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R E S E A R CH AR T I C L E
A profile of auditory-responsive neurons in the larval zebrafishbrain
Gilles Vanwalleghem1 | Lucy A. Heap1 | Ethan K. Scott1,2
1School of Biomedical Sciences, The
University of Queensland, St Lucia, QLD,
Australia
2The Queensland Brain Institute, The
University of Queensland, St Lucia, QLD,
Australia
Correspondence
Ethan K. Scott, School of Biomedical
Sciences, The University of Queensland, St
Lucia, QLD 4072, Australia.
Email: [email protected]; Gilles
Vanwalleghem, School of Biomedical
Sciences, The University of Queensland, St
Lucia, QLD 4072, Australia.
Email: [email protected]
Funding information
Grant sponsor: NHMRC Project Grant,
Grant number: APP1066887; Grant
sponsor: ARC Future Fellowship, Grant
number: FT110100887; Grant sponsor: A
Simons Foundation Explorer Award, Grant
number: 336331; Grant sponsor: Two ARC
Discovery Project Grants, Grant numbers:
DP140102036, DP110103612; Grant
sponsor: ARC LIEF, Grant number:
LE130100078.
AbstractMany features of auditory processing are conserved among vertebrates, but the degree to which
these pathways are established at early stages is not well explored. In this study, we have
observed single cell activity throughout the brains of larval zebrafish with the goal of identifying
the cellular responses, brain regions, and brain-wide pathways through which these larvae perceive
and process auditory stimuli. Using GCaMP and selective plane illumination microscopy, we find
strong responses to auditory tones ranging from 100 Hz to 400 Hz. We also identify different cat-
egories of auditory neuron with distinct frequency response profiles. Auditory responses occur in
the medial octavolateral nucleus, the torus semicircularis, the medial hindbrain, and the thalamus,
and the flow of information among these regions resembles the pathways described in adult fish
and mammals. The details of these patterns, however, indicate that auditory processing is still rudi-
mentary in larvae. The range of frequencies detected is small, and while different neurons have
distinct response profiles, most are sensitive to multiple frequencies, and distinct categories show
substantial overlap in their responses. Likewise, while there are signs of nascent spatial representa-
tions of frequency in the larval brain, this only faintly resembles the clear tonotopy seen in adult
fish and mammals. Overall, our results show that many fundamental properties of the auditory sys-
tem are established early in development, and suggest that zebrafish will provide a good model in
which to study the development and refinement of these pathways.
K E YWORD S
Auditory Pathways, Cochlear Nucleus, Fluorescence, RRID: AB_2315112, RRID: AB_2534077,
RRID: SCR_002234, RRID: SCR_001622, RRID: SCR_007198, RRID: SCR_002285, RRID:
SCR_002798, RRID: SCR_001905, Zebrafish
1 | INTRODUCTION
The auditory systems of fish draw information from a range of sensory
structures and neural pathways, and these vary from species to species
and across development. Fish sense sound waves as particle motion
via the otoliths in the inner ear. Amongst “hearing specialists” such as
zebrafish, the Weberian ossicles conduct high frequency swim bladder
vibrations to the inner ear, expanding the hearing range (Grande &
Young, 2004; Higgs, Rollo, Souza, & Popper, 2003; Popper & Fay,
1999). In zebrafish larvae, audition of frequencies above 100 Hz is
likely mediated solely by the saccular otoliths (Popper & Fay, 1993;
Yao, DeSmidt, Tekin, Liu, & Lu, 2016), as the utricular otoliths are
involved exclusively with vestibular perception (Riley & Moorman,
2000), and the Weberian ossicles are neither fully developed nor
functional in larvae (Grande & Young, 2004; Higgs et al., 2003). The
same holds true for the lagenar otolith, which may be involved in audi-
tion, but only develops 2 weeks post fertilization (Bever & Fekete,
2002; Riley & Moorman, 2000). Another potential source of auditory
input is the lateral line, which consists of hair cells similar to the ones
of the inner ear, but organized into neuromasts (Kalmijn, 1988; North-
cutt, 1981). In zebrafish larvae, lateral line afferents respond to
frequencies up to 100 Hz, but it is unclear whether or how this infor-
mation interacts with the rest of the auditory system (Levi, Akanyeti,
Ballo, & Liao, 2015).
Anatomical studies in adult teleost fish have shown auditory path-
ways similar to those in mammals. Auditory information from the VIIIth
nerve flows through the octavolateralis nuclei in the hindbrain to the
torus semicircularis (inferior colliculus in mammals) in the midbrain, and
J Comp Neurol. 2017;1–13. wileyonlinelibrary.com/journal/cne VC 2017Wiley Periodicals, Inc. | 1
Received: 1 March 2017 | Revised: 26 May 2017 | Accepted: 29 May 2017
DOI: 10.1002/cne.24258
The Journal ofComparative Neurology
then to the thalamus (Fay & Edds-Walton, 2008). It is unknown
whether this pathway is developed in larval zebrafish, as most studies
of hearing in zebrafish have looked at juveniles or adults (Cervi, Poling,
& Higgs, 2012; Higgs et al., 2003; Higgs, Souza, Wilkins, Presson, &
Popper, 2002; Mueller, 2012; Wang et al., 2015). The few studies
assessing larval hearing have used microphonic potentials to gauge
responses in the ears’ hair cells (Lu & DeSmidt, 2013; Rohmann, Tripp,
Genova, & Bass, 2014; Yao et al., 2016), and this work has shown
responses to tones ranging from 20 Hz to 400 Hz as early as 3 days
postfertilization (dpf) (Yao et al., 2016). By 5–6 dpf, larvae startle to
tones up to 1,000 Hz, although these responses require extremely
strong stimuli (Bhandiwad, Zeddies, Raible, Rubel, & Sisneros, 2013).
