the sessions ([200–500], [400–700], [600–1,000], [800–1,200], [1,000–1,400] ms).

### 4.8.5. Introduction of Single Stream

For the following 8 sessions, pure tones were introduced so as to form a single-stream oddball paradigm. Within a training session, the intensity of the pure tone was augmented from 0 dB up to the intensity level used for the target sound using 10 dB increment steps. The amount of hit trials required to augment the intensity was diminished across sessions (50-30-20-15-10-7-5-3). The mouse was required not to lick before the occurrence of the target, using the longest interval used in the previous training phase. The SOA varied depending on the frequency of the stream pure tone.

### 4.8.6. Introduction of Distractor Stream

For the following 4 sessions, pure tones were introduced so as to form another single-stream. Within a training session, the intensity of the extra tones were augmented after each 10 hit trials from 40 dB up to the level used for the target sound using 10 dB increment steps. The amount of hit trials required to present extra tones was diminished across sessions (20-15-10-7).

### 4.8.7. Reinforcing False Alarms

For the following 7 sessions, response to a No-Go sound (FA) was negatively reinforced by presenting a short noise burst upon the response, stopping any upcoming sound presentation, and subsequently applying a silent time out (random delay of 4–6 s). The vacuum valve was turned on after the time out had passed. If a response was made after having missed target, the sequence was terminated after presenting four regular tones past the target and the trial ended regularly (i.e., without time out).

### 4.8.8. Introduction of a Temporal Bias

For the following 5 sessions, the time window in which a target could occur was modified across session. For T1, only the window of the Low target varied so as to occur gradually later in the trial. Per session, the target window was fixed according to a trial block design (Early or Late block). On the final day of training, the Late window was ~6 s, and the Early window was ~2 s. Reinforcement of FA was always applied during this training stage.

### 4.9. Statistics

Blinding and randomization of neurophysiological data were not performed. Unless stated otherwise, results are presented as mean ± standard error of the mean. KS refers to the Kruskal Wallis test. WRS refers to the Wilcoxon rank sum test, and WSR to the Wilcoxon signed rank test.

Statistics on circular data were performed using the Kuiper and the Rayleigh tests (Wilkie, 1983). The vector strength (VS) was considered significant if $N \cdot VS^2 > k$ at the $\alpha$ level, where N was the number of phases used to compute the VS, and $k = [2.9957 \ 4.6052 \ 5.2983 \ 6.9078]$ for corresponding $\alpha$ values [0.05 \ 0.01 \ 0.005 \ 0.001] (Wilkie, 1983).

Throughout this text, the strength of p-value is indicated by stars: \*p < 0.05, \*\*p < 0.01, \*\*\*p < 0.001 unless stated otherwise.

Significant spiking response to a tone was measured using the activity in the 100 ms window pre-tone (spontaneous) and post (evoked) tone onset. A 10 ms bin PSTH was generated for each trial, and the evoked response at each bin was compared (KS test) against the spontaneous response. A cell was classified as significantly evoked if either 3 bins passed the threshold of $p < 0.01$ or if one bin passed the threshold of $p < 0.001$ (Bonferroni correction factor 10).

The discrimination performance (DP) for trial type (such as Early/Late) classification was computed using logistic regression and receiver operating characteristic (ROC) analysis. Smoothed-PSTH on single trials were generated for the two conditions to compare (5 ms bin, convolved with 20 ms std Gaussian function for smoothing). A logistic regression model was fit at each bin of the single trial responses. The ROC curve was computed using the probability estimates from the logistic regression model. The DP was defined as the area under the ROC curve (AUROC).

## 5. DATA STATEMENT

Data can be made available by the authors upon request.

### ETHICS STATEMENT

This study was carried out under a UK Home Office Project Licence (PPL 70/8837) in accordance with the Animals (Scientific Procedures) Act 1986 following the recommendations of Imperial College Animal Welfare and Ethical Review Board (AWERB).

### AUTHOR CONTRIBUTIONS

GC designed the study, conducted experiments and analysis, and wrote the manuscript. PC designed the study, contributed to analysis, and wrote the manuscript.

### FUNDING

This work was funded from the following sources: Bioengineering departmental Ph.D. studentship to GC; Medical Research Council Career Development Award (G1000512) and grants from the Human Frontier Science Program and the Biotechnology and Biological Science Research Council (BB/N008871/1) to PC.

### SUPPLEMENTARY MATERIAL

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fnbeh.2018.00205/full#supplementary-material
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**Conflict of Interest Statement:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Copyright © 2018 Chapuis and Chadderton. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
<figure><img src="image_48.png" /></figure>

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# How Many Clocks, How Many Times?
On the Sensory Basis and
Computational Challenges of
Circadian Systems

Jason Somers<fnref n="1" />,²*, Ross E. F. Harper<fnref n="1" />,<fnref n="3" /> and Joerg T. Albert<fnref n="1" />,²,<fnref n="3" />,⁴*

A vital task for every organism is not only to decide what to do but also when to do it. For this reason, “circadian clocks” have evolved in virtually all forms of life. Conceptually, circadian clocks can be divided into two functional domains; an autonomous oscillator creates a ~24 h self-sustained rhythm and sensory machinery interprets external information to alter the phase of the autonomous oscillation. It is through this simple design that variations in external stimuli (for example, daylight) can alter our sense of time. However, the clock’s simplicity ends with its basic concept. In metazoan animals, multiple external and internal stimuli, from light to temperature and even metabolism have been shown to affect clock time. This raises the fundamental question of cue integration: how are the many, and potentially conflicting, sources of information combined to sense a single time of day? Moreover, individual stimuli, are often detected through various sensory pathways. Some sensory cells, such as insect chordotonal neurons, provide the clock with both temperature and mechanical information. Adding confusion to complexity, there seems to be not only one central clock in the animal’s brain but numerous additional clocks in the body’s periphery. It is currently not clear how (or if) these “peripheral clocks” are synchronized to their central counterparts or if both clocks “tick” independently from one another. In this review article, we would like to leave the comfort zones of conceptual simplicity and assume a more holistic perspective of circadian clock function. Focusing on recent results from *Drosophila melanogaster* we will discuss some of the sensory, and computational, challenges organisms face when keeping track of time.

**Keywords:** circadian clock, biological oscillator, multisensory integration, bayesian modeling, *Drosophila melanogaster*, sensory conflict

## OPEN ACCESS

**Edited by:**
Gérard Manière,
Université de Bourgogne, France

**Reviewed by:**
Charlotte Helfrich-Förster,
Universität Würzburg,
Germany
François Rouyer,
UMR9197 Institut des Neurosciences
Paris Saclay (Neuro-PSI),
France

*Correspondence:
Joerg T. Albert
joerg.albert@ucl.ac.uk
Jason Somers
j.somers@ucl.ac.uk

**Received:** 06 July 2018
**Accepted:** 21 August 2018
**Published:** 11 September 2018

**Citation:**
Somers J, Harper REF and Albert JT (2018) *How Many Clocks, How Many Times? On the Sensory Basis and Computational Challenges of Circadian Systems*. Front. Behav. Neurosci. 12:211. doi: 10.3389/fnbeh.2018.00211

## INTRODUCTION

A good decision is not absolute. It varies depending on context. Foraging for food can be a good or bad decision depending on the presence of a looming predator. The key is to optimize behavior for the current context. But what if the context is in perpetual flux? Owing to the spin of our planet on its longitudinal axis, the vast majority of life on earth exists in 24-h cycles of environmental change.

<footnote n="1">¹Ear Institute, University College London, London, United Kingdom, ²The Francis Crick Institute, London, United Kingdom,</footnote>

<footnote n="3">³Centre for Mathematics and Physics in the Life Sciences and Experimental Biology (CoMPLEX), University College London, London, United Kingdom, ⁴Department of Cell and Developmental Biology, University College London, London, United Kingdom</footnote>
The sun rises and sets. Dawn comes and goes. Temperatures change, and both predator and prey alike sleep and wake. Thus, good decisions depend as much on *when* to do, as they do on *what* to do.

This critical importance of time for decision-making necessitates the existence of internal clocks. These “circadian” oscillators give temporal structure to behavior. The circadian system of *Drosophila melanogaster* presents a remarkable tool to investigate how external environmental changes can impact internal timekeeping that best prepares an organism for time-appropriate tasks. The circadian clock has a set period (i.e., one full cycles takes ~24 h) and its time, or phase, can be adjusted by incoming sensory information. This flexibility is paramount to a system that is orchestrating numerous behavioral and physiological processes while external stimuli are constantly changing.

In this review article we set out to highlight the complexities of the *Drosophila melanogaster* circadian clock, in which “decisions” must be made with regard to external environmental cues. These fundamental timing decisions are made by individual neurons in the context of multimodal sensory information. By discussing the complexities that exist on the molecular, cellular, network and behavioral level, we propose computational approaches that may be fruitful to gain further insight into the nuances of circadian systems.

## THE MOLECULAR CLOCK

To understand how the wider circadian system keeps time, we must first begin with the oscillatory building blocks that comprise it. Time is computed at the level of individual cells and integral to this cellular timekeeping is a molecular clock that is driven by the autonomous oscillations of so called “clock genes”. The autonomous oscillators are driven by a series of molecular transcriptional/translational feedback loops (TTFL) of which components autoregulate their own expression (Figure 1), reviewed in Hardin (2011).

The molecular clock starts with the transcription factors, CLOCK (CLK) and CYCLE (CYC), activating transcription of the primary TTFL genes, *per* and *tim*, through binding to E-box regions in their gene promoters (Hao et al., 1997; Allada et al., 1998; Darlington et al., 1998; Rutila et al., 1998). Transcription occurs between ~ZT4 to ~ZT18 (where ZT0 is lights on and ZT12 is lights off) and translation of these mRNAs generate the protein products PERIOD (PER) and TIMELESS (TIM). The PER protein is inherently unstable, and is rapidly targeted for proteasomal degradation by the kinase DOUBLETIME (DBT; Kloss et al., 1998, 2001; Price et al., 1998). However, the PER-DBT dimer can be stabilized through dimerization with TIM allowing the PER-TIM-DBT complex to accumulate in the cytosol, around 6–8 h after *per* and *tim* transcription activation or ~ZT12 (Curtin et al., 1995; Gekakis et al., 1995; Price et al., 1995; Zeng et al., 1996).

Phosphorylation of PER and TIM by the kinases, CK2 and SGG (Martinek et al., 2001; Lin et al., 2002; Akten et al., 2003), initiates nuclear accumulation of PER and TIM (Nawathean and Rosbash, 2004; Top et al., 2016). Inside

the nucleus, the active form of PER inhibits CLK-CYC activation of *per* and *tim* transcription between ~ZT18 to ~ZT4 (Lee et al., 1998, 1999; Bae et al., 2000; Menet et al., 2010). Consequently, PER and TIM concentrations decline in the cytosol, reducing nuclear abundance of PER and TIM, and ultimately removing the inhibition of CLK-CYC activity. The molecular cycle then starts again, *circa* 24 h later.

The primary TTFL is bolstered by additional interlocked loops. In a second TTFL CLK-CYC also activates transcription of *vrille* and *Pdp1ε/δ* between ~ZT4 and ZT16 (Blau and Young, 1999; Cyran et al., 2003). VRILLE (VRI) protein concentration peaks several hours before *PDP1ε/δ* (~ZT14 vs. ~ZT18) and both act at VRI/PDP1-boxes to modulate *Clk* and *cry* transcription (Cyran et al., 2003; Glossop et al., 2003). PER-TIM-DBT activity from the primary TTFL inhibits CLK-CYC activity interlinking the two loops. The *cry* gene encodes a blue-light photoreceptor that upon photic activation promotes TIM degradation essentially acting as a molecular reset switch of the primary TTFL (Emery et al., 1998; Stanewsky et al., 1998). In the third TTFL, CLK-CYC promotes expression of *clockwork orange* that competitively binds to the same E-box regions to negatively regulate CLK-CYC mediated transcription (Kadener et al., 2007; Lim et al., 2007; Matsumoto et al., 2007).

<figure><img src="image_49.png" /><figcaption>**FIGURE 1** | The interlocked core transcription/translation feedback loops (TTFL) of *Drosophila melanogaster*. The primary TTFL is initiated by the CLK-CYC mediated transcription of the *per* and *tim* genes. The PER protein is inherently unstable and DBT will direct it for degradation unless PER is stabilized by TIM. PER and TIM are phosphorylated (P) by CK2 and SGG to negatively regulate their own expression by repressing CLK-CYC activity. VRI and PDP1 are also produced by CLK-CYC activity, each of which have potentially opposing effects and offset timing to modulate *Clk* and *cry* transcription. Light activation of CRY leads to the degradation of TIM acting as a molecular reset of the TTFL cycling. CWO is a part of third TTFL that also acts as a repressor of CLK-CYC activity.</figcaption></figure>
## THE CENTRAL CLOCK—A NETWORK OF COUPLED OSCILLATORS

If the oscillating proteins of the TTFLs are the gears that direct timekeeping, the central pacemaker neurons are analogous to a synchronizer that sets a coherent time in context with the external environment. The architecture of this central pacemaker in flies consist of ~150 neurons comprising distinct subgroups identified in the brain through cytological staining of clock genes (Figure 2; Helfrich-Förster and Homberg, 1993; Helfrich-Förster, 1995). The subgroups are named after their morphology and anatomical location; each brain hemisphere has four small ventral lateral neurons (s-LN<sub>v</sub>) that express the pigment dispersing factor (PDF), a fifth PDF-negative s-LN<sub>v</sub>, four large PDF-positive ventral lateral neurons (l-LN<sub>v</sub>), six dorsal lateral neurons (LN<sub>d</sub>), 17 group one dorsal neurons (DN1), two group two dorsal neurons (DN2), ~40 group three dorsal neurons (DN3) and three lateral posterior neurons (LPN; Helfrich-Förster et al., 2007; Nitabach and Taghert, 2008; Schubert et al., 2018).

The dual oscillator model, originally hypothesized for nocturnal rodents (Pittendrigh and Daan, 1976), has historically been used to also describe the *Drosophila* crepuscular activity

patterns, proposing separate autonomous oscillators that control activity peaks observed at dusk and dawn. The PDF+ LN<sub>v</sub> are required for the fly's morning activity peak (and free-running rhythms; Renn et al., 1999; Grima et al., 2004; Stoleru et al., 2004), while the LN<sub>d</sub> and 5th PDF- LN<sub>v</sub> are important for the evening peak (Grima et al., 2004), thus, these two groups are labeled the morning (M) and evening (E) oscillators respectively. In practice these designations are too strict as the roles and hierarchy of some cellular oscillators change under different environmental conditions (Rieger et al., 2006, 2009; Murad et al., 2007; Picot et al., 2007).

Current pacemaker models depict a variable—both neurochemically and electrically—coupled neuronal network that differentially responds to incoming multimodal stimuli (Yao and Shafer, 2014; Schlichting et al., 2016; Yao et al., 2016) reviewed in detail in Hermann-Luibl and Helfrich-Förster (2015); Top and Young (2018).

