Understanding zebrafish cognition

Accepted Manuscript Title: Understanding zebrafish cognition Author: Darya A. Meshalkina Marina N. Kizlyk Elana V. Kysil Adam D. Collier David J. Echevarria Murilo S. Abreu Leonardo J.G. Barcellos Cai Song Allan V. Kalueff PII: DOI: Reference:

S0376-6357(16)30382-5 http://dx.doi.org/doi:10.1016/j.beproc.2016.11.020 BEPROC 3344

To appear in:

Behavioural Processes

Received date: Revised date: Accepted date:

29-7-2016 12-10-2016 30-11-2016

Please cite this article as: Meshalkina, Darya A., Kizlyk, Marina N., Kysil, Elana V., Collier, Adam D., Echevarria, David J., Abreu, Murilo S., Barcellos, Leonardo J.G., Song, Cai, Kalueff, Allan V., Understanding zebrafish cognition.Behavioural Processes http://dx.doi.org/10.1016/j.beproc.2016.11.020 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

Understanding zebrafish cognition

Darya A. Meshalkina1, Marina N. Kizlyk, Elana V. Kysil1, Adam D. Collier2, David J. Echevarria2,3, Murilo S. Abreu4, Leonardo J. G. Barcellos3,4,5, Cai Song6, and Allan V. Kalueff1,3,6,7,8*

1

Institute of Translational Biomedicine, St. Petersburg State University, St. Petersburg 199034,

Russia 2

Department of Psychology, University of Southern Mississippi, Hattiesburg, MS, USA

3

The International Zebrafish Neuroscience Research Consortium (ZNRC), 309 Palmer Court,

Slidell, LA, 70458, USA 4

Graduate Program in Pharmacology, Federal University of Santa Maria (UFSM), Av. Roraima,

1000, University City, Camobi, Santa Maria, RS, Brazil, 97105-900 5

Graduate Program in Bioexperimentation, University of Passo Fundo (UPF), BR 285, San Jose,

Passo Fundo, RS, Brazil, 99052-900 6

Institute for Marine Drugs and Nutrition, Guangdong Ocean University, Zhanjiang 330001, China

7

Ural Federal University, Ekaterinburg 620002, Russia

8

Neuropharmacology Laboratory, ZENEREI Research Center, Slidell, LA, 70458, USA

*

Corresponding author:

Allan V. Kalueff, PhD, Institute of Translational Biomedicine, St. Petersburg State University, St. Petersburg 199034, Russia Tel/Fax: +1-240-899-9571 Email: [email protected]

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Highlights 

Zebrafish are rapidly becoming a popular model organism in cognitive neuroscience research.

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Zebrafish continue to increase our understanding of brain cognition and its genetic and pharmacological modulation.

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Here, we discuss the utility of zebrafish in understanding cognitive phenotypes and their deficits

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Zebrafish models for mutant and small molecule screening are critical for identifying novel targets to treat cognitive deficits.

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Abstract

Zebrafish (Danio rerio) are rapidly becoming a popular model organism in translational and cognitive neuroscience research. Both larval and adult zebrafish continue to increase our understanding of cognitive mechanisms and their genetic and pharmacological modulation. Here, we discuss the developing utility of zebrafish in understanding cognitive phenotypes and their deficits, relevant to a wide range human brain disorders. We also discuss the potential of zebrafish models for high-throughput genetic mutant and small molecule screening (e.g., amnestics, cognitive enhancers, neurodevelopmental/neurodegenerative drugs), which becomes critical for identifying novel candidate genes and molecular drug targets to treat cognitive deficits. In addition to discussing the existing challenges and future strategic directions in this field, we emphasize how zebrafish models of cognitive phenotypes continue to form an interesting and rapidly emerging new field in neuroscience.

Keywords: zebrafish, cognition, experimental models, cognitive tasks, cognitive deficits, human cognitive disorders

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1. Introduction Animal and human cognition is a complex biological phenomenon of acquiring and processing information necessary to respond plastically to changes in the environment (Dukas 2004). Cognition includes various brain processes, such as learning and memory, decision-making, attention, categorization, evidence integration, probabilistic and numerical reasoning, as well as context-specific cognitive judgments (Baars and Gage 2010; Neisser 2014). Studying cognition represents a critical focus of translational biomedicine, and understanding how molecular and cellular activity produces cognition and behavior is one of the most exciting and unsolved research challenges (Lee and Silva 2009). This focus also has a prominent practical application in treating a wide range of cognitive deficits, including neurodegenerative disorders as well as other brain diseases where cognition is impaired (Tables 1 and 2). Furthermore, in addition to cognitive deficits associated with brain disorders, age-related cognitive decline is a part of normal, non-pathological aging that currently affects an increasing percentage of human population (Harada, Love, and Triebel 2013; Association 2015). Cognition encompasses some of the most complicated of brain processes, traditionally attracting more attention to model organisms with complex behavior, such as rodents, dogs and primates (Shettleworth 2001; Nagahara et al. 2009; Pavlov 1927). However, their phenotypic complexity may yield rather entangled results, leading to the difficulty in functional discrimination and prediction of cognitive processes. Therefore, simpler organisms with robust, clearly identifiable forms of behavior may represent an excellent complementary strategy to study cognitions. Here, we discuss mounting evidence that zebrafish (Danio rerio), a small Cyprinidae teleost fish, can be a promising novel organism for neuroscience research (Stewart, Braubach, et al. 2014; Kalueff, Echevarria, and Stewart 2014). Until recently, zebrafish and other fishes were neglected in cognitive studies due to their seemingly „primitive‟ brain morphology and unsophisticated, „instinctively-driven‟ behaviors (Kalueff, Echevarria, and Stewart 2014; Oliveira 2014). Recognizing excellent cognitive abilities of zebrafish, here we discuss the developing utility of zebrafish in understanding cognitive phenotypes and their deficits, highly relevant to understanding 4

normal coping behavioral strategies in animals, and in modeling a wide range human brain disorders. 2. What makes zebrafish an excellent model to study cognitive phenotypes? Among many advantages of zebrafish in this field, their relative practical/phenotypic simplicity over mammals, as well as the robustness of their phenotypes (Stewart, Braubach, et al. 2014; Kalueff, Echevarria, and Stewart 2014), help fill the gap between the in-vitro studies and complex mammalian models. In addition to complex well-defined behaviors in adult zebrafish (Kalueff et al. 2013), larval zebrafish execute a repertoire of simple, well-defined, and stereotyped sensorimotor behaviors that have accessible, characterized circuitry and provide a vertebrate system amenable to large-scale forward genetic and chemical screening (Wolman et al. 2014). Zebrafish cognition is related to the fact that they are highly social animals (Engeszer et al. 2007). While zebrafish attention is usually concentrated on conspecifics (which allows to use their image as a positive reinforcement, Table 3), disturbances in zebrafish sociality can be easily observable and increase the value of this model in studying deficits in social interactions, such as autism spectrum disorder (Stewart, Nguyen, et al. 2014). Like humans (but unlike rodents), zebrafish are a diurnal species which relies heavily on vision and reacts strongly to the images of other zebrafish or their natural predators (Table 3). Zebrafish larvae are transparent during the first days post fertilization, and some zebrafish strains (e.g., casper or sheer) remain transparent throughout their lifespan, thereby establishing zebrafish as a useful optogenetic model and providing easy access to developmental studies of cognitive functions (Del Bene and Wyart 2012). However, there are notable differences between zebrafish and mammalian morphology and neurophysiology. For example, the hippocampal trisynaptic circuitry critical for spatial learning in rodents is absent from zebrafish brain, albeit zebrafish appear to be capable of spatial learning (Karnik and Gerlai 2012; Arthur and Levin 2001), which is likely mediated by other brain regions (Sison and Gerlai 2010). Another example is tetrachromatic vision that allows zebrafish to distinguish a wider color range compared to humans and rodents, and must be recognized when selecting visual cues for zebrafish (Avdesh et al. 2012; Colwill et al. 2005). Although zebrafish 5

