developing zebrafish models of complex phenotypes ...

DEVELOPING ZEBRAFISH MODELS OF COMPLEX PHENOTYPES RELEVANT TO HUMAN BRAIN DISORDERS AN ABSTRACT SUBMITTED ON THE 10th DAY OF JANUARY 2013 TO THE NEUROSCIENCE PROGRAM IN PARTIAL FULFILLMENT OF THE REQUIREMENTS OF THE SCHOOL OF SCIENCE AND ENGINEERING OF TULANE UNIVERSITY FOR THE DEGREE OF DOCTOR OF PHILOSOPHY BY __________________________________________ JONATHAN MICHAEL CACHAT APPROVED: ___________________________ Jeffrey Tasker, Ph.D., as proxy for Allan Kalueff, Ph.D., Director

___________________________ David Corey, Ph.D. ___________________________ Jill Daniel, Ph.D.

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___________________________ Benjamin Hall, Ph.D.

Abstract Zebrafish (Danio rerio) are offering novel perspectives to investigate the nervous system as a genetically and molecularly tractable in-vivo model of complex vertebrate behavior. The balance of low cost, experimental agility and phenotypic complexity, unique to zebrafish, empowers researchers to study biochemical regulators of development, behavior and disease pathogenesis in ways previously unapproachable. Larval zebrafish assays have demonstrated the value of integrating behavioral tests with high-throughput quantification techniques by successfully identifying psychotropic compounds and predicting neurological targets based on large-scale analysis of variation in behavioral responses. Pre-clinical drug discovery and behavioral genetics stands to benefit greatly from high-throughput screening assays using adult zebrafish. However, a characterization and quantification technique of adult zebrafish behavioral phenotypes requires standardization before such assays can be realized. This dissertation provides characterization of adult zebrafish behavior following ethological and pharmacological experimental treatments in affective and social domains. Behavior is quantified manually, as well as using automated video-tracking software, and correlated with a physiological biomarker (i.e. whole-body cortisol) to verify phenotypic states. In addition, a novel method of neurophenotyping using three-dimensional swim trajectory reconstructions is presented to enable rapid identification of treatment specific movement patterns. Collectively, this research provides a foundation for future studies pursing high-throughput behavioral phenotyping in adult zebrafish, and their i application to modeling complex human disorders. v

DEVELOPING ZEBRAFISH MODELS OF COMPLEX PHENOTYPES RELEVANT TO HUMAN BRAIN DISORDERS A DISSERTATION SUBMITTED ON THE 10th DAY OF JANUARY 2013 TO THE NEUROSCIENCE PROGRAM IN PARTIAL FULFILLMENT OF THE REQUIREMENTS OF THE SCHOOL OF SCIENCE AND ENGINEERING OF TULANE UNIVERSITY FOR THE DEGREE OF DOCTOR OF PHILOSOPHY BY __________________________________________ JONATHAN MICHAEL CACHAT APPROVED: ___________________________ Jeffrey Tasker, Ph.D., as proxy for Allan Kalueff, Ph.D., Director

___________________________ David Corey, Ph.D. ___________________________ Jill Daniel, Ph.D. ___________________________ Benjamin Hall, Ph.D.

© Copyright by JONATHAN MICHAEL CACHAT, 2013 All Rights Reserved

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Acknowledgements I would like to thank first and foremost, Dr. Allan Kalueff, for allowing me to perform this research in his laboratory, and for his mentorship throughout my graduate career without which this would not have been possible. His continued support and dedication to pushing my abilities has instilled a mentality that anything is possible, given hard work and dedication. I would also like to thank him for including me on trips to international conferences and workshops, which allowed me to experience the joy of sharing science and ideas with an international community. Over the years, there have been several personnel in the Kalueff lab, from undergraduates to medical students, all of which I am very thankful for meeting and working with. In particular, I would like to thank Peter Hart, Peter Canavello, Adam Stewart, Siddharth Gaikwad, Evan Kyzar, Eli Utterback, Chris Collins, Jeremy Green, and Kyle Robinson for their dedication to both scientific and social successes. The importance of their support as lab members and close personal friends cannot be overstated. This research was also supported by a number of grants including: Tulane Synergy Grant, Louisiana Board of Reagents P-Fund, OPT-IN, NIDA R03 Grants awarded to Dr. Kalueff. Selected drug treatments were kindly supplied by the NIDA Drug Supply Program. My participation in the 2010 Society for Neuroscience conference was supported by Greater New Orleans Society for Neuroscience (GNOSN) Chapter’s Graduate Student Travel Award.

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I would like thank the Tulane University Neuroscience Program, including my committee members, Dr. Jill Daniel, Dr. Benjamin Hall, and Dr. David Corey, as well as Dr. Jeffery Tasker and Sherrie Calogero, who have provided invaluable advice, wisdom and support throughout my graduate career. Moreover, I am grateful to Dr. Ramgopal Mettu in Computer Science at Tulane University for assistance with MatLab and Python script programming. I would like to also thank Dr. Susan Kennedy and Dr. Maryann Martone for their shared vision of neuroscience research, advice and appreciation of challenging the status quo. I am also very grateful for collaborators on this project, Noldus Information Technologies, particularly Ruud Tegelenbosch, Dr. Lucas Noldus and Dr. Fabrizo Grieco, for support, feedback and access to EthoVision XT video-tracking software, Track3D and Social Interaction modules. I also acknowledge my collaborators at the University of Zurich, Ali Soleymani and Dr. Somayeh Dodge, for their assistance in applying movement pattern and trajectory analysis techniques to zebrafish spatiotemporal data, as well as assistance with MatLab programming. In addition, my family, especially my parents John and Holly Cachat, deserve special thanks for unwavering support and encouragement throughout my entire academic career, as well as my friends, both local and across the nation, who have always been there for me and made sure I was not alone during long hours, late nights and weekends dedicated to science.

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Table of Contents Chapter 1. Zebrafish (Danio rerio) ................................................................................. 1 1.1. Biology and Behavior ........................................................................................... 2 Chapter 2. Zebrafish in Neurobehavioral Research .................................................... 6 2.1. Larval Models ....................................................................................................... 9 2.2. Adult Models ...................................................................................................... 10 2.3. Challenges and Problems .................................................................................. 27 Chapter 3. Overview of Dissertation Research Strategy .......................................... 31 3.1. Experimental Strategy ........................................................................................ 33 Chapter 4. Methods and Materials ............................................................................... 38 4.1. Animals ................................................................................................................ 38 4.2. Drug Treatments ................................................................................................. 41 4.3. Behavioral Tests .................................................................................................. 44 4.4. Automated Behavior Quantification and Analysis ........................................ 52 4.5. Three-Dimensional Swim Path Reconstructions............................................. 57 4.6. Advanced Spatiotemporal Trajectory Analysis .............................................. 59 4.7. Whole-body Cortisol Concentration Assay ..................................................... 62 4.8. Statistical Analysis .............................................................................................. 62 4.9. Data Sharing ........................................................................................................ 63 Chapter 5. Modeling Affective Phenotypes ............................................................... 65 5.1. Models of Fear: Ethologically-Relevant Stimuli .............................................. 66 5.2. Models of Anxiety: Exposure to Pharmacological Treatments ..................... 72 5.3. Discussion and Translational Value ................................................................. 84 5.4. Conclusions ......................................................................................................... 91 Chapter 6. Modeling Phenotypes Related to Drugs of Abuse and Withdrawal .. 94 6.1. Drugs of Abuse ................................................................................................... 95 6.2. Acute and Repeated Withdrawal Paradigms .................................................. 98 6.3. Discussion and Translational Value ............................................................... 104 6.4. Conclusions ....................................................................................................... 107 Chapter 7. Modeling Phenotypes of Hallucinogenic Drug Action....................... 110 7.1. Lysergic acid diethylamide (LSD) .................................................................. 111 7.2. 3, 4-Methylenedioxymethylamphetamine (MDMA).................................... 118 7.3. Ibogaine.............................................................................................................. 120 iv

7.4. Discussion and Translational Value ............................................................... 128 7.5. Conclusions ....................................................................................................... 132 Chapter 8. Innovative Information Technology Approaches to Zebrafish Neurobehavioral Models ............................................................................................. 140 8.1. Automated Behavioral Quantification: Caveats, Correlations and Movement Parameters ................................................................................................................ 142 8.2. Three-Dimensional Reconstructions .............................................................. 155 8.3. Advanced Trajectory Analysis Techniques ................................................... 167 8.4. Discussion .......................................................................................................... 170 Chapter 9. Conclusion .................................................................................................. 173 9.1. Summary of Results.......................................................................................... 173 9.2. Future Directions for Translational Neurobehavioral Research with Adult Zebrafish ................................................................................................................... 177 Appendix A: Publications Related to Dissertation Research ................................ 183 Appendix B: Automated Analysis Scripts ................................................................ 186 References ...................................................................................................................... 200 Bibilography .................................................................................................................. 243

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List of Tables Table 1: Major Phenotypic Domains Modeled in Zebrafish ............................................ 11 Table 2: Experimental Treatments used in Behavioral Tests ............................................ 42 Table 3: Correlation of Manually and Automated Endpoints in Region Based Endpoints ................................................................................................................................ 145 Table 4: Correlation of Manually and Event-based Endpoints in Complex Behavioral Responses ................................................................................................................................ 146 Table 5: Evaluating Movement Parameter Values During Complex Behavioral Events ................................................................................................................................................... 147

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List of Figures Figure 1: Larval and Adult Zebrafish...................................................................................... 2 Figure 2: Natural Geographic Distribution of Zebrafish .................................................... 3 Figure 3: Adult Zebrafish Stress Axis ..................................................................................... 5 Figure 4: Experimental Organisms and Complexity of Behavioral Phenotypes ............. 7 Figure 5: Dissertation Research Approach ........................................................................... 32 Figure 6: Outline of Experimental Strategy ......................................................................... 35 Figure 7: Drug Withdrawal Treatment Schedules .............................................................. 43 Figure 8: Experimental Design of Behavioral Tests ........................................................... 45 Figure 9: Novelty-based Exploration and Affective Behavioral Tests ............................ 49 Figure 10: Social Behavior Tests ............................................................................................ 50 Figure 11: 3D Trajectory Reconstructions Coordinate Framework .................................. 58 Figure 12: Calculation of Straightness Index ....................................................................... 61 Figure 13: Indian Leaf fish and Oscar fish ........................................................................... 67 Figure 14: Effects of Sympatric and Allopatric Predator Exposure in NTT.................... 68 Figure 15: Effects of Acute Alarm Pheromone Exposure in NTT ..................................... 70 Figure 16: Representative Trajectory (2D) of Acute Alarm Pheromone in NTT ........... 71 Figure 17: Effects of Prolonged Alarm Phermone Exposure in NTT ............................... 71 Figure 18: Effects of Acute Caffeine Treatment in NTT .................................................... 73 Figure 19: Representative Trajectory (2D) of Acute Caffeine treatment in NTT........... 74 Figure 20: Effects of Acute Pentobarbital Treatment in NTT ........................................... 75 Figure 21: Effects of Acute Fluoxetine Treatment in NTT ................................................ 76 Figure 22: Effects of Chronic Fluoxetine Treatment in NTT............................................. 77 Figure 23: Representative Trajectory (2D) of Chronic Fluoxetine exposure in NTT .... 78 Figure 24: Effects of Acute Ethanol Treatment in NTT...................................................... 79 Figure 25: Effects of Chronic Ethanol Treatment in NTT.................................................. 79 Figure 26: Representative Trajectory (2D) of Chronic Ethanol Treatment in NTT ....... 80 vii

Figure 27: Effects of Acute Nicotine Treatment in NTT .................................................... 82 Figure 28: Representative Trajectory (2D) of Acute Nicotine Treatment in NTT ......... 83 Figure 29: Summary of Ethologically-Relevant Stimuli in NTT ...................................... 84 Figure 30: Summary of Pharmacological Treatments in NTT .......................................... 87 Figure 31: Effects of Acute Cocaine Treatment in NTT ..................................................... 95 Figure 32: Effects of Acute Morphine Treatment in NTT ................................................. 96 Figure 33: Effects of Chronic MorphineTreatment in NTT .............................................. 97 Figure 34: Representative Trajectory (2D) of Chronic Morphine treatment in NTT .... 98 Figure 35: Behavioral Effects of Ethanol Withdrawal in NTT ........................................ 101 Figure 36: Effects of Caffeine Withdrawal in NTT........................................................... 102 Figure 37: Effects of Repeated Morphine Withdrawal in NTT ...................................... 103 Figure 38: Summary of Drugs of Abuse and Withdrawal Treatments in NTT ........... 104 Figure 39: Overview of Treatments Modulating Affective Domains in Zebrafish .... 108 Figure 40: Effects of LSD Treatment in NTT ..................................................................... 114 Figure 41: Effects of LSD Treatment in NTT and on Swim Path ................................... 115 Figure 42: Effects of LSD Treatment in Light Dark Box .................................................. 116 Figure 43: Effects of LSD Treatment in Open-Field Test ................................................ 116 Figure 44: Effects of LSD Treatment in Social Prefence and Shoaling Tests ............... 117 Figure 45: Effects of LSD on whole-body cortisol concentrations ................................ 118 Figure 46: Effects of Acute MDMA Treatment in NTT ................................................... 119 Figure 47: Representative (2D) Swim Paths of MDMA Treatment in NTT ................. 120 Figure 48: Effects of Acute Ibogaine Treatment in NTT.................................................. 123 Figure 49: Effects of Acute Ibogaine Treatment in Light Dark Box ............................... 124 Figure 50: Effects of Acute Ibogaine Treatment in Open-Field Test and Homebase Formation ................................................................................................................................ 125 Figure 51: Effects of Acute Ibogaine Treatment on Social Domains ............................. 127 Figure 52: Summary of Hallucinogenic Treatments in NTT .......................................... 128 Figure 53: Effects of Body Wobble Noise on Turn Angle Values .................................. 143 viii

Figure 54: Using Track Smoothing to Eliminate Body Wobble Noise .......................... 144 Figure 55: Optimizing Automated Detection of Freezing Bouts .................................... 149 Figure 56: Optimizing Automated Detection of Freezing Duration.............................. 150 Figure 57: Optimizing Automated Detection of Erratic Movements ............................ 152 Figure 58: Grouping Anxiogenic and Anxiolytic Manipulations and Correlating Manual and Automated Endpoints ..................................................................................... 154 Figure 59: Behavioral Evaluation with 3D Temporal Reconstructions ......................... 156 Figure 60: Behavioral Evaluation with 3D Spatial Reconstructions ............................. 157 Figure 61: Using 3D Reconstructions to Optimize Mobility Threshold ....................... 158 Figure 62: Representative 2D Swim Traces: Control, Alarm Pheromone and Fluoxetine ................................................................................................................................................... 159 Figure 63: Representative 3D Temporal Reconstruction of Acute Alarm Pheromone 160 Figure 64: Representative 3D Temporal Reconstruction of Wild-Type Control ......... 161 Figure 65: Anxiogenic Movement Patterns – 3D Temporal ............................................ 163 Figure 66: Anxiolytic Movement Patterns – 3D Temporal .............................................. 164 Figure 67: Hallucinogenic Movement Patterns – 3D Temporal ..................................... 165 Figure 68: Representative Temporal and Spatial 3D Reconstructions of Nicotine and LSD-Evoked Swimming Profiles ........................................................................................ 166 Figure 69: Effects of Track Smoothing on Fractal d .......................................................... 168 Figure 70: Arena Segmentation Profiles illustrate differences between experimental treatments ................................................................................................................................ 169 Figure 71: Integrative Analysis of Zebrafish Trajectories to Identify TreatmentSpecific Profiles ...................................................................................................................... 180

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List of Abbreviations 2D 3D 5HT ACh ACTH AD ANOVA CB1 CPP CRF CV DA ELISA GABA Glu HPA HPI HTS ILF LDB LSD MDMA NE NMDA NTT OFT PD PPI sCV SEM SI SSRI tCV WD

Two dimensional Three dimensional Serotonin Acetylcholine Adrenocorticotropic hormone Alzheimer's disease Analysis of variance Cannabinoid receptor type 1 Conditioned-place preference Corticotrophin releasing factor Coefficient of variance Dopamine Enzyme-linked immunosorbent assay Gamma-aminobutyric acid Glutamate Hypothalamic-pituitary-adrenal axis Hypothalamus-pituitary-interrenal axis High-throughput screen Indian Leaf fish Light-Dark Box Lysergic acid diethylamide 3,4-methylenedioxy-N-methylamphetamine Norepinephrine N-Methyl-D-aspartic acid Novel tank test Open field test Parkinson's disease Pre-pulse inhibition Spatial coefficient of variance Standard error mean Straightness index Selective serotonin reuptake inhibitor Temporal coefficient of variance Withdrawal x

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Chapter 1. Zebrafish (Danio rerio) In 1981, George Streisinger published a seminal Nature article pioneering zebrafish developmental and genetic research by developing techniques to produce homozygous diploid clones on a large scale, representing the first genetic clone of a vertebrate organism (Streisinger, Walker et al., 1981). Following this formative research, zebrafish have become one of the most important vertebrate research species in genetics, developmental biology and neurophysiology in biomedical research (Grunwald & Eisen, 2002; Kimmel, 1989, 1993; Kimmel, Ballard et al., 1995). For example, as the subject of the first large-scale mutagenesis screen in a vertebrate, over 400 novel genes regulating vertebrate ontogenesis were identified (Driever, Solnica-Krezel et al., 1996; Granato & Nusslein-Volhard, 1996; Haffter, Granato et al., 1996). Over the past three decades, the accumulated genetic knowledge and experimental techniques has led to an increasing interest in using zebrafish as a model organism in behavioral genetics and behavioral neuroscience. Zebrafish-based paradigms are becoming increasingly popular in behavioral neuroscience and pharmacology research (Arslanova, Yang et al., 2010; Hortopan & Baraban, 2011; Trompouki & Zon, 2010). Due to the complexity of the vertebrate brain and the number of human brain disorders in which the understanding of etiological and pathological mechanisms remains rudimentary, zebrafish are beginning to fulfill the substantial need

2 for a suitable experimental organism able to model such diseases as well as facilitate investigation of their mechanisms (Gerlai, 2012). This introductory chapter provides a brief overview of zebrafish’s evolutionary history, nervous system biology and natural behavior to serve as an illuminating foundation of why zebrafish models have an encouraging future in neurobehavioral research and progressing the understanding of human brain disorders.

