Neuroscience 296 (2015) 26–38
REVIEW
SEEING THE WHOLE PICTURE: A COMPREHENSIVE IMAGINGAPPROACH TO FUNCTIONAL MAPPING OF CIRCUITS IN BEHAVINGZEBRAFISH
C. E. FEIERSTEIN, a R. PORTUGUES b ANDM. B. ORGER a*
aChampalimaud Neuroscience Programme, Champalimaud
Centre for the Unknown, Avenida Brasılia, Doca de
Pedroucos, Lisbon 1400-038, PortugalbMax Planck Institute of Neurobiology, Am Klopferspitz 18,
82152, Germany
Abstract—In recent years, the zebrafish has emerged as an
appealing model system to tackle questions relating to the
neural circuit basis of behavior. This can be attributed not
just to the growing use of genetically tractable model organ-
isms, but also in large part to the rapid advances in optical
techniques for neuroscience, which are ideally suited for
application to the small, transparent brain of the larval fish.
Many characteristic features of vertebrate brains, from
gross anatomy down to particular circuit motifs and cell-
types, as well as conserved behaviors, can be found in zeb-
rafish even just a few days post fertilization, and, at this
early stage, the physical size of the brain makes it possible
to analyze neural activity in a comprehensive fashion. In a
recent study, we used a systematic and unbiased imaging
method to record the pattern of activity dynamics through-
out the whole brain of larval zebrafish during a simple visual
behavior, the optokinetic response (OKR). This approach
revealed the broadly distributed network of neurons that
were active during the behavior and provided insights into
the fine-scale functional architecture in the brain, inter-indi-
vidual variability, and the spatial distribution of behaviorally
relevant signals. Combined with mapping anatomical and
functional connectivity, targeted electrophysiological
recordings, and genetic labeling of specific populations,
this comprehensive approach in zebrafish provides an
unparalleled opportunity to study complete circuits in a
behaving vertebrate animal.
This article is part of a Special Issue entitled: Contribu-
tions From Different Model Organisms to Brain Research.
� 2014 Published by Elsevier Ltd. on behalf of IBRO.
Key words: zebrafish, whole-brain imaging, neural circuits,
behavior, sensorimotor circuits.
http://dx.doi.org/10.1016/j.neuroscience.2014.11.0460306-4522/� 2014 Published by Elsevier Ltd. on behalf of IBRO.
*Corresponding author.
E-mail address: [email protected] (M. B.Orger).Abbreviations: AFs, arborization fields; OKR, optokinetic response;OMR, optomotor response; RGC, retinal ganglion cell.
26
Contents
The challenge of bridging scales in systems neuroscience 26
Identifying the circuits underlying behaviors 27
The zebrafish model system 28
Selected zebrafish contributions to systems and circuits neuro-
science 28
Retinal processing 28
Circuit mechanisms of vision 28
Spinal cord motor circuits 28
Brainstem motor circuits 29
Comprehensive imaging from neural populations 29
Whole-brain imaging of zebrafish larvae during optokinetic
behavior 30
Comparing activity across individuals—stereotypy of neuronal
responses 32
Localizing sensorimotor signals to different brain areas 32
Conclusions 32
Acknowledgments 35
References 35
THE CHALLENGE OF BRIDGING SCALES INSYSTEMS NEUROSCIENCE
Our brains must continuously integrate information from
the senses, past experience and internal states to plan
and execute appropriate behaviors. A central aim of
systems neuroscience is to understand how activity
dynamics in the complex, distributed neuronal networks
in the brain contribute to carrying out these tasks. This is
a particularly challenging problem because it requires an
integrated understanding of processes that span scales
which may differ by orders of magnitude (van Hemmen
and Sejnowski, 2005; Grillner, 2014), from the biophysical
properties of individual cells to networks of billions of inter-
connected neurons. Frequently, however, technical limita-
tions constrain analysis to one particular level. For
example, electrophysiology allows recordings with very
high fidelity and temporal resolution, but these recordings
are usually limited to one or a few neurons in a restricted
area. On the other hand, imaging methods that measure
activity patterns throughout the whole brain, such as
functional magnetic resonance imaging (fMRI), can
typically report only the pooled activity of many neurons.
Recent studies that applied in vivo calcium imaging to
the transparent brains of Caenorhabditis elegans and
zebrafish have shown great potential to bridge this
C. E. Feierstein et al. / Neuroscience 296 (2015) 26–38 27
gap by imaging large fractions of the brain at single-cell
resolution (Ahrens et al., 2013; Panier et al., 2013;
Schrodel et al., 2013; Portugues et al., 2014; Prevedel
et al., 2014).
IDENTIFYING THE CIRCUITS UNDERLYINGBEHAVIORS
The ability to obtain a whole-brain perspective is valuable
since even an extremely simple behavior may involve
several groups of neurons scattered throughout many
different brain areas. How can we identify the distributed
networks responsible for a particular behavior? One
approach is to trace out pathways anatomically, as
exemplified by the burgeoning field of connectomics,
which aims to comprehensively map connectivity among
large assemblies of neurons or even throughout whole
brains. The connectome of C. elegans has been
available for some time (White et al., 1986), and this
information has acted as a powerful guide, providing
constraints which have enabled rapid progress in the
identification of circuits underlying important functions
such as sensory processing, locomotor control and learn-
ing (Chalfie et al., 1985; Bargmann and Horvitz, 1991;
Mori and Ohshima, 1995; Tsalik and Hobert, 2003; Gray
et al., 2005; Ha et al., 2010). More recently, it has become
possible to apply this approach on a sufficiently large
scale to map the connections in substantial volumes of
brain tissue in both the rodent and fly visual systems
(Helmstaedter et al., 2013; Takemura et al., 2013). At
the same time, the description of the structure of networks
is not, by itself, sufficient to reliably predict circuit function,
since even a very complete map will lack essential pieces
of information needed to predict the resulting activity
dynamics, including intrinsic electrical properties of
neurons, the relative strengths of different connections,
and the impact of modulatory inputs (Bargmann and
Marder, 2013).
