# From Stimulus to Behavioral Decision-Making

Gérard Maniere, G. Coureaud

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FROM STIMULUS TO BEHAVIORAL
DECISION-MAKING

EDITED BY: Gérard Manière and Gérard Coureaud

PUBLISHED IN: Frontiers in Behavioral Neuroscience
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ISSN 1664-8714

ISBN 978-2-88963-463-7

DOI 10.3389/978-2-88963-463-7

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FROM STIMULUS TO BEHAVIORAL
DECISION-MAKING

Topic Editors:

**Gérard Manière**, Centre des Sciences du Goût et de l'Alimentation,
AgroSupDijon/CNRS UMR 6265/INRAE UMR1324/Université
Bourgogne-Franche-Comté, France

**Gérard Coureaud**, Centre de Recherche en Neurosciences de Lyon,
INSERM U1028/CNRS UMR 5292/UCBL1, France

The proper detection of stimuli coming from the environment is vital for any organism, in vertebrates as in invertebrates. Animals use different sensory modalities, independently or simultaneously, to detect them, such as audition, vision, olfaction, taste and touch. Indeed, in their ecosystems, animals constantly perceive various sensory signals. These stimuli come either from the environment (light, temperature, humidity, salinity...), or from other organisms (calls, colors, movements, pheromones, allomones, ...).

This eBook combines studies and comments (research articles, reviews, opinions ...), dealing with how animals detect a precise simple or complex sensory signal, confer a value to that signal, and respond to it by devoted and adaptive behaviors. This eBook provides a general overview for the detection of identified stimuli, and the mechanisms that lead to decision-making in animals. It also highlights the differences and similarities that may exist between vertebrates and invertebrates.

**Citation:** Manière, G., Coureaud, G., eds. (2020). From Stimulus to Behavioral Decision-Making. Lausanne: Frontiers Media SA. doi: 10.3389/978-2-88963-463-7
# Table of Contents

**04 Editorial: From Stimulus to Behavioral Decision-Making**
Gérard Manière and Gérard Coureaud

**07 Assessment of Appetitive Behavior in Honey Bee Dance Followers**
Mariel A. Moauro, M. Sol Balbuena and Walter M. Farina

**14 Octopamine Shifts the Behavioral Response From Indecision to Approach or Aversion in Drosophila melanogaster**
Gerbera Claßen and Henrike Scholz

**26 Attractant or Repellent? Behavioral Responses to Mammalian Blood Odor and to a Blood Odor Component in a Mesopredator, the Meerkat (Suricata suricatta)**
Henrik Pettersson, Mats Amundin and Matthias Laska

**34 fMRI-Based Brain Responses to Quinine and Sucrose Gustatory Stimulation for Nutrition Research in the Minipig Model: A Proof-of-Concept Study**
Nicolas Coquery, Paul Meurice, Régis Janvier, Eric Bobillier, Stéphane Quellec, Minghai Fu, Eugeni Roura, Hervé Saint-Jalmes and David Val-Laillet

**43 More Than Meets the Eye: The Impact of Materialism on Information Selection During Luxury Choices**
Catherine Audrin, Tobias Brosch, David Sander and Julien Chanal

**55 Current Status on the Functional Characterization of Chemosensory Receptors of Cydia pomonella (Lepidoptera: Tortricidae)**
Alberto Maria Cattaneo

**65 Using Temporal Expectation to Assess Auditory Streaming in Mice**
Gaëlle A. Chapuis and Paul T. Chadderton

**76 How Many Clocks, How Many Times? On the Sensory Basis and Computational Challenges of Circadian Systems**
Jason Somers, Ross E. F. Harper and Joerg T. Albert

**84 Gentle Handling Attenuates Innate Defensive Responses to Visual Threats**
Xuemei Liu, Chen Chen, Yuanming Liu, Zhijie Wang, Kang Huang, Feng Wang and Liping Wang

**94 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

**102 Visual Contrast Modulates Operant Learning Responses in Larval Zebrafish**
Wenbin Yang, Yutong Meng, Danyang Li and Quan Wen
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Check for
updates

# Editorial: From Stimulus to Behavioral Decision-Making

Gérard Manière¹* and Gérard Coureaud²*

¹Centre des Sciences du Goût et de l'Alimentation, AgroSup Dijon/CNRS UMR6265/INRA UMR1324/Université Bourgogne-Franche-Comté, Dijon, France, ²Centre de Recherche en Neurosciences de Lyon, INSERM U1028/CNRS UMR 5292/Université Claude Bernard Lyon 1, Bron, France

**Keywords:** behavior, sensory detection, invertebrates, vertebrates, stimuli

## Editorial on the Research Topic

### From Stimulus to Behavioral Decision-Making

In their natural environments, animals are continuously exposed to sensory complexity, due to the huge number and diversity of stimuli emitted and contained in their surroundings, within and between sensory modalities (somesthesis, olfaction and taste, audition, vision), and due to fluctuating and sometimes unpredictable conditions. Two main kinds of environmental factors have consequences on behavior in both solitary and gregarious species, abiotic factors (e.g., temperature, pressure, salinity, hygrometry, light/darkness, etc.) and biotic factors produced by organisms themselves (movements, displays, calls, colors, pheromones, etc.). These various stimuli regulate the life of animals and their interactions in the ecosystem. The present Research Topic aimed at providing illustrations, reflections, and elements of discussion about some of the stimuli that lead to decision making in vertebrates and invertebrates and about mechanisms that support their emission and/or action.

In response to environmental changes, animals adapt their physiology (homeostasis, etc.) and behavior (food seeking, avoidance of predators, social interactions, etc.) to survive. Responses to environmental stimuli may be either spontaneous, i.e., result from predispositions to perceive and process particular signals, or dependent on experience, i.e., be acquired through simple exposure, or associative/non-associative learning episodes. Learning may contribute to reduce or discard behavioral experiences that have negative consequences, as shown here in the original paper by Yang et al.. They found that the larvae of zebrafish could acquire by operant conditioning a new visual stimulus like pure-black pattern (conditioned stimulus) when it is associated with electric shock (unconditioned stimulus). The contrast of the visual stimulus (grayscale visual pattern) then modulates the learning responses and retention of the visual information in the larvae.

Animal behavior is also regulated by alternating day-night cycles over the nycthemeral period of 24 h, which can lead to the expression of circadian rhythmic behaviors. Some animals indeed have a daytime activity, whereas others have a nocturnal one. A fascinating question is how various sources of information are combined to sense a single time of the day in an animal. Actually, behavioral activities are mediated by internal clocks supported by neurophysiological mechanisms from the peripheral and/or central nervous system. Among these mechanisms, molecular processes may contribute to the internal clock and generation of the locomotor circadian rhythm. The mini-review by Somers et al. shows in particular that in *Drosophila*, locomotor activity depends on two brain oscillators, one that controls the morning locomotor activity and another that governs the evening activity. These clocks are autonomous oscillators, and the phase can be adjusted by light or thermal stimuli.

## OPEN ACCESS

**Edited and reviewed by:**
Denise Manahan-Vaughan,
Ruhr University Bochum, Germany

*Correspondence:
Gérard Manière
gerard.maniere@u-bourgogne.fr
Gérard Coureaud
gerard.coureaud@cnrs.fr

**Specialty section:**
This article was submitted to
Learning and Memory,
a section of the journal
Frontiers in Behavioral Neuroscience

**Received:** 29 November 2019
**Accepted:** 04 December 2019
**Published:** 10 January 2020

**Citation:**
Manière G and Coureaud G (2020)
Editorial: From Stimulus to Behavioral
Decision-Making.
Front. Behav. Neurosci. 13:274.
doi: 10.3389/fnbeh.2019.00274
Animals including humans may currently benefit from five main senses (more or less functional, depending on the species and on the developmental stages) allowing them to detect, discriminate, represent, and respond behaviorally to external stimuli.

Olfactory or gustatory organs in animals allow for detecting chemical cues merging from the ecosystem. These organs are fundamental, e.g., for food searching, identification of conspecifics, recognition of sexual partners, or detecting dangers such as predators or noxious compounds. For instance, some insect species are known to be major pest of crops. Many methods aim to use mating disruption to control insect populations using synthetic sex pheromones. In his review, Cattaneo shows the interest of the identification and functional characterization of the chemosensory receptors of *Cydia pomonella* (Lepidoptera: Tortricidae), a pest of apple, pear, and walnuts. A better knowledge and understanding of the olfactory mechanisms of pests would allow for a better control of their action by using the olfactory system as a target. About odor-guided searching for food, de Fouchier et al. show in invertebrates that caterpillars from a phytophagous insect, the moth-model pest *Spodoptera littoralis* (Noctuidae), respond behaviorally to plant volatiles. These chemical cues are ligands of olfactory receptors identified by the authors. By a complementary modeling approach, the authors pinpoint some receptors whose activation is related to caterpillar attraction. In vertebrates, Pettersson et al. show that attraction to mammalian blood odor is not exclusive to top predators (such as tigers and wolves), but that such an attraction may be also displayed by a small-bodied mesopredator, here the meerkat (*Suricata suricatta*). The study reveals that this attraction is triggered by a single molecule contained in mammalian blood, which seems to be the TED single molecule (trans-4,5-epoxy-(E)-2-decenal).

At the periphery of the olfactory system, olfactory receptor neurons sense odor cues. These sensory inputs are then processed by the brain to promote attraction or avoidance responses. Claßen and Scholtz show that in *Drosophila*, the behavior induced by an olfactory stimulus like ethanol can be shifted depending on specific activation of particular neurons. Indeed, the activation of a cluster of octopaminergic neurons leads to an attraction toward ethanol, while the activation of another group of octopaminergic neurons results in avoidance of the same stimulus. Thus, octopaminergic neurons are directly involved in decision making and contrasted behaviors in *Drosophila*.

Social insects live in colonies and communicate with conspecifics to transmit crucial information carrying adaptive values, e.g., enabling colony provisioning, defense, and reproduction. In hives, bees that have found and located a food source in the surroundings transfer multiple sensory information using the waggle dance to bee dance followers. In the present *Frontiers Research Topic, Moauro et al.* show that bee dance followers have better memory retention and higher gustatory responsiveness to sucrose than non-follower bees. The followers are more sensitive to environmental stimuli compared to non-followers and could decipher more easily the nature and localization of the food source encoded by the bee waggle dance.

Concerning taste and underlying neurophysiological processing, research in animal models may have fundamental impacts allowing for improving knowledge in human nutritional physiology, brain structures, and related behaviors. In their proof-of-concept study, Coquery et al. show that potentially appetitive (sucrose) or aversive (quinine) gustatory stimulations may be combined with functional MRI in minipigs to promote brain responses in part concordant with results already obtained in humans. This may constitute a promising way to run preclinical investigations that compare the integration of gustatory stimuli in healthy individuals or individuals suffering from dysgeusia or eating disorders.

In audition, certain signals carry crucial values in terms of adaptation and survival, as, for instance, in the case of signals that indicate the immediate presence of a predator. Animals must therefore remain attentive in order to select relevant auditory information that helps in deciding for an escape response. In their original study, Chapuis and Chadderton reveal that mice can detect a target sound from another in a sound mixture and that the activity of the auditory cortex is modulated during the different attentional states.

Surviving from threats such as predators depends also in some species on vision, and on acquired or innate defensive responses to visual stimuli. However, the regulation of innate fear responses by the additional perception of pleasantness/unpleasantness still remains debatable. In their original research, Liu et al. test the consequences of gentle handling of the innate response of mice to a visual looming stimulus. They show that handling attenuates the defensive response of mice in correlation with cortical plasticity, as the de-excitation of specific layers of the superior colliculus, and change in the connection between the cortex and the subcortex.

Finally, visual attention is also important in humans, for instance, as a contributor to consumer decision making. While individuals' determinants of this attention remain weakly understood, Audrin et al. propose here an original study assessing how visual attention can be modulated by psychological values in the context of forced-choice experiments proposing luxury/non-luxury ready-to-wear products. They show that participants with high vs. low levels of materialism display with visual attention to distinct information carried by the products (symbolic dimension vs. actual characteristics) that may end in the choice of a same product but for different reasons.

Thus, innate or learned responsiveness to stimuli and consecutive behaviors allow animals to adapt to their ecosystem and to provide for their vital needs such as food searching, sexual interacting with partners, and escaping from predators. To that goal, animals may modify their physiology and behavior by detecting changes in the environment. By being exposed at the same time or sequentially to stimuli from different sensory modalities like olfactory, visual, and auditory events, animals must combine multisensory information to adjust their behavior. The peripheral and central nervous systems play a major role in the integration of this information allowing for making a decision and *in fine* to display a coherent and efficient behavioral response.
## AUTHOR CONTRIBUTIONS

GM and GC conceived and supervised this Frontiers Research Topic and wrote and edited the editorial.

## ACKNOWLEDGMENTS

We would like to sincerely thank all the authors who participated in our Research Topic, whose manuscript was accepted or not, and the reviewers of the manuscripts.

**Conflict of Interest:** 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 © 2020 Manière and Coureaud. 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.
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# Assessment of Appetitive Behavior in Honey Bee Dance Followers

Mariel A. Moauro<sup>1,2†</sup>, M. Sol Balbuena<sup>1,2†</sup> and Walter M. Farina<sup>1,2*</sup>

<sup>1</sup>Laboratorio de Insectos Sociales, Departamento de Biodiversidad y Biología Experimental, Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires, Buenos Aires, Argentina, <sup>2</sup>Instituto de Fisiología, Biología Molecular y Neurociencias (IFIBYNE), CONICET, Universidad de Buenos Aires, Buenos Aires, Argentina

Honey bees transfer different informational components of the discovered feeding source to their nestmates during the waggle dance. To decode the multicomponent information of this complex behavior, dance followers have to attend to the most relevant signal elements while filtering out less relevant ones. To achieve that, dance followers should present improved abilities to acquire information compared with those bees not engaged in this behavior. Through proboscis extension response assays, sensory and cognitive abilities were tested in follower and non-follower bees. Individuals were captured within the hive, immediately after following waggle runs or a bit further from the dancer. Both behavioral categories present low and similar spontaneous odor responses (SORs). However, followers exhibit differences in responsiveness to sucrose and odor discrimination: followers showed increased gustatory responsiveness and, after olfactory differential conditioning, better memory retention than non-followers. Thus, the abilities of the dance followers related to appetitive behavior would allow them to improve the acquisition of the dance surrounding information.

**Keywords:** *Apis mellifera*, waggle dance, gustatory responsiveness, olfactory conditioning, proboscis extension response

**OPEN ACCESS**

**Edited by:**
Gérard Coureaud,
UMR5292 Centre de Recherche en Neurosciences de Lyon (CRNL),
France

**Reviewed by:**
Nina Deisig,
UMR7618 Institut d'Écologie et des Sciences de l'Environnement de Paris (IEES), France
Jean-Marc Devaud,
Université Toulouse III Paul Sabatier,
France

*Correspondence:
Walter M. Farina
walter@fbmc.fcen.uba.ar

**Received:** 22 February 2018
**Accepted:** 05 April 2018
**Published:** 27 April 2018

**Citation:**
Moauro MA, Balbuena MS and Farina WM (2018) Assessment of Appetitive Behavior in Honey Bee Dance Followers. *Front. Behav. Neurosci.* 12:74. doi: 10.3389/fnbeh.2018.00074

## INTRODUCTION

The waggle dance is a stereotyped behavior performed by *Apis mellifera* foragers which consists in an eight-shape figure on the vertical comb inside the hive (von Frisch, 1967). This complex behavioral display is considered a multicomponent signal (Grüter and Farina, 2009) which not only attracts nestmates to the dance surrounding but also informs the presence of a profitable food source (von Frisch, 1967; Seeley, 1989). Honey bees can acquire information about the location of the feeding site by following these maneuvers from behind or laterally (Michelsen, 2003; Díaz et al., 2007). The dance followers can also perceive and learn the odors of the collected food during interactions with the dancer (von Frisch, 1967; Farina et al., 2005; Díaz et al., 2007). In this way, both naïve and experienced foragers can acquire information from the waggle dance (Biesmeijer and de Vries, 2001; Biesmeijer and Seeley, 2005).

The honey bee dance takes place in particular comb areas named “dance floor” (Tautz and Lindauer, 1997), located at approximately 4–20 cm from the hive entrance, where dancers and dance followers come into contact (von Frisch, 1967; Seeley, 1995). Dance maneuvers increase the activity of bees in the dancer’s vicinity (von Frisch, 1923; Božič and Valentinčič, 1991; Thom et al., 2007). Thus, in this informational context, the levels of motivation and attention of the bees located in the dance surrounding might be enhanced by the presence of the excited dancers. As a result, follower bees are motivated to start foraging.

<footnote>† These authors have contributed
equally to this work.</footnote>
The shift from in-hive tasks to foraging involves changes in the responsiveness to external stimuli (Robinson, 1987; Robinson and Page, 1989; Seeley, 1989). Furthermore, bees performing different tasks within the colony also present different response thresholds (Ramírez et al., 2010). In this sense, Katz and Naug (2016) have shown that dancer and follower bees present different sensitiveness according to the individual and colony nutritional states. Creating a mismatch between these nutritional states and using a conditioning assay, they evaluated the proboscis extension response of fed and starved bees. Followers showed to be less sensitive to changes in the colony nutritional condition than dancers. However, incoming foragers can adjust their response to the nutritional status of the colony (Lindauer, 1954; Seeley, 1989; Farina, 2000; De Marco, 2006). Therefore, the presence of individuals responding differentially according to nutritional states would allow a better adjustment of the foraging activity of the entire colony. However, until now, it is unknown how different are the chemosensory and olfactory learning abilities of those bees located in the hive areas where the information related to the incoming resources is transmitted. Bearing this in mind, our aim is to study the sensory and cognitive capacities of bees involved in dance following and unemployed bees located next to the dance floor that did not follow dances at the moment they were captured. For this, we carried out behavioral assays testing sucrose responsiveness, spontaneous response to odors and ability to discriminate odors.

# MATERIALS AND METHODS

## Study Site and Animals

The experiments were performed during the summer-autumn seasons of 2015 and 2016 at the Experimental Field of the University of Buenos Aires, Argentina (34° 32'S, 58° 26'W). We used four observation hives (two colonies during 2015, henceforth: H1 and H2; and the other two during 2016, henceforth: H3 and H4) that consisted of two frames with brood, a mated queen and about 4000 workers of the European honey bees (*Apis mellifera*) each. The hives were contained between acrylic walls that had a 40 × 25 cm² window covered by a hinged door that allowed access to the colony during the assays. The observation hives were located individually inside a flight chamber (6 m length × 3 m wide × 2 m height), which was closed only during the experiments to prevent interference with other bee colonies. During the rest of the day, the flight chamber was opened for bees to have access to natural food sources.