## PERIPHERAL CLOCKS

In addition to the central pacemaker clocks, circadian oscillators also exist in several tissues outside the central nervous system and are likely to regulate organ or tissue specific functions. Peripheral clocks have largely been characterized through clock TTFL protein rhythms identified either by immunohistochemistry or by luciferase reporter expression being driven by clock gene promoters. Located in a number of tissues, peripheral clocks are heterogeneous in both their molecular machinery and their relationship to the central clock (Ito and Tomioka, 2016).

At the molecular level, CRY appears to act not only as a photoreceptor but also as a core component of the primary TTFL. In some peripheral clocks of *cry* mutants—in which CRY normally fulfils both roles—light entrainment in lost, however they also lose molecular oscillations of *per* and *tim* (Ivanchenko et al., 2001; Krishnan et al., 2001). In these peripheral clocks it appears CRY not only integrates photic information into the clock, but it also plays a role in driving oscillations of the clock, possibly acting as a repressor (Collins et al., 2006). Alternatively, CRY may act exclusively as a photoreceptor with persistent free running rhythms observed in certain tissues of *cry* mutants. This indicates that in these peripheral clocks CRY is not required to drive oscillations of the clock (Ito et al., 2008).

Phase relations between central clocks and peripheral clocks are also heterogeneous. For example, peripheral clocks in the Malpighian tubules and chemosensory sensilla can directly entrain to external stimuli and maintain entrained rhythms even without information from central clock neurons (Hege et al., 1997; Krishnan et al., 1999). A different phase relationship is observed between oenocytes and the central clock. PDF release from clock neurons into the hemeolymph can set the phase of the distally located oenocytes, which control rhythmic release of mating pheromones (Krupp et al., 2013). The different phase relationships identified between central and peripheral clocks add yet another layer of complexity to the timekeeping system.

<figure><img src="image_50.png" /></figure>
## LOCOMOTOR ACTIVITY RHYTHMS

Circadian rhythms exist at the cellular and network levels, yet it is the appropriate timing of behaviors that makes this system adaptive for the organism. Behavioral output of a circadian clock is typically measured as the organism's activity pattern; this is no different for *Drosophila*. Simple infra-red beam-breaking monitors record the activity of individual flies isolated in small glass tubes with enough food to last the duration of the experiment. These monitors are placed inside incubators in which the environmental conditions can be accurately controlled according to the test paradigm. In the simplest experiments, transitions between environmental conditions, or Zeitgebers ("time giver" in German), occur almost instantaneously (e.g., 12 h of light followed by 12 h of dark-LD). Here, *Drosophila* exhibits the previously mentioned bimodal activity pattern, with M and E activity peaks occurring at the Zeitgeber transition points. If the flies are then released into constant, free-running conditions, devoid of temporal information, rhythmic activity persists. However, the rhythms become almost exclusively unimodal possibly due to the merging of the previous M and E activity peaks (Wheeler et al., 1993; Helfrich-Förster, 2000). The timing of this free-running activity peak is a common read-out of "clock time" or circadian time. By adjusting the timing of cue onset/offset, the entrainability of the autonomous oscillator, which adjusts subsequent free-running rhythms to new schedules, can be tested. The speed of entrainment and relative amplitudes of autonomous oscillations depend on the strength and mode (e.g., light, temperature, vibration) of the specific Zeitgeber signal. Indeed, the precise activity profile observed depends on both the environmental conditions and the genetic background of the fly (Schlichting and Helfrich-Förster, 2015).

## ENTRAINMENT

Photic information is communicated to clock neurons both directly via CRY, and indirectly via visual photoreceptors residing in the compound eyes, ocelli and Hofbauer-Buchner eyelets. CRY mediates cell-autonomous perception of light in a number of tissues that harbor circadian rhythms via its interaction with TIM in the primary TTFL (Plautz et al., 1997; Emery et al., 2000; Lin et al., 2001). This direct input pathway makes the *Drosophila* circadian system particularly sensitive to light, capable of entraining to low light intensities (0.03 lux; Bachleitner et al., 2007), and can exhibit significant phase shifts following brief light pulses (Levine et al., 1994; Egan et al., 1999; Vinayak et al., 2013). Visual pathways to the clock are not as well defined and do not reset the clock with the same efficiency as CRY dependent pathways (Emery et al., 2000; Helfrich-Förster et al., 2001). However, it appears that a subset of rhodopsin photoreceptors communicate visual photic information to the clock by a novel phototransduction pathway (Stanewsky et al., 1998; Ogueta et al., 2018).

Temperature cycles (TC) can also entrain the circadian clock, and although this can occur in a tissue autonomous fashion in peripheral clocks, signaling from peripheral sensors

play an important role in entraining the central clock (Glaser and Stanewsky, 2005; Sehadova et al., 2009). Thermoreceptors, expressed in chordotonal organs (ChOs) have been identified in the thermotransduction pathway, relaying information to the clock across different TC regimes (Wolfgang et al., 2013; Chen et al., 2015). Furthermore, genes important to the structural integrity of ChOs have also shown importance for entrainment to TC (Glaser and Stanewsky, 2005; Sehadova et al., 2009). Specifically, *nocte* (*no circadian temperature entrainment*) was identified in a screen for mutants that could entrain to LD but not to TC (Glaser and Stanewsky, 2005). Interestingly, the *nocte* mutant cannot entrain to combined LD and TC, hinting at a role in a sensory integration pathway that appears to eventuate in the DN1 central clock neurons (Chen et al., 2018).

ChOs also play a role in entrainment to mechanical inputs and this appears dependent on where the ChO is located. ChOs are stretch sensitive organs that populate almost every exoskeletal joint mediating proprioception in the legs as well as hearing in the antennae (Kavlie and Albert, 2013). Flies with no functional ChOs fail to entrain to 12-h vibration/12-h silence cycles, whereas flies lacking only antennal ChOs entrain better than their wild type controls (Simoni et al., 2014). This may be another feedback loop in the circadian system where output activity feeds information back to the clock.

## NATURAL CONDITIONS

In order to perform more ecologically relevant experiments, environmental conditions can also be shifted in relation to each other in order to closely mimic natural conditions. This can be taken even further by placing the activity monitors outside the predictable conditions of the laboratory in the real-world environment. Under semi-natural "Summer" conditions an extra activity peak is observed that coincides with the temperature maxima termed the afternoon (A) peak (Vanin et al., 2012). Evidence suggests that A peak activity is induced by activation of the TRPA1 thermosensor in the AC neurons, rather than the circadian clock pacemakers (Tang et al., 2013; Green et al., 2015). The ecological relevance of the A peak is hypothesized to be an escape response from afternoon heat which is made evident by the occurrence of this peak being largely environmentally controlled. Closer observations of the fly activity support this hypothesis. Inactive flies at a preferable temperature (28°C) quickly retreat to the relative coolness of their food at an uncomfortable temperature (31°C) and are subsequently induced to erratic hyperactivity at noxious temperatures (35°C; Menegazzi et al., 2012). The phase of the A peak, however, still appears to be modulated by both the clock and environmental conditions (Menegazzi et al., 2012; Vanin et al., 2012). Interestingly, a functional clock appears to suppress the A peak at non-noxious temperatures. Clock mutant flies, deficient in either PER or CLK, exhibit A peak activity under milder TC in which wild-type flies exhibit bimodal activity patterns (Currie et al., 2009; Menegazzi et al., 2012; Vanin et al., 2012). This could be the clock mutant's lack of time perception being unaware of the daily afternoon peaks of temperature, whereas wild-type flies anticipate the regular afternoon increase
in temperature knowing it is nothing to be concerned about until a critical threshold is crossed at noxious temperatures. This is a more explicit decision-making process governed by the circadian clock—knowing what stimuli to respond to and what stimuli can safely be ignored.

## CONFLICT CONDITIONS

The complexity of the number of potential interactions that could occur between the central clock pacemakers and the peripheral clocks is only now starting to be realized. How many clocks contribute to the animal's overall sense of time? The historical view that one master pacemaker clock actively sets the phase of all the downstream clocks is being challenged. Perhaps the central pacemaker sets the time of central processes and regional peripheral clocks set time locally? To test this, offset environmental conditions have been used to understand the molecular, and subsequent behavioral repercussions, of receiving potentially conflicting environmental cues. During antiphasic conflict of light and temperature (a 12-h maximal misalignment between the two cues), the activity patterns of the flies demonstrate preferential entrainment to light (Yoshii et al., 2010; Harper et al., 2016). An assessment of the relative strength of photic and thermic input under these conditions suggests light to be the victor. However, observation of fly activity under other misalignment schedules reveals that flies appear to follow temperature cues when the misalignment is short (2-4 h) and light cues when the misalignment is long (>7 h). Misalignment between this (5-7 h) produces a novel behavior of sustained activity over the course of misalignment (termed “plateau” or P behavior; Harper et al., 2016). P behavior was not observed in the clock-less *per* mutants or the light-input pathway impaired *cry* mutants. The *per* mutant generally displayed arrhythmic behavior with brief startle responses to environmental changes while, due to their lack of the CRY photoreceptor, *cry* mutants entrained preferentially to temperature cues. The lack of the P behavior in these two clock mutants during sensory conflict demonstrates that the behavior depends on a functional clock and is not the result of environmental masking. Furthermore, a severe dampening of PER oscillations in the central pacemakers under conflict conditions support the hypothesis that sensory conflict is causing the abnormal P behavior. This contrasts with results from a subsequent study in peripheral clocks. During similar conflict conditions, bioluminescent PER reporters expressed in peripheral clocks, revealed peripheral molecular rhythms remained entrained to the light cue (Harper et al., 2017). Again, CRY expression appears to be key for this light cue entrainment in these tissues as peripheral molecular rhythms in *cry* mutants follow the temperature cue under conflict conditions. The fact that molecular rhythms in these peripheral clocks do not collapse as observed in the central clock could indicate that these peripheral clocks do not contribute to locomotor rhythms or alternatively that asynchrony between central and peripheral clocks results in abnormal behavior. Further studies are required to unpack this relationship.

## PROBABILISTIC CONSIDERATIONS AND FUTURE COMPUTATIONAL APPROACHES

The “circadian clock” is a complex, highly interconnected system, which attempts to determine—or predict—the state of the world from noisy or incomplete data. Probabilistic modeling provides a powerful conceptual framework for the theoretical, and experimental, analysis of such a system.

In a circadian setting, we can think of Zeitgebers as observable variables, from which the clock must compute the otherwise unobservable time of day. Bayesian integration provides an optimal algorithmic method by which to combine different sources of information. Here, the goal of a Bayesian observer (e.g., the “circadian clock”) is to compute the conditional density function that specifies the probability of the time of day from the given external cues (e.g., light and temperature). Further complexity is added by the temporal structure of circadian data, requiring that probability distributions be calculated over sequences of observations. Hidden markov models (HMMs) are one way of representing such temporal distributions and are used widely in tasks such as speech recognition (Rabiner, 1990), computational genomics (Eddy, 2004) and decoding neural spike data (Escola et al., 2011). More recently, HMMs have begun making their way into the circadian field, effectively modeling rhythms of both molecular (Bieler et al., 2014) and behavioral data (Harper, 2017). We expect such probabilistic frameworks to be crucial for understanding the relationships between circadian clock components across all levels of the system.

## DISCUSSION

Complexity exists at all levels of the circadian system. Multiple TTFL maintain the ticking of the individual cellular clocks. This timing can then be communicated, neurochemically or electrically, to other cells that use the information to set the phase of their own internal clocks and orchestrate downstream processes. However, the flow of this information is not unidirectional. Multiple central oscillators exchange timing information set by incoming sensory information to compute time. This raises a further question of how the circadian system weights each oscillator's contribution (a problem well-suited to probabilistic modeling).

Existing methods used to quantify circadian function are imperfect. They are typically measured in free running conditions in order to avoid masking effects that occur as a stimulus response to environmental condition transitions. This necessarily removes the context in which circadian clocks operate and results in rapidly dampened rhythms—both molecular and behavioral. Ideally the phase of the circadian system would be measured during the entrainment period to alleviate the rapid dampening effects of free run, and also reducing the overall length of the experiment. Again, probabilistic models pose an interesting tool for extracting key circadian metrics—such as phase, period and rhythm strength—from noisy time series data in the presence of changing Zeitgebers.
Finally, a better quantitative understanding of the computations—and possible conflicts—occurring in the circadian system is also relevant in the context of the multitude of clock-related pathologies in humans (Roenneberg and Merrow, 2016). These pathologies may be the result of the circadian system simply making the wrong decisions, e.g., due to conflicting data (light at night, social jetlag, trans-meridian travels, etc.). A deeper understanding of where the errors in the decision making processes occur may be a decisive first step toward identifying and treating these pathologies.

## AUTHOR CONTRIBUTIONS

JS, RH and JA together wrote and designed different aspects of the article.

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## FUNDING

RH received funding from the Engineering and Physical Sciences Research Council (EP/F500351/1) through UCL CoMPLEX. JS was supported by a European Research Council grant to JA (H2020-ERC-2014-CoG/648709/Clock Mechanics). JA was supported by grants from the Biotechnology and Biological Sciences Research Council (BB/L02084X/1) and the European Research Council (H2020-ERC-2014-CoG/648709/Clock Mechanics).

## ACKNOWLEDGMENTS

We are grateful for the valuable inputs that resulted from discussions with Ralf Stanewsky.

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**Conflict of Interest Statement:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Copyright © 2018 Somers, Harper and Albert. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
<figure><img src="image_51.png" /></figure>

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# Gentle Handling Attenuates Innate Defensive Responses to Visual Threats

Xuemei Liu¹,²†, Chen Chen¹†, Yuanming Liu¹†, Zhijie Wang¹, Kang Huang¹, Feng Wang¹* and Liping Wang¹*

¹ Shenzhen Key Lab of Neuropsychiatric Modulation and Collaborative Innovation Center for Brain Science, CAS Center for Excellence in Brain Science and Intelligence Technology, The Brain Cognition and Brain Disease Institute, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China, ² University of Chinese Academy of Sciences, Beijing, China

Innate defensive responses to threats are essential for animal survival. The complexity and variability of innate defensive behaviors can be due to individual experiences, environmental factors, and internal states. However, it is not completely understood if the gentle handling involved in sensory processing affects innate defensive responses to visual threats. Here, we report attenuation of innate defensive responses after gentle handling accompanied by de-excitation of the intermediate layer (IL) and deep layer (DL) of the superior colliculus (SC) but not of the superficial layer (SL). Our theoretical analysis of the c-Fos network revealed an increased correlation in module 1, which maybe generally functionally associated with fear emotional, a decreased correlation in module 2, which maybe generally functionally associated with sensory processing. The IL of the SC appeared to have the highest correlation with the two modules. We verified the dynamic activities of the IL of SC in response to overhead looming stimulus using fiber photometry. Retrograde labeling of 18 regions of interest (ROIs) showed that the IL received significant inputs from the cortical areas, thalamus, hypothalamus, and brainstem. These data suggest the sensory processing involved in the modulatory roles of the SC in innate fear processing.

**Keywords:** gentle handling, innate fear, sensory input, superior colliculus, overhead looming stimulus

## INTRODUCTION

Innate defensive responses to threats are essential for animal survival (LeDoux, 2012; Anderson and Adolphs, 2014; Janak and Tye, 2015; Tovote et al., 2015). Although defensive behaviors are stereotyped, their complexity and variability can be due to individual experiences (Morgan, 1896), environmental factors, and internal states (LeDoux, 2012; Anderson and Adolphs, 2014; Anderson, 2016). However, whether sensory input regulate the innate fear behavioral output is not well defined.