have no cortex or hippocampus (Panula et al. 2010), the analogous structures in the zebrafish brain appear to be biochemically and functionally similar, as zebrafish show complex cognition comparable to that seen in mammals (Parker et al. 2013). Additionally, zebrafish lack midbrain dopaminergic populations, such as the substantia nigra and ventral tegmental area (Panula et al. 2010), but are perfectly capable of behaviors attributed to these areas, which are also sensitive to dopamine-targeted pharmacological stimulation (Panula et al. 2010). From a cognitive neuroscience standpoint, the lateral pallium was proposed to play a role in spatial learning as an equivalent of mammalian hippocampus, whereas medial pallium (implicated in avoidance learning) may be equivalent to mammalian amygdala (Stewart, Ullmann, et al. 2014; Norton et al. 2011). Increased expression of the immediate early gene cfos, a measure of neuronal activation, was observed in both the medial and lateral pallium during the conditioning and drug (i.e., D-amphetamine) administration phase of a zebrafish conditioned place preference (CPP) assay (von Trotha, Vernier, and Bally-Cuif 2014). Interestingly, following conditioning, when zebrafish displayed drug seeking CPP behavior, the medial, but not the lateral pallium, showed increased cfos expression. Similarly, increased cfos expression has been observed in the zebrafish lateral pallium while avoiding a light source, suggesting that this region likely mediates choice behavior in zebrafish (Lau et al. 2011). Several features of the zebrafish endocrine and neurotransmitter systems increase their translational value for cognitive research. The most notable example is that zebrafish release cortisol as their primary stress hormone, which is advantageous over rodent models that release corticosterone (Kalueff, Stewart, and Gerlai 2014; Stewart, Braubach, et al. 2014). Another example is undetectable level of non-neural histamine (Eriksson et al. 1998), indicating that the effects of histamine-targeted drugs in zebrafish are only neural, without peripheral side-effects (Kaslin and Panula 2001). In line with this, the zebrafish genome contains the complete complement of human neural nAChR subunit genes, and for 8 of the 12 human genes only one zebrafish orthologue exists, whereas the 4 remaining human genes have two zebrafish homologs (Klee et al. 2012; Klee et al. 2011). 6

Given their relatively longer lifespan (depending on the strain, 3-5 years in zebrafish vs. 2-3 years in mice) (Gerhard et al. 2002), zebrafish also provide a good model of gradual decline in physical and neural phenotypes, critical for modeling age-related human disorders (Zhdanova 2011; Zhdanova et al. 2008; Stewart, Braubach, et al. 2014). Several biomarkers of aging can also be evaluated in zebrafish. For example, zebrafish telomere length increases until about 18 months of age when it begins to decrease, and zebrafish express telomerase in all tissues and at all stages of life, and show decreased telomerase reverse transcriptase (TERT) mRNA expression of older fish (over 2 years old) (Anchelin et al. 2011). Aged zebrafish have reduced generalization of adaptive associations, increased stereotypic and reduced exploratory behavior, and altered temporal entrainment. For example, young 1-year old zebrafish develop anticipatory behavior during the 7 day period of adaptation to a new feeding schedule, while middle-aged (2 years) and old (3 years) zebrafish develop this response more gradually. (Yu et al. 2006). Notably, old and young zebrafish perform similarly well in developing learned avoidance behavior. This is suggestive of differential roles of motivation in zebrafish learning across age groups. Development of cognitive abilities depends heavily on the environment, where enriched rearing conditions often results in increased learning performance (Kobayashi, Ohashi, and Ando 2002; Salvanes et al. 2013). The initial performance of zebrafish juveniles collected from the wild was lower than aquarium-reared animals, likely due to the higher inertness to explore the maze in wild population (although both groups show a gradual improvement in their performance) (Roy and Bhat 2016). The development of classical and operant learning behaviors in zebrafish increases with age, beginning at approximately week 3 of age and reaching performance levels similar to adult zebrafish at week 6 of age (Valente et al. 2012). Finally, given the higher restorative capacity of fish CNS (compared to mammals), using zebrafish models may help understand powerful compensatory mechanisms underlying cognitive aging in humans and other vertebrates (Zhdanova et al. 2008; Yu et al. 2006).

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3. Adapting zebrafish models to study learning and memory In general, a good animal model should possess three main attributes (Kalueff and Stewart 2015; Stewart and Kalueff 2015), including construct validity (conforming to the underlying rationale of the disease), face validity (mimicking some of the characteristics of the disease) and predictive validity (allowing the prediction of novel disease symptoms, or identification of disease treatments). Thus, disease modeling involves creating animal disease models, testing the model validity and identifying the range of its responses to the screened drugs (standard and those under investigation). Here, we discuss major cognitive domains and the value of zebrafish for their research. Learning is a change in behavior with experience (Lachman 1997) which mediates many aspects of animal behavior, including social interactions, foraging, navigation and predator avoidance (Spence et al. 2008). Learning often occurs as a result of continued stimulus exposure (e.g., non-associative learning), stimulus-stimulus associations (e.g., associative learning, or stimulus-response associations (e.g., operant conditioning). While learning represents an acquisition of information, and memory reflects the expression of such acquired information, for the purpose of animal testing these processes are usually considered to be interrelated and are often assayed simultaneously. A variety of learning protocols for zebrafish have been developed based on either pre-existing rodent protocols, mostly dependent on Pavlovian or operant conditioning (e.g., T-maze, CPP tasks), or originally zebrafish-specific (e.g., the C-bend habituation) (Bailey, Oliveri, and Levin 2015). Such learning protocols commonly employ pretest habituation trials to provide a general acclimation to test conditions. Zebrafish can demonstrate fear and stress when placed in a novel environment, which may suppress their learning and decrease test performance. The same also applies to handling conditions, as stressful handling may decrease zebrafish performance. Therefore, pretest trials are necessary prior to learning trials to provide acclimation to background conditions of the test apparatus and to experimental cues (e.g., unfamiliar food), to avoid nonspecific confounding factors affecting cognition.