## General Procedure

A group of foraging workers of each experimental colony was trained to visit an artificial feeder located 6 m from the hive that offered 50% weight/weight (w/w) unscented sucrose solution. Foragers that visited the feeder were marked with acrylic paint (ALBA-Argentina) on their thoraxes to distinguish them from the rest of the nestmates.

Dance maneuvers of bees that returned from the artificial feeder were observed carefully by eye. A successful follower attends 3-7 consecutive waggle-runs before leaving the hive (Judd, 1994). Despite of the short distance to the artificial

source, it could be observed waggle dances. Thus, we defined as dance followers those hive bees that were captured directly from the unloading area (dance floor) after following at least three waggle-runs. In addition, we avoided capturing followers that performed oral contact with the dancers in order to avert a gustatory experience which could affect the sensory and cognitive capacities evaluated here (see sections below; Farina et al., 2007; Martinez and Farina, 2008). Unemployed hive bees that were not observed to be involved in dance following (henceforth: non-follower bees) were caught at a distance of 10-20 cm from the dancers, depending on the size of the dancing area (considering the approximate followed waggle dance radius is about 1.5 cm), and were used to compare their spontaneous odor response (SOR), gustatory responsiveness and odor discrimination with the dance follower's responses. Bees that performed cell cleaning, food and wax processing or absolute repose were not captured. It is worth to note that bees of both categories were caught from combs without brood or food reserves.

The captured bees were anesthetized in the freezer (−18°C) for no more than 2 min and harnessed in small metal tubes that restrained body movement but allowed free movement of antennae and mouthparts (Frings, 1944; Bitterman et al., 1983; Matsumoto et al., 2012). After waking, bees were offered a drop of water to drink if they would be tested for their gustatory responsiveness or 30% w/w sucrose solution if they would be submitted to the differential conditioning protocol and then, housed in an incubator (30°C, 55% RH and darkness) for 90 min before assessing their response.

## Spontaneous Odor Response

The proboscis extension reflex (PER) is a reliable indicator for studying response to odors or sugared rewards, such as those found in nectar naturally (Frings, 1944; Page et al., 1998). Even though an odor is considered a neutral stimulus and usually the proboscis is not released by itself, a novice bee can still show a spontaneous response towards certain odors in the laboratory context (Guerrieri et al., 2005). For this procedure, two pure odors commonly present in floral fragrances (Knudsen et al., 1993; Raguso and Pichersky, 1999; Nouvian et al., 2015), Linalool (LIO) and Phenylacetaldehyde (PHE; Sigma-Aldrich, Steinheim, Germany), were used to test the SOR of bees from H1 and H2. A device that delivered a continuous airflow was used for the odorant application. Individually harnessed bees were exposed to a constant clean airstream (2.5 ml s⁻¹) delivered 2 cm away from their heads. A filter paper (30 × 3 mm) was impregnated with 4 µl of the pure odorant and placed inside a syringe. Each odor was delivered during 6 s when the airflow was redirected to pass through the syringe by means of an electric valve. A SOR was measured when the bee fully extended its proboscis towards any of the two odors, during odor delivery (Farina et al., 2005). Bees that responded to the mechanical air stimulus (6 s of clean airflow before and 3 s after odor presentation) were discarded, as well as bees that did not respond to 50% w/w sucrose solution after the gustatory response assay. The elapsed time between the first and the second odor presentation was 15 min, and the order was alternated from bee to bee.
## Gustatory Responsiveness

PER was also used to evaluate gustatory responsiveness, by determining a gustatory response score (GRS) per experimental bee (Page et al., 1998). It was tested on bees from H1 and H2 after going through SOR assay. At the beginning of the assay, water was offered in order to avoid confounding thirst effects. Bees were assayed by presenting sucrose solutions of increasing concentration (0.1, 0.3, 1, 3, 10, 30 and 50% w/w; Page et al., 1998). Usually, this protocol only tests the response to sucrose concentrations from 0.1% to 30% w/w; however, we added the 50% w/w solution in order to include those bees with high sucrose response thresholds. Between each concentration of sucrose solution, all bees were tested for their response to water. This was done to avoid potential effects of repeated sucrose stimulation that could lead to increased sensitization or habituation. The inter-stimulus interval between water and sucrose solution was an average of 3 min. At the end of the procedure, a GRS was obtained for each bee. This score was based on the times each bee responded to the different sucrose concentrations (Scheiner et al., 2001; Pankiw et al., 2004). The response was arbitrarily quantified with scores from one to seven, where one (1) represented a bee that only responded to the highest sucrose concentration (50% w/w), while a score of seven (7) represented an individual that responded to all concentrations tested. If a bee failed to respond to a concentration of sucrose in the middle of a response series (e.g., responded to 0.1, 0.3, 3 and 10% w/w, but did not respond to 1%), this response was considered to be an error and the bee was deemed to have responded to that concentration as well. A bee that did not respond to more than one of the sucrose concentration in the middle of a response series was excluded from the analyses (Mc Cabe et al., 2007; Martinez and Farina, 2008). The same happened for bees that did not respond to the 50% w/w concentration (positive control, score = 1) offered because we cannot assure if they were able to sense and respond. In addition, those bees that responded to all sucrose concentrations but to all water presentations, too, were excluded from analyses as they appeared not to be able to discriminate between sucrose solution and water, were too starved or just thirsty.

## Differential PER Conditioning

Two additional hives, H3 and H4, were exposed to 1.5 ml of a pure odor for at least 3 days before performing PER conditioning (H3: PHE, H4: LIO). The scent was provided by two small Petri dishes placed at the bottom of the hive containing a filter paper (3 cm diameter) soaked with 0.75 ml of the pure scent. The Petri's top lid was perforated to prevent bees touching the compound while volatiles could be dispelled. The volatile exposed in the hive had the purpose that bees associate it with a non-appetitive hive context (Farina et al., 2005; Díaz et al., 2007).

The bees captured from both colonies were subjected to a differential PER conditioning to analyze olfactory discrimination to two pure odors during training procedure (Bitterman et al., 1983): the rewarded odor (rewarded conditioning stimulus, CS+) was paired with 50% w/w sucrose solution (unconditioned stimulus, US), and the non-rewarded odor was presented alone

(non-rewarded conditioned stimulus, CS−) and was the same used in the hive as the pre-exposed odor. The same device described in SOR section was used for this assay. The volatile compound was delivered through a secondary air stream (2.5 ml s⁻¹) injected into the main airflow during the delivery of the odor. The bees were exposed to these stimuli five times each in a pseudo-randomized order (CS−, CS+, CS+, CS−, CS+, CS−, CS+, CS+, CS−). For each colony, differential PER conditioning was carried out using a pre-exposed odor as CS− and a non-exposed odor as CS+ (H3: CS− = PHE, CS+ = LIO; H4: CS− = LIO, CS+ = PHE). The inter-trial interval lasted 10–15 min between the CS presentations, depending on the number of individuals tested at one time (usually, n = 20–40). Only those bees that showed an unconditioned response (the reflexive extension of proboscis after applying a 50% w/w sucrose solution to the antenna, UR) and that did not respond to the mechanical airflow stimulus were used for the test. During the experiment in the per setup, a fan extracted the released odors to avoid contamination. Each learning trial lasted 40 s and we presented the CS for 6 s. Reinforcement (50% w/w sucrose) was delivered for 3 s after the onset of the CS+. Those bees that showed a spontaneous response to the odor in the first trial were discarded from the experiment. To evaluate whether the bees had formed a medium-term memory after they had all gone through the learning assay, bees stayed harnessed for 15 min and were then subjected to a non-rewarded presentation of both training odors (testing phase).

## Statistics

The effects of factors on all variables were assessed by means of Generalized Linear Models (GLM) or Generalized Linear Mixed Models (GLMM), depending on the type of factors included in the models. Models were fitted in R program (R Development Core Team, 2011) using the *glm* function for the former case and the *glmer* function of the *lme4* package (Bates et al., 2015) for the latter, in which fixed and random effects are specified via the model formula. We used the MuMln package which contains functions to streamline information-theoretic model selection and carry out model averaging based on the information criteria (Burnham and Anderson, 2002). The *dredge* function was used to perform the automated model selection with subsets of the global model; the set of models was generated with all possible combinations of the fixed factors. *AICc*, which is a second-order information criterion, was used to rank the models and to obtain model weights (Akaike, 1973). Unless the Akaike weight for the best model was high enough ($w_k \ge 0.8$, personal criterion), we could not consider that the predictors not selected were unimportant; so, in the cases in which the $w_k < 0.8$, we performed multimodel inference using the *model avg* function which calculates model averaged parameters with standard errors and confidence intervals to evaluate the significances.

The effect of the dance following behavior on SOR was assessed by means of a GLM with binomial error structure. Gustatory responsiveness was estimated through the GRS, which is a sum of the unconditioned responses to the sugar solutions presented in the procedure. Values include 1 through 7. The effect of the following or non-following behavior on GRS was
assessed by means of a GLM with Poisson error structure. In both cases, the initial model included *behavior* and *hive* as fixed factors, considering only additive effects. *Hive* was considered as a fixed factor because it had only two levels (Zuur et al., 2007). In the particular case of GRS, in which significant difference between the hives was found, we analyzed *behavior* factor in each hive separately by constructing data subsets.

The effect of behavior on olfactory discrimination was assessed by means of a GLMM with binomial error structure. The initial model to analyze the acquisition phase included *behavior*, *trial* and *hive* as fixed factors, contemplating only additive effects, and *bee* as a random factor. In the testing phase, none of the bees extended its proboscis towards the CS-. Therefore, the effect of behavior was only studied on conditioned response towards the CS+ and assessed by means of a GLM which included *behavior* and *hive* as fixed factors, considering additive effects.

# RESULTS

## Spontaneous Odor Response

We tested the SOR of follower and non-follower bees from H1 and H2. Each experimental bee was exposed to a single presentation of two odor stimuli in the per setup: LIO and PHE. Because we were interested in the response to the odors independently of their identity, we analyzed the SOR towards any of them. Then, we grouped the odors since there was no interaction with the behavior fixed factor (in all cases, there was a bit higher response to PHE; data not shown). For both hives, we found that, independently of the behavioral category analyzed (followers vs. non-followers), bees showed low SOR levels (ca. 25%) and there was no difference between them (H1: $N_F$ = 103, $N_{NF}$ = 88; H2: $N_F$ = 79, $N_{NF}$ = 81). Indeed, the minimal model did not include the behavioral factor (Figure 1, Supplementary Table S1; $SOR_{H1+H2} \sim$1). Thus, both followers and non-followers did not show odor preference. Although there was no significant difference between colonies, Figure 1 shows their result separately because, analyzing GRS of the same bees (see below), *hive* factor was included in the minimal model.

## Gustatory Response Scores

After SOR, our aim was to study if gustatory responsiveness was affected by the dance context. We evaluated the sucrose response thresholds of bees from H1 and H2 under the PER paradigm. The GRS was defined as the sum of positive responses throughout the presentation of increasing concentration of the sucrose solutions (Page et al., 1998). Our results show that the dance followers had a higher GRS than non-follower bees in both experimental colonies [median values: 4 for followers ($N_{H1}$ = 103, $N_{H2}$ = 88), and 3 for non-followers ($N_{H1}$ = 79, $N_{H2}$ = 81)]. It means that follower bees present higher sucrose responsiveness than non-followers: followers respond to lower sucrose concentrations (Figure 2, Supplementary Table S2; $GRS_{H1} \sim$ Behavior, $Z = -2.462, p = 0.0138$; $GRS_{H2} \sim$ Behavior, $Z = -3.899, p = 9.65e^{-05}$). Hence, the gustatory responsiveness seems to be dependent on the behavioral category which bees belonged to. Even though we found significant difference between colonies, it is important to highlight that the tendency

<figure><img src="image_5.png" /></figure>

**FIGURE 1** | Follower and non-follower dancing honey bees present similar probability of spontaneous odor response (SOR). Percentage of bees, followers (F, gray bars) and non-followers (NF, white bars) that extended their proboscises towards any of the two odors, Linalool (LIO) and Phenylacetaldehyde (PHE). Bees from Hive 1 (H1) and Hive 2 (H2) were tested. "N.S." indicates no statistical differences (behavior, as a factor, is not included in the final model; see "Results" section for details). The number of bees tested is shown inside the bars.

<figure><img src="image_6.png" /></figure>

**FIGURE 2** | Follower and non-follower dancing honey bees present different gustatory responsiveness. Gustatory response score (GRS) for followers (F, gray bars) and non-followers (NF, white bars) from Hive 1 (H1) and Hive 2 (H2). Medians (black line), quartiles as vertical boxes, and ranges with bars are shown. Median values: for followers, 4, and for non-followers, 3. Asterisks indicate statistical differences ("*p" < 0.05; "**p" < 0.001; see "Results" section for details). The number of bees tested is shown inside the boxes.

for both of them, related to *behavior* factor, was the same, which means there was no interaction between the analyzed factors.

## Odor Discrimination in Classical PER Conditioning

Odor discrimination were tested in bees from scent pre-exposed hives H3 and H4 (H3: $N_F$ = 51, $N_{NF}$ = 49; H4: $N_F$ = 29, $N_{NF}$ = 30) under PER conditioning procedure. Global Discrimination Index (DI) was defined for each bee as the number of trial pairs the bee succeeded in discriminating between the two odors, in other
<figure><img src="image_7.png" /><figcaption>**FIGURE 3** | Olfactory learning abilities of the follower and non-follower dancing honey bees. (A) Global Discrimination Indexes (Global DI) during acquisition, and (B) percentage of bees that extended their proboscis towards the rewarded conditioned stimulus (Conditioned Response, CR) during the testing phase performed 15 min after acquisition. Follower (F, gray bars) and non-follower bees (NF, white bars) from hive 3 (H3) and hive 4 (H4) were tested. "N.S." indicates no significant differences while asterisks indicate statistical differences (\*\*p < 0.01; see "Results" section for details). The number of bees tested is shown inside the boxes and the bars.</figcaption></figure>

words, if it extended its proboscis towards the CS+ but not to the CS− (Mengoni Goñalons et al., 2016). No difference was found during the acquisition between the two behavioral groups of bees (Figure 3A, Supplementary Table S3; ACQ<sub>H3+H4</sub>~Trial+1|Bee). However, the acquisition level reached by the follower bees at the last trial was between 10% and 15% higher than by the non-followers, in both hives (H3: *F* = 73%, *NF* = 63%; H4: *F* = 72%, *NF* = 57%). During the testing phase, the follower bees exhibited a higher Conditioning Response (CR) to CS+ than the non-follower ones (Figure 3B, Supplementary Table S4; TEST<sub>H3+H4</sub>~Behavior, *Z* = −2.724, *p* = 0.00646). Therefore, followers showed better memory retention than the non-follower when they were evaluated 15 min after the olfactory conditioning.

DISCUSSION

Through PER assays, we evaluated olfactory and gustatory responsiveness besides the ability to discriminate odors through classical conditioning in honey bee workers with different probabilities to be recruited as foragers. A brief time after capturing both dance follower and non-follower bees showed similar SOR levels but significant differences in the sucrose responsiveness and odor memory retention. Specifically, dance followers presented higher gustatory responsiveness (higher GRS levels) and better memory retention after olfactory conditioning than non-followers.

We did not take into account the age of the experimental
bees in this study. Previous reports suggest that age seems
not to be relevant within this social context. For instance,
individualized bees that followed dances showed a wide age range
(i.e., from 9 to 32 days old; Balbuena et al., 2012). Likewise,
a recent study evaluated the sucrose responsiveness of hive

bees captured from the dance floor or delivery area within
an age interval of 2–15 days old (Mengoni Goñalons et al.,
2016). No age-dependent relationship was found for the sucrose
responsiveness measured at the per setup in that study. In this
sense, the gustatory responsiveness of honey bees may be affected
by the informational context where individuals were caught from
(i.e., dance floor) more than by their age. Indeed, Martinez and
Farina (2008) showed that hive bees captured after receiving
food from a donor bee presented different sucrose responsiveness
according to the food quality received during the oral contact.

In the present study, bees captured in the dance context
(following dances but did not interact orally with a dancer)
showed higher gustatory responsiveness compared with those
bees captured at a distance of 10–20 cm from the dancer. High
GRS values correlate positively with improved performances
during olfactory conditioning as it was previously reported
(Scheiner et al., 2004; Mengoni Goñalons and Farina, 2015).
Consistent with this evidence, we found that dance followers
showed improved levels of memory retention after an olfactory
PER conditioning. However, when sucrose responsiveness
correlates with memory retention, also correlates with learning
performance (Scheiner et al., 2001, 2004; Mengoni Goñalons
and Farina, 2015). Here, we only found a correlation between
memory retention and gustatory responsiveness. Those previous
studies used absolute conditioning procedures to test it. In this
study, differential conditioning has been used. This protocol
evaluates the ability to associate an odor to a reward, but also the
capacity to distinguish it from another which is not linked to an
unconditioned appetitive stimulus. Here, our unrewarded odor,
CS−, is the same odor used that we used as the hive odor and
it would represent a nonappetitive context for the experimental
subjects. Thus, the differences in response to rewarded and
unrewarded odors would be bigger for those bees that are more motivated to acquire appetitive information. Although it was not significant, this tendency can be observed at the end of the learning performance for dance followers and clearly visualized while the trained odors were tested 15 min later. It is worth mentioning that we only evaluated memory retention at a medium-term scale. It is expected that highly motivated individuals not only learn faster but also recall longer, an issue which was not cover in the present study.

Changes in the motivation and attention levels of the dance followers would turn out to be more sensitive to any other environmental stimuli, a fact that might facilitate the decoding of spatial information transmitted as well as the acquisition of incidental cues such as floral odors carried by the waggle dancer. The role of the early odor-rewarded experiences acquired in the beehive as a stimulus that facilitates the decoding of waggle dance information at elder ages has been suggested (Balbuena et al., 2012). In that study, honey bees preferred to follow dancers scented with an early exposed and rewarded odorant, and even they were recruited to the feeding site scented with the early experienced odors more successful. As the presence of reward affects physiological states in honey bees within a short-term period (Hammer, 1997), the most vigorous dances, which indicate the presence of a highly profitable food source, might represent appetitive stimuli that facilitate a prompt acquisition of information within the dance context.

This study shows a correlation between sensory and cognitive performances and behavioral category based on the dance

context. Nevertheless, it does not show if there is a causal relationship. To do that, the life history of individual bees should be considered to determine whether bees with a low gustatory responsiveness tend to follow dances or even whether the sucrose responsiveness changes after dance following. Our results are a first approach to understand abilities of the dance surrounding bees, but this issue requires further analysis.

## AUTHOR CONTRIBUTIONS

MAM, MSB and WMF contributed to the conception and design of the study and contributed to the drafting and revision of the manuscript and approved the final version. MAM and MSB conducted the experiments. MAM analyzed the data.

