To link the development of the locomotor repertoire to the development of hindbrain V2a neurons and their spinal pathways, we systematically examined the ontogeny of these neurons, their topography and the onset of their projections to the spinal cord until 120 hr post-fertilization (hpf) when larvae are capable of exhibiting both stimulus-evoked crude locomotion that appears by 48 to 72 hpf and more refined spontaneous locomotion that appears by 96 to 120 hpf (Figure 1A) (Drapeau et al., 2002; McLean and Fetcho, 2009).

We first examined the ontogeny of hindbrain V2a neurons from 24 hr post fertilization (hpf) to 120 hpf with time lapse imaging (Figure 1B, n = 8 for each timepoint) of a transgenic line expressing EGFP under the control of the promoter of vsx2, a transcription factor specific to V2a neurons. This transcription factor has been shown to become active after the final cell division (Kimura et al., 2008). Therefore, the onset of EGFP expression in a V2a neuron in our transgenic line indicates the time of differentiation - which we use synonymously with the time of birth - of this neuron. At 24 hpf, one or two pairs of V2a neurons appeared in a segmental fashion (Figure 1B iii, iii’, 24 hpf), and then the number of V2a neurons increased dramatically until 60 hpf (Figure 1B iii, iii’, 60 hpf). After 72 hpf, the V2a cluster showed no drastic visible changes in the overall shape (Figure 1B iii, iii’, 72–120 hpf). As a fish is still only capable of generating crude forms of locomotion at 72 hpf, this suggests that the connectivity of its neurons and their electrophysiological properties need to mature to support refined locomotion.

We then examined where V2a neurons born at different time points resided in the hindbrains of 120 hpf fish that are capable of generating both the crude and refined forms of locomotion. We did this by photoconverting Kaede, a fluorescent photoconvertible protein, which was expressed under the control of the vsx2 promoter. We converted Kaede throughout the fish at different time points (24–96 hpf) in different groups of fish and imaged each of these groups at 120 hpf (Figure 2—figure supplement 1A i, n = 8 for each timepoint). Based on the presence of converted Kaede (shown in magenta), we distinguished neurons that already had Kaede at the time of photoconversion from those that started to express Kaede after the time of photoconversion (Kimura et al., 2006; Caron et al., 2008). In most rhombomeres, V2a neurons born by 24 hpf were located in the most lateral portions of the brain region occupied by this group of neurons (Figure 2—figure supplements 1C and 24 hpf conversion, magenta cells), whereas neurons born afterwards gradually filled up the space medial to the pre-existing ones (Figure 2—figure supplement 1C and 36–72 hpf conversion). Compared to photoconversion at earlier time points, photoconversion at 72 hpf revealed relatively few, if any, unconverted green cells, except in the rostral hindbrain (Figure 2—figure supplements 1C and 72 hpf conversion). This indicated that the neurogenesis of hindbrain V2a neurons was mostly complete by 72 hpf when fish are still only capable of producing the crude locomotion, suggesting that further developments of these neurons are required if they are to contribute to the development of locomotor behaviors.

Hindbrain V2a neurons with spinal projections (Video 1) are more likely to play roles in the development of the locomotor repertoire, therefore we examined the times of birth of these neurons in further detail. First, we focused on large spinal projecting neurons (reticulospinal neurons) that were labeled by dye injection in the spinal cord (Figure 2C–E, n = 4 for each timepoint). These neurons are identifiable across animals and are named based on their rostrocaudal (Figure 2C, left) and dorsoventral positions (Figure 2C, right) (Kimmel et al., 1982; Mendelson, 1986). The dorsal subpopulation of reticulospinal neurons (Figure 2C, MiD2i, MiD3i, RoM2, RoM3 and dorsal MiV1) corresponded to V2a neurons born by 24 hpf (Figure 2D), while the majority of ventral reticulospinal neurons (RoV3, ventral MiV1 and MiV2) corresponded to V2a neurons born by 36 hpf (Figure 2E; Video 2). This birth order is consistent with previous birthdating analysis of reticulospinal neurons based on the incorporation of a marker into replicating DNA (Mendelson, 1986). Other than the large spinal projection neurons that are labeled consistently by dye injection, a majority of the V2a neurons in the caudal hindbrain have also been shown to project to the spinal cord (Kimura et al., 2013) (Video 1). In this region, the cell bodies of the earliest-born V2a neurons were located lateral to the populations born afterwards (Figure 2F). This clear topographical organization allows us to readily identify the earliest-born population and the later-born population in the caudal hindbrain. Notably, this region contained the youngest group of neurons among the hindbrain V2a descending neurons we examined, suggesting their roles in the refined locomotion that develops later (Figures 2F and 48 hpf conversion).

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For the hindbrain V2a neurons to contribute to the development of the locomotor repertoire, they first need to establish connections to spinal neurons. To examine the development of spinal projections we optically back labeled V2a descending neurons from the spinal cord (‘optical backfill’) using photoconversion restricted to the rostral spinal cord (Kimura et al., 2013). This procedure was repeated at multiple developmental time points to examine the developmental sequence of spinal projections (Figure 3A, n = 6 for each timepoint). The optical backfill at 36 hpf labeled the dorsal V2a but not the ventral V2a reticulospinal neurons (Figure 3C) while the backfill at 60 hpf labeled both the dorsal and ventral reticulospinal neurons (Figure 3D), indicating that these neurons had projected to the rostral spinal cord in sequence based on their respective differentiation times. The V2a subpopulation in the caudal hindbrain showed a pattern similar to that indicated by the birthdating analysis (Figure 3E). This indicates that the sequential development of spinal projections based on differentiation time holds true for the caudal hindbrain V2a subpopulation as well. Among these descending neurons, the caudal medial V2a neurons were the last to project to the spinal cord. As these neurons had descending processes in the rostral spinal cord by 84 hpf, it is plausible that they contribute to the refined locomotion that appears as early as 96 hpf.

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Collectively, the results of our developmental analysis showed the timeline of neurogenesis and spinal projections of hindbrain V2a descending neurons relative to the development of the locomotor repertoire. Furthermore, we found that the birth order of these neurons dictated the positions of their cell bodies and the order of their spinal projections. Most importantly, this analysis established a way to identify the birthdates of these neurons based on the positions of their cell bodies. This set the stage for functional analysis of these neurons, wherein we characterized how neurons of different age were recruited during the distinct locomotor patterns that fish develop in sequence so as to examine the functional links between the development of V2a neurons and the development of locomotor behaviors.

To identify functional cell groups related to the crude and refined locomotor behaviors that appear in sequence during development, we examined the recruitment of hindbrain V2a neurons expressing the calcium indicator GCaMP6s (Chen et al., 2013) using whole-hindbrain two-photon volumetric imaging at 2 Hz in 120–132 hpf fish, which are capable of generating both forms of locomotion. Fish were paralyzed and axial motor activity was monitored using glass pipettes attached to motor nerves innervating axial muscles on both sides (Figure 4; Video 3). In this condition, fish spontaneously exhibited episodes of rhythmic axial motor activity (Figure 4A i; Figure 4B i). The maximum beat frequencies of these episodes matched those of slow spontaneous swims generated through tail-restricted movements in unparalyzed fish (12 fish, Figure 4C) (Mirat et al., 2013); Marques et al., 2018), suggesting that these episodes are fictive analogs of refined locomotor behavior. To elicit the crude locomotor behaviors, we used two stimulus paradigms (six fish for each paradigm). The first paradigm made use of a transient electrical pulse delivered to one side of the head to evoke locomotor episodes similar to escapes in unparalyzed condition (Figure 4A i). These episodes started with strong axial motor activity on the side contralateral to stimulation, which is consistent with the powerful whole-body bending away from the stimulus observed at the start of escape (Liu and Fetcho, 1999; Koyama et al., 2016). Maximum beat frequencies during these episodes was also significantly higher than those during spontaneous rhythmic activity (Figure 4C, p<0.001). This is consistent with fast rhythmic bending observed during escape (Mirat et al., 2013); Marques et al., 2018), further suggesting that these episodes correspond to escapes. The second paradigm employed a gradual mechanical stimulus applied to the front of the head to evoke locomotor episodes similar to struggles (Liao and Fetcho, 2008). These episodes exhibited powerful bursting activity (Figure 4B i), consistent with the powerful whole-body bending observed during struggles (Liao and Fetcho, 2008). The maximum beat frequencies of these powerful bouts of bursting activity were clearly lower than those of putative escapes (Figure 4C, p<0.001) and also statistically lower than those of spontaneous slow swims (Figure 4C, p<0.001). They also matched the frequencies previously reported for struggles (Liao and Fetcho, 2008). Peripheral motor nerve recordings from a single site on each side did not allow us to determine if the rostrocaudal patterns of axial motor activity were organized similarly to those observed in unparalyzed fish, but beat frequency and relative strength of axial motor activity were similar to those of axial movements observed in unparalyzed fish. With these three distinct locomotor patterns, we examined not only how hindbrain V2a descending neurons are recruited during crude forms of locomotion that develop early and more refined form of locomotion that develop later but also how the recruitment of these neurons changes in relationship to beat frequency and strength of axial motor activity.

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To examine neuronal recruitment, we first analyzed imaging data, focusing on the V2a descending neurons we had identified based on development. In the electrical shock paradigm (Figure 4A), almost all the identifiable dorsal neurons were recruited during putative escapes but primarily on the side ipsilateral to the leading axial motor activity (Figure 4A iii, blue traces). This is consistent with the primarily ipsilateral projections of these neurons (Kinkhabwala et al., 2011; Kimmel et al., 1982). On the other hand, the ventral MiV1 and caudal medial neurons showed stronger activity during weak spontaneous swimming. In the gradual mechanical stimulus paradigm, a subset of the neurons recruited during putative escapes were recruited when there was strong ‘struggle’ like motor activity on the ipsilateral side (Figure 4B iii-iv, blue traces close to the end). The ventral MiV1 and caudal medial cells were again recruited during weak spontaneous swimming. When we sorted these cell types based on their birthdate and examined their responses during these motor patterns, it became apparent that there was a systematic relationship between recruitment and birthdate (Figures 4C and 6 fish for each paradigm). The early-born group (Figure 4C,<24 hpf) did not show any clear response during spontaneous weak swimming, but many neurons in this group showed significant responses during putative struggles that exhibited strong locomotor activity but with low beat frequency. The responses in a few of these neurons were even stronger during putative escapes that exhibited strong locomotor activity with high beat frequency (Figure 4C, MiD2i, RoM2 and RoM3). The recruitment of the early-born group during both putative struggles and escapes indicates that this group of neurons underlies powerful whole-body movements observed in both struggles and escapes in the unparalyzed condition. The further recruitment of this group during putative escapes also indicates that such enhanced activity may lead to fast cyclic movements of the whole-body as observed in escapes. On the other hand, the intermediate-aged group (Figure 4C 24-36 hpf) showed mixed recruitment patterns; the subgroup in the rostral hindbrain (Figure 4C, RoV3) showed comparably strong activity during putative escapes and putative struggles but not during weak spontaneous swims whereas the subgroup in the middle hindbrain (Figure 4C, ventral MiV1) showed comparably strong activity in all three conditions. In contrast, the late-born group in the caudal hindbrain (Figure 4C 36-60 hpf) showed more activity only during weak spontaneous swimming. Interestingly, during stimulus-evoked strong swims, these neurons showed decreased activity instead, raising the possibility of inhibition during strong swims. Taken together, our findings indicate that a recruitment pattern that is based on the time of differentiation holds true for all the hindbrain V2a descending neurons that we identified based on development; the early-born group is recruited during the crude forms of locomotion (escapes and struggles) that appear early in development whereas the late-born group is recruited during the refined locomotion that appears later in development. This finding, along with the finding that spinal projections develop sequentially, suggests that distinct functional groups emerge in developmental sequence and contribute to the development of locomotor behaviors. Furthermore, the active set of hindbrain V2a neurons switched based on the type of ongoing motor activity, which is reminiscent of the recruitment of spinal interneurons (McLean et al., 2008). This raises the possibility that distinct functional groups of hindbrain V2a neurons may provide excitatory drive to corresponding spinal functional groups.