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Learning trials (typically grouped in sessions) are first needed to condition zebrafish to associate motivating unconditional stimuli (UCS) with conditional stimuli (CS). The number of training trials may vary depending on the task and the stimuli employed, and can range from a 1 trial (Collier et al. 2014) to upwards of 30 trials (Williams, White, and Messer 2002). The duration of intertrial intervials and the ratio of intertrial intervals to duration of training trials (i.e., CS presentation) is an important consideration in animal learning tasks (Shettleworth 2009). For example, longer intertrial intervals and a longer intertrial interval in comparison to the duration of training trials produces faster learning acquisition (Domjan 2003; Gibbon et al. 1977). Intersession intervals are usually days or weeks, and may be used to evaluate long-term memory capabilities. Following learning trials, test trials are conducted without motivating stimuli, to assess the association between the UCS and CS. Importantly, motivating stimuli (e.g., rewarding or aversive) are essential to successful learning protocols, as inappropriate stimuli can lead to the biased or lacking results (Gerlai 2016). A variety of rewarding or aversive cues can be employed as an UCS, selected examples of which are summarized in Table 3. Minimizing stress-inducing conditions in zebrafish used in cognitive tests is essential. This includes habituation of animals to the tanks and food used in experiments prior to forming conditioned responses (Grossman et al. 2011). Another method is beginning habituation trials with shoals of ~15-20 fish and then gradually decreasing their size over time. These practices reduce stress by acclimating zebrafish to the laboratory environment and diet, the novelty of the test apparatus, and to the effects of social isolation. Due to the diurnal activity of zebrafish, and that nighttime memory formation is suppressed by melatonin, better results in learning are obtained during the daytime (e.g., between 12:00-16:00 h) (Rawashdeh et al. 2007). Direct learning protocols are usually based on maze paradigms of varying shapes and sizes (designed to promote attraction or avoidance of selected target compartments) based on both associative and relational learning. Associative learning is the most widely studied form of learning and implies learning of the direct connection of a salient cue with a reward or punishment (Gerlai 2016; Mackintosh 1983; Hall 1991). The connection between stimuli can often causal and temporal in nature. A reward or 9

punishment usually corresponds to the UCS, and a salient cue – to the CS. Clearly, the CS must precede the UCS for the reflex to be established, and the CS and UCS may also be presented close in proximity or time (a feature called contiguity) (Gerlai 2016; Anderson 2000). Finally, relational learning, a complex form of associative learning, implies learning of an association of a complex and/or variable stimuli with an UCS. It is a subset of associative learning based on spatial orientation, and a clinically relevant phenotype that presents in early stages of various neurodegenerative disorders (Dickerson and Eichenbaum 2010). 3.1. Habituation and related learning Habituation is the simplest non-associative form of learning, based on the adjustment of responses to previous experience. During habituation, the subject‟s response to repeated presentation of the same stimulus gradually decreases and is unrelated to sensory adaptation, motor fatigue or injury (Rankin et al. 2009). Habituation allows the filtering out of irrelevant stimuli and to concentrate on important environmental changes critical for survival. In neurobehavioral research, habituation tests are a simple and fast alternative to direct learning tests, and particularly useful for automated screening strategies. Habituation can be successfully measured by evaluating zebrafish startle responses, of which there are several well-defined forms. The C-start is a rapid stereotypic bend of a fish into a C-shape (~6 ms onset latency) triggered by abrupt, salient sensory stimuli (e.g., auditory, visual or tactile) and controlled by a bilateral pair of large command neurons in the hindbrain (i.e., the Mauthner cells) (Eaton, Lee, and Foreman 2001). Allowing zebrafish to escape predatory attack, zebrafish C-start first appears in larvae at the 4th day post-fertilization (dpf) and exhibits habituation. In addition, there is also a slower form of C-start response, which does not require the Mauthner cells and can be distinguished by a longer onset (~30 ms). The O-start is provoked by a set of different stimuli, such as an abrupt absence of light for 1 s (“dark flash”), and represents a larger (then in C-start) bending of the fish body not involving the Mauthner cells. C-start and Ostart responses are followed by characteristic evading movements to escape potentially aversive stimuli, the habituation of which can be used to assess zebrafish learning (Eddins et al. 2010). 10

Habituation testing is especially useful when working with larval zebrafish, helping to establish and record responses in a rapidly developing organism of small size that permits highthroughput substance screening. For example, a recent screening of 1,760 bioactive compounds with already known molecular targets attempted to establish the agents affecting habituation (Wolman et al. 2014). Larval zebrafish assays study two forms of habituation: rapid and short-term. Rapid habituation, typically lasting between 1-15 min, is reliably elicited by low-frequency auditory stimulation, but not blocked by the NMDA receptor antagonist DL-2-amino-5-phosphonopentanoic acid (Roberts et al. 2011). Short-term habituation, sensitive to the NMDA receptor antagonists, lasts between 25-60 min and can be induced by spaced training using several 15-min sessions spaced with 5-min pauses (Roberts et al. 2011). Habituation testing is not limited to startle response assays, and can be based on many other types of behavior. For example, the novel tank test, usually applied to test adult zebrafish anxiety-like behaviors, reveals robust within- and inter-trial habituation (Wong et al. 2010). A number of pharmacological agents are effective in preventing (e.g., anxiogenic pentylenetetrazole, caffeine and alarm pheromone) or promoting (e.g., anxiolytic fluoxetine) zebrafish habituation in this model (Wong et al. 2010). Additionally, zebrafish with experimental lidocaine-induced anosmia show reduced anxiolytic-like behavioral effects of fluoxetine, suggesting these effects may be partly mediated by olfactory perception (Abreu et al. 2016). Albeit differing from habituation by involving discrimination (which in terms of brain computation requires different cognitive abilities), recognition behavior can also be assessed by decreased intensity of exploration of a novel object that becomes familiar following an exposure period. This easily performed test is often used in rats as a one-trial memory test (Steckler et al. 1998), and has recently been adapted for zebrafish (Antunes and Biala 2012). Objects differing in shape, color and size be presented to zebrafish in a familiar tank. The proportion of time spent in the vicinity of the stimulus object (e.g., pink sphere, yellow prism) reflects its overall familiarity, and decreases even on the second presentation (Lucon-Xiccato and Dadda 2014). The objects can also be presented on a computer screen, thereby markedly increasing the throughput of the 11

procedure (Braida, Ponzoni, Martucci, and Sala 2014). Overall, habituation tests have useful applications for learning assessment in zebrafish, due to the simplicity of testing and analysis, and amenability of multiple forms of zebrafish behavioral responses to be habituated. 3.2 Conditioned place preference and passive avoidance learning CPP and conditioned place avoidance (CPA) models are similar in experimental design, and typically assess direct association of a specific location in the test apparatus with a rewarding or aversive stimulus, respectively. In CPP and CPA paradigms, zebrafish learn to spend more time in the location associated with the attractive stimulus, and to avoid the location associated with the aversive stimulus (Mathur, Lau, and Guo 2011; Mathur and Guo 2010; Collier and Echevarria 2013; Lau et al. 2011). The efficiency of learning is measured by the time in proximity to these locations. For example, zebrafish develop CPP learning towards both food and morphine, whereas morphine (but not food) CPP can be diminished by an opioid antagonist naloxone (Lau et al. 2011), and the „too few’ zebrafish mutants with aberrant monoaminergic neurons do not develop morphine CPP (Lau et al. 2006). Furthermore, zebrafish CPP responses to acute diazepam, fluoxetine, risperidone and buspirone are abolished in anosmic fish (Abreu et al. 2016). Zebrafish display a CPA response to a concentration of 50% of standard doses of tricaine MS222 (ethyl 3aminobenzoate methanesulphate) and benzocaine, two commonly used fish anesthetics.(Readman et al. 2013). CPP paradigms are also available for zebrafish larvae. For example, in an apparatus consisting of several parallel chambers that were half-opaque and half-transparent and illuminated with two different light intensities, larval zebrafish displayed a slight initial preference for the brighter chamber and a strong preference towards the transparent chamber that allowed visual access to conspecifics (Hinz et al. 2013). During training, the darker environment was employed as a CS and paired with visual access to conspecifics in the transparent chamber. Larval zebrafish developed a CPP response as exhibited by a learned preference for the darker environment that had been previously paired with conspecifics, which persisted for 36 hr. This demonstrates the rewarding properties of social exposure in zebrafish and its utility as an UCS in learning paradigms. 12