## FUNDING

The authors would like to acknowledge funding support from Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET). This study was partly supported by grants from Agencia Nacional de Promoción Científica y Tecnológica (ANPCYT), University of Buenos Aires and CONICET to WMF.

## SUPPLEMENTARY MATERIAL

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fnbeh.2018.00074/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 Moauro, Balbuena and Farina. 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 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_8.png" /></figure>

# Octopamine Shifts the Behavioral Response From Indecision to Approach or Aversion in *Drosophila melanogaster*

Gerbera Claßen and Henrike Scholz*

Department of Biology, Institute for Zoology, Biocenter, Albertus-Magnus University of Cologne, Cologne, Germany

Animals must make constant decisions whether to respond to external sensory stimuli or not to respond. The activation of positive and/or negative reinforcers might bias the behavioral response towards approach or aversion. To analyze whether the activation of the octopaminergic neurotransmitter system can shift the decision between two identical odor sources, we active in *Drosophila melanogaster* different sets of octopaminergic neurons using optogenetics and analyze the choice of the flies using a binary odor trap assay. We show that the release of octopamine from a set of neurons and not acetylcholine acts as positive reinforcer for one food odor source resulting in attraction. The activation of a subset of these neurons causes the opposite behavior and results in aversion. This aversion is due to octopamine release and not tyramine, since in *Tyramine-β-hydroxylase* mutants (*Tβh*) lacking octopamine, the aversion is suppressed. We show that when given the choice between two different attractive food odor sources the activation of the octopaminergic neurotransmitter system switches the attraction for ethanol-containing food odor to a less attractive food odor. Consistent with the requirement for octopamine in biasing the behavioral outcome, *Tβh* mutants fail to switch their attraction. The execution of attraction does not require octopamine but rather initiation of the behavior or a switch of the behavioral response. The attraction to ethanol also depends on octopamine. Pharmacological increases in octopamine signaling in *Tβh* mutants increase ethanol attraction and blocking octopamine receptor function reduces ethanol attraction. Taken together, octopamine in the central brain orchestrates behavioral outcomes by biasing the decision of the animal towards food odors. This finding might uncover a basic principle of how octopamine gates behavioral outcomes in the brain.

**Keywords:** octopamine, Tβh, attraction, aversion, decision making, ethanol attraction, food odor

## INTRODUCTION

Animals must make constant decisions whether to respond to external sensory stimuli or not to respond. Depending on the internal condition of the animals, they might respond with approach, indifference or aversion. The observed indifference could be due to the inability to perceive the sensory information, the meaninglessness of the information, and/or the inability to decide to which information to respond. Other reasons for showing indifference include the inability of animals

## OPEN ACCESS

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

**Reviewed by:**
Yael Grosjean,
Centre National de la Recherche
Scientifique (CNRS), France
Thomas Roeder,
Christian-Albrechts-Universität zu
Kiel, Germany

*Correspondence:
Henrike Scholz
henrike.scholz@uni-koeln.de

**Received:** 18 April 2018
**Accepted:** 11 June 2018
**Published:** 03 July 2018

**Citation:**
Claßen G and Scholz H (2018) Octopamine Shifts the Behavioral Response From Indecision to Approach or Aversion in *Drosophila melanogaster*. *Front. Behav. Neurosci.* 12:131. doi: 10.3389/fnbeh.2018.00131
to translate the decision into motor output and the inability to execute the locomotion required to fulfill the task. The activation of positive and/or negative reinforcers might bias the behavioral response towards approach or aversion.

In nature, *Drosophila melanogaster* is attracted to ethanol-enriched fermenting food sources (Dudley, 2002; Zhu et al., 2003). For example, female flies search for and prefer fermenting food sources as a substrate to lay their eggs (Richmond and Gerking, 1979; Azanchi et al., 2013). The search for food sources depends on olfactory cues (Chow and Frye, 2009). Given the choice between two similar attractive food odor sources, male and female *Drosophila* are more attracted to ethanol-enriched food odors than to a similar odor without ethanol (Ogueta et al., 2010). The low ethanol concentration that elicits approach functions as a key odorant in the food odor mixture (Giang et al., 2017). The approach depends on the positive reinforcing action of the octopaminergic/tyraminergic neurotransmitter system, as neuronal activation of neurons expressing tyrosine decarboxylase 2 (dTdc2)—the enzyme required for tyramine and indirectly for octopamine synthesis (Cole et al., 2005)—results in attraction (Schneider et al., 2012). The voluntary movement to the site of neuronal activation resembles the voluntary self-administration of electric shocks into the rodent brain that are perceived by the animal as rewarding and uncover the function of reinforcing properties in the brain (Olds and Milner, 1954; Schneider et al., 2012). The optogenetic activating pattern used to stimulate reinforcing tyraminergic/octopaminergic neurons in *Drosophila* brain resemble the high and low frequency of activity observed in the octopaminergic VUMmx1 neuron of the honey bee carrying information of the unconditioned stimulus in olfactory associative appetitive reward learning (Hammer, 1993; Schneider et al., 2012). Activation of the tyraminergic/octopaminergic neurotransmitter system is not only sufficient to cause a bias, but it also might be required for the attraction. *Tyramine-β-hydroxylase* mutants (more specifically the *Tβh*<sup>nM18</sup> mutant)—lacking detectable level of octopamine and with increased TA levels—do not show ethanol-induced site attraction (Monastirioti et al., 1996; Schneider et al., 2012). The loss of attraction is not due to the loss of odorant perception (Schneider et al., 2012), although octopamine might modulate sensory input. For example, octopamine has been shown to reduce the sensory response of bitter-sensing neurons (LeDue et al., 2016).

The octopaminergic neurotransmitter system acts also as a positive reinforcer in other behaviors, such as positive associative olfactory learning and memory in *Drosophila*, (Schwaerzel et al., 2003). However, there is also evidence that the octopaminergic neurotransmitter system might function as a negative reinforcer in the regulation of behavior, as the octopaminergic neurotransmitter system suppresses courtship conditioning (Zhou et al., 2012). Therefore, the octopaminergic neurotransmitter system might do both: function as a positive and negative reinforcer depending on the behavior or condition analyzed. In a more neutral/general way, the octopaminergic neurotransmitter system might function by selecting the right behavioral response or deciding what

stimulus to respond to when multiple types of information are presented.

Evidence that octopamine acts as positive and negative regulator is also derived from observations of other insects. In locusts, octopamine release at one site of the ventral nerve cord evokes bouts of rhythmic flight motor activity and at another site suppresses neuronal activity related to oviposition behavior (Sombati and Hoyle, 1984). These results led Sombati and Hoyle (1984) to propose the “orchestration hypothesis” to describe the function of octopamine. For every set of a behavior, a neuronal network exists that can be selectively activated or inhibited by the release of octopamine and therefore allow suppression of opposing behaviors, such as the initiation of flight and suppression of egg laying (Sombati and Hoyle, 1984). However, whether similar selection processes also work in the central brain to modify behavioral output has not been investigated.

To investigate whether the octopaminergic neurotransmitter system is also required in the selection of behavioral programs in the central brain of *Drosophila*, we first addressed whether octopamine can act as negative and positive reinforcer for an odor-guided behavior that involves decision making. To evaluate whether the fly makes a decision, we used a binary choice assay consisting of two odor traps filled with the same food odor to control for the same sensory input. We activated different sets of tyraminergic/octopaminergic neurons using optogenetics and analyzed the consequences of this activation on the choice of the fly to enter a food odor trap. We next addressed whether indeed octopamine and not tyramine mediated these behavioral choices by introducing *Tβh*<sup>nM18</sup> mutants lacking octopamine to the same condition. We show that octopamine functions as a positive and negative reinforcer in odor-evoked behaviors depending on the sets of neurons that are activated. We provide evidence that the activation pattern used for optogenetic activation does not influence the function of the reinforcer. To analyze whether octopamine release shifts the behavioral outcome, we activated the octopaminergic neurons in the presence of a less attractive food odor in comparison to a more attractive ethanol-enriched food odor. We show that the release of octopamine is required to shift the attractiveness of an attractive food odor source to a less attractive food odor source. We further provide evidence that the attraction of ethanol-enriched food odors also requires the release of octopamine by pharmacologically altering octopamine levels and interfering with octopamine receptor function. Taken together, we provide evidence that octopamine is required to select behavioral responses and that the “orchestration of different behavioral circuits underlying behavioral elements” in the central brain is also mediated by octopamine.

# MATERIALS AND METHODS

## Fly Stocks

Flies were raised and kept on an ethanol-free standard cornmeal/molasses/yeast/agar medium on 12-h/12-h light/dark cycle at 25°C with 60% humidity. Flies carrying transposable elements were backcrossed for at least five generations to the *w*<sup>1118</sup> maintained in the laboratory to isogenize the genetic
background. The following fly lines were used: *w<sup>1118</sup>, Tβh<sup>nM18</sup>* (Monastirioti et al., 1996), *norpA-; UAS-ChR2; UAS-ChR2* (Schneider et al., 2012), *w<sup>1118</sup>; UAS-ReaChR; UAS-ReaChR* (Inagaki et al., 2014), *w<sup>1118</sup>; dTdc2-Gal4* (Cole et al., 2005) and *ChAT-Gal80* (Kitamoto, 2002) and *w<sup>1118</sup>; 6.2_2-Tβh-Gal 4* (Schneider et al., 2012).

## Optogenetic Site Attraction Assay

Flies expressing channelrhodopsin (ChR) or red activatable ChR were raised on food with 200 µl 250 mM all-*trans* retinal (ATR) dissolved in 100% ethanol or 200 µl 100% ethanol in the dark. Fifty three- to five-day-old male flies were collected using CO₂ anesthesia and recovered in the dark for at least 24 h in the presence of food with either 100 µl 100% ethanol or 100 µl 250 mM ATR. The behavioral experiments were performed as previously described (Schneider et al., 2012). Briefly, flies were given the choice between two odor traps filled with 1.5 ml of apple-mango-juice (Alnatura, Germany GTIN: 4104420071841) for 19 h overnight in the dark at 25°C and 60% humidity.

Both traps were illuminated with two different LEDs with the same intensity ranging from 800 lux to 1700 lux. For the blue light-inducible ChRs, the following two diodes were used: a blue light LED (465-485 nm, Cree, Germany) and a warm white LED (Cree, XLAMP, XR_E LED with 2600-3700K CCT) combined with a blue light filter (high pass filter, 510 nm, HEBO, Aalen, Germany) resulting in yellow light. For the redshifted ChR, the following diodes were used: a blue light LED (465-485 nm, Cree, Germany) and an amber light LED (595 nm, Cree, XRCAMB-L1-0000-00J01). All LEDs repeated the activation pattern of 2 s 40 Hz, 16 s 8 Hz and 2 s 0 Hz for 19 h. The next day, the attraction index (AI) was calculated.

## Olfactory Attraction Assay

The assay was performed as described previously (Ogueta et al., 2010). Briefly, 50 1- to 3-day-old male flies were collected using CO₂ anesthesia and recovered from the treatment for at least 24 h. The experiments were conducted for 19 h overnight at 25°C and 60% humidity on a cold white light plate or when using ChR on a dark surface. To determine ethanol attraction, flies choose between apple-mango juice and 5% ethanol-enriched apple-mango-juice. Prior to the test, flies were fed with different drugs as follows. The control group was fed on a solution containing 5% sucrose/5% red food color on filter paper. The experimental group received the same food with the drug. If not mentioned otherwise, the flies were starved for 3 h before treatment without any access to water. The concentration of 53 mM octopamine was fed for 1 h (300 µl), 50 mM clonidine for 3 h (200 µl), 200 nM naphazoline for 3 h (200 µl) and 25 mM yohimbine for 2 h (300 µl). Non-starved flies were fed with 3 mM epinstaine for 2 days (300 µl, daily remoistening of the filter paper) or with 345 mM tyramine (without 5% sucrose) for 18 h. For the ethanol pre-feeding experiments, flies were fed with 10% ethanol for 30 min (200 µl) after they were starved overnight in the presence of moistened filter paper. Flies were recovered in the presence of moistened filter paper (200 µl) to avoid intoxication during the behavioral experiments for 3.5 h.

## Statistical Analysis

Errors represent the standard error of the mean (SEM). Bars labeled with the letter “a” indicate differences from random choice as determined with a one-sample sign test. Because the data were normally distributed for comparison of two experimental groups, Student's T-test was used. For more than two groups, ANOVA and the Tukey-Kramer *post hoc* test were used. The significance levels are indicated as follows: \*P < 0.05, \*\*P < 0.01 and \*\*\*P < 0.001.

## RESULTS

In a binary choice assay, flies choose between two equal food odor traps (Ogueta et al., 2010). The presence of an attractive key odorant within the food odor biases the decision to move towards and enter the food odor trap containing the key odorant (Giang et al., 2017). The attraction for one odor trap in comparison to an equal second food odor trap can also be induced by activation of tyraminergic/octopaminergic neurons using the UAS-ChR2 transgene under the control of the *dTdc2-Gal4* driver (Schneider et al., 2012; **Figure 1A**). For these experiments, one odor trap is illuminated with blue light, resulting in activation of neurons expressing ChR2 in the presence of ATR, and the second odor trap is illuminated with yellow light with the same intensity to avoid a bias based on difference in light intensity (**Figure 1A**). To elicit the reinforcing properties of the tyraminergic/octopaminergic neurons, we used a sequence of light stimulation of 40 Hz for 2 s, 8 Hz for 16 s followed by 2 s of no light. This activation pattern mimics the response of the reinforcing VUMmx1 neuron in honey bees (Hammer, 1993; Schneider et al., 2012).

## The Activation of Octopaminergic Neurons Is Sufficient and Required for Attraction

The *dTdc2-Gal4* line drives transgene expression in at least 137 neurons (Busch et al., 2009). To narrow down the subset of neurons sufficient to induce site attraction, we restricted the expression of the *dTdc2-Gal4* driver using the *ChAT-Gal80* driver (Kitamoto, 2002; **Figure 1B**). The expression of the Gal80 repressor resulted in the expression of transgenes in 34 neurons in the brain, including 3-5 VUM neurons per segment and neurons within the G0b, G2b G3a/AL2, G3b, G5a and G5b, 5c clusters (see Supplementary Figure S1 in Schneider et al., 2012). Activation of these restricted sets of neurons using the UAS-ChR2 transgene under the control of the *dTdc2-Gal4* driver combined with the *ChAT-Gal80* driver did not result in site attraction (**Figure 1B**). The dTdc2 enzyme is required for the synthesis of tyramine and therefore indirectly required for octopamine synthesis (Cole et al., 2005). The *dTdc2-Gal4* line drives transgene expression in octopamine-expressing and 41 Tβh-positive neurons in the brain (Busch et al., 2009; Schneider et al., 2012). To address whether octopamine elicits attraction, we performed a similar experiment in Tβh<sup>nM18</sup> mutants. The Tβh<sup>nM18</sup> mutants lack the Tβh isoform that converts tyramine into octopamine and have no detectable levels of octopamine, but 8-fold significantly
<figure><img src="image_9.png" /></figure>

**FIGURE 1** | Activation of tyraminergic/octopaminergic/cholinergic neurons results in attraction. (A) Schematic of the site attraction assay (modified after Schneider et al., 2012). Two LEDs with the same light intensity but different wavelengths illuminate two similar odor traps with the same flicker frequency of 2 s at 40 Hz, 16 s at 8 Hz and 2 s at 0 Hz. If not otherwise indicated, this pattern was used for activation. The attraction index (AI) describes the fly number in the blue-illuminated odor trap minus the fly number in the yellow-illuminated odor trap in comparison to the total number of flies. (B) Neuronal activation using the *UAS-ChR2* under the control of the decarboxylase 2 (*dTdc2*)-Gal4 driver elicits site attraction for the blue illuminated odor trap (Als of the control and experimental group: 0.21 ± 0.14 and 0.6 ± 0.12, respectively; *n* = 34, 31). Removing the activation of cholinergic neurons by combining the *dTdc2*-Gal4 driver with the *ChAT*-Gal80 driver eliminates site attraction (Als for the control and experimental group: 0.08 ± 0.16 and -0.05 ± 0.17, respectively; *n* = 25, 23). (C) Activation of the *UAS-ChR2* transgene in a *dTdc2*-Gal4-dependent manner in *Tβh<sup>nM18</sup>* mutants elicits site aversion (Als of the control and experimental group: -0.05 ± 0.14 and -0.48 ± 0.15, respectively; *n* = 15, 12, and Als at 8 Hz in the control and experimental group: -0.09 ± 0.11 and -0.55 ± 0.08, respectively; *n* = 20, 19). (D) Removal of *UAS-ChR2* transgene activation by expression of *ChAT*-Gal80 eliminates aversion (Als of the control and experimental group: -0.07 ± 0.14 and -0.03 ± 0.13, respectively; *n* = 16, 16). Errors are SEM. The letter "a" indicates differences from random choice as determined by the one-sample sign test. Student's *T*-test was used to determine differences between two groups. \* *P* < 0.05 and \*\* *P* < 0.05. For data, see **Supplementary Table S1**.

increased levels of tyramine (Monastirioti et al., 1996). Activation of *dTdc2*-Gal4-targeted neurons in a *Tβh<sup>nM18</sup>* mutant background lacking octopamine resulted in loss of attraction and significant site aversion using two different activation frequencies (Figure 1C). This result suggests that octopamine release mediated attraction. The observed aversion might be due to increased tyramine levels or other neurotransmitter that were co-expressed in tyraminergic/octopaminergic neurons. To test whether activation of acetylcholine transferase (ChAT)-positive neurons elicits aversion, expression of the *dTdc2*-Gal4 driver in the *Tβh<sup>nM18</sup>* mutant background was restricted using the *ChAT*-Gal80 driver (Figure 1D) to reduce transgene expression to 16 neurons in the brain, including a subset of VUM neurons and neurons of the G3a/AL2 and G3b cluster (Schneider et al., 2012; see Supplementary Figure S1). Repression of activation in cholinergic neurons resulted in a loss of aversion, indicating that activation of ChAT-positive neurons mediated the aversion. Taken together, the results suggest that octopamine release from acetylcholine co-expressing neurons mediates attraction.