Strong vibrations may also activate the semicircular canals hair cells,
but these are not functional in 6 dpf larvae (Beck, Gilland, Tank, &
Baker, 2004).
With its transparency, and given the rise of light-based tools for
observing and manipulating neural activity, the larval zebrafish has
emerged as an important model for dissecting functional circuits in the
brain. So-called optophysiology, in which fluorescent indicators of
activity are monitored throughout the brain, has enabled the study of
the cell types and circuits involved in processing water flow sensation
(Thompson, Vanwalleghem, Heap, & Scott, 2016), proprioception
(Bohm et al., 2016; Fidelin et al., 2015), somatosensation (Douglass,
Kraves, Deisseroth, Schier, & Engert, 2008), and especially vision
(Bianco & Engert, 2015; Bianco, Kampff, & Engert, 2011; Del Bene
et al., 2010; Temizer, Donovan, Baier, & Semmelhack, 2015; Thompson
& Scott, 2016; Thompson et al., 2016; Vladimirov et al., 2014). Larvae
respond to auditory stimuli (Bhandiwad et al., 2013; Lu & DeSmidt,
2013; Rohmann et al., 2014; Yao et al., 2016), but aside from Mauthner
cell driven auditory escape responses (Mu, Li, Zhang, & Du, 2012; Tani-
moto, Ota, Horikawa, & Oda, 2009), little is known about their auditory
processing, and brain-wide optophysiology has not yet been applied to
this modality.
Here, we use a custom-built selective plane illumination micro-
scope (SPIM) to image the calcium indicator GCaMP6f throughout the
brain of 6 dpf larval zebrafish (Chen et al., 2013) while presenting pure
tones as auditory stimuli. We find responses to frequencies up to 800
Hz, but strong consistent responses only occur from 100 Hz to 400
Hz. A majority of the responsive neurons are located in the medial
octavolateralis nuclei (MON) and the medio-dorsal hindbrain, but
sparse responses take place in the thalamus and the torus semicircula-
ris. Using Granger causality, a statistical method to infer causality from
time series, we suggest that information flows through these regions in
a manner similar to what has been described in mammals.
2 | MATERIAL AND METHODS
Zebrafish (Danio rerio) larvae, of either sex, carrying the transgene
elavl3:H2B-GCaMP6f (Chen et al., 2013) were maintained at 28.58C on
a 14 hr ON/10 hr OFF light cycle. Adult fish were maintained, fed, and
mated as previously described by (Westerfield, 2000). All experiments
were carried out in nacre mutant elavl3:H2B:GCaMP6f larvae of the TL
strain (Chen et al., 2013). Larvae at 6 dpf were immobilized in 2% low
melting point agarose (Progen Biosciences, Australia) and imaged at 5
Hz on a custom-built SPIM (Thompson et al., 2016). The microscope
set-up was isolated from vibrations on a micro-g lab table (TMC, USA,
#63–534). In each larva, horizontal planes were imaged in either the
dorso-ventral or ventro-dorsal direction, at 10 mm increments from the
dorsal-most neurons in the brain to the deepest brain region that could
be clearly imaged using SPIM. For most larvae, this resulted in a stack
of images spanning roughly 250 mm dorso-ventrally, and capturing the
entire rostro-caudal and lateral extents of the brain. This means that
most of the brain was robustly sampled, but that some of the deepest
regions (composing the ventral-most 50 mm, approximately) may have
been missed in some larvae, and may therefore be underrepresented in
our dataset. All image acquisition and stimulus presentation was con-
trolled by lManager software (Edelstein, Amodaj, Hoover, Vale, &
Stuurman, 2010).
For all experiments, audio speakers (Logitech Z213) were posi-
tioned 10cm from the animal on both sides (left and right) and slightly
elevated to have a clear line of sight to the imaging chamber. Pure
tones of frequencies ranging from 100 Hz to 800 Hz were played for
1 s, with 5 s in between stimuli. The sound intensity was measured at
70 dB in air at the larva’s position, and was chosen to avoid acoustic
startle responses. The background level of noise was roughly 40 dB.
Each presentation of the stimulus train involved playing these eight fre-
quencies three times, in ascending, descending, and random order.
Since larval zebrafish perceive sound as particle motion, we measured
motion using an accelerometer (PCB Piezotronics, Q353B34) with a
sensitivity of 98.7 mV/g, attached to the imaging chamber. The signal
was acquired using National Instruments SignalExpress, a vibration
input module (NI-9234), and a DAQ (cDaq-9171). The data were proc-
essed using Matlab. The measured accelerations averaged 0.023 m/s2,
but the measured particle motion for 800 Hz was three times higher
than the other frequencies (0.086 m/s2) (Sup. table 1). These intensities
are in line with what has been used in previous studies (Bhandiwad
et al., 2013). One limitation of our measurements was the impossibility
of embedding the accelerometer in agarose and measuring how
embedding may affect the perceived stimulus. Previous studies have
shown that agarose embedding appears to lower the response thresh-
old of Rana catesbeiana tadpoles (Simmons & Flores, 2012).
