The zebrafish subcortical social brain as a model for ... · The zebrafish subcortical social brain as a model for studying social behavior disorders Yijie Geng and Randall T. Peterson*

REVIEW

The zebrafish subcortical social brain as a model for studyingsocial behavior disordersYijie Geng and Randall T. Peterson*

ABSTRACTSocial behaviors are essential for the survival and reproduction ofsocial species. Many, if not most, neuropsychiatric disorders inhumans are either associated with underlying social deficits or areaccompanied by social dysfunctions. Traditionally, rodent modelshave been used to model these behavioral impairments. However,rodent assays are often difficult to scale up and adapt to high-throughput formats, which severely limits their use for systems-levelscience. In recent years, an increasing number of studies have usedzebrafish (Danio rerio) as a model system to study social behavior.These studies have demonstrated clear potential in overcoming someof the limitations of rodent models. In this Review, we explore theevolutionary conservation of a subcortical social brain betweenteleosts and mammals as the biological basis for using zebrafish tomodel human social behavior disorders, while summarizing relevantexperimental tools and assays. We then discuss the recent advancesgleaned from zebrafish social behavior assays, the applications ofthese assays to studying related disorders, and the opportunities andchallenges that lie ahead.

KEY WORDS: Autism, Phylogenetic conservation, Model organism,Social deficit, Neuropsychiatric disorders, Behavioral assay

IntroductionSocial behavior – defined as beneficial interaction betweenindividuals in the same species – is essential for the survival andreproduction of social species, including humans and many othervertebrates. Social behavior involves specific behaviors such asconspecific preference, social communication, aggression andmating. Many, if not most, neuropsychiatric disorders are relatedto underlying social defects or are accompanied by socialdysfunctions. These include autism, which is associated withdeficits in processing social cues, andWilliam’s syndrome, which ischaracterized by an abnormally high enthusiasm for interacting withstrangers. Other disorders that are not primarily social (e.g.schizophrenia and depression) may still interfere with normalsocial functioning. Therefore, developing and studying animalmodels with social deficits has far-reaching implications for manyneuropsychiatric diseases, and studying these behavioral aspectsrequires developing specific behavioral assays.Rodents are traditionally used to model disorders associated with

social deficits. As highly social species, rodents possess many

complex social behavior traits that mimic human behaviors.Additionally, researchers have established sophisticated protocolsfor studying these behaviors in rodents (Hanell and Marklund,2014). These benefits make the rodents the current ‘go to’ modelsfor studying disorders associated with social deficits. However,rodent models are not without drawbacks. They are expensive andlabor intensive. They are predominantly nocturnal and highlysensitive to environmental disturbances such as light, sound,temperature changes and odors. Furthermore, they have not beenvery amenable to scalable or high-throughput assays. Thesedrawbacks pose a limit to the broader application of these modelsin disease research.

In recent years, the zebrafish has rapidly become an attractivemodel for studying behavioral disorders. Adult colonies can beefficiently maintained at high density. Zebrafish give birth to largeclutch sizes (>100 eggs per female for each round of breeding) andprovide ample offspring for experimental manipulations. Theembryos are small (∼0.7 mm in diameter) and develop ex uterowith no special supplementations needed except for water duringthe first week of development, enabling easily scalable embryonicexperimental perturbations. The transparent nature of zebrafishembryos during early development also facilitates imaging andanalysis of developmental events. Recent advances in genome-editing technologies such as CRISPR can be applied to zebrafishembryos (Hwang et al., 2013). Unlike rodents, zebrafish are diurnaland can perform behavioral tasks under a normal light setting.Because they remain submerged in water during behavioral tests,zebrafish are not easily affected by minor environmentalinterferences such as weak sounds and smells.

In this Review, we first discuss the neuroanatomical andneurophysiological evidence supporting the use of zebrafish tomodel human social behavior disorders (see Box 1; Figs 1, 2). Wethen describe the established experimental methods for studyingsocial behavior deficits and examples of using these assays to modelrelated human disorders. Finally, we explore relevant emergingtechnological advances and the opportunities and challenges that lieahead in applying these technologies to social disorder modelingusing zebrafish.

Zebrafish assays for studying social behavior and deficitsThere are typically two approaches to modeling a behavioraldisorder: by using an endophenotype (see Box 2 for a glossary ofterms) assay or a behavioral assay. In this Review, we focusprimarily on the behavioral approach.

As larval zebrafish develop into adults, their behavior becomesincreasingly complex. This is particularly pronounced for sociallyrelevant behaviors. In this section, we briefly review severalstereotypical social behaviors in zebrafish and discuss recentadvances in developing assay platforms for studying thesebehaviors. We begin by describing assay setups for specificaspects of social behaviors using traditional and new experimental

Department of Pharmacology and Toxicology, College of Pharmacy, University ofUtah, 30 S. 2000 East, Salt Lake City, UT 84112, USA.

*Author for correspondence ([email protected])

R.T.P., 0000-0002-4582-4690

This is an Open Access article distributed under the terms of the Creative Commons AttributionLicense (https://creativecommons.org/licenses/by/4.0), which permits unrestricted use,distribution and reproduction in any medium provided that the original work is properly attributed.

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© 2019. Published by The Company of Biologists Ltd | Disease Models & Mechanisms (2019) 12, dmm039446. doi:10.1242/dmm.039446

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mailto:[email protected]

http://orcid.org/0000-0002-4582-4690

methods. We then discuss emerging technologies for socialbehavior analysis that will help improve the robustness,consistency and resolution of the current assay methods,including computer vision, machine learning, computationalmodeling, robotics, and virtual reality (VR) technologies.

Social preference assaySocial preference behavior, or the innate tendency of an animal toobserve, mimic and approach a conspecific, is well conservedamong social vertebrate species. Often emerging early duringontogeny (Fantz, 1963), this simple and perhaps primitive form ofsocial behavior forms a necessary foundation for the later, higher-order social functions such as shoaling, schooling and othercomplex social interactions. This behavior is routinely tested inrodents using a three-chamber social preference assay.To study this behavior during development, it is desirable to

design experimental systems that can pinpoint its earliestemergence. Hinz and de Polavieja (2017) discovered thatzebrafish larvae start to show attraction toward a conspecific asearly as 6-7 days post-fertilization (dpf). Although weak at thisstage, the attraction quickly gets stronger each day duringdevelopment. An important note for the experimental setup is thatearly larval zebrafish are also attracted to borders such as the wall ofa Petri dish. Thus, to detect weak social attractions at the early

developmental stage, a testing chamber with a deep center andgradually shallower edge may be used to counter this ‘borderattraction’ by deterring the larvae from the border with shallowwater.

Dreosti et al. adopted a design not unlike the three-chambersocial preference assay for rodents and adult zebrafish (Dreostiet al., 2015): transparent windows divided a U-shaped test arenainto three compartments, including a middle test compartmentand two stimulus compartments (Fig. 3A). A test subject is placedinside the test compartment, and age-matched social stimulusfish are placed inside one of the two stimulus compartments, whilethe third compartment remains empty and thus stimulus free.The social preference of a test subject is quantified as the time itspends near the social stimulus fish. Two-week-old larvaeexhibited a weak social preference, whereas, by 3 weeks, thispreference behavior became highly robust (Fig. 3B). This systemsetup is simple to implement and does not require simultaneoustracking of more than one animal, but limits the test subject’sinput to visual cues, and prevents physical interactions betweenthe fish.

Social preference behavior of adult fish is typically tested inlarger three-compartmented tanks. Zebrafish of the same ordifferent (Engeszer et al., 2004) strains, animated images of fish(Gerlai, 2017), 3D-printed fish models (Bartolini et al., 2016) or

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Fig. 1. Previous theoretical models of the social brain. Brainstructures that constitute previous models of social brainnetworks, illustrated in a mammalian brain from a lateral view.(A) The social behavior network (SBN) (Newman, 1999). (B)The social decision-making (SDM) network (O’Connell andHofmann, 2011b), composed of the SBN and the mesolimbicreward system (MRS). AH, anterior hypothalamus; BLA,basolateral amygdala; BNSTm, medial bed nucleus of the striaterminalis; HIP, hippocampus; LS, lateral septum; MPOA,medial preoptic area; NAc, nucleus accumbens; PAG,periaqueductal gray; STR, striatum; VMH, ventromedialhypothalamus; VP, ventral pallidum; VTA, ventral tegmentalarea.

