figure 1G,I,J; Asante et al., 2010; Aoki et al., 2012; Sato et al., 2012). Collectively, these results indicate that head-fixed mice perform a rapid sensorimotor transformation in which information about the location and height of an obstacle is transformed into graded adjustments of the kinematics of all four limbs to avoid collision.

Although research on sensory-guided locomotion in humans and cats has focused largely on vision (Drew et al., 1996; McVea and Pearson, 2009), rodents may rely on additional sensory modalities such as whisker-mediated somatosensation (Kleinfeld et al., 2006; Grant et al., 2012). Hence, we next explored the contributions of both whiskers and vision to head-fixed obstacle clearance (experiment summarized in Video 3).

Mice were trained and tested with the obstacle illuminated on half of the trials (randomly interleaved) and the other half occurring in complete darkness (Figure 2A). When the obstacle could be sensed with both the eyes and whiskers (as in Figure 1), mice successfully cleared it at high rates (Figure 2B, green), adjusted the height of their paws corresponding to the height of the obstacle (Figure 2C–D, Figure 2—figure supplement 1B), and ran at high speeds (Figure 2E). However, they slowed down as they approached the obstacle (Figure 2E). Remarkably, without visual input mice maintained this level of performance without slowing down (Figure 2E). In the absence of visual input mice cleared the obstacle at similar rates (Figure 2B; p=0.19), matched the height of the paws to the height of the obstacle to a similar extent (Figure 2C–D, Figure 2—figure supplement 1B; p=0.57 for the leading forepaw), and maintained high speeds even as they cleared the obstacle (Figure 2E).

We next tested whether mice require whiskers to perform the task. All whiskers were trimmed on both sides of the face after mice reached steady-state performance. Although the trident whisker could not reach the obstacle, we trimmed it as well. When the obstacle was visible but mice had no whiskers, success rates dropped (Figure 2B; p<0.01) despite no significant change in the average height of steps over the obstacle (Figure 2C, Figure 2—figure supplement 1A). Rather than affecting overall step height, whisker trimming abolished the correlation between the height of the leading forepaw and that of the obstacle (Figure 2C–D, Figure 2—figure supplement 1B; p<0.001), suggesting that whiskers – rather than vision – are necessary for estimating obstacle height. In a separate set of experiments, performance was assessed as whiskers were gradually trimmed. The ability to adjust the paw height to the height of the obstacle required more than one whisker (Figure 2—figure supplement 1D), and the accuracy of paw landing positions deteriorated as more whiskers were trimmed (Figure 2—figure supplement 1E). These results suggest mice combine information from multiple whiskers to determine both the height and horizontal position of the obstacle.

Whereas whiskers provide high-fidelity information about nearby objects, vision may be used to detect objects at a distance and drive preparatory changes. In humans, for example, vision is thought to guide positioning of the trailing foot at an appropriate distance relative obstacles (Chou and Draganich, 1998; Patla and Greig, 2006). Interestingly, mice with whiskers but no vision positioned their trailing forepaw more accurately (with less variability) compared to mice with vision only (Figure 2F, Figure 2—figure supplement 1C; p<0.05), suggesting that although whisker information only becomes available at the last moment, mice can quickly respond with accurate modifications.

Finally, with whiskers trimmed and lights off, mice ran at least as fast relative to the vision and whiskers condition (Figure 2E; p=0.11). However, mice were no longer able to successfully clear the obstacles (Figure 2B; p<0.01) or match the height of their paws to the height of the obstacle (Figure 2C–D, Figure 2—figure supplement 1B), ruling out roles for other sensory modalities. Overall, these results demonstrate that whisker somatosensation is sufficient to drive rapid behavioral modifications during obstacle clearance.

Relying on whisker input to clear obstacles at high speeds seemingly poses a challenge. At the moment of whisker contact, a paw will intercept the obstacle within ~63 ms if no modifications are made, and the closest paw is only ~32 mm away from the obstacle (pooled across trials with and without vision) (Figure 3—figure supplement 1A; Materials and methods). Moreover, the state of the body is highly variable across trials at whisker contact (Figure 3—figure supplement 1B), implying that mice must rapidly integrate whisker sensory input with information about the state of the body to execute appropriate responses.