For phosphorylated Extracellular signal-Regulated Kinase (pERK)
staining, animals were fixed overnight in ice-cold 4% paraformaldehyde
in PBS immediately after presentation of a 10-min long auditory stimu-
lus train. After washing four times in PBS with 0.3% Triton (PBS-T)
animals were incubated in 150 mM Tris-HCl (pH9.0) for 5 min at 238C,
followed by 15 min at 658C. Larvae were washed in PBS-T again (2 3
10 min) before being incubated in 0.05% Trypsin-EDTA (Thermo Fisher
#25300054) on ice for 45 min. Animals were again washed in PBS-T
(3 3 15 minute washes) and were blocked in 5% goat serum, 1% BSA,
1% DMSO in PBS-T for 1 hr at room temperature. pERK was targeted
using Cell Signaling Technology’s Phospho-p44/42 MAPK (Erk1/2)
(Thr202/Tyr204) (D13.14.4E) (#4370, RRID: AB_2315112) Rabbit
primary mAb. This antibody was generated against a synthetic
2 | The Journal ofComparative Neurology
VANWALLEGHEM ET AL.
phosphopeptide and detects single or dual phosphorylated Erk1/2
(Filosa, Barker, Dal Maschio, & Baier, 2016; Randlett et al., 2015). Pri-
mary antibodies were added to the blocking solution at a concentration
of 1:500 for 72 hr at 48C. Larvae were washed in PBS-T (3 3 15 min)
and were incubated in secondary antibody coupled to AlexaFluor 546
(A-11010, RRID: AB_2534077) in blocking solution at a concentration
of 1:500 for 72 hr at 48C. Stained larvae were embedded in 2% low
melting point agarose, and confocal stacks of the entire animals were
taken on a ZeissLSM 710 inverted microscope with 3 mm slice inter-
vals. Consistent confocal settings were used for all animals.
Confocal stacks were stitched together using the Pairwise Stitch-
ing plugin for ImageJ (Preibisch, Saalfeld, & Tomancak, 2009). Image
registration of anti-tERK expression was performed against a model of
anti-tERK expression in the nervous system of larval zebrafish. This
was performed with Computational Morphometry Toolkit, RRID:
SCR_002234 using the command string -awr 010203 -T 8 -X 52 -C 8
-G 80 -R 3 -A ‘–accuracy 0.4’ -W ‘–accuracy 1.6’. Separately, experi-
mental and control animals were averaged using a custom-written Mat-
lab (RRID: SCR_001622) script (Randlett et al., 2015), which was then
incorporated into a local version of the Z-Brain Atlas.
Neomycin (Sigma, N6386) treatment involved a 1-hr incubation
with 200 mM neomycin in E3 media, followed by two washes in E3 and
a 1-hr recovery period (Harris et al., 2003). The effectiveness of the
neomycin treatment was assessed using DASPEI staining as performed
by Harris et al. (2003). Specifically, the presence or absence of 12 neu-
romasts (N, IO1–4, SO1–3, O1–2, OP1 and M2) per fish (4 controls
and 3 neomycin treated) was scored (Raible & Kruse, 2000).
In calcium imaging experiments, the aforementioned auditory stim-
ulus train was reused. Stimuli were generated in Audacity, RRID:
SCR_007198. Imaging was performed on a house-built SPIM micro-
scope, as previously described (Thompson & Scott, 2016; Thompson
et al., 2016). The essential components of this microscope are listed in
Table 1.
Motion artifacts caused by slow drift of the image or by spontane-
ous movements by the larva were corrected in Fiji, RRID:
SCR_002285, using “moco” (Dubbs, Guevara, & Yuste, 2016) followed
by a rigid body transformation in StackReg (Thevenaz, Ruttimann, &
Unser, 1998). The eyes and otoliths were manually cropped from the
images before segmentation to avoid artifactual signals. Segmentation
of individual cells was done with the MorphoLibJ morphological seg-
mentation tool (Legland, Arganda-Carreras, & Andrey, 2016). ROIs
smaller than 8 pixels total or larger than 200 pixels were excluded. We
averaged the fluorescence across each ROI, and DF/F was calculated
as previously described (Jia, Rochefort, Chen, & Konnerth, 2011). All
DF/F time series from the 15 fish were then analyzed as follows.
Stepwise linear regressions and location analyses were performed
in Matlab using custom scripts. The stepwise regression fits a linear
model to the data using the Bayesian information criterion to select
the predictors that improve the fit of the model. Predictors were built
for each of the eight frequencies, with a typical GCaMP6f response
occurring for each of the three presentations of the tone. The coeffi-
cient of determination (r2) of the linear regression models was used to
select auditory responsive neurons, and we chose a 0.15 threshold
based on the r2 distribution of our models to allow for conservative fil-
tering of the data. All statistical analyses were performed with Graph-
pad Prism 7.0, RRID: SCR_002798.
The correlation clustering approach (Bianco & Engert, 2015) was
performed in Matlab. From a given trace, we computed its correlation
coefficient with all the other fluorescent traces and merged it with the
highest correlated trace above our threshold of 0.85. This high thresh-
old was required because of the broad response profile of most neu-
rons. We repeated the process until no traces showed correlation
above 0.85 to the merged traces. We then started the process again
from another unmerged trace until no pairs of traces and merged traces
had a correlation above 0.85. Clusters were deemed representative if
they contained at least 10 neurons per fish in at least 5 fish. Only rep-
resentative clusters were considered for further analysis. The coordi-
nates of individual neurons were normalized by registering all larvae
onto each other using Matlab. Principal Component Analysis (PCA) was
used to define the axis of maximal variance in order to investigate pos-
sible tonotopy.
Granger causality was computed with the VARS and tseries pack-
age in R (RRID: SCR_001905), following the Toda-Yamamoto proce-
dure (Toda & Yamamoto, 1995). Briefly, we used both augmented
Dickey–Fuller (ADF) and Kwiatkowski–Phillips–Schmidt–Shin (KPSS)
test to assess for the stationarity of our time series and identify the
TABLE 1 SPIM parts list
Component Product Manufacturer
Beam expander LC1715 f5250 mm Concave Lens Thorlabs
Beam expander LA1708 f5 200 mm Convex Lens Thorlabs
Beam splitter BS PLATE 50 3 50 mm 50R/50T Edmund Optics
Fixed slit 6 3 50 mm slit Custom made
Cylindrical lens LENS CYL 50 3 25 mm 3 75 FL VISNIR Edmund Optics
Illumination objective XLFLUOR4X 0.28NA Olympus
Detection objective XLUMPFLN 20XW 1.0NA Olympus
Camera pco.edge 5.5 PCO
VANWALLEGHEM ET AL. The Journal ofComparative Neurology
| 3
maximal order of integration (Kwiatkowski, Phillips, Schmidt, & Shin,
1992; Said & Dickey, 1984). We then set up a vector autoregressive
(VAR) model of the maximal order of integration (1 in our case) and
used the Akaike Information criterion to select the optimal lag. We
ensured that there was no serial correlation using the portmanteau test
and performed the Johansen test to ensure the time series were coin-
tegrated (Johansen, 1991). Finally, we computed the pairwise Granger
causality of our time series (Granger, 1969).
