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: michael.orger@neuro.fchampalimaud.org (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)