Kinematic analysis of many trials revealed distinct strategies mice use to clear the obstacle (Figure 3A–B; Video 4). We focus our analysis on the forepaw in swing at whisker contact, as this paw is in the most immediate danger of colliding with the obstacle absent kinematic modifications. On some trials, this paw continues along its expected trajectory, whereas on other trials the step is shortened or lengthened relative to control steps. On shortened trials, the paw is placed in front of the obstacle such that the opposite paw can step over first, and on lengthened trials the paw usually (on 74% of trials) clears the obstacle in one large step. These step modifications were initiated rapidly. Within less than 30 ms of whisker contact the kinematic trajectory of the forepaw changed significantly (Figure 3—figure supplement 1C; Materials and methods). These strategies were no longer apparent with whiskers trimmed and lights off (Figure 3G, bottom). Both strategies emerged with or without vision (as long as whiskers were present; Figure 3G); we therefore pooled both trial types for these analyses.

We explored whether the strategy used on a given trial (lengthening vs. shortening the step of the paw in swing at whisker contact) is systematically related to the state of the body and/or the position of the obstacle. We identified features of the body state and obstacle position that are predictive of whether mice shortened or lengthened their step (omitting trials where no modifications were made [Materials and methods]) by sequentially adding them to logistic Generalized Linear Models (GLMs) based on their ability to improve the models’ accuracy (Materials and methods). Consistent with a deterministic decision-making process, models predicted the behavioral strategy with 73.0% accuracy using eight predictors (Figure 3C). Artificial neural networks trained with the same predictors had comparable accuracy of 73.1%, suggesting that the decision underlying this behavior may obey a relatively simple logic. Mice were less successful on trials in which their decision violated the predictions of the model (Figure 3—figure supplement 1E; p<0.001), suggesting that correct decision-making facilitates obstacle clearance.

The top features selected by the model suggest that the decision is influenced by both the state of the body and the position of the obstacle (Figure 3C,D). The top two features were obstacle proximity (the horizontal position of the obstacle at whisker contact; blue) and the horizontal paw position (orange). Mice are more likely to lengthen their step when whisker contact occurs early in the swing phase (when the horizontal paw velocity [purple] is low and the horizontal paw position [orange] is further back) and when mice are running faster (yellow). Furthermore, mice are less likely to lengthen their step when the obstacle is high (magenta) and far away (blue).

To better understand why mice lengthen or shorten their step on a given trial, we plotted kinematics for the forepaw in swing at whisker contact binned by the likelihood of the step being lengthened (Figure 3E). We compared these to the kinematics we would expect if no modifications were made (dashed gray traces; Materials and methods). A clear pattern emerged: the closer the paw would have landed to the obstacle, the more the step is shortened. However, when the paw would have landed very close to or beyond the obstacle, mice tend to extend the paw to clear it in one large step. This decision-making threshold can be clearly visualized by plotting the landing position probability distributions conditioned on the landing position expected if no modifications are made (Figure 3F).

The integrity of this decision-making process depends upon the whiskers. When mice have vision but no whiskers their paw no longer lands cleanly in front of or beyond the obstacle. Rather, the bimodal landing position distribution, which is characteristic of the decision-making process, becomes diffuse (Figure 3G). Furthermore, models trained on whiskers-only trials were less accurate when evaluated on light-only trials, suggesting that the decision-making process is meaningfully altered (Figure 3—figure supplement 1F, p<0.05, ‘W’ vs. ‘W → L’ conditions). Consistent with this interpretation, mice no longer lengthen or shorten their step based on where it would have landed if no modifications were made (Figure 3H; the predicted [x axis] and actual landing distance [y axis] are similar, such that the landing position distribution clusters around the unity line). Thus, although mice still make kinematic modifications with vision only, these modifications are less accurate and no longer evince the fast decision process that occurs when whiskers are present. Although we cannot rule out the possibility that vision could be utilized in more naturalistic, head-free circumstances, these results demonstrate that whiskers are sufficient for driving a rapid, body-state dependent sensorimotor transformation.

Finally, mice with neither whiskers nor vision fail to make modifications except when their paws collide with the obstacle (Figure 3G–H), and models trained on these trials have reduced accuracy (Figure 3—figure supplement 1F, p<0.05), ruling out roles for other sensory modalities. Collectively, these results show that mice lengthen or shorten their steps to avoid collision with the obstacle, choosing between these strategies by integrating information about their body state with sensory information obtained from the whiskers.