All procedures were performed with approval from the University
of Queensland Animal Welfare Unit in accordance with approval
SBMS/305/13/ARC.
3 | RESULTS
3.1 | Auditory responsive regions of the larval
zebrafish brain
As an initial means of identifying auditory responsive brain regions at 6
dpf, we played auditory stimuli, fixed the larvae, and stained for pERK
as an early marker of neuronal activation (Fig. 1) (Randlett et al., 2015).
The tones, ranging from 100 Hz to 800 Hz, were played at 70 dB for
10 min (see Methods) immediately prior to fixation. Auditory stimuli
resulted in an increase of the pERK signals in several brain regions (Fig.
1a’–f’), when compared to control larvae not exposed to auditory stim-
uli (Fig. 1a–f).
We observed clear responses along both vglut2 stripe 1 and vmat2
stripe 1 in the hindbrain (Fig. 1d’, red and blue outlines, respectively)
(Kinkhabwala et al., 2011; Randlett et al., 2015; Wen et al., 2008). This
region is known to receive lateral line, auditory, and somatosensory
input and corresponds to the MON in larval zebrafish (Liao & Haehnel,
2012). We also see an overall increase in the pERK signal in the medial
hindbrain, a region of the brain that is still histologically undifferentiated
in larvae (Fig. 1d’, along the midline) (Mueller & Wullimann, 2016). Con-
sistent with previous calcium imaging observations (Thompson et al.,
2016), we see auditory responses in the deep optic tectum (Fig. 1e’, red
outline). Other areas with auditory responses include a weak and diffuse
labelling in the torus semicircularis (Fig. 1f’, red outline) and a strong
labelling in the pallium (Fig. 1b’, c’, blue outlines). The subpallium (Fig. 1c’
cyan outline) also shows a weak increase in the pERK signal, but most of
the telencephalic signal appears localized in the pallium.
These results demonstrate that an auditory pathway in larval
zebrafish is present by 6 dpf, and that it spans both first order sensory
structures and more central brain regions. In order to gauge how these
broad patterns are manifested in the activity of individual neurons
within these structures, we next performed calcium imaging of auditory
processing using SPIM and the calcium indicator GCaMP6f.
3.2 | Auditory neurons respond strongly up to tones
ranging from 100 Hz to 400 Hz
We presented the same 70 dB, 100–800 Hz tones to immobilized lar-
vae expressing H2B-GCaMP6f pan-neuronally, and imaged calcium
dynamics across the brain using SPIM in order to track the responses
of individual neurons (Fig. 2a, b). The tones were presented three
times, in ascending, descending, and random order of frequencies (Fig.
2d, bottom). Movies were preprocessed using motion corrections and
manual cropping (Dubbs et al., 2016; Thevenaz et al., 1998), and indi-
vidual cells were morphologically segmented into distinct regions of
interest (ROIs, Fig. 2c) (Legland et al., 2016). Following the exclusion of
ROIs not corresponding to neurons (see Methods) the average fluores-
cence of each ROI was measured, and the change of fluorescence from
baseline (DF/F) was calculated at each time point (Fig. 2d) (Jia et al.,
2011; Legland et al., 2016).
To avoid the inclusion of spontaneously active nonauditory neu-
rons, we selected neurons active at least six times using correlation to
an average GCaMP6f spike (threshold of 0.75). Although this could
prevent us from finding truly specific neurons, we did not find any
groups of neurons that showed a significant and representative
response to a single frequency. Our approach yielded roughly 120,000
active neurons across eight animals. It has previously been shown that
the lateral line may contribute to hearing, especially at low frequencies,
in zebrafish larvae (Kalmijn, 1988; Levi et al., 2015; Northcutt, 1981).
To rule out the possibility that the lateral line is responsible for our
observed responses, we treated additional larvae with neomycin to
ablate the lateral line hair cells (Harris et al., 2003). Larvae lacking lat-
eral line hair cells (as demonstrated by a lack of DASPEI labelling of
their neuromasts, Table 2) showed similar responses to controls (see
Fig. 3b). This means that the observed responses result from otolith-
generated auditory signalling from the inner ear hair cells, since these
survive bath application of neomycin (Lombarte, Yan, Popper, Chang, &
Platt, 1993). However, we cannot exclude that the lack of response
from the lateral line may be caused by the embedding of the larvae in
agarose, which could impede the motion of the hair cells. Because
responses were equivalent between control and neomycin treated ani-
mals, we pooled our control data with an additional 130,000 respon-
sive neurons drawn from six neomycin treated larvae.
We next sought to identify, among the roughly 250,000 auditory
responsive neurons (Fig. 2e), those responding consistently to specific
tones. We used a stepwise approach to fit linear regression models to
all our fluorescent traces (see Methods). We then selected fluorescence
traces with an r2 value above 0.15 (15% of the data variance is
explained by the linear regression) for further analysis (Fig. 2f). This
resulted in roughly 70,000 traces corresponding to individual consis-
tently responding auditory neurons (Fig. 2g).