The more common approach to identifying the cells
involved in a specific task has been to record neural
activity during behavior or passive presentation of
sensory stimuli, and relate this activity to the ongoing
computations and task demands. This has typically
been done using recordings with extracellular electrodes
(Adrian, 1926), and usually from one neuron at a time.
In this way, it is possible to identify neurons that are tuned
to properties of sensory stimuli (Hubel and Wiesel, 1959),
or motor output (Georgopoulos et al., 1982), or whose fir-
ing reflects internal parameters linked to the ongoing com-
putations in different regions of the brain (Goldberg and
Wurtz, 1972). Although single-unit recordings account
for much of our knowledge regarding the signals carried
in different brain areas, more information can often be
extracted when data from several neurons are analyzed
together (Perkel et al., 1967; Brown et al., 2004;
Churchland et al., 2007; Miller and Wilson, 2008). Broad
and overlapping tuning curves will mean that representa-
tions are distributed over many cells (Erickson, 1968;
Georgopoulos et al., 1986; Young and Yamane, 1992),
and the temporal evolution of the population dynamics
may not be evident in the responses of single neurons
(Friedrich and Laurent, 2001; Briggman et al., 2005;
Mante et al., 2013; Kaufman et al., 2014). In many such
cases, the distribution of activity in the population may
nevertheless be established over many sequential sin-
gle-unit recordings. However, if important information is
encoded in the covariance between neurons, or in pat-
terns of activity that are not faithfully repeated from trial
to trial, it is necessary to record from two or more cells
simultaneously (Zohary et al., 1994; Meister et al., 1995;
Nicolelis et al., 1995; Singer and Gray, 1995; Vaadia
et al., 1995; Harris et al., 2003; Jones et al., 2007). While
electrophysiological methods have been developed to
record from many neurons (Nicolelis et al., 1993;
Meister et al., 1994), in vivo calcium imaging is becoming
an increasingly popular tool for population recordings,
particularly thanks to the development of genetically
encoded indicators (Miyawaki et al., 1997; Nakai et al.,
2001), which have undergone rapid recent improvements
in sensitivity and speed (Chen et al., 2013; Sun et al.,
2013; Thestrup et al., 2014). While the fidelity and tempo-
ral resolution of calcium imaging may not yet be a match
for the electrode, there are several advantages which
make it a very useful approach, including:
(1) Large populations of neurons can be imaged at
once, which allows high experimental throughput,
and also reduces recording bias. Importantly, it
may also reveal aspects of information processing
that are not captured by serial recordings from
one or a few cells, as discussed above.
(2) Recordings can be restricted to genetically defined
populations, including different neurotransmitter
classes, by cell-type specific expression of the indi-
cator, or a fluorescent co-label (Dıez-Garcıa et al.,
2005; Sohya et al., 2007; Yaksi et al., 2007; Kerlin
et al., 2010); and it is even possible to selectively
record the pooled activity from one class of neurons
(Naumann et al., 2010; Cui et al., 2013). Reading sig-
nals from genetically specified neurons has also
become possible, to some degree, by using electro-
physiology in combination with optogenetic stimula-
tion (Lima et al., 2009).
(3) Precise spatial information is retained, both in the
arrangement of neuronal cell bodies, and fine struc-
tural organization in neuropil (Ohki et al., 2005;
Komiyama et al., 2010; Nikolaou et al., 2012).
(4) Subcellular signaling can be resolved, allowing the
measurement of both spatial aspects of dendritic
processing (Borst and Egelhaaf, 1992; Svoboda
et al., 1997; Euler et al., 2002; Hill et al., 2013)
and the topography of synaptic inputs (Baden and
Hedwig, 2007; Bollmann and Engert, 2009; Peron
et al., 2009; Hopp et al., 2014).
(5) Recordings can be made in a minimally invasive
manner, especially in transparent organisms, in
some cases while they are freely moving (Clark
et al., 2007; Ben Arous et al., 2010; Naumann
et al., 2010; Faumont et al., 2011; Piggott et al.,
2011; Larsch et al., 2013; Muto et al., 2013).
(6) Information about cell morphology can be obtained
simultaneously, and the recorded cell can be
28 C. E. Feierstein et al. / Neuroscience 296 (2015) 26–38
tracked while being imaged chronically for days or
weeks (Bozza et al., 2004; Mank et al., 2008;
Andermann et al., 2010; Margolis et al., 2012).