Sensorimotor transformations are commonly thought to involve signals passing from sensory to motor areas of the cerebral cortex (see e.g. Drew and Marigold, 2015). Given the importance of whiskers in our task, we lesioned vibrissal primary sensory cortex (‘barrel cortex’) to examine its role in obstacle clearance behavior (Figure 4A, Figure 4—figure supplement 1A; Materials and methods). Mice performed the task in complete darkness with whiskers trimmed on one side of the face (Figure 2—figure supplement 1D–E). Ipsilateral lesions served as a control because barrel cortex receives information from contralateral whiskers. Because ipsilateral lesions had little impact (Figure 4—figure supplement 1B), we analyzed pooled contralateral and bilateral lesions (n = 8).

Barrel cortex lesions had small effects on obstacle clearance the following day that recovered within 4 days (Video 5). In animals with contra- or bilateral lesions (n = 8), there were no effects the first day post-lesion on basic locomotion, including running velocity, the height of the body above the wheel, and body angle (Figure 4B). Obstacle clearance rates decreased slightly the day immediately following contralateral lesions but recovered to baseline levels within 4 days (Figure 4C–D). The early effects appear to be due to decreased paw heights during obstacle clearance, which recovered on the same timescale as success rates (Figure 4E–F). Notably, contralateral barrel cortex lesions had no effect on the correlation between leading forepaw and obstacle height (Figure 4G–H).

Finally, we trimmed all whiskers to determine whether mice learned to compensate for the absence of barrel cortex by relying on other sensory modalities. As with non-lesioned animals (Figure 2), whisker trimming significantly decreased success rates (Figure 4D), drove decreases in the height of the leading forepaw as it approached the obstacle (Figure 4F), and abolished the correlation between the paw and obstacle height (Figure 4H). Collectively, these results indicate that barrel cortex is dispensable for this task. Due to the initial decrease in some performance measures, it is impossible to completely rule out involvement of barrel cortex in the pre-lesioned state. However, we suspect that these may be non-specific effects given that similar changes were observed after ipsilateral control lesions (Cohen’s d = 0.062 for control lesions vs. 0.133 for contralateral/bilateral lesions; compare Figure 4C and Figure 4—figure supplement 1B).

Manipulations of motor cortex are known to affect obstacle avoidance in cats (Beloozerova and Sirota, 1993; Drew et al., 1996) and freely moving rodents (Asante et al., 2010). However, the rapid obstacle avoidance described here involves distinct behavioral strategies and may therefore depend on different brain regions. We thus pharmacologically silenced and lesioned motor cortex to determine its necessity in our task.

Silencing forelimb motor cortex impaired obstacle clearance as well as basic aspects of head-fixed locomotion (Video 6). After unilateral injections of muscimol in the M1 rostral forelimb area (Tennant et al., 2011; Figure 5A) mice ran slower, lifted their paws to a lesser extent, and exhibited changes in gait such that the base of the tail was lower and angled contralateral to the side of the injection (Figure 5B, left, Figure 5—figure supplement 1A; p<0.05 for velocity, body angle, and tail height). Obstacle clearance also suffered considerably. Paws contacted the obstacle more frequently across obstacle heights (Figure 5C, left, Figure 5—figure supplement 1B–C), both because they were not lifted high enough (Figure 5E, Figure 5—figure supplement 1D–E) and because they tended to grab the obstacle (Figure 5—figure supplement 1F). The correlation between the height of the leading forepaw and that of the obstacle also decreased significantly (Figure 5D, left; p<0.01). The effects on paw height were greatest for the contralateral hindlimb (Figure 5—figure supplement 1D; p<0.001), and among the forepaws only the contralateral side had significantly decreased success rates (Figure 5—figure supplement 1B).

The effects on obstacle clearance persisted even when controlling for changes in baseline locomotion. We identified pairs of manipulated and control trials that were matched in aspects of baseline locomotion for each mouse (the top 20% of trials that were best matched by running velocity, body angle, and tail height at whisker contact [Materials and methods]). In this subpopulation of trials, locomotion in control and manipulated conditions was indistinguishable at whisker contact (Figure 5B, right; p=0.64, 0.47, and 0.47, for velocity, body angle, and tail height, respectively). Nonetheless, success rate and paw height deficits remained, consistent with a direct contribution of motor cortex to obstacle clearance (Figure 5C,E, Figure 5—figure supplement 1B–E; success rate: p<0.05; paw height: p<0.05 for ipsilateral forelimb and p<0.01 for other paws). A slight reduction in the paw-obstacle height correlation was still seen in matched trials, but this trend was not statistically significant (Figure 5D, right; p=0.22).