This CPP response was impaired when the NMDA antagonist MK-801 was administered during training (Hinz et al. 2013). In addition to the assessment of learning efficiency, zebrafish CPP responses provide a measure of the rewarding/appetitive potential of drugs. For example, this approach was used to assess the rewarding effects of cocaine (Darland and Dowling 2001), nicotine (Kedikian, Faillace, and Bernabeu 2013), ethanol (Parker, Annan, et al. 2014), MK-801 (Swain, Sigstad, and Scalzo 2004), salvinorin A (Braida et al. 2007) and other neuroactive agents. 3.3 Active avoidance learning Active avoidance reflects learning aimed to prevent the aversive stimulus from being applied and assesses operant conditioning, where the time spent in the associated compartment is typically measured to quantify learning (Bailey, Oliveri, and Levin 2015). The test may be performed in a two-compartment tank, such as a shuttle box with a narrow gate under a partition, where negative stimuli are delivered after failure to escape from the designated compartment where a CS is applied. Various cues may be used as a CS, including brief lighting of the compartment (Pradel, Schachner, and Schmidt 1999), designation of the compartments as light and dark (Rawashdeh et al. 2007; Xu et al. 2007) or simply varying the conditions of the test apparatus (Truong et al. 2014). UCS can consist of mild electric shock, swimming restriction and predatorrelated exposure (Table 3). The same paradigm can also be applied as the three-chamber tank, with the central chamber used as a starting section, and side compartments are positively and negatively reinforced (Arthur and Levin 2001). Developing this system, zebrafish learning (typically requiring >20 trials in the shuttle-box) can be accelerated to 7-trials if the first compartment the fish enter is paired with a negative UCS, and other as an „escape‟ compartment (Levin, Bencan, and Cerutti 2007; Levin et al. 2006). Notably, in active avoidance experiments, there are typically a group of “non-learners” that do not develop a consistent escape reaction and fail several trials, even at the end the learning session (26% in (Pradel, Schachner, and Schmidt 1999)). The active avoidance tests are also attractive because they may may utilize automatization without fish handling between trials. For example, crossing of the gates between the compartments can easily be detected with infrared beam detector, allowing for automatic turning on/off the CS and UCS (Xu et al. 2007). 13

This may also accelerate the learning session by shortening the time for each trial, thereby enhancing high-throughput drug or genetic screening. 3.4 Spatial alternation The aquatic spatial alternation tests usually deliver a stimulus to alternating sides of the tank (Williams, White, and Messer 2002). Accordingly, zebrafish must swim to the side where the stimulus will be before its delivery, and the response is measured as the distance to the stimulus side. Using food as an UCS, adult zebrafish can develop spatial alteration learning in just two days, whereas the long-term memory can be retrieved after 10 days period without training (Williams, White, and Messer 2002). A rewarding UCS can also be represented by the sight of a moving zebrafish shoal, delivered by computer screens (Pather and Gerlai 2009). Such learning protocols can consist of 30 trials of video presentation, being presented in a consistently alternating pattern between sides in a shuttle box apparatus. Zebrafish presented with animated images of conspecifics in an alternating manner learn to swim away from the animated shoal to the other side of the tank (where the image was expected) during the interstimulus interval (Pather and Gerlai 2009). Interestingly, such learning protocols demand not only valid spatial memory of tested animals, but also the ability to detect regularities in stimulus presentation, thereby evaluating multiple components of animal cognition. For example, this paradigm can be developed further by applying more complex patterns of stimuli and estimating the extent to which zebrafish can find these regularities and follow them. 3.5 Spatial and visual discrimination tasks Spatial and visual discrimination tasks investigate operant conditioning – represented by animals reproducing a previous behavior to receive positive reinforcement or escape a negative stimulus. These models include several traditional experimental tests (e.g., T-maze, hole-board maze, 3-chamber test, vertical plus maze) that mostly represent an aquatic translation of the same rodent tests (Myhrer 2003). Depending on the cues used for fish orientation, these tests can be used to assess associative or relational learning (Sison and Gerlai 2010). In associative learning tasks, the “correct” compartment (with positive reinforcement or lacking negative one) is marked by a salient 14

cue, such as a specific color (Colwill et al. 2005), a specific pattern, or a site of conspecific fish shoal (Al-Imari and Gerlai 2008). The cue can be placed in spatially different compartments to ensure direct association with the cue, but not with spatial arrangement. For the relational (mainly, spatial) learning, the “correct” compartment is not specifically marked, but is made constant in its position, according to the general maze and room environment (Sison and Gerlai 2010). Thus, the environment is considered to contain multiple subtle cues that must be detected in order to serve as a CS. The T-maze and other similar mazes usually consist of several arms, connected to the starting chamber by a door (that temporally constrains an animal to allow its acclimation and orientation before the trial start). After the animal acclimates to the starting chamber, the door is opened and the fish navigate the maze. The first trials are usually habituational, when fish get acquainted with the maze that may occur in groups and with all arms baited to reduce the stress (Grossman et al. 2011). The number of learning trials varies between protocols (e.g., 15-50) and is usually distributed between several training sessions. The test trial is conducted without the reward, and the entrances to different arms of the maze and the time spent in the “correct” arm, are evaluated. These measures not only characterize zebrafish learning per se, but also reflect overall exploratory activity in parallel. Interestingly, unpaired groups, in which the CS and UCS are presented at independent random locations, show more total arm entries than paired groups in which the CS and UCS are presented in close proximity, suggesting higher exploratory activity of unpaired animals that learn that the environment represents the possibility to find a rewarding UCS. (Sison and Gerlai 2010). Overall, the more possible choices that can be made in the maze apparatus – the wider range of responses can be measured, due to lowering of “no response”, basal value (Bilotta et al. 2005). For example, if by chance the two-armed maze produces a “no response” value of 50%, the threearmed maze improves this value to 33%. Although maze-based learning has a short trial period, it cannot be used as a high-throughput screening approach due to extensive human handling. For the approach with more choices, the hole-board arrangement can be applied instead, consisting of an open arena tank with centrally located board or box with holes, one of which is baited (Ruhl et al. 2014). Since the food location must not be obvious for zebrafish, the holes can be painted into the 15

color of the food bait (e.g., the red color of bloodworm), and the decreased latency to find food can be measured to quantify learning in this setup (Ruhl et al. 2014). 3.6 Stimulus generalization and discrimination Generalization is an extension of an already learned response to another, somewhat similar, stimulus. Generalization phenotypes in zebrafish can be measured by presenting several similar stimuli or the same stimulus in a new context. For example, zebrafish learn to avoid a red compartment paired with an electrical shock in a CPA apparatus, and continue to avoid a red arm in a t-maze apparatus, an effect that was greater in younger zebrafish (~1 year) compared to middle age (~2 years) zebrafish (Yu et al. 2006) Discrimination is a form of learning in which animals are trained to learn to distinguish between multiple stimuli options and respond to the correct stimuli that had been previously paired with an UCS (Sutherland and Mackintosh 2016). The T-maze paradigm discussed above is a good example of a discrimination task in zebrafish. In general, a 3-chamber apparatus consisting of a central starting chamber and left and right choice chambers colored blue or red, zebrafish learned to discriminate between the two colors options and avoid the assigned color that had been paired with a moving net stimulus (Arthur and Levin 2001). When the correct response parameters were reversed, performance initially declined but improved over time with repeated training, indicative of reversal learning. 3. Testing attention in zebrafish Attention is an important aspect of cognition which allows concentrating selectively on some aspect of information and ignoring others (Echevarria, Jouandot, and Toms 2011). This process may be active, with voluntary filtering of irrelevant stimuli, and passive, that results from involuntary responses to sensory inputs. In rodent studies, there are five components of attention: orienting (assessment of an animal's gaze and alertness), expectancy (a conditional readiness to the onset of stimulus), stimulus differentiation (a filter, calibrating the animals ability to differentiate between subjects), sustained attention (an ability to keep track of stimulus changes over time), and parallel processing (an ability to simultaneously identify different stimuli) (Bushnell 1998). Until 16