Activation of a Subset of Octopaminergic
Neurons Is Sufficient and Required for
Aversion

We next addressed whether activation of a subset of neurons included in the expression pattern of the *dTdc2*-Gal4 driver line also resulted in attraction. Therefore, we expressed the ChR2 transgene in three VUMa4 neurons in the SOG using the 6.2-*Tβh*-Gal4 driver (Figure 2). The 6.2-*Tβh*-Gal4 driver targets transgene expression in the three VUMa4 neurons and also to non-*Tβh* positive neurons (Schneider et al., 2012). In contrast to the whole set of VUM neurons included in the expression pattern of the *dTdc2*-Gal4 driver, the activation of neurons in 6.2-*Tβh*-Gal4 dependent manner resulted in significant aversion to the site of blue light activation (Figure 2A). Activation of a subset of dopaminergic neurons using the *TH*-Gal4 driver resulted in aversion (Schneider et al., 2012). To exclude that the observed aversion was due to activation of dopaminergic neurons, we combined the 6.2-*Tβh*-Gal4 with the *TH*-Gal80 driver to suppress expression in putative dopaminergic neurons (Figure 2A). Here, neuronal activation still resulted in significant
<figure><img src="image_10.png" /></figure>

**FIGURE 2** | Octopamine release causes aversion. (A) Neuronal activation under the control of the 6.2-*Tβh*-Gal4 driver elicits site aversion (Als of the control and experimental group: 0.02 ± 0.14 and -0.41 ± 0.13, respectively; *n* = 22, 19). Removal of the activation of dopaminergic neurons by combining the 6.2-*Tβh*-Gal4 driver with the *TH*-Gal80 driver does not influence aversion (Als for the control and experimental group: 0.1 ± 0.12 and -0.49 ± 0.14, respectively; *n* = 14, 14). Removing ChAT positive neurons by using the *ChAT*-Gal80 in combination with the 6.2-*Tβh*-Gal4 driver resulted in loss of aversion (Als of control and experimental group: -0.14 ± 0.17 and -0.08 ± 0.21 respectively; *n* = 13, 11). (B) Activation of the *UAS-ChR2* transgene under the control of 6.2-*Tβh*-Gal4 in *Tβh*<sup>nM18</sup> mutants eliminates aversion (Als of the control and experimental group: -0.17 ± 0.1 and -0.19 ± 0.08, respectively; *n* = 20, 19). For activation, the flicker frequency of 2 s at 40 Hz, 16 s at 8 Hz and 2 s at 0 Hz was used. Errors are SEM. Differences from random choice were determined using the one-sample sign test and are indicated by the letter "a". Student's *T*-test was used to determine differences between two groups. \* *P* < 0.05. For data, see **Supplementary Table S2**.

aversion (Figure 2A). Restriction of the Gal4 expression pattern of the 6.2-*Tβh*-Gal4 driver to non-cholinergic neurons using the *ChAT*-Gal80 driver resulted in loss of aversion showing that activation of ChAT positive neurons mediate aversion (Figure 2A). To investigate whether the observed aversion was also regulated by octopamine release, we activated neurons targeted by the 6.2-*Tβh*-GAL4 driver in *Tβh*<sup>nM18</sup> mutants lacking octopamine (Figure 2B). Here, the activation did not elicit site aversion or attraction. Therefore, the results suggests that octopamine can also mediate aversion depending on the sets of octopaminergic neurons activated.

## The Frequency and Intensity of Light Activation Influences the Behavioral Outcome

The frequency used to activate the *UAS-ChR2* transgene mimicked the frequency used to depolarize the VUMmx1 neuron in honey bees (Hammer, 1993; Schneider et al., 2012). The depolarization with electrodes in honey bees and the activation of the *UAS-ChR2* transgene in *Drosophila* using optogenetics with this frequency was sufficient to elicit the reinforcer (Hammer, 1993; Schneider et al., 2012). To further investigate whether the observed site attraction or site aversion was caused by a specific activation frequency, we used different light flicker frequencies

with a constant intensity to activate the *UAS-ChR2* transgene in a *dTdc2-GAL4*-dependent manner and analyzed the consequence of this activation on site attraction (Figure 3). Neither the short pulse of 40 Hz nor the long pulse of 8 Hz was sufficient to result in site attraction (Figure 3A). We next tested the average frequency of 11.5 Hz. The flicker pattern still did not result in site attraction. A reduction of 40–20 Hz within the 40–8 Hz pattern was sufficient to elicit a significant attraction. The observed frequency-dependent site attraction suggested that either a specific frequency or the kinetics of the transgene influenced the behavioral outcome. We next tested whether there was a similar frequency dependence behavioral outcome when using the 6.2-*Tβh*-Gal4 driver (Figure 3B). Activation of the *UAS-ChR2* transgene with an 8-Hz pulse followed by 4 s in the dark in a 6.2-*Tβh*-Gal4-dependent manner was sufficient to elicit site aversion. However, neither the reduction to 20 Hz for 2 s followed by 8 Hz for 16 s nor the activation by only 40 Hz resulted in site aversion. Next, we addressed whether the intensity of the light influenced the activation of the transgene (Supplementary Figure S1, Table S6). The flicker sequence used for activation was kept constant with a pattern of 40 Hz for 2 s followed by 8 Hz for 16 s. Changing the light intensity for neuron activation resulted in site attraction in a *dTdc2-GAL4*-dependent manner; however, the degree of attraction as measured in AI varied from 0.3 to 0.6. Changing the light
<figure><img src="image_11.png" /></figure>

<figure><img src="image_12.png" /></figure>

<figure><img src="image_13.png" /></figure>

<figure><img src="image_14.png" /></figure>

**FIGURE 3** | Frequency-dependent activation results in attraction. Different flicker patterns indicated above the panel were used to activate neurons in a *dTdc2*-Gal4-dependent manner (**A**, *C*) and 6.2-*Tβh*-Gal4-dependent manner (**B**, *D*). Only activation with a flicker of 2 s at 20 Hz, 16 s at 8 Hz followed by 2 s of silence continues to elicit site attraction (*A*, *Als* of the control and experimental group: -0.08 ± 0.14 and 0.55 ± 0.13, respectively; *n* = 27, 23). (**B**) The 8 Hz pattern is sufficient to elicit site aversion when used to activate neurons (*Als* of the control and experimental group: -0.11 ± 0.12 and -0.53 ± 0.11, respectively; *n* = 20, 14). (**C**) Activation with *UAS-ReaChR* in a *dTdc2*-Gal4 dependent manner leads to site attraction (2 s at 40 Hz, 16 s at 8 Hz activation pattern: *Als* of the control and experimental group: -0.06 ± 0.15 and 0.6 ± 0.08, respectively; *n* = 18, 18; 2 s at 20 Hz, 16 s at 8 Hz activation pattern: *Als* of the control and experimental group: -0.17 ± 0.1 and 0.6 ± 0.08, respectively; *n* = 13, 7; 16 s at 8 Hz activation pattern: *Als* of the control and experimental group: 0.15 ± 0.1 and 0.47 ± 0.11, respectively; *n* = 12, 8). (**D**) Activation with *UAS-ReaChR* in a 6.2-*Tβh*-Gal4-dependent manner results in site aversion (2 s at 40 Hz, 16 s at 8 Hz activation pattern: *Als* of the control and experimental group: -0.12 ± 0.1 and -0.42 ± 0.08, respectively; *n* = 23, 20; 2 s at 20 Hz, 16 s at 8 Hz activation pattern: *Als* of the control and experimental group: -0.17 ± 0.2 and -0.61 ± 0.1, respectively; *n* = 23, 16; 16 s at 8 Hz activation pattern: *Als* of the control and experimental group: -0.05 ± 0.17 and -0.77 ± 0.05, respectively; *n* = 18, 17). Errors are SEM. Differences from random choice as determined by the one-sample sign test are labeled with the letter "a". Student's *T*-test was used to determine differences between two groups with significance levels as follows: \* *P* < 0.05, \*\*\* *P* < 0.001. For data, see **Supplementary Table S3**.

intensity for neuron activation in a 6.2-*Tβh*-Gal4-dependent manner only resulted in differences between the control and experimental group when neurons were activated with 1200 lux (Supplementary Figure S1B). Thus, the activation frequencies of the *UAS-ChR2* transgene and light intensities influence the behavioral outcome.

To address, independent of the kinetics of the blue light sensitivity of the *ChR2* transgene, whether neuronal activation

of tyraminergic/octopaminergic neurons induced site attraction, we used the *UAS-ReaChR* transgene for neuronal activation (Figure 3C; Inagaki et al., 2014). The *ReaChR* encodes a redshifted channel rhodopsin variant with an activation light spectrum between orange and red, higher photocurrents and faster kinetics (Lin et al., 2013). Activation of neurons using the *UAS-ReaChR* transgene in a *dTdc2*-Gal4-dependent manner was sufficient to elicit site attraction, when the
tyramine/octopamine-positive neurons were activated by amber light (Figure 3C). All tested activation patterns resulted in attraction. In addition, activation of neurons with three different flicker frequencies in a 6.2-Tβh-Gal4-dependent manner caused site aversion (Figure 3D). Therefore, the activation pattern of the neurons did not appear to be frequency-dependent but rather based on the kinetic of the transgene. Independent of the transgene, the activation of neurons in a *dTdc2*-*Gal4*-dependent manner was sufficient to elicit site attraction, and activation of neurons in a 6.2-Tβh-Gal4-dependent manner was sufficient to elicit site aversion.

## Octopaminergic Neurons Are Required to Switch the Behavioral Response

The release of octopamine from acetylcholine co-expressing neurons mediates attraction for the site of neuronal activation. The site attraction could be due to reinforcement or due to a behavioral switch. To test whether the octopamine release reinforced the positive association with one site of the behavioral choice paradigm, we wanted to analyze whether octopamine release could increase a pre-existing attraction. Normally flies show attraction to 5% ethanol-enriched food odors when given the choice between food odor and ethanol-enriched food

odor (Schneider et al., 2012). To analyze whether octopamine release increased this attraction, flies expressing the UAS-ChR2 transgene under the control of the *dTdc2*-*Gal4* driver were offered a choice between a food odor trap and a 5% ethanol-enriched food odor trap that was illuminated with neuron-activating flickering blue light (Figure 4A). Activation of tyraminergic/octopaminergic neurons did not significantly increase the attraction for the ethanol-enriched food odor trap. To test whether the activation of tyraminergic/octopaminergic neurons would switch a preexisting ethanol attraction to the site of blue light activation, we enriched the yellow light-illuminated food odor trap with ethanol (Figure 4B). Here, as expected, control flies were significantly more attracted to the ethanol-enriched yellow-illuminated food odor trap. The activation of tyraminergic/octopaminergic neurons significantly suppressed this ethanol attraction, indicating that tyraminergic/octopaminergic neurons are not directly involved in attraction rather mediating the switch between two choices.

To independently address whether octopamine release mediated switches between behavioral elements rather than being directly involved in the execution of behavior, we reanalyzed the choice behavior of *Tβh*<sup>nM18</sup> mutants (Figure 4C). In contrast to control flies, *Tβh*<sup>nM18</sup> mutants failed to show attraction to 5% ethanol-enriched food odors (Schneider et al.,

<figure><img src="image_15.png" /></figure>

**FIGURE 4** | Octopaminergic neurotransmitter shifts behavioral outcomes. (A) Neuronal activation using the blue light-sensitive UAS-ChR2 transgene in a *dTdc2*-*Gal4*-dependent manner results in attraction to the blue light-illuminated odor trap. Addition of 5% ethanol to the blue light-illuminated trap did not change this attraction significantly (Als of the control and experimental group: 0.66 ± 0.07 and 0.55 ± 0.1, respectively, *n* = 25, 17). (B) Addition of 5% ethanol to the yellow light-illuminated trap results in a significant reduction of the attraction to the blue light-illuminated food odor trap (Als of the control and experimental group: 0.68 ± 0.09 and 0.28 ± 0.15, respectively, *n* = 20, 18). (C) Flies normally prefer the 5% ethanol-enriched food odor trap over the plain food odor trap, and *Tβh*<sup>nM18</sup> mutants differ significantly from the control in their behavior and do not show attraction. Pre-feeding control flies with 10% ethanol-enriched sucrose solution results in a similar degree of attraction to the 5% ethanol-containing food odor trap. Ethanol pre-feeding in *Tβh*<sup>nM18</sup> mutants results in a significant attraction to the 5% ethanol-containing food odor trap (Als for *w*<sup>1118</sup>: 0.44 ± 0.06 and with EtOH: 0.22 ± 0.08; for *w*<sup>1118</sup>, *Tβh*<sup>nM18</sup>: −0.07 ± 0.07 and with EtOH: 0.38 ± 0.06; *n* = 33, 31, 17, 20). The errors are SEM. The differences from random choice were determined using the one sample- sign test, and significance is indicated by the letter “a”. Student’s *T*-test was applied to determine differences between two groups, and ANOVA followed by Tukey *post hoc* analyses were used for comparisons among more than two groups. Significant differences are indicated as follows: \* *P* < 0.05, \*\* *P* < 0.01 and \*\*\* *P* < 0.001. For data, see **Supplementary Table S4**.
2012; **Figure 4C**). To address whether *Tβh*<sup>nM18</sup> were unable to show attraction *per se*, we pre-exposed flies to ethanol by feeding a 10% ethanol-enriched 5% sucrose solution for 30 min. After 3.5 h of recovery, the attraction to ethanol-enriched food odors was analyzed in the binary choice assay (**Figure 4C**). Ethanol pre-fed control flies showed an attraction to ethanol-enriched food odors, and ethanol pre-feeding of *Tβh*<sup>nM18</sup> mutants resulted in a similar significant ethanol attraction. These results showed that ethanol pre-feeding to *Tβh*<sup>nM18</sup> mutants restored the attraction to ethanol-containing food odors. Thus, *Tβh*<sup>nM18</sup> mutants were able to develop the attraction but failed to choose ethanol-enriched food odors in an ethanol naïve choice situation over non-ethanol-containing food odors. Taken together, activation of the octopaminergic/tyraminergic neurotransmitter system can shift the attraction, and *Tβh*<sup>nM18</sup> fail to show ethanol attraction because they cannot shift their behavioral response.

Octopamine Is Required for Ethanol
Attraction

To determine whether the loss of ethanol attraction in ethanol naïve *Tβh*<sup>nM18</sup> was due to the loss of octopamine, we performed pharmacological experiments (Figure 5). First, we fed *Tβh*<sup>nM18</sup> mutants with 53 mM octopamine—a concentration that restored the egg laying defect of the *Tβh*<sup>nM18</sup> mutants (Monastirioti et al., 1996)—and analyzed their ethanol attraction (Figure 5A). In addition, we fed control flies with octopamine to investigate whether increased octopamine levels could increase attraction. Feeding with OA restored the loss of ethanol attraction in *Tβh*<sup>nM18</sup> mutants to control levels, but it did not alter the attraction of *w*<sup>1118</sup> flies (Figure 5A). To independently confirm that octopamine signaling was required for attraction, *w*<sup>1118</sup> flies were fed with 3 mM of the antagonist epinastine for octopamine receptors, a concentration that has been shown to effectively interrupt TfAP-2-induced hyperactivity in *Drosophila* (Williams et al., 2014). Epinastine-fed control flies showed a similar significant loss of ethanol attraction to *Tβh*<sup>nM18</sup> mutants (Figure 5B). To confirm that octopamine receptor signaling was required for ethanol attraction, we fed *βh*<sup>nM18</sup> mutant and control flies with two octopamine receptor agonists—50 mM clonidine or alternatively 200 nM naphazoline (Evans, 1981; Evans and Maqueira, 2005)—and investigated the effects on attraction (Figures 5C,D). The octopamine receptor agonist naphazoline and clonidine should restore octopamine receptor signaling in *Tβh*<sup>nM18</sup> mutants. Clonidine turned the loss of ethanol attraction of *Tβh*<sup>nM18</sup> mutants into attraction, but it did not influence the attraction of control flies (Figure 5C). Naphazoline also turned the loss of attraction into ethanol attraction in *Tβh*<sup>nM18</sup> mutants (Figure 5D). However, naphazoline also altered the attraction in control animals by reducing the attraction, suggesting that too much activation of octopamine receptors might result in aversion or that naphazoline activates additional receptors that mediate aversion. To address the putative function of increased tyramine levels on ethanol attraction, tyramine receptor signaling was blocked in control flies and *Tβh*<sup>nM18</sup> mutants by feeding with the tyramine receptor antagonist yohimbine. Feeding with

25 mM yohimbine—a concentration that rescues the *Tβh*<sup>nM18</sup>
mutant phenotype in flight initiation and maintenance (Brembs

A

<figure><img src="image_16.png" /></figure>

B

<figure><img src="image_17.png" /></figure>

C

<figure><img src="image_18.png" /></figure>

D

<figure><img src="image_19.png" /></figure>

E

<figure><img src="image_20.png" /></figure>

F

<figure><img src="image_21.png" /></figure>

**FIGURE 5** | Octopamine is required for ethanol attraction. (A) Feeding with 53 mM octopamine restores the loss of attraction to ethanol-containing food odors in *Tβh*<sup>nM18</sup> mutants (Als for *w*<sup>1118</sup>: 0.4 ± 0.05 and with octopamine: 0.45 ± 0.11; for *w*<sup>1118</sup>, *Tβh*<sup>nM18</sup>: 0.06 ± 0.1 and with octopamine: 0.39 ± 0.07; *n* = 25, 21, 19, 22). (B) Blocking OA receptors by feeding 3 mM epinastine eliminates the attraction of control flies similarly to the observed loss of attraction in *Tβh*<sup>nM18</sup> mutants (Als for *w*<sup>1118</sup>: 0.45 ± 0.08 and with epinastine: 0.11 ± 0.11; for *w*<sup>1118</sup>, *Tβh*<sup>nM18</sup>: 0.02 ± 0.15; *n* = 29, 28, 19).

(C) *Tβh*<sup>nM18</sup> mutants fed with 50 mM clonidine show a significant attraction to ethanol-containing food odors (Als for *w*<sup>1118</sup>: 0.33 ± 0.04 and with clonidine: 0.27 ± 0.03; for *w*<sup>1118</sup>, *Tβh*<sup>nM18</sup>: 0.1 ± 0.08 and with clonidine: 0.47 ± 0.05; *n* = 30, 28, 29, 31). (D) Feeding 200 nM naphazoline to *Tβh*<sup>nM18</sup> mutants restores the loss of attraction to ethanol-containing food odors and significantly reduces the attraction of the control group (Als for *w*<sup>1118</sup>: 0.59 ± 0.04 and with naphazoline: 0.31 ± 0.08; for *w*<sup>1118</sup>, *Tβh*<sup>nM18</sup>: 0.11 ± 0.09 and with naphazoline: 0.45 ± 0.07; *n* = 32, 33, 29, 33).

(E) Blocking TA receptors by feeding with 25 mM yohimbine does not alter the attraction to ethanol-containing food odors (Als for *w*<sup>1118</sup>: 0.38 ± 0.08 and with yohimbine: 0.41 ± 0.07; for *w*<sup>1118</sup>, *Tβh*<sup>nM18</sup>: 0.01 ± 0.11 and with yohimbine: 0.09 ± 0.1; *n* = 40, 40, 21, 24). (F) Increasing tyramine levels in control flies do not significantly alter attraction, but they significantly induce attraction in *Tβh*<sup>nM18</sup> mutants (Als for *w*<sup>1118</sup>: 0.42 ± 0.05 and with tyramine: 0.32 ± 0.09; for *Tβh*<sup>nM18</sup>: −0.01 ± 0.09 and with tyramine: 0.38 ± 0.08).