Overhead looming stimuli that mimic aerial predators have been shown to trigger stereotyped defensive responses across species (Yilmaz and Meister, 2013; Muijres et al., 2014; Temizer et al., 2015). Recently, looming-evoked defensive behavior and its underlying neural circuitry via the superior colliculus (SC) have received increasing attention (Yilmaz and Meister, 2013; Shang et al., 2015; Wei et al., 2015; De Franceschi et al., 2016; Huang et al., 2017; Vale et al., 2017; Evans et al., 2018; Li et al., 2018; Salay et al., 2018). SC is a laminated retinal

## OPEN ACCESS

**Edited by:**
Gérard Coureaud,
INSERM U1028 Centre de Recherche en Neurosciences de Lyon, France

**Reviewed by:**
Jeffrey B. Rosen,
University of Delaware, United States
Rafael S. Maior,
Universidade de Brasilia, Brazil

*Correspondence:
Feng Wang
feng.wang@siat.ac.cn
Liping Wang
lp.wang@siat.ac.cn

†These authors have contributed
equally to this work

**Received:** 27 June 2018
**Accepted:** 24 September 2018
**Published:** 18 October 2018

**Citation:**
Liu X, Chen C, Liu Y, Wang Z, Huang K, Wang F and Wang L (2018) *Gentle Handling Attenuates Innate Defensive Responses to Visual Threats*. Front. Behav. Neurosci. 12:239. doi: 10.3389/fnbeh.2018.00239
recipient structure, of which its superficial layer (SL) receives direct retina input and primarily responds to visual stimuli. The intermediate layer (IL) and deep layer (DL) of the SC contain neurons that respond to multimodal (somatosensory, auditory, visual) sensory inputs (Wurtz and Goldberg, 1971; Stein, 1984).

Laboratory housing, husbandry, and handling methods have been shown to affect the internal state of the animals (Grandin, 1997; Poole, 1997; Deacon, 2006; Hurst and West, 2010; Clarkson et al., 2018). Unpleasant somatosensations such as tail handling and rough handling reduce the response to reward and increase stress levels (Grandin, 1997; Clarkson et al., 2018). Conversely, a gentle touch plays an essential role in the pleasant somatosensory processing conserved across species (Lumpkin et al., 2010; Abraira and Ginty, 2013). In mammals, a gentle touch is the expression of preference between mates, parenting, and affiliation (Barnett, 2005; Ebisch et al., 2016). It has also been demonstrated that massage has positive effects on health in human, grooming reduces stress levels in primates, and tactile stimulation alleviates the effects of stress in fish (Field et al., 2005; Soares et al., 2011). Our previous study demonstrated that stress accelerates the defensive responses to looming in mice (Li et al., 2018). However, it remains unknown if the repeat pleasant somatosensation of gentle handling affects innate defensive behaviors to visual threats.

Here, we found that gentle handling attenuated innate defensive responses to overhead looming. In addition, the IL and DL (but not the SL) of the SC had significantly reduced activities in response to overhead looming after repeated gentle handling. Functional analysis of the c-Fos network revealed that the highest correlation was between the IL of the SC in two modules among the 16 ROIs. Moreover, we verified the dynamic activities of the IL of SC to overhead looming stimulus using fiber photometry. Retrograde labeling of 18 ROIs showed that the IL had significant inputs from the cortical areas, thalamus, hypothalamus, and brainstem, suggesting the potential modulatory roles of the SC in innate fear and sensory processing.

# MATERIALS AND METHODS

## Animals

All husbandry and experimental procedures in this study were approved by the Animal Care and Use Committee at the Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences (Shenzhen, China). Male (6 weeks of age) C57BL/6 mice obtained from Beijing Vital River Laboratory Animal Technology Co., Ltd. (Beijing, China) were kept in a quiet room (22–25°C) with a 12 h light-dark cycle, and were given food and water *ad libitum*.

## Viral Injection

Mice were anesthetized with intraperitoneal (i.p.) injection of pentobarbital sodium (0.5% w/v, 80 mg/kg) and placed in a stereotaxic frame (RWD Life Science, Shenzhen, China). Microinjection needles were inserted unilaterally directly above the IL of the SC, using the following coordinates from Bregma: AP, -3.85 mm, ML, ±0.8 mm, and DV, -1.85 mm. The cholera

toxin subunit B (CTB) Alexa Fluor 594 Conjugate and adeno-associated virus (AAV) expressing EGFP driven by a neuron calcium/calmodulin-dependent kinase II (CaMKII) promoter (AAV9-CaMKIIα-GCaMP6-EGFP; BrainVTA Co., Ltd., China) were injected using a 10 µL microsyringe with a 33 gauge metal needle (Hamilton; Sigma, United States) connected to the UltraMicroPump 3 microsyringe pump injector (World Precision Instruments, United States) and its controller (Micro4; World Precision Instruments) at a rate of 100 nL/min. After injection, the needle was left in place for an additional 10 min to allow diffusion of the virus particles away from the injection site, and then it was slowly withdrawn.

## Histology, Immunohistochemistry, and Microscopy

To quantify the expression of c-Fos positive neurons in the whole brain induced by overhead looming stimuli after consecutive 7 days of gentle handling or no-handled, the two group of mice were sacrificed 1.5 h after presentation of overhead looming stimuli on the 8th day. Mice were perfused by overdosing with chloral hydrate (10% w/v, 300 mg/kg, i.p.) and transcranially perfused with cold 1 M PBS followed by ice-cold 4% paraformaldehyde (PFA; Sigma) in 1 M PBS. Brains were removed and submerged in 4% PFA at 4°C overnight to fix, and then transferred to 30% sucrose to equilibrate. The coronal brains sections (40 µm) were obtained with a cryostat microtome (CM1950; Leica, Germany). Freely floating sections were washed with PBS, and blocked for 1 h at room temperature in blocking solution containing 0.3% Triton X-100 and 10% normal goat serum (NGS). Then the sections were incubated overnight with rabbit monoclonal anti-c-Fos (1:300, #2250; Cell Signaling Technology, United States) diluted in PBS with 3% NGS and 0.1% TritonX-100. The sections were incubated for 1 h at room temperature with Alexa Fluor 488 goat anti-rabbit secondary antibody (1:200; Jackson Laboratory, United States). Finally, the sections were mounted, coverslipped with DAPI (1:50,000, #62248; Thermo Fisher Scientific, United States), and photographed using the Olympus VS120 virtual microscopy slide scanning system (Olympus; Japan). The images were acquired using identical gain and offset settings, and analyzed with ImageJ, Image Pro Plus, and Adobe Photoshop software. ROIs were traced with reference to the “The mouse brain in stereotaxic coordinates, George Paxinos and Keith B. J. Franklin,” and c-Fos immunoreactivity was quantified using Image Pro Plus that was checked by comparing to manual counts by a trained double-blind observer.

## Behavior Assay

### Gentle Handling

After obtained from the laboratory animal center, the mice have 1 week to habituation to the new animal facility and cage homes. Gentle handling was performed in the arena.

Briefly, mice were placed individually on a piece of medical absorbent cotton for 3 min handling once per day for 7 consecutive days. Handling sessions were performed by gently and thoroughly touching the mouse body through the cotton.
New cages with the previous bedding were used to house the mice that had been handled. Handling sessions were performed in the same room by the same experimenter. The mice in the non-handled group received the same approach without the handling treatment, including habituation to the looming box on the 7th day and receive three times upper visual looming stimuli on the 8th day.

## Looming Stimulation Test

The behavioral arena was a 40 cm × 40 cm × 30 cm closed plexiglass box with a shelter nest in the corner and an LCD monitor on the ceiling to present the looming stimulus (LS). The LS was a black disk that expanded from 2 to 20 degrees in diameter; it was presented 15 times in 5.5 s. The stimulation was manually triggered by the experimenter when the mice were in the farthest area away from the shelter. The handling group of the mice have experienced the recurrent 7 days gentle handling. On the 7th day, the mice in gentle handled group and non-handled group have 10 min to habituation to the looming box. Then on the 8th test day, after 3 min pre-test session, all the mice have subjected three times of the upper visual black looming stimulation. And the duration between each of the stimulation is no less than 3 min.

## Fiber Photometry

A fiber photometry system (ThinkerTech, Nanjing) was used to record the calcium signals of CaMKIIα-positive neurons in the IL of the SC in response to upper visual field looming stimulation. Two to 3 weeks following AAV9-CaMKIIα-GCaMP6-EGFP injection into the IL, an optical fiber (220 µm O.D., 0.37 NA; Newdoon, China) was placed above the IL of the SC. One week after the surgery, mice took 10 min to habituation to looming box the day before the test day. In the test day, mice received three times upper visual field black looming stimulations and three times upper visual field white looming stimulations. The upper visual field white looming here is used as a control which does not evoke the flight behavior.

To record the fluorescence signals, a 480 nm excitation light from a LEDs (CREE XPE), reflected of a dichroic mirror with a 435-488 nm reflection band and a 502-730 nm transmission band (Edmunds Inc.) and coupled to a long optical fiber (220 µm O.D., 0.37 NA, 2 m long, Thorlabs, Inc.). The laser intensity at the fiber tip was 20-30 µW to minimize GCaMP bleaching. GCaMP fluorescence was filtered with a GFP bandpass (Filter 525/39; Thorlabs, Inc., United States), detected by the sensor of a CMOS camera (Thorlabs, Inc. DCC3240M). A Lab view program is developed to control the CMOS camera and acquire calcium signal in about 50 Hz. The behavior event signal is recorded by a DAQ card (NI, usb-6001) in 1000 Hz using the same program.

## Statistical Analysis

All data values are presented as the mean ± SEM. Mann-Whitney *U* test was used to analyze statistical differences using GraphPad Prism 7 software. Statistical significance was set at \*p < 0.05, \*\*p < 0.01, \*\*\*p < 0.001, \*\*\*\*p < 0.0001. Functional network analyses of the c-Fos dataset were computed with

the Rubinov and Sporns (2010) Brain Connectivity Toolbox<fnref n="1" /> in MATLAB R2016 (The Mathworks Inc.) Graph theoretical analysis of c-Fos data between gentle handling and non-handling conditions was used to calculate the relationship among the 16 ROIs. Correlations among the 16 ROIs arranged by rank-order were conducted using Kendall's Tau correlation coefficient. Community correlation network and participation coefficients were analyzed according to previous studies, and a threshold value of 0.35 was set (Newman, 2006; Power et al., 2013; Vetere et al., 2017; Rogers-Carter et al., 2018).

# RESULTS

## Gentle Handling Attenuates Defensive Responses to Overhead LS

After 1 week habituation in the laboratory, mice underwent 7 consecutive days of gentle handling. To determine whether gently handling affects the innate defensive responses of mice, we used a behavioral assay with a rapidly expanding dark disk stimulus that mimicks an approaching predator (Yilmaz and Meister, 2013; Shang et al., 2015; Wei et al., 2015; De Franceschi et al., 2016; Huang et al., 2017; Vale et al., 2017; Salay et al., 2018). In response to repeated LS (three trials, intervals longer than 3 min), gently handled mice had an increased latency to onset flight behavior (Figure 1B), increased latency of flight to nest (Figure 1C), decreased duration hiding in nest (Figure 1D), and decreased locomotion speed (Figure 1E) compared with the non-handled group (n = 12 in each group, Mann-Whitney *U* test, \*p < 0.05, \*\*p < 0.01, \*\*\*p < 0.001, \*\*\*\*p < 0.0001). Taken together, these results demonstrate that long-term repeated handling attenuates innate defensive responses to visual threat stimuli. These data suggest that instinctive behavioral output is sensitive to tactile stimulation.

## Gentle Handling Reduces the Activities of SC Neurons in Response to Overhead Looming Stimulus

To test whether gentle handling affects SC responses to LS, we performed c-Fos labeling in the gently handled and non-handled groups after the behavioral test. The gently handled group had attenuated innate defensive behavior (Figure 1) accompanied by a decrease of c-Fos expression in the IL and DL of the SC, but not in the SL of the SC (Figure 2; n = 6 mice in each group, Mann-Whitney *U* test, *U* = 8, *p* = 0.132, *U* = 2, \*\*p = 0.0087, *U* = 4, \*\*p = 0.0260). These data indicate that gentle handling reduced the activities of the IL and DL of the SC in response to the black looming in the upper visual field.

## Analyses of the c-Fos Functional Network in Response to Overhead Looming Stimulus After Gentle Handling

To determine the functional network connectivity, we performed c-Fos mapping in 16 ROIs in response to overhead LS between

<footnote n="1">¹https://sites.google.com/site/bctnet/</footnote>
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gently handled and non-handled mice. The 16 ROIs are selected by the expression of c-Fos which is comparatively higher than some other brain regions, also the brain regions what we choose, is most like generally functionally associated with the “fear” and “sensory” processing. Graph theoretical analysis was used to divide the 16 ROIs into two modules, in which the functional

correlation of ROIs in each module was higher than that between modules (Figure 3A).

Our paradigm used in the current study is overhead life-threatening looming stimulation which mimic the predatory in the upper visual field. And most ROIs in module 1 (Figure 3D, purple dots) maybe generally functionally associated
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<figure><img src="image_54.png" /></figure>

**FIGURE 2** | (A) The timeline and bar graph showing reduced c-Fos expression in the gently handled group compared with the non-handled group in response to LS. (B) Representative images showing c-Fos expression in the SC of gently handled or non-handled groups in response to LS (SL, superficial layer of SC; IL, intermediate layer of SC; DL, deep layer of SC); Scale bars = 100 µm. Mann-Whitney *U* test, **P** < 0.01, *U* = 2; \*P* < 0.05, *U* = 4.

with “fear” related, such as different layer of superior colliculus, ventral tegmental area, basolateral amygdala, anterior cingulate cortex, pontine gray, piriform cortex. And the recurrent long-term (7 days continuously stimulation) gentle handling should transmit sensory input signal. And most ROIs in module 2 (Figure 3D, green dots) maybe generally maybe generally functionally associated with “sensory” related, such as primary somatosensory cortex, barrel field; primary somatosensory cortex, trunk region; primary visual cortex. After gentle handling, the correlation was decreased in module 1 and increased in module 2, and the correlation across modules 1 and 2 was increased (Figure 3B). The participation coefficients were calculated to determine the hubs in the functional correlation-based networks (Power et al., 2013; Vetere et al., 2017; Rogers-Carter et al., 2018). The nodes with the highest participation coefficients across two modules included brain areas such as the IL and DL of the SC, S1BF, V1, PVT, and PAG (Figure 3C). Functional network analysis indicated that compared to the SL and DL, the IL of the SC was more densely connected with other nodes related to innate fear and sensory perception.