In order to identify categories of neurons with distinct response
profiles, we adapted a clustering approach first used by Bianco and
Engert (2015). Briefly, we clustered highly correlated traces together
and averaged their responses; we repeated the process until no unclus-
tered traces showed correlation coefficient above 0.85 to the merged
traces. This produced five clusters with distinct responses (Fig. 3a).
Although each has unique tuning properties, they also have commonal-
ities. All the clusters show broad tuning from 100 Hz to 300 Hz, and
most also show strong responses up to 400 Hz (Fig. 3c). All clusters
also show similar distributions between neomycin treated and control
larvae (Fig. 3b), especially cluster 1, which given its selective response
4 | The Journal ofComparative Neurology
VANWALLEGHEM ET AL.
FIGURE 1 Auditory responsive brain regions. pERK signals are shown in magenta, H2B-RFP reference is shown in grey (a,c,e) or green(other subpanels) (Randlett et al., 2015). Images represent averages of 5 experimental and 3 control animals. Each row show horizontalimages from dorsal (First row; �50 mm below the skin), intermediate (Second row; �100 mm below the skin) to ventral (Third row;�150 mm below the skin). (a), (c) and (e) show the major brain regions outlined as follow. (a) Optic tectum in red, Cerebellum in cyan,Habenula in green. (c) Optic tectum in red, Cerebellum in cyan, pretectum in green and pallium in blue. (e) Torus semicircularis in red,pallium in blue, subpallium in cyan, thalamus in green and posterior lateral line ganglion in pink. Larvae were embedded in agarose and
presented with no auditory stimuli (a1,b,c1,d,e1,f)) or with auditory stimuli (a2,b1,c2,d1,e2,f1). Higher magnification images (boxes) areshown in rightmost panels (b,d,f). Stronger signals are seen in larvae exposed to auditory tones in both glutamatergic (red) and vmat2stripe 1 (blue), as well as in the medial hindbrain (along the midline gap) (a1–2,b-b1). D1 shows stronger signals in the optic tectumand pallium (red and blue, respectively). Finally, we see an increase in the pERK signal in torus semicircularis, pallium and subpallium(f1, red, blue, and cyan, respectively). The thin dashed white lines indicate major boundaries between fore-, mid- and hindbrain. Scalebars: 100 mm
VANWALLEGHEM ET AL. The Journal ofComparative Neurology
| 5
to the 100 Hz stimulus (Fig. 3d), might have been expected to be
driven by the lateral line (Levi et al., 2015). Furthermore, we did not
observe auditory responses in the posterior lateral line ganglion neu-
rons. Overall, these results suggest that the lateral line plays no role in
the auditory perception of 6 dpf zebrafish larvae in the 100–800 Hz
bandwidth and at this moderate sound intensity.
In contrast to prior studies using microphonic potential recordings
(Lu & DeSmidt, 2013; Yao et al., 2016), we observe responses, albeit
weak ones, up to 800 Hz (Fig. 3c). Using linear regression, we esti-
mated the tuning properties of each cluster to the different frequencies
and organized the clusters from low to high frequency preferences
(Fig. 3d). Cluster 1 appears strongly tuned to 100 Hz, cluster 2 is
slightly more tuned to 200 Hz, as is cluster 3. Cluster 4 appears more
tuned to 100 and 300 Hz, whereas cluster 5 appears more tuned to
400 Hz. These are relatively subtle distinctions, however, and all clus-
ters except for cluster 1 have broad tuning from 100 Hz to 400 Hz.
3.3 | Brain-wide distribution of responsive clusters,
and the flow of information through the brain
Next, we explored the ways in which these clusters were represented
spatially across auditory responsive brain regions, and asked whether
FIGURE 2 Functional imaging and image processing workflow. (a) shows a single frame from SPIM imaging of a elavl3:H2B:GCaMP6f 6dpflarva. Scale bar: 50 mm. (b) shows a higher magnification image of the indicated area in (a) with individual cell nuclei visible, and (c) showsthe ROIs after morphological segmentation (see Methods) representing individual neurons within this small region of the brain. (d) showsindividual DF/F traces for the two neurons highlighted (red and green) in (c). The bottom row of (d) indicates the stimulus train presentedwith ascending, descending, and random ordering of the 8 frequencies. (e) shows a raster plot of the color-coded DF/F for the 250,000 neu-rons from the 15 fish in our dataset. The coefficient of determination (r2) values of the stepwise linear regression to our 8 predictors of the250,000 neurons from (e) are shown in (f), with a red dashed line indicating our 0.15 r2 threshold. (g) shows the raster plot for the resulting70,000 neurons with an r2 value above 0.15, color-code is the same as (e)
TABLE 2 DASPEI staining, positive neuromasts
Neuromasts Control (n54) Neomycin treated (n53)
N 4/4 0/3
SO1–3 11/12 0/9
IO1–4 15/16 0/12
OP1 – M2 8/8 0/6
O1 – O2 8/8 0/6
DASPEI labelling of 12 neuromasts (N, IO1–4, SO1–3, O1–2, OP1 andM2) per fish (4 controls and 3 neomycin treated), 1 indicate presence oflabelling, while 0 is the absence of labelling of the neuromast. N5 nasal,IO5 infraorbital, SO5 supraorbital, OP5opercular, O5otic,M5mandibular.
6 | The Journal ofComparative Neurology
VANWALLEGHEM ET AL.
different frequencies were represented in spatially distinct manners. By
mapping the neurons belonging to our five clusters back onto the cor-
responding locations within the brain (Fig. 4a, b), we identified four
brain regions that contained a majority of the auditory responsive neu-
rons: the torus semicircularis, the thalamus, the medial hindbrain, and
the MON (Fig. 4c). These brain regions were defined based on the Z-
brain atlas as in Fig. 1 (Randlett et al., 2015). Among these, the MON
contained a majority of the consistently auditory-responsive neurons.