To see if our observations about neuronal recruitment extend to the complete set of hindbrain V2a neurons, we performed regression analysis (Miri et al., 2011b) (Figure 5). In the electrical pulse paradigm, we used two regressors to distinguish the activity related to shock-induced putative escapes from activity related to weak spontaneous swimming (Figure 5A ii). The activity map for the shock-evoked putative escapes (Figure 5A iii, left; Figure 5A iv, magenta; Video 4, magenta) revealed a large population of recruited neurons on the side contralateral to the stimulus (ipsilateral to the leading side of motor activity), consistent with their primarily ipsilateral projections (Kinkhabwala et al., 2011). This includes the dorsal early-born group and the rostral intermediate group (RoV3) in the rostral hindbrain (Figure 5A v, magenta) and a large number of the lateral early-born neurons in the caudal hindbrain (Figure 5A vi, magenta). The activity map for spontaneous weak swimming revealed the middle intermediate group (ventral MiV1) in the rostral hindbrain (Figure 5A iii, right; Video 4, green) and a large number of the medial late-born neurons in the caudal hindbrain (Figure 5A iii, right; Figure 5A vi, cyan; Video 4, green). For the gradual mechanical stimulus paradigm that produced putative struggles that varied in latency, we constructed three regressors based on the strength of the axial motor activity: two regressors for the strong motor activity for each side to capture the struggle related activity and one regressor for the weak motor activity to capture the activity related to spontaneous weak swims (Figure 5B ii). The activity maps for the strong motor activity (Figure 5B iii, left and right; Figure 5B iv, yellow and magenta; Video 5, yellow and magenta) revealed a large population of neurons ipsilateral to the side of motor activity (Figure 5B iii, left and right) consisting of a subpopulation of the dorsal early-born group, the rostral intermediate group (RoV3) in the rostral hindbrain (Figure 5B v), and a large number of the early-born lateral neurons in the caudal hindbrain (Figure 5B vi, magenta). The map for spontaneous swimming revealed the same set of the neurons shown for spontaneous swimming occurring in the electrical pulse paradigm (Figure 5B iii, middle; Figure 5B iv, cyan; Video 5, cyan). These regression maps were reproducible across fish (Figure 5—figure supplement 1) and further support the differentiation time dependent recruitment pattern revealed by the ROI analysis. The activity maps for putative escapes and struggles were surprisingly similar, especially in the caudal hindbrain (Figure 5—figure supplement 1). This supports the idea that the early-born population contributes to the crude whole-body movements observed in both struggles and escapes. However, this analysis also made it clear that the activity of the early-born group is further enhanced during escapes compared to struggles, especially in the rostral hindbrain (Figure 5—figure supplement 1), suggesting again that further activation of this population leads to fast cyclic movements of the whole-body, as observed in escapes. Interestingly, these maps also revealed a striking functional separation of neuropil in the caudal hindbrain (Figure 5A vi; Figure 5B vi; Figure 5—figure supplement 1C). The neuropil active during weak spontaneous swimming was located lateral to the neuropil active during putative struggles (Figure 5B vi, filled arrowheads; Figure 5—figure supplement 1C, Struggle, arrowheads). This functional segregation is the opposite of the functional segregation of cell bodies in the caudal hindbrain (Figure 5B vi, open arrowheads). This neuropil separation was maintained when additional cells were recruited for shock-induced escapes (Figure 5A vi, filled arrowheads; Figure 5—figure supplement 1C, Escape, arrowheads), suggesting that these additional cells also followed this functional separation. Over all, this raises the possibility that these functional neuronal groups project their descending axons through distinct regions in the neuropil.

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To this point, we showed a correspondence between the birth order of hindbrain V2a neurons and their involvement in distinct forms of locomotion that appear in sequence. This suggests that distinct functional cell groups emerge in the hindbrain and project to the spinal cord in sequence and contribute to the diversification of locomotor repertoire. Furthermore, the similarity in the recruitment of hindbrain and spinal functional groups as well as the spatial segregation of the neuropil of the hindbrain functional groups both raise the possibility that parallel pathways connect matching sets of hindbrain and spinal functional groups. Having established a link between the birth order and function of hindbrain V2a neurons, we next examined the spinal organization of descending processes from hindbrain functional groups.

To understand how the spinal projections from hindbrain V2a neurons are organized to support the development of the locomotor repertoire, we examined if the aforementioned sequentially generated functional groups of hindbrain V2a neurons exhibit distinct innervation patterns to the spinal cord. First, we examined the overall neuropil organization of hindbrain and spinal V2a neurons based on the time of differentiation by photoconverting early-born V2a neurons at 24 and 36 hpf (Figure 6A, n = 6 fish for each timepoint). In the hindbrain (Figure 6A iii), and throughout the spinal cord (Figure 6A iv-vi), the neuropil from V2a neurons born before 24 hpf (Figure 6A iii-vi, magenta arrowheads) was located medial to the neuropil from neurons born after 24 hpf (Figure 6A iii-vi, green arrowheads), consistent with the functional segregation of neuropil we observed in the caudal hindbrain. The neuropil from V2a neurons born before 36 hpf was still located medial to the neuropil from neurons born after 36 hpf (Figure 6A viii-xi, arrowheads). However, it extended more toward the lateral surface than the neuropil from V2a neurons born before 24 hpf (Figure 6A xii). This suggested that axonal processes from new neurons were continuously being added laterally to preexisting processes, forming layers based on the time of differentiation.

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To see if this neuropil organization was maintained in the descending processes of hindbrain V2a neurons, we photoconverted V2a neurons only in the hindbrain (Figure 6B). The Gal4-UAS system was used to gain enough expression of Kaede to be able to visualize the spinal processes of hindbrain V2a neurons. Putative presynaptic terminals were also labeled with synaptophysin-GFP driven by the Gal4-UAS system. To visualize the spinal projections from only the early-born and intermediate-aged hindbrain V2a groups, UV light was shone on the hindbrain at 42 hpf (Figure 6B ii-v, n = 8 fish). This conversion time was selected to account for the delay in the expression of Kaede driven by the Gal4-UAS system and to match the age groups labeled by the photoconversion of Tg(vsx2:Kaede) at 36 hpf. The descending processes from the early-born and intermediate-aged groups reached the caudal part of the spinal cord (Figure 6B v) and were in the medial part of the neuropil in the rostral and middle spinal cord (Figure 6B iii-iv, magenta arrowheads). To visualize the spinal projections from almost all the hindbrain V2a neurons, we photoconverted Kaede in the hindbrain at 96 hpf and imaged them at 120 hpf (Figure 6Bvi-ix, n = 8 fish). The descending processes from hindbrain V2a neurons covered almost all the neuropil region formed by V2a neurons (Figure 6B vii-ix, magenta arrowheads). Comparison of the lateral extents of the descending processes of the early-born and intermediate-aged groups (Figure 6B x, blue lines) and all groups (Figure 6B x, orange lines) indicated that the descending processes from the late-born group were most prominent in the lateral neuropil in the rostral and middle spinal cord. Taken together, these results indicated that the spinal projections from hindbrain V2a neurons were also layered based on the time of differentiation and that the late-born group had shorter descending processes than the early-born and intermediate-aged groups. The difference in the axon length across different age groups is consistent with the pattern previously revealed by single-cell labeling (Kinkhabwala et al., 2011). These observations support the notion that the sequentially generated hindbrain functional groups provide parallel spinal pathways.

To examine the pattern of connections made by each pathway onto possible postsynaptic spinal neurons, we highlighted the descending processes of early-born and intermediate-aged hindbrain V2a neurons by photoconversion in the hindbrain, and additionally backfilled the following two spinal cell types with a far-red dye. The first cell type is primary motoneurons (PMN) which are among the earliest-born neurons in the spinal cord (Myers et al., 1986) (Figure 6B xi, n = 8 fish). They are only recruited during the strongest of movements in contrast to secondary motoneurons (SMN) which are also recruited during weaker movements (McLean et al., 2007; Menelaou and McLean (2012); Wang and Brehm, 2017). The second cell type is multipolar commissural neurons (MCoD), which are later-born excitatory interneurons in the rostral spinal cord (Figure 6B xii, n = 8 fish). They are only active during slow swimming and have monosynaptic connections to SMNs in the caudal spinal cord (McLean et al., 2008; Fetcho and McLean, 2010). We found that PMNs were located close to the medial neuropil region occupied by the spinal projections from the early-born hindbrain V2a population, whereas MCoDs were located close to the lateral neuropil region occupied by the spinal projections from the late-born population. Thus, these results support the notion that there are parallel pathways between the hindbrain and the spinal cord that are separable by differentiation time.

The innervation patterns exhibited by these hindbrain V2a age groups are consistent with the type of axial muscle activity observed during the locomotor patterns that these age groups participate in: direct activation of PMNs throughout the spinal cord by the early-born group is expected to lead to the powerful whole-body bends observed during the crude and strong forms of locomotion (escape and struggle), whereas indirect activation of SMNs in the caudal spinal cord through MCoDs should lead to slow bending of the caudal tail observed during the more refined and weaker locomotion. Thus, these observations raise the possibility that each age group contributes to kinematics specific to the locomotor pattern in which this group participates.

So far, we experimentally investigated the overall organization of the hindbrain V2a population and the results suggest that birthdate-dependent parallel circuit organization underlies the development of locomotor behaviors. To examine this more rigorously and to gain deeper insights about the biophysical properties of V2a neurons and their descending pathways, we examined various subsets of V2a reticulospinal neurons in depth.

Neuronal excitability and synaptic inputs in V2a pathways vary in accordance with birthdate-related recruitment pattern

To electrophysiologically examine the birthdate-related recruitment pattern, we focused on MiV1 neurons, a small cluster of V2a reticulospinal neurons in rhombomere 4 (Figure 7A), which included neurons varying in birthdate and recruitment pattern (Figures 2, 4 and 5).

