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Evolutionarily conserved brainstem architecture enables gravity-guided vertical navigation [1]

['Yunlu Zhu', 'Departments Of Otolaryngology', 'Neuroscience', 'Physiology', 'The Neuroscience Institute', 'New York University Grossman School Of Medicine', 'New York', 'United States Of America', 'Hannah Gelnaw', 'Franziska Auer']

Date: 2024-11 

Larval zebrafish navigate depth by maintaining a consistent heading over a series of swim bouts

We first examined whether larval zebrafish maintain a consistent heading as they navigate in depth [48]. To measure behavior, we used a high-throughput real time Scalable Apparatus to Measure Posture and Locomotion (SAMPL) [49]. SAMPL records body position and posture in the pitch axis (nose-up/nose-down) as larval zebrafish swim freely in depth (Fig 1A). We examined freely swimming larvae from 7 to 9 days postfertilization (dpf) in complete darkness. We measured the trajectory of swim directions relative to horizontal and observed both upward and downward swim bouts (Fig 1B and 1C), indicating that larvae climb and dive in the water column. To quantify the spread of individual swim directions, we defined variability as the median absolute deviation (MAD) of swim bout trajectories (Fig 1D).

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TIFF original image Download: Fig 1. Larvae navigate depth in series of consecutive bouts with consistent heading. (A) Sample swim trajectories of 7 dpf larvae in the x/z axes. All trajectories begin from the left. Dots represent fish locations 60 ms apart (down-sampled from 166 Hz data for visualization). Arrows mark swim directions of individual bouts in the blue trajectory. Scale bar: 1 mm. (B) Time series data of the blue trajectory in A. Horizontal dashed line in the upper panel indicates the 5 mm/s threshold for bout detection. Vertical lines label the time of peak speed for each bout. Lower panel plots directions of movement (black) and body posture in the pitch axis (orange). (C) Polarized histograms (frequency polygons) of bout directions of WT zebrafish. n = 123,849 bouts from 537 fish over 22 experimental repeats. (D) Schematic illustrations of bout direction variability. A wide distribution of bout directions indicates high variability. (E) Directions of the following bout plotted as a function of the current plot. Correlation coefficient is plotted in (F). Slope of the best fitted line is plotted in (G). n = 63,681 bout pairs from 537 fish over 22 experimental repeats. (F) Serial correlation (autocorrelation) of swim directions across observed consecutive bouts and shuffled bouts. N = 22 experimental repeats. 95% correlation confidence intervals are shown as shaded error bands. (G) Slope of the best fit line of swim directions of bout(n+lag) vs. bout(n) is defined as the swim direction consistency. N = 22 experimental repeats. 95% confidence intervals of the estimated slope are shown as shaded bands. (H) Veering is quantified as the absolute change of swim directions between adjacent bouts, averaged through a bout series. A bout sequence with greater direction changes results in higher veering. (I) Veering across 4 consecutive bouts (observed) and shuffled bouts are plotted as histograms. Median values are shown as dashed lines. n = 17,155 sets of 4 bouts. P median-test < 1e-16. (J) Distribution of depth changes, defined as the displacement on the z axis, of a single swim bout. n = 123,849 bouts from 537 fish over 22 repeats. (K) Cumulative depth changes through 4 consecutive bouts, separated by the swim direction of the first bout. (L) Schematic illustration of “depth change efficacy.” A trajectory with higher efficacy achieves a greater depth change (displacement on the z axis) with the same swim distance and starting direction. (M) Histograms of depth change divided by distance across 4 swim bouts. Values of −1 or 1 represent trajectories pointing straight down or up, respectively, on the vertical axis. (N) Depth change/swim distance ratio during series of 4 bouts plotted as a function of the swim direction of the first bout. Depth change efficacy is defined as the slope of best fit line. n = 17,155 sets of 4 bouts. (O) Depth change efficacy of observed and shuffled bouts. Errors indicate standard deviations. N = 22 experimental repeats. P t-test = 8.65e-37. See also Table 1 for parameter definitions and statistics. All code and data can be found at DOI: 10.17605/OSF.IO/AER9F. https://doi.org/10.1371/journal.pbio.3002902.g001

The depth change resulting from a single swim bout was small (0.34 [0.57] mm, median of absolute depth displacement with interquartile range [IQR]), so we hypothesized that larval zebrafish integrate a series of swim bouts to adjust their depth effectively. We quantified and parameterized the statistics of short series of sequential bouts. Directions of consecutive bouts were highly correlated (Fig 1E), determined by the coefficient of determination of direction (Fig 1F), and highly consistent, defined as the slope of the best-fit line between directions of consecutive bout (Fig 1G and Table 1 for parameter definitions and statistics). As the series continued, bout direction became increasingly less correlated with the first bout (Fig 1F and 1G) reaching chance level around the 10th consecutive bout (S1 Fig). To quantify the amount of direction change during consecutive bouts, we defined veering as the mean of absolute direction differences between adjacent bouts (Fig 1H). Compared to shuffled bouts, fish veered significantly less during observed consecutive bouts (Fig 1I; 5.61 [6.85° versus 23.82 [16.03°, observed versus shuffled, median with IQR, P median-test < 1e-16), indicating that larval zebrafish maintain stable swim directions through a series. Consequentially, a bout series results in cumulative changes in depth (Fig 1K). In addition to swim directions, swim distances also affect depth displacement. A trajectory that allows effective depth change should allocate more displacement to the vertical axis (Fig 1L). We quantified the ratio of displacement in depth versus Euclidean swim distance and found that observed bout sequences showed a wider distribution (Fig 1M) and was highly correlated with swim directions (Fig 1N). To quantify how effectively larvae change depth, we defined depth change efficacy as the best fitted line between depth/distance ratio and the direction of the first bout in the sequence (Fig 1N). Compared to shuffled bouts, fish show significantly higher efficacy (Fig 1O; 1.31e-2 ± 1.09e-3 versus 1.37e-4 ± 1.09e-3, observed versus shuffled, mean ± SD, P t-test = 8.65e-37), indicating that, given the swim direction of a bout, fish exhibit effective depth change following consecutive bouts in the sequence.

We conclude that larvae change depth effectively by performing a series of swim bouts with consistent heading. The parameters of consistency, veering, and efficacy define their ability to navigate in depth.

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[1] Url: https://journals.plos.org/plosbiology/article?id=10.1371/journal.pbio.3002902

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