1.1. Biology and Behavior Zebrafish (Danio rerio) are a relatively small, cyprinid (commonly known as the minnow family) with adult fish averaging around 3-4 cm in length (Figure 1), and larvae (5 days post fertilization) averaging approximately 0.33 cm in length (Spence, Gerlach et al., 2008; Suli, Watson et al., 2012).

Figure 1: Larval and Adult Zebrafish At approximately 5 days post-fertilization larval zebrafish (upper) begin to display active, freelyswimming behavioral activity. The adult wild-type zebrafish (pictured below) is approximately 2 months old and represents the age, size and strain of the zebrafish used in this study (Fero, Yokogawa et al., 2010). Note, figure not to scale.

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Figure 2: Natural Geographic Distribution of Zebrafish The natural geographic distribution of zebrafish. Primarily found in low-lying areas, surround major rivers such as the Ganges and Brahmaputra river basins. Black dots indicate recorded occurrences (Spence, Gerlach et al., 2008).

The primary geographic habitat of wild-type zebrafish is most concentrated around the Ganges and Brahmaputra river basins (Figure 2) in northern India, Nepal and Bangladesh (FrazerBallinger et al., 2007; Spence, Gerlach et al., 2008). In these monsoon areas, zebrafish are found occupying low-lying river basins that are standing or slow-moving bodies of water, notably along the edge rice fields (Talwar & Jhingran, 1991). With food supported by the rice and zooplankton, and a water bed composed of loose, fine-grain silt this ecological niche provides and protects zebrafish shoals from potential predator fish most often found within the rivers (Spence, 2010). As a vertebrate species, zebrafish share substantial anatomical and physiological homology with mammals, including humans. The central nervous system of zebrafish is

4 comprised of all major structures, and comparative functional similarities between brain regions and modulatory circuits have been established (Ma, 2003; Panula, Chen et al., 2010; Panula, Sallinen et al., 2006; Wullimann, 1997; Wullimann & Mueller, 2004). While not identical to the elaborated brain of higher order vertebrates, the zebrafish CNS may represent the core, evolutionarily conserved pathways. Zebrafish also possess all major neurotransmitters, including glutamate (Huang, Haug et al., 2012), glycine (Fucile, Jan et al., 1999) and GABA (Mueller, Vernier et al., 2006), serotonin (Lillesaar, 2011; Lillesaar, Stigloher et al., 2009; Maximino & Herculano, 2010; Maximino, Lima et al., 2012), dopamine (Kastenhuber, Kratochwil et al.; Rink & Wullimann, 2001, 2002a; Ryu, Holzschuh et al., 2006; Tay, Ronneberger et al., 2011), acetylcholine (Mueller, Vernier et al., 2004), and histamine (Kaslin & Panula, 2001; Peitsaro, Kaslin et al., 2004; Peitsaro, Sundvik et al., 2007). In mammals, the hypothalamic-pituitary-adrenal (HPA) axis mediates the endocrine system during the stress response (Alsop & Vijayan, 2008). Under stress, the periventricular nucleus of the hypothalamus produces corticotrophin releasing factor (CRF), which is delivered to the anterior pituitary gland via the hypothalamichypophyseal portal blood vessel system (Suzuki, Kawasaki et al., 2009). CRF stimulates the anterior pituitary gland, causing release of adrenocorticotropic hormone (ACTH) into the blood stream (Tsigos & Chrousos, 2002). When stimulated by ACTH, the adrenal cortex synthesizes glucocorticoid hormones that modulate the stress response (Dedovic, Duchesne et al., 2009; Pruessner, Dedovic et al.).

5 A similar evolutionarily conserved mechanism has been found in zebrafish (Alsop & Vijayan, 2009; Alsop & Vijayan, 2008; To, Hahner et al., 2007), whose hypothalamus-pituitaryinterrenal (HPI) axis is homologous to the HPA axis (Figure 3). With cortisol as the main mediator of the physiological response to stress,

Figure 3: Adult Zebrafish Stress Axis Endocrine stress responses in zebrafish are regulated by the hypothalamus-pituitary-interrenal (HPI) axis, homologous to the HPA axis in mammals (Cachat, Canavello et al., 2010c).

zebrafish may be an excellent model for endocrine research (Winberg, Nilsson et al., 1997). This experimental ability to parallel physiological biomarkers with behavioral phenotypes for individual zebrafish provides researchers with an important tool for investigating stress-related phenomena. Therefore, with genetic, physiological and neurological homologies to mammals, including humans there is a growing interest in the use of adult zebrafish in neurobehavioral research and behavioral genetics.

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Chapter 2. Zebrafish in Neurobehavioral Research Understanding of how nervous system physiology relates to behavior, cognition, and various brain disorders requires in-vivo investigation. As such, animal models continue to be indispensable for screening psychotropic drugs or genetically modified animals, testing neurobiological hypotheses and finding candidate biomarkers or therapeutic targets for human diseases (Van Der Staay, Arndt et al., 2009). While rarely articulated, it is widely recognized that a single model organism cannot accurately mimic the complexity of human brain disorders (Mcarthur & Borsini, 2006; Nestler & Hyman, 2010). Over the last decades, neuropsychiatry research has consolidated the range of animal species as laboratory model organisms (Manger, Cort et al., 2008). Genetic, molecular, and biological development investigations are commonly performed using C. elegans and Drosophila models, whereas behavioral, cognitive and developmental research most actively applies mammalian (rodent or primate) animal models (Steenbergen, Richardson et al., 2011). Notwithstanding insights and discoveries made with these species, it is recognized that mammalian models are limited by an inability to achieve cost-effective, high-throughput behavioral screens for drug discovery and translational genomic assays (Gama Sosa, De Gasperi et al., 2012; Guo, Wagle et al., 2012; Jesuthasan, 2012; Mathur &

7 Guo, 2010). Thus, contemporary neurobehavioral research is challenged by two urgent needs; 1) developing low-cost, in-vivo high-throughput behavioral screening assays for drug discovery and translational genomic research and 2) standardizing and enhancing methodologies for objective acquisition and analysis of behavioral data (Burne, Scott et al., 2011; Morris, 2009). On-going efforts to integrate behavioral data are further complicated by inter-laboratory variability, the selection of behavioral endpoints, data acquisition and statistical analysis (Benjamini, Lipkind et al., 2010; Kalueff, Wheaton et al., 2007). Zebrafish are rapidly expanding as a model species in neurobehavioral research because they possess a unique balance between experimental practicality and phenotypic complexity (Figure 4) (Gerlai, 2012).

Figure 4: Experimental Organisms and Complexity of Behavioral Phenotypes As an experimental organism, zebrafish represent a unique balance displaying complex behavioral responses while also sharing the genetic knowledge and techniques of traditional invertebrate species (Cachat, Canavello et al., 2010c).

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They are inexpensive to maintain and breed, as a single mating pair produces hundreds of progeny. Embryonic development is external and optically transparent, providing a window for developmental biologists to probe genetic and molecular regulators of organogenesis. Within five days, the progeny become freely swimming, feeding larvae. These attributes, along with established genetic and molecular tractability, position zebrafish on par with traditional invertebrate animal models, C. elegans and Drosophila (Burne, Scott et al., 2011). Conversely, as a vertebrate, the homology to higher order animal models (i.e. rodents) make it a suitable candidate for modeling mammalian phenotypic domains (Gerlai, 2010). Thus, zebrafish represent an experimental organism with distinctive abilities to model genetic and molecular aspects of vertebrate development, behavior and disease with translational implications. This chapter will outline recent developments in the field of zebrafish neurobehavioral research, and emerging topics for future studies in this field. Despite several ongoing discussions regarding the definition and usage of the term “model” in biomedical research (Meunier, 2012), in this dissertation the use of “model” reflects a reductionist convention and refers to the use of animal species in an experimental setting to isolate and mimic some aspects of complex biological and neurological phenomena in order to more quickly and ethically understand related human conditions.

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2.1. Larval Models Larval zebrafish have emerged as a popular model for a number of brain pathologies. Larvae display learning, sleep, drug addiction, and other quantifiable neurobehavioral phenotypes (Best & Alderton, 2008). Another advantage of using zebrafish larvae is the ability to study multiple animals simultaneously within a highthroughput battery (Best & Alderton, 2008; Creton, 2009). Soon after birth, zebrafish begin to display basic swimming locomotion activity (Budick & O'malley, 2000). As a result, behavioral assays monitoring multiple larvae in parallel are widely used as highthroughput screens for genetic research and drug discovery (Fan, Cowden et al., 2010; Rihel, Prober et al., 2010). The strength of larval models is in a proven ability to perform behavioral experiments in a high-throughput manner, the ease of genetic manipulations, and simple, well-defined behavioral responses (Budick & O'malley, 2000; Lockwood, Bjerke et al., 2004; Renier, Faraco et al., 2007; Rubinstein, 2006). The value and promise of larval zebrafish research for biomedical research is exemplified by recent large-scale screening assays. One of the biggest challenges in discovering novel therapeutic compounds is predicting how novel compounds will effect complex behavioral phenotypes. To approach this obstacle, (Rihel, Prober et al., 2010) and (Kokel, Bryan et al., 2010) developed high-throughput, quantitative assays that enable thousands of psychotropic drugs to be evaluated in parallel. By collecting multidimensional behavioral data from larval zebrafish activity, they were able to generate hierarchical clusters of tested compounds according to similarity in behavioral profiles.

10 In doing so, the effects of well characterized psychotropic compounds was conserved in clustered groups and the mechanisms of action in poorly characterized compounds could be confidently predicted (Kokel, Rennekamp et al., 2012; Rihel & Schier, 2012). Larval zebrafish possess certain translational limitations for neurobehavioral research, being less complex behaviorally and morphologically. Moreover, the behavioral endpoints recorded in larval zebrafish research may not be fully translated (or possess good homology) to those recoded in adult behavioral research. Also, larval models have somewhat limited developmental applications, for example, lacking fully established mediatory and endocrine systems (Kimmel, Ballard et al., 1995), as well as some neural circuits and projections (Kastenhuber, Kratochwil et al., 2010). Thus, larval research may be unable to fully replace the adult zebrafish studies. Although testing multiple adult fish simultaneously may be an interim solution, a better strategy may be to accept both approaches and use them complementarily to advance zebrafish research.

2.2. Adult Models While larval research is currently represented more in the literature, there is growing interest in adult zebrafish behavioral assays. This is due to the fact that adult zebrafish display more complex behaviors spanning social (i.e. aggression (Filby, Paull et al., 2010), shoaling (Engeszer, Barbiano et al., 2007; Engeszer, Ryan et al., 2004; Maaswinkel, Zhu et al., 2012; Miller & Gerlai, 2007; Miller & Gerlai, 2011), courtship), affective (i.e. fear/anxiety (Guo, Wagle et al., 2012; Jesuthasan, 2012; Waldman, 1982), drug abuse (Cachat, Canavello et al., 2010a; Khor, Jamil et al., 2011; Mathur & Guo, 2010,

11 2011) and cognitive (i.e. attention (Echevarria, Jouandot et al., 2011), spatial learning (Salas, Broglio et al., 2006), latent learning (Gomez-Laplaza & Gerlai, 2010), associative learning (Gould & Kalueff, 2011)) domains. Summarized in Table 1, the growing interest in zebrafish has resulted in model paradigms to investigate a wide range of nervous system functioning (Norton & Bally-Cuif, 2010) and disorders (Becker & Rinkwitz, 2012; Guo, 2004; Ingham, 2009; Lieschke & Currie, 2007).

Table 1: Major Phenotypic Domains Modeled in Zebrafish Behavioral domains Anxiety/fear-related behavior

Cognitive behavior Social behavior

Reward-related behavior Sexual behavior Pain-related behavior Sensory behavior Sleep behavior Neurological phenotypes Neurodegenerative disorders

Selected references (Blaser, Chadwick et al., 2010; Cachat, Stewart et al., 2011; Egan, Bergner et al., 2009; Jesuthasan & Mathuru, 2008; Maximino, Da Silva et al., 2011; Maximino, De Brito et al., 2010a; Maximino, De Brito et al., 2010b; Maximino, Marques De Brito et al., 2010; Norton & Bally-Cuif, 2010) (Best, Berghmans et al., 2008; Braubach, Wood et al., 2009; GomezLaplaza & Gerlai, 2010; Grossman, Stewart et al., 2011; Rosemberg, Rico et al., 2011) (Al-Imari & Gerlai, 2008; Filby, Paull et al., 2010; Miller & Gerlai, 2007; Oliveira, Silva et al., 2011; Wright & Krause, 2006; Wright, Nakamichi et al., 2006; Wright, Rimmer et al., 2003) (Al-Imari & Gerlai, 2008; Braida, Limonta et al., 2007; Bretaud, Li et al., 2007; Cadet, 2009; Kily, Cowe et al., 2008; Lau, Bretaud et al., 2006; Ninkovic & Bally-Cuif, 2006; Ninkovic, Folchert et al., 2006; Stewart, Wong et al., 2011; Webb, Norton et al., 2009) (Darrow & Harris, 2004) (Gonzalez-Nunez & Rodriguez, 2009; Macho Sanchez-Simon & Rodriguez, 2009) (Bhinder & Tierney, 2012; Braubach, Wood et al., 2009; Huang & Neuhauss, 2008; Huang, Tschopp et al., 2009; Shamchuk & Tierney, 2012; Tierney, 2011; Tierney, Baldwin et al., 2010; Tierney, Ren et al., 2008; Tierney, Sekela et al., 2011) (Zhdanova, 2006) (Best & Alderton, 2008; Hortopan & Baraban, 2011; Hortopan, Dinday et al., 2010) (Bandmann & Burton, 2010; Flinn, Bretaud et al., 2008; Kabashi, Brustein et al., 2011; Kabashi, Champagne et al., 2010; Rinkwitz, Mourrain et al., 2011; Sallinen, 2009)