Beyond these regions, there was a very sparse distribution of auditory
responses in the rest of the brain (including the tectum and cerebellum)
representing 2.8% of the responding cells. The distributions of clusters
across the brain regions showed that all were widely distributed, with-
out any obvious enrichment of particular clusters in particular regions
(Fig. 4c).
Spatial organization of the auditory responses to different frequen-
cies (tonotopy) is a common feature of auditory processing in mammals
(Bourk, Mielcarz, & Norris, 1981; Echteler, 1985b; Kandler, Clause, &
Noh, 2009; W. Lippe & Rubel, 1985; W. R. Lippe, 1987; Muniak &
Ryugo, 2014). Since the majority of our auditory responses were found
in the MON, and since the corresponding cochlear nuclei in mammals
are tonotopic, we analyzed our responses in this region for tonotopy.
We normalized the xyz coordinates for each fish and used principal
component analysis to find the axes of maximal variance on the pooled
coordinates of all the clusters. The main variance axis (explaining 85%
of variation) that we identified was close to the dorso-ventral axis (108
off the vertical axis, Fig. 4b dashed red line 1). In mapping our five clus-
ters along this axis, we found a significant trend in which lower-
frequency clusters were located more ventrally than higher-frequency
clusters (Fig. 4d). No clear trend emerged from the second component,
which explains a remaining 8% of the variance (rostro-caudal axis,
dashed red line 2), but we see significant differences between the clus-
ters, with cluster 4 more rostral and cluster 3 more caudal (Fig. 4e). The
torus semicircularis is known to be tonotopic in adult carp (Echteler,
1985b), but PCA did not reveal any significant difference in the cluster
position in our larval zebrafish.
Because the brain regions that we have identified are homologous
to regions that respond to auditory stimuli in mammals, we were inter-
ested in whether auditory information flows through these circuits in a
manner similar to how it does in mammals. To address this, we used
Granger causality to infer the directional flow of information among
the different brain regions identified in Fig. 4c (Cadotte, DeMarse, He,
& Ding, 2008; Granger, 1969; Seth, Barrett, & Barnett, 2015). Time
FIGURE 3 Clustering of auditory responsive neurons. (a) shows a raster plot of the color-coded DF/F for neurons composing the fiveclusters obtained after correlation merging. The number of neurons contributing to each cluster is indicated to the left. (b) indicates the
distribution of cells (in %) in each cluster across neomycin treated (grey) and untreated animals (white). The mean DF/F traces (1/2 95%confidence interval (CI), dashed line) for all neurons in each of the five clusters are shown in (c). The bars underneath each graph representthe times during which each pure tone was presented to the larva. (d) shows the tuning curve (normalized estimated coefficients 1/2 95%CI) to the eight frequencies for the neurons in each of our five clusters
VANWALLEGHEM ET AL. The Journal ofComparative Neurology
| 7
FIGURE 4 Spatial organization of auditory responses, and the flow of auditory information through the brain. (a) shows a maximumintensity projection of the locations of auditory responsive neurons, coded by cluster (colors maintained from Fig. 3, Scale bars: 50 mm), and(b) contains a rotated view of a 3D interpolated model of (a). The dorso-ventral axis is indicated, as well as the two components of the PCAfor the cluster coordinates (red dashed line). Major brain regions are outlined as follow; optic tectum in red, thalamus in green and MON inblue (c) Each of the four major auditory responsive brain regions contains a mix of cells from all clusters. (d) shows the mean score (1/2SEM) along the first component of the PCA (from b) for each cluster within the MON. There are no significant differences among the clus-ters (Kruskal Wallis test with Dunn’s multiple comparison test), but there is a significant linear trend (ANOVA test for Trend, p5 .0076). (e)shows the mean score (1/2 SEM) along the second component of the PCA (from b) for each cluster within the MON. Significant differen-ces are indicated by asterisks (Kruskal Wallis test with Dunn’s multiple comparison test, p values: red versus green50.029; red versusorange50.0414; blue versus pink50.0013 and red versus pink <0.0001)
FIGURE 5 Granger causality among the auditory brain regions. (a) p value (top) and F-statistics (bottom) of the Granger causality computa-tions among the four brain regions outlined in Fig. 4. (b) Summary model of (a), where the width of the arrows is proportional to the F-statof the causality, and only significant (Bonferroni corrected p value of .01/12) links are shown
8 | The Journal ofComparative Neurology
VANWALLEGHEM ET AL.
series X is said to Granger cause another time series Y if the past of X
contains more information than only the past of Y to predict the future
of Y. The average neural responses of each brain region were tested
for Granger causal relations to the responses in each of the other audi-
tory responsive regions. The resulting significant predictions of Granger
causality show a flow of information from the MON and the hindbrain
to the torus semicircularis (Fig. 5). Granger causality also suggests that
the thalamus is driven by the MON, the hindbrain, and the torus semi-
circularis. There also appear to be weaker flows of information from
the torus semicircularis and thalamus back to the hindbrain and the
MON, which may reflect the existence of feedback loops. Alternatively,
these weak apparent causal links may simply result from the regions’
being driven by a common stimulus. Overall, these results confirm the
expected flow of information among the different brain regions, based
on auditory processing in mammals (Demanez & Demanez, 2003;
Grothe, Pecka, & McAlpine, 2010; Kandler et al., 2009).