## Dynamic Activation of the IL of SC in Response to Looming Overhead Stimulus

The SC, especially the SL of the SC, receives direct retinal input and mediates visual threat processing (Shang et al.,

2015, 2018). To investigate whether the IL of the SC receiving somatosensory input reflects visual threat signals, we injected AAV9-CaMKIIα-GCaMP6s into the SC and optical fibers were implanted into the IL. Fiber photometry was used to record calcium transients in CaMKIIα-positive neurons in the IL in response to a black looming in the upper visual field (Figure 4A). A white looming in the upper visual field was used as a control, as it does not trigger flight-to-nest behavior (Figures 4B,C). The IL of SC CaMKIIα-positive neurons exhibited a significant increase in the activity after presentation of a black LS in the upper visual field (9.52 ± 2.13%, ΔF/F mean) compared with the white LS in the upper visual field (0.98 ± 0.74%, ΔF/F mean). Calcium signal onset to looming overhead stimulus rapidly increased (15% of peak signal as the onset time point) with a latency of 0.71 ± 0.18 s and peak signal of 19.08 ± 3%, ΔF/F (Figures 4D,E). Taken together, these data demonstrate the IL of SC CaMKIIα-positive neurons encode visual threat processing.

## Retrograde Labeling of Input to the IL of the SC

To visualize inputs to the IL, the CTB Alexa Fluor 594 Conjugate CTB was used for tracing. Two weeks after microinjection of CTB into the IL, the brain was sectioned to 40 µm, and every third section was processed for subsequent analysis. Across the entire brain, the most abundant labeling was found in the cortex (Aux, V1, S1, ACC, TeA, EcT, PRh, EnT), thalamus (LD, LP, AV),
A

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C

<figure><img src="image_56.png" /></figure>

B

<figure><img src="image_57.png" /></figure>

D

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**FIGURE 3** | (A) Correlation matrix showing the functional correlation among ROIs, SLSC (superficial layer of superior colliculus), ILSC (intermediate layer of superior colliculus), DLSC (deep layer of superior colliculus), BLA (basolateral amygdala), ACC (anterior cingulate cortex), Pir (piriform cortex), PG (pontine gray), VTA (ventral tegmental area), VMH (ventromedial hypothalamic nucleus), DMH (dorsal hypothalamic area), V1 (primary visual cortex), S1BF (primary somatosensory cortex, barrel field), S1TR (primary somatosensory cortex, trunk region), TRN (tegmental reticular nucleus), PVT (paraventricular thalamic nucleus), PAG (periaqueductal gray matter). (B) Two modules were classified by graph theoretical analysis. (C) Participation coefficients were calculated to determine the degree of each node connected to multiple networks. (D) Functional network nodes were exhibited by network visualization (module 1 is in purple, module 2 is in green).

and hypothalamus (LH, DMH, VMH, ZI), brainstem (LC, DRN) (Figure 5). These data suggest that the IL of the SC receives broad cortical inputs from visual cortex, auditory cortex, and somatosensory cortex, as well as autonomic inputs from LC, LH, and thalamic nuclei (Saper, 2002).

DISCUSSION

The innate defensive reactions to threats are vital for survival. Although innate fear is instinctive and unconditional, the expression of innate fear can be affected by the circuits of motivation, arousal, emotional state, and reinforcement (LeDoux, 2012; Anderson and Adolphs, 2014). Tactile stimuli is a fundamental form of sensory perception conserved across species (Ratcliffe, 2012). Gentle handling has been shown to reduce stress, modify cognitive behavior, and improve the relationship between handlers and animals (Harlow and Suomi, 1970; Antoniazzi et al., 2017). Furthermore, gentle handling has been proven to decrease depression and reduce anxiety-like behavior in laboratory mice (Grandin, 1997; Madruga et al., 2006). Visually evoked defensive responses to overhead looming stimulus are initiated from the SC and lay downstream of the SC

(Wei et al., 2015; Shang et al., 2018), but whether gentle handling affects innate visual threat processing is largely unknown.

In the current study, we demonstrated that gentle handling attenuates innate defensive reactions to visual threat stimuli, consistent with findings from previous studies (Grandin, 1997; Madruga et al., 2006). These studies provide a possibility that gentle handling might change the social interaction with experimenter and animal to improve the animal's performance. Moreover, it's possible that gentle handle induced the neurotransmitters such as oxytocin and dopamine release or lower corticotropin releasing hormone (CRH) to reduce emotionality, promote calm, and increase the ability to address visual threat situation. (Harlow and Suomi, 1970; Fenoglio, 2006; Ellingsen et al., 2014; Scheele et al., 2014; Peled-Avron et al., 2016).

Our c-Fos network data analysis revealed that after gentle handling, the correlation became weaker in module 1, but was stronger in module 2, and the functional connection across modules 1 and 2 became much stronger, as seen in **Figure 4B**. This suggests that gentle handling reduces activity in fear emotional-related regions but enhances the sensory connection response to visual threat. Functional network analysis demonstrated the IL and DL of the SC, S1BF, V1, PVT, and
A

<figure><img src="image_59.png" /></figure>

B

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C

<figure><img src="image_61.png" /></figure>

D

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E

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**FIGURE 4** | (A,B) The timeline and schematic of fiber photometry recording of LS-induced Ca²⁺ transient. (C) AAV9-CaMKIIα-GCaMP6-GFP expression in the IL of the SC; Scale bars, left = 100 µm; right = 10 µm. (D) Representative heatmap exhibits LS-induced Ca²⁺ transient across three trials. (E) Mean GCaMP signal response to black looming in the upper visual field and white looming (n = 6 mice in black looming, n = 5 mice in white looming, \*\*\*\*p < 0.0001, Mann-Whitney U test, U = 117).

PAG, these brain areas have the highest participation coefficients across two modules. IL and DL of SC are thought to be involving in defensive behavior and are also functionally as the multisensory integration center (Sahibzada et al., 1986; Mooney et al., 1992). And in our study, IL and DL of SC are vital hubs to process both the sensory and visual threatening signals. S1BF and V1 are a part of primary sensory cortex, previous studies have defined the S1BF encode the representation of a sensory stimulus shaped by associative fear learning and V1 inputs to SC increase the response magnitude to looming (Gdalyahu et al., 2012; Zhao et al., 2014). Our recurrent gentle handling supposed to enhance the cortical plasticity and change the connection between cortex and subcortex which could lead to a change of defensive responses respond to overhead looming. PVT is a part of thalamus, which is recognized as relay station to transfer the sensory information, has proved to contribute to threatening events and fear memory (Penzo et al., 2015). PAG has defined to orchestrates sensory and motor (Koutsikou et al., 2015) and

initiation of escape to looming stimuli (Evans et al., 2018). Taken together, all these brain regions functionally as hubs in powerful way in sensory and fear related networks.

The SC is a laminate sensory-motor structure. The SL of SC is thought to process primarily visual signal, whereas the IL and DL of SC exert sensory responses and multimodal integration, contributing to saccadic eye movements and head movements (Wurtz and Goldberg, 1971; Stein, 1984). Our CTB retrograde tracing data indicated that the IL of the SC receives broad cortical and autonomic inputs. Another possible mechanism may be multimodal integration of the IL-SC, in which recurrent somatosensory stimuli enhance visual discrimination (Macaluso et al., 2000, 2002). Our c-Fos mapping data demonstrated that, gentle handling affects visual threat signals by preferentially influencing different layer of SC. That's c-Fos expression in gently handled mice was significantly higher in the IL and DL of the SC but not in the SL of the SC compared with the non-handled group respond to overhead looming stimuli.
<figure><img src="image_64.png" /></figure>

In addition, our c-Fos functional network analysis confirmed that the IL has more intensive connections with the other regions than the DL and SL of the SC. However, the different functions of IL and DL have not been well defined. Thus, the neural mechanisms underlying interlaminar modulation of the SC needs further investigations (Mooney et al., 1992; Saito and Isa, 2005; Cohen and Castro-Alamancos, 2010; Ghitani et al., 2014).

The results of this study indicate that innate defensive responses are sensitive to external conditions, which are vital factors that influence animals' behaviors by affecting the internal state of brain. Our data consistent with the previous studies suggest that the environmental condition (e.g., housing, husbandry, habituation, handling, transport) and standard of

behavioral protocol are crucial for animal behavioral tests, especially the innate behavioral test (Grandin, 1997; Poole, 1997; Deacon, 2006; Baumans, 2009; Hurst and West, 2010; Clarkson et al., 2018).

Our current study revealed that gentle handling changes the activation of key brain regions that respond to danger signaling, indicating that these regions are vital for processing the interaction between sensory perception and fear emotion. The fact that recurrent sensory stimuli affect innate fear processing suggest that related neural circuits may be causally responsible for psychiatric diseases such as autism, of which the core characteristics are sensory and emotion deficits (Pernon et al., 2007; Cascio et al., 2012; Robertson and Baron-Cohen, 2017).
## AUTHOR CONTRIBUTIONS

XL, LW, and FW designed the experiments. CC performed the behavioral tests. YL conducted the fiber photometry recording. CC, YL, and XL performed the histological studies. XL, CC, YL, ZW, and KH analyzed the data. XL, LW, and FW wrote the manuscript.

## FUNDING

This work was supported by the National Natural Science Foundation of China (Grant Nos. NSFC 31630031, 81425010,

91632303, and 31600860), the International Partnership Program of Chinese Academy of Sciences (Grant No. 172644KYS820170004), the External Cooperation Program of the Chinese Academy of Sciences (Grant No. GJHZ1508), the Guangdong Provincial Key Laboratory of Brain Connectome and Behavior (Grant No. 2017B030301017), the Shenzhen Municipal People's Government (Grant Nos. JCYJ20150529143500959 and JCYJ20160429190927063), the Shenzhen Discipline Construction Project for Neurobiology DRCSM (Grant No. [2016]1379), Ten Thousand Talent Program, and the Helmholtz-CAS Joint Research Grant, GJHZ1508, and also supported by Guangdong Special Support Program.

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**Conflict of Interest Statement:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Copyright © 2018 Liu, Chen, Liu, Wang, Huang, Wang and Wang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
<figure><img src="image_65.png" /></figure>

# Behavioral Effect of Plant Volatiles Binding to *Spodoptera littoralis* Larval Odorant Receptors

Arthur de Fouchier†, Xiao Sun†, Gabriela Caballero-Vidal, Solène Travaillard†, Emmanuelle Jacquin-Joly and Nicolas Montagné*

Institut National de la Recherche Agronomique (INRA), Sorbonne Université, CNRS, IRD, UPEC, Université Paris Diderot, Institute of Ecology and Environmental Sciences of Paris, Paris and Versailles, France

OPEN ACCESS

**Edited by:**
Gérard Manière,
Université de Bourgogne, France

**Reviewed by:**
Alisha Anderson,
Commonwealth Scientific and
Industrial Research Organisation
(CSIRO), Australia
Merid Negash Getahun,
International Centre of Insect
Physiology and Ecology, Kenya

*Correspondence:
Nicolas Montagné
nicolas.montagne@sorbonne-
universite.fr

†Present address:
Arthur de Fouchier,
Laboratoire d'Ethologie
Expérimentale et Comparée (LEEC),
Université Paris 13, Sorbonne Paris
Cité, Villetaneuse, France
Xiao Sun,
Biotic Interaction and Biosecurity
Lab, School of Life Sciences, Henan
University, Kaifeng, China
Solène Travaillard,
Aix-Marseille Université,
CNRS, Institut de Biologie du
Développement de Marseille,
Marseille, France

**Received:** 12 June 2018
**Accepted:** 18 October 2018
**Published:** 12 November 2018

**Citation:**
de Fouchier A, Sun X, Caballero-Vidal G, Travaillard S, Jacquin-Joly E and Montagné N (2018) Behavioral Effect of Plant Volatiles Binding to Spodoptera littoralis Larval Odorant Receptors. *Front. Behav. Neurosci.* 12:264. doi: 10.3389/fnbeh.2018.00264

Phytophagous insects use volatile organic compounds (VOC) emitted by plants to orient towards their hosts. In lepidopteran pests, crop damages are caused by larval stages—the caterpillars—that feed extensively on leaves or other plant tissues. However, larval host plant choice has been poorly studied, and it is generally admitted that caterpillars feed on the plant where the female laid the eggs. The mobility of caterpillars has been generally overlooked even though several studies showed that they can orient towards odors and change host plant. Recently, a large number of odorant receptors (ORs) tuned to plant volatiles have been characterized in the model pest moth *Spodoptera littoralis* (Noctuidae). In the present work, we identified nine of these deorphanized ORs as expressed in *S. littoralis* caterpillars. In order to understand whether these ORs are involved in host searching, we tested the behavioral significance of their ligands using a larval two-choice assay. This OR-guided approach led to the identification of nine plant volatiles, namely 1-hexanol, benzyl alcohol, acetophenone, benzaldehyde, (Z)3-hexenol, (E)2-hexenol, indole, DMNT and (Z)3-hexenyl acetate, which are active on *S. littoralis* caterpillar behavior, increasing our knowledge on larval olfactory abilities. To further explore the link between OR activation and behavioral output induced by plant volatiles we used a modeling approach, thereby allowing identification of some ORs whose activation is related to caterpillar attraction. These ORs may be promising targets for future plant protection strategies.

**Keywords:** insect, olfaction, olfactory receptor, volatile organic compound, crop pest, caterpillar, Lepidoptera, Noctuidae

## INTRODUCTION

Holometabolous insects are characterized by two mobile developmental stages with drastically different morphologies and physiologies. The larval stage constitutes a period of active feeding and growth, while the adult stage is a period devoted to reproduction and dispersal. Larvae and adults thus have different life styles, are not in competition for the same resources, and develop independent adaptations in response to different selective pressures. This distinction between adults and larvae is particularly striking in Lepidoptera. While larvae (or caterpillars) are actively feeding on their host plant, the adults generally live only a few days and feed on the nectar of flowers (Powell, 2009). Almost all plant species are damaged by caterpillars, many of which are pests of both crops and stored products (Stehr, 2009).
Host plant choice is a crucial task for phytophagous insects, and it is highly dependent on the sense of smell. The detection of plant-emitted volatile organic compounds (VOC) has been the subject of intense research, notably in crop pest insects (Bruce and Pickett, 2011; Bruce et al., 2015). In a number of lepidopteran pests, VOCs have been identified as attractants towards host plants, as repellents towards non-host or damaged plants or as oviposition stimulants (Saveer et al., 2012; Borrero-Echeverry et al., 2015). However, despite the impact of caterpillars on crop production, most studies focused on the adults and little is known about larval olfaction. A well-admitted theory, referred as “mother knows best,” assumes a strong selective pressure for females to lay their eggs on the plant where the larvae will have the highest performance (Jaenike, 1978; Carrasco et al., 2015). However, in some species it has been demonstrated that the caterpillars can leave the plant on which they hatched to select another host plant (Soler et al., 2012; Gamberale-Stille et al., 2014). Consistently, caterpillars exhibit attraction or repulsion behaviors towards VOCs of ecological significance (Carroll and Berenbaum, 2002; Huang and Mack, 2002; Singh and Mullick, 2002; Carroll et al., 2006, 2008; Castrejon et al., 2006; Becher and Guerin, 2009; Mooney et al., 2009; Piesik et al., 2009; Poivet et al., 2012; Zhu et al., 2016; Di et al., 2017) and are even able to perform associative learning (Blackiston et al., 2008; Salloum et al., 2011). This indicates that olfaction may play a more prominent role than initially expected in host plant choice of caterpillars, which could lay foundation for the development of novel pesticide-free strategies for fighting against those insects.