Unilateral lesions affected behavior similarly to muscimol (Figure 5F–J, Figure 5—figure supplement 1; Video 6). There were deficits in success rates, paw heights, and paw height correlations in addition to deficits in baseline locomotion (Figure 5G–J, left, Figure 5—figure supplement 1). The effects on obstacle clearance remained in subsets of pre- and post-lesion trials matched for characteristics of baseline locomotion (Figure 5G–I, right, Figure 5—figure supplement 1). These effects on obstacle avoidance are generally consistent with a previous study in freely moving mice (Asante et al., 2010). Notably, performance largely recovered over a week (Figure 5K). Further studies will be required to determine whether the deficits observed immediately after motor cortex manipulations reflect a genuine role for motor cortex in sensory-guided locomotion or acute off-target effects (Kawai et al., 2015; Otchy et al., 2015).

We next asked whether manipulations of primary motor and sensory cortices affect the decision to lengthen or shorten strides to clear obstacles.

After motor cortex lesions, the forepaw in swing at whisker contact was still lengthened or shortened to avoid collision with the obstacle (Figure 6A), and the body state and obstacle position remained important determinants of the chosen strategy: models trained on pre- and post-lesion sessions were comparably accurate in predicting behavior (Figure 6—figure supplement 1A; p=0.06), mice were still more likely to lengthen steps if their paw would have landed closer to the obstacle (Figure 6B), and the relationships between obstacle position, body state, and behavioral strategy were similar (Figure 6C). We observed the same general pattern of results for muscimol injections into motor cortex, other than a small decrease in model accuracy (6.1% decrease; Figure 6—figure supplement 1C–G; p<0.05). The paw in swing at whisker contact tended to land closer to the obstacle in both muscimol and lesion conditions (Figure 6—figure supplement 1B,D), an effect that is likely attributable to execution deficits similar to those described above (Figure 5). It appears that mice still decide to lengthen or shorten their steps, as evinced by the bimodality of landing position distributions (Figure 6A,B), but are less capable of executing the chosen strategy.

Barrel cortex lesions also had minimal impact on the decision-making process. The forepaw in swing at whisker contact was still lengthened or shortened to avoid collision with the obstacle (Figure 6D); models trained on pre- and post-lesion sessions were comparably accurate in predicting behavior (Figure 6—figure supplement 1H; p=0.67); mice were still more likely to lengthen steps if their paw would have landed closer to the obstacle (Figure 6E); and the relationships between obstacle position, body state, and behavioral strategy were similar (Figure 6—figure supplement 1J). Lesions caused the forepaw in swing at whisker contact to land closer to the obstacle (Figure 6—figure supplement 1I), but this effect mostly recovered after 4 days, the same period over which other small performance deficits recovered (Figure 4). Finally, after complete whisker trimming the landing position of the paw was not modified to avoid contact with the obstacle (Figure 6D), the paw landed much closer to the obstacle (Figure 6—figure supplement 1I), and the accuracy of the models decreased significantly (Figure 6—figure supplement 1H; p<0.01). This verifies that mice did not learn to use a different sensory modality to guide decision-making.

Whisking is important for guiding locomotion in rodents (Grant et al., 2012; Arkley et al., 2014; Sofroniew et al., 2014; Grant et al., 2018). Whiskers span the ground where rodents subsequently place their forepaws (Grant et al., 2018) and become protracted during high speed locomotion, suggesting they serve as collision detectors (Arkley et al., 2014; Sofroniew et al., 2014). Our results support this hypothesis, demonstrating that mice use whiskers to detect obstacles, determine their location, and drive rapid responses to avoid collision even at high speeds.

Although mice had access to visual information several hundred milliseconds prior to whisker contact, the only visually guided anticipatory adjustment we observed was a reduction in running speed on light-on trials. Hence the strategies for obstacle avoidance adopted by mice in the present task contrast with the precise visually guided adjustments of gait, for example of the positioning of the penultimate step, observed in humans (Higuchi, 2013) and cats (Drew et al., 1996). We cannot rule out that head-fixation and the absence of full-field optic flow in our task disrupted the normal use of vision. Nevertheless, given that mice are largely nocturnal and have relatively poor visual spatial acuity, the rapid whisker-based gait adjustments observed here are likely to be ethologically significant.