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robotically controlled biomimetic zebrafish (Kopman et al., 2013;Ruberto et al., 2016, 2017) can be used as social stimuli.Researchers can place different stimuli in the two testcompartments to assess preference. For example, wild-type (WT)fish typically prefer a WT conspecific over a nacre (Box 2) fish(Braida et al., 2012). Interestingly, this tendency is reversed by

oxytocin, vasopressin, and amphetamine derivatives (Braida et al.,2012; Busnelli et al., 2016; Ponzoni et al., 2016). A WT test fishremembers a familiar fish from this assay for at least 24 h, andprefers to interact with an unfamiliar fish over a familiar one(Madeira and Oliveira, 2017). A two-compartment design has alsobeen implemented for testing adult (Liu et al., 2018a) and juvenile(Patowary et al., 2019) fish, with social preference assessed by theproximity of a test subject to the social stimulus fish.

As will be demonstrated in examples in later sections, a three- ortwo-chamber social preference assay is frequently used to assesssocial preference of individuals subjected to different experimentaltreatments. A major benefit of this approach may be that testsubjects are examined individually, as opposed to in groups as willbe described in shoaling and schooling assays, such that the degreeof social preference for each test subject can be easily quantified.A three-chamber assay setup also enables one to assess preferencebetween two types of stimuli, such as two different fishstrains. However, restrictions in sensory inputs and physicalinteractions limit the assay’s ability to evaluate more complexmodes of social behaviors.

ShoalingGroups of zebrafish naturally form compact aggregations, abehavior called shoaling, which emerges as early as 15 dpf (Hinzand de Polavieja, 2017). Benefits of shoaling may include betterdetection of and defense against predators, enhanced foraging andincreased mating choices (Krause and Ruxton, 2002). While moststudies choose the number of fish to be tested in a shoaling assayarbitrarily, a number that balances between minimizing animalusage and reducing variability may be estimated using a methodbased on Shannon entropy (Box 2) (Eguiraun et al., 2018).

Traditionally, shoaling is examined by two-dimensional (2D)video recording and analysis. A number of freely availablecomputer vision programs and commercial software have beendeveloped for detecting this behavior in grouped zebrafish(reviewed in Franco-Restrepo et al., 2019). Methods forquantifying shoaling behavior in a 2D recording typically don’trequire the fish to be individually distinguishable. Measurements ofinter-individual distance and nearest-neighbor distance arecommonly used to quantify the tightness of a shoal (Miller andGerlai, 2007). Alternatively, analyzing a shoal by Delaunaytriangulation (Box 2) can provide a unique measurement for therelative positions of each fish in a shoal configuration, as well as itsoverall tightness (Xiao et al., 2015). The trajectory of a shoal can beobtained by tracking its centroid (Eguiraun et al., 2018).

Because fish behave in a three-dimensional (3D) space, 3Drecording and analysis systems are being developed using mirrors(Maaswinkel et al., 2013b; Audira et al., 2018b), multiple cameras(Macrì et al., 2017; Qian and Chen, 2017; Bishop et al., 2016; Al-Jubouri et al., 2017;Wang et al., 2017b; Butail and Paley, 2012) or asingle camera with depth-sensing capability (Kuroda, 2018) toassess shoaling more accurately. The complex 3D trajectories of fishhave been modeled using a long short-term memory (Box 2)network (Wang et al., 2017b).

WT zebrafish form tight shoals. As will be discussed in latersections, experimental perturbations can lead to changes in shoalcohesion. A reduction in shoal cohesion is often interpreted asdecreased social interactions among members of the group.However, simple measurements of aggregation cannot fully revealthe complex, interactive and inter-dependent forces betweenindividuals (Katz et al., 2011) or the collective dynamics of agroup (Rosenthal et al., 2015).

Box 1. The ‘subcortical social brain’ and its evolutionaryconservation between zebrafish and mammals

Complex higher-order human social behaviors, such as face recognition,social cognition, perception of social signals, social judgement, socialdecision making and theory of mind, rely substantially on cortical input(Adolphs, 2003). The cerebral cortex is widely considered to be themajorcontroller of these higher-order social behaviors (Adolphs, 2009;Blakemore, 2008, 2012; Frith, 2007). Human studies have identifiedspecific cortical brain regions, such as the medial prefrontal cortex(mPFC) and the posterior superior temporal sulcus (pSTS), thatcontribute to these functions (Adolphs, 2009; Frith, 2007; Blakemore,2008, 2012). This focus on cortical inputs sometimes overlooks thecritical functions that subcortical brain regions play in regulating socialbehavior. In fact, a complex network of subcortical brain regionsassociated with social behavior exists and is highly conserved amongall vertebrates (Newman, 1999; O’Connell and Hofmann, 2011b). Here,we conceptualize a ‘subcortical social brain’ (SSB) based on theoreticalframeworks and recent experimental findings.

Originally suggested for mammals, Newman (1999) proposed a core‘social behavior network’ (SBN) based on evidence from neuroendocrineand behavior studies. The SBN consists of several brain regions as‘nodes’, including the medial amygdala, medial bed nucleus of striaterminalis, lateral septum, preoptic area, anterior hypothalamus,ventromedial hypothalamus and the midbrain periaqueductal gray/central gray (Fig. 1A). In this model, each node responds to a varietyof social stimuli, and all nodes collaboratively respond with a distinctpattern to modulate different behavioral outputs.

O’Connell and Hofmann (2011b) pointed out the importance of themesolimbic reward system (MRS), consisting of the ventral tegmentalarea, nucleus accumbens, basolateral amygdala, striatum, ventralpallidum, hippocampus and several regions overlapping with the SBN,in social behavior. They further argued that the SBN and MRScollectively constitute a larger social decision-making (SDM) network(O’Connell and Hofmann, 2012, 2011b) (Fig. 1B). Finally, theydemonstrated that this network is largely conserved between zebrafishand mammals (O’Connell and Hofmann, 2011a, 2012). Functionalanalysis of immediate early genes after social interaction supports thishypothesized network (Teles et al., 2015).

Recent studies have linked additional subcortical brain regions to socialbehavior, and homologous structures for these socially relevant brainregions, such as the dorsal raphe (Dölen et al., 2013), lateral habenula(Golden et al., 2016) and cerebellum (Carta et al., 2019), are also presentin zebrafish (Yokogawa et al., 2012; Amo et al., 2010; Heap et al., 2013).The anatomical and functional conservation of these SSB componentsare summarized in Table 1 and Fig. 2.

Although many cortical regions in the mammalian brain are also relevantto social behavior by serving executive functions during socialinteractions and have been discussed elsewhere (Adolphs, 2009;Blakemore, 2008, 2012; Frith, 2007), we chose to focus this Review onthe SSB for its relevance to zebrafish social behavior. We argue that itsstrong evolutionary conservation suggests a critical role in supporting thesurvival and reproduction of not only fish, but also other vertebratespecies, including humans. Knowledge acquired from investigating theSSB may therefore provide valuable insights into human social behaviordisorders (Lord et al., 2000).

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SchoolingIn addition to shoaling, a group of zebrafish can ‘school’. Whileshoals are simple aggregations of individual fish, schools are shoalsthat exhibit polarized formations and synchronized motions.Density and group size affect shoal cohesion, but not polarization(Shelton et al., 2015). Acute treatment with alcohol strongly affectsshoal polarization but only modestly inhibits cohesion, whereasnicotine significantly reduces cohesion but modestly affectspolarization (Miller et al., 2013). These differences indicate thatschooling and shoaling are two differentially regulated behaviorsand that assessing both behavioral endpoints together may moreeffectively characterize the effects of experimental treatments. Tanget al. (2018) developed an unsupervised machine learning approachto examine schooling of adult zebrafish. Using this assay, theauthors classified group behavior into distinct stereotypical states ofpolarization, and found that genetic mutations (see later sections fordetails) can alter the proportion of time spent or the tendency totransition between these states. While this approach provides aninnovative way to quantitatively evaluate the propensities of a groupto adopt stereotypical states of schooling, it is limited to detectingstatic patterns of group formation as a whole and cannot revealdynamic interactions among group members.

AggressionAdult male zebrafish fight to establish dominance and hierarchy,and to compete for important resources such as food and mates(Huntingford and Turner, 1987). A simple way to assay aggressivebehavior is by introducing a target for the test subject to attack.

A mirror is often used to allow the test subject to attack its ownreflection (Zabegalov et al., 2019). Alternatively, a dummy fish or avideo recording of another fish can trigger aggression (Way et al.,2015). The number of times a test subject exhibits aggressivebehavior, such as biting and charging, is counted to quantify itslevel of aggressiveness. Although this assay provides a simplemeans to quantify aggression, the lack of physical contact betweenaggressors and targets limits its ability to mimic natural fightingbehaviors. Interestingly, live fish have not been used as targets inthis assay setup. Instead, when two fish interact through atransparent window, their behaviors were typically interpreted associal interaction (such as in a two-compartment social preferenceassay) rather than aggression.