12 Zebrafish models offer a unique opportunity to target multiple neurobehavioral domains including both neuropsychiatric and neurological disease models. In the sections below, I will summarize progress in three major domains in which zebrafish research has been successfully applied and will be examined in this research: affective (fear/anxiety) domains, covering behavioral phenotypes relevant to a spectrum of neuropsychiatric conditions, social domains, in which behavioral phenotypes have been used to model aspects of autism spectrum disorder and schizophrenia and cognitive domains, representing learning and memory phenotypes with relevance to neurodegenerative disorders and Alzheimer’s disease. Affective Domains: Novelty-based Behavioral Paradigms Anxiety and anxiety-spectrum disorders are becoming increasingly prevalent in modern society, requiring new therapeutic approaches and treatments (Koenig, George et al., 1994; Murphy, 1986; Twenge, 2000). Affective disorders are also complex, showing high co-morbidity within and outside the anxiety spectrum (Anu, Annamari et al., 2008; Beghi, Allais et al., 2007; Bisson, Ehlers et al., 2007; Fawcett, Cameron et al., 2010). As drug discovery shifts toward targeting specific pathways and molecular determinants, versatile translational experimental models are important for pre-clinical drug screening (Proetzel & Wiles, 2010). Although constant refinement of existing experimental paradigms is necessary (Ramos, 2008), it is crucial to make further conceptual advances in this field (Kalueff, Laporte et al., 2008; Kalueff, Ren-Patterson et al., 2008), especially because of the domination of single-domain animal models of anxiety and the lack of

13 complex models that target several different domains and their interplay (see (Kalueff, Laporte et al., 2008; Kalueff & Murphy, 2007; Kalueff, Ren-Patterson et al., 2008; Laporte, Egan et al., 2010) for details). As a relatively young field, neurobehavioral research with adult zebrafish has adapted traditional rodent paradigms for use with this aquatic species. Recent studies have characterized adult zebrafish behavior in several novelty-based paradigms, reporting thigmotaxis, geotaxis and scototaxis – each classically related to the interpretation of affective phenotypes (Champagne, Hoefnagels et al., 2010; Dlugos & Rabin, 2003; Levin, Bencan et al., 2006; Levin, Bencan et al., 2007; Maximino, Benzecry et al., 2012; Maximino, Da Silva et al., 2011; Maximino, De Brito et al., 2010a; Maximino, De Brito et al., 2010b) Similar to rodent open field test (Choleris, Thomas et al., 2001; Prut & Belzung, 2003; Redolat, Perez-Martinez et al., 2009), the Novel Tank Test (NTT) evaluates the natural neophobic response of zebrafish, expressed in reduced exploration, increased freezing and/or unorganized erratic locomotion (Cachat, Stewart et al., 2010a; Levin, Bencan et al., 2007; Sackerman, Donegan et al., 2010). In contrast, reduced neophobic fear (lower-anxiety) in this test is accompanied by increased exploration with reduced freezing and fewer erratic bouts (Egan, Bergner et al., 2009; Gerlai, 2003; Stewart, Kadri et al., 2010) Like the open field test used in rodent models, which exhibit anxiety-like behavior by staying close to the walls (thigmotaxis), but increase exploration as they

14 become acclimated to the new environment (Choleris, Thomas et al., 2001), exposure to a novel environment evokes a robust anxiety response in zebrafish (Blaser & Gerlai, 2006), as they dive to the bottom (geotaxis) until they become acclimated and explore the upper regions of the tank. Typical endpoints in this test include the latency to enter the top, the number of transitions to the top, time spent in top, top:bottom time ratio, the number of fear/escape-like erratic movements, as well as freezing frequency and duration (Cachat, Canavello et al., 2010a; Levin, Bencan et al., 2007; Stewart, Kadri et al., 2010; Wong, Elegante et al., 2010). The Light-Dark Box is traditionally used in rodent behavioral neuroscience, and is based on the innate aversion to open illuminated areas (scototaxis) (Bourin & Hascoet, 2003). Previous research has shown that while anxiolytic compounds can facilitate exploratory activity (i.e. increased entries and duration in the light part), anxiogenic drugs cause the opposite effect (Bourin & Hascoet, 2003). Importantly, this test is now being applied to zebrafish, in which they exhibit a natural preference for the dark side (Serra, Medalha et al., 1999). Several different modifications exist for the fish light-dark box test (e.g., (Blaser, Chadwick et al., 2010; Serra, Medalha et al., 1999), consistently demonstrating the utility of light-dark situation to model zebrafish anxiety (Stewart, Kadri et al., 2010). The Open Field Test (OFT), another apparatus traditionally used in experimental biopsychology in rodents (Carola, D'olimpio et al., 2002; Choleris, Thomas et al., 2001; Koplik, Salieva et al., 1995; Walsh & Cummins, 1976), also offers a promising new area of

15 research in zebrafish. For example, some studies have applied the open field test to larval models (Lockwood, Bjerke et al., 2004). The utility of the open field test for adult zebrafish research also seems very logical. As in mice, zebrafish exhibit a natural tendency to stay close to walls of the apparatus, especially the corners. As they habituate to a novel arena, zebrafish predictably increase exploration, by crossing and spending more time within the center of the testing arena. Overall, zebrafish exploration appears to be driven by the same, evolutionarily conserved factors as rodent behavior, which is much better studied and understood. These factors include the balance between exploration (novelty-seeking, curiosity) and avoidance of aversive stimuli (thigmotaxis, scototaxis), thereby reconfirming the use of zebrafish in experimental and comparative biopsychology research. Finally, unlike rodent models, zebrafish behavior is three-dimensional, and includes an additional vertical dimension (geotaxic; top-bottom behavior), thereby introducing a novel aspect to their exploration-based phenotypes (Stewart, Kadri et al., 2010). Drug Abuse and Addition Animal sensitivity to acute and chronic drugs of abuse (including both rewardrelated and other behavioral effects) is an important phenotype (Cachat, Canavello et al., 2010a; Gerlai, Lahav et al., 2000; Gerlai, Lee et al., 2006; Ninkovic & Bally-Cuif, 2006) which correlates with the drug’s abuse potential. The sensitivity to drugs of abuse revealed strong genetic determinants of both increased (Lindemann, Meyer et al., 2008; Martin, Ledent et al., 2000; Michna, Brenz Verca et al., 2001) and decreased (Krall,

16 Richards et al., 2008; Thomsen, Hall et al., 2009; Trigo, Renoir et al., 2007) risks of drug abuse. Both animal and clinical models reveal striking parallels in their sensitivity to cocaine (Reichel & Bevins, 2010), amphetamine (Mathews, Morrissey et al., 2010),

benzodiazepines (Straub, Carlezon et al., 2010), ethanol (Heilig, Egli et al., 2010), nicotine (Jackson, Walters et al., 2009), opiates (Solecki, Turek et al., 2009), and other

psychotropic compounds (Melichar, Daglish et al., 2001; Passie, Halpern et al., 2008). Zebrafish models are also sensitive to a wide range of psychotropic compounds. While these responses will be only briefly discussed here, they generally parallel rodent and clinical observations, thereby confirming the translational value of zebrafish models.

Zebrafish models have been extensively used to study the effects of ethanol. A Ushaped dose-response curve has been observed in adult zebrafish (Gerlai, Lahav et al., 2000), also showing strain-dependent variations in their responses to ethanol (Gerlai, Ahmad et al., 2008), as well as reduced shoaling and increased aggressiveness (Echevarria, Hammack et al., 2010). In contrast, chronic ethanol treatment has an anxiolytic effect on zebrafish behavior (Egan, Bergner et al., 2009), also altering the expression of multiple brain genes, some of which are implicated in the addiction (Kily, Cowe et al., 2008) and similarly affected by ethanol in mammals (Heilig, Egli et al., 2010; Sircar & Sircar, 2006). Nicotine exposure produces strong effects on zebrafish place preference and learning (Grossman, Utterback et al., 2010; Kily, Cowe et al., 2008), along with the established effects of drugs of abuse that affect cognitive functions give further validity

17 to the modeling phenotypes of drug abuse in zebrafish. Chronic nicotine exposure in larval zebrafish leads to reduced swimming and impairs their startle response (Parker & Connaughton, 2007). In adult zebrafish, acute administration of nicotine has an anxiolytic-like effect (Levin, 2010; Levin, Bencan et al., 2006; Levin, Bencan et al., 2007) similar to its effect in humans (Murray, 1991) and rodents (Jackson, Walters et al., 2009). While zebrafish show a clear preference for cocaine in the CPP paradigm, there are also several strains with a decreased sensitivity in this model (Darland & Dowling, 2001; Lopez-Patino, Yu et al., 2008). Overall, zebrafish CPP models display a substantial similarity to rodent cocaine CPP studies (Dietz, Wang et al., 2007). Adult zebrafish treated acutely with mild doses of cocaine display arousal (e.g., circling, fin extension), increased aggressiveness, and decreased visual sensitivity (Darland & Dowling, 2001). Higher concentrations of cocaine reduce fish responses (Darland & Dowling, 2001) despite the high levels of cocaine in the brain (Lopez-Patino, Yu et al., 2008). The sensitivity of larval zebrafish to amphetamine (Irons, Macphail et al., 2010) generally parallels a similar locomotor response observed in mammals. While low concentrations of amphetamine increase activity, higher concentrations of this drug markedly reduce zebrafish locomotion (Ninkovic & Bally-Cuif, 2006; Webb, Norton et al., 2009). The rewarding properties of amphetamine have been reported in adult zebrafish in the CPP test (Ninkovic & Bally-Cuif, 2006), and also parallel those seen in rodents (Mathews, Morrissey et al., 2010).

18 Benzodiazepines are known to activate the reward system in rodents (Straub, Carlezon et al., 2010), and have also been tested in zebrafish models. For example, both chlordiazepoxide and diazepam display anxiolytic-like effects in adult zebrafish. While chlordiazepoxide increases exploratory behavior in the light/dark box paradigm, it does not affect vertical localization in the NTT (Bencan, Sledge et al., 2009; Sackerman, Donegan et al., 2010). In contrast, diazepam increases exploration in the novel tank,

exhibiting a biphasic response for low to moderate doses (Bencan, Sledge et al., 2009). Although hallucinogenic drugs have been extensively studied in rodents, they have only recently been tested in zebrafish. For example, salvinorin A, one of the most potent hallucinogens, exhibits rewarding properties in the CPP, accelerates zebrafish swimming in acute low doses, and reduces locomotion (evoking low-velocity "trancelike" state) at high doses (Braida, Limonta et al., 2007). Recently resurrected interest in psychedelic drug research (Dyck, 2005; Gonzalez-Maeso & Sealfon, 2009; Passie, Halpern et al., 2008) guided the motivation to investigate these drugs in zebrafish. Tolerance and withdrawal Commonly observed for drugs of abuse in both clinical (Joseph, Reichling et al.; Roberts & Dollard) and animal (Aley & Levine, 1997; Gerlai, Lee et al., 2006) studies, tolerance is the progressive reduction of drug sensitivity, which requires higher doses to obtain the same effects. Tolerance represents an important drug abuse-related phenotype, mediated by the brain’s adaptive mechanisms (Nagy, 2008; Popik, Kamysz et al.; Wang, Krishnan et al., 2007). Recent studies have confirmed tolerance in adult

19 zebrafish, reporting that after chronic exposure to ethanol, zebrafish have a reduced response to the acute effects of the drug (Gerlai, Lee et al., 2006). Another study reported tolerance following chronic ethanol exposure, which was also influenced by zebrafish genotype (strain) (Dlugos & Rabin, 2003). Tolerance is also seen in zebrafish following chronic exposure to nicotine (Kily, Cowe et al., 2008), collectively paralleling known rodent and clinical findings. Withdrawal is another key phenotype associated with drug abuse (Cachat, Canavello et al., 2010a), extensively studied in various rodents following cessation of ethanol (Morris, Kelso et al., 2010), cocaine (Santucci & Rosario, 2010), benzodiazepines

(De Ross, Castilho et al., 2008), and opiates (Becker, Gerak et al., 2010). These symptoms are also sensitive to various pharmacological (Bhutada, Mundhada et al., 2010; Rawls, Baron et al., 2010), genetic (Morice, Denis et al., 2010) and behavioral (Saadipour, Sarkaki et al., 2009; Smith & Yancey, 2003) factors. Withdrawal is believed to be mediated by homeostatic mechanisms, in which counter-regulatory processes produce deleterious effects when a drug is abruptly removed (Bayard, Mcintyre et al., 2004; Cruz, Berton et al., 2008; Ista, Van Dijk et al., 2010; Khong, Sim et al., 2004; Nagy, 2008; Tyrer & Seivewright, 1984). Researchers have recently turned their attention to studying withdrawal phenomena in zebrafish. Acute discontinuation of drug treatment – acute (single) withdrawal – is a common form of withdrawal, evoking strong behavioral effects in humans and rodents (Ashton, 1984; Jonkman, Risbrough et al., 2008; Kokkinidis,

20 Zacharko et al., 1986; Koob, Stinus et al., 1989; Wiese, Shlipak et al., 2000). In all studies, the most common behavioral manifestations of withdrawal include anxiety, seizures, lethargy, and pain (Gowing, Ali et al., 2009; Harris & Gewirtz, 2004; Joseph, Reichling et al.; Minozzi, Amato et al.; Strong, Kaufman et al., 2009). In zebrafish, acute ethanol discontinuation increases zebrafish shoaling behavior (Gerlai, Chatterjee et al., 2009), whereas cocaine withdrawal evokes marked hyperlocomotion marked by erratic movements and increased exploratory behavior (Lopez-Patino, Yu et al., 2008; Lopez Patino, Yu et al., 2008). In humans, chronic drug abuse represents a cyclical process of repeated reward and withdrawal. Therefore, to more accurately model clinical withdrawal phenomena, repeated withdrawal models are needed in addition to acute withdrawal studies. Repeated drug withdrawal paradigms have been recently developed for rodents, showing that both the rat and human share common triggers of relapse (such as the drug of abuse, stress, stimuli, or the environment conditioned to the drug of abuse), and that withdrawal selectively potentiates responses to anxiogenic stimuli (Fendt & Mucha, 2001; Harris & Aston-Jones, 2003; Jonkman, Risbrough et al., 2008; Miczek & Vivian, 1993; Vorel, Liu et al., 2001). The complexity of withdrawal phenotypes (Cooper & Haney, 2009; Cruickshank & Dyer, 2009; Henningfield, Shiffman et al., 2009; Martinotti, Nicola et al., 2008; Prat, Adan et al., 2009; Shoptaw, Kao et al., 2009; Teixeira, 2009; Wu, Pan et al., 2009), as well as the difficulty in modeling withdrawal syndrome in animals (Becker, 2000; Braw,

21 Malkesman et al., 2008; Keane & Leonard, 1989), represent another challenge. Potentially interesting directions of research may focus on neurochemical alterations, neural circuits, and the long-term consequences (Li, Li et al., 2008; Nava, Caldiroli et al., 2006; Shi, Li et al., 2009; Zhang & Liu, 2008) of drug withdrawal in zebrafish. The genomic profiling of zebrafish withdrawal also provides further insights, including altered gene expression in the zebrafish brain following chronic drug treatment and withdrawal (Gerlai, Chatterjee et al., 2009; Gerlai, Lee et al., 2006; Kily, Cowe et al., 2008). Sex differences have been reported for zebrafish withdrawal-related behaviors (Lopez Patino, Yu et al., 2008) paralleling sexual dimorphism in human (Fox, Garcia et al., 2006) and rodent (Alves, Magalhaes et al., 2008; Butler, Smith et al., 2009; Strong, Kaufman et al., 2009; Taylor, Tio et al., 2009) withdrawal responses, therefore increasing population and construct validity of these models. Social Domains: Shoaling and Social Preference Tests Social behaviors are commonly seen in zebrafish, granting the ability of modeling social phenotypes in this species. In rodents, social defeat stress has been shown to induce anxiety and depression in the “loser” animals (Becker, Zeau et al., 2008; Koolhaas, De Boer et al., 1997). While most research in social defeat has been focused on higher organisms (Bjorkqvist, 2001; Koolhaas, De Boer et al., 1997), zebrafish are also capable of establishing dominant-subordinate relationships and exhibiting agonistic behavior (Filby, Paull et al., 2010; Larson, O'malley et al., 2006). As for a highly social species, novel behavioral paradigms are being developed for zebrafish to induce and