3.3 | Stimulus intensity
Finally, we explored how the intensity (volume) of the stimulus is
encoded. Stimulus intensity could be represented by rate coding within
responsive neurons, by the number of neurons responding, or by popu-
lations of neurons with distinct sensitivities. We selected the 400 Hz
tone to approach this issue. Supra-threshold stimuli may trigger startle
responses and not accurately reflect environmental stimuli (Bhandiwad
et al., 2013). As such, our strongest stimulus was at 80 dB, which is
twice the intensity used in the previous experiments. We then
decreased the intensity by factors of two, down to 40 dB, or one six-
teenth of the highest intensity. The stimulus train consisted of three
repetitions, one of decreasing intensity tones, followed by increasing
intensities, and finally randomly ordered intensities. Reliably responsive
auditory neurons were selected as before (Fig. 6a, b).
The data were analysed as for our frequency experiments, and we
obtained two clusters of responses (Fig. 6c). The two clusters show
similar response profiles, with a sharp drop in the strength of neural
response below 60 dB (Fig. 6d). The main difference is a plateau-like
response profile between 80 dB and 60 dB for cluster 1 (Fig. 6d, pur-
ple), while cluster 2 shows a more linear decrease in intensity, with a
comparatively smaller drop of response strength between 60 dB and
50 dB. This difference notwithstanding, the two clusters show broadly
similar tuning, and there is no compelling reason to believe that they
are playing distinct functional roles. Since DF/F is a loose proxy for the
number of spikes (Akerboom et al., 2012; Chen et al., 2013), this is con-
sistent with the idea that the intensity of the sound stimulus is rate
encoded in larval zebrafish. This conclusion is strengthened by the fact
that volume-specific clusters are absent, although it does not rule out
temporal coding that calcium imaging would not reveal.
4 | DISCUSSION
To our knowledge, this is the first investigation of auditory processing
at the whole brain level with cellular resolution in any system, and it
demonstrates the feasibility of using calcium imaging to look at audi-
tory processing across the brain.
FIGURE 6 Neural responses to sound intensity. (a) Provides a raster plot of the DF/F from 160,000 auditory responsive neurons acrossseven fish, and (b) gives the same information for the 50,000 neurons with a r2 value above .15. Raster plots for cluster 1 (top) and 2 (bottom)are shown in (c). Mean DF/F traces (1/2 95% CI) and normalized estimated coefficients (1/2 95% CI) for the responses to the five intensitiesare shown in (d) and (e), respectively, for these two clusters. The number of neurons belonging to each cluster in indicated in (d)
VANWALLEGHEM ET AL. The Journal ofComparative Neurology
| 9
In mammals, the primary auditory pathway involves information
coming from the VIIIth nerve, which is then processed in the cochlear
nuclei. The cochlear nuclei then project to the inferior colliculus
(homologous to the torus semicircularis in fish) and then the thalamus,
which then projects to the auditory cortex (Kandler et al., 2009; Web-
ster, 1992). We have confirmed that the organization of the auditory
pathway in larval zebrafish is similar to that of mammals. The MON,
where we observe our first and most robust auditory responses, is part
of the rhombic lip, specifically in the lateral glutamatergic stripe that
develops into the octavolateralis nucleus (Liao & Haehnel, 2012; Wulli-
mann et al., 2011). This region expresses atoh1 and ptf1a, which give
rise to excitatory and inhibitory neurons, respectively, in the homolo-
gous cochlear nuclei of mammals (Farago, Awatramani, & Dymecki,
2006; Fujiyama et al., 2009). Auditory information flows through the
octavolateralis nuclei in the hindbrain to the torus semicircularis in the
midbrain, and then to the thalamus in the forebrain. These results con-
firm anatomical studies in adult teleosts (Echteler, 1985a; Fay & Edds-
Walton, 2008; Mueller, 2012; Northcutt, 2006), and reinforce the util-
ity of zebrafish model for studying auditory processing that appears to
be grossly conserved from larval fish through mammals.
Interestingly, pERK staining suggests that there are telencephalic
responses to auditory stimuli, but these were not detected by our cal-
cium imaging. Several possibilities exist to explain these results. Telen-
cephalic responses may reflect a more complex encoding of stimulus
properties, which may not be apparent using calcium imaging. Another
possibility is that the high rate of spontaneous activity in the telen-
cephalon (data not shown) prevents the identification of specific audi-
tory responses using our approach. However, previous studies have
shown that the dorsal part of the telencephalon receives auditory input
in the carp and the goldfish (Echteler, 1985a; Northcutt, 2006). Based
on these observations, we speculate the auditory responsive region of
the pallium we observed may correspond to the future dorsomedial
and dorsocentral divisions.
The lateral line has been implicated in directionality of the auditory
startle response (Mirjany, Preuss, & Faber, 2011), but it is unclear
whether it plays a role in normal audition for stimuli that are sub-
threshold for a startle response. We find that ablating the lateral line
does not affect the auditory processing, at least in the 100–800 Hz
range, and at 70 dB, consistent with previous observations of lateral line
responses to lower frequency, but higher intensity sounds (Levi et al.,
2015; Mirjany et al., 2011). Information from the lateral line, like audi-
tory information, flows through the octavolateral nucleus and the torus
semicircularis (Liao & Haehnel, 2012), so it is possible that these modal-
ities converge on downstream premotor circuitry that mediates behav-
ioral responses to stimuli across a range of frequencies and volumes.
Further imaging studies of water-flow responses mediated by the lateral
line, registered against auditory responses, will be needed to resolve
whether and how these streams of sensory information interact.
The processing of different frequencies is spatially organized in
auditory pathways across the animal kingdom. This tonotopy has been
observed in the cochlear nuclei of mice, cats, and chickens (Bourk et al.,
1981; Lippe & Rubel, 1985; Muniak & Ryugo, 2014). Tonotopy has also
been observed in the torus semicircularis of the carp and in the auditory
system of Drosophila (Echteler, 1985b; Lai, Lo, Dickson, & Chiang, 2012).