Dyadic fighting assays examine aggression in a more naturalsetting. Although fighting behaviors are highly complex,stereotypical bouts can be repeatedly observed throughout a fight(Teles and Oliveira, 2016b; Zabegalov et al., 2019). Traditionally, ahuman observer monitors the process, manually annotates thesebehavioral bouts and keeps track of the outcomes of a fight (Chouet al., 2016). Alternatively, a recently developed analysis pipelineautomatically annotates stereotypical fighting behavior with sub-second precision (Laan et al., 2018) (Fig. 3C), demonstrating greatpromise in applying unsupervised machine learning methods tostudying complex natural behaviors.

The social hierarchy of a group can be assessed from dyadicfighting outcomes. Changes in social status have been associatedwith an individual’s altered motor activity (Clements et al., 2018),reproductive success (Paull et al., 2010), and other physiological

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Fig. 2. The subcortical social brain (SSB) inzebrafish and mouse. (A) The teleost SSBillustrated from a lateral view. (B) Themammalian(rodent) SSB illustrated from a lateral view. Areaswith the same color mark regions that arehomologous between teleosts and mammals.For regions with different nomenclaturesbetween teleosts and mammals, thecorresponding mammalian nomenclatures areappended after the teleost nomenclature inparentheses. AH, anterior hypothalamus; ATN,anterior tuberal nucleus; BLA, basolateralamygdala; BNSTm, medial bed nucleus of thestria terminalis; CB, cerebellum; Dl, lateral dorsaltelencephalon; Dm, medial dorsaltelencephalon; DR, dorsal raphe; HIP,hippocampus; LHb, lateral habenula; LS, lateralseptum; MeA, medial amygdala; MPOA, medialpreoptic area; NAc, nucleus accumbens; PAG,periaqueductal gray; POA, preoptic area; PT,posterior tuberculum; STR, striatum; Vc, centralventral telencephalon; Vd, dorsal ventraltelencephalon; VHb, ventral habenula; VMH,ventromedial hypothalamus; VP, ventralpallidum; Vs, supracommissural nucleus of theventral telencephalon; VTA, ventral tegmentalarea; VTN, ventral tuberal nucleus; Vv, ventralnucleus of the ventral telencephalon.

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and health consequences (Filby et al., 2010a). Social animals adjusttheir behavior based on their status within a group, a phenomenoncalled social plasticity, which is also studied using aggression assays

(Teles et al., 2016a; Maruska et al., 2019; Guayasamin et al., 2017;Sykes et al., 2018).

An animal can observe interactions between other individuals anduse this information to adjust its own future behavior, a phenomenonnamed ‘social eavesdropping’. A common assay uses a two-chambertest arena divided by a one-way transparent window. An observer fishis placed in the chamber on the see-through side of the window,allowing it to observe the outcome of a fight in the opposing chamberwithout interacting with these fish (Abril-de-Abreu et al., 2015a). Avideo recording of a fight can also be used to train the observer fish(Abril-de-Abreu et al., 2015b). The time the observer fish spends inthe vicinity of the observation window quantifies its attention. Theobserver fish remembers the participants and the outcome of the fightand adjusts its future dominant or submissive behaviors toward theseindividuals accordingly. Attentiveness toward a fight activates geneslinked to neuronal plasticity, memory formation and alertness (Lopeset al., 2015).

As an assay with clearly separated binary outcomes, theaggression assay has been used to examine transcriptional (Malkiet al., 2016; Oliveira et al., 2016) and neurophysiological (Teles andOliveira, 2016a; Pavlidis et al., 2011; Filby et al., 2010b) outcomesof winning or losing a fight. Such effects were also examined in fishwith different social statuses (Sneddon et al., 2011; Larson et al.,2006; Teles et al., 2016b).

MatingHighly stereotypical mating behaviors have been described forzebrafish (Darrow and Harris, 2004), although automated computervision methods remain to be developed for mating behavior analysis.Several reports have created VR mimetics of the fish sailfin molly(Poecilia latipinna) and have used these animated animals to study themating choices of live fish (Gierszewski et al., 2018, 2017; Mülleret al., 2017a), an approach that may be transferrable to zebrafish.Zebrafish imprint visual and olfactory cues at 6 dpf, and use thisphenotypic template to match and avoid mating with its own kin asadults (Gerlach et al., 2008; Hinz et al., 2013). Interestingly, exposureto non-kin cues does not result in successful imprinting (Gerlach et al.,2008), suggesting that additional mechanisms exist that regulate kinrecognition (Biechl et al., 2016; Gerlach et al., 2019).

Social learningZebrafish can learn from their peers. Social learning may helpindividuals in a group acquire public knowledge on resources suchas food and threats such as predators without each individual payingthe costs for learning. Naïve fish can learn an escape route (Lindeyerand Reader, 2010) or to find food (Zala and Määttänen, 2013) froma knowledgeable demonstrator fish. A separate study, however,reported that observer fish were unable to learn how to find food in amaze from demonstrators (Roy and Bhat, 2017). Wild-caughtzebrafish are typically more timid than their domesticatedcounterparts. Wild zebrafish become bolder when exposed todomestic fish, without changing the level of boldness of thedomestic fish, and this change in behavior persists after removal ofthe domesticated fish (Zala et al., 2012).

Tracking individuals in a group using computer visionTo examine group dynamics during behavioral assays in moredetail, researchers have explored methods to track individual fish ina group. This is a difficult task, as zebrafish swim in a 3D space, andindividuals in a group will unavoidably cross over each other ina camera’s view. Conventional solutions focus on derivingalgorithms for predicting the trajectories of each fish. Before each

Box 2. Glossary

Alarm substance: also known as Schreckstoff (startle/shocksubstance); a chemical alarm signal released by injured fish thatinduces fear in conspecifics. A method for extracting zebrafish alarmsubstance can be found in the report by Schirmer et al. (2013).

Bayesian decision theory: a statistical method that calculates thetradeoff between various decisions using Bayesian estimation.

Chemogenetics: the modification of biological macromolecules, suchas proteins, to interact with previously unrecognized ligands. EngineeredG protein-coupled receptors (GPCRs), such as designer receptorexclusively activated by designer drugs (DREADDs) (Armbruster et al.,2007), can be activated by an otherwise inert ligand to modulate theactivity of genetically modified neurons.

Delaunay triangulation: for a given set of discrete points in a plane, aDelaunay triangulation generates a network of triangles between thesepoints,which ensures that nopoint is inside the circumcircle of any triangle.

Endophenotype: an experimentally measurable trait that geneticallysegregates with an illness. Frequently used in psychiatric diseaseresearch to connect higher-order complex behavioral symptoms togenetics.

Fetal alcohol spectrum disorders: a group of conditions, includingphysical and behavioral problems, that can occur in a person prenatallyexposed to alcohol.

Fetal valproate syndrome: a condition that can occur in a person byprenatal exposure to the anti-seizure medication valproic acid (sodiumvalproate).

Immediate early gene: a gene that is activated rapidly but oftentransiently at the transcription level in response to certain stimuli.

Long short-term memory: abbreviated as LSTM; a recurrent neuralnetwork architecture used in deep learning. It is particularly suitable foranalyzing and making predictions for time-series data containing lags ofunknown duration between important events.

Morphant: in zebrafish, gene expression levels can be knocked down atthe early embryonic stage through embryonic injection of morpholinoantisense oligonucleotides. A zebrafish treated with a morpholino totemporarily inhibit expression of a targeted gene is called a morphant.

nacre: a zebrafish genetic mutation (Lister et al., 1999). Homozygousnacremutants lackmelanophores, a type of pigment cell, and are thereforepartially transparent and visually different from wild-type zebrafish.

Optogenetics: an experimental method that uses light to controlactivation and/or inhibition of genetically modified neurons expressinglight-responsive ion channels.

Shannon entropy: also known as ‘information entropy’; provides ameasurement of the predictability of the value of a variable. In Eguiraunet al. (2018), Shannon entropy was used to measure the predictability ofthe trajectory of a shoal’s centroid.

Transfer entropy: a measurement of the amount of directed transfer ofinformation between two random processes. It provides a quantification ofthe cause-and-effect relationships between (possibly) coupled time series.