The peripheral olfactory system of caterpillars is generally composed of three olfactory sensilla located on the antennae, and four to five olfactory sensilla located on the maxillary palps (Grimes and Neunzig, 1986; Laue, 2000; Vogt et al., 2002; Roessingh et al., 2007; Poivet et al., 2012; Zielonka et al., 2016). These sensilla house the olfactory sensory neurons that express transmembrane odorant receptor (OR) proteins, which bind odorants and allow signal transduction (Leal, 2013). The repertoires of ORs expressed in caterpillar tissues have been identified only in a few species, such as the silkworm *Bombyx mori* (Tanaka et al., 2009), the cotton bollworm *Helicoverpa armigera* (Di et al., 2017) and the cotton leafworm *Spodoptera littoralis* (Poivet et al., 2013). In this latter species, 15 ORs (further referred as SlitORs) tuned to plant VOCs have been recently deorphanized (de Fouchier et al., 2017), i.e., their ligands have been identified **(Supplementary Figure S1)**. These VOCs are mainly short-chain alcohols, aldehydes or esters (also referred as green leaf volatiles, abundantly released from damaged leaves), aromatics and terpenes (most of them being ubiquitous odorants, present in high amounts in floral bouquets). However, the effect of these SlitOR ligands on the behavior of *S. littoralis* larvae remains largely unknown. Among them, only 1-hexanol (a green leaf volatile) has been shown to be attractive at high dose toward 2nd and 3rd-instar larvae (Rharrabe et al., 2014).

In the present work, we first re-examined the expression pattern of the 15 deorphanized SlitORs in adult and larvae olfactory organs, and identified nine as expressed at the larval

stage. We then used a simple bioassay to carry out a systematic behavioral analysis of 14 VOCs previously identified as ligands of these nine SlitORs. Using this OR-guided approach, we found 1-hexanol, benzyl alcohol, acetophenone, benzaldehyde, (Z)3-hexenol, (E)2-hexenol, indole, DMNT and (Z)3-hexenyl acetate as active on the behavior of *S. littoralis* caterpillars, increasing our knowledge on larval olfactory abilities. Building on the results of these behavioral assays and on our previous knowledge of SlitOR response spectra (de Fouchier et al., 2017), we used a modeling approach in order to identify possible correlations between the activation of SlitORs and the behavioral response of caterpillars. By doing so, we highlighted ORs whose activation may be critical for larval attraction towards plant volatiles.

# MATERIALS AND METHODS

## Insects and Chemicals

*S. littoralis* larvae were reared on a semi-artificial diet (Poitout and Bues, 1974) at 22°C, 60% relative humidity and under a 16 h light: 8 h dark cycle. The panel of odorants tested was composed of 14 synthetic molecules (**Supplementary Table S1**) previously shown to be active on SlitORs expressed at the larval stage (de Fouchier et al., 2017). Odorants were diluted in paraffin oil (Sigma-Aldrich, St. Louis, MO, USA), except indole that was diluted in hexane (Carlo-Erba Reagents, Val de Reuil, France). The odorants were used at concentrations of 100, 10, 1, 0.1 or 0.01 µg/µl.

## RNA Isolation and Reverse-Transcription PCR

Fifty *S. littoralis* male and female adult antennae and 50 pairs of 4th-instar larvae antennae and maxillary palps were dissected and immediately placed in TRIzol™ Reagent (Thermo Fisher Scientific, Waltham, MA, USA) for total RNA extraction. After isolation using phenol-chloroform, RNA was purified using the RNeasy Micro Kit (Qiagen, Venlo, Netherlands), including a DNase I treatment. RNA purity and quantity were measured on a NanoDrop™ ND-2000 spectrophotometer (Thermo Fisher Scientific). cDNA synthesis was performed using 1 µg of total RNA as template, with the iScript Reverse Transcription Supermix (BioRad, Hercules, CA, USA). PCRs were performed using the LightCycler® 480 SYBR Green I Master mix (Roche, Basel, Switzerland) under the following conditions: 95°C for 5 min, followed by 40 cycles of denaturation (95°C for 10 s), hybridization (58–62°C—depending on primer pairs—for 15 s) and elongation (72°C for 15 s). Primer pairs were designed from SlitOR nucleotide sequences using Primer3Plus<fnref n="1" />. All primer sequences, annealing temperatures and expected product sizes are listed in **Supplementary Table S2**. Orco, the obligatory OR co-receptor (Malpel et al., 2008; Leal, 2013), was used as control for the four tissues. For each amplification, negative controls consisted of amplifications run on DNase-treated RNAs and water templates. The amplification products were loaded on 1.5% agarose gels and visualized using GelRed™

<footnote n="1">¹http://www.bioinformatics.nl/cgi-bin/primer3plus/primer3plus.cgi</footnote>
Nucleic Acid Gel Stain (Biotium, Fremont, CA, USA). Tissue dissections, RNA extractions and RT-PCR experiments were repeated three times at different periods, to serve as biological replicates.

## Behavioral Experiments

Two-choice behavioral assays were performed using S. littoralis 3rd and 4th-instar larvae, starved for 16–22 h prior to experiments. The behavioral assay consisted in placing 10 caterpillars in the center of a Petri dish. Filter papers were placed at two opposite sides of the dish. One was loaded with 10 µl of an odorant solution and the other with 10 µl of the corresponding solvent. Each odorant concentration was tested 10–15 times. For each experiment, 10 Petri dishes (containing 10 different odorants) and one control dish with solvent on both sides were recorded during 15 min. In each dish, two zones were defined around the filter papers, an “odorant” zone and a “solvent” zone (the layout of the zones are visible in Figure 1). The number of caterpillars in each zone was counted 2, 5, 10 and 15 min after the beginning of the experiment.

## Data Analysis and Modeling

For each time point, a preference index (PI) was calculated using the following formula:

$$PI = (N_{\text{odorant}} - N_{\text{solvent}})/(N_{\text{total}})$$

$N_{\text{odorant}}$ being the number of larvae in the odorant zone, $N_{\text{solvent}}$ being the number of larvae in the solvent zone and $N_{\text{total}}$ being the total number of larvae in the assay. As this

PI varies between −1 and 1, a positive value means that the odorant is attractive and a negative value indicates repellency. To test for the statistical significance of the observed PI, we compared the value to a theoretical value of 0 with a Wilcoxon two sided unpaired test using R (Package stats version 3.3.2).

In order to compare observed PIs with responses of the SlitORs (in spikes.s⁻¹) when expressed in the *Drosophila* empty neuron system (de Fouchier et al., 2017), we performed multiple linear regressions using the “step” and “lm” function of R (Package stats version 3.3.2). To obtain the most efficient equation, we performed stepwise linear regressions relating PI with all possible interactions between the larval SlitOR responses (SlitOR7, 14, 19, 24, 25, 27, 28, 29 and 31). As odorant stimulus quantities used in electrophysiology experiments cannot be directly related to quantities used in the present behavior experiments, we built models for different electrophysiology-behavior odorant quantity relationships (1:1, 1:1/10, 1:1/100 and 1:1/1,000). We selected the equation with the highest $R^2$ and refined it performing another stepwise multiple linear regression. This model relates the PI with all the interactions between the factors with an impact significantly different from zero (Pr(>t) p ≤ 0.05) in the previously selected model. To further simplify the model, we performed a last multiple linear regression relating PI with only additive interactions of the previously used variables.

We also built some models to further test the importance of the different SlitORs in predicting larval PI. One using all possible interactions between the responses of SlitOR14, 19, 28, 29 and 31, and four other models using linear regressions of the PI explained by the response from only SlitOR7, 24, 25 or 27.

<figure><img src="image_66.png" /></figure>

# RESULTS

## Expression of SlitORs at the Larval Stage

The expression pattern of 15 previously deorphanized SlitORs in male and female adult antennae, larval antennae and larval maxillary palps (4th-instar larvae) was re-investigated using RT-PCR. As found previously, all SlitORs were expressed in male and female antennae. Among them, nine SlitORs were also expressed in larval tissues (Figure 2). Five ORs were expressed in larval antennae (SlitOR14, 19, 24, 28 and 31), and four ORs were expressed in both larval antennae and maxillary palps (SlitOR7, 25, 27, 29). Altogether, these nine ORs were previously found to detect 20 plant VOCs (Supplementary Figure S1) among a panel of 50 molecules from different chemical classes, when expressed in the *Drosophila* empty neuron system (de Fouchier et al., 2017). We then selected a panel of 14 of these odorants, chosen based on the distinct OR activation patterns they elicit, in order to test their effect on larval behavior.

## Behavior of S. littoralis Caterpillars Toward SlitOR Ligands

We assessed the valence of plant VOCs for S. littoralis caterpillars by describing their repartition in a two-choice
<figure><img src="image_67.png" /><figcaption>**FIGURE 2** | Tissue-specific expression of larval *S. littoralis* odorant receptors (ORs) identified by RT-PCR. Each RT-PCR was repeated three times on three separate RNA extractions. Only SlitORs found to be expressed in larval antennae or maxillary palps in the three replicates are shown.</figcaption></figure>

bioassay (Figure 1) using a PI over a period of 15 min. Figure 2 reports the PIs measured at 2.5 min for the different VOCs at different doses. PIs measured for other time points are presented in Supplementary Figure S2. For 2-phenyl acetaldehyde, 1-indanone, (E)-ocimene and eugenol, we observed no significant attraction (PI > 0) or repulsion (PI < 0), at any dose and any time. Benzyl alcohol, acetophenone, benzaldehyde, indole, 1-hexanol, (Z)3-hexenol and (E)2-hexenol were attractive at least at one dose, with the highest PI measured at 2.5 min (Figure 3). 1-hexanol displayed the strongest attraction, with a mean PI of 0.50 at 100 µg, and 0.44 at 10 µg. Benzyl alcohol was attractive over the wider range of doses, from 100 down to 1 µg per filter paper. Benzaldehyde elicited attraction at 100 and 10 µg, and acetophenone only at 100 µg. Indole was attractive at 10 and 0.1 µg only and (E)2-hexenol

was attractive only at 1 µg. For most of these VOCs, the PI tended to decrease over time (Supplementary Figure S2), which suggests that sensory adaptation occurred. The only stimulus that remained attractive over time was acetophenone, when presented at the highest dose (100 µg). (Z)3-hexenyl acetate differed from the previous VOCs as doses of 100 and 10 µg were found to be attractive after 5 min of experiment, and not after 2.5 min (Supplementary Figure S2).

At 2.5 min, benzaldehyde (at 0.1 µg) was the only VOC found to be repulsive (Figure 3). (Z)3-hexenyl acetate (1 µg) was repulsive after 5 min, and (E)2-hexenal and DMNT also induced a negative PI (for 0.1 and 100 µg, respectively) at 15 min of observation (Supplementary Figure S2).

## Modeling of the Relationship Between SlitOR Activation and Behavioral Activity Induced by Their Ligands

We next aimed to identify which of the SlitORs could be linked to attraction or repulsion towards plant VOCs. To assess the correlation between the valence of odorants and their activation pattern of ORs, we built models relating caterpillar PIs measured here with larval SlitOR responses to the same odorants (previously characterized in de Fouchier et al., 2017). We used stepwise multiple linear regressions, taking into account all possible interactions between the variables. The equations of the first models built are available in Supplementary Datasheet 2. The multiple linear regression giving the highest adjusted R² (0.6861) was the one using a 1:1 relationship between quantities used in behavior and electrophysiology experiments (Table 1).

To identify the SlitORs whose activation is the most critical to the valence of plant odorants for caterpillars, we refined the equation of the 1:1 model. For this, we performed stepwise multiple linear regressions taking into account all possible interactions between the factors with an effect significantly different from zero in the 1:1 model (Pr(>t) *p* ≤ 0.05). This model was able to describe the variation of PIs from the responses of 5 SlitORs (SlitOR7, 14, 24, 25 and 27; F-Test, *p* ≤ 0.001, R² = 0.6366, Table 1, Figure 4A and Supplementary Figure S3). The equation of the refined model is given in Supplementary Datasheet 2. The intercept value of this model was not different from 0 (Pr(>t) *p* ≥ 0.05), which predicts that an absence of SlitOR activation would result in an absence of behavioral output. In this refined model, activation of SlitOR24 was predicted to have a positive effect by itself on PIs (Pr(>t) *p* ≤ 0.05), whereas activations of SlitOR7, 25 and 27 were predicted to have an

<table><caption>**TABLE 1** | SlitOR/behavior multiple linear regression model statistics.</caption><thead><tr><th>Model</th><th>Adjusted R<sup>2</sup></th><th>Residual standard error</th><th>F-test</th><th>Shapiro test</th></tr></thead><tbody><tr><td>Model 1:1</td><td>0.6861</td><td>0.09647</td><td>***</td><td>***</td></tr><tr><td>Model 1:1/10</td><td>0.6225</td><td>0.1048</td><td>***</td><td>NS</td></tr><tr><td>Model 1:1/100</td><td>0.5795</td><td>0.1106</td><td>***</td><td>*</td></tr><tr><td>Model 1:1/1000</td><td>0.3061</td><td>0.142</td><td>***</td><td>NS</td></tr><tr><td>Refined 1:1 model</td><td>0.6366</td><td>0.1038</td><td>***</td><td>**</td></tr><tr><td>Minimal 1:1 model</td><td>0.6115</td><td>0.1073</td><td>***</td><td>NS</td></tr></tbody></table>

Statistics associated with the models of *S. littoralis* caterpillars PIs. The Shapiro Test column indicates the *p*-value of a normality test for the distribution of the model residuals. *** *p* ≤ 0.001, ** *p* ≤ 0.01, * *p* ≤ 0.05, NS: *p* > 0.05.
<figure><img src="image_68.png" /><figcaption>**FIGURE 3** | S. littoralis larval PI measured 2.5 min after exposure to different odorant stimuli. Box plots show the median PI and the 25th and 75th percentiles (n = 8–15). Outliers are indicated with black dots. *p*-values are indicated using a color code (Wilcoxon test).</figcaption></figure>

<figure><img src="image_69.png" /><figcaption>**FIGURE 4** | Predicted PI plotted as a function of the observed PI for the refined (A) and minimal models (B). Red lines depict the linear trend while the overlaying gray band is the SE for the fit.</figcaption></figure>

effect on PIs only through OR co-activation. SlitOR14 associated coefficients were not different from 0 (Pr(>t) *p* ≥ 0.05).

As the refined model had a complicated equation (20 terms), we then built a simpler model to predict the behavior using only

additive interactions. The equation of this minimal model is:

$$PI = a + b \times SlitOR7 + c \times SlitOR24 + d \times SlitOR25 + e \times SlitOR27$$
with *SlitORx* as the ORx responses to the considered odorant in spikes.s⁻¹ and *a-e* as coefficients. The values of these coefficients (available in **Supplementary Datasheet 2**) were all different from 0 (Pr(>t) *p* ≤ 0.05), except for the intercept. The *R*² value for this model was 0.6115 (Table 1, Figure 4B and Supplementary Figure S3), which is comparable to the performances of the refined 1:1 model. SlitOR24 had the highest coefficient (2.6070 × 10⁻³, *p* ≤ 0.001), which further supports a link between this receptor and neuronal circuits driving attraction in *S. littoralis* larvae. It is interesting to note that the coefficient associated with SlitOR7 was negative (−5.0528 × 10⁻³, *p* ≤ 0.05). This predicts that activation of SlitOR7 has a negative effect of the PI of *S. littoralis* caterpillars.