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First, we examined the spatial organization of neurons in this cluster based on the time of differentiation, as indicated by photoconversion-based birthdating from 25 hpf to 37 hpf (Figure 7B, n = 6 fish for each timepoint). We found that newly generated V2a neurons were systematically located ventral to preexisting ones (Figure 7B iii), in contrast to a previous study reporting that late-born neurons are located dorsal to early-born neurons except in rhombomere 6 (Kinkhabwala et al., 2011). To see if this inverted organization was due to migration, we performed time lapse imaging from 25 hpf to 37 hpf (Figure 7—figure supplement 1A, n = 6) and from 48 hpf to 78 hpf (Figure 7—figure supplement 1B, n = 6). We found that newly generated neurons were indeed initially located dorsal to preexisting ones (Figure 7—figure supplement 1A iii) but the subpopulation of neurons born before 48 hpf migrated ventrally after 48 hpf (Figure 7—figure supplement 1B iii, magenta cells; Video 6). This supported the idea that the inverted organization we observed in this region was indeed due to radial migration. This analysis revealed a fine-scale birthdate-related topographical organization, which our previous analyses overlooked, and provided an opportunity to examine in detail the relationship between ontogeny and recruitment pattern. For the following recruitment analysis, we used dorso-ventral position as a proxy for birthdate, taking advantage of the fine-scale birthdate-related topographical organization (see Materials and methods). We first identified dorsal MiV1 based on its position just ventral to M-cell, its soma larger than the nearby younger cells, and the presence of photoconverted Kaede. We then defined normalized dorso-ventral position with ‘0’ being the dorsal edge of dorsal MiV1 and ‘1’ being the ventral edge of the most ventral MiV1. Cells other than dorsal MiV1 with the position values lower than 0.5 were categorized as middle MiV1 while those with the position values higher than 0.5 were categorized as ventral MiV1.

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To examine the activity of MiV1 neurons during locomotion, we performed whole-cell recordings in a paralyzed fictive swim preparation (Figure 7C–F). We delivered electrical shocks to the tail to induce putative escapes that showed fast frequency bursting activity in axial motor recording (Figure 7C ii, left). Locomotor episodes with slower frequency bursting activity were observed spontaneously (Figure 7C ii, right) as in the functional imaging experiments. However, whole-cell recordings allowed us to examine how subthreshold and spiking activity changes in relationship to beat frequency within each swim episode. The dorsally located early-born MiV1 neurons (n = 8, position: 0.14–0.18) showed strong depolarization leading to action potentials during the fastest phase of fast swim episodes evoked by electrical shock to the tail (Figure 7C ii, left orange traces, shaded cycle). However, they showed no clear depolarization during spontaneous slow swim episodes (Figure 7C ii, right orange traces). The middle MiV1 neurons that belong to the intermediate age group (n = 13, position: 0.30–0.48) showed clear spiking activity at the fastest phase of the shock-induced fast swimming. They also showed clear rhythmic depolarization leading to occasional firing during spontaneous slow swimming (Figure 7C ii, green traces). The ventral late-born MiV1 neurons (n = 23, position: 0.64–0.93) showed clearly rhythmic firing during both shock-induced fast swimming and spontaneous slow swimming (Figure 7C ii, blue traces). Spiking activity showed clear statistical interaction between the time of differentiation and the speed of locomotion (Figure 7D, p<0.001), suggesting that groups of neurons that differed by age were recruited differently depending on the speed of locomotion. Indeed, the dorsal and middle MiV1 neurons showed significant modulation of activity based on the speed of locomotion (Figure 7D). Subthreshold activity also showed similar interaction (Figure 7E, p<0.001), suggesting that this recruitment pattern can be explained at least partly by their distinct synaptic inputs. At the same time, the MiV1 group showed systematic differences in their intrinsic excitability based on their time of differentiation: the older neurons showed lower input resistances and more hyperpolarized membrane potentials compared to younger ones (Figure 7F, Rin, Vrest). This is similar to the previously reported relationship between input resistance and differentiation time in the spinal cord and the caudal hindbrain V2a population (McLean et al., 2007; Kinkhabwala et al., 2011). Furthermore, we also found a systematic change in the membrane time constants: the older neurons showed faster membrane time constants than the younger ones (Figure 7F, Tau). This comports well with the selective recruitment of the older neurons during fast tail beats. Thus, taking advantage of whole-cell recordings, we showed that these V2a reticulospinal neurons exhibited a systematic change in the spiking and subthreshold activity within each swim episode, depending on the speed of locomotion. Furthermore, we revealed differential synaptic inputs and intrinsic properties linked to the birthdate-related recruitment of V2a reticulospinal neurons.

Spinal projections from individual hindbrain V2a neurons are organized based on their respective birthdates and innervate functionally matched spinal groups

Now that we had shown birthdate-related recruitment and its potential mechanisms in hindbrain V2a reticulospinal neurons, we examined their spinal projections in detail by labeling individual cells in each age group with dye electroporation in a series of transgenic lines labeling specific spinal neurons (Figure 8). We found distinct projection patterns across age groups at a single-cell level in a manner consistent with the overall age-related projection pattern of hindbrain V2a descending neurons, as revealed by photoconversion.

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The early-born dorsal group (n = 8, position: 0.13–0.19) sent relatively thick axons through the medial longitudinal fasciculus and exhibited extensive axon collaterals throughout the spinal cord (Figure 8B ii). All the cells in this group had clear puncta-like structures - putative presynaptic terminals - in close proximity to the somata of PMNs (Figure 8B iv, 8 out of 8 cells) and SMNs (Figure 8B v, 4 out of 4 cells) but not spinal V2a neurons (Figure 8B vi, 0 out of 7 cells). Their collaterals were in the medial part of V2a neuropil (Figure 8B viii, ix) and clearly separated from the cell bodies of MCoDs located in the lateral part of the neuropil (Figure 8B vii vii’, 0 out of 4 cells).

All the cells in the intermediate age group (n = 24, position: 0.32–0.48) had puncta-like structures on the ventrally located SMNs (Figure 8C v, 9 out of 9 cells) but only a third of the cells had puncta on the dorsally located PMNs (Figure 8C iv, 8 out of 24 cells). On the other hand, there was clear somatic innervation to the spinal V2a neurons in most of the cells (Figure 8C vi, 10 out of 13 cells). Again, their collaterals were in the medial part of the neuropil (Figure 8C viii, ix) and clearly separated from the laterally displaced MCoDs (Figure 8C vii, vii’, 0 out of 15 cells).

Axon collaterals from the late-born ventral group (n = 26, position: 0.68–0.92) were sparse (Figure 8D ii) and located mostly in the ventral part of the neuropil (Figure 8D viii-xi). The main axon tract was in the lateral part of the neuropil where no cell bodies of motoneurons and spinal V2a neurons are present (Figure 8D iv-vi). Most of them showed clear somatic innervations to MCoDs (Figure 8D vii, 23 out of 26 cells) but far fewer or no innervations to the soma of motoneurons and spinal v2a neurons (Figure 8D iv-vi, 0 out of 26 for PMN, 4 out of 10 for SMN and 1 out of 11 for spinal V2a neuron).

At the population level, there were statistically significant differences in the dorsal extents of spinal axon arborizations across age groups (Figure 8E, p<0.001). The axon arborization of each age group was also localized in a distinct medio-lateral position in the neuropil, as evident from coronal views (e.g. Figure 8B x-xi and 8D x-xi) and from their axon arborizations relative to the laterally displaced MCoDs (panels vii in Figure 8B, C and D). This is also in accordance with the chronological layering of V2a spinal projection revealed by photoconversion. These differences in axon arborization were reflected in the somatic innervation patterns to spinal neurons (Figure 8F). The interaction between MiV1 age groups and spinal cell types was statistically significant (p<0.001), meaning that each age group had distinct somatic innervations based on the spinal cell types. Indeed, the dorsal early-born group was the only group that consistently innervated the cell bodies of early-born motoneurons, PMNs, whereas the ventral late-born group was the only group that had somatic innervation to late-born interneurons, MCoDs. Thus, our results provided cellular-level anatomical evidence that hindbrain V2a neurons form parallel spinal pathways arranged based on birthdate and set the stage for electrophysiological analysis of these connections.

Chronologically organized parallel synaptic connections to spinal neurons with distinct biophysical properties

Population- and cellular-level anatomy of hindbrain V2a neurons suggests that they exhibit distinct innervation patterns based on their time of differentiation: early-born neurons innervate early-born spinal neurons such as PMN while late-born neurons innervate late-born spinal neurons such as MCoD. To confirm these putative synaptic connectivity patterns and examine their properties in detail, we performed a series of paired whole-cell recordings from hindbrain descending neurons and spinal cord neurons (Figure 9).

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First, we examined the synaptic connectivity of dorsal MiV1 (n = 22, position: 0.12–0.17), the early-born V2a descending neurons in rhombomere 4, to PMN (Figure 9A; n = 14) and MCoD (Figure 9B; n = 8). The firing of an action potential in dorsal MiV1 led to a postsynaptic potential in PMN in most pairs (Figure 10A; 13 out of 14). In eight cases, the EPSPs had two clear components: a short latency component and a relatively longer latency component (Figure 9A iii). We tested the monosynaptic nature of the connections by raising magnesium and calcium concentrations to minimize the contribution of polysynaptic pathways (Figure 9A iii). In these cases (n = 7), the responses persisted, suggesting that they were mediated by monosynaptic connections. The longer latency component was blocked by a mixture of glutamatergic blockers, leaving only the shorter latency component (Figure 9A iii; n = 7). The longer latency component was also variable across trials while the short latency component was stable across trials. Therefore, the initial short latency response was probably due to an electrical synapse, and the later response that followed this was due to a glutamatergic synapse. On the other hand, none of the MCoDs showed a postsynaptic response to an action potential elicited in the dorsal MiV1 (Figure 9B; 8 out of 8). Taken together, our results indicate that in a manner consistent with their spinal projection patterns, dorsal MiV1s, the early-born V2a neurons in rhombomere 4, show selective synaptic connectivity to PMNs, the early-born spinal neurons.

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In contrast, ventral MiV1s (n = 29, position: 0.67–0.91), the late-born V2a descending neurons in rhombomere 4, showed the opposite pattern of synaptic connectivity. None of the PMNs showed a postsynaptic response to an action potential elicited in ventral MiV1 (Figure 9C; 0 out of 8), while one-third of MCoDs examined showed a clear postsynaptic potential (Figure 9D; 8 out of 21). The responses in these MCoDs persisted in high magnesium and calcium solution, suggesting they were generated monosynaptically. In four out of eight cases, we observed clear biphasic responses (Figure 9D iii), the latter component of which was blocked by glutamatergic blockers (Figure 9D iii; n = 4). The amplitude of the short latency component was stable across trials in contrast to the later component mediated by glutamatergic synapses, suggesting that the early component was probably due to electrical synapses. Thus, in a manner consistent with their spinal projection patterns, ventral MiV1s showed selective monosynaptic connectivity to MCoDs, the late-born spinal interneurons that are only active during slow swimming (McLean et al., 2008).