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crossover, the program calculates the most likely trajectory of eachfish, so that, immediately after the two fish are separated,the algorithm assigns identities to each fish based on how welltheir new trajectories match the predictions. This method frequentlyintroduces errors and unavoidably fails after long periods oftracking.To solve this problem, the de Polavieja lab developed idTracker,

which identifies and tracks individuals using a distinct digitalfingerprint generated for each fish (Pérez-Escudero et al., 2014)(Fig. 4A). Crossover events still interfere with tracking, but only

temporarily, as identities are reassigned after each crossover basedon the fingerprint. Thismethod enables researchers to acquire insightsto previously difficult-to-observe behaviors in a group, such asterritorial behavior. Their recently updated method, idTracker.ai,simultaneously tracks 100 individuals using deep learning with animpressive identification accuracy of greater than 99.9% (Romero-Ferrero et al., 2019). Building on the idTracker approach, severalrecent attempts have achieved multi-individual identification andtracking, with varying degrees of success (Bai et al., 2018; Qianand Chen, 2017; Qian et al., 2016, 2014; Wang et al., 2016a).

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Fig. 3. Examples of zebrafish social behavior assays. (A) The three-chamber social preference assay. Adapted fromDreosti et al. (2015), where it was publishedunder a CC-BY licence. A test subject (TS) is placed inside a U-shaped chamber. Social stimulus (SS) fish are placed inside one of the chambers at the end of theU-shaped arena, separated from the test subject’s chamber bya transparent window. The other end of theU-shaped arena is left emptyas a control stimulus. (B) Three-week-old zebrafish develop a robust social preference. A test subject visits the two compartments randomly if both compartments are empty; if social stimulus fish areintroduced into one compartment, the test subject is attracted to and interacts intensively with the social stimulus fish. Adapted fromDreosti et al. (2015), where it waspublished under a CC-BY licence. Red, movements in the social interaction zone; black, movements in the middle zone; blue, movements in the control zone.(C) Analysis of fighting behavior usingmachine learning. Adapted fromLaanet al. (2018), where itwas published underaCC-BY licence. Imagesare acquired (1) andtwoanimals in the test arenaare tracked individually (2). Fractions of the trackingcoordinates aremanuallyannotated for fightingbehavior (3). This is thenused to traina neural network (4), which automatically detects attacks by generating an attack score for each fish (5). An ethogram is generated based on the attack score (6).

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Computational modeling of collective behaviorsComputational modeling has been applied to studying groupbehaviors of zebrafish and has generated valuable insights intogroup dynamics of fish and other species such as humans (Madirolasand de Polavieja, 2015). Previous methods largely ignored individualbehaviors and focused primarily on examining static features ofcollective behavior at each time point, such as group cohesioncalculated based on the distribution of individuals’ positions, andpolarization, which is assessed by individuals’ orientations. Thislimitation may be attributed to limited understanding of stereotypicmotions and difficulties in continuous tracking of individuals.Although ignoring individual identities allows each fish to betreated as a particle and aids the application of machine learningmethods to characterize group behavior as a whole (Butail et al.,2013b), it inevitably limits further investigations on how individualsmake behavioral decisions in a group.One approach to overcome these limitations is to examine the

behaviors of isolated individuals given different social cues, such asby using a three-chamber social preference setup (Fig. 3A)(Arganda et al., 2012; Porfiri and Ruiz Marín, 2017). Researchershave modeled individual behavioral rules in response to the motionof a social stimulus fish using theoretical frameworks based on the

Bayesian decision theory (Box 2) (Arganda et al., 2012) and transferentropy (Box 2) (Porfiri and Ruiz Marín, 2017). Other studies firstimproved continuous tracking of individuals and thencomputationally modeled pairwise interactions using the optimalcontrol theory (Laan et al., 2017), deep attention networks (Heraset al., 2018), transfer entropy (Butail et al., 2016) and other data-driven methods (Zienkiewicz et al., 2018) to reveal how pairs ofindividuals attract, repulse and align with each other.

Recent studies have applied computational and machine learningmethods tomodel individual stereotypicalmotions ofCaenorhabditiselegans (Stephens et al., 2008; Brown et al., 2013), fruit flies (Bermanet al., 2014), mice (Wiltschko et al., 2015) and zebrafish (Marqueset al., 2018; Mwaffo et al., 2017; Zienkiewicz et al., 2015).Combining individual behavioral modeling with continuousindividual tracking provides an opportunity to investigate groupdynamics and individual decision-making with higher resolution. Inone study, individual motions alternated between acceleration anddeceleration bouts. The kinetics of these bouts could be described bysigmoid and exponential functions, respectively. Individual zebrafishmotions were found to alternate between a ‘passive’ behavioral mode,in which behaviors of an individual are unaffected by other groupmembers, and an ‘active’ mode, in which an individual’s behavior

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Intensity map: a 2D histogram showing how many pairs of pixels are at a given distance and

have a given sum of intensities

i1

i1+i2

Fig. 4. Examples of emerging technologies for social behavior assays and analysis. (A) idTracker for tracking individuals in a group. The raw images ofeach fish were first segmented to identify the body of each fish. Two pixels with intensities i1 and i2 are highlighted, which are separated by a distance, d. An intensitymap is generated for each fish to show how many pairs of pixels are at a certain distance (d) and have a certain sum of intensities (i1+i2). This intensity map isused to identify each individual fish. Adaptedwith permission fromPerez-Escudero et al. (2014). This image is not published under the terms of the CC-BY licence ofthis article. For permission to reuse, please see Perez-Escudero et al. (2014). (B) Avirtual reality (VR) fish that mimics a 23-dpf real fish. Reproduced with permissionfrom Stowers et al. (2017). This image is not published under the terms of the CC-BY licence of this article. For permission to reuse, please see Stowers et al.(2017). (C) A self-propelled robotic fish. Reproduced with permission from Butail et al. (2013a), where it was published under a CC-BY licence.

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adjusts to social input from the group. This framework predictedbehaviors of individuals with high precision (Harpaz et al., 2017).

Virtual realityCollective behaviors such as shoaling are mutual: each individual isdriven by social cues emitted by its shoal mates and at the same timeemits social signals that influence its shoal mates. Owing to theinteractive nature of this closed-loop feedback system, it is difficult todisentangle a social input from the outputs it triggered. VR systemshave the advantage of providing socially relevant inputs in a controlledand isolated manner. A projected virtual object moving in a way thatmimics the characteristic kinetics of zebrafish swim bouts wassufficient to trigger shoaling of juvenile fish. Other previouslyimplicated social cues, such as a fish-like shape or pigmentationpattern, were not required to trigger this behavior (Larsch and Baier,2018). In another example, a virtual zebrafish was created to mimic a23-dpf fish (Stowers et al., 2017) (Fig. 4B). In this interactive VRsystem, the movement trajectory of the virtual zebrafish wasprogrammed to be influenced to varying degrees by the trajectory ofa real fish. When set to be strongly influenced by the real fish, thevirtual fish spent most of the time following the real fish and thusminimally affected the real fish’s typical trajectory.Gradually reducingthe level of social feedback resulted in the virtual fish exerting astronger influence on the trajectory of the real fish, and therefore itseemed to ‘lead’ the real fish. Continued reduction in the degree ofsocial feedback, however, eventually decreased the influence of thevirtual fish on the real fish and led to its failure in leading.

‘Robot zebrafish’Robotically controlled biomimetics have been developed to mimicanimal behaviors and socially interact with animals such as fruitflies (Zabala et al., 2012) and cockroaches (Halloy et al., 2007),providing valuable insights into the social behaviors of thesespecies. Similarly, a number of research groups have developedrobotic fish (Cazenille et al., 2018). These systems are oftencomposed of two parts: a biomimetic fish dummy and a roboticcontrol system. The fish dummies can be directly fixed to a robotarm, indirectly linked to andmoved by a robotic mechanism throughmagnetic coupling, or self-propelled (Butail et al., 2013a) (Fig. 4C),enabling them to ‘swim’ under water. Zebrafish respond to arobotically controlled dummy in a three-compartment socialpreference assay (Kopman et al., 2013; Ruberto et al., 2016,2017). Compared to VR, a major advantage of robotic systems istheir ability to provide physical contact between a biomimetic andan animal. Therefore, other groups have developed robot zebrafishthat can come into physical contact with real zebrafish shoals(Cazenille et al., 2018; Polverino et al., 2012). However, the keyfeatures that allow a biomimetic fish to be socially integrated into agroup of fish are still being debated. While many efforts focused onidentifying socially attractive morphologies such as shape, size andpigmentation patterns for the dummy, other studies argued that arobot’s behavior, such as its trajectories and movement kinetics,exert a greater influence on its ability to socially integrate into ashoal (Cazenille et al., 2018).