THE ZEBRAFISH MODEL SYSTEM
Given the benefits of using optical methods to record and
also manipulate neural activity, zebrafish have emerged
in recent years as a promising model organism in
systems and circuit neuroscience (Friedrich et al.,
2010). Their neural development is rapid, with the first ret-
inal ganglion cell (RGC) axons leaving the eye at 34-h
post-fertilization (Stuermer, 1988) and behavioral
responses to visual stimuli appearing within the first
3 days of life (Easter and Nicola, 1996). One-week-old lar-
vae, just four millimeters in length, can follow stimuli with
their eyes, track and capture small, moving prey, avoid
predators and stabilize their position in moving water
(Easter and Nicola, 1997; Neuhauss, 2003; Portugues
and Engert, 2009). Many of these behaviors can be repro-
duced in head-restrained larvae, and, by using appropri-
ately timed, closed-loop presentation of visual feedback,
it is even possible to elicit naturalistic sequences of coor-
dinated movements (Portugues and Engert, 2011; Trivedi
and Bollmann, 2013). At this early stage, the zebrafish
brain consists of roughly 100,000 neurons, and is trans-
parent and sufficiently small, measuring about
800 � 400 � 300 lm, that the whole volume can be
imaged at subcellular resolution within the field of view
of a typical microscope. Moreover, recent advances in
transgenic technology mean that new stable lines with
cell-specific expression of genetic tools can be generated
cheaply and rapidly (Abe et al., 2011; Suster et al., 2011).
SELECTED ZEBRAFISH CONTRIBUTIONS TOSYSTEMS AND CIRCUITS NEUROSCIENCE
Thanks to these advantages, the zebrafish model has
been used to address important questions in many
different areas of systems and circuits neuroscience.
While this is not intended to represent a comprehensive
list (functional imaging studies in zebrafish are
described more completely elsewhere: Kettunen, 2012;
Renninger and Orger, 2013), we highlight below a few
examples of important recent discoveries and observa-
tions that originated in the zebrafish model, which have
potentially broad relevance for neural circuit function in
other organisms.
Retinal processing
Imaging has been used to reveal novel aspects of
synaptic function in the zebrafish retina, with
implications for visual processing. Using a synaptically
targeted indicator, Dreosti et al. made the surprising
discovery of all-or-nothing calcium spikes at the bipolar
cell synapse, which were precisely time-locked to visual
stimuli, challenging the textbook view of graded
transmission (Baden et al., 2011; Dreosti et al., 2011).
Further investigations revealed a surprising triphasic rela-
tionship between luminance changes and vesicle release
in some bipolar cell terminals, and suggested a role for
this unusual response in efficient coding of fluctuating
light stimuli (Odermatt et al., 2012). In another study
(Wang et al., 2014), some long-standing questions sur-
rounding the mechanistic origin of lateral inhibitory signals
from horizontal cells to photoreceptor terminals were
addressed. Using a genetically encoded pH sensor tar-
geted to the synaptic cleft of cone terminals in zebrafish,
it was shown that light-evoked synaptic alkalinization due
to a change in proton flux across horizontal cell mem-
branes is sufficient to mediate this process (Wang et al.,
2014).
Circuit mechanisms of vision
RGCs in zebrafish project to at least 10 arborization fields
(AFs) with the vast majority innervating the optic tectum
(AF 10) (Burrill and Easter, 1994). Within the tectum,
RGCs terminate in four different layers. Multicolor labeling
of single axons using Brainbow (Livet et al., 2007) demon-
strated that RGC arbors, can be further separated into at
least 10 distinct sublaminae, each a few microns in thick-
ness (Robles et al., 2013). Each sublamina receives input
from a distinct combination of RGC types, suggesting that
the tectum may receive segregated input from parallel
visual processing streams. Functional studies revealed
that RGCs sensitive to different directions of whole-field
motion target different tectal sublaminae (Gabriel et al.,
2012; Nikolaou et al., 2012), and that neurons in the tec-
tum integrate these inputs to generate distinct tuning
characteristics (Hunter et al., 2013).
The tectum is not required for the optokinetic
response (OKR) (Roeser and Baier, 2003), but Kubo
et al., using optogenetic activation and silencing, identi-
fied a region, in the vicinity of retinal AF 9, that is both nec-
essary and sufficient to drive smooth eye movements
(Kubo et al., 2014). Imaging systematically from cell
bodies surrounding this area, in the area pretectalis, in
response to different combinations of horizontal motion
presented to the two eyes, they identified several classes
with different response profiles. These included both
monocular neurons, and binocular neurons sensitive to
same-direction (‘‘translational’’) and opposite direction
(‘‘rotational’’) motion between the two eyes. Importantly,
because they had a comprehensive picture of the func-
tional types in the population, and their locations, they
were able to propose, based on minimal Boolean logic,
the simplest connectivity pattern that could explain the
observed responses, providing a straightforward circuit
hypothesis which can now be tested experimentally
(Kubo et al., 2014).
Spinal cord motor circuits
As reviewed extensively elsewhere (Fetcho and McLean,
2010), a large body of work has established fundamental
principles of recruitment of spinal neurons during different
modes of locomotion (McLean et al., 2007, 2008). Recent
evidence has indicated that motor neuron pools in zebra-
fish are divided into discrete modules with different pat-
terns of recruitment and muscle innervation, a finding
with important implications for motor control as well as
the evolution of more complex locomotor circuits
C. E. Feierstein et al. / Neuroscience 296 (2015) 26–38 29
(Gabriel et al., 2011; Ampatzis et al., 2013; Bagnall and
McLean, 2014). The small size of zebrafish has allowed
three recent studies to investigate the supraspinal control
of locomotion by assessing the contribution of a small
midbrain nucleus of spinal projecting neurons to both pos-
tural control and swimming speed (Severi et al., 2014;
Thiele et al., 2014; Wang and McLean, 2014). These
studies show how the interplay between the axonal pro-
jection patterns of these specific neurons and variations
of biophysical properties of spinal neurons along the dor-
sal–ventral axis result in the implementation of specific
locomotor maneuvers and how identified neurons can
contribute selectively to modulation of different parame-
ters of behavior.