To see if other V2a descending neurons and non-V2a descending neurons show similar connectivity patterns based on time of differentiation, we also looked at the connectivity of early-born V2a neurons in rhombomere 6, MiD3i, (Figure 9E,F) and the earliest born descending neurons in rhombomere 4, the Mauthner cell (M-cell), (Figure 9G,H) to PMNs and MCoDs. In seven out of seven cases, a clear biphasic postsynaptic potential was observed in PMNs in response to an action potential in MiD3i (Figure 9E). This response persisted in high magnesium and calcium solution, suggesting that they were monosynaptic. Once again, the early component was stable across trials while the late component was variable and blocked by glutamatergic blockers (Figure 9E iii; n = 7), suggesting that they were electrical and glutamatergic, respectively. The M-cell also showed similar, but much stronger connections. In 14 out of 14 cases, PMNs showed very strong postsynaptic potentials leading to spiking activity in response to a single action potential in the M-cell (Figure 9G iii). These responses also persisted in high magnesium and calcium solution, suggesting that the connections were monosynaptic. These responses were completely blocked by a cholinergic blocker (Figure 9G iii; n = 10), indicating that they were mediated by cholinergic synapses, in accordance with existing literature (Koyama et al., 2011; Clemente et al., 2004). MCoDs on the other hand showed a long latency complex postsynaptic potential in response to one M-cell action potential, suggesting that the connection is probably polysynaptic (Figure 9H iii; 13 out of 13 cases). Most, if not all, of the responses disappeared in high magnesium and calcium solution, further supporting that they are mediated polysynaptically (Figure 9H iii; n = 10). In sum, our findings indicate that both types of early-born descending neurons make synaptic connections to the early-born PMNs but not to the late-born MCoDs.

Overall, the connection probability was statistically different depending on the hindbrain cell type and the spinal cord cell type (Figure 9I; main effect of hindbrain cell type, p<0.001; main effect of spinal cell type, p<0.0001). In fact, PMNs and MCoDs received synaptic inputs from completely different sets of reticulospinal neurons, with each set corresponding to distinct age group (Figure 9I). Thus, these results showed electrophysiologically that early-born and late-born hindbrain neurons form parallel pathways to the spinal cord and these pathways are organized based on the time of differentiation, supporting the idea that parallel hindbrain-spinal cord circuits emerge in sequence during development.

We then examined biophysical properties of these synaptic connections. We first examined differences in conduction velocity among connected pairs (Figure 9J): the youngest ventral MiV1/MCoD pairs were the slowest among the connected pairs. The middle-aged pairs (dorsal MiV1/PMN and Mid3i/PMN) were slower than the oldest M-cell/PMN pairs. We also examined differences in the time constants of synaptic potentials, excluding the M-cell/PMN pair that showed spiking activity (Figure 9K). The youngest ventral MiV1/MCoD pairs showed significantly longer time constants than the older V2a neurons (Figure 9K). With respect to the amplitudes of the synaptic potentials, the M-cell/PMN pair was the only pair that was statistically different from other pairs (Figure 9L). However, consistent with previous studies (McLean et al., 2007; Menelaou and McLean, 2012), the early-born PMNs and the late-born MCoDs examined here differed significantly in input resistance (92.3 ± 18.2 M Ohm and 514 ± 172 M Ohm, respectively; Wilcoxon test, p<0.001), suggesting that the underlying synaptic currents were systematically stronger for the early-born hindbrain neurons than the late-born hindbrain neurons. Altogether, we found systematic differences in conduction velocities, synaptic decay times and synaptic currents between the two parallel pathways: the early-born pathway is equipped with fast conduction velocities, fast time constants of synaptic potentials and stronger synaptic currents while the late-born pathway is equipped with slow conduction velocities, slow time constants of synaptic potentials and weaker synaptic currents. The differences in the biophysical properties as well as connectivity patterns are consistent with the distinct types of axial motor activity observed during the locomotor patterns in which these pathways participate. The net effects of the distinct biophysical properties and connectivity patterns associated with the early- and late-born pathways is that the former could more quickly bring the PMNs throughout the spinal cord to their firing thresholds, whereas the latter could more slowly bring the SMNs in the caudal spinal cord to their firing thresholds. As a consequence, activation of early-born pathways could produce more powerful whole body bending observed during different crude forms of locomotion (escape and struggle)(McLean et al., 2008; Liao and Fetcho, 2008), whereas activation of late-born pathways could produce weaker bending of the caudal tail region with a long rostrocaudal delay, as observed during the refined locomotion (McLean et al., 2008). In sum, these results suggest that the parallel hindbrain-spinal cord circuits that develop in sequence and have distinct connectivity patterns and biophysical properties contribute to the distinct locomotor patterns that zebrafish display in sequence.

Systematic characterization of the development, recruitment, and connectivity of V2a descending neurons led us to hypothesize that early-born hindbrain V2a descending neurons contribute to the powerful whole-body movements observed during the stimulus-evoked locomotion that develops early in development while their late-born counterparts contribute to the weaker tail-restricted movements observed during the spontaneous locomotion that develops later. To test this hypothesis, we separately ablated either the early-born or the late-born hindbrain V2a descending neurons and at 5 days post-fertilization (dpf) examined the effects of each of these ablations on a few distinct patterns of locomotion (Figure 10).

To examine a wide range of swimming patterns in the freely swimming condition, we employed two experimental conditions: auditory-evoked escape responses (Figure 10A ii) and spontaneous swimming (Figure 10A iii). In each condition, we monitored movements of the fish using a high-speed camera (Figure 10A i). Auditory stimulus-elicited escape episodes consisted of large and fast changes in head orientation as well as body bends (Figure 10A ii), these being characteristic of the crude locomotion that develops early. On the other hand, spontaneous swim episodes mostly consisted of small changes in head orientation, and slow and small bends restricted to the caudal portion of the tail (Figure 10A iii), and these are typical of the more refined locomotion that develops later. The distinctiveness of these two swim types became quite apparent when for each swim episode the maximum angular velocity (see Materials and methods) was plotted against the maximum body bend amplitude (Figure 10A iv). In this plot, the points corresponding to escapes and slow swims clearly segregated into distinct clusters. Consistent with the findings of a previous study (Burgess and Granato, 2007), when we looked at the distribution of the amplitude of the first total body bend in each spontaneous swim episode we found it to be multimodal, suggesting that spontaneous swim episodes consisted of distinct subtypes (Figure 10A v). A Gaussian mixture model fit indicated that the best fit to the distribution was using a linear combination of three distinct gaussian distributions, suggesting that there were potentially three subcategories of spontaneous swim episodes (see Materials and methods). The timeseries of body bends and head orientation in each category showed consistent motor patterns (Figure 10A vi), further supporting the idea that fish at 5 dpf exhibit at least three distinct motor patterns spontaneously. We referred to these patterns as ‘scoot’, ‘routine turn’ and ‘high-angle turn’ based on the nomenclature adopted in previous studies (Burgess and Granato, 2007; Marques et al., 2018). Combined with auditory-evoked escape responses, we examined four distinct motor patterns in total (Figure 10A vi).

We focused on two age groups of hindbrain V2a descending neurons based on their times of differentiation and their recruitment patterns. The first group consisted of neurons born before 24 hpf (10 fish for the ablated group; 10 fish for the control group). We targeted all these neurons in the hindbrain as the optical backfill and Ca²⁺ imaging indicated that all of them were descending neurons involved in strong locomotion. We visualized these neurons by photoconverting Tg(vsx2:Kaede) at 24 hpf, and at 60 hpf we ablated these neurons by femtosecond laser pulses (Figure 10B i; 131 ± 17.3 cells/fish). We delivered multiple pulses to each neuron (typically 5–10 pulses/neuron) until the fluorescence signal suddenly decreased. Neurons labeled with photoconverted Kaede disappeared with no clear unintended damage to the nearby cells and processes (Figure 10B ii). To confirm that these neurons did not recover before the behavior experiments, we imaged them again at four dpf (Figure 10—figure supplement 1A). Comparison with the control group at 4 dpf clearly showed that these early-born neurons did not recover from ablation. The second group of neurons targeted for ablation consisted of neurons born from 42 hpf to 60 hpf (Figure 10B iii; 10 fish for the ablated group; 10 fish for the control group). We focused on the neurons in the dorsocaudal hindbrain because the optical backfill and Ca²⁺ imaging indicated these neurons are descending neurons involved in spontaneous slow locomotion. We identified these V2a neurons based on the lack of photoconverted Kaede at 60 hpf after the photoconversion at 42 hpf (Figure 10B iii) and their relative position to the nearby early-born neurons (508 ± 15 cells/fish; see Materials and methods). Femtosecond laser pulses ablated these neurons with no clear unintended damage to the nearby early-born neurons (Figure 10B iv). Comparison with the control group at 4 dpf clearly showed the reduction of the late-born neurons in the dorsocaudal hindbrain (Figure 10—figure supplement 1B).

To assess if ablations had resulted in global changes in swim episodes, we examined total swim distance per episode, maximum swim velocity per episode, mean swim velocity per episode, episode duration, and total number of bends per episode (see Materials and methods) for each of the four distinct swim types (Figure 10—figure supplement 2). We found that for scoots all of the foregoing global parameters were significantly reduced in the late-born V2a neuron ablation group (Figure 10—figure supplement 2B, p<0.01, corrected for multiple comparisons, Holm test). For routine turns, these ablations produced a slight reduction in swim distance and mean swim velocity, but at a lower significance level (Figure 10—figure supplement 2B, p<0.05, corrected for multiple comparisons, Holm test). This is consistent with our prediction that the late-born V2a neuron contributes to the weaker tail-restricted movements observed during spontaneous swims. On the other hand, ablation of early-born V2a neurons did not result in changes in any of these global parameters. Neither ablation of the early-born or the late-born neurons resulted in a change in the onset latency of escape (Figure 10—figure supplement 2A). The lack of phenotype for the early-born group ablation is perhaps not surprising as the recruitment and connectivity of the early-born V2a neurons suggest that the this group is likely to contribute most to the powerful body bends observed during the initial phase of escapes.

To examine the effect of ablations on a bend-by-bend basis, for each swim type, we compared the amplitudes and durations of body bends between ablated and matched control groups (Figure 10C). This analysis is based on the assumption that each swim type is so stereotypical that one could compare identically-numbered bends across episodes. We assess this assumption based on the timing of each bend with respect to the onset of the episode (Figure 10—figure supplement 3): we examined how discriminable the time of maximum amplitude of each bend is from that of the preceding bend (see Materilas and methods). We found that the discriminability during escapes dropped suddenly after the sixth bend but for other swim types discriminability decreased more gradually until the tenth bend which marked the end to most of these episodes (Figure 10—figure supplement 3B ii). Based on this observation, we decided to perform bend-by-bend analysis up to the 10th bend across all swim types for consistency. Relative to the control group, the early-born V2a ablation group exhibited significant decreases in the amplitudes of the first, second and fourth bends, as well as a decrease in the duration of the first bend of escape (Figure 10C i, Escape; p<0.01, corrected for multiple comparisons, Holm test). However, we observed no statistically significant changes in the three other slower locomotor patterns (Figure 10C i, Scoot, R-turn and High-angle turn). On the other hand, in the late-born V2a ablation group we saw no clear changes in the escape response (Figure 10C ii, Escape). Instead, this group showed a decrease in the amplitudes of bends in the later phase of scoot (Figure 10C ii, Scoot; p<0.01, corrected for multiple comparisons) but not the other two spontaneous swim patterns involving turning (Figure 10C ii, Routine turn and High-angle turn). These observations comport well with the recruitment pattern and the spinal projections we observed earlier for the neurons we ablated here. Furthermore, the fact that we observed significant changes only in the late phase of scoots suggests that late-born V2a neurons may play a role in maintaining the excitatory drive in the spinal cord during slow forward locomotion.