Popular assays and their variationsAmong the assays discussed above, the social preference, shoalingand aggression assays may be the most frequently used. Possiblereasons for their popularity may be that they are relatively easy to setup, have intuitive relevance to social behaviors in humans, and havesimple and quantifiable readouts. These assays are commonly usedto assess changes in social behavior induced by disease-relevant

treatments, as discussed further in the next section. Many variationsexist for each assay, including but not limited to dimensions of thetest platforms, numbers of animals used, types of stimulus(particularly for social preference and aggression assays), andquantification criteria and methods. This poses a potential challengefor the field, as the diversity of assays complicates interpretation ofresults and comparison between studies. It is worth noting thatsimilar diversity is also widely present in rodent assays, which arecurrently still considered the gold standard for measuring socialbehavior. Nevertheless, efforts should be made to standardizecurrent behavioral assay formats in zebrafish. Carefully designedexperiments should be performed to evaluate these variations andprovide recommendations for the optimal formats of each assay.

Disease-relevant social-deficit models in zebrafishIn this section, we discuss recent advances in using zebrafish tomodel human social-behavior-related disorders. Although non-behavioral endpoints exist, including anatomical changes andendophenotypes, behavioral assays most directly demonstrate therelevance of these models to actual human behavioral disorders.Therefore, we focus on studies that model these disorders usingsocial behavior assays. We categorize these studies based on thedifferent methods used to induce social deficits.

Genetic modelsTechnologies such as CRISPR, transcription activator-like effectornucleases (TALENs) and zinc-finger nucleases (ZFNs) have beenimplemented in zebrafish to generate genetic models. Forward-genetics methods can also generate mutants through randommutagenesis.

Autism risk genesModulating autism-related genes in zebrafish can induce autism-related phenotypes. However, the endpoints assessed in thesestudies have primarily focused on developmental and physiologicalchanges or other comorbid behavioral symptoms of autism such asanxiety, sleep disorders and seizures. For example, cntnap2knockout induced night-time hyperactivity (Hoffman et al., 2016),and chd8 morphants (Box 2) and mutants developed macrocephaly(Sugathan et al., 2014; Bernier et al., 2014). Researchers havestarted examining social behavior deficits in more recent studies.Knocking out the autism gene shank3b (Durand et al., 2007)induced deficits in shoaling, social preference and kin recognition(Liu et al., 2018a). Zebrafish with mutant sam2, ortholog to thehuman FAM19A2 gene, were found to have shoaling (Choi et al.,2018) and social preference (Ariyasiri et al., 2019) deficits. Thehuman FAM19A2 gene is located in the 12q14.1 locus, home to acopy-number variation (CNV) associated with intellectual disabilityand autism (Autism Genome Project et al., 2007).

Zebrafish also demonstrated its rapid disease-modeling capabilityin a recent study in which a novel autism risk gene, CEP41, wasidentified by whole-exome sequencing. The zebrafish CEP41morphant showed deficits in social preference behavior (Patowaryet al., 2019), providing experimental support for this new autismrisk gene. A CRISPR-based targeted mutagenesis studysystematically evaluated 35 autism and schizophrenia risk genesin an unsupervised machine learning assay for schooling (Tanget al., 2018). Significant behavioral changes were observed in theimmp2l and scn1labmutants; immp2l knockout enhanced shoaling,whereas heterozygous mutation in scn1lab seemed to suppress allevident social interactions between individuals. Their humanortholog, IMMP2L, is associated with Tourette syndrome (Petek

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et al., 2001), and SCN1A is associated with autism (Weiss et al.,2003) and Dravet syndrome (Wolff et al., 2006). Several othermutations also altered shoaling and schooling, but to a lesser degree.

Intellectual-disability risk genesIntellectual disability is often comorbid with autism. Zebrafishknockout of dyrk1aa, an ortholog of the human Down syndromegeneDYRK1A, induced shoaling and social preference impairments(Kim et al., 2017b). Fragile X syndrome is a form of humanintellectual disability caused by a loss-of-function mutation of thefragile X mental retardation 1 (FMR1) gene (Wu et al., 2017).Interestingly, knocking out of zebrafish fmr1 caused precociousdevelopment of shoaling behavior, a phenomenon interpreted as aresult of hyperactivity and increased anxiety (Wu et al., 2017),although such a phenomenon does not seem to be present in humanpatients with fragile X syndrome (Tranfaglia, 2011).

Schizophrenia risk genesThe zebrafish ortholog of the schizophrenia risk gene DISC1induced impaired shoaling response to stress when mutated (Eachuset al., 2017). Acute exposure to alarm substance (Box 2) or osmoticstress increased shoal cohesion in 5-dpf WT fish but not disc1mutants, suggesting its role in the development of thehypothalamic-pituitary-interrenal (HPI) axis, the fish equivalentof the hypothalamic-pituitary-adrenal (HPA) axis. Knocking outadra1aa and adra1ab, the two zebrafish orthologs of humanADRA1A, causes fish to freeze in tight groups for prolonged periodsof time (Tang et al., 2018). Polymorphisms in the promoter regionof the ADRA1A gene have been associated with schizophrenia(Clark et al., 2005), although not without controversies (Huanget al., 2008; Clark et al., 2006). While the freezing behavior found infish is significant, whether or how this deficit translates to humandisease phenotypes may require further investigation.

Other genetic models that cause social deficitsResearchers serendipitously discovered increased aggression in thespiegeldanio strain, an fgfr1at3R705H/t3R705H mutant (Norton et al.,2011), during routine stock maintenance. This mutant showedincreased mirror biting behavior and novel-object exploration,reminiscent of behavioral phenotypes seen in aggression-boldnesssyndrome. However, association of the human FGFR1 withaggression has not been reported.Leptin is generally known as an appetite regulator, but recent

evidence has shown that it also plays roles in behavioral regulation(Morrison, 2009). Knockout of lepa by TALENs resulted in reducedaggression in a mirror-biting assay and reduced shoaling (Audiraet al., 2018a). The authors argue that a dysregulated HPI/HPA axismay be responsible for the social deficit phenotype. In humans, anelevated leptin level has been associated with autism (Ashwoodet al., 2008; Blardi et al., 2010; Raghavan et al., 2018) and Rettsyndrome (Blardi et al., 2007, 2009), whereas a decrease in leptin islinked to schizophrenia and depression (Kraus et al., 2001; Atmacaet al., 2003).

Gene expression modulation modelsChanges in gene expression levels have been associated with socialdisorders. For example, CNV in chromosome region 16p11.2 islinked to autism (Sebat et al., 2007). Overexpression of the 29 genesencompassed by the 16p11.2 CNV in zebrafish identified KCTD13as an inducer of microcephaly (Golzio et al., 2012). Suppression ofthe same gene by morpholino resulted in macrocephaly (Golzioet al., 2012). Zebrafish morphants in several of these genes also

showed deficits in brain-ventricle and midbrain development(Blaker-Lee et al., 2012). Although modulation of geneexpression at the larval or adult stages is also possible usinginducible expression systems (Chiu et al., 2016), this has not beenutilized to establish social-deficit models in zebrafish.

Chemically induced models: embryonic and maternal exposureBoth genes and the environment contribute to the development ofsocial behavior. For example, environmental factors are estimated toaccount for 41% of autism risk (Gaugler et al., 2014). In fact, anumber of environmental toxins, such as bisphenol A (BPA) (Steinet al., 2015), polychlorinated biphenyls (PCBs) (Lyall et al., 2017)and pesticides (von Ehrenstein et al., 2019), have been associatedwith elevated autism risk through epidemiological research, andwere investigated in rodent models (Yu et al., 2011; Jolous-Jamshidiet al., 2010; Lan et al., 2017; Mullen et al., 2012). The zebrafishprovides a powerful model for studying environmental factors thataffect social development, especially given the simplicity ofcompound administration through water immersion. Thissubsection summarizes findings on how chemical exposure priorto or during embryonic development affects social behavior.

Alcohol and other abused drugsAlcohol consumption during pregnancy can lead to fetal alcoholspectrum disorders (FASDs; Box 2). Patients with less-severe FASDcan exhibit social deficits without anatomical changes (Seguin andGerlai, 2018). Zebrafish embryos that were briefly (2 h) exposed tolow levels (up to 1%) of alcohol develop to adults with no grossanatomical changes but show dose-dependent reductions in socialpreference to virtual (Fernandes and Gerlai, 2009) or live (Buske andGerlai, 2011) social stimuli. This effect is likely mediated byimpairments in the dopaminergic and serotoninergic systems(Fernandes et al., 2015; Buske and Gerlai, 2011). Embryonicexposure to another commonly abused drug, ketamine, did notsignificantly alter shoaling behavior (Félix et al., 2017a,b).

Prescription drugsWhen taken during pregnancy, some common prescription drugsmay have side effects or toxicity that affect the development ofsociality in humans. In addition, due to their continuous usage andemission, pharmaceuticals often accumulate faster than they areremoved from the environment and are considered pseudo-persistent contaminants (Mackay et al., 2014), making themaccessible to humans through environmental exposure.