Errors are SEM, and the letter "a" indicates differences from random choice as
determined by the one-sample sign test. Student's T-test was used to
determine differences between two groups and ANOVA followed by the Tukey
*post hoc* test for more than two groups. \*P < 0.05, \*\*P < 0.01, \*\*\*P < 0.001.
For data, see **Supplementary Table S5**.
et al., 2007)—did not alter the attraction to ethanol in control or in mutant flies (Figure 5E). To independently address whether increased tyramine levels altered ethanol attraction, 345 mM tyramine was fed to control flies, and the effects on attraction to ethanol were analyzed (Figure 5F). Control flies fed with tyramine did not show a significantly reduced attraction to ethanol-containing food odors. Feeding tyramine to the $Tβh^{nM18}$ mutant significantly induced attraction to ethanol-containing food odors, suggesting that feeding with tyramine was effective and might function at high levels as an agonist for octopamine receptors, or alternatively, activation of tyramine receptors might also promote the induction of attraction to ethanol-containing food odors. Taken together, octopamine is required for the attraction to ethanol, and increased TA levels in $Tβh^{nM18}$ mutants are not responsible for the loss of attraction.

## DISCUSSION

The release of octopamine from acetylcholine co-expressing tyraminergic/octopaminergic neurons is necessary to induce a bias in a behavioral choice between two identical food odor traps, resulting in attraction for one odor trap. The release of tyramine or acetylcholine from the same set of cholinergic neurons results in aversion. In addition, the release of octopamine from VUMa4 neurons acts as a negative reinforcer when flies choose between two identical food odor traps and causes aversion. Therefore, octopamine mediates aversion and attraction depending on the set of neurons activated. In **Figure 6** we provide an overview summarizing these results. The behavioral outcome of neuronal activation depends on the kinetics of the channelrhodopsin transgene used for activation. Nevertheless, independent of the transgene, the activation of tyraminergic/octopaminergic neurons is sufficient to elicit site attraction, and the activation of VUMa4 neurons is sufficient to elicit site aversion. Given the choice between an attractive ethanol-enriched food odor and a less attractive food odor, the activation of tyraminergic/octopaminergic neurons is able to shift the attraction to the less attractive food odor. The induction of attraction for ethanol-enriched food odors in a binary choice also requires the function of octopamine, supporting the requirement for octopamine to trigger the behavior towards approach, e.g., ethanol attraction.

## Octopamine Acts as Positive and Negative Reinforcer

In *Drosophila*, octopamine has been shown to function as a positive reinforcer in several behaviors that involve the processing, evaluation and behavioral response of olfactory information. Innate attraction to ethanol-containing food odors requires the activation of tyraminergic/octopaminergic neurons, and mutants lacking octopamine fail to show attraction (Schneider et al., 2012). In more complex behaviors such as olfactory learning and memory, octopamine functions as a positive reinforcer, since $Tβh^{nM18}$ mutants fail to show a positive association or attraction to a previous sucrose rewarded odor (Schwaerzel et al., 2003). Similar to the adult nervous system, in the developing nervous system of 3rd

instar larvae, activation of tyraminergic/octopaminergic neurons substitutes for reward in olfactory larval learning, and the activity of tryaminergic/octopaminergic neurons is required for olfactory appetitive learning and memory (Schroll et al., 2006; Selcho et al., 2014). Here, we show that activation of tyraminergic/octopaminergic neurons is sufficient to bias the decision toward one of two similar odor traps and that $Tβh^{nM18}$ mutants fail to show attraction upon activation of the same set of neurons (Figure 1). Octopamine release from VUMa4 neurons results in aversion, indicating that octopamine functions as negative reinforcer in an olfactory choice situation (Figure 2). Eliminating octopamine release from 6.2-Tβh-Gal4-targeted neurons by introducing $Tβh^{nM18}$ mutants clearly eliminated aversion, showing that octopamine release is required for aversion. Another observation supporting that octopamine acts as a negative reinforcer was derived from the observation that the octopamine receptor agonist naphazoline reduced attraction in control flies (Figure 5D). There is evidence that the octopaminergic system might also function as negative reinforcer in other behaviors. In olfactory aversive learning and memory, when an odor is paired with an electric shock, $Tβh^{nM18}$ mutants show reduced learning abilities, indicating an impairment of the negative reinforcing function of the electric shock (Iliadi et al., 2017). Furthermore, activation of tyraminergic/octopaminergic neurons suppresses courtship conditioning, another form of aversive associative learning (Zhou et al., 2012).

<figure><img src="image_22.png" /></figure>

**FIGURE 6** | Model for octopamine induced attraction and aversion. The circles highlight the Tbh positive neurons targeted by the *dTdc2*-Gal4 driver in the adult brain (Schneider et al., 2012). The Tbh is the rate limiting enzyme for octopamine synthesis (Monastirioti et al., 1996). Activation of these neurons using UAS-ChR2 elicits site attraction (Figure 1). When the Gal4 expression of the driver is restricted by co-expression of ChAT-GAL80 driver the flies choose both odor traps equally (Figure 1). The neurons that are not affected by the ChAT-Gal80 repressor are indicated in open circles. They only express Tbh and activation of these neurons results in indifferences (Figure 1). The ChAT and Tbh expressing neurons are indicated by gray circles and they are responsible—when activated—to mediate attraction. Within these set of positive neurons, the activation of the VUMa4 neuron (indicated by a circle that is half black and half gray) results in aversion (Figure 2). The VUMa4 neurons are Tbh positive (Schneider et al., 2012). Since the ChAT-Gal80 repressor eliminates the aversion caused by activation of the 6.2 Tbh-Gal4 driver, the VUMa4 neuron is also ChAT positive (Figure 2). The activation of the VUMa4 neuron results in aversion, but when the VUMa4 neuron is activated together with other octopaminergic neurons, attraction occurs. This result is consistent with the idea that the activation of a second set of octopaminergic neurons can overrule the octopamine-induced aversion by the VUMa4 neuron. For data, see **Supplementary Table S6**.
On the cellular level, opposing neuronal functions for octopamine have been previously observed. For example, in the ventral nerve cord of locusts, octopamine release causes, at one site, bouts of rhythmic flight motor activity and, at another site, suppression of neuronal activity related to egg laying (Sombati and Hoyle, 1984). The opposing roles of octopamine might also be explained by the activation of different classes of octopamine receptors that either activate or reduce neuronal activity, both of which have been identified in *Drosophila melanogaster* (Evans and Maqueira, 2005). In addition to the negative reinforcing function of octopamine, an additional neurotransmitter mediates aversion in the *dTdc2-Gal4*-targeted neurons, since the introduction of *Tβh<sup>nM18</sup>* mutants resulted in significant aversion. One possible candidate for this is tyramine. For example, feeding the tyramine receptor antagonist yohimbine to *Tβh<sup>nM18</sup>* mutant larvae partially restored the observed reduced locomotion in the mutants, suggesting that the reduced locomotion was due to an increased tyramine level and that tyramine might counteract the function of octopamine (Saraswati et al., 2004). However, feeding with the tyramine receptor antagonist yohimbine did not interfere with attraction to ethanol in control flies, nor did it alter the loss of attraction in *Tβh<sup>nM18</sup>* mutants, suggesting that tyramine is not responsible for the observed aversion. Furthermore, feeding tyramine to control flies did not suppress the attraction to ethanol-enriched food odors (Figure 5F). Rather, feeding tyramine to *Tβh<sup>nM18</sup>* mutants resulted in attraction, suggesting that high levels of tyramine might also function also as a positive reinforcer. Another candidate is acetylcholine. Neuronal activation of neurons in a *dTdc2-Gal4*-dependent manner might also co-release other neurotransmitters that negatively regulate attraction. Not all neurons targeted by the *dTdc2-Gal4* line are octopaminergic or Tβh-positive (Busch et al., 2009; Schneider et al., 2012), and *ChAT-Gal80* represses *dTdc2-Gal4*-dependent transgene expression (Schneider et al., 2012). Consistent with this observation, some of the targeted VUM neurons express *ChAT* (Sayin et al., 2018). However, activation of VUMa4 neurons in *Tβh<sup>nM18</sup>* mutants eliminates the aversion, clearly indicating that octopamine, in addition to possible other neurotransmitter systems, might induce aversion. Taken together, octopamine functions as a positive and negative reinforcer in response to olfactory cues such as food odors.

## Octopamine Biases Behavioral Outcomes

How does a neurotransmitter mediate both negative and positive reinforcement to an olfactory cue? In associative olfactory short-term learning and memory, dopamine mediates two different sets of neurons: positive and negative reinforcing properties (Claridge-Chang et al., 2009; Aso et al., 2010; Liu et al., 2012). The same phenomenon might also be true for octopaminergic neurons. Octopamine-dependent activation of the VUMa4 neuron results in aversion, but when the VUMa4 neuron is activated together with other octopaminergic neurons, attraction occurs. This result indicates that the activation of a second set of octopaminergic neurons can overrule the octopamine-induced aversion by the VUMa4 neuron. Such an attraction might be the primary behavioral outcome when

octopamine release is depleted, as supported by the observation that *Tβh* mutants with no detectable levels of octopamine fail to show attraction and feeding control flies with the octopamine receptor antagonist epinstaine results in a reduction of ethanol-induced attraction rather than increased attraction. Feeding the agonist of octopamine—naphazoline- to control flies also reduces the attraction to ethanol enriched food odor (Figure 5D). This could be due to the activation of octopamine dependent negative reinforcement resulting in aversion.

If octopamine mediates both attraction and aversion to olfactory cues, then a situation must arise where octopamine-deficient flies respond with indecision in a choice situation, which is indeed the case. *Tβh<sup>nM18</sup>* mutants fail to show attraction to ethanol-enriched food sources. The indecision to choose between the two odor sources might depend on the inability to detect ethanol within food odors, the lack of a positive reinforcer, inability to make a decision or failure to perform the motor skills required for the task. *Tβh<sup>nM18</sup>* mutants sense ethanol and food odors and can distinguish between them (Schneider et al., 2012), eliminating the possibility that failures in odor perception account for the indifference. During olfactory learning and memory, odor ethanol functions as a positive reinforcer (Kaun et al., 2011), and *Tβh<sup>nM18</sup>* mutants show attraction to ethanol-containing food odors when they are pre-exposed to ethanol (Figure 4C), showing that the mechanism resulting in positive reinforcement is still functional in *Tβh<sup>nM18</sup>* mutants. Furthermore, *Tβh<sup>nM18</sup>* mutants still enter food odor traps, showing that the motor ability of the flies and the motivation to seek food is still intact. Consistent with a function in decision making, the activation of octopaminergic/tyraminergic neurons shifts the attraction from the ethanol-enriched food odor to a less attractive odor (Figure 4B) and therefore biases the behavioral outcome.

Are there other behaviors where flies normally must decide to respond to odor information and where *Tβh<sup>nM18</sup>* mutants fail to make the right decision or correct response? After exposure to a novel olfactory stimulus such as ethanol, normal flies show an increased startle response as measured by an increase in locomotion (Scholz, 2005). The *Tβh<sup>nM18</sup>* mutants not only fail to show a startle response but also show a repression of locomotor activity that can be contributed to the lack of octopamine, mimicking a freezing response. The immobility is turned into activity when the ethanol stimulus is sufficiently strong (Scholz, 2005). Another example that involves a behavioral choice is the associative appetitive and aversive olfactory learning paradigm that is analyzed in the Tully-Quinn paradigm (Tully and Quinn, 1985). In this paradigm, flies must demonstrate what they learned in a binary choice between two odors. Before the experiments, the odor concentrations are balanced so that they evoke a similar behavioral response to ensure that the strength of the presented odor stimuli is comparable. *Tβh<sup>nM18</sup>* mutants fail to respond to the positive reinforced odor with attraction within the given time, but they respond with aversion to the negative reinforced odor (Schwaerzel et al., 2003) or only respond in a reduced manner (Iliadi et al., 2017).
The difference between appetitive and aversive learning defects in *Tβh*<sup>nM18</sup> mutants might be due to the strength of the reinforcer. The lack of comparability of the strength of the reinforcer between aversive and appetitive olfactory learning and memory is also observed in the degree of association formed between the odor and the reinforcer, as reflected in differences in the learning score. Flies show lower learning scores when a positive reinforcer, such as sucrose, is paired with an odor compared with when a negative reinforcer, such as an electric shock, is paired (Schwaerzel et al., 2003). The influence of the threshold of the reinforcing stimulus is also observed in the behavioral response of *Tβh*<sup>nM18</sup> mutants when they are pre-exposed to ethanol and respond with ethanol attraction in the binary choice paradigm (Figure 4C). Here, the pre-exposure to ethanol odor is sufficiently strong to elicit attraction. In summary, octopamine orchestrates the behavioral response to odors. It remains to be investigated whether this finding holds true for other sensory information, such as taste and vision, and how octopamine integrates different external and/or internal information to bias the behavioral outcome.

## ETHICS STATEMENT

The animal studies including the model organism *Drosophila melanogaster* were conducted in agreement with the regulations of the DFG and the land North Rhine Westphalia.

## AUTHOR CONTRIBUTIONS

GC and HS: conception and design of the work; acquisition, data analysis and interpretation. GC: original draft—writing. HS: supervision, project administration and funding acquisition.

## FUNDING

This research was supported by Deutsche Forschungsgemeinschaft (DFG) 656/7-2, DFG 656/5-1 to HS, SFB1340 to HS and Bundesministerium für Bildung und Forschung (BMBF) SysMedAlc.

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## ACKNOWLEDGMENTS

We thank the Scholz Lab for scientific discussion. We thank the two reviewers for insightful comments and suggestions.

## SUPPLEMENTARY MATERIAL

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fnbeh.2018.00131/full#supplementary-material

**FIGURE S1** | Effect of light intensity on behavioral outcome. The same flicker frequency of 2 s at 40 Hz, 16 s at 8 Hz was used for all experiments, but the intensity of both LEDs was changed simultaneously. (A) Different intensities indicated above the panel were used to activate neurons with a blue light-sensitive UAS-ChR2 under the control of the *dTdc2-Gal4* driver. All the used intensities resulted in attraction for the site of activation (Als for the 1700 lux control and experimental group: -0.07 ± 0.12 and 0.32 ± 0.1, *n* = 20, 20; for 1000 lux: 0.21 ± 0.14 and 0.6 ± 0.12, *n* = 34, 31; for 800 lux: 0.02 ± 0.09 and 0.43 ± 0.1, respectively, *n* = 39, 33). (B) Neurons activated with a blue light-sensitive UAS-ChR2 under the control of the 6.2-*Tβh*-Gal4 driver with different intensities showed significant aversion when illuminated with 1200 lux, but not 1700 lux or 1000 lux (Als for the 1700 lux control and experimental group: -0.31 ± 0.14 and -0.62 ± 0.11, *n* = 20, 16; for 1200 lux: -0.02 ± 0.13 and -0.41 ± 0.13; *n* = 22, 19 and for 1000 lux: 0.12 ± 0.13 and -0.02 ± 0.12, respectively; *n* = 21, 19). (C) Activation with the amber-sensitive UAS-*ReaChR* in a *dTdc2-Gal4*-dependent manner leads to site attraction for the amber-illuminated site (Als for the 1200 lux Als of control and experimental group: -0.06 ± 0.15 and 0.6 ± 0.08, *n* = 18, 18; Als for the 800 lux 0.21 ± 0.11 and 0.1 ± 0.14, respectively, *n* = 14, 5). Errors bars are SEM. The one-sample sign test was used to determine differences from random choice, and significant differences are labeled with the letter “a”. Student’s *t*-test was used to determine differences between two groups with significance levels as follows: \* *P* < 0.05, \*\* *P* < 0.01, \*\*\* *P* < 0.001. Data for **Supplementary Figure S1**, see **Supplementary Table S6**.

**TABLE S1** | Data for **Figure 1**.

**TABLE S2** | Data for **Figure 2**.

**TABLE S3** | Data for **Figure 3**.

**TABLE S4** | Data for **Figure 4**.

**TABLE S5** | Data for **Figure 5**.

**TABLE S6** | Data for **Supplementary Figure S1**.

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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.

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# Attractant or Repellent? Behavioral Responses to Mammalian Blood Odor and to a Blood Odor Component in a Mesopredator, the Meerkat (Suricata suricatta)

Henrik Pettersson¹, Mats Amundin¹,² and Matthias Laska¹*

¹ Department of Physics, Chemistry and Biology, Linköping University, Linköping, Sweden, ² Kolmården Wildlife Park, Kolmården, Sweden

It is well-established that the odor of mammalian blood is attractive to top predators such as tigers and wolves and aversive to prey species such as mice and rats. Recent studies have shown that the mammalian blood odor component *trans*-4,5-epoxy-(E)-2-decenal (TED) elicits corresponding behavioral responses in these two groups of mammals. Here we assess whether a mesopredator, that is, a small-bodied carnivorous mammal that is both predator and prey, is attracted to or repelled by the odor of mammalian blood and TED. To this end, we assessed the behavior of a group of 15 captive meerkats (*Suricata suricatta*) when presented with wooden logs that were impregnated either with horse blood or with TED, and compared it to their behavior toward a fruity odor (*iso*-pentyl acetate) and a near-odorless solvent (diethyl phthalate). We found that the meerkats displayed significantly more interactions with the odorized wooden logs such as sniffing and pawing when these were impregnated with the two prey-associated odors compared to the two non-prey-associated odors. Most importantly, no significant difference was found in the number of interactions with the wooden logs impregnated with horse blood and TED, respectively. These results demonstrate that meerkats, despite being small-bodied mesopredators, are clearly attracted to the odor of mammalian blood. Further, the results suggest that a single blood odor component can be as efficient as the odor of real blood in eliciting behavioral responses in this herpestid mammal, similar to previous findings in feline and canine top predators.

**Keywords:** blood odor, epoxydecenal, behavior, mesopredator, meerkats, *Suricata suricatta*

## INTRODUCTION

The vast majority of naturally occurring odor stimuli are highly complex mixtures composed of dozens or even hundreds of volatile compounds (Ohloff, 1994; Sell, 2014). Olfactory systems are therefore faced with the problem to recognize the identity of an odor stimulus despite permanent fluctuations in its composition or intensity due to changes in the odor source itself (e.g., ripening of a fruit) or the environment (e.g., convection of volatiles in air currents) (Riffell et al., 2009). How the olfactory system achieves and maintains stimulus identity of complex odor mixtures is still not fully understood (Thomas-Danguin et al., 2014).