Brainstem motor circuits
While the relationship between domains of transcription
factor expression and neuronal identity has been well
established in the spinal cord (Briscoe et al., 2000;
Goulding et al., 2002), it was unclear if and how the same
principles could be applied to circuits in the hindbrain.
Kinkhabwala and colleagues demonstrated that transcrip-
tion factor stripes extend from the spinal cord into the
medulla, but display a medio-lateral rather than dorso-
ventral organization (Kinkhabwala et al., 2011). Each
stripe is associated with neurons of a particular morphol-
ogy and neurotransmitter identity, such as glutamatergic
ipsilateral descending neurons. Using kaede photocon-
version to label the birth order of neurons, they found that
each stripe showed a dorso-ventral gradient of age. Fur-
thermore, targeted electrophysiological recordings
showed that there was a striking relationship between
the tail-beat frequency at which neurons were recruited
during fictive swimming and their location, with older,
more ventral, neurons becoming active only during higher
frequency bouts of swimming. From this observation, they
propose a model in which circuits for different behaviors
develop in a temporal sequence, each drawing from a
pool of available neuron types originating from the differ-
ent expression zones (Kinkhabwala et al., 2011). In a
companion study, Koyama et al. combined systematic
paired patch recordings, and confocal reconstructions of
neuronal morphology, to reveal how a circuit mediating
Mauthner-cell initiated escapes is constructed from this
modular architecture (Koyama et al., 2011). Interesting
insights from the zebrafish into the mechanisms of neural
integration in the brainstem circuits mediating eye move-
ments are reviewed in detail elsewhere in this issue
(Joshua and Lisberger, 2014).
Comprehensive imaging from neural populations
Several studies have taken advantage of the small size of
the zebrafish brain to make comprehensive recordings of
activity from particular populations of neurons. In an adult
brain preparation, Yaksi and colleagues mapped the
spatiotemporal dynamics of responses throughout most
of the olfactory bulb (Yaksi et al., 2007). Analysis of the
temporally deconvolved population responses showed
that an initially chemotopic output pattern evolved rapidly
into a sparse representation of odor identity. Repeating a
similar approach for olfactory target structures in the tel-
encephalon (Yaksi et al., 2009), they showed differences
in coding between subpallial and pallial regions, with the
former showing broad odor tuning, and the latter contain-
ing cells that responded more specifically to particular
odor combinations.
The habenula, a key relay station between the
forebrain and neuromodulator systems, has received
considerable attention in the zebrafish, due to its
pronounced asymmetries in morphology, gene
expression, innervation, axonal projections and
functional responses (Concha et al., 2000; Hendricks
and Jesuthasan, 2007; Kuan et al., 2007; Bianco et al.,
2008; Miyasaka et al., 2009; deCarvalho et al., 2014;
Dreosti et al., 2014), as well as its apparent central role
in determining behavioral choices (Agetsuma et al.,
2010; Lee et al., 2010). Krishnan et al. developed a
simple, wide-field epifluorescence system for rapid
three-dimensional imaging using fast focusing and
deconvolution, and applied this method to reveal, with
single-cell resolution, the dynamics in response to differ-
ent concentrations of multiple odors throughout the whole
habenula (Krishnan et al., 2014). They found that popula-
tion activity in the right dorsal habenula varied with the
concentration of a socially relevant odor (a bile salt),
and provided evidence, using pharmacology and abla-
tions, that this region mediates a switch from attraction
to avoidance at high concentrations.
In addition to revealing spatial patterns of activity,
volume imaging from individual animals provides an
unbiased method to identify rare or sparsely distributed
cell types. Previous work by Orger and colleagues
aimed to determine which reticulospinal cells in the
zebrafish brainstem were active when the fish was
swimming forward or turning (Orger et al., 2008). They
imaged systematically through the whole population while
presenting stimuli moving in different directions. While
many cells were activated by forward motion, only a few
were preferentially activated by leftward and rightward
motion, and these were small and weakly labeled, and
could easily have been overlooked by a more selective
sampling strategy. Knowing the spatial locations of all
the cells that were active during turns, it was possible to
systematically ablate them, and show that the fish could
no longer perform optomotor turns toward the ablated
side (Orger et al., 2008). Subsequent studies showed that
the same ventral neuron groups are required, in general,
for the fish to make routine turns, for example during
spontaneous swimming or phototaxis (Huang et al.,
2013). Since the set of neurons associated with a partic-
ular type of swim may be distributed across several retic-
ulospinal groups, the chances of ablating exactly the right
combination to specifically eliminate a single behavior, in
the absence of a functional map, may be very small.
Moreover, knowing the context in which the cells are
active makes it possible to assay a more targeted set of
behaviors and identify more subtle phenotypes (Liu and
Fetcho, 1999; Severi et al., 2014).
Some of the greatest potential the zebrafish model
offers lies in the ability to monitor population activity
across multiple brain regions, or even throughout the
30 C. E. Feierstein et al. / Neuroscience 296 (2015) 26–38
entire brain. In one study, different brain volumes were
imaged across many paralyzed fish, during adaptation
of the fictive optomotor response (OMR) to different
closed-loop gains (Ahrens et al., 2012). Activity patterns
were correlated with the visual motion, gain changes or
fictive motor output and the resulting data were subse-
quently aligned to a reference brain with an accuracy of
20–25 lm to yield brain-wide correlation maps. As
described in more detail below, we recently imaged activ-
ity through most of the fish brain at micron resolution, gen-
erating whole-brain functional maps from individual
animals performing optokinetic behavior, while partially
restrained (Portugues et al., 2014). Light-sheet imaging,
which allows for faster acquisition rates than two-photon
laser scanning microscopy, can been used to acquire
data nearly simultaneously from large portions of the brain
(Panier et al., 2013). This approach allowed the recording
of spontaneous whole-brain activity from agarose-embed-
ded larvae at around 1 Hz, resulting in the identification of
functionally defined three-dimensional structures span-
ning multiple regions (Ahrens et al., 2013), and is compat-
ible with simultaneous recording of fictive visual behavior
(Vladimirov et al., 2014). Light-field imaging is an
approach that promises even faster volume acquisition
rates, by capturing the data from multiple focal planes in
a single camera exposure (Levoy et al., 2009; Broxton
et al., 2013). This method was successfully applied
recently to functional imaging in both worms and zebrafish
(Prevedel et al., 2014), although several critical hurdles,
such as sample bleaching and very long data processing
times, still remain to be addressed.