The effects of pharmaceuticals on social development can beconveniently modeled in zebrafish. For example, prenatal exposureto valproic acid can lead to fetal valproate syndrome (Box 2) inhumans. Embryonic exposure to valproic acid or sodium valproateinduced deficits in social preference behavior (Bailey et al., 2016;Baronio et al., 2018; Dwivedi et al., 2019; Zimmermann et al.,2015) but not aggression (Zimmermann et al., 2015). Impairmentsin the histaminergic (Baronio et al., 2018) and purinergic(Zimmermann et al., 2017) systems likely mediate this effect.

Embryonic exposure to 2 nM retinoic acid, an importantsignaling mediator in development, decreased social preference toa video of shoaling fish without inducing neural tube malformationsor elevated death rate (Bailey et al., 2016).

Fluoroquinolones and tetracyclines are β-diketone antibiotics(DKAs) widely used in humans and animals. A 3-month exposureto a mixture of six DKA species, starting from birth, increasedshoaling at a low concentration (6.25 mg/l) but inhibited shoalcohesion at a higher concentration (25 mg/l) (Wang et al., 2016b).

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Environmental chemicalsExpansion of the chemical industry in the past century has greatlyincreased the number of environmental chemicals, yet only afraction of these have been studied for their effects on thedevelopment of social behavior. Some commonly foundenvironmental toxins have been studied in the zebrafish socialbehavior model. Benzo[α]pyrene often forms during organic-mattercombustion and is found in cigarette smoke, diesel exhaust andgrilled foods. When tested across three generations, benzo[α]pyreneinduced a shoaling deficit in the first but not the subsequentgenerations (Knecht et al., 2017).Chemical flame retardants are added to many household products.

Among these, the brominated flame retardant (BFR) polybrominateddiphenyl ethers (PBDEs) were widely used until the early 2000s.Althoughnow largely phased out due to toxicity concerns, they persistin the environment, which can lead to continuous low-level exposure.When exposed to low doses of either of two prominent PBDEs, BDE-99 and BDE-47, zebrafish exposed to BDE-99 but not BDE-47exhibited reduced social preference behavior (Glazer et al., 2018b).Another study reported elevated shoaling between pairs of zebrafishlarvae following embryonic BDE-47 treatment (Zhang et al., 2017).Interestingly, the same group also tested two BDE-47 metabolites, 6-OH-BDE-47 and 6-MeO-BDE-47, using the same experimentalsetup, and found that 6-MeO-BDE-47 but not 6-OH-BDE-47inhibited shoaling (Zhang et al., 2018). Another BFR,tetrabromobisphenol A, heightened aggression in males but not infemales (Chen et al., 2016a).BFRs have been largely replaced by a newer class of flame

retardants, the organophosphate flame retardants (OPFRs).Currently, little is known about the potential developmentalneurotoxicity of these chemicals. Six commonly used OPFRsshowed no negative effect on shoaling behavior through embryonicexposure (Glazer et al., 2018a; Oliveri et al., 2015). A mixture ofBFRs and OPFRs, FM 550 did, however, induce shoaling deficits(Bailey and Levin, 2015).The organophosphorus pesticide dichlorvos (Altenhofen et al.,

2019) and the neonicotinoid pesticide imidacloprid (Crosby et al.,2015) showed no effect on social behavior.Endocrine-disrupting chemicals (EDCs) such as xenoestrogen

have been suspected of affecting social behavior. Indeed, embryonicexposure to 17α-ethinylestradiol enhanced social preferencebehavior (Volkova et al., 2015).BPA increased time spent near a mirror, but reduced male attacks

on the mirror (Weber et al., 2015).The heavy metals lead and arsenic were also tested, with

lead exposure increasing aggression in a mirror test (Weber andGhorai, 2013) and arsenic showing no effect on social behavior(Dipp et al., 2018).

Maternal exposureBecause humans develop in utero, environmental risk factors forsocial-behavior-related disorders must access the fetus throughmaternal exposure. Although exposure mechanisms in egg-layingfish and placental animals are significantly different, it is possible tomodel some aspects of this exposure mechanism in zebrafish. Forexample, exposing adult female zebrafish to a mixture of the water-soluble fraction of crude oil and lead was found to suppress shoalingbehavior in their offspring (Wang et al., 2016c).

Chemically induced models: adult exposureAdult zebrafish can be used for pharmacological and toxicologicalstudies. Drugs can be easily administered by direct water

immersion, which enables drug absorption through the skin andgill, or oral ingestion. Both acute and chronic drug exposure can beconducted with good temporal control, as drugs can be added andremoved at precise time points. To overcome potential issues in drugsolubility and to enable pharmacokinetic analyses, drugs can also beapplied by oral administration (Kulkarni et al., 2014; Dang et al.,2016) or intraperitoneal injection (Samaee et al., 2017). Drugabsorption and metabolism can be measured by mass spectrometry(Villacrez et al., 2018).

Dietary componentsChronic exposure to dietary components can affect a body’snutritional and toxicological balance, which in turn modulate theoverall health of an animal through regulation of metabolism andgene expression. Trace elements such as selenium and zinc areessential nutrients for mammals but are neurotoxic at excessivelevels and their neurobehavioral effects on social behavior are notwell understood. Chronic (60 days) exposure to selenomethionine, anaturally occurring selenoamino acid found in cereal grains,grassland legumes and soybeans (Whanger, 2002), suppressedshoaling in adult fish, potentially due to alterations of theserotonergic pathway (Attaran et al., 2019). Chronic (21 days)exposure to zinc chloride reduced mirror-biting behavior(Sarasamma et al., 2018). Hyperprolinemia is an inheriteddisorder of proline metabolism deficiency and has been associatedwith schizoaffective disorders (Jacquet et al., 2005; Oreši�c et al.,2011). To examine the effect of excess proline on social behavior,adult fish were exposed to 1.5 mM proline for 7 days. Impairmentsin social preference and other schizophrenia-related behaviors werefound and rescued by the atypical antipsychotic drug sulpiride butnot the typical antipsychotic haloperidol (Savio et al., 2012).

Environmental chemicalsDirect short-term (days) exposure to the herbicides glyphosate(Bridi et al., 2017) and atrazine (Schmidel et al., 2014) reducedaggressive behavior and shoaling, respectively, whereas an 18-dayexposure to intraperitoneally injected paraquat did not significantlyaffect social interaction (Bortolotto et al., 2014). Acute exposure togold resulted in a temporary reduction in social preference behaviorthat may be related to elevated oxidative stress; the social inhibitioneffect was short lived and the treated fish recovered within severalhours (Strungaru et al., 2018). Chronic exposure to the EDC BPAreduced courtship behavior in females but increased theiraggression towards mating competitors; females also preferredcontrol males over BPA-treated males during courtship tests (Liet al., 2017a). Nonylphenol, another EDC and xenoestrogencompound, inhibited aggression and social preference behaviorsby chronic exposure (Xia et al., 2010). 17α-ethinylestradiol, asynthetic estrogen and major component in oral contraceptive pills,is excreted from the human body in high amounts and accumulatesin the environment. Its impact on zebrafish social behavior wereexamined in several studies to assess its influence on aquaticanimals, revealing changes in social hierarchy and courtship in fishfollowing exposure (Coe et al., 2008, 2009; Colman et al., 2009;Filby et al., 2012). Another EDC, triclosan, had inconsistent effectson social preference behavior (Liu et al., 2018b; Zang et al., 2019).