22 quantify social phenotypes, such as shoaling (Green, Collins et al., 2012) and social preference (Pham, Raymond et al., 2012). The shoaling test primarily relies on average interfish distance within a group of fish as an index of group cohesion, while the social preference test examines an individual zebrafish’s preference for being in proximity to other zebrafish (see section 4.3.4 and 4.3.5). The development of technologies to enable high-throughput analysis of these adult behavioral tests may contribute substantially to biomedical research by allowing large scale mutagenesis screening-based identification of molecular mechanisms involved in vertebrate social behavior. The zebrafish has also been suggested for the analysis of the mechanisms of autism spectrum disorders (Tropepe & Sive, 2003). The number of genetic factors underlying autism spectrum disorders is believed to be much less than in such neuropsychiatric conditions as anxiety or schizophrenia, and thus animal genetic models have been generated with much hope. Importantly, several of the genes implicated in the human disease have been shown to have homologs in zebrafish (Mathur & Guo, 2010). It may therefore be possible to recapitulate some aspects of autism spectrum disorders by selectively targeting these genes and testing the effect of the genetic or pharmacological manipulations on developmental as well as behavioral characteristics in zebrafish. Schizophrenia Schizophrenia is a neurodevelopmental disorder that often manifests first during adolescence. The zebrafish model system provides an opportunity to study both the

23 genetic and developmental basis of schizophrenia as well as various pathological processes affecting neurogenesis, cell-fate determination and neuronal migration (Morris, 2009). Unlike in the case of autism spectrum disorders the number of genes involved in schizophrenia may be extremely large (hundreds) and many of these genes may only have a minor ‘‘predisposing’’ effect, which have hindered the unraveling of the mechanisms of the disease. Nevertheless, some of the genes implicated in schizophrenia have been identified in zebrafish. For example, DISC1, a schizophrenia susceptibility gene, has been shown to play roles in cell migration and differentiation in the zebrafish neural crest (Drerup, Wiora et al., 2009) as well as in the development of oligodendrocytes and neuronal lineages developing from olig2 expressing precursor cells. In addition to delineating the cellular and molecular roles of some schizophrenia associated genes, there is already one example for a genetic manipulation to affect zebrafish behavior. SHANK3 is a synaptic scaffolding protein whose gene was recently identified to carry mutations in some patients suffering from schizophrenia and was also found in autistic patients. Morpholino-induced knock down of the expression of the corresponding gene resulted in robust morphological abnormalities as well as impaired swimming in response to tactile stimulation in the zebrafish larva (Gauthier, Champagne et al., 2010). It may be noted that the specificity of the morpholino-induced changes may be questionable given that the attempt to rescue the phenotype by injection of wild type or mutant SHANK3 mRNA sequences led only to partial success at best. Last, psychopharmacological experiments have already started to be utilized with

24 zebrafish in the analysis and modeling of schizophrenia. One behavioral endophenotype often argued to be an important aspect of schizophrenia is reduced prepulse inhibition (PPI). PPI is believed to be a measure of sensory gating. It is induced by employing a weak stimulus (the prepulse), which is expected to inhibit the reaction to a subsequent stronger startling stimulus (the pulse). Larval zebrafish exhibit PPI of the acoustic startle response similarly to what has been demonstrated in rodents (Mathur & Guo, 2010). Dopamine agonists in the zebrafish larvae can disrupt PPI, an alteration that is reversed by antipsychotic drugs similarly to the mammalian situation (Braff, Geyer et al., 2001). In addition to these promising psychopharmacology results, a forward genetic screen has already isolated a mutant ‘‘Ophelia’’, which exhibited reduced PPI (Burgess & Granato, 2007). Cognitive Domains: Learning and Memory As a form of spatial working memory, habituation has long been used in neuroscience research to study cognition and its experimental modulation (Bolivar, 2009; Kandel, 2001; Rankin, Abrams et al., 2009; Salomons, Van Luijk et al., 2010). Representing a reduction in responses to novelty over time (Leussis & Bolivar, 2006; Thompson & Spencer, 1966), within-trial (intra-session) habituation is observed in multiple species as an evolutionarily conserved, adaptive behavior relevant to exploration and cognition (Angelucci, Vital et al., 1999; Bolivar, 2009; Clay, Bloomsmith et al., 2009; Dubovicky, Tokarev et al., 1997; Eisenstein, Eisenstein et al., 2001; Johnson & Wuensch, 1994; Maroun & Akirav, 2008; Mello, Benetti et al., 2008; Raymond, Chanin et al., 2012; Thompson &

25 Madigan, 2007; Turner, Beidel et al., 2005). Possessing significant genetic and physiological homology to other vertebrates, zebrafish (Danio rerio) are becoming increasingly popular in neurobehavioral research of affective and cognitive phenotypes (Best, Berghmans et al., 2008; Cachat, Stewart et al., 2010a; Egan, Bergner et al., 2009; Miklósi & Andrew, 2006; Stewart, Cachat et al., 2010a; Stewart, Wu et al., 2011). Zebrafish display strong anxiety-like behavior in various novelty-based paradigms, including the novel tank (Grossman, Stewart et al., 2011; Levin, Bencan et al., 2007; Wong, Elegante et al., 2010), light-dark box (Macphail, Brooks et al., 2009), open field (OFT) (Champagne, Hoefnagels et al., 2010; Stewart, Cachat et al., 2013) and startle (Eddins, Cerutti et al., 2009; Levin, Aschner et al., 2009) tests. These behaviors also habituate in novelty-based tests, demonstrating high sensitivity to experimental manipulations and confirming the utility of zebrafish models to study both affective and cognitive phenomena (Raymond, Chanin et al., 2012; Wong, Elegante et al., 2010). Since zebrafish swimming is also characterized by three-dimensional locomotion, they offer the additional value of an ‘extra’ (vertical) dimension of locomotion for in-depth behavioral analysis using this species (Cachat, Stewart et al., 2010b; Cachat, Stewart et al., 2011; Grossman, Utterback et al., 2010). Mounting evidence shows that zebrafish represent an excellent species to study various behavioral syndromes (Moretz, Martins et al., 2007; Wisenden, Sailer et al., 2011). However, as our understanding of the complexity of zebrafish behavior grows (Cachat, Stewart et al., 2011; Champagne, Hoefnagels et al., 2010; Maximino, De Brito et

26 al., 2010b; Stewart, Cachat et al., 2010a), the extent to which these behaviors habituate remains unclear. From a theoretical point of view, the sensitivity to anxiety and the ability to habituate may reflect either inter-related or independent behavioral phenomena (Kalueff & Murphy, 2007). For example, a specific behavior can be highly sensitive to anxiogenic factors, but show low or unaltered habituation (e.g., habituate equally well in both control and experimental groups, or habituate in controls but not in experimental cohorts). Although human (Mauss, Wilhelm et al., 2003; Thayer, Friedman et al., 2000) and rodent (Plamondon & Khan, 2005; Salomons, Van Luijk et al., 2010) literature supports a complex interplay between anxiety and habituation. Understanding adult zebrafish phenotypes relevant to learning and memory has translational implications to several neurodegenerative disorders. Progress on modeling aspects of these disorders using zebrafish is highlighted below. Neurodegenerative disorders The two key endophenotypes of Alzheimer’s disease (AD) include a buildup of amyloid-beta plaques in the nervous system, and a parallel production of uncoordinated meshwork of neurofibrillary tangles caused by damaged Tau protein (Best & Alderton, 2008; Paquet, Bhat et al., 2009). As suggested, these neuronal damages can lead to memory impairment, and have specifically been noted in 50% of patients with dementia (Vandenberghe & Tournoy, 2005). Therefore, it is possible that learning and memory acquisition in zebrafish can be effectively modeled based on amyloid-beta plaques and

27 tangled neuron formation. Paradigms which use ASR, raised platform, or T-maze arenas can assess learning and memory capabilities in fish (Best, Berghmans et al., 2008), including tauopathic zebrafish (Barut & Zon, 2000; Paquet, Bhat et al., 2009) highly relevant to AD. Likewise, Parkinson’s disease (PD), the most common movement disorder in humans, is also well-studied in zebrafish (Paquet, Schmid et al., 2006; Shankaran, Schmid et al., 2006). In addition, various PD-inducing drugs have also been successfully evaluated in both larval and adult zebrafish (e.g., (Guo, 2009).

2.3. Challenges and Problems Although an extensive amount of research has been performed in zebrafish models and a variety of phenotypes investigated, an important drawback is that the zebrafish behavior is not well characterized (Gerlai, 2002; Kane, Salierno et al., 2005; Kane, Salierno et al., 2004). Overall, this reflects the current weakness of zebrafish as a model organism in behavioral neuroscience – its use in genetics and neurobiology have been extremely powerful, but its behavioral phenotypes are largely uncharacterized due to a lack of available, validated behavioral test paradigms (Sison, Cawker et al., 2006). Moreover, there is a significant absence of zebrafish research programs that apply genetic and behavioral approaches in parallel (Gerlai, 2012). It is widely acknowledge that comprehensive examination of the adult fish behavioral repertoire in response to various anxiogenic, anxiolytic and other known psychotropic drugs is absolutely necessary (Agid, Buzsaki et al., 2007; Blaser &

28 Rosemberg, 2012; Cachat, Stewart et al., 2011; Gerlai, 2011; Kabashi, Brustein et al., 2011; Kabashi, Champagne et al., 2010; Luca & Gerlai, 2012; Markou, Chiamulera et al., 2009; Savio, Vuaden et al., 2012). Thus, in order to realize the full potential of the zebrafish as an integrative, translationally relevant vertebrate model, there is a need to develop and standardize both behavioral endpoints and assays (Cheng & Members of the Zebrafish, 2008). Traditional high-throughput screens (HTS), performed in vitro, have advanced biomedical knowledge tremendously (Kokel, Bryan et al., 2010; Kokel & Peterson, 2008; Rennekamp & Peterson, 2012). For many diseases, molecular mechanisms are not well understood and thus cannot yet be reduced to biochemical or cell-based assays. Of the diseases that deeply impact human health worldwide, this is particularly true for psychiatric and neurological disorders (Becker & Rinkwitz, 2012; Clark, Boczek et al., 2011; Kabashi, Brustein et al., 2011; Kokel, Rennekamp et al., 2012; Rihel, Prober et al., 2010; Stein & Steckler, 2010; Tamplin, White et al., 2012). In order to discover novel genetic or molecular regulators of disease etiology and pathology, in vivo phenotypic analysis of whole organisms is a critical necessity (Agid, Buzsaki et al., 2007; Gama Sosa, De Gasperi et al., 2012; Williams & Hong, 2011). Analysis of behavior facilitates rapid and effective dissection of contributing changes in the nervous system across multiple levels of analysis (i.e. signaling pathways, molecular activity and gene expression), and thereby can identify novel targets with potential therapeutic value (Blackiston, Shomrat

29 et al., 2010; Dickinson, 2000; Kane, Salierno et al., 2005; Kane, Salierno et al., 2004; Rennekamp & Peterson, 2012; Tierney, 2011). The combination of genetic and molecular approaches with HTS technology will not deliver the expected contributions to biomedical research and pre-clinical drug discovery if not paralleled by an increase in capacity to interpret and quantify behavioral phenotypes (Cryan & Holmes, 2005; Ramos, 2008). As a novel animal species to neuroscience research, comprehensively dissecting adult zebrafish behavioral responses is a necessary and critical process before targeted genetic or physiological screens can be confidently hypothesized and performed (Agid, Buzsaki et al., 2007; Blaser & Rosemberg, 2012; Luca & Gerlai, 2012; Markou, Chiamulera et al., 2009; Savio, Vuaden et al., 2012). All behaviors involve locomotion and thus with appropriate methods, are quantifiable (Benjamini, Lipkind et al., 2010). Unlike the movement of mammalian models, fish movement occurs in three dimensions. This presents different challenges to interpretation and quantification, but also provides valuable opportunities to collect more detailed, realistic behavioral data. The lack of standardization in the field, and the numerous technical challenges that face the development of a versatile system with the necessary capabilities, comprise a significant barrier keeping molecular developmental biology labs from integrating behavior analysis endpoints into their pharmacological and genetic perturbations (Blackiston, Shomrat et al., 2010).

30 To approach these bottlenecks in contemporary zebrafish neurobehavioral research, a comprehensive, systematic analysis of behavioral responses across various phenotypic domains is necessary – requiring multiple experimental treatments and behavioral paradigms. In theory, this research approach would enable a large repository of standardized behavioral responses to be collected across various drug treatment schedules, as well as multiple behavioral domains. Moreover, improving standardization and reliability of phenotypic quantification through automated videotracking tools would also support this effect by reducing potential subjective variation of human observers. With a database of raw spatiotemporal data, categorized by experimental treatment details, and made available to the research community – global characteristics of translationally relevant phenotypic domains could then be reached using data mining approaches as well as community input and exchange. My interest in following this research path arises from a confidence in the well-documented potential zebrafish models could have for neurobehavioral research, particularly in preclinical drug discovery, if these current limitations can be alleviated.

31

Chapter 3. Overview of Dissertation Research Strategy The overarching goal of this dissertation is to advance the characterization of adult zebrafish behaviors, and progress comprehensive quantification and dissection of phenotypic profiles with translational relevance to neuropsychiatric disorders. I selected this avenue of research to address existing challenges and problems critically restricting the power of zebrafish models for neurobehavioral research (outlined in Section 2.3). In order to empower high-throughput research paradigms integrating genetic and behavioral approaches in parallel, my research seeks to develop novel techniques and innovative approaches to automated behavioral quantification technologies for adult zebrafish behavioral paradigms. The Kalueff laboratory has recently established integrative approaches to investigate behavioral and physiological profiles in affective, social and neurological phenotypic domains by evaluating a wide-range of ethological and pharmacological treatment in multiple behavioral tests. In particular, this research group developed modifications to traditional behavioral tests, including a two-zone variation of the NTT and three dimensional behavioral quantification, as well as novel techniques to measure physiological biomarkers, using a more sensitive human salivary cortisol ELIZA kit to measure cortisol levels in individual fish (Egan, Bergner et al., 2009). These methodological advancements increase the throughput of behavioral experiments,

32 provide multi-dimensional quantification of behavioral responses and enable correlation of behavioral and physiological endpoints for each subject tested. My research approach capitalizes on these developments in order to comprehensively characterize and distinguish variance in behavioral and physiological responses to treatments with established mechanisms of action (Figure 5).

Figure 5: Dissertation Research Approach Experimental challenges (predator exposure, drug treatments) are used to induce targeted physiological changes and evoke complex behavioral responses. The behavioral and physiological responses observed can then be evaluated across multiple phenotypic domains.

Central Hypothesis I posit that using a large-scale, integrative approach to quantify behavioral and physiological phenotypes in adult zebrafish following treatment with psychotropic

33 agents modulating distinct neurological systems will generate highly granular, data dense behavioral profiles clustering around conserved mechanisms of action. I further postulate that automated video-tracking software to collect movement parameters and three-dimensional spatiotemporal trajectories will enable the detection and dissection of behavioral profiles in adult zebrafish. Assessing these responses in affective, social and neurological domains, and paralleling observed phenotypes with those observed in rodent and clinical research will 1) increase the validity of zebrafish models for human brain disorders and 2) enable prediction of the mechanism of action in novel or poorly characterized psychoactive drugs.