**OPEN ACCESS**

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

**Reviewed by:**
Sylvia Anton,
Institut National de la Recherche
Agronomique (INRA), France
Markus Fendt,
Universitätsklinikum Magdeburg,
Germany

*Correspondence:
Matthias Laska
malas@ifm.liu.se

Received: 26 January 2018

Accepted: 03 July 2018

Published: 23 July 2018

**Citation:**
Pettersson H, Amundin M and Laska M (2018) Attractant or Repellent? Behavioral Responses to Mammalian Blood Odor and to a Blood Odor Component in a Mesopredator, the Meerkat (*Suricata suricatta*).
Front. Behav. Neurosci. 12:152.
doi: 10.3389/fnbeh.2018.00152
One possible strategy for an animal to recognize a complex odor mixture is to rely on only one or a few “key” or “character impact” compound(s) which determine its odor identity (Dunkel et al., 2014). This strategy, of course, requires that the compound in question is reliably present in the odor mixture and that it can be reliably detected against the noise of the other compounds (Nehring et al., 2013). Thus, a high olfactory sensitivity for such a “key” or “character impact” compound should be expected in species for which the corresponding odor mixture is behaviorally relevant (Laska et al., 2005; Sarrafchi et al., 2013).

Behavioral tests assessing the ability of animals to recognize and to properly respond to the presentation of behaviorally relevant odors yielded rather mixed results concerning the efficiency of single compounds that are part of a complex odor mixture to elicit such adaptive behavioral responses. With regard to food odors, for example, recent studies found that frugivorous mammals do not seem to rely on single compounds to assess the degree of ripeness of a fruit but rather on the relative abundance of several compounds (Hodgkison et al., 2013; Nevo et al., 2015). Concerning the olfactory recognition of predators by prey species via body-borne odors, some studies found that single compounds are sufficient to elicit avoidance responses whereas other studies failed to find such effects or reported the behavioral responses to be weaker compared to those elicited by the full odor mixture (for a review, see Apfelbach et al., 2017).

The odor of blood has been shown to be attractive to mammalian top predators such as tigers (Nilsson et al., 2014) and wolves (Arshamian et al., 2017) and to be aversive to mammalian prey species such as the mouse (Sandnabba, 1997; Lahger and Laska, 2018) and the rat (Stevens and Saplikoski, 1973; Hornbuckle and Beal, 1974; Mackay-Sim and Laing, 1981). Interestingly, the tigers and wolves were equally attracted to and the mice were equally repelled by *trans*-4,5-epoxy-(E)-2-decenal (TED), a single component of mammalian blood odor which has been described by humans as having a typical “metallic, blood-like” odor quality (Buettner and Schieberle, 2001). These findings suggest that TED might indeed be a “key” or “character impact” compound which determines or at least contributes to the reliable recognition of blood odor. This notion is further supported by the finding that mice are extraordinarily sensitive to TED with olfactory detection thresholds in the ppt (parts per trillion) range (Sarrafchi and Laska, 2017).

Meerkats (*Suricata suricatta*) are carnivorous mammals belonging to the mongoose family (Herpestidae). They have well-developed olfactory brain structures (Gittleman, 1991; van Valkenburgh et al., 2014) and are known to strongly rely on olfactory cues in the context of social communication (Jordan, 2007; Mares et al., 2011; Leclaire et al., 2013). Similarly, meerkats have been reported to use their sense of smell for predator avoidance (Hollén and Manser, 2007; Zöttl et al., 2013) and for foraging and food selection (Leclaire, 2017). Due to their small body mass of 0.7–1.2 kg (van Staaden, 1994), they are typical mesopredators, meaning that they are both predators of smaller prey species and, at the same time, prey to larger predators. Their diet, although primarily based on arthropods, includes up to 20% (by volume) of small-bodied mammals and other vertebrates such as birds, eggs, lizards, and snakes (van Staaden, 1994; Doolan and

Macdonald, 1996). Therefore, it should be interesting to assess whether meerkats are attracted to or repelled by the odor of blood and if they also display the same behavior toward TED.

It was therefore the aim of the present study to (1) assess behavioral responses of meerkats to mammalian blood odor and to the mammalian blood odor component *trans*-4,5-epoxy-(E)-2-decenal, (2) to compare their behavioral responses to those toward a fruity odor and a near-odorless control, and (3) to compare their behavioral responses to those of top predators and prey species tested previously on the same odor stimuli.

# MATERIALS AND METHODS

## Ethics Statement

The experiments reported here comply with the *Guide for the Care and Use of Laboratory Animals* (8th edition, National Research Council, 2011) and also with current Swedish laws. They were performed according to a protocol approved by the ethical board of the Swedish Board of Agriculture (Jordbruksverket, protocol # 31-2647/10).

## Animals

The study was conducted at Kolmården Wildlife Park, near Norrköping, Sweden. A group of 15 meerkats (*S. suricatta*), comprising twelve males and three females ranging from a few months to 10 years of age, was observed. All animals were born in captivity. The enclosure of the meerkats was composed of an indoor and an outdoor part, connected by a sliding door. The indoor enclosure was 40 m² and had a ground substrate of sand. It contained standing brush material, tree stumps of a varied height, and different hiding places such as wooden nest boxes. The outdoor enclosure was 330 m² with mainly earth as ground substrate. In addition to tree stumps, the outdoor enclosure also had a grassy area, coniferous trees, rocks and bushes scattered throughout the area. The ground substrate of both the indoor and the outdoor enclosure allowed the animals to dig burrows and tunnels. The meerkats could freely choose between the indoor and the outdoor enclosure during the daytime, but were kept indoors over night. They were provided with food three times per day (in the morning, that is, about 60 min prior to the start of the day's observations; around noon, that is, during the hour between the morning and the afternoon observations; and in the afternoon, that is, after the end of the day's observations). Their food consisted of mice, baby chicken, pieces of meat from different even-toed ungulate species (such as deer and antelope, but not from odd-toed ungulate species such as horses), cat food pellets (Four Friends Senior, Västerås, Sweden), fruit (banana, apple, different berries), crickets, mealworms, chicken eggs.

## Odor Stimuli

The four odor stimuli used were:

Blood from a domestic horse (*Equus ferus caballus*). The blood was collected directly after the horse was euthanized and immediately deep-frozen in aliquots of 0.5 ml at -20°C. On the morning of each testing day, five aliquots of
horse blood were thawed and warmed up to approximately 25°C.

*trans*-4,5-epoxy-(E)-2-decenal (CAS# 134454-31-2), henceforth abbreviated as TED. This odorant has been identified as a volatile component in mammalian blood and evokes a typical “metallic, blood-like” odor quality in humans (Buettner and Schieberle, 2001). It was presented at a dilution of 1:100 (in diethyl phthalate) from a stock solution of 5 mg/ml.

The rationale for using these two odor stimuli was that horse blood and *trans*-4,5-epoxy-(E)-2-decenal had also been used in previous studies assessing behavioral responses of top predators (Nilsson et al., 2014; Arshamian et al., 2017) and a prey species (Lahger and Laska, 2018) to blood odor.

*iso*-pentyl acetate (CAS# 123-92-2). This odorant has been identified as a volatile component in a variety of fruits and evokes a typical “banana-like” odor quality in humans (Burdock, 2009). It was presented at a dilution of 1:1,000 (in diethyl phthalate).

Diethyl phthalate (CAS# 84-66-2). This organic solvent is near-odorless and was used both for diluting the two monomolecular odorants mentioned above and as a blank stimulus.

The concentrations mentioned above for the blood odor component TED and for the fruity odor (*iso*-pentyl acetate) were chosen in order to provide stimuli that were clearly detectable for humans, but not overwhelmingly strong so that a relatively close contact to the odor source was necessary to detect them.

## Experimental Procedure

The four different odor stimuli were presented to the animals using wooden (spruce) logs of 48 cm × 4.5 cm × 4.5 cm. Each log was impregnated with 0.5 ml of a given odor stimulus immediately prior to each presentation. The odor stimulus was applied on the two largest surfaces of a log using a micropipette and then spread over the surface using a paint brush. Plastic gloves were used whenever the logs were handled to avoid that they were contaminated with human odor. Five logs, impregnated with the same odor stimulus, were used during a given experimental day.

At the start of each experimental day, five freshly odorized wooden logs were placed into the outdoor enclosure of the meerkats. Care was taken to present the wooden logs to the animals at positions that differed between experimental days and, at the same time, allowed the experimenter to see all five logs at least at the start of the observation. Immediately after the logs were put in place, the animals were allowed access into the outdoor enclosure and were observed for 3 h in the morning and 3 h in the afternoon (between 08:00 a.m. and 04:00 p.m.). At the end of each experimental day the wooden logs were removed from the enclosure. Experiments were only performed on non-rainy days to prevent the odor stimuli from being washed away by the rain. At least 1 day was interspersed between consecutive presentations of odorized wooden logs. Each of the four odor stimuli was presented for a total of five times in a pseudorandomized order which resulted in a total of 20 experimental days.

Continuous sampling was used to record the occurrence of each interaction with a wooden log which was visible to the experimenter. A total of twelve behaviors involving different kinds of interaction with or immediate behavioral responses to the inspection of an odorized wooden log were considered (Table 1 and Figure 1). During one experimental day per odor stimulus, the duration of the behaviors was also recorded using a stopwatch.

## Data Analysis

Differences in the frequency of occurrence of behavioral responses between odor stimuli were assessed using the Chi-square test. Within-species comparisons of the duration of behavioral responses were performed using the Friedman test. Correlational analyses were performed by calculating Spearman rank-order coefficients *r*<sub>s</sub> which were tested for significance by computing *z*-scores. As the number of animals differed between the species that were compared, we calculated the number of interactions *per animal* by dividing the total number of interactions observed in a given species by the number of animals. All statistics were performed using SPSS, version 22.0.

# RESULTS

## Number of Interactions

Across all 20 observation days with the four different odor stimuli combined the meerkats interacted in total 2850 times with the odorized wooden logs. Ten out of the twelve behaviors described in the ethogram were observed (Table 2). Sniffing was by far the most frequently displayed behavior with 1970 interactions, representing 69% of all observations. Pawing was another frequently observed behavior with 834 occurrences, accounting for 29% of all observations. Thus, sniffing and pawing combined accounted for >98% of all interactions with the odorized wooden logs that the meerkats displayed. Licking

<table><caption>TABLE 1 | Ethogram of all behaviors considered in the present study.</caption><thead><tr><td>Functional term</td><td>Description</td></tr></thead><tbody><tr><td>Sniffing</td><td>Using the nose to investigate a wooden log</td></tr><tr><td>Pawing</td><td>Using the paw or claws to scratch a wooden log</td></tr><tr><td>Licking</td><td>Using the tongue to investigate a wooden log</td></tr><tr><td>Biting</td><td>Using the teeth to investigate a wooden log</td></tr><tr><td>Toying</td><td>Moving or otherwise manipulating a wooden log</td></tr><tr><td>Flehmen</td><td>Curling of the upper lip and "grimacing" when investigating a wooden log</td></tr><tr><td>Self-impregnating</td><td>Rubbing the face or other body part at a wooden log</td></tr><tr><td>Scent-marking</td><td>Depositing body-borne odors onto a wooden log</td></tr><tr><td>Orientating</td><td>Turning head, ears, or eyes following an interaction with a wooden log</td></tr><tr><td>Guarding</td><td>Resting close to or on top of a wooden log</td></tr><tr><td>Vocalizing</td><td>Producing sounds during or following an interaction with a wooden log</td></tr><tr><td>Fleeing</td><td>Rapidly moving away following an interaction with a wooden log</td></tr></tbody></table>
<figure><img src="image_24.png" /><figcaption>**FIGURE 1** | The two behaviors performed most often by the meerkats. (Upper) A meerkat sniffing at an odorized wooden log. (Lower) A meerkat pawing at an odorized wooden log.</figcaption></figure>

and flehmen were never displayed by the meerkats with any of the four odor stimuli.

## Comparison Between Odor Stimuli

A comparison between the four odor stimuli showed that the meerkats displayed a significantly higher number of interactions with the wooden logs when these were odorized with horse

blood compared to when they were odorized with the fruity odor ($\chi^2 = 83.701, P < 0.0001$) and the blank control ($\chi^2 = 27.597, P < 0.0001$). Similarly, the meerkats interacted significantly more often with the wooden logs when these were odorized with the blood odor component TED compared to the fruity odor ($\chi^2 = 70.689, P < 0.0001$) and the blank control ($\chi^2 = 20.263, P < 0.0001$). In contrast, the number of interactions with the wooden logs did not differ significantly between the horse blood and the blood odor component TED ($\chi^2 = 0.572, P = 0.450$).

## Duration of Interactions

The mean duration of interactions with the odorized wooden logs was $3.1 \pm 5.5$ s for the four different odor stimuli combined (Table 3).

No significant differences in the mean duration of interactions with the wooden logs were found between any of the four odor stimuli (Friedman ANOVA: $\chi = 6.01, P = 0.111$).

## Variability Between Sessions

No significant correlation between the number of interactions with the wooden logs across the five sessions was found with any of the four odor stimuli (Spearman test, horse blood: $r_s = -0.70, P > 0.05$; blood odor component: $r_s = -0.60, P > 0.05$; fruity odor: $r_s = -0.70, P > 0.05$; solvent: $r_s = -0.60, P > 0.05$). However, with all four odor stimuli the number of interactions with the wooden logs was higher in the first compared to the fifth session (Figure 2). Accordingly, a non-significant trend for a decrease in the animals' interest in the wooden logs across sessions was found as indicated by the negative correlation coefficients.

## DISCUSSION

The results of the present study demonstrate that meerkats, small-bodied mesopredators, are clearly attracted to, and not repelled by, the odor of blood. Further, they show that the blood odor component trans-4,5-epoxy-(E)-2-decenal is as efficient in

<table><caption>**TABLE 2** | Number of interactions with the odorized wooden logs in the meerkats ($n = 15$).</caption><thead><tr><th>Behavior</th><th>Horse blood</th><th>Blood component</th><th>Fruity odor</th><th>Blank control</th><th>Total</th></tr></thead><tbody><tr><td>Sniffing</td><td>598</td><td>551</td><td>378</td><td>443</td><td>1970</td></tr><tr><td>Pawing</td><td>252</td><td>258</td><td>126</td><td>198</td><td>834</td></tr><tr><td>Licking</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr><tr><td>Biting</td><td>0</td><td>2</td><td>2</td><td>3</td><td>7</td></tr><tr><td>Toying</td><td>1</td><td>6</td><td>0</td><td>0</td><td>7</td></tr><tr><td>Flehmen</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr><tr><td>Self-impregnating</td><td>0</td><td>1</td><td>1</td><td>0</td><td>2</td></tr><tr><td>Scent-marking</td><td>0</td><td>0</td><td>0</td><td>4</td><td>4</td></tr><tr><td>Orientating</td><td>4</td><td>2</td><td>7</td><td>3</td><td>16</td></tr><tr><td>Guarding</td><td>1</td><td>0</td><td>2</td><td>1</td><td>4</td></tr><tr><td>Vocalizing</td><td>0</td><td>1</td><td>0</td><td>0</td><td>1</td></tr><tr><td>Fleeing</td><td>0</td><td>4</td><td>1</td><td>0</td><td>5</td></tr><tr><td><strong>Total</strong></td><td><strong>856</strong></td><td><strong>825</strong></td><td><strong>517</strong></td><td><strong>652</strong></td><td><strong>2850</strong></td></tr></tbody></table>
<table><caption>**TABLE 3** | Duration of interactions with the odorized wooden logs in the meerkats (n = 15).</caption><thead><tr><td>Horse blood</td><td>Blood component</td><td>Fruity odor</td><td>Blank control</td><td>Total</td></tr></thead><tbody><tr><td>2.2 ± 1.9</td><td>3.1 ± 6.3</td><td>4.5 ± 8.0</td><td>2.7 ± 4.0</td><td>3.1 ± 5.5</td></tr></tbody></table>

Given are mean (±SD) values in seconds.

eliciting behavioral attraction responses in *S. suricatta* as the odor of real blood.

## Comparison Between Species

Our finding that the meerkats were clearly attracted to the odor of blood and to the blood odor component TED is not trivial considering that they are small-bodied mammals which are prey to a variety of larger-bodied predators (van Staaden, 1994). Thus, it should not have been surprising if the meerkats in our study would have behaved like mice and rats, that is, like a prey species and accordingly would have avoided the odor of blood (Hornbuckle and Beal, 1974; Lahger and Laska, 2018). If we further consider that the diet of meerkats is not exclusively based on smaller vertebrates but also includes a variety of non-vertebrate food items such as arthropods and plant material (van Staaden, 1994), it is actually somewhat unexpected that the meerkats behaved like top predators such as tigers and wolves (Nilsson et al., 2014; Arshamian et al., 2017) and were attracted to blood odor. This, in turn, suggests that the attraction to blood odor displayed by carnivorous mammals of the feline and canine families may be an evolutionarily old trait as the mongoose family (Herpestidae) to which the meerkats belong split from the other carnivore families about 25 million years ago (Nyakatura and Bininda-Emonds, 2012).

A comparison of the behavioral responses of the meerkats in the present study to those of several species of top predators tested previously with the same method and odor stimuli (Nilsson et al., 2014; Arshamian et al., 2017) shows that the

meerkats displayed the highest number of interactions *per animal* with the odorized wooden logs (Table 4). This was true both when considering all four odor stimuli combined and separately.

The total number of interactions with the odorized wooden logs *per animal* displayed by the meerkats was almost five times higher than that of the Asian wild dogs, and more than three times higher compared to the African wild dogs and Siberian tigers, respectively. The South American bush dogs and the Eurasian wolves displayed a higher interest *per animal* to all four odor stimuli than the other two top predators, but still lower numbers compared to the meerkats. This raises the question as to possible reasons underlying these between-species differences in their degree of interest toward the odorized wooden logs. Several possible explanations should be considered:

Although the possibility that differences in enclosure size might have affected the likelihood of an animal to encounter and interact with the odorized wooden logs cannot be ruled out completely, it seems unlikely to explain the above-mentioned between-species differences in interest as correlations between enclosure size (m²/animal) and interest in the odorized wooden logs (interactions/animal) were not significant for any of the four odor stimuli considered separately (Spearman test: real blood odor: $r_s = -0.49, p = 0.33$; TED: $r_s = -0.43, p = 0.40$; fruity odor: $r_s = -0.60, p = 0.21$; solvent: $r_s = -0.49, p = 0.33$) or combined ($r_s = -0.43, p = 0.40$). Similarly, although subadult mammals tend to be more reactive to novel objects and odor stimuli than adult conspecifics (Glickman and Sroges, 1966), differences in the age composition of the studied carnivore species are also unlikely to explain the observed between-species differences in interest toward the odorized wooden logs as with all six species under consideration > 90% of all individuals were adults. Whether between-species differences in overall activity level may account for the finding that the meerkats displayed the highest interest in the odor stimuli used here needs further investigation. Although we did not systematically record the overall activity of the meerkats across the 6 h of observation per experimental day, it

<figure><img src="image_25.png" /><figcaption>**FIGURE 2** | The number of interactions with the odorized logs across the five sessions performed per odor stimulus. circles: blood odor component TED [trans-4,5-epoxy-(E)-2-decenal]; squares: horse blood; triangle: fruity odor (iso-pentyl acetate); diamond: blank control (diethyl phthalate).</figcaption></figure>
<table><caption>TABLE 4 | Number of interactions per animal with the odorized wooden logs.</caption><thead><tr><th>Species</th><th>Horse blood</th><th>Blood component</th><th>Fruity odor</th><th>Blank control</th><th>Total</th></tr></thead><tbody><tr><td>Meerkats<sup>1</sup></td><td>57.1</td><td>55.0</td><td>34.5</td><td>43.5</td><td>190.0</td></tr><tr><td>Eurasian wolves<sup>2</sup></td><td>48.1</td><td>45.0</td><td>17.6</td><td>14.4</td><td>125.1</td></tr><tr><td>African wild dogs<sup>3</sup></td><td>11.8</td><td>23.6</td><td>10.6</td><td>7.9</td><td>53.9</td></tr><tr><td>Asian wild dogs<sup>3</sup></td><td>13.4</td><td>14.2</td><td>6.3</td><td>4.3</td><td>38.2</td></tr><tr><td>South American Bush dogs<sup>3</sup></td><td>54.0</td><td>52.1</td><td>29.7</td><td>22.4</td><td>158.2</td></tr><tr><td>Siberian tigers<sup>3</sup></td><td>27.7</td><td>27.2</td><td>5.7</td><td>4.8</td><td>65.3</td></tr></tbody></table>

(Data from ¹present study, ²Arshamian et al., 2017, ³Nilsson et al., 2014).

was obvious that they, similar to the other carnivore species tested in previous studies, alternated between phases of rest and phases of activity. Thus, there were at least no obvious between-species differences in this parameter.