WHOLE-BRAIN IMAGING OF ZEBRAFISHLARVAE DURING OPTOKINETIC BEHAVIOR
The OKR is a reflexive behavior that consists of a
smooth, tracking eye movement in response to whole-
field rotational motion, interrupted by fast reset
saccades, and is thought to serve to reduce or cancel
retinal slip. This behavior is found throughout the
animal kingdom (Walls, 1962; Masseck and Hoffmann,
2009), and, although every type of animal has different
constraints and specializations, for example due to
foveal vision or lateral vs. frontal eyes, the sensorimotor
transformation that occurs during OKR presumably
relies on similar circuit and neural computations across
species. In a recent study (Portugues et al., 2014), we
set out to map neural activity dynamics with single-cell
resolution through the whole brains of fish while they
performed this behavior, reasoning that such a compre-
hensive map would be a significant step toward under-
standing the organization and function of the underlying
network.
The OKR appears in zebrafish at an early stage, and
is reliably evoked by a rotating pattern of vertical stripes
at 5 days post-fertilization (Easter and Nicola, 1997;
Beck et al., 2004). We imaged from awake, partially
restrained larvae with pan-neuronal expression of the
genetically encoded calcium indicator GCaMP5G
(Akerboom et al., 2012), while they responded to sinusoi-
dally rotating patterns which elicited the OKR (Fig. 1A, B).
Our custom-built setup allowed the brain to be stably
imaged using two-photon excitation, while the eyes and
tail were free to move. The fish’s behavior was tracked
with a camera, revealing robust and consistent responses
over many hours (Fig. 1B). Therefore, we could gather
data sequentially from hundreds of planes, under similar
behavioral conditions, sampling the whole brain at less
than 1 lm resolution in all three dimensions. Most cells
in the brain, at this age, have cell bodies 3–6 lm in
diameter. Fig. 1C shows color-coded maps of response
magnitude, and phase relative to the stimulus, at various
depths in the brain, superimposed on a grayscale image
of the brain anatomy. Individual cell bodies can be clearly
distinguished, based on nuclear exclusion of the GCaMP,
and groups of active voxels that colocalize with single
neuronal somata, as well as with neuropil structures,
are evident. In addition to the fluorescence time-series
for each voxel that describes the neuronal activity, each
sequentially recorded slice is accompanied by the
time-course of the stimulus presented and high-speed
recordings of behavior. From 400 to 600 such planes,
we can build three-dimensional maps of the average
activity dynamics, composed of around 200 million
voxels. Fig. 1D shows a projection from two viewpoints
of all identified active regions in an example fish, color-
coded by response phase.
Although phase-locked responses were detected in
only a small percentage of the neurons in the imaging
volume (<5% of voxels imaged), the active areas were
widely dispersed. Responses were found in structures
throughout the brainstem including multiple retinal
ganglion AFs, the periventricular layers of the optic
tectum, and areas in the hindbrain such as the
cerebellum and the inferior olive, even spanning regions
in the forebrain such as the habenula (Fig. 1C, D). At the
same time, even within individual regions, the activity
pattern could be quite sparse. For example, although
activity was reliably observed in the optic tectum,
responses were restricted to much less than 1% of all
neurons. The sparse and broadly distributed nature of the
network that is engaged during a relatively simple,
reflexive behavior, such as the OKR, highlights the
benefits of being able to record activity systematically
throughout the brain.
Neuronal responses were locked to different phases
of the stimulus, with different brain areas showing
distinct phases of activation relative to the rotating
stimulus (Fig. 1B–D). Areas that receive input from
the retina and are therefore likely engaged in the
representation of sensory information, such as the
tectum and pretectum, responded earlier in the stimulus
cycle than areas associated with motor output, revealing
the dynamics of information flow across brain regions
during this behavior.