3.1. Experimental Strategy The experimental strategy of my dissertation research follows three specific aims: Specific Aim 1: Characterize and Quantify Behavioral Phenotypes of Zebrafish exposed to Experimental Treatments in Affective and Social Domains: Behavioral responses following acute exposure to anxiogenic (increasing anxiety) and anxiolytic (decreasing anxiety) factors were manually observed and scored in behavioral tests targeting affective domains (the novel tank (NTT), open field (OFT) and light-dark box (LDB) tests). Acute anxiogenic treatments will include predator exposure and treatment with alarm pheromone and caffeine. Affective modulation will also be examined with anxiolytic drugs. Anxiolytic treatments include pentobarbital, fluoxetine, ethanol and nicotine. Observed behavioral profiles in affective domains will also be correlated to

34 physiological biomarkers of HPI-axis (stress response) activation (whole-body cortisol concentrations). Since affective disorders often show with high comorbidity with drug abuse in humans, this project will also examine the behavioral and physiological responses of zebrafish to selected drugs of abuse, including cocaine and morphine, as well as responses following caffeine, morphine, and ethanol withdrawal. Although pentobarbital and nicotine could be considered drugs of abuse the goal of these experiments was to examine pharmacogenic treatments targeting different neurotransmitter systems than those investigated previously, and more prominently the ability of adult zebrafish to model withdrawal syndromes observed from these drugs in a clinical setting. In addition to affective domains relevant to anxiety disorders and drug abuse, there is a growing interest to investigate hallucinogenic drug actions, which exert powerful effects on affective and cognitive domains in both humans and animal models. In order to understand how these compounds maybe used in a clinical setting, this research evaluates several hallucinogenic agents, including lysergic acid diethylamide (LSD), 3,4-methylenedioxy-N-methylamphetamine (MDMA) and ibogaine, in behavioral tests targeting affective and social domains (social preference test, shoaling test). Specific Aim 2: Develop Automated Quantification Techniques of Behavioral Endpoints: Following characterization of behavioral and physiology profiles across each domain (Figure 5), automated video tracking will be used to quantify zebrafish

35 behavioral in respective tests (Figure 6). As the use of video-tracking software in rodent neurobehavioral research is well established, very limited studies have used this approach in adult zebrafish behavioral tests. Expanding on developments previously established in the Kalueff laboratory, video-tracking software (EthoVision XT 7-8.5; Noldus Information Technologies; Wageningen, Netherlands) will be used to complement and potentially replace the requirement of manually scoring zebrafish behavior, as complex, multifaceted behavioral events (i.e. erratic movement) could potentially be defined by a set of numerical attributes, and become analytically tractable.

Figure 6: Outline of Experimental Strategy First, zebrafish activity in behavioral tests will be observed and manually quantified. After establishing a general characterization of treatment specific phenotypes, techniques will be improved to automated behavioral quantification. Automation using video-tracking software enables highly granular spatiotemporal data that will be used to reconstruct the swim path or trajectory for each zebrafish, this will promote the discovery of novel endpoints and refinements to automated quantification.

36 Overall, automated quantification with video-tracking software will be thoroughly evaluated to ensure that accurate, reliable and reproducible detection of the zebrafish behavior is achieved. In addition, basic movement parameters (i.e. distance, velocity) and more complex trajectory endpoints (i.e. angular velocity, meandering, and trajectory curvature) (Figure 6) will be examined in three-dimensional trajectory reconstructions, generated from spatiotemporal data quantified by video tracking software, for potential to improve phenotypic profile dissection. Specific Aim 3: Identify New Phenotypic Features by evaluating 3D Trajectory Reconstructions: Once a standard and optimized set of video-tracking protocols has been established for adult zebrafish behavioral assays, the final phase of my research will evaluate the potential of using highly granular, raw spatiotemporal data of individual fish for use in data-mining statistical analysis of zebrafish phenotypes (Figure 6). Movement paths are a highly organized time-series that contain typical events and patterns of behavior. This aim seeks to determine if interactive 3D trajectory reconstructions can significantly facilitate the recognition of sequential patterns by researchers, and the development of algorithmic definitions to analyze and detect to novel differences in zebrafish behavior. Addressing aspects outlined in each specific aim in collectively, this study has the potential to develop new methods for analyzing zebrafish behavior in affective, social and neurological research domains. First, by establishing correlations to phenotypic profiles observed in rodent models and clinical research following related

37 experimental treatments will strengthen the validity of adult zebrafish models in behavioral neuroscience and behavioral genetics. Secondly, this study has the potential to significantly empower neurophenotyping in adult zebrafish models by integrating manual observation and automated acquisition of behavioral parameters while seeking naturalistic, more relevant 3D representations of phenotypic profiles as they occur. Successful application of trajectory segmentation techniques to dissect micro-events in zebrafish movement will provide significant methodological advancements and enable high-throughput, data mining of adult zebrafish behavioral profiles capable of rapidly, and objectively characterizing and clustering experimental treatments. The application of this technology in future research on neuropsychiatric diseases or the neurobiology/genetics of complex behavioral phenotypes is widely anticipated to have a large impact across biomedical disciplines, resulting in significant advancements for diagnosis, treatment and prognosis options for several human brain disorders (Fleming & Alderton, 2012; Gerlai, 2012; Klee, Schneider et al., 2012; Kokel, Rennekamp et al., 2012; Parker & Brennan, 2012; Rennekamp & Peterson, 2012; Richendrfer, Pelkowski et al., 2012; Rihel & Schier, 2012; Tamplin, White et al., 2012). The results of this dissertation research are reflected in a series of peer-reviewed publications (Appendix A), in which the dissertation was the first author of 4 published articles.

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Chapter 4. Methods and Materials 4.1. Animals The nature of neurobehavioral research requires animal models and cannot be replaced by in vitro or in silco designs. These studies were performed in-vivo using wild type short-fin (SF) zebrafish (Danio rerio) because they represent a lower-order vertebrate species with the practical and experimental attributes of an invertebrate species, and are widely used in zebrafish neurobehavioral research (Gerlai, Chatterjee et al., 2009; Gerlai, Fernandes et al., 2009; Yu, Tucci et al., 2006). A total of ~1850 zebrafish were used in this project. Adult zebrafish (~ 6-8 months old) were obtained from a local distributor (50 Fathoms Pet Shop, Metairie, LA). All animals were given at least 3 weeks to acclimate to the Zebrafish Facility (see section 4.1.1) prior to behavioral testing. All experimental procedures in this study were conducted in full compliance with ethical standards set forth by the National Institutes of Health (NIH) and Tulane University's Institutional Animal Care and Use Committee (IACUC) to minimize the pain and discomfort of the experimental animals (Westerfield, 2007). A standard of 10-15 animals per group was used in initial experiments, to ensure adequate sample size for each group, based on previous experience, published zebrafish studies from this study (Cachat, Stewart et al., 2010a; Egan, Bergner et al., 2009) and other groups (Gerlai, Chatterjee et al., 2009; Gerlai, Fernandes et al., 2009), and a-priori sample

39 size calculations (power: 0.8, large effect size > 0.5, probability of a Type 1 error (α) = 0.05). Both sexes of zebrafish (approximately 50:50% of males and females) were used in this study, similar to other previously published studies from established zebrafish laboratories (Blaser & Gerlai, 2006; Fernandes & Gerlai, 2009; Gerlai, Fernandes et al., 2009). A standard of 30 animals per experimental cohort was used in analyses involving movement pattern and trajectory analysis research. This number was chosen to provide statistically meaningful comparison between cohorts, based on a-priori sample size calculations with (power: 0.8, large effect size > 0.5, probability of a Type 1 error (α) = 0.05). Obtained data per animal is very granular, and used to increase the potential number of predictors to differentiate between experimental groups. The long-term goal is to identify unique phenotypic profiles in zebrafish, which are distinctly related to the pharmacodynamic profiles of the proposed drug treatments. Data-mining and machine learning techniques require dense and highly granular training data sets in order to make confident predictions on unknown data sets in the future. Examining multiple doses and several behavioral phenotyping techniques can compensate for the lower sample size if necessary.

4.1.1. Animal Care and Housing This research was conducted within the laboratory of Dr. Allan Kalueff, occupying ~1200 sq. ft. of space within the Department of Pharmacology at the Tulane University Medical Center (TUMC), and ~400 sq. ft. of space within TUMC Vivarium.

40 Zebrafish were housed in a fully operational Zebrafish Facility, maintained by the laboratory’s personnel and staff of the TUMC DCM. Animals were housed in a standalone Aquatic Habitats bench top system (Apoupka, FL). This system is a self-contained system, capable of housing of up to 1000 zebrafish that automatically cycles the water (maintained at 26+2°C by water heaters) through mechanical and biological filters as well as a UV sterilizer. All fish were exposed to a standard 12:12-h light cycle (on: 6.00 AM, off: 18.00 PM), consistent with the zebrafish standard of care (Westerfield, 2007). Fish were fed Tetramin Flakes (Tetra Werke; Blacksburg, VA) by DCM personnel once a day. All tank water is treated with non-acidic Prime™ chemical treatment (removing chlorine, chloramines, and ammonia) before fish are placed into housing tanks. Additionally, all water is tested daily for chlorine and ammonia levels prior to placing fish into housing tanks. In the case that excessive ammonia is detected in any tank, a partial (~50%) water change is carried out immediately. Discomfort and injury was limited to which is unavoidable in the conduct of scientifically valuable research. Zebrafish did not undergo any procedure deemed to cause significant pain or distress; included behavioral tests are based on observing zebrafish natural exploratory and emotionality behaviors, and did not cause any pain or distress. Animals were euthanized with 5% Tricaine (MS 222, buffered to pH = 7.0) followed by rapid decapitation and tissue collection/processing for further analyses, procedures consistent with the recommendations of the AVMA Guidelines on Euthanasia.

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4.2. Drug Treatments Several well-established pharmacological treatments were used in this research, representing traditional neurobehavioral modulators of affective and neurological domains (see Table 2). All drug treatments were obtained from Sigma Aldrich (USA), or through the NIDA Drug Supply program and treatments were administered via water immersion in a 1L pre-treatment beaker, prior to behavioral testing. Based on internal pilot studies, as well as previously published research (as referenced within respective sections) pretreatment times were selected from those that produced robust behavioral effects and preventing lethal or neurotoxic effects.

4.2.1. Alarm Pheromone Extraction Alarm pheromone was extracted from epidermal cells of euthanized zebrafish (Speedie & Gerlai, 2008). A sterile razor blade was used to make 10-15 shallow slices on each side (one at a time) of the fish body, damaging the epidermal cells. Care was taken to prevent drawing blood, which would contaminate the pheromone solution. The damaged side of the body was then washed in a Petri dish filled with 10 mL of distilled water for 5 min, placed on ice bucket to prevent degradation of the extracted pheromone. This dish was shaken gently to ensure water wash throughout the lacerations. This procedure was then repeated on the opposite side of the zebrafish. On average, 10-20 donor zebrafish underwent the alarm pheromone extraction procedure.

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Table 2: Experimental Treatments used in Behavioral Tests Pharmacology reflects the primary neurotransmitter system(s) targeted by each compound, rather than the complete psychopharmacological profile. For withdrawal (WD), the treatment column contains the pretreatment length following by the withdrawal period (also see Figure 7).

Treatment Ethological

Dose

Treatment

Domain

Pharmacology

Predator Exposure

-

0, 24, 72 h

Affective

Ethological

Alarm Pheromone

7 mL

5, 30 min

Affective

Ethological

Pharmacological Caffeine

250 mg/L

20 min

Affective

Adenosine

Caffeine WD

50 mg/L

1 wk, 12 hr

Affective

Adenosine

Drug Abuse

DA, 5HT, NE

Cocaine

1, 12.5, 25 mg/L

Ethanol

0.3% vol/vol

5 min, 1 wk

Affective

GABA, 5HT, DA

Ethanol WD

0.3% vol/vol

1 wk, 12 hr

Drug Abuse

GABA, 5HT, DA

Fluoxetine

100 - 1000 µg/L

Affective

SSRI

Ibogaine

20 min

20 min, 2 wk

10, 20 mg/L

20 min

Hallucinogen

5HT, Glu, Opiate

25, 50, 250 µg/L

20 min

Hallucinogen

5HT

MDMA

10 -120 mg/L

20 min

Hallucinogen

5HT, NE, DA

Morphine

1, 2, 5 mg/L

Drug Abuse

Opiate

Drug Abuse

Opiate

LSD

20 min, 2 wk 2 wk, 3h/day

Morphine WD

1.0 mg/L

Nicotine

10 mg/L

5 min

Affective

ACh

5, 10, 20 mg/L

20 min

Affective

GABA

Pentobarbital

Abbreviations: DA, Dopamine; 5HT, Serotonin; GABA, gamma-aminobutyric acid; ACh, Acetylcholine; Glu, Glutamate; SSRI, Selective Serotonin Reuptake Inhibitor; NE, Norepinephrine; LSD, Lysergic acid diethylamide; MDMA, 3,4-methylenedioxy-Nmethylamphetamine.

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4.2.2. Drug Withdrawal Treatment Paradigms In withdrawal experiments (Figure 7), fish were administered caffeine or ethanol for 1 week in their respective home tanks, which were then filled with drug-free water for 12 h before behavioral testing.

Figure 7: Drug Withdrawal Treatment Schedules Following acute or chronic treatment, zebrafish were placed in drug-free water for a single withdrawal period or repeated withdrawal schedule before behavioral and physiological assays were performed.

For extended withdrawal experiments (Figure 7), morphine and caffeine were administered chronically for 2 weeks within their home tanks. On the day of testing, fish were placed in drug-free water for 12 h (ethanol) or 48 h (morphine). The doses and the duration of chronic treatment and withdrawal were selected based on the Kalueff laboratory’s pilot data confirming the lack of non-specific toxic/sedative effects of these drugs, as well as based on known biological half-lives of drugs used here (morphine,

44 caffeine > ethanol) and were also similar to those used in other withdrawal studies in zebrafish (Lopez-Patino, Yu et al., 2008; Lopez Patino, Yu et al., 2008). Repeated withdrawal trials were performed in this study on zebrafish treated with ethanol or morphine (Figure 7). Briefly, after 1-week chronic treatment, fish were placed into exposure tanks with fresh untreated water for 3 h at a time, twice per day for 1 week prior to testing. Drug-free control fish were placed into treated water with no drugs added, and chronic drug treatment groups were placed into water containing concentrations of the drug identical to the treatment. Following 3 h exposure trials, animals were returned to their respective drug-treated home tanks. After 1 week of repeated withdrawal, fish were taken from home tanks and placed in exposure tanks for a final 3 h withdrawal session prior to behavioral testing.

4.3. Behavioral Tests Behavioral testing (Figure 8) was performed between 11:00 and 15:00 h using tanks with water adjusted to the home tank temperature. Prior to testing, fish were individually pre-treated in a 1-L plastic beaker to either drug or drug-free vehicle (control) solution. During testing, zebrafish behavior was manually quantified by 2–3 trained observer that are blind to drug treatments (refer to details below for test specific endpoints). Zebrafish activity was also video-recorded (640x480; 30 fps) for subsequent automated analysis in video-tracking software using laptop computers with one or more USB 2.0 web cameras (720p HD widescreen LifeCam Cinema; Microsoft Corp., Redmond, WA).

Figure 8: Experimental Design of Behavioral Tests Following pretreatment, zebrafish behavior is manually scored and video-recorded for subsequent video-tracking analysis. Data from manual observation and each camera view is then statistically analyzed for group differences, phenotypic profiles are correlated with physiological biomarkers, and 3D swim path reconstructions are created for careful evaluation of spatiotemporal variance or patterning.

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4.3.1. Novel Tank Test The novel tank test (NTT), used to assess exploratory and affective domains, was a 1.5-L trapezoidal tank (15 cm height × 28 cm top × 23 cm bottom × 7 cm 137 width; Aquatic Habitats, Apopka, FL) maximally filled with treated water and divided into two equal, horizontal regions (top/upper and bottom/lower zones) by a line marking the outside walls (Figure 9) (Cachat, Stewart et al., 2010a; Egan, Bergner et al., 2009). Following pre-treatment, fish were individually placed in the novel tank and observed for 6-30 min. Manually scored behavioral endpoints in the NTT included: latency to enter the top zone of the tank (s), time spent in top zone (s), number of transitions to top zone, the number of erratic movements (unorganized, sharp changes in direction with high velocity along the bottom of the tank), as well as the number and duration of freezing bouts (total absence of movement, except for eyes and gills for 2 s or longer along bottom of tank) (Cachat, Stewart et al., 2010a; Egan, Bergner et al., 2009; Levin, Bencan et al., 2007). NTT activity was video-recorded by two web cameras (corresponding side and top views) positioned ~50 cm away from the testing tank. Videos were then acquired in video-tracking software to quantify raw spatiotemporal data and movement parameters (see section 4.4.1). Within video-tracking software, the NTT tank was divided into two virtual areas (mirroring top/upper and bottom/lower zones) in order to quantify regional activity in addition to standard movement parameters (Cachat, Canavello et al., 2010b).