recently, attention functioning in zebrafish was usually inferred from results of learning tasks. For example, zebrafish are more attentive towards a pair of conspecifics that are interacting and fighting than non-interacting conspecifics, as measured by body position (orientation) of the observer fish (Abril-de-Abreu, Cruz, and Oliveira 2015). Such behavioral models and phenomena (e.g., social eavesdropping in zebrafish) become a useful, conceptually novel addition to the repertoire of zebrafish cognitive tasks in assessing attention, as they do not rely on discrimination of stimuli. A visual object recognition test designed to assess zebrafish attention employed a long and narrow tank with two static or animated geometric shapes (Braida, Ponzoni, Martucci, and Sala 2014). Zebrafish were first acclimated to the testing apparatus without stimulus presentation, then were presented a geometric shape for 10 min to become familiar with, and then later presented a different and novel shape stimulus for 10 min. Zebrafish tended to investigate the novel stimulus as exhibited by more time spent in vicinity of it, indicating increased attention and discrimination learning. Increased attention was given to moving shape stimuli in this model, suggesting that moving stimuli are powerful cues to be employed in studies of zebrafish attention (Braida, Ponzoni, Martucci, and Sala 2014). Cholinergic drug treatment with nicotine (nicotinic agonist) increased performance, while scopolamine (muscarinic antagonist) and mecamylamine (nicotinic antagonist) decreased performance. Moreover, attentional set formation has also been described in zebrafish. Zebrafish learned to discriminate between two color options as indicated by six entries into the color chamber designated as correct that had been paired with food delivery (Parker et al. 2012). Following learning, the correct color choices were reversed and training was repeated until the learning criteria were established once again. A novel pair of colors was then instituted, learning criteria were established, and the correct color choices reversed another time. Zebrafish learned to respond to the correct color in each of the four phases of this test, suggestive of an attentional set formation in this assay. The five-choice serial reaction time task is commonly employed to evaluate attentional performance in rodent models (Robbins 2002; Bari, Dalley, and Robbins 2008). An automated five-choice serial reaction time task has recently been developed for zebrafish, the apparatus of 17

which consists of two halves separated by a gate, with one half being a food delivery area paired with a green LED light, and the other half being the stimulus area consisting of five yellow LED lights (Parker, Brock, et al. 2014). Following a 1-week habitation, zebrafish were pre-trained during week two by being restricted to the food delivery area and administered food while the green LED was illuminated. During week three of pre-training, the gate was lifted and all five yellow LED lights were illuminated. Following an entry to any of the yellow LED apertures conditional reinforcement took place by illuminating the green food light and lowering the gate and administering a food reward once zebrafish approached the food compartment (Parker, Brock, et al. 2014). Zebrafish were then trained in the five-choice serial reaction time test in two phases, employing methods similar to the final phase of pre-training, except with only one stimulus light being illuminated and using a pre-stimulus interval consisting of a delay between the start gate being raised and the stimulus light being illuminated. Phase 1 (weeks 4-5) consisted of 30 seconds of stimulus duration with a one second pre-stimulus interval on a fixed schedule and phase 2 (weeks 6-9) consisted of the same stimulus duration with a five second pre-stimulus interval on a variable schedule. Zebrafish increased their response accuracy during both phases of training with no differences in approach or return latency to the yellow and green stimuli, respectively. This task is an undoubtedly valuable addition to the repertoire of cognitive tests employed with zebrafish. However, there are notable differences between zebrafish and mammalian performance in this task worth discussing. For example, zebrafish proportion of correct responses is lower (~60%) than in rodents (~80-90%), and zebrafish show longer response and return latencies (~5 s) than in rodents (~1 s) (Parker, Brock, et al. 2014). While zebrafish appear to be capable of forming anticipatory responses and attentional sets, there are likely differences between the cognitive capacity of fish and mammals. Developing attentional tasks in zebrafish further, paired with, for example, reverse and forward-genetic procedures to identify novel genes and pathways underlying cognitive (e.g., attention) deficits, is one promising application of zebrafish models for translational research of such complex brain pathologies as attention deficit hyperactivity disorder (ADHD).

18

4. Pharmacological modulation of cognitive phenotypes in zebrafish Zebrafish are particularly useful for pharmacology studies of their brain functioning due to easy and controllable methods of substance administration. For example, substances are commonly delivered by immersing the fish into the tank with substance solution to allow it to diffuse through the gills into the bloodstream (Goldsmith 2004). Moreover, until 8 dpf, the blood-brain barrier of zebrafish remains more permeable, allowing for penetration of the drugs that usually could not cross it (Fleming, Diekmann, and Goldsmith 2013). Thus, the zebrafish early larvae signaling pathways are quite amenable to pharmacological manipulations. However, addressing concerns of drug solubility and pharmacodynamics modeling, many other drugs may be administered orally (Kulkarni et al. 2014) or intraperitoneally (Parker et al. 2012), and complemented with assessing the drug concentration directly in the brain tissue. A traditional way to model long-term memory disturbances pharmacologically is to use drugs inhibiting protein synthesis, which may be applied to zebrafish studies. For instance, 10 M cycloheximide (Wolman et al. 2014) or 5 mg/ml puromycin (Hinz et al. 2013) stopped gene expression and prevented synaptic growth, responsible for long-term memory formation. Multiple substances that modulate the action of neurotransmitter receptors are also widely used in zebrafish cognitive studies. For example, nicotine is a widely used drug of abuse with potential properties of a cognitive enhancer, acting on the corresponding subtype of acetylcholine receptor (Levin 2011; Eddins et al. 2009; Matta et al. 2007; Levin and Chen 2004). It has a biphasic effect on learning in many vertebrate species, with low doses enhancing learning and high doses inhibiting learning (Levin et al. 2006). For zebrafish, these doses represent 50 and 100 mg/l, respectively, and begin acting within 3 min after immersion (Levin and Chen 2004). Nicotine affects zebrafish learning in various paradigms, including the aquatic T-maze (Braida, Ponzoni, Martucci, Sparatore, et al. 2014), three-chamber spatial discrimination (Levin et al. 2006; Levin and Chen 2004) and CPP (Kedikian, Faillace, and Bernabeu 2013) tests. The inhibiting effects of nicotine on learning at high doses can be explained by the development of non-specific effects which can be abolished by pretreatment with nicotine antagonist mecamylamine (Levin, Bencan, and Cerutti 2007; Levin et al. 19