To further confirm the importance of those four SlitORs for models performance in predicting the observed PI, we tried to build a model using all interactions between all the SlitORs except SlitOR7, 24, 25 and 27. The stepwise multiple linear regressions method was unable to produce a model from these variables, thus highlighting the importance of these receptors for the response of caterpillars to the VOCs tested. We also built models using the responses from only SlitOR7, 24, 25 or 27. The *R*² values for these models were respectively: 0.15, 0.48, 0.19 and 0.04. The values of the coefficients of the intercept and of the SlitOR response were different from 0 (Pr(>t) *p* ≤ 0.05), except for the intercept of the model based on SlitOR24. These observations support that SlitOR24 is the most important receptor to predict the PI observed for the plant volatiles we tested.

DISCUSSION

Building upon the previous identification of ligands for a large number of *S. littoralis* ORs, we aimed at identifying behaviorally active odorants for caterpillars, which are pests feeding on a wide range of plants, notably economically important ones (Salama et al., 1971; Cabello, 1989; Thöming et al., 2013; von Mérey et al., 2013; Proffit et al., 2015). Nine *S. littoralis* ORs were confirmed to be expressed in larval chemosensory organs, namely the antennae and the maxillary palps. Our “OR-guided” strategy, by which we tested molecules active on these larval SlitORs, appeared as a good strategy as we could identify plant VOCs being behaviorally active when presented alone, most of them being attractive to caterpillars. Following that work, it will be of interest to test the effect of blends of these VOCs. It has been shown in *H. armigera* that a mixture of the best ligands of four ORs was the most attractive stimulus for first-instar larvae (Di et al., 2017), and one would expect that the same holds true for *S. littoralis*.

Our study complements a former study (Rharrabe et al., 2014) that investigated 11 odorants commonly emitted by plants, identifying only a small part of them as behaviorally active. In this previous work, eugenol was found to be repellent and 1-hexanol attractive. Here, attraction towards 1-hexanol could be reproduced in our assay but eugenol was inactive. This discrepancy could be explained by the fact that odorants and controls were presented together with food pellets in the aforementioned study while we used only filter papers as odor source. Hence, it is likely that repellent VOCs for *S. littoralis*

caterpillars may be identified only when given the choice between
food sources (or food odors) with or without the VOC.

Another interesting difference between these two types of
behavioral assays is that the presence of food will make the larvae
stay on the food source once they have made a choice. In our
experiments, larvae resumed foraging after their initial choice,
which enabled to observe a decrease of the PI in most cases,
likely due to sensory adaptation. Another possible explanation
for this PI decrease would be that the volume of the Petri dish
has been rapidly saturated with the odor, leading to a loss of the
odor gradient necessary for larval orientation.

A similar OR-guided approach was recently used on another
species of pest caterpillars, *H. armigera*, and led to the
identification of several OR ligands that were active on the
behavior of first-instar larvae (Di et al., 2017). Even if *S. littoralis*
and *H. armigera* both belong to the same family (Noctuidae)
and are both highly polyphagous herbivores, their larval OR
repertoires seem to differ drastically. Indeed, the orthologs of
only three of the nine larval SlitORs were also found to be
expressed in *H. armigera* larvae (Di et al., 2017). The same holds
true when comparing with the more distantly related species *B.
mori* (Tanaka et al., 2009). Accordingly, a limited number of
odorants identified as active on *S. littoralis* larvae are also active
on other species, and vice versa.

The most attractive VOC (i.e., with the highest PI) was
1-hexanol, an ubiquitous plant volatile (Knudsen et al., 2006),
which has been observed to be attractive for caterpillars of the
Tortricidae *Lobesia botrana* (Becher and Guerin, 2009). Among
other attractive compounds for *S. littoralis* larvae, (Z)3-hexenol
was also observed to be attractive to *L. botrana* and *H. armigera*
(Di et al., 2017), but not to *B. mori* (Tanaka et al., 2009). (Z)3-
hexenyl acetate is a volatile released by plants that suffered attacks
from insects and it has been reported to serve as a chemical
message between plants (Frost et al., 2008; Helms et al., 2014).
It has been observed to be attractive for the larvae of *S. littoralis*
(this study), *H. armigera*, *L. botrana*, and *B. mori*. This suggests
that (Z)3-hexenyl acetate is an important cue for a large spectrum
of lepidopteran species. However, at a lower dose (1 µg), it
is also the most repulsive VOC for *S. littoralis* caterpillars.
Further experiments specially designed for the identification
of repellents would be necessary to confirm this repulsive
effect, but *S. littoralis* might use (Z)3-hexenyl acetate to detect
and avoid damaged plants. Indeed, it has been demonstrated
previously that *S. littoralis* larvae are able to discriminate between
different leaves of a host plant and show a preference for young
leaves, this preference being modified by herbivore damage
(Anderson and Agrell, 2005). (Z)3-hexenyl acetate is detected via
the activation of several ORs (de Fouchier et al., 2017). Their
differential activation pattern relative to the dose may encode
the concentration, as previously hypothesized for pheromone
receptors detecting the same pheromone component in adults
(de Fouchier et al., 2015).

From the comparison of behavior results with our previous
results on SlitOR deorphanization (de Fouchier et al., 2017), we
built models that can predict PI values for odorants based on
their OR activation pattern. Results of this modeling approach
suggest that larval attraction depends on the activation of a
particular subset of ORs (i.e., circuit-based) rather than on the summed response of the entire OR repertoire. This will be possible to confirm this hypothesis only when the complete larval OR repertoire will be characterized. In *D. melanogaster*, similar linear regression-based approaches allowed to predict larval behavior from the responses of only five ORs (Kreher et al., 2008). Still in *D. melanogaster*, a strong link has been identified between larval attraction and activation of two larval ORs, DmelOR42a and DmelOR42b (Kreher et al., 2008; Asahina et al., 2009; Grewal et al., 2014). Here, models supported that SlitOR24, 25 and 27 are involved in pro-attraction neuronal circuits, while SlitOR7 activation would antagonize attraction. Activation of the first three receptors, especially SlitOR24, seems to be sufficient to trigger attraction of *S. littoralis* toward different concentrations of odorants. This will need further experimental validation, notably by identifying new ligands for these receptors and testing their behavioral effect, but it could be a promising way to identify new compounds that could impact the behavior of this important crop pest.

## AUTHOR CONTRIBUTIONS

AF, EJ-J and NM designed the study. AF, XS and ST performed behavioral experiments. GC-V performed molecular biology

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## FUNDING

This work has been funded by Inra, Sorbonne Université and the French National Research Agency (ANR-16-CE21-0002-01 and ANR-16-CE02-0003-01). AF and GC-V received doctoral fellowships from Inra and the National Council of Science and Technology of Paraguay, respectively. XS received a grant from the China Scholarship Council (CSC).

## ACKNOWLEDGMENTS

The authors thank Christelle Monsempes, Marie-Christine François and Françoise Bozzolan for their help with molecular biology experiments and Matthieu Dacher for his help with data modeling.

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The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fnbeh. 2018.00264/full#supplementary-material

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**Conflict of Interest Statement:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Copyright © 2018 de Fouchier, Sun, Caballero-Vidal, Travaillard, Jacquin-Joly and Montagné. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
# Visual Contrast Modulates Operant
Learning Responses in Larval
Zebrafish

Wenbin Yang¹,²*, Yutong Meng¹,², Danyang Li¹,² and Quan Wen¹,²,³*

¹ Hefei National Laboratory for Physical Sciences at the Microscale, Center for Integrative Imaging, School of Life Sciences, University of Science and Technology of China, Hefei, China, ² Chinese Academy of Sciences Key Laboratory of Brain Function and Disease, Hefei, China, ³ Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, China

The larval zebrafish is a promising vertebrate model organism to study neural mechanisms underlying learning and memory due to its small brain and rich behavioral repertoire. Here, we report on a high-throughput operant conditioning system for zebrafish larvae, which can simultaneously train 12 fish to associate a visual conditioned pattern with electroshocks. We find that the learning responses can be enhanced by the visual contrast, not the spatial features of the conditioned patterns, highlighted by several behavioral metrics. By further characterizing the learning curves as well as memory extinction, we demonstrate that the percentage of learners and the memory length increase as the conditioned pattern becomes darker. Finally, little difference in operant learning responses was found between AB wild-type fish and *elavl3:H2B-GCaMP6f* transgenic fish.

**Keywords:** zebrafish larvae, behavioral neuroscience, learning, vision, high-throughput imaging, automated image analysis

## INTRODUCTION

In operant conditioning, an animal learns to correlate its behavioral responses with consequences. Responses leading to satisfying consequences are reinforced whereas those leading to negative consequences are weakened or discarded. This form of associative learning has been intensively studied on behavioral, cellular, and molecular levels (Freund and Walker, 1972; Brembs et al., 2002; Nargeot and Simmers, 2011; Ishikawa et al., 2014), and many factors, such as dopaminergic signaling (Wise, 2004; Wassum et al., 2011; Steinberg et al., 2013) and Hebbian plasticity (Bi and Poo, 1998; Cassenaer and Laurent, 2007; Froemke et al., 2010), are known to play critical roles. Nevertheless, it remains largely elusive how *in vivo* learning rules, by which local synaptic plasticity and reward signaling must be integrated across distributed brain circuits, subserve adaptive animal behaviors. To make progress, it would be illuminating to measure neural activity of defined cell types at the whole-brain scale during the entire learning process.

The larval zebrafish is a promising vertebrate model to identify brain-wide mechanisms underlying learning and memory: its small brain is a great compromise between system complexity and simplicity. Recently, it has become possible to perform whole-brain imaging of calcium activity in freely behaving larval zebrafish (Cong et al., 2017; Kim et al., 2017). Whereas, fish are well-established animal models to study learning and memory (Davis and Agranoff, 1966; Agranoff and Davis, 1968), few associative learning paradigms have been developed for zebrafish larvae. Li (2012) reported operant learning in head-fixed larvae, in which fish learned to correlate the relief of

## OPEN ACCESS

**Edited by:**
Gérard Manière,
Université de Bourgogne, France

**Reviewed by:**
Sen Song,
Tsinghua University, China
Peng Cao,
National Institute of Biological
Sciences (NIBS), China
Tod R. Thiele,
University of Toronto, Canada

*Correspondence:
Wenbin Yang
young24@mail.ustc.edu.cn
Quan Wen
qwen@ustc.edu.cn

**Received:** 03 September 2018
**Accepted:** 07 January 2019
**Published:** 24 January 2019

**Citation:**
Yang W, Meng Y, Li D and Wen Q (2019) Visual Contrast Modulates Operant Learning Responses in Larval Zebrafish.
Front. Behav. Neurosci. 13:4.
doi: 10.3389/fnbeh.2019.00004
aversive heat stimulus with biased tail turning. Valente et al. (2012) showed that 1-week larvae were unable to perform an operant learning paradigm, in which fish must learn to swim to the other half of an arena to avoid electroshocks. Other reports demonstrated that larval zebrafish could be classically conditioned: they could associate the conditioned stimulus (CS)—a moving spot with the unconditioned stimulus (US)—a touch of the body (Aizenberg and Schuman, 2011). Social reward, such as visual access to conspecifics, could also be paired with a distinct visual environment cue during classical conditioning in larval zebrafish (Hinz et al., 2013).

Zebrafish have sophisticated vision. Adult zebrafish can distinguish colors (Colwill et al., 2005; Zimmermann et al., 2018) and visual patterns with different orientations (Colwill et al., 2005). Spatial and non-spatial visual learning tasks have been studied in adult zebrafish (Arthur and Levin, 2001). The visual system of zebrafish develops rapidly. 70-80 hpf (hour after post-fertilization) larval zebrafish can respond to abrupt light intensity change (Easter and Nicola, 1996), and exhibit optokinetic responses to rotating illuminated stripes (Huang and Neuhauss, 2008; Portugues and Engert, 2009; Mueller and Neuhauss, 2010). Seven-days-old larval zebrafish show strong optomotor responses to sophisticated motion stimuli (Orger et al., 2000; Roeser and Baier, 2003; Orger and Baier, 2005). However, much less is known about how properties of visual stimuli would modulate learning process in larval zebrafish.

Here, we report a modified operant conditioning paradigm (Valente et al., 2012) in freely swimming larval zebrafish, a system that combines a high-throughput automated training process and a toolkit for post-data analysis and storage. We use our new paradigm to investigate how visual contrast modulated the operant learning responses in larvae, characterized by both the positional and turning metrics. The measurements of learning curves provide a way to investigate memory extinction in larvae zebrafish. Moreover, we compare the differences between wild-type and transgenic fish in operant learning responses and memory extinction. We demonstrate that learning responses can be enhanced by a visual conditioned pattern that is darker than the background and that the percentage of learners as well as the memory length increase with darker conditioned patterns.

# MATERIALS AND METHODS

## Ethical Statement of Animals-Using

Handling and care of all animals were conducted in strict accordance with the guidelines and regulations set forth by University of Science and Technology of China (USTC) Animal Resources Center, and University Animal Care and Use Committee. Both raising and training protocols were approved by the Committee on the Ethics of Animal Experiments of the USTC (permit number: USTCACUC1103013).

## Animals and Raising

Zebrafish (Danio rerio) of the genotype *elavl3:H2B-GCaMP6f* and AB wild-type fish were used in all experiments. All tested fish were 7-10 dpf (day past fertilization) larvae. They were bred, raised, and housed in the same environment. Fish were fed two times per day from 6 dpf with paramecium in the morning (8-9 a.m.) and evening (6-7 p.m.) before being used in experiments. Water was replaced with E2 medium (Cunliffe, 2003) in the morning (8-9 a.m.) and evening (6-7 p.m.). Water temperature was maintained at 28.5°C. Light was turned on at 08:00 a.m. and off at 10:00 p.m.

## Experimental Setup

The behavioral system with custom software suites and supporting hardware was built to achieve an end-to-end high-throughput experimental workflow (Figure 1A).

### Hardware

Zebrafish swam freely in custom-built acrylic containers with transparent bottoms. Each container was divided into four arenas separated by opaque walls. The arena's size is 3 cm × 3 cm × 1 cm, with water filled (Supplementary Figure 7). Each arena held one fish. Three CMOS cameras (Basler aca2000-165umNIR, Germany) with adjustable lens (Canon, Model EF-S 18-55mm f/3.5-5.6 IS II, Japan) simultaneously captured swimming behaviors at 10 frames per second. Three infrared LEDs (Kemai Vision, China, model HF-FX90, wavelength 940 nm) illuminated each container from below. A 700 nm long-pass filter (Thorlabs FEL0700, US.) was positioned in front of each camera to block visible light and to facilitate online imaging processing with custom software BLITZ. Visual stimuli were projected onto three containers (PIQS Projector S1, 14.6 × 7.85 × 1.75 cm, 854 × 480 pixels). Electroshocks (100 ms, 9 Volt/3 cm) were delivered via two platinum filaments, one on each side of the arena. Shock delivery at each arena was controlled by custom software BLITZ via a 16-channel relay (HongFa JQC-3FF, China). Room temperature was controlled by an air-conditioner at 27°C.