Neuroactive chemicalsNeuroactive chemicals have been applied to adult fish directly toinvestigate how different neurotransmitter pathways contribute tothe regulation of social behavior and to examine a drug’s therapeuticpotential in treating social disorders. Oxytocin (OT) and arginine-

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vasopressin (AVP) are neuropeptides known to regulate socialbehavior in mammals. Their zebrafish homologs, isotocin (IT) andvasotocin (AVT), together with the mammalian OT and AVP, wereexamined in a social preference assay in which a WT test subjectwas placed betweenWT and nacre fish. Control- and vehicle-treatedfish prefer to stay close to the WT social stimulus, whereasincreasing doses of all four neuropeptides first reversed thispreference, and then returned it to baseline, meaning that, atmedium doses, the treated fish preferred to stay closer to the nacrethan to the WT social stimulus (Braida et al., 2012). A syntheticoxytocin receptor ligand, dOTK2–C8, elicited a similar preference-reversal phenotype (Busnelli et al., 2016).The dopaminergic system has been implicated in reward and

social responses. Not surprisingly, the dopamine D1 receptorantagonist SCH23390 significantly reduced social preference in theWTAB zebrafish strain. Interestingly, researchers failed to observea similar effect in another WT zebrafish strain, demonstratingnatural variation in behavioral responses to neuroactive chemicals indifferent zebrafish strains (Scerbina et al., 2012). The commonprescription drugs fluoxetine (Giacomini et al., 2016) andbenzodiazepines (Giacomini et al., 2016; Schaefer et al., 2015)both inhibited shoaling. Fluoxetine also inhibited the offensiveaggression behavior in dominant fish while suppressing freezingbehavior in the subordinate fish (Theodoridi et al., 2017).The glutamatergic N-methyl-d-aspartate receptor antagonist MK-

801, commonly used to inhibit memory formation, has been used tocreate fish models with autism- and schizophrenia-like behavioraldeficits. Acute exposure to MK-801 decreases social preference(Dreosti et al., 2015), shoaling (Maaswinkel et al., 2013a) andaggression (Zimmermann et al., 2016), an effect rescued byoxytocin and the oxytocin receptor agonist carbetocin(Zimmermann et al., 2016). Atypical antipsychotics sulpiride andolanzapine also reversed MK-801-induced social impairment, yetthe typical antipsychotic haloperidol failed to reverse thisphenotype (Seibt et al., 2011).Nicotine significantly inhibits shoal cohesion but only mildly

affects polarization, whereas ethanol strongly affects polarizationwithin a fish school but only modestly inhibits shoal cohesion (Milleret al., 2013). In a social novelty test (Ariyasiri et al., 2019), control fishtypically prefer to interact with a novel over a familiar fish. Ethanolexposure significantly suppressed this novelty preference behaviorwithout affecting sociality in a three-chamber social preference test(Ariyasiri et al., 2019). Individual fish also respond differently toethanol. While ‘shy’ individuals typically spent more time near ashoal than ‘bold’ fish, ethanol increased shoaling in bold fish butinhibited shoaling in shy fish (Araujo-Silva et al., 2018). The acutemild inhibitory effect of ethanol on sociality is enhanced by taurine, acommon supplement in energy drinks (Fontana et al., 2018). Taurinealso prevented alcohol-induced elevated aggression (Fontana et al.,2016).While acute ethanol exposuremildly inhibits shoaling, chronic(8 days) exposure to ethanol surprisingly increases shoal cohesion(Müller et al., 2017b). Chronic ethanol exposure dramatically loweredfertility when at least one of the mating partners was treated, and thisinhibition was fully reversed by a 9-week withdrawal program(Dewari et al., 2016).The psychotropic drug lysergic acid diethylamide (LSD)

inhibited shoaling (Green et al., 2012; Grossman et al., 2010) butnot social preference behavior (Grossman et al., 2010). Similarto the effects of oxytocin and arginine-vasopressin receptoragonists, the amphetamine derivatives 2,5-dimethoxy-4-bromo-amphetamine hydrobromide, para-methoxyamphetamine and 3,4-methylenedioxymethamphetamine generated an inverted-U-shaped

curve in the nacre/WT social preference assay, shifting socialpreference fromWT conspecifics to nacre and then back to WT fishat progressively increasing doses. Ketamine (Riehl et al., 2011) andibogaine (Cachat et al., 2013) both inhibited group cohesion.

Stressor-induced modelsExternal stressorsUnpredictable chronic stress (UCS) and developmental socialisolation (DSI) are often applied to animal models to mimic theenvironmental stressors that may contribute to psychiatric disorderdevelopment in humans. Zebrafish UCS assays apply differentcombinations of chronic stressors – such as restraint, social isolation,overcrowding, tank or water change, cold/heat, being chased by a net,dorsal body exposure in shallow water, exposure to air in a net,predator presence, and alarm substance – for varying durations (daysto weeks). Stressors are often randomized on different days to ensureunpredictability. UCS assays with different stress protocols havegenerated inconsistent results, including increased (Chakravarty et al.,2013), first increased and then decreased (Piato et al., 2011), orunaltered (Fulcher et al., 2017) shoaling behavior after UCS. Acutestress by harassing the fish with a pen net prior to a behavioral testdecreased social preference behavior but increased aggression(Giacomini et al., 2016). The effect of DSI on shoaling alsoremains controversial, as different reports have found it decreased(Shams et al., 2018) or did not alter (Fulcher et al., 2017) shoaling.

Physiological stressorsAn induced inflammatory response by inoculating fishwith formalin-inactivatedAeromonashydrophila reduced social preferencebehavior(Kirsten et al., 2018), consistent with a previous report linking theimmune system with social behavior in mice (Filiano et al., 2016).Traumatic brain injury (TBI) by pulsed, high-intensity focusedultrasound to the adult zebrafish brain increased shoaling cohesion(McCutcheon et al., 2017), although it may be difficult to determinethe exact location and degree of brain damage caused by such adiffusive injury method. Hunger reduced aggression in females butnot in males, possibly due to the females’ stronger need to conserveenergy compared to males (Ariyomo and Watt, 2015).

Circuit manipulation modelsDifferent parts of the subcortical social brain (SSB) play differentroles in social behavior. Manipulating these brain regions and neuralcircuits through targeted neuronal inhibition, ablation and activationusing genetic, optogenetic and chemogenetic (Box 2) approachescan help improve our understanding of the mechanisms regulatingdifferent aspects of social behavior. For example, targetedexpression of tetanus neurotoxin to silence the lateral or medialsubregion of the dorsal habenula (Table 1; Fig. 2) resulted inpredispositions to lose or win a fight, respectively, revealing a dualcontrol system for conflict resolution (Chou et al., 2016). In anotherstudy, manual (by inserting a 27½ G needle) and genetic ablationsof a population of neurons in the ventral telencephalon inhibitedsocial interactions, as quantified by failure to adjust orientationagainst a social stimulus fish (Stednitz et al., 2018). The ablatedregion is believed to be homologous to the mammalian lateralseptum (Table 1; Fig. 2), a region implicated in social behavior inmammals (Clarke and File, 1982; Shin et al., 2018).

Emerging technologies for modeling social behaviordisorders in zebrafish: opportunities and challengesIn this section, we discuss emerging technologies that canpotentially improve modeling of social behavior disorders in

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zebrafish. These can be broadly categorized as the ‘next generation’methods for high-throughput model generation and drug testing,high-resolution functional brain imaging, and high-precision circuitmanipulation for studying the circuit-level mechanisms ofbehavioral deficits. These research goals, limitations of currentmethodologies and potential solutions to overcome these limitationsare summarized in Table 2.

High-throughput genome editing for disease modelingMany neuropsychiatric disorders with social deficits have a stronggenetic basis. Advanced genetic and genomic technologies haveenabled researchers to find hundreds of genes that contribute to risksof developing neurological diseases. Given that the zebrafish isrelatively inexpensive and easy to manipulate genetically comparedto rodents, it has great potential as an experimental model to studythese disease risk genes.CRISPR is a popular technology for genome editing in zebrafish

due to its simplicity and speed (Hwang et al., 2013; Prykhozhijet al., 2017; Prykhozhij and Berman, 2018). Researchers haveattempted to improve throughput by developing more scalablemethods. Current approaches are based on pooled CRISPRtargeting followed by individual genotyping and separation, buthave yet to provide a truly high-throughput output (Varshney et al.,2015; Shah et al., 2015). Several possible approaches may improvethe current methods. Pooled CRISPR followed by early genotypingof live larvae using a recently developed approach (Lambert et al.,2018) can significantly speed up the turnover for each round ofgenotyping. Robotically controlled and fully automated embryonicinjection methods (Zhao et al., 2018) also have the potential to

increase the throughput of CRISPR delivery. Automated feedingsystems such as Tritone (Aquatic Solutions) may facilitate thehusbandry of large numbers of mutant lines generated by high-throughput CRISPR editing. Finally, if large numbers of disease-related mutants are generated, expanding the capacity of zebrafishstock centers may be needed (Table 2).