Imaging methods, as compared to electrode
recordings, not only provide information about the
functional properties of different areas, but they also allow
precise spatial mapping of the responses. Voxelwise
analysis of the response phase shows that activity within
most regions is not synchronous, but instead shows
smooth spatial gradients of activation. Taken together
Fig. 1. (A) Eliciting the optokinetic response (OKR). Top, Larvae were presented with a rotating radial striped pattern. Bottom, Eye position was
determined as the eye angle relative to the midline. (B) Larvae responded by tracking the movement of the grating with a conjugate movement of the
eyes. Stimulus rotation was sinusoidally modulated (gray, stimulus velocity). Top color bar indicates the mapping of phases relative to the stimulus
cycle onto color (used in (C) and (D)). (C) Activity phase maps highlight the dynamics of activation of different brain areas. Far left, Image of a six-
day-old larval zebrafish, indicating the imaging area in subsequent figures. Scale bar = 1 mm. Remaining images: Color-coded representation of
activity at three different planes, in dorsal view and overlaid on a compressed grayscale image of the average GCaMP5G fluorescence as an
anatomical reference. Each voxel is color-coded according to the phase of its response at the stimulus frequency (see color bar in (B)). Responses
are spatially smoothed with a 1-lm gaussian filter. Boxed region highlights the ability to image activity with single-cell resolution. Arrow points to
activity in fine sublaminae of the tectal neuropil. (D) Bottom, Rendered dorsal view of all automatically segmented ROIs in one fish, color-coded
according to the phase of their response at the stimulus frequency (see color bar in (B)). Top, Lateral view of ROIs in the left half of the brain. (E)
Activity was stereotyped across fish. Stereotypy was defined as the distance that needs to be traveled in another fish to find a voxel with similar
temporal profile (i.e., similar phase of maximum activation); thus, smaller distances indicate higher stereotypy. Shown are minimum projections of
the median distance (across 13 fish), in dorsal (bottom) and lateral (top) views. Scale bars = 50 lm. Some panels adapted from Portugues et al.
(2014).
C. E. Feierstein et al. / Neuroscience 296 (2015) 26–38 31
with observations of gradients of physiological and
functional properties in the zebrafish spinal cord,
brainstem and oculomotor circuitry (Fetcho and McLean,
2010; Kinkhabwala et al., 2011;Miri et al., 2011a), this sug-
gests that such functional topography may be a general
organizing feature of sensorimotor circuits.
32 C. E. Feierstein et al. / Neuroscience 296 (2015) 26–38
COMPARING ACTIVITY ACROSSINDIVIDUALS—STEREOTYPY OF NEURONAL
RESPONSES
The active areas, while widely dispersed, nevertheless
showed a conspicuously ordered spatial arrangement,
which is particularly evident when one compares the
patterns on the left and right sides of the brain. The
whole network has a striking symmetry, right down to
individual neurons and groups of neurons, with opposite
structures in the brain active 180� out of phase. The
notable exception to this is in the dorsal habenula,
where responses are heavily biased toward the left side,
consistent with other work investigating the distribution
of visual and olfactory signals in this structure (Dreosti
et al., 2014). This led us to ask: how consistent is this pat-
tern from one fish to another? In invertebrate systems, it is
common to find identifiable neurons across animals
(Selverston, 2010; for an example see O’Shea and
Williams, 1974), and tracing of single axons in Drosophila,aligned to a reference brain, revealed that even long-
range projections may show stereotyped organization,
on the order of a few microns, across individual animals
(Jefferis et al., 2007). In other cases, though, studies have
found more random organization (Lu et al., 2009; Caron
et al., 2013). To address this question, all the imaged
brains were first aligned to a reference stack using a
non-rigid deformation. We then asked, for each detected
region of interest: how far do you have to travel, on aver-
age, in the brain of another fish to find a region active at a
similar phase? For a large portion of the regions active
during OKR, including the pretectum, cerebellum, haben-
ula, and an extensive hindbrain network, this was around
1–5 lm, which is on the order of a single neuronal cell
body, and indicates a high degree of stereotypy
(Fig. 1E). This highly consistent organization across fish
brains suggests that what we learn from an individual
brain can, at least for simple behaviors such as the
OKR, be straightforwardly extrapolated to other fish.
One practical advantage of this is the ability to use the
functional maps obtained to guide targeted ablation,
imaging or photoactivation to areas of interest, or to com-
pare with the distribution of molecular or genetic markers
(Ronneberger et al., 2012). Moreover, the high degree of
stereotypy allowed us to combine data from multiple fish,
improving the signal-to-noise in areas that were weakly
active, or dimly labeled, in individual fish, thus providing
a more comprehensive map of activity during behavior
(see Fig. 4 in Portugues et al., 2014).
LOCALIZING SENSORIMOTOR SIGNALS TODIFFERENT BRAIN AREAS
Next, we asked how this pattern of activity reflects the
processing of behaviorally relevant information in
different parts of the network. When using this simple
stimulus to drive the OKR, the sensory input and motor
outputs are highly correlated, and the left and right eyes
move together in a conjugate fashion. Therefore, in
order to reveal what signals are present in different
areas, we employed a richer stimulus set, in which the
same basic sensory cues were presented, but in
different combinations, giving rise to more variable and
complex sequences of motor output (Fig. 2A). Taking
into account the response kernel of GCaMP5G (Fig. 2B;
see (Miri et al., 2011b)), we then constructed a set of vari-
ables, based on the properties of the sensory stimulus,
measured motor outputs, and other behaviorally relevant
parameters (we will refer to them as regressors, following
Miri et al., 2011b, although here we are measuring the
correlation of each of these variables with imaging data).
Local regions of activity could be identified which corre-
lated strongly with different regressors related to both
sensory and motor features including eye position, stimu-
lus velocity and swimming episodes (Fig. 2C), as well as
intermediate steps of sensorimotor processing. For exam-
ple, we made the unexpected observation that some
wide-field motion-selective neurons in the optic tectum
appear to integrate information from the two eyes,
although the tectum only receives direct inputs from the
contralateral eye. These neurons responded phasically
when the direction of motion to the two eyes was the
same, consistent with translational movement, but their
responses were suppressed when motion occurred in
opposite directions during a rotating stimulus, similar to
some neurons in the area pretectalis described above
(Kubo et al., 2014).
Using our data sets we could then examine, in an
unbiased manner, how these signals are distributed
through the brain, and compare this distribution across
animals. We extracted the fluorescence time courses for
an array of overlapping �5 lm cubes tiling the whole
imaging volume, and identified the best matching
regressor for each. Voxels matching particular
regressors were tightly localized to particular areas, with
very few found outside a few dense regions. These
locations were also highly consistent between fish.