2006). Scopolamine is an antagonist of muscarinic acetylcholine receptors and is frequently used to investigate learning associated with muscarinic receptors due to its pro-amnestic and explorationreducing properties. These effects were corroborated in zebrafish using a CPA paradigm (Kim et al. 2010; Richetti et al. 2011). Glutamatergic N-methyl-D-aspartate (NMDA) receptor is critical for learning, memory and synaptic plasticity. As a noncompetitive NMDA antagonist MK-801 (dizolcipine) acts as an amnestic agent, and in some zebrafish studies it was only effective when applied before, but not after, learning sessions (Xu et al. 2007). In other studies, MK-801 was effective both in treatment before and after training (Blank et al. 2009). The main differences between the two protocols was in the duration of training procedure (30 vs. 1 trials) and in the method of drug administration (injection vs. incubation in the antagonist solution). Data obtained in rodents also demonstrate mixed results, depending on the duration of training and the pause before the treatment. Effects of NMDA receptor modulation was also assayed in zebrafish larvae, where MK-801 and another noncompetitive NMDA receptor antagonist ketamine both inhibit startle response habituation without altering fish motor performance (Wolman et al. 2014). In contrast, a competitive NMDA antagonist DL-2-amino-5-phosphonopentanoic acid failed to affect C-start habituation in zebrafish larvae (Roberts et al. 2011), likely due to the specificity of the Mauthner cell circuitry pharmacology. Cannabinoid signaling plays an important role in the response to novelty and emotionally linked plasticity. Zebrafish express a cannabinoid receptor CB1 in their homolog of amygdala, the medial pallium. Acute treatment with cannabinoid agonists marijuana/tetrahydrocannabinol (THC) and WIN55,212-2, or antagonists rimonabant and AM-281, had no effect of zebrafish performance in the discriminative learning or conditioned avoidance tasks, whereas chronic treatment with agonists impaired aversive learning in the conditional avoidance task (Ruhl et al. 2015). CB1 blockage induced general enhancement of cognitive functions, whereas THC administration in zebrafish impaired spatial, but not associative, learning (Ruhl et al. 2014).

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Some substances with a wide specificity to different CNS receptor classes affect learning in zebrafish. For example, lysergic acid diethylamide (LSD) somewhat improves zebrafish performance in the T-maze, with a reduction of anxiety-like behavior (Grossman et al. 2010). Salvinorin A, the hallucinogenic compound from the Salvia plant, was found to be reinforcing for zebrafish, increasing the time the fish spent in the drug-associated compartment in the CPP model, whereas antagonists for kappa-opioid receptors and CB1 individually blocked the reinforcing properties of salvinorin A in zebrafish (Braida et al. 2007). Since histamine exerts conflicting action on learning in rodent models, zebrafish studies can help clarity its basic effects on learning. For example, supplementation with the histamine precursor L-histidine facilitates associative learning in zebrafish (Cofiel and Mattioli 2009), whereas the suppression of histamine synthesis by histidine decarboxilase inhibitor -FMN worsens their Tmaze performance (Peitsaro et al. 2003). The selective serotonin reuptake inhibitor (SSRI) fluoxetine is also of special interest in cognitive studies, because it is widely used clinically as an antidepressant medication. In zebrafish it impairs performance on a modified plus maze without otherwise affecting locomotion or activity levels (Pittman and Lott 2014), while diminishing anxiety-related behavior (also see (Egan et al. 2009)). Nitric oxide (NO) signaling is another important aspect of memory consolidation, and NO metabolism is targeted by many substances, some of which have already been tested in zebrafish. However, the effects for NO synthesis inhibitors are controversial since the injection of L-NAME after active avoidance training trials impairs memory retention in zebrafish and mammals (Xu et al. 2007), suggesting a shared role of this substance in memory consolidation, whereas ammonium pyrrolidine dithiocarbamate enhanced zebrafish larvae habituation (Wolman et al. 2014). Piracetam is a derivative of γ-aminobutyric acid and a promising nootropic drug with an unidentified target, with overt cognitive-enhancing and anxiolytic actions in humans and rodents (Grossman et al. 2011). In adult zebrafish, piracetam enhances the T-maze performance, but only when introduced chronically (Grossman et al. 2011). An interesting model of experimentally evoked cognitive deficit, chronic glucose exposure (Capiotti et al. 2014) supports the link between diabetes mellitus and neurodegeneration (Biessels et 21

al. 2006; de la Monte 2009). For example, incubation of fish in 111 mM glucose for 2 weeks induces memory impairments and acetylcholine dysregulation. In contrast, glucose wash-out slightly improves the phenotype in this model, whereas prominent improvements are achieved by the galantamine (acetylcholine esterase inhibitor) treatment (Capiotti et al. 2014). Methylphenidate is a drug used for treatment of attention deficit hyperactivity disorder (ADHD) (Volkow and Swanson 2003). It inhibits dopamine and (to a less extent) norepinephrin, elevating their levels in the synaptic cleft. In zebrafish, developmental methylphenidate exposure at 1-5 dpf reduces accuracy on the three-chamber spatial discrimination task when the fish were tested in young adulthood (Levin 2011). This suggests that the developmental nature of this exposure may have altered behavioral plasticity in zebrafish, manifesting as impairments in adapting to environment. Methylphenidate also increases anticipatory responses in the five-chamber serial reaction time task in adult zebrafish, along with the number of trials completed in a session, similar to what is observed in rats (Navarra et al. 2008) 5. Neurotoxicological studies Rich evidence on drug modulation of zebrafish cognition comes from neurotoxicology studies. For example, ethanol is one of the most widespread toxins that causes cognitive deficits following developmental exposure in both larval and adult fish (Carvan et al. 2004; Fernandes et al. 2014; Parker, Annan, et al. 2014; Parker et al. 2016). These changes have been suggested to represent a complex of fetal alcohol spectrum disorder (FASD) with cognitive deficits (Li et al. 2015; Luchiari, Salajan, and Gerlai 2015; Pittman and Lott 2014). However, some studies show somewhat improved CPP learning and reduced seeking behavior by low concentrations of alcohol treatment (Chacon and Luchiari 2014) - the results that can be explained by anxiolytic activity of an acute alcohol treatment, which, in turn, may facilitate learning. A recent model of Parkinson‟s disease in zebrafish was established using paraquat herbicide toxicity (Bortolotto et al. 2014), the chronic exposure to which is neurotoxic for dopaminergic neurons, producing a characteristic decrease in locomotor activity and spatial memory, but unaltered anxiety. Other models of Parkinson disease in zebrafish can be based on 1-methyl-422

phenyl-1,2,3,6-tetrahydropyridine (MPTP), 6-hydroxydopamine (6-OHDA) or rotenone, meriting further studies of zebrafish cognitions. The recent work associated polybrominated diphenyl ether (PBDE) flame retardants with cognitive and other behavioral deficits showed that zebrafish larvae exposed to PBDE-47 yield deficits in memory retention (Truong et al. 2014). Zebrafish exposed to toxic pesticide chlorpirifos during juvenile stage showed significantly impaired habituation and facilitation, than their unexposed counterparts (Eddins et al. 2010). The popular solvent dimethylsulphoxide (DMSO) may produce developmental toxicity, affecting cognition. For example, a low 0.1 % DMSO exposure of larvae impairs learning (Chen, Wang, and Wu 2011; Truong et al. 2014), albeit typically not affecting adult zebrafish behavior (Sackerman et al. 2010). Therefore, DMSO usage for dissolving substances for screening purposes in zebrafish models must be carefully controlled. Finally, genotoxic stress of gamma irradiation produces an early decline in zebrafish cognitive abilities: animals irradiated at 6 months of age, developed cognitive deficits 7 months later (Yu et al. 2006). However, the process was associated with other signs of senescence, including impaired motor performance, lower muscle mass, poorer reproductive abilities, slower fin regeneration and decreased melatonin production (Yu et al. 2006). Thus, such effects may be rather non-specific to be considered for cognitive deficit modeling, and investigating the causal relationship of genotoxic stress on memory impairments is an interesting avenue for future research. 6. Selected genetic models relevant to cognitive analyses in zebrafish Zebrafish have long been used in genetic studies, which have recently been supported by the sequencing of the zebrafish genome published in 2013 (Howe et al. 2013). Zebrafish possess a high degree of genomic similarity with other vertebrate species, and 71% of human genes have at least one zebrafish orthologue (Amores et al. 2011; Postlethwait et al. 2000). Compared to humans, many regions of the zebrafish genome have undergone inversions and translocations, and some have duplicated. Compared to other vertebrates, the zebrafish ancestor underwent an additional round of whole-genome duplication called the teleost-specific genome duplication (Meyer and Schartl 1999), resulting in more than one copy of neurotransmitter receptor or transporter genes 23