To gain assurance that we compared the same set of spontaneous locomotor behaviors between the control and ablated groups, we compared the distributions of the first body bend we used to subcategorize spontaneous locomotor behavior (Figure 10—figure supplement 4). Although there were slight differences in the peak amplitudes of the probability distributions across the groups, the overall shapes of the distributions were consistent, indicating that we compared the same sets of locomotor behaviors across the control and ablated groups. To examine this more closely for scoot behavior, which showed significant deficits upon ablation of late-born caudal hindbrain neurons, we examined the time course of the amplitude of body bending during this behavior (Figure 10—figure supplement 5). Traces from all the episodes of scoot from the ablated fish (Figure 10—figure supplement 5, orange) and the control fish (Figure 10—figure supplement 3, blue) are overlaid for each ablation group. In both ablation groups, the ablated and control groups both exhibited the small amplitude body bends with slow left-right alternation (Figure 10—figure supplement 5) that is characteristic of scoot behavior (Figure 10A vi). Furthermore, the overlaid traces of the late-born V2a ablation and control groups clearly revealed that the bend amplitudes of scoots decreased in the later phase of scoot in the ablated group (Figure 10—figure supplement 5A). This further strengthened the idea that late-born V2a neurons of the caudal hindbrain play a role in the maintenance of excitatory drive during slow forward locomotion. To summarize, our ablations did not change the set of distinct locomotor behaviors fish exhibit spontaneously.

While examining the results of ablations, we noticed that the early-born and the late-born control groups exhibited significantly different bend amplitudes during scoot (Figure 10C i, Scoot; Figure 10C ii, Scoot, p<0.01). Comparison to an independent control group that was not subjected to photoconversion suggested that photoconversion at 42 hpf impacted the kinematics of scoot at five dpf (data not shown). Although this does not undermine the observed significant decrease in bend amplitudes of scoot in the late-born ablation group, it does raise some concerns about the lack of significant effects in the early-born ablation group. For instance, it is conceivable that ablation of early-born neurons does not reveal a decrease in scoot bend amplitudes because of a floor effect, wherein the observed bend amplitudes somehow represent the lowest values that the fish can inherently display in order to produce a scoot (Figure 10C i, Scoot). However, we think this is unlikely because we noticed a slight increase rather than decrease in bend amplitudes after ablation of the early-born neurons (Figure 10C i, Scoot). In any case, the effects of photoconversion itself further justifies our use of matched control for each ablation group.

In conclusion, with ablation experiments we confirmed our hypothesis about the functional roles of early-born and late-born V2a neurons in freely swimming fish. Moreover, the double dissociation of the two locomotor patterns indicates that the underlying V2a descending pathways are largely independent, in accordance with their parallel circuit arrangement resulting from the layered growth of their spinal projections. This suggests that the layered development of new connections in the nervous system underlies its ability to integrate new circuits into existing circuitry in such a way as to allow the different circuits to operate autonomously. Interestingly, previous studies showed that the ablation of ventral reticulospinal neurons, the hindbrain V2a neurons we classified as the intermediate-aged group, affects slow but large turns with no clear kinematic effects on forward locomotion and suggested that the descending pathway for turning behaviors is independent of the one for forward locomotion (Huang et al., 2013; Orger et al., 2008). Collectively, this suggests that the array of locomotor behaviors fish display after birth are supported by sequentially developing parallel hindbrain-spinal cord circuits, with each circuit accounting for a specific range of kinematics and dynamics through its unique connectivity pattern and biophysical properties.

Zebrafish larvae were obtained from an in-house breeding colony of wild-type adults maintained at 28.5°C on a 14–10 hr light-dark cycle. Embryos were raised in a separate incubator but at the same temperature and on the same light-dark cycle. Embryos were staged (Kimmel et al., 1995) and only the ones with normal development were used in the study. All experiments presented in this study were conducted in accordance with the animal research guidelines from the National Institutes of Health and were approved by the Institutional Animal Care and Use Committee and Institutional Biosafety Committee of Janelia Research Campus (16-145).

The following previously published transgenic lines were propagated to Casper background and used in this study (see Supplementary file 1): TgBAC(vsx2:GFP)(Kimura et al., 2006); TgBAC(vsx2:Kaede)(Kimura et al., 2006); TgBAC(vsx2:Gal4)(Kimura et al., 2013); Tg(UAS:Kaede)(Davison et al., 2007); Tg(UAS:synaptophysin-EGFP)(Heap et al., 2013); Tg(UAS:GCaMP6s) (Muto et al., 2017); Tg(mnx1:TagRFPT)(Jao et al., 2012); TgBAC(islet1:GFP)(Higashijima et al., 2000); Tg(Dbx1b:Cre, vglut2a:lRl-GFP)(Koyama et al., 2011). For examining cholinergic transmission in the Mauthner cell, we used the paralytic mutant, relaxed, to avoid the use of the cholinergic blocker α-bungarotoxin for the induction of paralysis (Koyama et al., 2011).

TgBAC(vsx2:EGFP) was anesthetized in tricaine methanesulfonate (MilliporeSigma, E10521, St Louis, MO) dissolved in system water at 160 mg/L (hereafter referred as MS-222) and then embedded in 1.6% low melting point agar (MilliporeSigma, 2070-OP, MO). Volumetric images of hindbrain V2a neurons were acquired with a custom two-photon microscope. 25 × 1.1 NA objective lens (Nikon Instruments, CFI Apo LWD 25XW 1300 nm, NY) was used and EGFP was excited at 940 nm (MKS Instruments, Mai Tai HP, Deep See, MA). For each developmental time point, a separate group of fish was used to avoid the potential developmental effects of the imaging procedure (>6 fish per time point). For time lapse imaging, fish were kept in 0.5% agar and temperature was maintained at 28.5°C with a temperature controller (Luigs and Neumann, TC07, Germany).

Fish expressing the photoconvertible fluorescent protein, Kaede, in V2a neurons (TgBAC(vsx2:Kaede)) were photoconverted at 24, 36, 48, 60, 72 and 96 hr post fertilization (hpf) to identify V2a neurons that existed at a given developmental time point using a procedure similar to the one described previously (Kimura et al., 2006; Caron et al., 2008). Briefly, UV light was shone on around 10 fish within a drop of system water for 30–60 s using a stereo dissection microscope (Olympus, MVX10, PA) equipped with a DAPI filter (Chroma Technology, 49901, VT) and a halogen bulb (Excelitas Technologies, X-Cite 120PC Q, MA). Then, the fish were transferred back into the incubator and housed in a box that filtered out UV wavelengths from the light within the incubator. This prevented photoconversion of additional developing neurons by the ambient light in the incubator. The fish remained in the incubator until 120 hpf, at which point they were imaged with a confocal microscope (Zeiss, LSM710, Germany) with 20 × 1.0 NA objective lens (Zeiss, W Plan-Apochromat 20x/1.0, Germany) (>8 fish per time point). A group of fish converted at 24 and 36 hpf (six fish per time point) were injected with a dye in the spinal cord (Liu and Fetcho, 1999) at 4 days post-fertilization (dpf) to label reticulospinal neurons. A far red dye was used to avoid contaminating the red channel used for imaging photoconverted Kaede (Alexa Fluor 680 dextran, MW: 10,000, Thermo Fisher Scientific, MA). These neurons are identifiable across animals and are named based on their rostrocaudal positions (Figure 2C, left) and dorsoventral positions (Figure 2C, right) (Kimmel et al., 1982; Mendelson, 1986). We used the same naming convention used previously except for a V2a reticulospinal neuron that was born by 24 hpf in rhombomere 4. This cell was the most dorsal cell within V2a reticulospinal neurons in rhombomere four but was slightly ventral to MiM1. Just as MiM1 refers to a single identifiable cell that is just ventromedial to the Mauthner cell (Mendelson, 1986), we decided to refer to this neuron as dorsal MiV1.

Fish expressing Kaede in V2a neurons through the Gal4-UAS system were photoconverted in the rostral spinal cord (muscle segment 5 to 7) at 36, 60,84 and 108 hpf to identify hindbrain V2a neurons that had spinal projections at a given developmental time point as described previously (Kimura et al., 2013). Briefly, fish were anesthetized in MS-222 and embedded ventral-side up in 0.5% low-melting point agar and then the rostral spinal cord (muscle segment 5 to 7) was illuminated for 2 min with 405 nm LED (Lumencor, Spectra X, OR) in an inverted microscope (Nikon Instruments, Eclipse Ti-E, NY) using a 60x objective lens (Nikon Instruments, CFI Plan Apo VC 60XWI, NY). This procedure was repeated three to five times with an interval of 15–20 min. The fish were then kept in the dark until 120 hpf and imaged with a confocal microscope (Zeiss, LSM 710, Germany) (>6 fish per time point). The expression of Kaede in the earliest born hindbrain V2a neurons, driven by the Gal4/UAS system was delayed roughly 6 hr relative to the one driven directly by the V2a promoter (data not shown). Theoretically, it is possible that some descending neurons are not labeled with this procedure due to 1) inefficient transport on small axons, or 2) fast turnover back to unconverted Kaede. However, it is unlikely that we are missing cells because of insufficient diffusion time (36 hr to 84 hr) because the converted Kaede diffused to the cell bodies of the caudal V2a neurons that are previously speculated to have thin axons in less than 12 hr (Video 1). Turnover time of converted Kaede was more than 96 hr in the early-born population (see Figure 2—figure supplement 1 for example). This is longer than the diffusion time we used for our optical backfill experiments (36 hr to 84 hr). Thus, the second possibility is also unlikely.

The genetically encoded calcium indicator GCaMP6s (Chen et al., 2013) was expressed in hindbrain V2a neurons using the Gal4-UAS system (Tg(vsx2:Gal4; UAS:GCaMP6s)). Fish at 120–132 hpf were paralyzed with α-bungarotoxin (MilliporeSigma, 203980, MO) and then embedded in 1.6% low-melting point agar. The agar around the tail and the nostrils was released using a fine tungsten pin. Axial motor nerve activity was recorded through fire-polished glass pipettes placed on the dorsal intermyotomal cleft on both sides. A gentle suction (~15 mm Hg) was applied through a pneumatic device to gently break the skin. The signal was amplified (1000x) and bandpass filtered (100–1000 Hz) through an extracellular amplifier (NPI, EXT-02B, Germany) and digitized at 6 kHz with PCIe-6363 (National Instruments, TX) using a custom C# program (Source Code File 1). Clear motor activity was typically observed within 20 min. The whole hindbrain was imaged with a custom two-photon microscope equipped with a resonant scanner (Thorlabs, MPM-2PKIT) and a piezo objective scanner (PI, P-725K129, Germany) at a volume rate of 2 Hz (512 × 256, 30 slices, 7 μm z step) using ScanImage (Vidrio Technologies, VA). 940 nm, 80 MHz femtosecond laser pulses were used to excite GCaMP6s (MKS Instruments, Mai Tai HP, Deep See, MA). A gradual mechanical stimulus was delivered by gradually contacting the rostral part of the head with a fire polished glass pipette controlled by a motorized manipulator (Luigs and Neumann, Junior RE, Germany). A brief electrical stimulus (0.5–1 ms in duration, 2–10 V) was delivered through a glass electrode placed on the side of the head using a stimulus isolator (Digitimer Ltd., DS-2, England). The inter-stimulus interval was from 50 s to 2 min. The stimulus amplitude was adjusted to evoke strong motor activity consistently. Each experiment lasted between 20 and 40 min and thus contained 2400–4800 stacks.