### Software Suites

Custom C++ software BLITZ (Behavioral Learning In The Zebrafish), which inherited the coding style from MindControl (Leifer et al., 2011), processed three video streams in parallel to obtain real-time head, center, tail positions and heading angle by using the Pylon library (Basler AG, Germany) and the open source computer vision library (OpenCV) (Bradski, 2000). The program also rendered visual pattern and programmable electroshocks delivery based on the timeline and real-time fish motion parameters. Experimental information (e.g., experiment start time, visual pattern index, electroshock delivery information, and fish motion parameters) were recorded in YAML files. Raw videos were recorded.

The BLITZ software is available at https://github.com/Wenlab/BLITZ.

Another custom MATLAB (The MathWorks, Inc.) software ABLITZER (the Analyzer of BLITZ Results) was used to import YAML files, to visualize data, as well as to perform the behavioral and statistical analysis.

<footnote>**Abbreviations:** dpf, days post-fertilization; fps, frames per second; SEM, standard error of the mean; BLITZ, behavioral learning in the zebrafish; ABLITZER, the analyzer of BLITZ results.</footnote>
<figure><img src="image_70.png" /><figcaption>**FIGURE 1** | High-throughput automated behavioral training system for acquiring and analyzing operant conditioning responses in larval zebrafish. (A) Schematics of the behavioral system. Each arena held one fish. Cameras, projector, relay were controlled by the custom software BLITZ. Custom software ABLITZER imported the BLITZ-produced behavioral data, analyzed them, and visualized the results. (B) The operant conditioning paradigm: top is the experiment procedure and bottom is the detailed steps during the training phase.</figcaption></figure>

The ABLITZER software is available at https://github.com/Wenlab/ABLITZER.

## Experimental Procedure

Fish were fed at least an hour before being used in the experiment. Fish were placed via a Pasteur pipette (Nest, US) from the raising tank to the experimental arenas. The behavioral experiment would not run until fish started moving around to avoid startle responses to novel stimuli. Fish in the paired-group were trained first with the self-control protocol (see below), and then with the operant conditioning protocol. Fish in the unpaired-group were trained first with the self-control protocol, and then with the unpaired operant conditioning protocol (see below).

Fish used in the paired-group and unpaired-group were all naive fish.

## Operant Conditioning Protocol

This operant conditioning protocol was modified from Valente's learning paradigm (Valente et al., 2012). Here, fish would experience four different phases in order: baseline phase, training phase, blackout phase, and test phase (Figure 1B).

First, in the 10-min baseline phase, the visual pattern beneath each arena would flip between the CS at the top (Supplementary Figures 5A,C,E) and CS at the bottom (Supplementary Figures 5B,D,F) with a random duration that was uniformly sampled from 30 to 45 s.

Second, in the 20-min training phase, both the update of visual patterns and the delivery of electroshocks were dependent upon fish's behavior. After the visual pattern was updated (including the first visual pattern in the training stage), there was a 7-s delay for fish to make decisions before electroshocks were delivered based on their positions. If fish were in the CS zone after the delay time, whole-arena shocks would be delivered every 3 s until fish
<table><caption>**TABLE 1** | Visual contrasts of all conditioned patterns.</caption><thead><tr><th></th><th>Mean RGB value</th><th>Grayscale value</th><th>Visual contrast</th><th>Light irradiance (μW/cm²)</th></tr></thead><tbody><tr><td>Grayscale 0</td><td>(0, 0, 0)</td><td>0</td><td>-128</td><td>9.9</td></tr><tr><td>Grayscale 32</td><td>(32, 32, 32)</td><td>32</td><td>-96</td><td>28.9</td></tr><tr><td>Grayscale 43</td><td>(43, 43, 43)</td><td>43</td><td>-85</td><td>30.8</td></tr><tr><td>Grayscale 64</td><td>(64, 64, 64)</td><td>64</td><td>-64</td><td>36.9</td></tr><tr><td>Grayscale 96</td><td>(96, 96, 96)</td><td>96</td><td>-32</td><td>53.7</td></tr><tr><td>Red-black checkerboard</td><td>(128, 0, 0)</td><td>43</td><td>-85</td><td>40.7</td></tr><tr><td>White-black checkerboard</td><td>(128, 128, 128)</td><td>128</td><td>0</td><td>102.4</td></tr><tr><td>Background (grayscale 128)</td><td>(128, 128, 128)</td><td>128</td><td>0</td><td>108.1</td></tr></tbody></table>

In the table, the first column lists all conditioned patterns used in the experiments. Visual contrast = grayscale value of conditioned pattern - grayscale value of background (128).

escaped from the CS zone. After fish stayed in the Non-CS zone for 48 s, the visual pattern (CS zone at the top or bottom) would update with equal probability. The whole procedure would repeat (Figure 1B bottom).

After the training phase, there was a 1-min blackout phase to deprive all visual stimuli.

Finally, in the last 18-min test phase, to ask whether fish could develop the association between the CS and US, the visual pattern interchanged every 2 min between the CS at the top and CS at the bottom.

## Self-Control Conditioning Protocol

All phases were identical to the operant conditioning protocol, except for no electroshock delivery.

## Unpaired Operant Conditioning Protocol

All phases were identical to the operant conditioning protocol except for the training phase, in which electroshocks, without pairing with visual patterns, were randomly delivered across the 20-min duration.

## Behavioral Analysis

### Visual Contrast

The visual contrast was defined as the grayscale value difference between the conditioned pattern and the background pattern (pure-gray) (see Table 1 for more details). The light irradiance was measured with a power meter (PM16-130, Thorlabs) that averaged the received light over 50 s.

### Pre-screening

We defined data quality as the percentage of non-frozen frames. Frames were considered frozen when fish did not move for over 1 s. Fish with data quality lower than 0.95 were excluded from the analysis since those fish did not swim spontaneously and frequently. Fish with poor data quality were considered unhealthy.

The positional index was defined as the percentage of frames when fish were in the non-CS zone.

### Turning Analysis

We scored a turning event when the heading-angle-change between two consecutive frames exceeded 15 degrees. Fish would get +1 score when performing an escape turn, and -1 score

when returning to the CS zone. Fish in the Non-CS zone executed an escape turn when they approached the midline (within twice body length) and then turned back (Supplementary Figure 6). The turning index was defined as

$$ turning index = \frac{1}{2} + \frac{s(+)+s(-)}{(|s(+)|+|s(-)|) \cdot 2} $$

where, s(+), and s(-) are positive and negative scores, respectively. In this way, the turning index would fall between 0 and 1, the same range as the positional index.

### Distance to the Mid-line

This was defined as a signed Euclidean distance from the fish head position to the mid-line. The sign was -1 when fish were in the CS zone and +1 when fish were in the non-CS zone.

### Learning Analysis

To evaluate whether fish learned the operant conditioning task, we divided the entire operant conditioning protocol time into 24 2-min-epochs. The learning responses diminished in the absence of electroshocks during the test phase, which is known as memory extinction (Myers and Davis, 2007). The extinction point was computed as the first time when the positional index within an epoch dropped below the baseline. The recall period was defined from the starting time of test phase to the extinction point. Here we use memory length or recall period interchangeably. If the positional indices in the recall period were significantly higher than those in the baseline phase, fish were classified as learners (The unpaired t-test was applied). The extinction rate was defined as

$$ extinction rate = \frac{PI_1 - PI_2}{Memory Length} $$

where, PI₁ is the positional index of all learners of the first peak at the beginning of test phase, PI₂ is the positional index of all learners of the nearest valley to the extinction point.

The positional index increase is the difference between the mean positional index in the recall period and the mean index in the baseline period; the turning index increase is the difference between the mean turning index in the recall period and the mean index in the baseline period.
<figure><img src="image_71.png" /><figcaption>**FIGURE 2** | Zebrafish larvae showed enhanced learning responses in an operant conditioning task. (A) Analysis of the positional index suggests that learners showed significant enhancement of learning responses after training, whereas non-learners did not (CS zone was the red-black checkerboard pattern; t-test, *p* = 6.52e-12 for (Continued)</figcaption></figure>
**FIGURE 2** | learners, *p* = 0.3849 for non-learners, and *p* = 0.0020 for all fish). (B) Learners also showed significant differences in the turning index (t-test, *p* = 2.28e-6 for learners, *p* = 0.8606 for non-learners, and *p* = 0.0099 for all fish). (C) Learning curves using the positional index in the experiment group. Fish were classified as learners (red shaded line) and non-learners (green shaded line). The entire training process was divided into 2-min epochs (CS zone: red-black checkerboard). (D) A typical learner's behavioral trace, characterized by the relative position to the midline. The red-black checkerboard conditioned pattern was presented to the animal. A positive distance implies fish staying in the non-CS zone (also see section Materials and Methods). Each red dot represents the delivery of one electroshock. (P1: baseline phase; P2: training phase; P3: blackout phase; P4: test phase). (E) Analysis of the positional index suggests that fish did not show significant learning responses when the white-black checkerboard was presented as the conditioned pattern (t-test, *p* = 0.8832 for the experiment group, *p* = 0.2493 for the self-control group). We found that only one fish could be classified as learner. Because no significant learning response was found in the experiment group, we did not carry out unpaired-control experiments. (F) Analysis of the turning index suggests that fish did not show significant learning responses (t-test, *p* = 0.3750 for the experiment group, *p* = 0.7089 for the self-control group. There was no unpaired-control group because no significant learning response was found in the experiment group). All error bars are SEM. ** *p* < 0.01, *** *p* < 0.0001.

<table><caption>TABLE 2 | Percentages of learners in AB wild-type and transgenic fish.</caption><thead><tr><th></th><th>Grayscale 0</th><th>Grayscale 32</th><th>Grayscale 43</th><th>Grayscale 64</th><th>Grayscale 96</th><th>Red-black checkerboard</th><th>White-black checkerboard</th></tr></thead><tbody><tr><td>elavl3</td><td>21/44 (50%)</td><td>11/39 (28%)</td><td>9/39 (23%)</td><td>8/38 (21%)</td><td>1/33 (3%)</td><td>27/104 (26%)</td><td>1/37 (3%)</td></tr><tr><td>AB/WT</td><td>22/44 (50%)</td><td>8/36 (22%)</td><td>10/46 (22%)</td><td>11/44 (25%)</td><td>1/41 (2%)</td><td>17/68 (25%)</td><td>1/16 (6%)</td></tr></tbody></table>

The numerator is the number of learners and the denominator is the total number of fish.

## Statistical Analysis

The paired *t*-tests were used to compare the difference of the same fish between different phases in the same conditioning protocol; whereas the unpaired *t*-tests were used for the comparison between fish trained with the unpaired operant conditioning protocol and those with operant conditioning protocol. The sample size exceeded 20 for all tests.

## RESULTS

### Larval Zebrafish Show Significant Learning Responses in an Operant Conditioning Task

In our modified operant conditioning task (Figure 1B), larval zebrafish Tg (elavl3:H2B-gcamp6f) freely swam in an arena divided by two distinct patterns, each of which was projected onto one half of a transparent floor. In all cases, a pure-gray (grayscale-128) visual pattern was presented on the non-CS zone, and the CS patterns were presented on the other half. The CS was paired with the US—moderate electroshocks. The delivery of US and the update of visual patterns depended upon fish's positions and time (see Materials and Methods for detailed experimental procedures).

To scale up the training process, we developed a high-throughput operant conditioning system (Figure 1A) with custom supporting software suites BLITZ and ABLITZER (see Materials and Methods) that allowed training twelve fish simultaneously. BLITZ provided a fully automated workflow from video capture, online image processing, to visual stimuli presentation and electroshocks delivery for all behavioral protocols. Raw experimental data were then imported, analyzed and visualized by ABLITZER.

First, we tested several stimulus patterns in our operant conditioning task. When the red-black checkerboard was used as the conditioned pattern, 7–10 dpf zebrafish larvae showed significant learning responses (Figures 2A,B,

Supplementary Videos 1, 2), evaluated based on two metrics—the positional index and turning index (see Material and Methods). By analyzing individual fish behavior, we classified 27 out of 104 (26%) fish as learners (Table 2) (see section Materials and Methods for criteria). Learners showed significant increase in the positional and turning indices after training, whereas non-learners did not (Figures 2A,B).

Larval zebrafish have an innate positive light preference. Therefore, we developed two control settings: the self-control conditioning protocol in which no electroshock was delivered and the unpaired operant conditioning protocol in which electroshocks were randomly delivered (see Materials and Methods). Results from the two control settings were compared with those from the operant conditioning protocol to determine whether fish established the association (Supplementary Figures 11,J). Figure 2C shows how the learning curves of learners, non-learners, and all fish, characterized by the positional index, changed during the entire learning process. Figure 2D shows a typical trajectory of a learner who tended to avoid the conditioned visual pattern after training.

We asked whether spatial features of the checkerboard alone could induce learning responses. However, when using the white-black checkerboard as the conditioned pattern, we found that fish showed little learning response after training (Figures 2E,F).

### Visual Contrast Modulates the Percentage of Learners in the Operant Conditioning Task

We asked whether visual contrast—the grayscale value difference between a conditioned pattern and background (grayscale-128)—would modulate learning. Because light irradiance from the projector increases with the grayscale value of an image (Table 1 and Supplementary Figure 4), we tested whether conditioned patterns with varying grayscale values (0, 32, 43, 64, and 96) would modulate operant learning responses.
A

<figure><img src="image_72.png" /></figure>

B

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G

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H

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C

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D

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I

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J

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E

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F

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K

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L

<figure><img src="image_83.png" /><figcaption>**FIGURE 3** | Visual contrast was the key parameter to modulate operant learning responses. (A) The percentage of learners decreased with the grayscale value of conditioned patterns. (B) A typical learner's behavioral trace. Pure-black (grayscale-0) was used as the conditioned pattern. (C) Analysis of the positional index (t-test,</figcaption></figure>

(Continued)
**FIGURE 3** | *p* = 1.98e-11 for the learners, *p* = 0.9492 for the non-learners, and *p* = 2.03e-06 for all fish). Pure-black (grayscale-0) was used as the conditioned pattern. (D) Learners also showed significant increase in the turning index (t-test, *p* = 1.22e-5 for learners, *p* = 0.6491 for non-learners, and *p* = 0.0057 for all fish).

(E) Analysis of the positional index (t-test, *p* < 0.00001 for learners, *p* = 0.3252 for non-learners, and *p* = 0.0018 for all fish). Grayscale-32 was the conditioned pattern. (F) Learners also showed significant increase in the turning index (t-test, *p* = 0.0049 for learners, *p* = 0.4923 for non-learners, and *p* = 0.0331 for all fish).

(G) Analysis of the positional index (t-test, *p* = 0.0005 for learners, *p* = 0.3182 for non-learners, and *p* = 0.0128 for all fish). Grayscale-43 was the conditioned pattern. (H) Learners also showed significant increase in the turning index (t-test, *p* = 0.0022 for learners, *p* = 0.5067 for non-learners, and *p* = 0.0498 for all fish).

(I) Analysis of the positional index (t-test, *p* < 0.0001 for learners, *p* = 0.0927 for non-learners, and *p* = 0.0009 for all fish). Grayscale-64 was the conditioned pattern.