(Musa, Lehrach, and Russo 2001). For instance, zebrafish possess two isoforms of the serotonin transporter - sertA and sertB (Wang et al. 2006). A large body of zebrafish neurodevelopmental data further implicate genes of interest in modeling cognitive impairments, even though the behavioral data to support such links remains rather sparse (Table 4). For example, the DISC1 (disrupted in schizophrenia 1) and NRG1 (Neuregulin 1) are well-known candidate genes whose down-regulation has been linked to schizophrenia. In zebrafish, their morpholino knockdown promotes hindbrain oligodendrocytes and cerebellar neurons deficiency, likely due to the inhibition of Olig2-positive precursors differentiation into neurons and oligodendrocytes (Wood et al. 2009). Heterozygous acetylcholine esterase deficiency (achesb55/+) mutation increases the level of extracellular acetylcholine, causing delayed cognitive impairments without affecting young 1 year old fish (Yu et al. 2006). Also, faster relearning after extinction in young acetylcholinesterase mutants vs. young wild-type fish indicates faster establishment of an attention set. Likewise, the mutant fish demonstrated more intense alternation patterns (which can be interpreted as an elevated exploratory behavior), thereby further implicating the acetylcholine system in zebrafish ageing as in human ageing (Yu et al. 2006). Genetic modeling of type 1 neurofibromatosis can also be relevant to learning deficits (Shin et al. 2012). In humans, this disease is evoked by the mutation in the NF1 gene, coding for neurofibromin protein (which normally inactivates RAS proto-oncogene). The mutation of this gene‟s orthologue in zebrafish recapitulates the main features of the disease, including spontaneous tumors, coloration abnormalities as well as learning and motor deficits (Shin et al. 2012). Learning deficits in the larvae in this model were further corroborated by the test of habituation of C-start response (Shin et al. 2012). Pharmacological manipulation of downstream signaling targets of NF1 reveals the difference of the roles of each pathway – RAS or adenilate cyclase – in zebrafish memory formation (Wolman et al. 2014). Inhibition of downstream targets of RAS proto-oncogene – MAPK and PI3K – improves long-term memory consolidation and recall, but does not affect the short-term memory. Conversely, acute pharmacological enhancement of cAMP signaling restores short, but not long-term memory (Wolman et al. 2014). 24

Fragile X syndrome (FXS) is another genetic disease with cognitive impairments which can be modeled in zebrafish (Qian et al. 2013). FXS is the most frequent inherited form of human mental retardation, caused by the absence of FMRP protein, coded by the FMR1 gene. The transcription of this gene is silenced by the CGG repeat expansion in the 5‟-untranslated region of the gene. Zebrafish FMRP has 72% amino acid identity to that of human, and fmr1 knockout zebrafish possess anxiolytic-like responses (increased exploratory behavior in the light/dark and open-field tests), impaired avoidance learning, suppressed long-term potentiation and enhanced long-term depression – the phenotypes which strikingly parallel human and rodent FXS (Qian et al. 2013). As already mentioned, the high-throughput automatic startle habituation test screened the zebrafish mutants with impaired learning from the library of ethylnitrosousea-induced mutants (Wolman et al. 2014). This „forward genetics‟ approach identified several well-known (e.g., fish lacking a pyruvate carboxylase a enzyme, responsible for the glutamate synthesis in neurons) as well as previously unknown mutants (e.g., fish lacking pregnancy associated plasma protein-aa, PAPP-AA). PAPP-AA is extracellular metalloprotease that enhance local insulin-like growth factor (IGF) signaling by cleaving IGF binding protein 4 (IGFBP4), which normally restricts IGF from signaling through cell-surface IGF receptors. As IGF receptor can activate downstream PI3K, restored habituation in this model was achieved with PI3K- and Akt-activating substances (Wolman et al. 2014). Genetic mutation of Per1b has also been reported in zebrafish, mimicking ADHD (Huang et al. 2015). The mutant zebrafish demonstrate hyperactivity and impulsivity with memory impairments and lower dopamine level, characteristic for clinical ADHD. This zebrafish gene is orthologous to the human PER1, the gene that regulates dopamine levels by controlling its metabolism and maintaining dopaminergic neurons differentiation and viability, and linked to human ADHD symptoms as well as for circadian rhythms regulation. Predictably, the Per1b mutant zebrafish responded to ritalin and deprenyl, the two treatments officially recommended for the ADHD patients (Huang et al. 2015). Another recent genetic model of zebrafish ADHD-like 25

behavior is based on down-regulating the lphn3.1 gene, a zebrafish orthologue of a human gene coding for Latrophilin 3 (LPHN3), a putative adhesion-G protein-coupled receptor (Lange et al. 2012). In this model, loss of lphn3.1 impairs patterning of dopaminergic neurons in the ventral diencephalon, and causes a hyperactive, impulsive motor phenotype in zebrafish, which can be reversed by anti-ADHD drugs methylphenidate and atomoxetine (Lange et al. 2012). Collectively, such interesting translational studies emphasize the growing role of zebrafish in modeling human brain disorders with aberrant cognitive phenotypes, such as ADHD. 7. Conclusions Zebrafish have gained much attention as an intermediate model between the in-vitro cellular/molecular methods and the expensive in-vivo mammalian assays for mechanism investigation and drug discovery (Stewart, Braubach, et al. 2014; Kalueff, Echevarria, and Stewart 2014). The zebrafish is also rapidly gaining popularity due to good genetic and developmental characterization, as well as straightforward amenability to new research fields, such as Cas9 genome editing and optogenetics (Auer and Del Bene 2014; Del Bene and Wyart 2012). Thus, the main obstacle for zebrafish cognitive studies remains to overcome the prepossession about weakness of the fish learning, that is nevertheless already fading with every new paper appearing in the field. Clearly, there is much work to be done in the field of zebrafish cognition and its pharmacological and genetic manipulation, especially since the whole classes of important drugs remain understudied in zebrafish, including cognitive enhancers, antipsychotics and drugs of abuse. The standard for rodent pharmacological models of cognitive abnormalities are not established in the details required for their wide application. For example, while zebrafish have proved their utility as a model for the search of mnemotropic cognitive enhancers (Grossman et al. 2011), there was only one high-throughput cognitive screening of well-known substances influencing CNS signaling (Wolman et al. 2014). Thus, although this study identified new potential genetic targets for cognitive processes modulation, data on new compounds acting on learning or attention remain scarce. 26