What brain regions transmit the whisker information required for obstacle clearance? Barrel cortex neurons are capable of responding to whisker input at short latencies (as fast as 10 ms) (Crochet and Petersen, 2006; Constantinople and Bruno, 2013; Yu et al., 2019), exhibit locomotor-related activity (Ayaz et al., 2019), and send projections to subcortical motor areas (Aronoff et al., 2010; Bosman et al., 2011). Moreover, barrel cortex has been implicated in other forms of whisker-guided locomotion (e.g. wall-tracking) (Sofroniew et al., 2015; Ayaz et al., 2019). Although recent work has shown that barrel cortex is dispensable for object detection (Hong et al., 2018), obstacle clearance additionally requires determining the location of the obstacle by integrating information across multiple whiskers and rapidly adjusting motor output accordingly. Nonetheless, mice were able to perform the task without barrel cortex. Lesions caused minor decreases in success that recovered within 4 days, and the correlation between the height of the leading forepaw and that of the obstacle was unaffected. It cannot be ruled out that the initial performance decrease reflects a role for barrel cortex in the pre-lesioned state. However, control lesions also caused minor decreases in performance, suggesting that the effects of barrel cortex lesions are at least partially attributable to non-specific effects of the manipulation. Further work is needed to resolve this question fully.

The barrel cortex lesion results suggest that subcortical structures are sufficient for transmitting whisker sensory information in this task. Although whisker sensory input is widely distributed in the rodent brain (Kleinfeld et al., 1999; Aronoff et al., 2010; Bosman et al., 2011), two particularly intriguing candidate structures are the cerebellum and the spinal trigeminal nucleus (Sp5). The cerebellum receives extensive whisker somatosensory (Yatim et al., 1996; Rancz et al., 2007; Bosman et al., 2011; Proville et al., 2014) and locomotor input (Arshavsky et al., 1972a; Arshavsky et al., 1972b; Armstrong, 1986; Powell et al., 2015), projects to subcortical regions involved in locomotor control (Armstrong, 1986; Capelli et al., 2017; Fujita et al., 2020) as well as directly to the spinal cord (Liang et al., 2011), has been implicated in normal and skilled locomotion (Armstrong and Marple-Horvat, 1996; Morton et al., 2004; Aoki et al., 2013; Darmohray et al., 2019), and has been proposed as a fast route by which sensory-guided behaviors may be coordinated (Stein and Glickstein, 1992). Sp5 provides what is likely to be the fastest route between whisker input and motor output; a subpopulation of Sp5 neurons (mainly located within the oralis and interpolaris subdivisions) has been shown to project monosynaptically to forelimb motor neurons (Esposito et al., 2014). Although precise patterns of muscle activation may not be computed in Sp5, such a direct pathway appears well suited to rapidly trigger spinal programs for obstacle avoidance, as discussed further below.

We found that obstacle avoidance involves a rapid sensorimotor decision wherein mice lengthen or shorten their steps depending on the state of the body and a whisker-derived estimate of the obstacle location. Although such sensorimotor decisions are presumably vital for fast locomotion over complex terrain, to our knowledge they have not been thoroughly studied in the laboratory. It is notable that while the logic underlying the decision process is relatively simple, revealing it involved the analysis of a large amount of high-dimensional data (>100,000 trials and >150 million video frames). Hence the present results highlight the power of machine learning for the quantitative analysis of behavior (Krakauer et al., 2017; Datta et al., 2019).

The cerebral cortex is widely associated with cognitive and perceptual decision-making (Gold and Shadlen, 2007; Cisek and Kalaska, 2010; Carandini and Churchland, 2013) and is thought to coordinate online responses to perturbations during reaching movements in primates (Nashed et al., 2014; Gallivan et al., 2018). Nonetheless, cortical manipulations had minimal effects on the decision underlying obstacle clearance. A subcortical locus for the decision is consistent with its rapidity and fits with a rich body of literature supporting the view that even ‘low-level’ reflexes possess considerable sophistication (Poppele and Bosco, 2003; Rossignol et al., 2006). Spinal reflexes are adaptively modulated during locomotion by muscle and skin afferents in a manner that depends upon the phase and speed of locomotion (Grillner, 1975; Rossignol et al., 2006), reminiscent of the phase and speed dependency we observed. Remarkably, even the basic motor program for stepping over obstacles may be present in the spinal cord, as shown by studies of spinalized cats (Forssberg et al., 1975; Forssberg et al., 1977). Projections from brainstem vestibular nuclei are also known to drive contextually appropriate corrective modifications during locomotion (Pompeiano and Allum, 1988; Murray et al., 2018). Similarly here, hindbrain pathways may transmit descending whisker signals that are integrated with information about the state of the body within spinal circuits. The decision-making process would thus be defined by feedback rules governing the relationship between whisker sensory input, body state, and subsequent locomotor modifications.