Two-photon data was first corrected for mismatch between the odd and even scan lines by cross correlation if necessary and then the shift of the sample was corrected by cross correlation to a reference volume. Then GCaMP6s signal from identifiable V2a neurons were examined in relationship to fictive swim bouts. The regions of interest (ROI) for V2a-positive reticulospinal (RS) neurons (Mid2i, Mid3i, RoM2, RoM3, RoV3, dorsal MiV1, and ventral MiV1/2) were drawn based on their distinct segmental distribution, dorsoventral position and soma morphology. First, dorsal early-born V2a RS neurons were identified based on their large and laterally displaced cell bodies and used as a landmark for each rhombomere (RoM2, RoM3, dorsal MiV1 and Mid2i, Mid3i) (Video 1). Then, the younger ventral V2a RS neurons in each rhombomere were identified based on their smaller cell bodies ventral to the dorsal early-born neuron. This was confirmed further with spinal backfill (Kimmel et al., 1982). The ROIs for caudal hindbrain V2a neurons were drawn based on their stereotypical positions as examined in the birthdating analysis: the early-born caudal neurons were displaced laterally from the rest of caudal V2a neurons while the late-born caudal neurons were located dorsally, close to the midline. Then the fluorescence time course (F(t)) for each ROI was extracted and the baseline fluorescence (F₀) was estimated as the bottom 20th percentile of the whole timeseries. Then ΔF(t)/F₀ was calculated as follows.

To detect weak spontaneous swimming activity reliably, the axial motor nerve activity was processed by taking a windowed standard deviation of the original signal with a 10 ms moving window as described previously (Ahrens et al., 2012). Then, a threshold for swimming activity was selected to detect the weak spontaneous swimming activity reliably from the transformed signal. In the gradual mechanical stimulus experiment, the latency of strong swimming activity from the onset of the stimulus was not consistent across trials. Thus, we used the following procedure to detect the strong bursting activity. For each swim bout, the maximum burst amplitude of the transformed signal was extracted independently for left and right channels (Figure 3B i). Then, the distribution of the maximum burst amplitude was log-transformed to reduce skew in the distribution and fitted as a Gaussian mixture distribution with two components to estimate the distribution of the maximum burst amplitude that corresponds to weak spontaneous swims. This estimated probability distribution corresponding to weak spontaneous swims was used to compute a value whose probability of falling in this distribution is less than 0.1% (Figure 4B i). The swim episodes with the maximum burst amplitude above this value were categorized as strong swims induced by the stimulus (Figure 4B i; Figure 3C, ‘Push’). The detected episodes matched with the slow and strong bursting activity (as assessed by eye). In the electrical pulse stimulus experiment, fast swim episodes were elicited reliably by the stimulus and were categorized as shock-induced fast swims (Figure 3C, ‘Shock’). The remaining spontaneous swim episodes were categorized as spontaneous swims (Figure 3C, ‘Sponta swim’). To derive the ΔF/F0 response related to these swim events (Figure 3C), ΔF/F0 signal was aligned at the onsets of a given swim type and averaged over trials for each cell. Only the fictive motor signal ipsilateral to the cell being examined was considered for the following reasons: 1) hindbrain V2a neurons are primarily ipsilaterally projecting neurons (Kinkhabwala et al., 2011; Cepeda-Nieto et al., 2005). 2) most of the neurons related to the strong swims responded only when there is a strong axial motor activity on the ipsilateral side (Figure 3A and B). The peak of the mean ΔF/F0 response was detected for each neuron and pooled for a given cell type to test if they showed activity consistently during a given swim type (Figure 3C, top row). To test if swim types had effects on the activity of a given cell type, an one-way ANOVA with a factor for swim types was used. All possible comparisons were tested with Tukey’s multiple comparison test (Figure 3C, bottom row).

To map the hindbrain V2a neurons recruited during these swim bouts in an unbiased way, voxel-level regression analysis was done with regressors representing the distinct types of swim bouts. Each regressor was constructed by convolving the corresponding fictive motor signal (as defined above) with a GCaMP6s impulse response function modeled as the rise and decay exponentials (0.5 s rise and 2 s decay) and then standardized for a given experimental session by subtracting its mean from its values and then dividing these new values by its standard deviation (Figure 4A ii, 4B ii). Voxel time course (Y) was fitted with these standardized regressors (X) using the following linear model.

The standardized coefficient (β) was estimated by ordinary least square and then T value for each voxel was calculated based on standardized coefficient (β) and residual (ε). Correction for multiple comparisons was done with the false discovery rate (FDR) (Miri et al., 2011b). The threshold for T maps was set at P_FDR <0.05.

Maps of birthdate, descending projection and activity of hindbrain V2a neurons were registered to Zebrafish Brain Browser (ZBB) atlas (Marquart et al., 2015; Marquart et al., 2017) using the procedure described in Marquart et al. (2017) with minor modifications. Briefly, the volume of vsx2:Gal4 provided by ZBB was used as a reference for all the registration of V2a population. Then the reference images for each map was created using either the summed volume of unconverted and converted Kaede for the birthdate and descending projection maps, or the time-averaged volume for activity maps. Affine and symmetric diffeomorphic registrations were performed using Advanced Normalization Tools (Avants et al., 2008; Avants et al., 2011) using the parameters described for live samples (Marquart et al., 2017).

The photoconvertible protein, Kaede, and GFP fused to synaptophysin were expressed in V2a neurons using the Gal4/UAS system (Tg(vsx2:Gal4;UAS:Kaede;UAS:synaptophysin-EGFP)). Hindbrain V2a neurons were photoconverted with the same set up used for optical backfill but with a shorter total exposure time (1 round of 1 min exposure). The photoconversions were done at 42 and 96 hpf to visualize the spinal projections of hindbrain V2a neurons that existed at a given developmental time point. The fish were then kept in the box that filtered out UV until the imaging at 120 hpf. A series of confocal images were taken in the hindbrain and the spinal cord from the dorsal side (Zeiss, LSM710, Germany). In a separate set of experiments, hindbrain V2a neurons were photoconverted at 42 hpf and spinal motoneurons in the rostral spinal cord were labeled by the injection of a far-red dye (Thermo Fisher Scientific, Alexa Fluor 680 dextran, MW: 10,000, MA) in the axial muscles at 108 hpf. In yet another set of experiments, spinal interneurons in the rostral spinal cord were labeled instead by the injection of the far red dye in the caudal spinal cord (28th to 30th muscle segment) in a manner described previously (Hale et al., 2001).

Volumetric images of V2a neurons labeled with converted and unconverted Kaede in the hindbrain and the spinal cord were 3D-rendered in Imaris (Bitplane, Switzerland). Then maximum intensity projection views from the dorsal side were created in the area of interest. The views were carefully oriented based on anatomical landmarks such as axons from V2a neurons to minimize the error from tilt of the 3D volume. Then the intensity distribution in the neuropil along the mediolateral axis in the projection images was calculated in Fiji (Fiji is just Image J) from a line profile extending from the lateral surface of the cell bodies of the most laterally located V2a neurons to the lateral surface of the neuropil from V2a neurons. The line thickness was set to 20 μm in Fiji to average the intensity profiles over 20 μm in the rostrocaudal direction. The intensity of converted Kaede and the mediolateral position were normalized in each fish and the average intensity profiles and their standard errors were plotted as a function of the normalized mediolateral position in the neuropil.

Patch-clamp recordings of hindbrain neurons were performed with a procedure similar to the one described previously (Kimura et al., 2013), but with some modifications. Five-day-old fish were paralyzed with α-bungarotoxin (MilliporeSigma, 203980, MO) that was dissolved in system water (1 mg/ml). After successful paralysis, larvae were anesthetized with MS-222 and then secured with etched tungsten wires (pins) through the notochord to a Sylgard-coated glass-bottom dish containing extracellular solution (134 mM NaCl, 2.9 mM KCl, 1.2 mM MgCl₂, 2.1 mM CaCl₂, 10 mM HEPES, and 10 mM glucose, adjusted to pH 7.8 with NaOH). Then, the head was rotated and secured ventral side up with tungsten pins placed through the ears and the rostral part of the jaw (Figure 7A ii). The ventral surface of the hindbrain was carefully exposed by removing the notochord using an tungsten pin and fine forceps. The skin of the middle region of the body was removed with a pair of forceps to gain access to the peripheral nerves from spinal motoneurons. Locations of motor nerve recordings were from the 10th to 15th muscle segment.

Neurons were targeted based on fluorescence and scanned Dodt gradient contrast images acquired with a custom two-photon microscope. 40x (Nikon Instruments, CFI Apo 40XW NIR, NY) or 25x (Leica, HCX IRAPO L 25x/0.95 W, Germany) objective lens was used. Descending hindbrain neurons were identified by the fluorescence signal from Tg(vsx2:EGFP), Tg(vsx2:Kaede) or spinal backfill (see above). MiV1 neurons were located in rhomobomere 4, medial and ventral to Mauthner cells which can be easily identified by Dodt gradient contrast. The subclass of MiV1 neurons was targeted based on photoconversion of Kaede (see above) and its stereotypical position. We also quantified dorso-ventral position based on volumetric images acquired after the recording as a proxy of birthdate, taking advantage of the fine-scale birthdate-related topographical organization. First, we made a coronal optical section including all the neurons in this cluster. The plane of the section was carefully adjusted so that it was parallel to the V2a processes running lateral to MiV1 neurons. Then, we identified dorsal MiV1 based on its close proximity to M-cell, its soma larger than the nearby younger cells, and the presence of photoconverted Kaede. We then defined normalized dorso-ventral position with ‘0’ being the dorsal edge of dorsal MiV1 and ‘1’ being the ventral edge of the most ventral MiV1. The cells other than dorsal MiV1 with the position smaller than 0.5 were categorized as middle MiV1, while those with the position larger than 0.5 were categorized as ventral MiV1.

Whole-cell recordings were established using the standard procedure and the signals were recorded using EPC 10 Quadro amplifier (HEKA Electronik, Germany) and PatchMaster (HEKA Electronik). The resistance of the electrode was 8 to 12 MOhm. The intracellular solution contained (in mM) 125 mM K-gluconate, 2.5 mM MgCl₂, 10 mM EGTA, 10 mM HEPES and 4 mM Na²ATP adjusted to pH 7.3 with KOH. A junction potential using this intracellular and extracellular solution (see above) has been calculated at 16 mV, which would result in a shift of measured potentials 16 mV in the negative direction. Because this would not affect our conclusions, we did not correct for it. The solution also contained 0.01% of Alexa Fluor 568 hydrazide or Alexa Fluor 647 hydrazide (Thermo Fisher Scientific). Z stacks were acquired with a custom two-photon microscope to confirm morphology and locations of the recorded neurons. Input resistances were calculated from an average of 5 hyperpolarizing square current pulses between 20 and 80 pA. This range of current injection produces a linear current-voltage response in all the MiV1 neurons examined.