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4.3.2. Open Field Test The open field test (OFT), designed to evaluate exploratory and affective phenotypes, was performed in a white plastic container (21 cm diameter, 24 cm height) filled to a depth of 12 cm with treated water (Figure 9). Following pre-treatment, adult zebrafish were individually placed in the center of the testing tank and video-recorded for 6-30 min (Stewart, Cachat et al., 2010a; Stewart, Cachat et al., 2010b; Stewart, Gaikwad et al., 2012). Open field test activity was video-recorded with one web camera (top view) positioned ~50 cm away from the top of the OFT tank. Videos were then acquired in video-tracking software to quantify raw spatiotemporal data and movement parameters (see section 4.4.1). Within the video-tracking software, the bottom of the tank was divided into two virtual zones, center and periphery (a circular zone with inner boundary 5 cm from the testing tank wall) enabling quantification of regional activity. In addition to standard movement parameters, primary behavioral measures included the time spent (s), distance traveled (m), number of visits and time spent in central and peripheral zones, and the ratio of time spent in the center zone over total testing time (Stewart, Cachat et al., 2010a; Stewart, Cachat et al., 2010b; Stewart, Gaikwad et al., 2012).

4.3.3. Light-Dark Box The light–dark box test (LDB), based on innate preference for darker environments in zebrafish and used to assess affective domains (similar to rodent models), was a rectangular tank (15 cm height × 30 cm length × 16 cm width) filled with treated water to a depth of 12 cm and divided into two equal regions (black and white

48 colored tank walls) (Figure 9). Following pre-treatment, zebrafish were individually placed into the black half and video-recorded for 6 min (Grossman, Utterback et al., 2010; Maximino, De Brito et al., 2010a; Stewart, Kadri et al., 2010). In the LDB, manually scored endpoints included: latency to enter (s), time spent (s), average entry duration (s), and the number of entries to the white half. Additionally, the ratio of time spent in the white half over the total testing time was calculated for both cohorts. Activity in the black half was not quantified, due an inability to distinguish the test subject from the black walls neither manually or with video-tracking software. In this test behavior was videorecorded by one web camera (top view) positioned ~50 cm away from the testing tank (Grossman, Utterback et al., 2010). Videos were then acquired in video-tracking software to quantify raw spatiotemporal data and movement parameters (see Video-Tracking for details). In video-tracking software, the LDB testing tank was divided into one virtual zone, corresponding to the white half, in order to automatically quantify regional activity in addition to standard movement parameters.

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Figure 9: Novelty-based Exploration and Affective Behavioral Tests Similar to behavioral tests used in mammalian models, affective phenotypes are inferred from modulation of thigmotaxis (center/periphery), geotaxis (top/bottom) and scototaxis (light/dark).

4.3.4. Shoaling Test The shoaling test was performed to evaluate how experimental treatments modulate pro-social behavior in adult zebrafish. Following pretreatment, 4-8 zebrafish were group-tested in a 1.5-L trapezoidal tank (15 cm height × 28 cm top × 23 cm bottom × 7 cm 137 width; Aquatic Habitats, Apopka, FL) (Figure 10). Shoaling behavior was video-recorded for 6 min by one web camera (side view) positioned ~50 cm away from the testing tank (Green, Collins et al., 2012; Grossman, Utterback et al., 2010; Miller & Gerlai, 2007; Pham, Raymond et al., 2012). Manual scoring of each shoal was performed by capturing 8 static frames from the last 3 min of the recorded videos spaced 20 s apart, resulting in a total of 16 frames for each cohort. Frames were analyzed in ImageTool v3.0 (UTHSCSA; San Antonio, TX) (Wilcox, Dove et al., 2002). A trained observer properly calibrated the images (using real world spatial distances) and manually measured

50 distance (cm) between each fish in the group. The measures obtained from each frame are then averaged, resulting in the average inter-fish distance for control and experimental cohorts. Videos are also acquired in video-tracking software to quantify raw spatiotemporal data and movement parameters (see section 4.4.1). For this test, the social-interaction module (SIM) of EthoVision XT enabling tracking of multiple subjects during a single trial was also used. The SIM module also provides a measure of proximity duration (s) defined as the average amount of time an individual fish spent within 0.5 cm to another fish within the shoal (Green, Collins et al., 2012; Grossman, Utterback et al., 2010).

Figure 10: Social Behavior Tests In the shoaling test, in instinctual drive to remain in proximity of conspecifics is evaluated over the entire group. The Social Preference test is based on the same instinctual drive but an individual’s behavior, rather than the group, is the focus of this analysis.

4.3.5. Social Preference Test The social preference test, was performed examine zebrafish social behavior and locomotor activity in a model similar to the mouse social preference paradigm. The test

51 was performed in a modified T-maze apparatus (50 cm length, 10 cm width, 10 cm height; Ezra Scientific, San Antonio, TX) (Figure 10). By sectioning off one arm of the TMaze, a Plexiglas corridor (50 cm) is created with three primary regions (conspecific, center and empty zones) each separated by transparent sliding dividers (Grossman, Utterback et al., 2010; Pham, Raymond et al., 2012). The conspecific zone contained an age-matched zebrafish and the empty zone did not contain any animals. To avoid lateral bias in the experimental cohorts, the left/right location of the conspecific fish was alternated between trials. Following pre-treatment, the test fish were introduced individually to the center zone and temporarily restrained within this zone (30 s) by additional transparent sliding doors (Grossman, Utterback et al., 2010; Pham, Raymond et al., 2012). Following the restraining interval, the two temporary sliding dividers were gently lifted, and test zebrafish was free to move within the full center zone for 6 min. Trained observers manually scored zebrafish activity, recording the following measures: number of center entries, time spent in center (s), the number of conspecific arm entries, the number of empty arm entries, total arm entries, and time spent (s) in the each zone of the testing apparatus. Additionally, the ratio of time spent in the conspecific arm over the empty arm and number of entries into the conspecific arm over total zone entries was calculated based on manually recorded data. The social preference test were videorecorded by one web camera (top view) positioned ~50 cm away from the testing tank. Videos were then acquired in video-tracking software to quantify raw spatiotemporal

52 data and movement parameters (see section 4.4.1). Within the video-tracking software, the testing tank was divided into three virtual areas (mirroring conspecific, center and empty zones) in order to automate the quantification of regional activity in addition to standard movement parameters (Cachat, Kyzar et al., 2013; Grossman, Utterback et al., 2010; Pham, Raymond et al., 2012).

4.4. Automated Behavior Quantification and Analysis Once respective behavioral tests are complete, each recorded video was transferred to a high-performance desktop computer dedicated to performing automated quantification of zebrafish behavior using video-tracking software.

4.4.1. Video-Tracking Video analysis was performed in Noldus EthoVision XT 7 - 8.5 (Noldus Information Technologies; Wageningen, Netherlands), an industry standard for automated animal behavior quantification. In addition, Social Interaction Module of EthoVision was used to track multiple zebrafish in shoals. This protocol is similar to (Cachat, Canavello et al., 2010b; Cachat, Stewart et al., 2011), with several modifications reflecting technology updates. Within EthoVision, a new experiment was created for each test and named using a standard format to ensure proper identification and simplify retrieval across multiple experimental tests. Videos were then copied into the “Media Files” folder of the respective experiment being analyzed. After this initial framework and file organization is established, the software must be appropriately configured for the experiment before

53 videos can be acquired including the Trial List, Manual Score Settings, Arena Settings, Trial Control Settings and Detection Settings (Cachat, Stewart et al., 2011). The Trial List is used to enumerate and identify each video that was analyzed. This list provides system level information (i.e. acquisition status, recording duration, video file location) and allows for the creation of user-defined variables (Cachat, Stewart et al., 2011). For each experiment, “FishGroup”, “FishLabel” and “CameraView” custom variables were established in the Trial List. “FishGroup” was used to distinguish between experimental cohorts (i.e. Control, DrugDose1, DrugDose2), “FishLabel” was made to identify individual subjects within respective experimental cohorts (i.e. Control1, Control2, Control3), and “CameraView” denoted the position of the camera used to record the corresponding video under analysis (i.e. Top, Side). Lastly, the correct number of trails was added to the Trial List to reflect the total videos to be acquired (number of fish + number of camera views) in experiment being analyzed. Within EthoVision XT, Manual Score Settings enable the user to record behaviors that cannot be detected automatically by pressing a pre-defined key during automated acquisition when the behavior or event occurs. Manual, event-based scoring was performed by a trained observer on mutually exclusive behavioral events established based the testing paradigm being analyzed. For NTT, OFT and LDB, behavioral states of swimming (“S”), erratic movement (“E”) and freezing (“F”) were scored using respective keyboard keys (Cachat, Stewart et al., 2011). Swimming was defined as

54 normal, continuous motion involving caudal and pectoral fins, erratic movements and freezing were defined as described previously (see section 4.3.1). Additionally, for each behavioral test the respective regions of interest were established in Arena Settings using shape-drawing tools and appropriately labeled (refer to methods above for test specific zones). All testing arenas were calibrated with two straight lines (horizontal and vertical) connecting the physical edges of the testing tank. This calibration converts pixel coordinates to the real-world coordinates with the length of each line (cm or m) input into the Arena Settings. During calibration, the origin axes (0, 0) were placed at the center of each arena in order to standardize spatial coordinate data across trials and experimental tests (Cachat, Canavello et al., 2010b; Cachat, Stewart et al., 2011). For top view videos for 3D reconstructions (not as primary view of behavioral test), a single rectangular zone was made and the origin axes were placed along the rear (or back) wall of the testing tank. Trial Control settings provide information to automate the acquisition of each experimental trial. For each experiment, the Trial Control settings were made to start acquisition after the center-point of the subject has is detected in the testing arena for less than or equal to 1.00 s. Depending on the behavioral test being acquired, the acquisition stopped after a delay of 6 min (360.0 s) and EthoVision was prepared for the next trial. Detection settings were selected to most accurately acquire zebrafish behavior. In general, it is important to ensure that there is good contrast between the subject and the

55 background of the testing arena. For each experiment, an image of the empty testing arena is captured and selected as the reference image. Dynamic Subtraction was selected as the Detection Method, using a scan window to limit detection of light glares or other rouge points (Cachat, Canavello et al., 2010b; Cachat, Stewart et al., 2011). Videos were acquired at the maximal sampling rate (30.0 fps) frames per second and the subject was set darker than the background reference image. In the event of poor detection for a particular experiment, these settings were modified as necessary. Acquisition was performed in real-time, without “Detection determines speed” selected to allow the manual observer to accurately score fish behavior using the manual event-based keystroke settings. The independent variables were viewed following acquisition of every video to ensure the video acquired is properly identified within the Trial List. After acquisition, the movement path of each fish was manually inspected for abnormalities (i.e. missing samples, reflection clustering, or rouge detection points) using the Track Editor features in EthoVision. Trials with widespread abnormalities were reacquired after adjusting necessary settings. The Track Editor permits manual correction of individual track points as well as interpolation of missing points based on the first previous and next non-missing points. From the Track Editor, standard two dimensional (2D) swim paths (with color representing experimental group) was saved as image files for future reference. In order to reduce the effects of rogue misdetection points, body wobble noise and other potential confounds inherent to all video-tracking systems, Track Smoothing settings using average moving LOWESS local regression over

56 10 samples and Minimal Distance Moved threshold (MDM) of 0.0003 m (Cachat, Stewart et al., 2011). Movement parameters for all trials were calculated in “Total” (cumulative over entire testing duration) and “TimeBins” (cumulative per minute over the testing duration) results established by the Data Profile of EthoVision XT. In the Analysis Profile, the software is instructed on which movement parameters and endpoints to calculate for each Data Profile. In general, this included: “InZone” (latency (s), duration (s), frequency; test specific zones), Behavior (duration, frequency; all manually eventbased scored behaviors), Distance moved (m, total), Velocity (m/s, mean), Turn Angle (°, mean; absolute and relative), Turning Rate (°/s, absolute angular velocity), Turn Bias (°/s, relative angular velocity), Meander (°/m, mean; absolute and relative), Rapid Movement (duration and frequency of movement with velocity above 4 m/s, averaged over 10 samples), Slow Movement (duration and frequency of movement with velocity below 0.3 m/s, averaged over 10 samples) and Mobility (calculated based on percentage of area changed between consecutive frames; duration and frequency of high mobility (greater than 70% area changed), immobility (less than 3.0% area changed) and mobile (area changed between 3.1% and 69.9%). Following calculation, movement parameters, custom endpoints and raw spatiotemporal track data was exported from EthoVision XT as an Excel spreadsheet for subsequent processing and statistical analysis (Cachat, Stewart et al., 2011).

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4.5. Three-Dimensional Swim Path Reconstructions While track smoothing (LOWESS, MDM) and missing value interpolation is conducted within EthoVision XT, raw track data files still require several pre-processing steps prior to creating 3D trajectory reconstructions (Cachat, Stewart et al., 2011). In order to automate, standardize and increase the throughput of these pre-processing procedures a custom MatLab (Mathworks; Natick, MA) script has been written (Appendix B - Scripts). This script processes each track file individually, removing unnecessary trial identification information, reformatting the raw data such that the first row contains dependent variable labels, creates new columns for movement, behavior and mobility and lastly, creates a new excel spreadsheet named with trial identification information contained within the original data. As exported from EthoVision each polynomial endpoint, movement, behavior and mobility, are reported as binary events in separate columns for each state (0 for no, 1 for yes). The script scans each polynomial dependent variable (i.e. Rapid Movement, Slow Movement) for “1” (yes, state is occurring) and creates a new column (i.e. Movement) that contains both “RapidMove” and “SlowMove” strings positioned at the exact time in which they occur (Cachat, Canavello et al., 2010b; Cachat, Stewart et al., 2011). Processed track files are then imported into RapidMiner 5.2 (Rapid-I GmbH; Dortmund, Germany) and stored in a local repository unique to each experiment. Each column (dependent variable) was designated as either a numerical or nominal valuetype based on its contents and no special attributes were assigned. 3D Swim Trajectory

58 Reconstructions are created by loading the track data in a Scatter 3D Color plot. 3D Temporal Reconstructions were created by selecting X-center, Time, and Y-center to the X, Y- and Z-axes, respectively (Figure 11). Spatial reconstructions were generated in a similar manner, with X-center (side-view), X- center (top-view) and Y-center (side-view) plotted on the X, Y- and Z-axes (Cachat, Canavello et al., 2010b; Cachat, Stewart et al., 2011).

Figure 11: 3D Trajectory Reconstructions Coordinate Framework Using a Scatter 3D Color plot, 3D temporal reconstructions plot X, Y and Time from a single camera. 3D spatial reconstructions require a second camera to capture and plot spatiotemporal data along the zaxis.

Dependent variables were actively cycled across the path using the Color attribute, and tracks were explored using rotation and zooming features. For comparison, axis ranges were standardized and reconstructions were saved as image files. Representative reconstructions for each experimental manipulation were selected by comparing the complete set of 2D and 3D swim path images, rating from 1 to n based on the level of activity (1 = low activity) relative to each other (by three observers on a

59 consensus basis) and choosing the middle track as representative (Cachat, Stewart et al., 2011). 3D Spatial Reconstructions require two cameras to plot horizontal (x, front view), vertical (y, front view) and side-side (x or y, top view) data, depicting zebrafish 3D activity within the actual testing arena (Figure 11). This research also applied Track3D (Noldus Information Technologies; Wageningen, Netherlands), a supplement for EthoVision XT developed originally for insects; to quantify 3D movement parameters based two camera views (Cachat, Stewart et al., 2011).

4.6. Advanced Spatiotemporal Trajectory Analysis Each drug treatment appears to have a general “style” of modulation on zebrafish swimming trajectories, established by qualitative evaluation of 3D trajectory reconstructions. In order to quantify features in zebrafish behavioral responses not adequately reflected or detected in traditional global position/activity level endpoints, raw spatiotemporal track data was analyzed with several techniques that are able to quantitatively detect and distinguish patterns, sequences and events within and between spatiotemporal data. These include trajectory analysis, arena segmentation and multivariate methods. The goal of these analysis techniques is to identify emergent behavioral states induced by experimental treatments sensitively and dose-dependently such that treatments with similar psychopharmacological profiles can be distinguished. Spatiotemporal track data exported from EthoVision XT 8.5 was reformatted into a time-series with x- and y- coordinates. Therefore, spatiotemporal points within a

60 zebrafish swim path are represented by a triplet containing a x-, and y-coordinate and time (j); {𝑥𝑗 , 𝑦𝑗 , 𝑡𝑗 } for 𝑗 = 0, … , 𝑛 − 1

In order to calculate the distance moved between two points, the analysis was performed by; 𝐷 = √(𝑋𝑛 − 𝑋𝑛−1 )2 + (𝑌𝑛 − 𝑌𝑛−1 )2

With this information, the following trajectory analysis methods were used to detect and describe sequential micro-events in the movement paths of zebrafish tested in novelty-based behavioral tests (i.e. NTT). Straightness Index (SI) is the most basic method for extracting information on the structure animal’s path. Given a trajectory, the straightness index is a ratio of the beeline distance between the first and last points to the total distance traveled (Figure 12). SI values range from 0 to 1.0, with 1.0 representing a straight path and 0.0 representing a highly circumscribed or circular path. The straightness indexes, as well as subsequent geometric measures are highly dependent on the step length (portion of trajectory analyzed) and sampling interval (number of points in step). For example, if both the step length and sampling interval are large the average straightness index will approach 0.5. Several different values are examined to ensure micro- and macro-level

61 patterns are characterized. The use of 3D reconstructions to visualize and observe how the index values change with respect to the window parameters facilitates this effort.