Furthermore, there are clear limitations of zebrafish cognitive models and cross-species differences to consider in a balanced manner here. For example, zebrafish perform worse than 4 other teleost species (i.e., guppies, Siamese fighting fish, angelfish, redtail splitfin) in a task discriminating between two groups of black geometric figures differing in number or discriminating between a filled black triangle or a black outline of a circle (Agrillo et al. 2012). However, zebrafish are capable of discriminating between 3D objects differing in color, shape, and color+shape, but not between objects differing in size (Oliveira et al. 2015). Thus, it may be that zebrafish do not perform well in tasks in which the stimuli employed differ in number/size because the differences are not salient enough for zebrafish to detect. The gene-duplication event in teleosts may also increase the difficulty of translational research between zebrafish and humans (Kalueff, Echevarria, and Stewart 2014). However, we view the genome duplication in zebrafish as both the difficulty and the opportunity for translational research. For example, with the availability of excellent powerful genetic tools for zebrafish (Auer and Del Bene 2014; Aluru, Jenny, and Hahn 2014), knocking-in/out or -down one of the two copies of the gene may provide additional insights into CNS genetic pathogenetic causes, where a better granularity of genetic ablation may be a clear advantage (rather than a difficulty) for translational research, especially evident for mutations which are lethal in mammals but can be viable in zebrafish (Kalueff, Echevarria, and Stewart 2014). Considering neurodegenerative diseases, zebrafish are quite often used to assess the role of implicated genes in normal development, but are rarely used to assess the behavior of mutant fish and even more, to model disease states. For example, presenilin 1 (psen1) is one of the key genes in Alzheimer disease pathogenesis. Although its knockout in mice is perinatally lethal (Shen et al. 1997), producing skeletal abnormalities, neurogenesis impairments and brain hemorrhages, the psen1 knockdown zebrafish are viable, fertile and develop normally (Sundvik, Chen, and Panula 2013). These fish have an unusual dynamic of histaminergic neurons, in 7-dpf animals lower, and in 1-year fish - higher than in controls (likely due to disrupted Notch signaling, that regulates neuron formation). While the larval psen1 knockdown fish also display lower locomotor activity, their cognitive phenotypes yet remain to be investigated. 27

Among teleosts, the natural history of zebrafish does not impose on them specific ecological demand for more developed cognitive capacities (for example, unlike zebrafish, cleaner wrasse seem to have more developed specific abilities due to their cooperative behavior with client reef fish (Salwiczek et al. 2012)). Likewise, zebrafish models may be further problematic for studying cognition because they are gregarious (i.e., live in shoals), which stresses them when placed in isolation in experimental setups to solve cognitive or other behavioral tasks (Kalueff, Echevarria, and Stewart 2014). However, we argue that experimenters can turn this potentially confounding response of zebrafish to isolation into an advantage, because more can now be made of studies of zebrafish social cognition (and its deficits), which may not only assess stress associated with isolation, but can also target a rather complex animal cognitive ability. Thus, the case for zebrafish as a model for cognitive studies needs to critically consider their advantages and disadvantages, in order to better justify the developing utility of zebrafish in this field, especially empowered by the techniques available for imaging and manipulating neural circuits in larval and adult fish (Stewart, Braubach, et al. 2014; von Trotha, Vernier, and Bally-Cuif 2014; Oliveira 2014; Parker et al. 2013). Additionally, considering zebrafish developmental stage is important for selecting a good model for cognitive assays. For example, the well-recognized advantages of using zebrafish as a model organism are mainly present at the larval stage, when the transparency of the body allows cell imaging in vivo (even when performing a behavioral task) (Krishnan et al. 2014; Del Bene and Wyart 2012). From purely behavioral standpoint, the advantages of using adult zebrafish for cognitive studies are not much better than of using other teleost fish, such as goldfish (that has been traditionally used in the past in comparative studies of learning and memory). However, combined with a fully sequenced zebrafish genome (Howe et al. 2013), the availability of transparent zebrafish strains (e.g., casper), and the development of newest gene-editing tools for this species (Auer and Del Bene 2014), this markedly increases the value of adult zebrafish for translational neuroscience and cognitive research (Stewart, Ullmann, et al. 2014).

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Finally, current investigations of zebrafish cognitive models appear to be mainly focused on replicating results obtained in mammalian models (Caramillo et al. 2014; Stewart et al. 2011; Wong et al. 2010; Levin 2011; Eddins et al. 2009; Levin and Chen 2004). Instead, we argue that one of the main strategic goals of zebrafish modeling can be to identify and target the functions that are difficult to treat in more complicated models, thereby allowing for a more clear-cut phenotypic dissection and a faster rate of therapeutic discovery. Combined with good characterization of zebrafish genetics and development, this can generate novel important insights in the near future.

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Table 1. Summary of major cognitive deficits

Deficit

Details and phenotypes

Delirium

A short-term disturbance of consciousness, that is accompanied by a quick change between mental states and may be caused by intoxication or deprivation of some substance

Dementia

A group, mainly defined by memory loss, which is accompanied by other cognitive function disturbances, for example attentional deficits. It is usually caused by trauma, infection or neurodegenerative disease

Amnesia

A discreet memory impairment without symptoms of other cognitive disorders, the reasons of which are usually unknown

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Table 2. Summary of major cognitive disorders

Disorder

Details and phenotypes

Schizophrenia

Problems with attention, memory, judgment and decision-making

Autism spectrum disorder

Aberrant social learning

Attention deficit hyperactivity disorder

Selective attention problems

Posttraumatic stress disorder

Decreased memory, planning and problem-solving

Drug addiction

Decreased learning and reasoning

Neurodegenerative disorders

Accelerated cognitive ageing in Alzheimer and Parkinson disease

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Table 3. Selected examples of positive and negative motivational states evoked in zebrafish neurobehavioral experiments

Factors

Experimental details

References

Food

Brine shrimps, bloodworms or other small-sized foods

(Ruhl et al. 2014)

Visual

Sight of zebrafish shoals (real or video) as a social reward

(Al-Imari

Positive

and

Gerlai

2008) Bright red or green colors of natural preference

(Avdesh et al. 2012)

Visual/Vestibular Preference of a deeper container

(Peitsaro et al. 2003)

Pharmacogenic

(Mathur, Lau, and Guo

Preference for morphine and other drugs of addiction

2011; Mathur, Berberoglu, and Guo 2011; Bretaud et al. 2007) Olfactory

Unfamiliar, unrelated opposite sex conspecific olfactory (Gerlach and Lysiak 2006) cues

Negative Visual

Predator image (e.g., Indian leaf fish)

(Gerlai 2016)

Visual

Aversion to the blue color

(Avdesh et al. 2012)

Tactile

Turbulence from unfamiliar fish net or stick

(Arthur

and

Levin

2001) Nociceptive

Mild electric shock

(Pradel, Schachner, and Schmidt 1999)

Pharmacogenic

Avoidance of selected anxiogenic drugs

(Braida et al. 2007)

Others

Swimming restriction in small space

(Levin and Chen 2004)

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Table 4. Selected genetic models relevant to cognitive analyses in zebrafish Zebrafish mutant

Relevant disorder

Behavioral assays used

References

achesb55/+

Alzheimer‟s disease

Entrainment to a spatial cue, entrainment (Yu et al. 2006) to a temporal cue, CPP, T-maze

nf1a + nf1b

Neurofibromatosis

C-start response habituation

(Shin et al. 2012)

type 1 O-start response habituation fmr1

Fragile X syndrome

per1b

ADHD

(Wolman et al. 2014) Light/dark test, open-field test, inhibitory (Ng, Yang, and Lu 2013) avoidance Inhibitory avoidance, two-choice serial (Huang 2015) reaction time test, mirror attack test,

et

al.

locomotor activity lphn3.1

ADHD

Locomotor activity,

(Lange et al. 2012)

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