Electrophysiological analysis of recruitment of MiV1 neurons was performed in a manner similar to the previous studies (McLean et al., 2007; McLean et al., 2008). Fast fictive swimming was induced by a brief electrical stimulus (<1 ms in duration at 1–10 V) delivered via a pair of tungsten electrodes (A-M systems, WA) placed near the tail. Slow swimming often occurs spontaneously but is also induced by flashes of light. Cycle frequencies of axial motor activity were computed by taking the reciprocals of the time interval between each burst and the next one. The spiking and subthreshold activity that precede each cycle were extracted and their relationship to cycle frequency was examined as follows. Spikes were detected based on the threshold determined for each cell. Subthreshold activity was examined based on the lowpass-filtered (60 Hz) voltage trace, which effectively isolated rhythmic subthreshold activity during swimming. The maximum depolarization for each cycle was calculated by subtracting its peak from the baseline which was defined as the minimum voltage in the 200 ms time window before the swim onset. In order to statistically test if each age group changes spiking and subthreshold activity as a function of cycle frequency, each cycle was classified as fast (>35 Hz) or slow (<35 Hz) cycle and fitted with the following linear mixed models with a random effect for cell using the nlme package in R (https://cran.r-project.org/web/packages/nlme/index.html). Number of spikes was log-transformed to satisfy the assumptions of the linear model. The effect of cycle speed was tested by comparing the activity for each age group with correction for multiple comparisons using the multcomp package in R (https://cran.r-project.org/web/packages/multcomp/index.html).

Electroporation of Alexa Fluor 647 dextran dye (MW 10,000; Thermo Fisher Scientific) into single neurons was performed as described previously (Koyama et al., 2011). MiV1 neurons were targeted based on GFP expression in Tg(vsx2:GFP) and/or spinal backfill with Texas Red dextran dye (MW 10,000; Thermo Fisher Scientific) along with scanned Dodt gradient contrast under a custom two-photon microscope at 5 days old. One day after electroporation, the hindbrain was imaged with a confocal microscope (Zeiss, LSM 710) from the dorsal side to confirm the dorsoventral position of the electroporated MiV1 neurons. Multiple z stacks from the side were also imaged from hindbrain to the caudal end of spinal cord to cover the entire spinal projection. Stacks were then stitched together with stitching plugin available through Fiji (Preibisch et al., 2009). When spinal backfill was used to visualize descending neurons, the spinal segments caudal to the injection site (21st to 23rd muscle segment) were excluded from imaging. Stitched stacks were 3D rendered in Imaris (Bitplane, Switzerland). To visualize the spinal projection in coronal view, axonal arborization was reconstructed with neuTube (http://www.neutracing.com/) and then visualized in Imaris. Three to four muscle segments were used to render coronal views in the rostral spinal cord (6th to 9th muscle segment) and in the middle spinal cord (14th to 17th muscle segment). All the cells examined in the study had their main axon in the ventral spinal cord and had collaterals innervating the dorsal spinal cord. The dorsal extent of the axonal arborization was quantified relative to the total dorsoventral extent of spinal cord as described previously (McLean et al., 2007). To statistically examine if the extent of spinal arborization depends on hindbrain cell type and the position in the spinal cord, the dorsal extent of arborization was fitted using the following linear mixed effects model with a random effect for cell using nlme package in R.

Putative presynaptic terminals were identified based on their characteristic puncta-like structure. Then their apposition to fluorescently labeled spinal neurons was examined. Primary motoneurons were identified using Tg(mnx1:TagRFPT). Secondary motoneurons were identified using TgBAC(islet1:EGFP). Spinal V2a neurons (also called circumferential ipsilateral descending neuron (CiD)) were identified using TgBAC(vsx2:EGFP). Multipolar commissural neurons (MCoD) were identified by spinal backfill from the caudal spinal cord (see above). To statistically examine if hindbrain cell type explained the variance in their spinal innervation patterns, the presence of innervation to the spinal neurons was fitted with the following generalized linear mixed effects model with a random effect for cell and a logit link function for a binomial distribution using lme4 package in R (https://cran.r-project.org/web/packages/lme4/index.html).

Whole-cell recordings from hindbrain neurons were done as described above. The spinal cord was also exposed by removing the muscles overlying the cell of interest using the standard procedure described previously (Drapeau et al., 1999). Dorsal and ventral MiV1 neurons were targeted as described above. Mid3i was identified based on the GFP signal from Tg(vsx2:EGFP), its stereotypical segmental location (rhombomere 6) and morphology (laterally elongated soma and its position relative to nearby reticulospinal neurons in the same segment). Mauthner cell was identified based on its segmental position (rhombomere 4) and its large soma which is clearly visible under Dodt gradient contrast. Spinal motoneurons and interneurons (MCoDs) were targeted with Dodt gradient contrast and/or fluorescence either from backfill (see above) or from transgenic lines (Tg(mnx1:TagRPFT) for motoneurons; Tg(Dbx1b:Cre, vglut2a:lRl-GFP) for MCoDs). Primary motoneurons are dorsally located large cells close to the lateral surface of the spinal cord. MCoDs are ventrally located smaller cells but also close to the lateral surface of the spinal cord. PMNs were sampled at muscle segment 17 to 19. MCoDs were sampled at muscle segments 11 to 13. To examine if a given connection is monosynaptic, extracellular solution with a high concentration of divalent cations (4x Mg²⁺ and 4x Ca²⁺) was perfused to increase the firing threshold of potential relay neurons. Only the connection between Mauthner cell and MCoD was almost completely abolished, which is also consistent with its long latency. Thus, this connection was excluded from subsequent analysis. To examine the composition of electrical and chemical synapses for a given connection, chemical synapses were blocked by a cocktail of D-AP5 (Tocris, 0106, MN) and NBQX (Tocris, 0373, MN) for glutamatergic synapses and by mecamylamine (Tocris, 2843, MN) for cholinergic synapses.

To statistically examine if hindbrain and spinal cell types had significant effects on the presence of monosynaptic connections, the presence of monosynaptic connection was fitted with the following generalized linear model with a logit link function for a binomial distribution using lme4 package in R.

The differences in conduction velocity, postsynaptic potential (PSP) amplitude and PSP half decay time across connected pairs of the monosynaptic responses were statistically examined with Kruskal-Wallis test. Post-hoc comparisons were done with Dunn’s multiple comparison test.

We ablated two groups of V2a neurons based on the developmental and recruitment analyses we performed (Figures 2–4). The first group consisted of the early-born V2a neurons that were recruited during strong swims. Procedurally, they were defined as hindbrain neurons that contained photoconverted Kaede at 60 hpf after photoconversion at 24 hpf in Tg[vsx2:Kaede] (Figure 10B i). The second group consisted of the late-born V2a neurons in the caudal hindbrain that were recruited during spontaneous weak swims. Procedurally, they were defined as neurons that did not contain photoconverted Kaede at 60 hpf after photoconversion at 42 hpf and met the following spatial criteria (Figure 10B iv). First, their somata were caudal to rhombomere six where the early-born neurons were located in dorsal hindbrain (Kinkhabwala et al., 2011) Second, they were dorsal to the early-born neurons in the caudal hindbrain. These neurons born after 42 hpf probably do not include all the caudal neurons active during weak swims (see Figure 5A vi and Figure 2F). Thus, we may underestimate the contribution of the caudal V2a neurons to spontaneous weak swims. However, we chose this to make sure there is no unintended damage to the most lateral early-born neurons so that we can see distinct locomotion phenotypes for each ablation group. The time of ablation was matched in the two groups to minimize the difference in the procedure.

For every fish that was randomly selected for ablation from the pool of fish that had been photoconverted, one or two more fish were chosen in the same way to serve as a negative control(s). The fish to be ablated was anesthetized in MS-222 for 2 min before being mounted dorsal-side up in 1.6% low melting point agarose and placed under our custom two-photon microscope where the ablations took place. Both the fish in which the ablations were carried out and the fish chosen as control were kept under anesthesia for the entire duration of the ablations. Before proceeding to ablations, we first imaged hindbrain V2a neurons labeled with unconverted and converted Kaede using 1020 nm, 80 MHz femtosecond laser pulses (MKS Instruments, Insight, Deep See, MA) with a 25x objective lens (Leica, HCX IRAPO L 25x/0.95 W, Germany). Then, we proceeded to ablations starting from the most ventral target cells to increasingly more dorsal cells to minimize light scattering that could result from preceding ablations. For a given depth, each target cell was exposed to femtosecond laser pulses for 5 ms with spiral scans centered on its soma (outer diameter, 3 um) before targeting the next target after a minimum 5 s interval using ScanImage (Vidrio Technologies, VA). We used 920 nm or 1020 nm laser (196 mW or 142 mW after objective lens, respectively). This procedure was repeated up to ten times until the cells at a given depth were deemed ablated based on visual inspection. After ablating all the cells of interest, we imaged hindbrain V2a neurons again to assess the quality of ablation. Then, the fish was gently removed from the embedding agarose and both this fish and the control fish were housed singly in separate dishes until later use on the day of behavior imaging, which was at 5 dpf. To examine the potential recovery of target cells after the ablation, a subset of the ablated fish and the control fish was selected for imaging at four dpf. From the time of photoconversion till the day of behavior, the dishes containing the fish were enclosed in our UV-opaque box to prevent further conversion of Kaede.

At 5 dpf, ablated and control fish were transferred singly and in random sequence to a 50 mm diameter circular arena where they were allowed to swim freely while being imaged from above with a high-speed camera (Miktrotron, MC-1362, Germany). To confine the movements of the fish to the yaw (x-y) plane, the depth of the water in the arena was limited to 2 mm. Before the start of imaging, each fish was allowed to acclimate to the arena for a minimum of 20 min before their behavior was imaged. If even after 20 min the fish spent a majority of the time being stationary or swimming very close to the edge of the arena (as they tend to do soon after being transferred into the arena from their home dish), then the wait period was increased until the fish started showing frequent swims in trajectories that took them far from the arena edge. Following this acclimation period, fish were first imaged at 300 frames per second (fps) for a total 5 min while they swam spontaneously. After this 5-min period, fish were imaged continuously while they were presented with a series of alternating vibration and dark flash stimuli - although in this paper we did not use data from dark flash responses - at 1 min intervals. A time period of 100 ms before and 1400 ms after each stimulus were imaged at 500 fps, whereas the intervening 1 min periods between stimuli were imaged at 30 fps. The vibration stimulus was delivered via a sound speaker in contact with the acrylic base plate upon which the fish arena was placed. The sound stimulus consisted of two cycles of 500 Hz sine wave at (peak-to-peak acceleration$\approx 100m{s}^{-2}$). For the dark flash stimulus, the ambient light was turned off for 300 ms.

Images collected during the behavior sessions were first sorted by frame rate. For images collected at 30 fps we only tracked the fish’s head position, whereas for images collected at 300- and 500 fps, we estimated the fish’s head orientation and body curvature as well.