Figure 12: Calculation of Straightness Index The solid line represents the actual path traveled by the subject between points (x 1, y1) and (x2, y2). To calculate SI, the beeline distance (length of dotted line) is divided by the total distance traveled (length of solid line).

Fractal Dimension (d) is an index of a line or trajectory geometric variation (Benhamou, 2004). A path is considered to be fractal if it possesses self-similarity, meaning that properties of the path are identical when evaluated at different scales. Essentially, fractal dimension d is a more complex measure of a path’s straightness index. Fractal d is reported in a range from 1 to 2, with 1.0 representing a straight path and 2.0 representing a highly circumscribed, or circular path. Fractal dimensions were calculated using Fractal v 5.20, an open source program that is able to perform various forms of fractal analysis (Kearns, Nams et al., 2010; Nams, 2006a, 2006b). Due to its origins in geology and studies of animal habitat formation, fractal dimension d assumes very large (approaching infinite) spatial scales. Several techniques permit analysis of path texture dynamically with variable scaling components. These were performed in recognition that the range of recorded zebrafish behavior is limited

62 by the size of the arena and the minimal resolution in which a behavioral event can be detected.

4.7. Whole-body Cortisol Concentration Assay Whole-body samples were taken from fish used in respective behavioral tests. Individual body samples obtained from experimental and control cohorts were homogenized in 500L of ice-cold 1× PBS buffer (Egan, Bergner et al., 2009). The homogenizing rotor blade was then washed with an additional 500L of PBS and collected in a 2 mL tube containing the homogenate. Samples were transferred to glass extract-O tubes and cortisol was extracted twice with 5 mL of diethyl ether (Fisher Scientific, USA). After ether evaporation, the cortisol was reconstituted in 1 mL of 1× PBS. To quantify cortisol concentrations, ELISA was performed using a human salivary cortisol assay kit (Salimetrics LLC, State College, PA). ELISA plates were measured in a VICTOR-WALLAC plate reader using the manufacturer’s software package. Wholebody cortisol levels were determined using a 4-parameter sigmoid minus curve fit based on the absorbencies of standardized concentrations, and presented as relative concentrations per gram of body weight for each fish, as described in (Cachat, Stewart et al., 2010a; Egan, Bergner et al., 2009).

4.8. Statistical Analysis Statistical differences in behavioral and physiological data was assessed using either Mann-Whitney U-test or ANOVA (factor: group), followed by Tukey post-hoc

63 tests (α = 0.05). Experimental designs with three or more testing groups were statistically analyzed using ANOVA (factor: group, dose), followed by Tukey post-hoc tests (α = 0.05). All TimeBin (per min) data were statistically evaluated using repeated measures ANOVA (factor: minute) followed by Tukey post-hoc test (vs. respective minute in control group). For analysis of homebase formation, data was analyzed using a chi-square (χ2) or Mann–Whitney U-test. The χ2 test were performed to analyze the spatial distribution of homebase-related behaviors, comparing actual percentages of time spent, number of visits and distance traveled in each zone with theoretical random (by-chance) distribution of these indices. χ2 data was first calculated for each endpoint, each OFT tank and each individual fish. Since various experiments used 2-3 trained observers, inter-rater and intra-rater reliability was assessed using Spearman correlation coefficients comparing each observer’s ratings on the same behavioral test data.

4.9. Data Sharing Recognizing the importance of scientific data sharing and dissemination, the results this dissertation are published in traditional academic media, including scholarly journals, books and conference abstracts. To make these new research techniques publicly available, we presented data at a number of local, national and international neuroscience conferences, including various professional meetings in USA and Europe, such as Experimental Biology (EB), Society for Neuroscience (SfN), Stress and Behavior

64 (ISBS) and Behavior, Biology and Chemistry (BBC) conferences. Selected drug treatments and collected phenotypes were included as part of the Zebrafish Neurophenome (ZND) database (www.tulane.edu/~znpindex/search), recently established by the Kalueff Lab, in collaboration with Tulane University Innovative Learning Center, to facilitate sharing of zebrafish behavioral findings (Kyzar, Zapolsky et al., 2012). My dissertation text and resulting publications will be hosted and distributed through my personal website (www.jcachat.com), in addition to technical documentation, outlining the analysis techniques, and a manual with tutorials and instructions on the proper use of features with the software and 3D approaches, with relevant references and supplemental information.

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Chapter 5. Modeling Affective Phenotypes Affective or mood disorders are highly widespread neuropsychiatric conditions, including anxiety and stress disorders, depression, obsessive compulsive disorders and various forms of phobia, and contribute markedly to worldwide disease burden (Baldwin & Garner, 2008). The World Health Organization estimates that the lifetime prevalence of this cluster of human brain diseases is 49.6%, making them the most commonly observed mental illnesses in humans (Kessler, Berglund et al., 2005; Kessler, Sonnega et al., 1995). Despite the profoundly negative effectives of these disorders on public health, progress in understanding their etiology and pathology has drastically diminished in recent years. Due to the influence of both genetic and environmental influences behind these diseases, it has been difficult to trace the mechanism behind affective disorders (Gerlai, 2012). The pursuit for new therapeutic drugs and modulating alternative molecular targets than the current drug classes has come to a near standstill as potential structural analogs become exhausted (Nestler & Hyman, 2010). There is a critical need for novel in-vivo approaches modeling behavioral symptoms with high validity, and permitting concurrent analysis of regulatory changes at genetic, cellular and molecular biological levels. Experimental animal models of affective disorders have been successfully used in rodents, based on predator

66 avoidance/escape defensive behavior (Blanchard, Hebert et al., 1998), risk assessment (Ohl, Arndt et al., 2008; Ohl, Roedel et al., 2002) and the motivational conflict between exploration and avoidance in a novel environment (Belzung and Agmo, 1997b, Kurt et al. , 2000, Ribeiro and De Lima, 1998). Predatory exposure and novelty-based behavioral paradigms, analogous to those used in rodents have been recently developed for zebrafish to assess affective domains, as described previously (see section 4.3). The purpose of modeling affective phenotypes was to characterize zebrafish affective responses and further validate potential utility of this model in neurobehavioral anxiety research. Paralleling evidence obtained in clinical settings and rodent behavioral research, the present experiments evaluate the behavioral and physiological phenotypes of adult zebrafish to natural (ethological) and pharmacological experimental treatments in the NTT.

5.1. Models of Fear: Ethologically-Relevant Stimuli The use of naturalistic stimuli to modulate animal behavior is argued as the most appropriate experimental approach if underlying biological mechanisms of behavior are the goals of investigation (Bourin, Petit-Demouliere et al., 2007; Gerlai, 2011; Rodgers, Cao et al., 1997) By evaluating species-specific traits, phylogenetic (evolutionarily selected alleles) and phenogenetic (behavioral phenotypes evolved through environmental conditions) variation are given priority focusing on evolutionarily conserved neural circuits and signaling pathways (Gerlai, Crusio et al., 1990). An alternative approach is examining genetic or pharmacological experimental treatments

67 on animal behavior in standardized laboratory settings, which strive to limit the influence of genetic and environmental variations as much as possible. These approaches are complementary and equally valuable in behavioral neuroscience and behavioral genetics, and both have been included in this dissertation research.

5.1.1. Predator Exposure Predator exposure is a common technique used to induce stress and fear in experimental animal models (Maximino, De Brito et al., 2010b). In zebrafish, visual (via a computer screen) and olfactory (via predator tank water) contact to the Indian Leaf fish (Nandus nandus), a sympatric predator (found naturally in same geographic habit), has been shown to evoke anxiogenic behavioral responses (Bass & Gerlai, 2008; Gerlai, Fernandes et al., 2009) as well as elevations in cortisol levels (Barcellos, Ritter et al., 2007). In the following study this research

Figure 13: Indian Leaf fish and Oscar fish Images obtained from fishbase.org

exposed zebrafish to the Indian Leaf fish and the Oscar fish (Astronotus ocellatus) a very aggressive allopatric predator (naturally occurring in a different geographic habitat) (Figure 13).

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Figure 14: Effects of Sympatric and Allopatric Predator Exposure in NTT Behavioral responses to acute (5 min, n = 10) and chronic (24 h, n = 13; 72 h, n = 11) exposure to the Indian Leaf fish (ILF) and acute exposure (10 min, n = 12) to Oscar fish. Data presented as Mean ± SEM, *p < 0.05, **p < 0.01, ***p < 0.005, #p = 0.05–0.08 (trend) vs. controls, Mann-Whitney U-test (Cachat, Canavello et al., 2010b).

Zebrafish acutely exposed to the Indian Leaf fish (Figure 14, top) spent significantly more time in the upper half, and a trending decrease in transitions to the upper half (Cachat, Canavello et al., 2010b). Chronic exposure resulted in significant responses reflecting a preference for the upper half, in addition to increased erratic movements (Figure 14, middle). In contrast, acute exposure to the Oscar fish (Figure 14, bottom) did not significantly alter zebrafish behavior (Cachat, Canavello et al., 2010b).

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5.1.2. Alarm Pheromone In addition to the visual and direct contact with a predator, indirect exposure with sensory cues (i.e. odor, sound) from the predator or previously exposed conspecifics (through social facilitation) can activate a stress response in animals (Bourin, Petit-Demouliere et al., 2007). In zebrafish, an analogous stimulus used to evoke anxiety is alarm pheromone exposure. Originally described in 1941 (Von Frisch, 1941), the pheromone is naturally synthesized in epidermal cells of fish and serves as a very advantageous evolutionary adaptation. During a predator attack the epidermal cell membrane is damaged, releasing the alarm pheromone and alerting nearby conspecifics of the danger. Previous studies in zebrafish have shown that alarm pheromone exposure induces a strong fear response, characterized by faster swimming with spontaneous rapid turns, increased freezing and markedly increased bottom dwelling (Rehnberg, 1988; Speedie & Gerlai, 2008). Stress axis activation has also been reported following alarm pheromone exposure, measured by cortisol activation (Barcellos, Ritter et al., 2007). The research sought to verify these results as well as evaluate the temporal nature of the observed behavioral response. In the NTT, acute alarm pheromone exposure resulted in a significantly longer latency to enter the upper half, fewer transitions and reduced time spent in the upper half, as well as increasing erratic movements, freezing bouts and freezing duration. Acute exposure also resulted in a significant increase in whole body cortisol

70 concentration, while the total distance traveled and average velocity during the sixminute trial did not differ between control and experimental groups (Figure 15).

Figure 15: Effects of Acute Alarm Pheromone Exposure in NTT Zebrafish (n = 60) were exposed to alarm pheromone (7 mL, 5 min) prior to behavioral testing in the novel tank test and cortisol analysis. Data presented as Mean ± SEM, *p < 0.05, **p < 0.01, ***p < 0.005, MannWhitney U-test, vs. controls (Cachat, Stewart et al., 2011).

71 This robust reduction in upper half exploration observed in acutely exposed zebrafish, compared to matched controls, is exemplified in representative (2D) swim path reconstructions (Figure 16).

Figure 16: Representative Trajectory (2D) of Acute Alarm Pheromone in NTT These swim paths illustrate the lack of upper half exploration observed in wild-type controls.

Zebrafish exposed to alarm pheromone for a prolonged period did not result in any significant alterations in behavioral endpoints compared wild-type, drug free controls (Figure 17). In addition, prolonged exposure did not significantly vary whole body cortisol concentrations (data not shown).

Figure 17: Effects of Prolonged Alarm Phermone Exposure in NTT Zebrafish (n = 10) were exposed to alarm pheromone (7 mL, 30 min) prior to the novel tank test. Data presented as Mean ± SEM. (Egan, Bergner et al., 2009).

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5.2. Models of Anxiety: Exposure to Pharmacological Treatments Pharmacological approaches to understand behavioral neuroscience are an important complement to ethological-focused approaches. Rather than strictly maintaining the differences found in an animal’s natural habit, this type of research strives to eliminate, as much as possible, genomic, environmental and experimental variability (Gerlai, Crusio et al., 1990). Animal strains are breed and maintained for genetic homogeny, and behavior is evaluated in tightly controlled settings (Chakraborty, Hsu et al., 2009). The nature of this approach is a particularly important consideration for developmental genetics and large-scale, high-throughput behavioral assays (Driever, Solnica-Krezel et al., 1996; Lessman, 2011; Steimer, 2011).

5.2.1. Caffeine Caffeine is a central nervous system stimulant that acts primarily as an antagonist at adenosine receptors, and has modulatory effects on affective and cognitive domains. In both humans (Smith, 2002) and rodents (El Yacoubi, Ledent et al., 2000), moderate to high caffeine consumption can increase of anxiety and related anxious behaviors. The effects caffeine exerts on learning and memory are also reported as dose and time dependent (Angelucci, Vital et al., 1999). Adenosine signaling is inhibitory and has anxiolytic action, the fact that caffeine has antagonistic effects on adenosine suggests a role of this system in anxiety pathogenesis (Correa & Font, 2008; Kulkarni, Singh et al.,

73 2007). Given genetic, developmental and environmental factors influencing affective disorders, zebrafish models could contribute significantly to this research and it was hypothesized that zebrafish would show similar affective responses to caffeine treatment (Egan, Bergner et al., 2009). Moreover the effects of caffeine on adult zebrafish behavior have not yet been evaluated, prior to this research.

Figure 18: Effects of Acute Caffeine Treatment in NTT Adult zebrafish (n = 15) were treated acutely with caffeine (250 mg/L, 20 min). Data presented as Mean ± SEM, *p < 0.05, **p < 0.01, ***p < 0.005, vs. controls, Mann-Whitney U-Test (Cachat, Stewart et al., 2011).

74 In the NTT, acute pretreatment with caffeine (Figure 18) significantly reduced the number of transitions to the upper half, and evoked more frequent, longer freezing bouts in caffeine-treated fish. On average, the latency to and time spent in the upper half, as well as erratic movements did not display significant modulation. Caffeinetreated zebrafish did have significantly higher whole body cortisol levels, in addition to traveling less total distance and a lower average velocity throughout the trial period (Figure 18) (Cachat, Stewart et al., 2011).

Figure 19: Representative Trajectory (2D) of Acute Caffeine treatment in NTT This representative swim paths illustrated the lack of upper half exploration, and predominate bottom dwelling profile observed in caffeine treated zebrafish.

Bottom dwelling behavior and an overall lack of novel tank exploration is evident in the representative 2D swim trajectory following acute caffeine treatment, compared to wild-type controls (Figure 19).

5.2.2. Pentobarbital Pentobarbital, a long established GABAergic modulator, was acutely delivered at several doses prior to testing in the NTT (Figure 20).

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Figure 20: Effects of Acute Pentobarbital Treatment in NTT Adult zebrafish (n = 8 – 10 per group) were treated acutely with pentobarbital (5, 10 and 20 mg/L, 20 min). Data presented as Mean + SEM; **p < 0.01, one-way ANOVA (factor: dose) with Tukey post hoc test vs. controls (Stewart, Wu et al., 2011).

A one-way ANOVA test (factor: dose) revealed that the drug significantly affects top transitions (F(3, 37) = 3.5, P < 0.05) and the time spent in top (F(3, 37) = 3.5, P < 0.05) in adult wild type (short-fin) zebrafish. While an overall dose-dependent trend appears to emerge, ANOVA did not provide significance for the latency, distance, velocity, erratic, and freezing behavior.