Whether the observed between-species differences in interest toward the odorized wooden logs might reflect generic differences in their use of the sense of smell for exploring objects and/or odors is hard to decide. All six carnivore species are known to possess well-developed olfactory brain structures (Gittleman, 1991; van Valkenburgh et al., 2014) and all have been reported to use olfactory cues for social communication and foraging (Nowak, 2005). However, this does not exclude the possibility that the importance and use of the sense of smell may indeed differ among these carnivores. Thus, at this point no conclusive answer can be given as to why the meerkats of the present study displayed a markedly higher interest toward all four odor stimuli compared to the other carnivore species.

## Recognition of the Odor Identity of a Complex Odor Mixture

With regard to the reliable recognition of behaviorally relevant odors which are almost always complex mixtures of volatiles the olfactory system can adopt two different strategies:

The first strategy implies that the olfactory system relies on only one or a few “key” or “character impact” compounds which are part of a complex mixture and are used for the recognition of an odor. Accordingly, this strategy requires olfactory receptors that are highly specific to a given ligand and central circuits which allow for a quick and hard-wired translation of a chemical stimulus into a specific behavioral response (Wilson and Stevenson, 2006). Further, this strategy requires that the “key” or “character impact” compound in question is reliably present in the odor mixture and that it can be reliably detected against the noise of the other compounds that are part of the odor mixture as well as against the chemical background noise in the environment (Nehring et al., 2013). Thus, this strategy also requires a high olfactory sensitivity for such a compound in species for which the corresponding odor is behaviorally relevant.

The second strategy implies that the olfactory system relies on either the full bouquet of volatiles that comprise an odor mixture or at least on a larger proportion of its components for the recognition of an odor. Accordingly, this strategy does not require olfactory receptors to be highly specific but instead requires central circuits to create a unitary percept or “odor object” from a complex mixture of volatiles which is stable against

fluctuations in the composition and intensity of the mixture components (Wilson and Stevenson, 2006). Thus, this strategy requires the olfactory system to generate patterns of activation which allow for fine discrimination of similar odor mixtures and a cut-off criterion which allows for distinguishing the odor object in question from similar odor objects. An example for the first strategy, in which the olfactory system relies on “key” or “character impact” compounds for odor recognition, would be the perception of certain pheromones, e.g., the rabbit mammary pheromone 2-methylbut-2-enal which has been identified as a component of rabbit milk and elicits a hard-wired behavioral response in rabbit pups (Coureaud et al., 2003; Schaal et al., 2003). This strategy is often connected to innate behavioral responses. An example for the second strategy, in which the olfactory system relies on a complex mixture of volatiles for odor recognition, would be the perception of individual body odors (Beauchamp and Yamazaki, 2003). This strategy is often connected to learned behavioral responses.

To which of these two strategies do our findings of the present study as well as those of previous studies on behavioral responses to blood odor and to the blood odor component trans-4,5-epoxy-(E)-2-decenal fit?

Our finding that the blood odor component TED elicited the same high degree of interest in the meerkats as the odor of real blood suggests that this monomolecular compound may serve as a “key” or “character impact” compound for the odor of blood in this and other carnivore species. However, we would like to emphasize that we do not know whether TED is “the” or just “a” component of blood odor that induces a behavioral attraction response. Considering the high number of volatiles that comprise the odor of blood (Kusano et al., 2013) it is not feasible to test all of them with a given species of animal. However, gas chromatography-olfactometry showed that TED was the only blood odor component described as having the typical “metallic, blood-like” quality that is characteristic for the odor of blood as perceived by humans (Rachamadugu, 2012).

Trans-4,5-epoxy-(E)-2-decenal is a product of lipid peroxidation (Buettner and Schieberle, 2001). Considering that this biochemical process is ubiquitous in the metabolism of mammals, it is very likely that TED is present in the blood odor of all mammals. Thus TED is likely to fulfill the prerequisite of the first strategy mentioned above of being reliably present in the odor mixture. Unfortunately, the olfactory detection threshold for TED is not known for any of the predator species that are attracted by this blood odor component. However, human
subjects and mice which are both repelled by the odor of TED (Arshamian et al., 2017; Lahger and Laska, 2018) have been shown to be extraordinarily sensitive to this compound with threshold values in the ppt (parts per trillion) range (Buettner and Schieberle, 2001; Sarrafchi and Laska, 2017). Thus, there is at least circumstantial evidence that TED may fulfill the prerequisite of a “key” or “character impact” compound of being detectable at low concentrations.

To the best of our knowledge, no olfactory receptor has been identified so far as having TED as its specific ligand in any species. Thus, we do not know yet if TED may fulfill the prerequisite of the first strategy mentioned above of having an olfactory receptor that is highly specific to this ligand. Future studies should therefore aim at testing the specificity and sensitivity of olfactory receptors for TED.

Finally, we do not know whether the attraction response to both blood odor and TED observed in mammalian predators is innate, which would support the notion that the first strategy mentioned above might apply, or whether the attraction response is acquired. Thus, future studies should assess behavioral responses of newborn predators to these odors. The finding that laboratory-born mice that were naïve with regard to the odors of blood and TED clearly avoided both odors (Lahger and Laska, 2018) suggests that behavioral responses to TED might indeed be innate rather than acquired.

## The Bipolar Behavioral Effect of Blood Odor and TED

Most of the behaviorally relevant odors studied so far have one thing in common: they elicit a certain behavioral response in a given species, or in a given group of species, and they fail to elicit any response in other species, or other groups of species. It is a hallmark of pheromones, for example, to be species-specific and thus to elicit adaptive behavioral responses, e.g., mating or aggregation, in conspecifics but not in heterospecifics (Wyatt, 2014). Similarly, food odors usually elicit adaptive behavioral responses, e.g., foraging for or inspection of a food item, in those species that feed on a given type of food but are usually ignored

by species that do not include this type of food into their diet (Stoddart, 1980). Predators are attracted by the odor of their prey whereas non-predators are usually indifferent to the odor of the prey species in question (Conover, 2007). Accordingly, prey species are repelled by the odor of their natural predator whereas non-prey species usually are not (Parsons et al., 2017).

The odors of blood and of its component trans-4,5-epoxy-(E)-2-decenal appear to be rather unique in the sense that they elicit a bipolar behavioral effect: an attraction response in predators (Nilsson et al., 2014; Arshamian et al., 2017) and an avoidance response in prey species (Hornbuckle and Beal, 1974; Lahger and Laska, 2018). In this context it is interesting to note that the avoidance response to both blood odor and TED displayed by mice suggests that these odors are likely to contain a warning cue rather than a fear cue as the animals significantly avoided these odor stimuli without showing behavioral indicators of fear such as freezing or defecation (Lahger and Laska, 2018). Similar behavioral responses indicative of a warning cue, but not a fear cue, have been reported in rats when presented with the odor of sick conspecifics (Arakawa et al., 2010). Future studies should therefore aim at elucidating the neural basis of these opposing behavioral responses, for example whether there are “labeled line”-like neural pathways mediating the connection between the perception of blood odor or of the blood odor component TED and behavioral attraction or avoidance responses.

## AUTHOR CONTRIBUTIONS

HP, MA, and ML conceived the study, analyzed the data, and wrote the manuscript. HP collected the data. All authors critically revised the manuscript, approved the final version, and agree to be accountable for its content.

## ACKNOWLEDGMENTS

The animal caretakers at Kolmården Wildlife Park involved in this study are gratefully acknowledged for their help and support.

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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 Pettersson, Amundin and Laska. 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.
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# fMRI-Based Brain Responses to Quinine and Sucrose Gustatory Stimulation for Nutrition Research in the Minipig Model: A Proof-of-Concept Study

Nicolas Coquery¹, Paul Meurice¹, Régis Janvier¹, Eric Bobillier¹, Stéphane Quellec², Minghai Fu³, Eugeni Roura³, Hervé Saint-Jalmes⁴ and David Val-Laillet¹*

¹ INRA, INSERM, Univ Rennes, Nutrition Metabolisms and Cancer, NuMeCan, Rennes, France, ² IRSTEA, UR OPAALE, Rennes, France, ³ Centre for Nutrition and Food Sciences, Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, St. Lucia, QLD, Australia, ⁴ CLCC Eugène Marquis, Inserm, LTSI-UMR 1099, Université de Rennes, Rennes, France

## OPEN ACCESS

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

**Reviewed by:**
Andy Wai Kan Yeung,
The University of Hong Kong,
Hong Kong
Hubert Preissl,
Institut für Diabetesforschung und
Metabolische Erkrankungen (IDM),
Germany

*Correspondence:
David Val-Laillet
david.val-laillet@inra.fr

Received: 02 May 2018

Accepted: 03 July 2018

Published: 24 July 2018

**Citation:**
Coquery N, Meurice P, Janvier R, Bobillier E, Quellec S, Fu M, Roura E, Saint-Jalmes H and Val-Laillet D (2018) fMRI-Based Brain Responses to Quinine and Sucrose Gustatory Stimulation for Nutrition Research in the Minipig Model: A Proof-of-Concept Study. *Front. Behav. Neurosci.* 12:151. doi: 10.3389/fnbeh.2018.00151

The minipig model is of high interest for brain research in nutrition and associated pathologies considering the similarities to human nutritional physiology, brain structures, and functions. In the context of a gustatory stimulation paradigm, fMRI can provide crucial information about the sensory, cognitive, and hedonic integration of exteroceptive stimuli in healthy and pathological nutritional conditions. Our aims were (i) to validate the experimental setup, i.e., fMRI acquisition and SPM-based statistical analysis, with a visual stimulation; (ii) to implement the fMRI procedure in order to map the brain responses to different gustatory stimulations, i.e., sucrose (5%) and quinine (10 mM), and (iii) to investigate the differential effects of potentially aversive (quinine) and appetitive/pleasant (sucrose) oral stimulation on brain responses, especially in the limbic and reward circuits. Six Yucatan minipigs were imaged on an Avanto 1.5-T MRI under isoflurane anesthesia and mechanical ventilation. BOLD signal was recorded during visual or gustatory (artificial saliva, sucrose, or quinine) stimulation with a block paradigm. With the visual stimulation, brain responses were detected in the visual cortex, thus validating our experimental and statistical setup. Quinine and sucrose stimulation promoted different cerebral activation patterns that were concordant, to some extent, to results from human studies. The insular cortex (i.e., gustatory cortex) was activated with both sucrose and quinine, but other regions were specifically activated by one or the other stimulation. Gustatory stimulation combined with fMRI analysis in large animals such as minipigs is a promising approach to investigate the integration of gustatory stimulation in healthy or pathological conditions such as obesity, eating disorders, or dysgeusia. To date, this is the first intent to describe gustatory stimulation in minipigs using fMRI.

Keywords: fMRI, brain, pig, nutrition, gustatory stimulation, visual stimulation, hedonism
# INTRODUCTION

The pig and minipig models are now recognized as one of the most prominent large animal model for human nutritional physiology (Roura et al., 2016; Roura and Fu, 2017) and neuroimaging studies (Sauleau et al., 2009; Clouard et al., 2012b; Val-Laillet et al., 2015). Given the exponential use of magnetic resonance imaging in human, many efforts have to be made in order to adapt MRI setup to the pig and minipig specificities. Regarding brain investigation with functional MRI (fMRI) and, to our knowledge, only few studies were performed in pigs and minipigs, including different kinds of stimulation paradigms and stimuli, such as visual (Fang et al., 2006; Gizewski et al., 2007), somatosensory (Fang et al., 2005; Duhaime et al., 2006), pharmacological (Mäkiranta et al., 2002), and deep-brain stimulations (Min et al., 2012; Knight et al., 2013; Paek et al., 2015; Gibson et al., 2016; Settell et al., 2017). To date, no study has been performed with fMRI to explore the brain responses to gustatory stimulations for nutrition research.

Olfactory and gustatory stimulations have already been widely investigated in the pig and minipig models. Roura and Fu (2017) and Val-Laillet (2018) have recently provided complete reviews on this topic and the scope of the methodologies used for this purpose come from ethology (Clouard et al., 2012a,c; Clouard and Val-Laillet, 2014) to electrophysiology (Danilova et al., 1999) and nuclear imaging, such as single photon emission computed tomography (SPECT) or positron emission tomography (PET; Clouard et al., 2012a, 2014b; Val-Laillet et al., 2015; Val-Laillet, 2018). These approaches provide either information about a long-term integration of the stimulation, i.e., behavior or nuclear imaging, or a direct but local impact, i.e., electrophysiology. Furthermore, nuclear imaging, usually performed with an acquisition time frame of several minutes, can only inspect the global brain changes, such as in brain perfusion for SPECT with hexa-methyl-propyl-amineoxime (HMPAO) detection, or glucose metabolism for PET with fluoro-deoxyglucose detection. fMRI solely can provide information on the acute response (15–20 s) to a repeated single and short stimulation in a global brain analysis, making this methodology of high interest for nutritional studies. Moreover, different kinds of stimulations and their controls can be investigated during the same imaging session, contrary to paradigms using nuclear imaging, which significantly increases the methodological strength and statistical comparison power between stimulations, in addition to temporal resolution, and decreases the costs and constraints for animal experimentation on large animals.

The couple pleasant vs. aversive stimulation is currently gaining more attention for nutritional studies in human. Indeed, this approach is being used to decipher the organization of the gustatory cortex, e.g., the insular cortex (Rudenga et al., 2010; Dalenberg et al., 2015) and the associated brain areas (Zald et al., 2002; van den Bosch et al., 2014; Field et al., 2015). This approach is also used to investigate the impact of aging (Hoogeveen et al., 2015) or pathologies, such as obesity (Szalay et al., 2012), on those brain areas. Besides species differences in terms of taste detection and integration between human and the pig or minipig

model (Roura and Fu, 2017), the pleasant vs. aversive impact of compounds have been already investigated with behavioral exploration and nuclear imaging in pigs (Clouard et al., 2012b,c) but not with fMRI.

In this study, we aim to validate in the minipig model a fMRI-based analysis of brain responses to sucrose vs. quinine stimulation as a model of pleasant vs. aversive stimulation paradigm. For this purpose, we first validate the experimental and analysis setup with a visual stimulation based on a previous study (Gizewski et al., 2007). This first attempt of fMRI analysis of gustatory stimulation should be of high interest for nutritional studies for basic research and pre-clinical purposes.

# MATERIALS AND METHODS

## Animals

Experiments were conducted in accordance with the current ethical standards of the European Community (Directive 2010/63/EU), Agreement No. C35-275-32, and Authorization No. 35-88. The Regional Ethics Committee in Animal Experiment of Brittany has validated and approved the entire procedure described in this paper (Project No. 2015051312053879). A total of six 1-year-old 30-kg male Yucatan minipigs were used in this study. The pigs were housed in individual pens (150 cm × 60 cm × 80 cm) and had free access to water. A chain was suspended in each pen to enrich the environment of the animals and fulfill their natural disposition to play. The room was maintained at ~24°C with a 13:11-h light–dark cycle.

## Anesthesia

Pre-anesthesia was performed with an intramuscular injection of ketamine (5 mg/kg – Imalgene 1000, Merial, Lyon, France) in overnight-fasted animals. Isoflurane inhalation (Aerane 100 ml, Baxter SAS, France) was used to suppress the pharyngotracheal reflex and then establish a surgical level of anesthesia, 3–5 and 2–3% v/v, respectively. After intubation, anesthesia was maintained with 2.5% v/v isoflurane and mechanical respiration allowed adjustment of respiratory frequency at 20 breathing/minute with a tidal volume of 450 ml. Cotton wool with an additional headset were used to conceal the animal’s ears.

## Visual and Gustatory Stimulation

For visual and gustatory stimulation, we used a custom-made stimulation apparatus, which was located outside the magnet-shielded room (5-m distance) and used to deliver both visual and gustatory stimulations upon synchronization with the MRI system.

### Visual Stimulation

Both eyes were entirely covered by an opaque cap. The stimulation apparatus produced the visual stimulation that was conducted to the animal’s right eye cap with an optical fiber. Visual simulation consisted of flashlight at a frequency of 8 Hz. A block paradigm was used: 20-s stimulation ON,
20-s stimulation OFF, repeated 15 times. The entire stimulation protocol duration was about 10 min.

## Gustatory Stimulation

Animals were equipped with an oral apparatus allowing gustatory stimulation as previously described (Clouard et al., 2012a) and consisting of three tubes, one for each solution, with an additional circular tube for continuous aspiration of liquid. Quinine (10 mM, Q1125-25G, Sigma-Aldrich, St. Quentin Fallavier, France) and sucrose (5%, S/8560/65, Fisher Chemical, Leics, United Kingdom) were solubilized in artificial saliva (Hellekant et al., 1997). In order to obtain the highest brain responses for each stimulation, three blocks of stimulation were performed in the following order, from the least to the most persistent solution in mouth: control stimulation with artificial saliva, sucrose stimulation, and quinine stimulation, each stimulation being repeated 15 times. Each stimulation consisted of oral stimulation (5 s, 24 mL/min), 25-s pause, rinse with artificial saliva (15 s, 24 mL/min), and pause (15 s). The entire stimulation protocol duration was about 45 min.