All experimental protocols were approved by the Columbia University Institutional Animal Care and Use Committee (protocol AC-AABG4566). Adult male wild-type mice (C57BL/6) aged >8 weeks postnatal were used for all experiments. Mice were purchased from Taconic Biosciences (Hudson, NY) and housed in an on-site animal facility on a 12 hr light-dark cycle. Experiments occurred during the light cycle.

Mice received subcutaneous injections of sustained release buprenorphine (0.75 mg/kg) the morning of surgery. Mice were subsequently anesthetized with isoflurane (1.5–2%) and placed in a stereotaxic frame. The skull was exposed and a custom steel headplate (5 × 25 × 1 mm) was attached to the skull with dental cement such that the surface of the headplate was parallel with the horizontal plane connecting bregma and lambda. Mice recovered for 3 days before experiments began.

Four days after surgery mice began water deprivation and habituation to head fixation on the running wheel. Mice were head fixed on the wheel once daily for about 30 min and were rewarded with drops of water for moving forward on the wheel. On the first day, 0.1–0.3 m of forward movement was rewarded. This amount was increased over ~6 days to 5.4 m (nine wheel rotations), which took ~5–9 days. The position of the head was sometimes fixed further down (ventral) or back (posterior) to encourage running early in training. The head position was gradually adjusted (~1–2 mm per day) until it reached a standard position that was the same for all mice.

Obstacle training then began. Three obstacles were introduced between every reward (Figure 1—figure supplement 1A). The movement of the obstacle was matched to the movement of the wheel to simulate moving toward a stationary object. The three obstacles started moving 0.9, 2.7, and 4.5 m after the previous reward, but the positions were jittered ±100 mm (uniformly sampled on this interval) to prevent mice from memorizing the position at which the obstacles arrive. Obstacles were 0.31 m away from the mouse when they started moving; this distance was jittered ±15 mm (uniformly sampled on this interval). The height of the obstacle (the vertical distance between the highest point on the wheel and the highest point of the obstacle) was randomized uniformly across trials between 4 and 10 mm. The behavior occurred in darkness other than a light emitted from the inside of the obstacle that was engaged in a random 50% of trials unless otherwise stated.

To encourage mice to step over (rather than on) the obstacle, it was equipped with a capacitive touch sensor that detected when mice grabbed it. Grabbing the obstacle triggered a white noise auditory stimulus and engagement of a solenoid break that prevented the wheel from moving. Mice were trained daily with obstacles for ~1–2 weeks until performance stabilized, at which point wheel breaks were rarely triggered (~3% of trials). Experiments then began. Mice unable to run quickly on the wheel (>0.3 m/s after ~2 weeks of training) were excluded from all experiments (~15% of mice). For all training and experiments mice performed one session per day.

After the final session, mice were anesthetized with ketamine/xylazine and perfused with 4% paraformaldehyde. Brains were sectioned at 100 μm using a cryostat and stained with DAPI. Cortical lesion sizes and locations were determined by tracing lesion boundaries in ImageJ (Schindelin et al., 2012) and plotting them on a schematic of the mouse brain using custom MATLAB software.

After training mice to perform the task, a ~0.5 mm diameter craniotomy was performed unilaterally over the left or right forelimb motor cortex (1.5 mm lateral, 0.25 mm anterior of bregma) while mice were anesthetized with isoflurane gas. Craniotomies were covered with Kwik-Sil (World Precision Instruments). After 1–2 days of additional training, mice were placed on the wheel while 74 nL total volume of either muscimol (5 μg/μL in saline) or saline was injected at depths of 400 and 700 microns beneath the surface of the brain using a Nanoject II (Drummond). Mice were taken off the wheel for 20 min before the behavioral session commenced. Each mouse received two alternating sessions each of muscimol and saline, with the order counterbalanced across mice (with one session per day). The two sessions for each condition were pooled for all analyses.