To extract the fish’s head position, we first computed a background image and subtracted this from all images to remove background from them. The background image was computed by averaging a set of 1000 temporally uniformly spaced images from the set of all images. After background subtraction, for convenience, the pixel values were multiplied by -1 so that the fish’s eyes would go from being the darkest to the brightest spots in the image. The images were then smoothed by convolving each image with a 1 mm wide 2D Gaussian kernel. The convolved images were then segmented using an automatically estimated threshold (multithresh function in MATLAB) to isolate within each image a blob (contiguous set of pixels) of the brightest pixels that included the fish’s eyes as well as the swim bladder. The centroid of these isolated pixels was used as the fish’s head position. The centroid obtained in this manner was reliably located between the eyes and the swim bladder in all image frames and for frames in which the fish did not move, the centroid typically shifted by less than 0.17 mm ($\approx$3 pixels) between frames (confidence interval of 95%).

For quantifying the fish’s tail curvature, we needed to reliably isolate as many pixels on the fish’s body as possible so that the movements of the tail could be accurately tracked during swimming. For this, we used a custom MATLAB script (available upon request) that used an iterative procedure to obtain a threshold for image binarization and isolation of fish pixels.

Following the detection of fish pixels, the images were binarized by setting fish pixels to 1 and the rest of the pixels to 0. The MATLAB function bwmorph was then used on the binary images to 'thin' the fish pixels down to a set of connected pixels that spanned the length of the fish and coarsely bisected it into two lateral halves. To transform these 'midline' pixels into a smoother curve that better tracked the spine of the fish, we followed the procedure described in (Huang et al., 2013), wherein the integer-valued image coordinates of the midline pixels were weighted by the intensities of the surrounding pixels to obtain a new decimal-valued set of pixels. The curve obtained by this procedure was then interpolated using cubic splines to obtain a final smooth curve ($C$) of 50 points.

To characterize swimming, we looked at changes in the body posture and head orientation over time. We used the midline curve ($C$) obtained using the procedure described above to characterize body posture. First, we computed tangent vectors ${\displaystyle \hat{i}}$ along the points on $C$ and used the angle differences between adjacent tangent vectors to compute local curvatures. We then cumulatively summed these curvatures along the tail, starting from the point closest to the head centroid. This way we obtained a set of angles along the midline that represented the angular difference between the tangent at a given point along the midline and the tangent at head centroid. The total curvature ($K$) was computed as the sum of all the local curvature along the tail.

To obtain the head orientation, the first 10 points (the 10 points closest to the head centroid) of the midline curve $C$were sampled and a straight line $\left(L\right)$ was fit to those points. The head orientation (${\varphi }_H$) was then estimated as the absolute angle of the vector extending from the point on $L$ farthest from the head centroid to the point on $L$ closest to the head centroid.

To identify distinct swim episodes, we used the total curvature time series, ${\rm K}\left(t\right)$, after low pass filtering it at 70 Hz to remove high-frequency noise. We then used a custom MATLAB script to identify the times corresponding to the onset and offset of each swim episode. Briefly, the absolute function of the filtered curvature timeseries (|${\rm K}\left(t\right)|$) was convolved with a 200 ms gaussian kernel and in the first step the onsets of swim episodes were identified as the points where the transformed timeseries ($S\left(t\right)$) crossed and stayed above a threshold of 4o for at least 50 ms. Then, for each onset a corresponding offset was identified as the first point in $S\left(t\right)$ after this onset where the signal returns and stays below the threshold for at least 50 ms. Then, in a second step to improve on the loss of temporal resolution resulting from the convolution of $K\left(t\right)$ to get $S\left(t\right)$, we updated each onset by replacing it with the time of the peak in the second derivative of $S\left(t\right)$ that occurred immediately before this onset.

To sort spontaneous swim episodes into distinct subtypes we used the amplitudes of the first bend within each swim episode because it was shown in a previous study (Burgess and Granato, 2007) that this single metric could be used to effectively categorize spontaneous swims into at least two distinct groups. To obtain the decision boundaries for sorting spontaneous swimming episodes from our dataset into distinct swim categories we extracted the first bend amplitude from each episode and fit a Gaussian Mixture (GM) model to the data using the scripts available in the scikit-learn library (http://scikit-learn.org/stable) in Python. For training the GM model, we used data from 15 fish (6056 swim episodes) in which V2a neurons had not been photoconverted (Figure 10A, iv-v) because photoconversion could have potentially altered swimming characteristics. Our model incorporated three Gaussian components because this resulted in the lowest value for the Akaike Information Criterion (AIC) (Figure 10A, iv bottom), indicating that our dataset consisted of three distinct spontaneous swim types. Finally, we used the trained GM model on the entire spontaneous swimming dataset to assign each episode to one of the three categories. To visually inspect the swim episodes from each category we used a custom script to temporally align all the episodes (curvature time series) within a category in such a way as to produce the highest correlations among them, and then plotted the traces atop of each other (Figure 10A,vi). For aligning escape episodes which have highly nonstationary beat frequencies, correlations were computed using a restricted time window of 80 ms after the episode onset. As with the training of the GM model, we only used data from non-photoconverted fish to make these plots. The plots showed that the swim episodes within each category were quite similar not just with respect to the amplitudes of their first bends, which we had used to predict swim categories, but with respect to subsequent bends as well. Furthermore, the overlaid traces revealed that swim episodes within a category were very similar to each other, but swim episodes across categories were quite distinct. This confirmed that as in the study by Burgess and Granato (2007), we could use the amplitude of just the first bend of each swim episode to effectively categorize spontaneous swims into distinct subtypes.

In the case of escape swims, we treated them as belonging to a single category because they were all produced in response to the same vibration stimulus and because they are very rarely, if ever, observed in the absence of a stimulus. Thus, for comparing the individual bend amplitudes and periods between control and ablated fish, we did not sort escapes in the same way as spontaneous swims.

To evaluate the effects of ablations on swimming, we computed two types of swim parameters from each swim episode and compared these across ablated and control groups of fish. The first type of parameters were global in that each of these parameters described some aspect of a swim episode using a single number. In this study, the global swim parameters we examined were total swim distance per episode, episode duration, mean swim velocity per episode, maximum swim velocity per episode, total number of body bends per episode, and onset latency of the episode (only for escapes because these were triggered using a stimulus of very short duration). The other type of swim parameters we computed were local in that they were a set of numbers that described a swim episode at different stages. Here, the local parameters we examined are the amplitudes and durations (or periods) of the different body bends that comprise a swim episode.

To estimate the traveling distance per episode, we tracked the spatial position of a point on the fish’s body just caudal to the swim bladder. We chose to track this point instead of the head centroid because it captured the fish’s translation in space without being susceptible to oscillations of the head, such as those conspicuously observed during escapes. The total distance per episode was then computed as the sum of the distances traveled by the tracked point from one frame to another over all the image frames containing a single swim episode. The duration of an episode was simply the time elapsed from the detected onset to the offset of the episode (see above), and the mean swim velocity was obtained by dividing the total swim distance per episode by the duration of the episode. To compute the maximum swim velocity, the temporal derivative of the fish’s spatial position was first computed to obtain the instantaneous swim velocity. The maximum of this timeseries during the course of an episode was the maximum swim velocity per episode. The number of bends per episode was computed by detecting and counting the total number of peaks in the curvature timeseries corresponding to a single swim episode.

Bend amplitudes and bend periods within a swim episode were computed after detecting peaks within the curvature timeseries as follows:

where $n$ corresponds to bend number, $A\left(n\right)$ and $P\left(n\right)$ are the $n^{th}$ bend amplitude and period respectively, $t_n$ is the time at the $n^{th}$ peak, and $t_{on}$ is the time of swim onset. Peak angular velocities within a swim episode were computed in a similar manner from the angular velocity time series $\omega \left(t\right)$, which is the temporal derivative of the timeseries $K\left(t\right)$.

To quantitatively examine how the stereotypy of swim episodes within each swim category changes over the course of an episode, which was one of the criteria used to limit the analysis of bend amplitude and periods first 10 bends of an episode (see Results), we utilized ROC (Receiver Operating Characteristic) analysis. We first computed the probability distributions of the peak times of each of the bends of a swim episode (Figure 10—figure supplement 3A) for all swim categories. Here, the peak time of a bend refers to the time elapsed from the onset of the episode to the time when that bend reaches peak amplitude. Then, from these probability distributions, we estimated the discriminability of pairs of adjacent bends using ROC analysis. For each pair of adjacent bends up to the 10th bend, we used the probability distributions of their peak times to compute ROC curves (Figure 10—figure supplement 3B i). The area under the curve (AUC) for an ROC curve generated for a given bend pair (say, the second and third bend) indicated how discriminable that pair of bends was with respect to their peak times. To examine how discriminability of pairs of bends changes for each pair of successive bends, we plotted the AUC values computed for all the bend pairs against bend pairs ordered in succession (Figure 10—figure supplement 3B ii).

To determine if the global and local swim parameters we computed were significantly different in control and ablated fish, we carried out statistical tests using software packages implemented in R. The data was first fit using a mixed effects model (nlme package) in which the swim parameter of interest (swim distance, bend amplitude, bend period, etc) was treated as the response variable while the categorical variables treatment, ablation group, and swim type were treated as the explanatory variables. The variable treatment took two values that indicated if the source of a given data sample was an ablated or a control fish. The variable ablation group also assumed two values that indicated if a data point had been collected from a fish in which old (birth-time < 24 hpf) or young (42 hpf <birth time<60 hpf) V2a neurons had been ablated. Finally, the variable swim type indicated which of the aforementioned swim categories the data point belonged to. In addition to the above explanatory variables, the identity of the fish was modeled as a random variable; the values within this last variable uniquely identified each fish that contributed to our dataset. The model also took into account interactions between the independent variables. We used this model-based approach to separately assess the effects of ablation on any of the variables of interest to us. This approach requires that the variables of interest be normally distributed, so to check for normality, we generated quantile-quantile (Q-Q) plots of the data, fit a straight line to the points and computed the R2 value to assess how good the fit was. R2 values greater than 0.8 were deemed good. Based on this criteria, all the swim parameters we looked at except escape onset latencies followed a normal distribution. However, when latencies were log-transformed and normality assessed on the transformed data, the R2 value exceeded our cutoff value of 0.8. So, the statistical test was carried out on log-transformed escape latencies. Significance was assessed at p<0.01, and the Holm correction for multiple comparisons was made.

Here, swim parameter can be any of the following: total swim distance per episode, episode duration, mean swim velocity per episode, maximum swim velocity per episode, total number of bends per episode, onset latency of escape, bend amplitude, bend period.

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

We are grateful to the present and past members of our lab, especially to Jamien Shea who supported the early phase of this work and Masashi Tanimoto who provided critical feedback to the manuscript. We are also thankful to James Fitzgerald, Tzumin Lee, Misha Ahrens and Erik Snapp for providing critical feedback and to the members of Misha Ahrens’ lab for their feedback during joint lab meetings. We thank all the members of the aquarium facility at Janelia for providing excellent care to the fish used in this study. We greatly appreciate the expertise and equipment provided by Janelia Experimental Technologies, especially by Vasily Goncharov and Christopher McRaven. Finally, we thank the scientific and administrative community at Janelia for creating an environment that allows for the focused pursuit of fundamental scientific questions.

Animal experimentation: All experiments presented in this study were conducted in accordance with the animal research guidelines from the National Institutes of Health and were approved by the Institutional Animal Care and Use Committee and Institutional Biosafety Committee of Janelia Research Campus. (16-145).

© 2019, Pujala and Koyama

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