## MRI Image Acquisition

Image acquisition was performed on a 1.5-T magnet (Siemens Avanto) at the Rennes Platform for Multimodal Imaging and Spectroscopy. Acquisitions were performed using a combination of coils (Body and Spine matrix coils) for optimized signal to noise ratio acquisition. *T1-weighted anatomical image acquisition:* a MP-RAGE sequence was adapted for adult minipig anatomy (1.2 mm × 1.2 mm × 1.2 mm, NA = 2, TR = 2400 ms, TE = 3.62 ms, TI = 854 ms, FA = 8°, acquisition duration 15 min). *BOLD signal acquisition:* an echo planar imaging sequence was adapted for minipig head geometry (TR/TE: 2500/40 ms, FA: 90°, voxel size: 2.5 mm × 2.5 mm × 2.5 mm). The first four acquired volumes were excluded for the data analysis, meaning that no stimulation was performed during this period.

## Data Analysis and Statistical Image Analysis

Data analysis was performed with SPM12 (version 6906, Wellcome Department of Cognitive Neurology, London, United Kingdom). After slice timing correction, realignment, and spatial normalization on a pig brain atlas (Saikali et al., 2010), images were smoothed with a Gaussian kernel of 4 mm. *Voxel-based statistic:* first-level (within-individual contrast) and second-level (within-group contrast) statistics were assessed with a threshold set at *p* < 0.05 to produce the brain maps of activation. No suprathreshold voxels were detected with FDR correction at *p* < 0.05. *ROI-based statistic:* anatomical ROIs from the Saikali pig atlas (Saikali et al., 2010) were extracted and ROI-based statistic was performed using the Marsbar toolbox with an uncorrected *p*-value threshold set at 0.05 for the whole ROI. Statistical analysis was performed either on single stimulation, e.g., changes compared to baseline, or between stimulations differences. Data from only five minipigs were used for visual analysis due to a

complication during acquisition of the visual stimulation in one animal.

# RESULTS

## fMRI Setup and Statistical Analysis Validation With Visual Stimulation

We first aimed to validate the experimental setup, the acquisition, and the statistical analysis pipeline with a visual stimulation paradigm such as previously described (Fang et al., 2006; Gizewski et al., 2007). We could easily distinguish a significant BOLD response in the left occipital lobe (Figure 1A). This cluster was mostly found in the primary visual system V1. The ROI-based statistical analysis confirmed a significant BOLD response in V1 but also in V2 (Figure 1B).

## Brain Responses to Artificial Saliva Stimulation

With artificial saliva stimulation, we could detect a large activation pattern in olfacto-gustatory centers (Rolls, 2005) such as the insular cortex, the prepyriform area, and thalamic regions, but also in other brain areas (Figure 2A). The ROI-based statistical analysis validated the increased BOLD responses within these brain regions (Figure 2B).

All stimulations (saliva, sucrose, and quinine) were able to activate, but in different subparts, the primary somatosensory

<figure><img src="image_27.png" /></figure>

### B.

<table><caption>**FIGURE 1 | (A)** Horizontal maps of global brain BOLD responses to a visual stimulation (white flash, 8 Hz). *p*-Value threshold = 0.05; DV, dorsal-ventral position in mm related to the posterior commissure. **(B)** Related ROI-based statistical analysis with ROIs from Saikali atlas (Saikali et al., 2010). ROI abbreviations are detailed at the bottom of the panel (A). Cerebellar brain ROIs were excluded, *p*-values <0.05 are presented in black normal font style, and *p*-value between 0.05 and 0.1 are presented in gray italic.</caption><thead><tr><th>ROI</th><th>Hemisphere</th><th>t-value</th><th>p (uncor.)</th></tr></thead><tbody><tr><td>V2: Sec. Visual Cortex</td><td>L</td><td>15.58</td><td>&lt;0.001</td></tr><tr><td>V1: Prim. Visual Cortex</td><td>L</td><td>3.90</td><td>0.009</td></tr><tr><td>DL Cingulate Cortex</td><td>L</td><td>2.79</td><td>0.025</td></tr><tr><td>Parahippocampal Cortex</td><td>R</td><td>2.54</td><td>0.032</td></tr><tr><td>Amygdala</td><td>L</td><td>2.51</td><td>0.033</td></tr><tr><td>DL Cingulate Cortex</td><td>R</td><td>2.28</td><td>0.042</td></tr><tr><td>Orbitofrontal Cortex</td><td>R</td><td>2.13</td><td>0.050</td></tr><tr><td>V2: Sec. Visual Cortex</td><td>R</td><td>2.07</td><td>0.053</td></tr></tbody></table>
<figure><img src="image_28.png" /></figure>

## B. Artificial Saliva

<table><thead><tr><th>ROI</th><th>L/R</th><th>t-value</th><th>p (uncor.)</th></tr></thead><tbody><tr><td>NST</td><td>R</td><td>4.41</td><td>0.003</td></tr><tr><td>PrePyriform Area</td><td>L</td><td>3.68</td><td>0.007</td></tr><tr><td>Fusiform Gyrus</td><td>L</td><td>3.57</td><td>0.008</td></tr><tr><td>Som. Asso. Cortex</td><td>R</td><td>3.22</td><td>0.012</td></tr><tr><td>Putamen</td><td>L</td><td>3.08</td><td>0.014</td></tr><tr><td>Clastrum</td><td>R</td><td>2.83</td><td>0.018</td></tr><tr><td>Nucleus Accumbens</td><td>L</td><td>2.79</td><td>0.019</td></tr><tr><td>Insular Cortex</td><td>L</td><td>2.68</td><td>0.022</td></tr><tr><td>LD Thalamic Nucleus</td><td>L</td><td>2.66</td><td>0.022</td></tr><tr><td>MD Thalamic Nucleus</td><td>R</td><td>2.54</td><td>0.026</td></tr><tr><td>Olfactory Bulb</td><td>L</td><td>2.53</td><td>0.026</td></tr><tr><td>Hippocampus</td><td>R</td><td>2.25</td><td>0.037</td></tr><tr><td>MD Thalamic Nucleus</td><td>L</td><td>2.17</td><td>0.041</td></tr><tr><td>Insular Cortex</td><td>R</td><td>2.07</td><td>0.046</td></tr><tr><td>Premotor Cortex</td><td>L</td><td>2.03</td><td>0.049</td></tr><tr><td>Premotor Cortex</td><td>R</td><td>1.94</td><td>0.055</td></tr><tr><td>NST</td><td>L</td><td>1.82</td><td>0.064</td></tr></tbody></table>

## C. Sucrose

<table><thead><tr><th>ROI</th><th>L/R</th><th>t-value</th><th>p (uncor.)</th></tr></thead><tbody><tr><td>Prim. Motor Cortex</td><td>R</td><td>2.71</td><td>0.032</td></tr><tr><td>Putamen</td><td>L</td><td>2.02</td><td>0.050</td></tr><tr><td>PrePyriform Area</td><td>R</td><td>1.91</td><td>0.057</td></tr><tr><td>Prim. Somatosensory Cortex</td><td>R</td><td>1.87</td><td>0.060</td></tr><tr><td>VA Thalamic Nucleus</td><td>R</td><td>1.72</td><td>0.073</td></tr><tr><td>Central Thalamic Area</td><td>R</td><td>1.65</td><td>0.080</td></tr><tr><td>Insular Cortex</td><td>R</td><td>1.59</td><td>0.087</td></tr></tbody></table>

## D. Quinine

<table><thead><tr><th>ROI</th><th>L/R</th><th>t-value</th><th>p (uncor.)</th></tr></thead><tbody><tr><td>Fusiform Gyrus</td><td>L</td><td>7.25</td><td>&lt;0.001</td></tr><tr><td>Nucleus Accumbens</td><td>L</td><td>3.92</td><td>0.006</td></tr><tr><td>Parahippocampal Cortex</td><td>L</td><td>2.66</td><td>0.022</td></tr><tr><td>VA Thalamic Nucleus</td><td>R</td><td>2.29</td><td>0.035</td></tr><tr><td>LD Thalamic Nucleus</td><td>R</td><td>2.11</td><td>0.044</td></tr><tr><td>Orbitofrontal Cortex</td><td>L</td><td>1.94</td><td>0.055</td></tr><tr><td>Putamen</td><td>L</td><td>1.82</td><td>0.064</td></tr><tr><td>Caudate Nucleus</td><td>L</td><td>1.75</td><td>0.070</td></tr><tr><td>Caudate Nucleus</td><td>R</td><td>1.70</td><td>0.075</td></tr><tr><td>A Enchorhinal Cortex</td><td>L</td><td>1.64</td><td>0.081</td></tr><tr><td>Substancia Nigra</td><td>R</td><td>1.54</td><td>0.092</td></tr></tbody></table>

**FIGURE 2** | (A) Horizontal maps of global brain BOLD responses to artificial saliva, sucrose, quinine, and between sucrose and quinine stimulations. *p*-Value threshold = 0.05; DV, dorsal-ventral position in mm related to the posterior commissure. ROI-based statistical analysis with ROIs from Saikali atlas (Saikali et al., 2010) for (B) artificial saliva, and (C) sucrose, (D) quinine. ROI abbreviations are detailed at the bottom of the panel (A). Cerebellar brain ROIs were excluded, *p*-values <0.05 are presented in black normal font style, and *p*-value between 0.05 and 0.1 are presented in gray italic. Activation and decreased activation are separated by a line and are organized in *p*-value decreasing order.

cortex, the somatosensory association cortex, the dorsal posterior cingulate cortex, the insular cortex, and the putamen (Figure 2A). For instance, sucrose and quinine stimulation promote brain activation in the most anterior part of the insular cortex, which is in accordance with taste encoding (Rolls, 2016). However, sucrose and quinine stimulations promoted a reduced number of activated voxels in the brain (sucrose: *n* = 174 activated voxels and quinine: *n* = 222 activated voxels) compared with artificial saliva stimulation (*n* = 399 activated voxels), as illustrated in the insular cortex (Figure 2A). This is illustrated in the sucrose vs. artificial saliva and in the quinine vs. artificial saliva brain maps in which we detected fewer activated voxels with quinine stimulation than with artificial saliva stimulation

(Figure 3A). The ROI-based statistical analysis also showed more statistically activated brain regions with artificial saliva stimulation (*n* = 15 for *p* < 0.05, Figure 2B) than with sucrose (*n* = 2 for *p* < 0.05) or quinine stimulation (*n* = 4 for *p* < 0.05, Figures 2C,D).

## Brain Responses to Sucrose Stimulation

Compared with artificial saliva stimulation, the sucrose stimulation also promoted activations, but in different subparts, in the anterior prefrontal cortex, the dorsal posterior cingulate cortex, a larger activation in the putamen, and an additional activation in the entorhinal cortex and the caudate nucleus (Figure 2A).
<figure><img src="image_29.png" /></figure>

## B. Sucrose vs Artificial Saliva

<table><thead><tr><th>ROI</th><th>L/R</th><th>t-value</th><th>p (uncor.)</th></tr></thead><tbody><tr><td><strong>Sucrose</strong></td><td></td><td></td><td></td></tr><tr><td>&gt; Saliva Central Thalamic Area</td><td>R</td><td>2.52</td><td>0.026</td></tr><tr><td>&gt; Saliva Pulvinar Nuclei</td><td>L</td><td>1.81</td><td>0.065</td></tr><tr><td><strong>Saliva</strong></td><td></td><td></td><td></td></tr><tr><td>&gt; Sucrose LD Thalamic Nucleus</td><td>L</td><td>-6.35</td><td>&lt;0.001</td></tr><tr><td>&gt; Sucrose MD Thalamic Nucleus</td><td>R</td><td>-3.69</td><td>0.007</td></tr><tr><td>&gt; Sucrose Fusiform Gyrus</td><td>L</td><td>-3.32</td><td>0.011</td></tr><tr><td>&gt; Sucrose Hippocampus</td><td>R</td><td>-3.26</td><td>0.011</td></tr><tr><td>&gt; Sucrose Olfactory Bulb</td><td>L</td><td>-3.21</td><td>0.012</td></tr><tr><td>&gt; Sucrose Prepyriform Area</td><td>L</td><td>-2.83</td><td>0.018</td></tr><tr><td>&gt; Sucrose Nucleus Accumbens</td><td>L</td><td>-2.55</td><td>0.026</td></tr><tr><td>&gt; Sucrose Premotor Cortex</td><td>R</td><td>-2.00</td><td>0.051</td></tr><tr><td>&gt; Sucrose A Entorhinal Cortex</td><td>R</td><td>-1.98</td><td>0.052</td></tr><tr><td>&gt; Sucrose DA Cingulate Cortex</td><td>L</td><td>-1.95</td><td>0.054</td></tr><tr><td>&gt; Sucrose Parahippocampal Cortex</td><td>R</td><td>-1.89</td><td>0.059</td></tr><tr><td>&gt; Sucrose NST</td><td>R</td><td>-1.83</td><td>0.082</td></tr><tr><td>&gt; Sucrose Nucleus Accumbens</td><td>R</td><td>-1.52</td><td>0.094</td></tr><tr><td>&gt; Sucrose Substantia Nigra</td><td>R</td><td>-1.49</td><td>0.098</td></tr><tr><td>&gt; Sucrose Globus Pallidus</td><td>R</td><td>-1.48</td><td>0.100</td></tr><tr><td>&gt; Sucrose Prm. Motor Cortex</td><td>R</td><td>-1.46</td><td>0.100</td></tr></tbody></table>

## C. Quinine vs Artificial Saliva

<table><thead><tr><th>ROI</th><th>L/R</th><th>t-value</th><th>p (uncor.)</th></tr></thead><tbody><tr><td><strong>Quinine</strong></td><td></td><td></td><td></td></tr><tr><td>&gt; Saliva LD Thalamic Nucleus</td><td>L</td><td>1.98</td><td>0.051</td></tr><tr><td>&gt; Saliva A Entorhinal Cortex</td><td>L</td><td>1.73</td><td>0.072</td></tr><tr><td>&gt; Saliva Geniculate Nuclei</td><td>L</td><td>1.67</td><td>0.078</td></tr><tr><td>&gt; Saliva Fusiform Gyrus</td><td>L</td><td>1.52</td><td>0.095</td></tr><tr><td><strong>Saliva</strong></td><td></td><td></td><td></td></tr><tr><td>&gt; Quinine Prepyriform Area</td><td>L</td><td>-2.31</td><td>0.035</td></tr><tr><td>&gt; Quinine LD Thalamic Nucleus</td><td>L</td><td>-2.24</td><td>0.038</td></tr><tr><td>&gt; Quinine Hippocampus</td><td>R</td><td>-2.24</td><td>0.038</td></tr><tr><td>&gt; Quinine Claustrum</td><td>R</td><td>-2.13</td><td>0.043</td></tr><tr><td>&gt; Quinine Globus Pallidus</td><td>L</td><td>-1.78</td><td>0.068</td></tr><tr><td>&gt; Quinine Olfactory Bulb</td><td>L</td><td>-1.77</td><td>0.069</td></tr><tr><td>&gt; Quinine Parahippocampal Cortex</td><td>R</td><td>-1.64</td><td>0.081</td></tr><tr><td>&gt; Quinine Claustrum</td><td>L</td><td>-1.59</td><td>0.086</td></tr><tr><td>&gt; Quinine insular Cortex</td><td>L</td><td>-1.58</td><td>0.088</td></tr><tr><td>&gt; Quinine NST</td><td>L</td><td>-1.54</td><td>0.092</td></tr><tr><td>&gt; Quinine NST</td><td>R</td><td>-1.51</td><td>0.096</td></tr><tr><td>&gt; Quinine Premotor Cortex</td><td>R</td><td>-1.50</td><td>0.097</td></tr></tbody></table>

## D. Quinine vs Sucrose

<table><thead><tr><th>ROI</th><th>L/R</th><th>t-value</th><th>p (uncor.)</th></tr></thead><tbody><tr><td><strong>Quinine</strong></td><td></td><td></td><td></td></tr><tr><td>&gt; Sucrose Fusiform Gyrus</td><td>L</td><td>6.16</td><td>&lt;0.001</td></tr><tr><td>&gt; Sucrose Caudate Nucleus</td><td>R</td><td>3.63</td><td>0.008</td></tr><tr><td>&gt; Sucrose Geniculate Nuclei</td><td>R</td><td>3.03</td><td>0.015</td></tr><tr><td>&gt; Sucrose Nucleus Accumbens</td><td>L</td><td>2.64</td><td>0.023</td></tr><tr><td>&gt; Sucrose A Entorhinal Cortex</td><td>L</td><td>2.20</td><td>0.040</td></tr><tr><td>&gt; Sucrose DA Cingulate Cortex</td><td>L</td><td>1.99</td><td>0.051</td></tr><tr><td>&gt; Sucrose Orbitofrontal Cortex</td><td>L</td><td>1.83</td><td>0.064</td></tr><tr><td>&gt; Sucrose Substantia Nigra</td><td>R</td><td>1.77</td><td>0.068</td></tr><tr><td>&gt; Sucrose LD Thalamic Nucleus</td><td>R</td><td>1.73</td><td>0.072</td></tr><tr><td>&gt; Sucrose Olfactory Bulb</td><td>L</td><td>1.48</td><td>0.100</td></tr><tr><td><strong>Sucrose</strong></td><td></td><td></td><td></td></tr><tr><td>&gt; Quinine Globus Pallidus</td><td>L</td><td>-3.76</td><td>0.007</td></tr><tr><td>&gt; Quinine Claustrum</td><td>R</td><td>-2.15</td><td>0.042</td></tr></tbody></table>

**FIGURE 3** | (A) Horizontal maps of global brain responses between quinine and artificial saliva, as well as sucrose and artificial saliva stimulations. *p*-Value threshold = 0.05; DV, dorsal-ventral position in mm related to the posterior commissure. ROI-based statistical analysis with ROIs from Saikali atlas (Saikali et al., 2010) for (B) sucrose vs. artificial saliva, (C) quinine vs. artificial saliva, and (D) quinine vs. sucrose (uncorrected *p*-values). ROI abbreviations are detailed at the bottom of the panel (A). Cerebellar brain ROIs were excluded, *p*-values <0.05 are presented in black normal font style, and *p*-value between 0.05 and 0.1 are presented in gray italic.

The ROI-based statistical analysis validated in part the global brain changes and showed an activation of the primary motor cortex and the putamen, a tendency toward activation of five other brain structures (Figure 2C).

The sucrose vs. artificial saliva brain map corroborated these observations (Figure 3A). Compared to artificial saliva, sucrose

promoted a decreased activation in the prepyriform area, the dorsal anterior cingulate cortex, the anterior and the ventral part of the anterior prefrontal cortex, the anterior part of the dorsal lateral prefrontal cortex, the fusiform gyrus, the ventral part of the caudate nucleus, the nucleus accumbens, the amygdala, and both higher activation and decreased activation in the insular