Fig. 2D shows superimposed projections, from dorsal
and anterior views, of all voxel locations correlated with
example sensory and motor variables, which were
identified in the brains of seven individual fish. The
distributions form either matched lateralized pairs of
clusters, as shown for left eye and right eye position
signals, as well as left and right side stimulus velocity,
or broad symmetric structures, as shown for swimming-
related activity. Thus, the broadly distributed pattern of
activity shown in Fig. 1D can be decomposed into local
modules subserving particular aspects of the
sensorimotor task.
CONCLUSIONS
The ability to rapidly identify which neurons are active
during a particular behavior, or constitute specific
functional classes, even when they are very few, or are
distributed across a wide area, provides a powerful
head start in deciphering the circuit mechanisms that
underlie the behavior. However, it is important to
recognize that such mapping studies are not, by
themselves, a solution to such questions, but instead
are a foundation for further investigations. Essential next
steps will be to determine the molecular genetic identity,
morphology and connectivity of the identified neurons,
Fig. 2. (A) A set of four stimuli was used to dissociate sensorimotor signals (top): a sinusoidally rotating grating, the same gratings presented on the
left or right visual fields alone, and gratings rotating in opposite directions for each eye, thus resembling forward and backward motion. These stimuli
elicit particular combinations of eye and tail movements (bottom). Gray shades indicate the four stimuli periods. (B) The time series of the stimulus
presented and the behavior-related variables are convolved with an exponential kernel reflecting the measured decay time constant of GCaMP5G
(Chen et al., 2013). These convolved traces (regressors) represent the fluorescence that would be recorded if activity was perfectly correlated to
each of those variables (Miri et al., 2011b; Portugues et al., 2014). (C) Different ROIs showed activity that was strongly correlated with different
behavioral variables. Here we show some examples; for each, the mean (across stimulus repeats) fluorescence trace and the mean predicted
fluorescence trace are overlaid. A schematic of the four stimuli is shown above the top center plot. Gray boxes indicate the duration of each of the
four stimuli. (D) Sensory and motor variables were differentially represented in different brain areas. Distribution of voxels that best correlated with
eye position, stimulus motion and swimming (minimum correlation 0.3) averaged across seven fish. For each regressor, a z-sum projection and a
coronal sum projection are shown. Scale bars = 50 lm. Some panels adapted from Portugues et al. (2014).
C. E. Feierstein et al. / Neuroscience 296 (2015) 26–38 33
and to demonstrate through gain- and loss-of-function
experiments what role they actually play in shaping the
observed responses.
Many transgenic lines exist which allow expression of
genes in particular classes of neurons. These may be
generated by random enhancer trapping (Scott et al.,
34 C. E. Feierstein et al. / Neuroscience 296 (2015) 26–38
2007; Asakawa et al., 2008), or by directed attempts to
label populations expressing particular genetic markers
(Suster et al., 2009). For hindbrain circuitry, systematic
sets of driver lines have been generated which target neu-
rons based on both the transcription factor domains that
define different basic neuronal classes and also many of
the important neurotransmitter systems (Kinkhabwala
et al., 2011; Satou et al., 2013). Performing the same
mapping studies in fish where these populations are spe-
cifically labeled will not only provide more detailed infor-
mation on the morphology and projections of the
different neurons, but also useful information on their pos-
sible circuit function: are they excitatory or inhibitory? Do
they have commissural axons? The fact that the activity
patterns in many regions are highly stereotyped, down
to the level of a few microns, is helpful from the perspec-
tive of identifying useful driver lines. Collections of high-
resolution, 3D-maps of gene expression patterns, aligned
to a standard brain (Ronneberger et al., 2012), can be
compared with similarly aligned functional maps, given a
‘‘bridging’’ transformation between their respective refer-
ence frames, making it possible to quickly search for lines
whose expression falls in areas of interest.
Within the network of active neurons correlated with
different aspects of a particular behavior, it will be
critical to be able to up- and down-regulate activity in
specific subpopulations in order to identify which of
them play a direct role in shaping the behavioral
response, and to test hypotheses about circuit
organization. Genetic ablations can be a powerful way
to target a defined population, providing a very specific
promoter exists, and can even be executed at defined
developmental stages using nitroreductase, an enzyme
with produces cytotoxic products when provided with an
appropriate substrate (see, for example, Agetsuma
et al., 2010). Cells that can be identified based on mor-
phology, location, or functional characteristics, can be tar-
geted for ablations using high-power, pulsed lasers. For
instance, single-neuron ablations in the reticulospinal sys-
tem have been used to identify the reticulospinal neurons
necessary for certain defined types of swim (Liu and
Fetcho, 1999; Kohashi and Oda, 2008; Orger et al.,
2008; Huang et al., 2013), or even particular kinematic
parameters of individual swim bouts (Severi et al., 2014;
Thiele et al., 2014), and similar methods have also been
used to generate lesions in more broadly defined areas
(Roeser and Baier, 2003; Mu et al., 2012; Krishnan
et al., 2014). A particularly powerful approach is to com-
bine genetic specificity with the spatial control of optical
methods, for example, using the phototoxic protein
KillerRed (Lee et al., 2010). Optogenetics, the use of
light-gated channels or ion pumps that allow rapid and
reversible bidirectional optical control of neural activity,
is particularly well suited to application in zebrafish
(Portugues et al., 2013). Among other things, optogenetic
loss- and gain-of-function manipulations have served
to identify a hindbrain area responsible for saccadic
eye movements (Schoonheim et al., 2010), to generate
single spikes in sensory neurons that trigger an escape
response (Douglass et al., 2008), and to identify a role for
cerebrospinal fluid contacting neurons in the spinal cord
in the control of locomotion (Wyart et al., 2009). Since
many behaviors of the larval fish can be performed in a
restrained preparation that is suitable for imaging,
neurons can be targeted for ablation or manipulation
based on functional characteristics. The effect of these
perturbations can be easily evaluated by continuously
monitoring the effect on behavior and circuit function in
the same preparation.