(J) Learners also showed significant increase in the turning index (t-test, *p* = 0.0108 for learners, *p* = 0.3694 for non-learners, and *p* = 0.0480 for all fish). (K) Analysis of the positional index (t-test, *p* = 0.3130 for the experiment group, *p* = 0.0749 for the self-control group). Grayscale-96 was the conditioned pattern. (L) Analysis of the turning index suggested that fish did not show significant learning responses (t-test, *p* = 0.3360 for the experiment group, *p* = 0.3019 for the self-control group).

All error bars are SEM. \* *p* < 0.05, \*\* *p* < 0.01, \*\*\* *p* < 0.001, \*\*\*\* *p* < 0.0001.

In the case of pure-black (grayscale-0), half of the fish population (21 out of 42, **Table 2**) can be classified as learners. They all showed significant increase in the positional index and the turning index after training (Figures 3C,D, **Supplementary Figures 1A,B**). **Figure 3B** showed a typical behavioral trace of a learner (see also **Supplementary Videos 3, 4**).

In the case of grayscale-96, however, fish showed little increase in the positional and turning indices (Figures 3K,L). We found only one fish can be classified as learner. In the other cases, a fraction of fish population exhibited learning responses (Figures 3E-J, **Supplementary Figures 1C-H**). The percentage of learners decreased with grayscale value (Figure 3A). Reversing the conditioned pattern and the background pattern in our paradigm, however, failed to elicit learning responses.

The red-black checkerboard and the grayscale-43 conditioned pattern had the same mean grayscale value (see **Table 1**). Consistently, similar percentages of fish (26 vs. 23%, **Table 2**) could learn the two conditioned patterns, respectively. Together, these results suggest that the visual contrast, not spatial checkerboard features, contributed to the operant learning responses.

## Visual Contrast Modulates Memory Extinction in the Operant Conditioning Task

To investigate how the operant conditioning behavior changed over time, we divided the entire process into epochs (excluding the blackout phase). Every 2-min interval is one epoch. The baseline phase has five epochs; the training phase has 10 epochs; and the test phase has nine epochs.

We defined the memory extinction point as the first time when the positional index within an epoch dropped below the mean index in the baseline phase; we defined the duration from the start of the test phase to the extinction point as the memory length. Memory length shorter than two epochs (e.g., fish may stay still in the non-CS zone) were excluded (see Materials and Methods).

We plotted the learning curves—the positional index vs. time—for learners and non-learners (Figures 4A-D). In the case of pure-black, the learning curve of learners rose and approached the maximum near the end of training; during the test phase, the learning curve remained high across the entire test phase (Figure 4A). Note that a large percentage of fish (16/21) did not show memory extinction (Figure 4G).

In the cases of other grayscale conditioned patterns, the learning curves of learners also reached their peaks near the end

of training. However, all curves decayed after several epochs in the test phase (Figures 4B-D). The memory extinction points were similar (grayscale-32 at 45 min, grayscale-43 at 41 min, grayscale- 64 at 41 min). Figures 4E,F illustrate a typical animal that learned the association and then experienced memory extinction in the test phase. The fish started swimming more in the CS-zone near 43 min.

In Figure 4G, we compared the distribution of memory lengths across all grayscale conditioned patterns. The mean memory length was the highest (970 s, **Table 4**) and the rate of extinction (see Materials and Methods) was the lowest in the case of pure-black conditioned pattern (Table 3).

We also found that the mean memory lengths (756 vs. 813 s) were similar when the red-black checkerboard and grayscale-43 pattern were used as the CS (Table 4). The percentages of learners that did not show memory extinction were also similar (12/27 vs. 5/9) in the two groups.

## Wild-Type Fish Show Similar Learning Responses in the Operant Conditioning Task

To test whether the learning effect was strain-specific, we also performed the operant conditioning task in AB wild-type fish. Like transgenic fish, the percentage of learners in AB fish also decreased with the grayscale value of conditioned patterns (Figure 5H, **Supplementary Figures 2C-H, 3**): half of the fish population could be classified as learners in the case of pure-black (Figures 5A-C, **Supplementary Figures 2A,B**); whereas only one in the case of grayscale-96 (Supplementary Figures 2I,J). Some AB fish can learn the association between the red-black checkerboard and US (Figures 5D-F, **Supplementary Figures 2K,L**), but not between the white-black checkerboard and US (Supplementary Figures 2M,N).

We note that in the case of pure-black CS, more wild-type fish exhibited memory extinction (Figure 5I). Figure 5G shows a learner with memory extinction in the test phase. Fish started to swim more in the CS zone at 41 min.

## DISCUSSION

### Operant Learning in Larval Zebrafish

Operant learning allows animals to avoid danger or to find potential reward in a complex environment (Skinner, 1984). An earlier study (Li, 2012) demonstrated an operant learning paradigm for head-fixed zebrafish larvae, where ~75% larvae
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**FIGURE 4** | Visual contrast modulated memory extinction. (A–D) Learning curves for different grayscale conditioned patterns. (E) A typical learner's behavioral trace. Grayscale-32 was the conditioned pattern. The blue triangle indicates the extinction point. (F) A magnification of the test phase in (E). (G) Memory length distribution for each conditioned pattern (grayscale values 0, 32, 43, 64, 96; RBC: red-black checkerboard). Blue: short-term memory fish (240–500 s); orange: mid-term memory fish (500–1,000 s); yellow: fish that did not show memory extinction during the entire test phase. All error bars are SEM.
<table><caption>**TABLE 3** | Memory extinction rates.</caption><thead><tr><th></th><th>Grayscale 0</th><th>Grayscale 32</th><th>Grayscale 43</th><th>Grayscale 64</th><th>Red-black checkerboard</th></tr></thead><tbody><tr><td>elavl3 (min<sup>-1</sup>)</td><td>3.83e-3</td><td>4.68e-3</td><td>1.00e-3</td><td>3.00e-2</td><td>8.11e-3</td></tr><tr><td>AB/WT (min<sup>-1</sup>)</td><td>9.82e-3</td><td>8.33e-3</td><td>1.01e-2</td><td>2.01e-2</td><td>2.04e-2</td></tr></tbody></table>

<table><caption>**TABLE 4** | Mean memory lengths of learners.</caption><thead><tr><th></th><th>Grayscale 0</th><th>Grayscale 32</th><th>Grayscale 43</th><th>Grayscale 64</th><th>Red-black checkerboard</th></tr></thead><tbody><tr><td>elavl3 (s)</td><td>970</td><td>807</td><td>813</td><td>570</td><td>756</td></tr><tr><td>AB/WT (s)</td><td>725</td><td>690</td><td>744</td><td>665</td><td>691</td></tr></tbody></table>

learned to correlate a positive consequence—the relief of heat exposure—with biased tail turning in the absence of conditioned cue.

In the current work, we demonstrated that a significant proportion of 7–10 dpf larval zebrafish showed significant operant learning responses when a darker-than-background conditioned pattern, such as a pure-black pattern, was paired with a punishment—moderate electroshock. In an earlier study (Valente et al., 2012), it was reported that 1-week larvae showed no significant learning response. Several factors may explain the discrepancy.

First, we observed little learning response when the white-black checkerboard or grayscale-96 pattern was paired with the US (only one fish learned the contingency), consistent with Valente’s result. Enhancement of learning was observed, however, when a darker visual pattern, such as a pure-black pattern, was paired with the US.

Second, in our modified paradigm, fish possessed more opportunities to learn the contingency between the CS and US during the training period: when fish stayed in the non-CS zone for more than 48 s, the positions of CS and non-CS patterns would update. In Valente’s paradigm, however, no visual pattern updates were implemented when fish stayed within the non-CS zone.

## Visual Contrast Is the Key Parameter to Modulate Operant Learning Responses

Larval zebrafish can detect light intensity change at a very early age (Easter and Nicola, 1996; Emran et al., 2008). Opsins that are sensitive to long and short wavelength light start to be expressed in cone photoreceptors at 50 hpf (Raymond et al., 1995). During optomotor behaviors, 7 dpf larvae can detect motion stimuli by computing the spatiotemporal correlation of light intensities from nearby pixels in a visual pattern (Orger et al., 2000; Orger and Baier, 2005). Here, our study shows that the visual contrast, rather than spatial features in a visual pattern, is critical in modulating larval zebrafish operant learning responses.

Many studies have demonstrated that larval zebrafish exhibit positive phototaxis (Steenbergen et al., 2011; Chen and Engert, 2014; Guggiana-Nilo and Engert, 2016). In our behavioral paradigm, the behavioral metric baselines

(e.g., positional index) were computed first before the training phase (see Materials and Methods). Light intensity could shift the baselines due to an innate illuminance bias. Significant changes in the behavioral metrics during and after operant conditioning (see **Figure 2**), however, require an explanation that goes beyond the innate avoidance response.

Here we speculate that this visual-contrast-dependent learning may arise from the crosstalk between phototaxis and fear conditioning circuits. Both phototaxis and US-triggered fear responses involve habenula (Agetsuma et al., 2010; Zhang et al., 2017), a specialized brain region where a direct association between a CS and fear may occur through synaptic plasticity. According to this model, a CS would trigger fear responses, and learning leads to a stronger association between visual-related inputs and escape responses. These predictions can potentially be tested by combining our behavioral system with whole brain calcium imaging in freely behaving larval zebrafish (Cong et al., 2017).

## Memory Extinction in the Operant Conditioning Task

Memory extinction is an active learning process where an animal learns to dissociate the conditioned response and CS in the absence of US (Myers and Davis, 2007). In our assay, the extinction point was defined as the first epoch whose positional index dropped below the mean index of the baseline. In addition, fish that did not keep a high level of positional index for at least two epochs were not counted as learners (see Materials and Methods). When the pure-black pattern was used, few learners showed memory extinction before the test phase ended. The distribution of memory length (**Figure 4G**) is consistent with that in a recent classical conditioning paradigm in larval zebrafish (Aizenberg and Schuman, 2011). When other grayscale patterns were used as the CS, mean memory lengths were further reduced (**Table 4**).

We also computed the rate of memory extinction (see Materials and Methods) for different CS patterns. We found that the extinction rate increased with the grayscale value of conditioned patterns for both wild-type and transgenic strains (**Table 3**). Also, the extinction rate in wild-type strain was higher
<figure><img src="image_91.png" /><figcaption>**FIGURE 5** | Strain differences in the operant learning task. (A) Learning curves of wild-type (AB) fish. Pure-black was the conditioned pattern. (B) Analysis of the positional index of wild-type fish presented with the pure-black conditioned pattern (t-test, *p* < 0.0001 for learners, *p* = 0.5672 for non-learners, and *p* < 0.0001 for all fish). (C) Learners also showed significant increase in the turning index (t-test, *p* = 0.0082 for learners, *p* = 0.4561 for non-learners, and *p* = 0.0094 for all fish). (D) Learning curves of wild-type fish. Red-black checkerboard was the conditioned pattern. (E) Analysis of the positional index of wild-type fish presented with the red-black checkerboard conditioned pattern (t-test, learners: *p* = 0.0001; non-learners: *p* = 0.4768; all: *p* = 0.0082). (F) Wild-type learners also showed significant increase in the turning index (t-test, *p* = 0.0035 for learners, *p* = 0.2514 for non-learners, and *p* = 0.0087 for all fish). (G) A typical wild-type learner's behavioral trace. (H) Percentage of learners vs. the grayscale value of conditioned patterns for transgenic (elavl3) and wild-type fish. (I) Memory length distribution for wild-type fish. All error bars are SEM. *** *p* < 0.01, **** *p* < 0.0001.</figcaption></figure>

than that in the transgenic strain when grayscale-0 was the CS (Figures 4G, 5I).

The above observations suggest that the dissociation between conditioned patterns and behavior could also be modulated by the visual contrast. Recent studies in Drosophila (Felsenberg

et al., 2017, 2018) showed that aversive memory formation and extinction involved different neural circuits and they competed with each other during decision-making. Identifying the neuronal circuitries underlying memory representation and extinction might help us test the hypothesis.
# Strain Differences in the Operant Conditioning Task

We performed operational conditioning for both *elavl3:H2B-GCaMP6f* (in the *casper* mutant background) transgenic fish and wild-type (AB) fish and found that visual contrast modulated learning behaviors in similar ways (Figure 5). Nevertheless, more wild-type fish exhibited memory extinction when the pure-black conditioned pattern was used. A previous study (Parker et al., 2013) has systematically characterized the behaviors of wild-type (TU) fish and *casper* mutants. No difference was found in adult fish, yet wild-type fish showed a significant increase in anxiety when treated with 1-pheyl-2-thiourea (PTU), a drug that is used to maintain the transparency of embryos. Whether differences in the internal state of the two strains led to the difference of memory extinction in our behavioral paradigm requires further investigation.

# High-Throughput Behavioral Assays for Learning and Memory in Larval Zebrafish

Larval zebrafish are amenable to high-throughput screen due to their transparency, small size and high permeability to small molecules (Kokel et al., 2010; Rihel et al., 2010). Though most systems are designed for drug or genetic screens (Rihel et al., 2010; Gehrig et al., 2018; Yang X. et al., 2018), here we have developed a high-throughput behavioral training system with custom supported software suites. Compared with previous work (Pelkowski et al., 2011; Hinz et al., 2013), the BLITZ software has enabled a fully automatic control of video capture, online image processing, visual pattern presentation and electroshocks delivery, making it an easily adaptable system for various purposes. Our complementary ABLITZER software also allows users to import, analyze and visualize data with well-structured classes and functions.

In its current version, our system cannot deal with situations of overlapping larvae, whose identities are hard to assign based on the current tracking algorithm. An earlier work (Mirat et al., 2013) showed that accurately tracking multiple larvae in groups over long periods of time were feasible. Integration of their algorithm with BLITZ may allow the study of social interactions of larval zebrafish in the future (Buske and Gerlai, 2014).

In conclusion, we have developed a high-throughput operant conditioning system for larval zebrafish. When using

electroshocks as the US and the red-black checkerboard or pure-black pattern as the CS, we found that a significant portion of larval zebrafish population could acquire operant learning. We also compared the learning responses between AB wild-type fish and transgenic fish. The percentage of learners and memory length strongly depended upon the visual contrast. Future work that combines imaging and genetic tools should help identify neural mechanisms underlying the visual-contrast-dependent learning behavior.

# AUTHOR CONTRIBUTIONS

WY conceived the study, designed, and built the behavioral setup, developed the software suites, designed, carried out the experiments, wrote the manuscript, and conceived the figures. YM helped carry out the experiment and conceive the figures. DL helped design, build the behavioral setup, and conceive the figures. QW helped design the experiments and wrote the manuscript.

# FUNDING

This work was funded by National Science Foundation of China Grants NSFC-31471051 and NSFC-91632102, the Strategic Priority Research Program of the Chinese Academy of Sciences (Pilot study, grant XDPB10).

# ACKNOWLEDGMENTS

We would like to thank Bing Hu for kindly providing the housing for zebrafish, Wanzhuo shi, Yuming Chai, Kexin Qi, and Kun He for invaluable technical help; Florian Engert, Caroline Wee, Max Nikitchinko, and Armin Bahl for kind guidance and inspirations in behavioral neuroscience. One of the authors also thank the Neuroscience Pioneer Club (NPC) for inspiring discussions. This manuscript has been released as a pre-print at BioRxiv (Yang W. et al., 2018).

# SUPPLEMENTARY MATERIAL

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fnbeh.2019.00004/full#supplementary-material

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