We used the Kinemouse Wheel (Warren and Hoffman, 2018) to capture two orthogonal views of the mouse simultaneously at 250 Hz. We trained a single DeepLabCut network (Mathis and Warren, 2018; Mathis et al., 2018) to track the positions of body parts and the obstacle in both views, and then stitched the tracking together to reconstruct the body pose in three dimensions. In both the top and bottom view, we tracked all four paws, the base of the tail, the middle of the tail, the nose, and the obstacle.

We initially trained the model on ~200 frames that were labeled using a custom MATLAB GUI. We then manually identified frames with erroneous tracking, corrected these frames, included them in an expanded training set, and retrained the model. This process was repeated until the model was highly accurate and the training set consisted of ~1000 images. The final model’s average error was 1.02 pixels (0.27 mm) on the training set and 2.29 pixels (0.60 mm) on the test set.

To further enhance tracking performance, we (1) removed low confidence tracked locations (those beneath a threshold of 0.99), (2) removed tracking when features violated a velocity constraint (i.e. when a feature jumped a large distance in adjacent frames), (3) applied temporal median filtering with a window size of 3 frames, (4) removed tracking when the x position of a feature in the top view was not close to that of the same feature in the bottom view (the x values of the same feature should approximately match because this dimension is shared in the two views), and (5) interpolated the small number of missing values.

To allow direct comparison with freely moving mice, we analyzed kinematics in ‘un-head-fixed’ coordinates by subtracting the displacement of the wheel (as determined by a high-resolution rotary encoder [U.S. Digital S5-720-250-IE-S-B]) from the kinematic measurements. After this transformation, the position of the obstacle is constant, whereas the mouse moves forward in space.

In a subset of mice (n = 4), we assessed performance as whiskers were gradually trimmed (Figure 2—figure supplement 1D). There were six conditions, beginning with all whiskers intact and ending with only the delta whisker remaining on one side (depicted in Figure 2—figure supplement 1D). Specifically, mice were first tested with (1) all whiskers intact; then (2) all whiskers trimmed on one side of the face; then (3) all but columns A-E, rows 1–3, gamma, and delta trimmed; then (4) columns A-C additionally trimmed; then (5) column D additionally trimmed; and finally (6) only the delta whisker remaining. Performance was assessed for a minimum of 3 days in each condition before proceeding to the next condition.

All decision-making analyses focus on the forepaw that is in swing at whisker contact. Because the forepaws are typically out of phase with one another (Figure 1H), there is usually a single forepaw in swing at whisker contact. However, on some trials both forepaws were in swing, or both were in stance. These trials were excluded from all decision-making analyses.

To determine the speed with which mice responded to whisker input we compared the kinematic trajectories of the forepaw in swing at whisker contact to the trajectories that would be expected if no obstacle had been presented (Figure 3—figure supplement 1C). We restricted the analysis to trials in which the paw was mid-swing.

For each trial, we used k-nearest neighbors to collect a family of 40 matched control steps that were as close as possible to the pre-contact kinematic trajectory for the trial (control steps were those occurring before contact with the obstacle). Averaging these steps yields an estimate of what the paw would have done if no obstacle were presented. Subtracting the actual trajectory from this estimate gives us a measure of kinematic change (in x, y, and z) as a function of time relative to whisker contact for each trial.

To verify that these differences did not emerge due to failures in our ability to estimate the kinematics that would have occurred if no obstacle were presented, we performed the same analysis on control steps, using the antepenultimate step to predict what would have happened in the penultimate step before whisker contact. There was very little deviation between the predicted and actual trajectories for control steps (black traces in Figure 3—figure supplement 1C).

Reaction time was estimated as the moment at which kinematics diverged from the control trajectories. For each trial, we determined the moment at which the trajectory deviates more than 2.5 standard deviations from the population of matched control steps for that trial. This yields a median latency estimate of 24 ms. We emphasize that this is only an estimate of the reaction time; errors are introduced both by the estimation of the moment of whisker contact, as well as the estimation of the kinematics that would have occurred if no obstacle were presented.

All statistical comparisons are paired t-tests, with statistical significance denoted as *p<0.05, **p<0.01, and ***p<0.001. To validate the use of parametric statistics, we used Kolmogorov-Smirnov tests to check distribution normality for paw heights, success rates, wheel velocity, body angle, tail height, the landing distance of the paw in swing at contact, model accuracies, and the variability of the trailing forepaw landing distance. We performed these tests on the baseline data collected for the 20 mice included in Figures 1 and 3. We failed to reject the null hypothesis that the data are normally distributed for all measures.