The ability to image the brain non-invasively over
prolonged periods also opens the possibility of following
the neural circuit changes that underlie learning and
memory, but this depends on the development of robust
learning paradigms for the larval fish. Classical
conditioning assays for week-old larvae based on both
rewarding social stimuli, and aversive touch have been
described (Aizenberg and Schuman, 2011; Hinz et al.,
2013). In the latter case, calcium imaging in the cerebel-
lum was used to map the distributions of neural popula-
tions responding to the conditioned and unconditioned
stimuli and track the changes in selectivity during learning
(Aizenberg and Schuman, 2011). Another robust form of
learning shown by the larvae is short-term motor adapta-
tion, elicited by changing the gain of closed-loop presen-
tation of visual feedback, driven by real or fictive tail
movements (Portugues and Engert, 2011; Ahrens et al.,
2012). The fish can cycle through many repetitions of
high- and low-gain conditions, which has enabled system-
atic mapping of the populations active at different stages.
However, most learning assays have been developed for
adult, not larval, fish, and only work at ages where non-
invasive imaging is no longer straightforward. Recently
though, single-cell resolution functional imaging in super-
ficial brain areas has been shown in fish as old as
4 weeks (Jetti et al., 2014), at which age fish do begin
to show a mature capacity for learning (Valente et al.,
2012). It is possible, using more invasive preparations,
to image neuronal populations in adult zebrafish and this
approach was used to record changes in population activ-
ity in telencephalic areas proposed to be part of a circuit
homologous to the mammalian cortico-basal ganglia loop
(Aoki et al., 2013). In that study, the authors identified
areas in the dorsal telencephalon where cue-related activ-
ity appeared following training. Interestingly, the precise
area which became active for a particular cue was depen-
dent on the task contingencies the fish associated to that
cue.
An interesting line of investigation will be to compare
the sets of neurons activated during different behaviors
in the same animal. Are there pathways dedicated to
particular responses, or do different behaviors emerge
from the patterns of responses in common sets of
neurons (Shaw and Kristan, 1997)? At the motor end, evi-
dence suggests that different swimming behaviors in zeb-
rafish may share common reticulospinal output pathways
(Sankrithi and O’Malley, 2010; Huang et al., 2013), and,
similarly, behaviors that result in eye movements are
likely to converge on a common oculomotor system
(Buttner-Ennever and Horn, 1997). At the sensory end,
distinct systems may process particular general types of
visual information, which are important for many different
behaviors. The optomotor and OKRs are, respectively,
C. E. Feierstein et al. / Neuroscience 296 (2015) 26–38 35
swimming and eye-tracking behaviors, which are elicited
by similar global motion patterns. The area pretectalis
neurons imaged by Kubo et al. show a diverse array of
responses to combinations of horizontal whole-field
motion presented to the two eyes, and it is proposed that
they provide input for both the OKR and OMR (Kubo
et al., 2014). How behavioral decisions emerge from the
dynamic interactions of many interconnected local net-
works of neurons, each dedicated to particular functions,
is a critical, but relatively unexplored, question in neuro-
science (Sompolinsky, 2014). Addressing it will likely
require that we measure activity in large populations of
neurons, both within and across areas. It has been dem-
onstrated for behaviors ranging from invertebrate sensori-
motor choices to complex cognitive tasks in primates that,
to understand either the mechanisms of decision making
within local circuits, or the functional coupling of informa-
tion across areas, it is often necessary to look at whole
population activity, rather than single neurons (Briggman
et al., 2005; Mante et al., 2013; Kaufman et al., 2014).
The ability, in zebrafish, to record simultaneously both
local and global population activity makes it a promising
system to address this question.
In summary, we have described a comprehensive
imaging approach in behaving zebrafish, that can be
applied to many different behaviors. This has allowed us
to explore the functional architecture of the zebrafish
brain, delineating areas involved in sensory processing,
motor generation, or the combination and transformation
of information that happens in between. A
comprehensive knowledge of the distribution of
functional classes in these regions makes it possible to
trace the flow of information from one area to the other,
and generate hypotheses about the possible circuit
connectivity, which can then be tested with more
targeted electrophysiological or anatomical tracing
experiments. In the future, the ability to study behavioral
circuits that are defined from sensory input to motor
output, to monitor single-neuron activity throughout the
whole brain and to optically manipulate individual
cellular components will make the zebrafish a very
powerful system to study the circuit mechanisms
underlying behavior.
Acknowledgments—RP was supported by the Max Planck Soci-
ety. MBO was supported by Marie Curie Career Integration Grant
PCIG09-GA-2011-294049 and by grant PTDC/NEU-NMC/1276/
2012 from the Fundacao para a Ciencia e a Tecnologia (FCT).
CEF was supported by a postdoctoral fellowship from Fundacao
para a Ciencia e a Tecnologia (FCT). We would like to acknowl-
edge Florian Engert for support and insightful discussions about
the work.
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(Accepted 19 November 2014)(Available online 27 November 2014)