We have observed a trend from low to high frequency across the MON,
roughly aligned with the dorso-ventral axis. This gradient is not sharp,
possibly due to the early developmental stage at which we perform our
imaging. It is known that the tonotopic maps are refined with age and
experience, so it is possible that this tonotopy is represented more
clearly in later stages. Furthermore, the hearing range of larval zebrafish
is constrained by the lack of the Weberian ossicles (Higgs et al., 2002;
Higgs et al., 2003; Kandler et al., 2009; Cervi et al., 2012; Wang et al.,
2015). As such, a full tonotopic map may await the formation of the
Weberian ossicles, with the associated expansion of frequencies
detected. More generally, we find that our clusters, while unique, are
broadly tuned and overlap extensively in the frequencies to which they
respond. They are also spread across the responsive brain regions with-
out pronounced specificity from region to cluster. All of these observa-
tions point toward a relatively immature auditory system, possibly
capable more of the rudimentary detection of low frequency sounds
than the fine discrimination of a range of auditory stimuli.
We speculate that the regions we observe in the hindbrain (MON
and medial hindbrain) will develop into the known teleost auditory nuclei.
The region we call the MON based on previous studies (Kinkhabwala
et al., 2011) should develop into the known auditory octaval nuclei,
namely the descending and anterior octaval nuclei (Echteler, 1984,
1985b; Meredith & Butler, 1983; Mueller, 2012; Northcutt, 1981). We
speculate the large medial hindbrain response we observe may also take
part in the development of the aforementioned octaval nuclei; it also
may be involved in the development of the secondary octaval population
(Echteler, 1984; McCormick & Hernandez, 1996). We suspect that the
auditory responsive region of the thalamus we observed develops into
the dorso/central posterior nucleus, a known recipient of auditory infor-
mation (Kirsch, Hofmann, Mogdans, & Bleckmann, 2002; Mueller, 2012).
More developmental studies in larvae and juveniles will be required to
elucidate how these various nuclei form and how tonotopy develops.
One limitation of this study is the use of calcium imaging; calcium
indicators such as GCaMP can only inform us of rate or population
coding in the brain. Subtle temporal coding of information, as takes
place for frequency discrimination in mammals (Frisina, 2001; Micheyl,
Schrater, & Oxenham, 2013), is currently unresolvable with the intrinsi-
cally slow responses of calcium indicators. Nonetheless, we see that
rate coding of the frequency may be present in the MON. Further-
more, we see clear indications of rate coding for the auditory stimulus
intensity, consistent with what is observed in mammals (Moore, 2003;
Sachs & Abbas, 1974). The encoding of the intensity of the stimulus, or
loudness, appears to be linear before reaching a plateau 20 dB above
our weakest stimulus. This saturation of the firing rate is also observed
in mammalian neurons (Moore, 2003; Sachs & Abbas, 1974).
Interestingly, we find that auditory responsive neurons are highly
consistent, responding in nearly 100% of trials to their relevant stimuli.
This stands in contrast to sensory coding in the visual system of larval
zebrafish, where a wide range of response probabilities exist, and
where different neurons show sharply different stimulus tuning (Bianco
& Engert, 2015; Del Bene et al., 2010; Orger, Smear, Anstis, & Baier,
2000; Temizer et al., 2015; Thompson & Scott, 2016; Thompson et al.,
2016). This is likely a function of the depth of sensory discrimination
10 | The Journal ofComparative Neurology
VANWALLEGHEM ET AL.
across these modalities. Vision is the primary means of hunting for
larval zebrafish (Bianco & Engert, 2015; Bianco et al., 2011; Gahtan,
Tanger, & Baier, 2005; Randlett et al., 2015) and also mediates preda-
tor avoidance (Dunn et al., 2016; Temizer et al., 2015), and behavioral
responses to wide-field motion (Easter & Nicola, 1997; Orger & Baier,
2005; Orger et al., 2000; Roeser & Baier, 2003). Accordingly, larvae
must be able to distinguish among numerous types of visual stimuli.
Audition at this developmental stage may simply allow the larva to per-
ceive generic auditory stimuli with some level of volume perception.
This simpler requirement would logically be subserved by correspond-
ingly simpler sensory coding, as manifested in the invariant responses
observed from auditory neurons.
In summary, we have shown that auditory processing is functional
in larval zebrafish, and that distinct but functionally overlapping catego-
ries of auditory responsive neurons carry out this processing. The over-
all architecture of the system resembles that of mammals, although the
range of frequencies detected, the frequency selectivity of individual
neurons, and the topographic representation of frequency are all rudi-
mentary. The major brain regions involved, the apparent flow of infor-
mation among these regions, and the encoding of the stimulus
intensity are all similar to that seen in adult fish and mammals. These
results suggest that the larval zebrafish will be a suitable platform for
exploring the ways in which auditory processing develops and integra-
tes with the processing of other sensory modalities.
ACKNOWLEDGMENTS
We thank Misha Ahrens for the elavl3:H2B:GCaMP6f transgenic line.
Support was provided by an NHMRC Project Grant (APP1066887),
ARC Future Fellowship (FT110100887), a Simons Foundation
Explorer Award (336331), and two ARC Discovery Project Grants
(DP140102036 & DP110103612) to E.K.S.; an EMBO Long-term
Fellowship to G.V; and an Australian Postgraduate Award to L.A.H.
Imaging work was performed in the Queensland Brain Institute’s
Advanced Microscopy Facility and generously supported by ARC
LIEF LE130100078.
CONFLICT OF INTEREST
None declared.
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SUPPORTING INFORMATION
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porting information tab for this article.
TABLE S1. Acceleration measurement of tones.
How to cite this article: Vanwalleghem G, Heap LA, Scott EK. A
profile of auditory-responsive neurons in the larval zebrafish
brain. J Comp Neurol. 2017;00:1–13. https://doi.org/10.1002/
cne.24258
VANWALLEGHEM ET AL. The Journal ofComparative Neurology
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