High-throughput chemical screening for disease modeling and drugdiscoveryBoth genes and the environment contribute to the development ofsocial behavior. The development of some social-related disordersis also believed to be affected by environmental factors such asprenatal exposure to certain chemicals. The zebrafish has been apopular model for in vivo chemical screening (Rennekamp andPeterson, 2015). Its ex utero development allows embryos to beexposed to potentially toxic chemicals during early embryogenesis.The zebrafish larva is small, and a large number of larvae can fit intoa compact imaging arena, enabling high-throughput behavioralprofiling and phenotype-based drug discovery (Jordi et al., 2018;Bruni et al., 2016; Kokel et al., 2010, 2012; Rihel et al., 2010).These features make zebrafish an attractive model for systematicallyidentifying potential environmental risk factors that contribute todisease etiology by high-throughput chemical and behavioralscreening. Although technologies are readily available to exposezebrafish embryos, larvae or adults to chemicals in a high-throughput or scalable manner, a social behavior testing systemcapable of operating in a high-throughput or scalable fashion has yetto be developed (Table 2). The establishment of such a high-throughput social behavior assay in zebrafish would enable

Table 1. Anatomical and functional conservation of the subcortical social brain between mammals and zebrafish

Mammalian structures Mammalian functions Homologous zebrafish structures

Anterior hypothalamus (AH) Aggression (Falkner and Lin, 2014) Ventral tuberal nucleus (VTN) (Xie and Dorsky, 2017)Basolateral amygdala (BLA) Social fear (Qi et al., 2018) Medial dorsal telencephalon (Dm) (von Trotha et al.,

2014, Perathoner et al., 2016)Cerebellum (CB) Social reward (Carta et al., 2019) CB (Bae et al., 2009)Dorsal raphe (DR) Social isolation (Matthews et al., 2016) DR (Yokogawa et al., 2012)Hippocampus (HIP) Social memory (Hitti and Siegelbaum, 2014; Okuyama

et al., 2016; Meira et al., 2018)Lateral dorsal telencephalon (Dl) (von Trotha et al., 2014)

Lateral habenula (LHb) Aggression (Flanigan et al., 2017), social play (van Kerkhofet al., 2013), social defeat (Wang et al., 2017a)

Ventral habenula (VHb) (Amo et al., 2010)

Lateral septum (LS) Aggression (Leroy et al., 2018), early-life stress-inducedsocial dysfunction (Shin et al., 2018)

Ventral nucleus of the ventral telencephalon (Vv)

Medial amygdala (MeA) Sexual behavior (Ferrero et al., 2013), sex discrimination ofsocial cues (Yao et al., 2017), social informationprocessing (Li et al., 2017b), social recognition(Takayanagi et al., 2017), aggression and parenting(Tachikawa et al., 2013)

Supracommissural nucleus of the ventral telencephalon(Vs) (Perathoner et al., 2016)

Medial bed nucleus of the striaterminalis (BNSTm)

Stress response (Lebow and Chen, 2016), sexual behaviorand parenting (Tsuneoka et al., 2015)

Supracommissural nucleus of the ventraltelencephalon (Vs)

Medial preoptic area (MPOA) Sexually dimorphic behaviors (Wei et al., 2018), socialreward (McHenry et al., 2017), parenting (Fang et al.,2018; Wu et al., 2014; Brown et al., 2017)

Preoptic area (POA) (Xie and Dorsky, 2017)

Nucleus accumbens (NAc) Social reward (Dölen et al., 2013) Dorsal ventral telencephalon (Vd)Periaqueductal gray/central gray(PAG/CG)

Defensive behavior (Deng et al., 2016) PAG/CG

Striatum (STR) Pair bonding (Báez-Mendoza and Schultz, 2013) Dorsal (Vd) and central (Vc) ventral telencephalonVentral tegmental area (VTA) Social reward (Hung et al., 2017) Posterior tuberculum (PT)Ventral pallidum (VP) Sexual behavior, social affiliation (Smith et al., 2009) N.A.Ventromedial hypothalamus (VMH) Aggression (Hashikawa et al., 2017), defensive behavior

(Wang et al., 2019), sexual behavior (Nomoto andLima, 2015)

Anterior tuberal nucleus (ATN) (Xie and Dorsky, 2017)

Recent experimental evidence of SSB conservation between zebrafish andmammals are referenced in column 3. Additional references covering earlier researchcan be found in the review by O’Connell and Hofmann (2011b). Structures are listed in alphabetical order.

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researchers to systematically screen chemicals for their disease-inducing risks and discover new drugs for treating social behaviordeficits in humans.

Neural activity imaging and circuit manipulation technologies forsocial disorder modelsThe circuit-level mechanisms underlying social behavior anddisorders are not well understood. The zebrafish is a promisingmodel for elucidating these mechanisms because, compared torodents, it is relatively easy to image due to its small size andtransparent nature. Furthermore, it is amenable to facile circuitmanipulations through genetically targeted ablation, or optical andchemical activation or inhibition. Although many of thesetechnologies are already established in zebrafish, they have rarelybeen used to investigate social behavior.Here,we discuss the potentialto use imaging and circuit manipulation techniques to study brainactivities in social behavior and disease models (Table 2).Real-time calcium imaging has long been applied to larval

zebrafish less than a week old. Researchers recently developed atwo-photon calcium imaging approach that enables real-time brainimaging of 3-week-old zebrafish (Jetti et al., 2014; Vendrell-Llopisand Yaksi, 2015), a stage when robust social preference behavior isdeveloped. Although, in the current methods, the fish must berestrained for imaging, combining this method with VR technologymay allow brain activity imaging during a virtual social interaction.In addition, with the recent advancements in real-time brain imagingof freely moving larvae (Kim et al., 2017a; Cong et al., 2017; Mutoand Kawakami, 2016), it may one day be possible to developcalcium imaging methods for freely moving 3-week-old fish duringphysical social interactions. Alternatively, whole-brain or whole-body tissue clearing methods such as CLARITY and PACT havebeen successfully applied to adult zebrafish (Cronan et al., 2015),and could enable post-hoc analysis of whole-brain activity patternsduring social interactions by examining the expression of immediateearly genes (Box 2).Conventional methods in zebrafish manipulate circuit activity by

inhibiting neuronal activity through expression of neurotoxins suchas tetanus or botulinum toxins, or ablation of targeted neurons byexpressing nitroreductase (Curado et al., 2008). Newer technologiessuch as optogenetics (Förster et al., 2017) and chemogenetics (Chenet al., 2016b) (Box 2) can be applied to zebrafish to activate orinhibit certain brain regions. It is also possible to combine bothmethods by applying an optically switchable compound to activatecertain neurons (Lam et al., 2017). These newer methods canactivate specific neurons with reversible temporal control.

ConclusionsAnimal models for behavioral disorders are inevitably confrontedwith questions about their validity. This is true for emerging modelorganisms such as the zebrafish as well as for established ones suchas rodents. Care must be taken when interpreting results acquiredfrom animal models that attempt to mimic symptoms of humanconditions. This is especially true for complex traits such as socialbehaviors. Nevertheless, behavioral assays often provide readoutsthat are more relevant to the core symptoms of human behaviordisorders than other assays that examine changes in anatomy,physiology or endophenotypes. Therefore, efforts in advancing thecurrent assay and analysis methods of social behavior are necessaryto facilitate progress in disease research.

The extensive data discussed in this Review support the idea thatmany of the most fundamental elements of social behavior – e.g.conspecific association, communication, establishment of hierarchies,social eavesdropping, aggression andmating – are conserved in socialvertebrates from teleosts to mammals. Vertebrates as evolutionarilydistant as zebrafish and rodents share numerous genetic,pharmacological, neuroanatomical and behavioral similaritiesrelevant to social behavior. Common structures in the SSB mayprovide a biological foundation for social behavior conservationamong vertebrates. We argue that the more complex, higher-ordersocial behaviors in humans must be understood as layered on top ofthe SSB, and studying the zebrafish SSB can therefore have directimplications for understanding sociality in humans. Given thenumerous methods currently available for studying various aspectsof social behavior in zebrafish, the existing zebrafish models of socialdeficits and the technologies (established and emergent) for high-throughput experimentation, we anticipate that the zebrafish SSBwillbecome an increasingly important model for understanding thebiology of sociality in health and disease.

Competing interestsThe authors declare no competing or financial interests.

FundingThis research received no specific grant from any funding agency in the public,commercial or not-for-profit sectors.

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Table 2. Potential applications of emerging technologies to improve social disorder modeling in zebrafish.

Research goals Limitations of current methods Possible solutions

High-throughput genome editing fordisease modeling

• Mutagenesis methods are low throughput• Labor-intensive husbandry• Limited resources for husbandry of mutant lines

• Rapid genotyping for fast turnaround• Automated microinjection• Automated fish husbandry• Expansion of fish husbandry capacity

High-throughput behavioralscreening for disease modeling ordrug discovery

• Current social behavior assays are low throughput • High-throughput or scalable social behavior assays

Investigating circuit-levelmechanisms underlying deficits ofsocial disease models

• Lack of real-time imaging capability for older or freelymoving fish

• Lack of whole-brain activity imaging methods• Lack of circuit activation methods• Low temporal resolution in circuit manipulation

approaches

• Adopt recent advances in real-time brain imaging, whole-body hydrogel tissue chemistry methods, andoptogenetic- and chemogenetic-based circuitmanipulation approaches

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