5.2.3. Fluoxetine Serotonergic mechanisms are strongly implicated in human (Deakin, 1998; Eison, 1990; Hoes, 1982; Morgan, Grillon et al., 1995) and animal anxiety (Handley & Mcblane, 1993a, 1993b; Heisler, Pronchuk et al., 2007). Since selective serotonin reuptake inhibitors (SSRIs) are potent modulators of brain serotonin (Esler, Lambert et al., 2007; Goldstein & Goodnick, 1998), behavioral effects of fluoxetine on zebrafish merit further research.

76 Zebrafish possess a well-developed serotonergic system (Stewart, Wu et al., 2011) which makes them an ideal model for such analyses.

Figure 21: Effects of Acute Fluoxetine Treatment in NTT Behavioral responses of zebrafish (n = 10-16) to acute fluoxetine exposure (100-1000 µg/L, 20 min) in the novel tank test. Data presented as Mean + SEM; one-way ANOVA (factor: dose) with Tukey post hoc test vs. controls. (Stewart, Wu et al., 2011)

A one-way ANOVA test (factor: dose) revealed an overall effect on time frozen (F(4, 51) = 1.94, P < 0.05), and post-hoc Tukey test demonstrated this effect difference was between 100 and 1000 µg/L. Overall, compared to controls, acute treatment (20 min) with the SSRI fluoxetine (100-1000 µg/L) did not significantly alter adult zebrafish behavior in the NTT (Figure 21). Chronic exposure to fluoxetine (100 µg/L, 2 wk) resulted in robust behavioral responses in the NTT (Figure 22). Compared to untreated controls, drug-treated fish

77 displayed a significantly lower latency to enter the upper half, as well as increased transitions and time spent within the top region. Erratic movements and freezing behaviors were also significantly decreased in this fluoxetine treated cohort. In parallel to behavioral data, fluoxetine-treated fish also showed significantly lower whole-body cortisol concentrations compared to control group (Cachat, Stewart et al., 2011; Egan, Bergner et al., 2009).

Figure 22: Effects of Chronic Fluoxetine Treatment in NTT Zebrafish behavioral responses following chronic fluoxetine (100 µg/L, 2 wk; n = 30) treatment. Data presented as Mean ± SEM, *p < 0.05, **p < 0.01, ***p < 0.005, vs. controls, Mann-Whitney U-test (Cachat, Stewart et al., 2011).

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Figure 23: Representative Trajectory (2D) of Chronic Fluoxetine exposure in NTT These swim paths illustrate the lack of upper half exploration observed in wild-type controls.

Representative 2D swim paths following chronic fluoxetine treatment (Figure 23), illustrate these behavioral effects with predominate upper half exploration displayed by drug-treated fish compared to wild-type controls.

5.2.4. Ethanol As described in (Egan, Bergner et al., 2009), acute treatment with ethanol significantly decreased the latency to enter the top half, as well as increased transitions to and time spent within the upper region. Erratic movements, freezing bouts and duration were not significantly affected (Figure 24). A similar behavioral profile was observed following chronic ethanol treatment (Figure 25), in which all indices of upper half exploration were significantly elevated. In addition, significant decreases in erratic movements, and freezing bouts were observed, while freezing duration trended to decrease. The total distance traveled and average velocity were not significantly different between chronically treated ethanol and control cohorts. This behavioral

79 profile of ethanol-treated zebrafish was paralleled with a significant reduction in wholebody cortisol concentration compared to matched control fish.

Figure 24: Effects of Acute Ethanol Treatment in NTT Zebrafish behavioral responses (n = 10) following acute ethanol (0.3%, 5 min) treatment. Data presented as Mean ± SEM, *p < 0.05, **p < 0.01, vs. controls, Mann-Whitney U-test (Egan, Bergner et al., 2009).

Figure 25: Effects of Chronic Ethanol Treatment in NTT Zebrafish behavioral responses (n = 35) following chronic ethanol (0.3%, 1 wk) treatment. Data presented as Mean ± SEM, *p < 0.05, **p < 0.01, ***p < 0.005, vs. controls, Mann-Whitney U-test (Cachat, Stewart et al., 2011).

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Figure 26: Representative Trajectory (2D) of Chronic Ethanol Treatment in NTT These representative swim paths illustrate pronounced exploration around the novel tank in zebrafish chronically treated with ethanol in comparison to a typical wild-type control zebrafish.

Representative 2D swim path of chronic ethanol compared to wild-type controls (Figure 26), illustrates that chronically treated ethanol fish explore throughout the upper half and middle regions of the novel tank.

5.2.5. Nicotine Nicotine exposure produces strong effects on zebrafish place preference and learning (Kily, Cowe et al., 2008; Levin & Chen, 2004). Although learning and memory are not specifically addressed here, the established effects of drugs of abuse that affect cognitive functions give further validity to the zebrafish model of drug abuse (Gerlai, Lee et al., 2006; Grossman, Utterback et al., 2010; Lopez-Patino, Yu et al., 2008; Ninkovic & Bally-Cuif, 2006). Chronic nicotine exposure in larval zebrafish leads to reduced swimming and impairs their startle response (Parker & Connaughton, 2007). There is a precedent for nicotine research in zebrafish (Levin, Bencan et al., 2007; Levin & Chen, 2004; Levin, Limpuangthip et al., 2006) and the research indicates

81 Although 100 mg/L produces the most reliable anxiolytic effects (Levin, Bencan et al., 2006), and they are dose-dependent (Levin, 2010; Levin, Bencan et al., 2007; Levin, Limpuangthip et al., 2006) Acute exposure to nicotine prior to the NTT resulted in a significant reduction in the latency to the upper half, the number of transitions to the upper half while significantly increasing the time spend within the upper half (Figure 27). There was also a significant reduction in erratic movements and freezing behavior compared to matched controls, while nicotine-treated fish also displayed a significant increase in whole body cortisol concentrations (Figure 27).

Figure 27: Effects of Acute Nicotine Treatment in NTT Zebrafish behavioral responses (n= 40) following acute nicotine (10 mg/L, 5 min) exposure quantified with video tracking software. Data presented as Mean ± SEM, *p < 0.05, ***p < 0.005 vs. controls, Mann-Whitney U-test (Cachat, Stewart et al., 2011)

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Figure 28: Representative Trajectory (2D) of Acute Nicotine Treatment in NTT A representative trajectory of acute nicotine treatment show distinct upper half exploration, with practically no time spent along the bottom of the novel tank.

The representative 2D swim path of a zebrafish acutely treated with nicotine illustrates a predominate upper half exploration profile, in which it appears that these fish spend most of the NTT trial time along the upper water surface (Figure 28).

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5.3. Discussion and Translational Value 5.3.1 Models of Fear: Ethologically-Relevant Stimuli

Figure 29: Summary of Ethologically-Relevant Stimuli in NTT Effects of etiologically relevant stimuli on behavior and cortisol included in this research. Green upward arrows represent statistically significant increase relative to matched controls, red downward arrows reflect significant decrease in the respective endpoint vs. matched controls and yellow slanted arrows represent a trend (p = 0.05-0.08) upwards or downwards compared to the control cohort. Dash lined (“-“) represents non-significant effects, “na” reflects that test was not performed for given experiments. ILF, Indian Leaf Fish.

Predator Exposure. Acute and chronic exposure to the Indian Leaf fish resulted in difference behavioral profiles in adult zebrafish (Figure 29). In general, the observed preference for the upper region of the novel tank is not a typical characteristic of

85 anxiogenic phenotypes in the NTT (Egan, Bergner et al., 2009; Levin, Bencan et al., 2006). In previous studies, zebrafish were exposed to the Indian Leaf fish using visual images presented on a computer screen or by being placed in the predator's home tank water (Barcellos, Ritter et al., 2007; Bass & Gerlai, 2008; Gerlai, Fernandes et al., 2009). In this research’s experiments, zebrafish were in direct contact with the Indian Leaf fish by placing both fish within the same tank. While the significant preference for the upper regions of the novel tank are not indicative of an anxiogenic profile, it is believed that this is due to the fact that the Indian Leaf fish predominantly situated itself at the bottom of the exposure tanks. Therefore, it appears that the zebrafish displayed a distinct learned avoidance behavior by moving to the area least likely to be occupied by the Indian Leaf fish. This avoidance behavior paralleled with a significant increase in erratic movements suggests that a fear-like response, and interpretation of an anxiogenic profile can be supported. Comparing these results with those of Oscar fish exposure further supports this observation, in which upper half exploration was not significantly affected (Figure 29). These results suggest a greater fear of sympatric (compared to allopatric) predators. This suggests the importance of a genetic, innate influence on the zebrafish fear response. In general, two possible explanations for predator-avoidance behavior include learned anti-predatory responses (following exposure to a harmful predator), or instinctive avoidance behavior (Cachat, Canavello et al., 2010b).

86 Alarm Pheromone. In the NTT, acute exposure to alarm pheromone resulted a robustly significant reductions in measures representing upper half exploration, increased erratic movements and freezing as well as elevated cortisol concentrations (Figure 29). Replicating the results of previous studies (Barcellos, Ritter et al., 2007; Rehnberg, 1988; Speedie & Gerlai, 2008), this behavioral profile represents an exemplary anxiogenic phenotype and further validate the NTT as a valid behavioral paradigm to evaluate affective domains in adult zebrafish. The alarm pheromone exposure did not significantly alter the total distance traveled or average velocity during the behavioral trial indicating that locomotion was not confounded by neurological abnormalities. Representative 2D swim paths (Figure 16) show the bottom dwelling pattern that is representative of an anxiogenic phenotype, however, these 2D trajectory paths are unable adequately visualize more definitive features of an anxiogenic profile such as freezing bouts and erratic movements (Egan, Bergner et al., 2009). Prolonged exposure to alarm pheromone did not cause significant behavioral modulation compared to control fish (Figure 29). This suggests that behavioral reactions to alarm pheromone occur within short time span, and are quickly habituated to. Given the evolutionary function of alarm pheromone as a social signal to nearby conspecific of danger, rapidly responding would represent the most successful survival strategy to avoid danger and also detect the pheromone before dissipation through river water (Egan, Bergner et al., 2009).

87 5.3.2 Models of Anxiety: Exposure to Pharmacological Treatments

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na

na

na

Transitions to upper half Time in upper half, s Erratic movements Freezing bouts Freezing duration, s

Cortisol Concentration

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Chronic Ethanol

Acute Ethanol

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Latency to upper half, s

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Pharmacological Treatments

Figure 30: Summary of Pharmacological Treatments in NTT Effects of pharmacological treatments on behavior and cortisol included in this research Green upward arrows represent statistically significant increase relative to matched controls, red downward arrows reflect significant decrease in the respective endpoint vs. matched controls and yellow slanted arrows represent a trend (p = 0.05-0.08) upwards or downwards compared to the control cohort. Dash lined (“-“) represents non-significant effects, “na” reflects that test was not performed for given experiments.

Caffeine. Adult zebrafish acutely exposed to caffeine displayed several endpoints suggestive of an anxiogenic profile in the NTT, including reduced transitions to the upper half, markedly increased freezing behavior and elevated cortisol concentrations (Figure 30). In addition, there was a significant increase in erratic movements during the last half of the NTT (data not shown). Compared to matched controls, there was also a significant increase in erratic movements during the last half of the NTT trial (data not

88 shown). This behavioral profile suggests an anxiogenic phenotype and the representative 2D swim path depicts bottom dwelling behavior characteristic of anxious behavior. Supporting the translational value of adult zebrafish behavioral models, the anxiogenic profile observed here in zebrafish parallels both human responses to caffeine challenge (Childs, Hohoff et al., 2008) and rodent data on anxiogenic effects of caffeine (El Yacoubi, Ledent et al., 2000; Sudakov, Medvedeva et al., 2001). Adenosine has an inhibitory effect on the brain, and exerts robust anxiety reduction in rodents (Kulkarni, Singh, 2007). In contrast, its non-selective antagonist caffeine acts as an anxiogenic agent, as shown in clinical (Lara, 2010), rodent (Bradley et al. , 2010, Kulkarni, Singh, 2007) and zebrafish studies (Egan, Bergner, 2009). The anxiogenic effect seen here in zebrafish is in line with human response to caffeine challenge (Childs et al., 2008) and rodent data on anxiogenic effects of caffeine (El Yacoubi, 2000; Sudakov et al., 2001). In a similar manner to the representative alarm pheromone swimming trajectory (Figure 16) these 2D trajectories to not permit a thoroughly observation of the spatiotemporal dynamics of freezing behavior or erratic movements, as well as a closer dissection of distance and velocity parameters. Movement parameters quantified with video-tracking software found significant differences in the distance moved and average velocity of caffeine treated fish. As a CNS stimulant, it may be expected that caffeine exposure may increase, rather than decrease these measures of locomotion. Signs of neurological abnormalities that effect would affect natural swimming motion were not

89 observed during manual observation and therefore these results most likely reflect significantly increased anxiety-related freezing behavior. Pentobarbital. Pentobarbital administration evokes sedation in zebrafish (Figure 30), consistent with its effects in humans and animals (Abruzzi, 1964; Atkins, Rustay et

al., 2000) Fluoxetine. Acute fluoxetine did not significantly affect zebrafish behavior (Figure 30). In contrast, acute SSRI treatment has been reported to evoke anxiety in humans (Belzung, 2001; Enginar, Hatipoglu et al., 2008; Goodnick & Goldstein, 1998) and rodents (Bagdy, Graf et al., 2001; Drapier, Bentue-Ferrer et al., 2007; Kurt, Arik et al., 2000; Silva, Alves et al., 1999), and citalopram was anxiolytic in this model (Sackerman, Donegan et al.). While the lack of zebrafish anxiety following acute fluoxetine (Figure 30) contradicts clinical and rodent findings, acute SSRIs may exert complex behavioral profiles, including anxiolysis (Hascoet, Bourin et al., 2000; Lightowler, Kennett et al., 1994; Molewijk, Van Der Poel et al., 1995; Varty, Morgan et al., 2002). Furthermore, the lack of anxiogenic effects of acute SSRI may also be due to permeability to serotonin of the blood-brain barrier in teleosts (Khan & Deschaux, 1997), counterbalancing potentially anxiogenic effects of the sharp elevation of brain serotonin caused by these drugs. Chronic treatment with fluoxetine demonstrates the potential of this model to detect anxiolytic drug response, by inducing an overall increase in time spent in the top portion, lower latency to top exploration, and higher average top transitions. The

90 reduced levels of cortisol seen in the same groups of fluoxetine-treated fish (Figure 30) give further support to the efficacy of the zebrafish model to span across levels of analysis. Behavioral and physiological data from this study agree strongly with that seen in previously published in rodent studies showing that chronic exposure to fluoxetine reduces anxiety (Dulawa, Holick et al., 2004; Norcross, Mathur et al., 2008), corticosterone responses (mice (Norcross, Mathur et al., 2008)) and HPA sensitivity (rats (Lowry, Hale et al., 2009; Szymanska, Budziszewska et al., 2009)). Ethanol. Similar to ethanol-induced anxiolysis in mice (Houchi, Warnault et al., 2008), acute ethanol exposure produced effects consistent with a reduced anxiety state in zebrafish (Figure 30). Chronic exposure to ethanol similarly induced increased time spent in the upper tank portion, and showed a reduced cortisol levels (Figure 30). Collectively, these findings serve to further validate the zebrafish model of anxiety in its behavioral, pharmacological and endocrine aspects. Nicotine. In adult zebrafish, acute administration of nicotine has an anxiolyticlike effect (Levin, 2010; Levin, Bencan et al., 2006; Levin, Bencan et al., 2007) (Figure 30) similar to its effect in humans and rodents (Jackson, Walters et al., 2009). Nicotine has varied effects on anxiety in humans and animals (Angelucci, Vital et al., 1999; Butler, Smith et al., 2009; Childs, Hohoff et al., 2008; El Yacoubi, Ledent et al., 2000; Smith, 2002); these effects are frequently correlated with tobacco addiction (Rogers, Heatherley et al., 2005). In humans, stress increases the rate of smoking (Comer, Haney et al., 1997); this

91 relation between stress and nicotine seeking is also found in rodents (Sukhotina, Zvartau et al., 2004). In rodents, nicotine has been found by some investigators to have anxiolytic effects as measured by choices on an elevated plus maze (El Yacoubi, Ledent et al., 2000). However, others have found nicotine-induced anxiogenic or mixed effects in rodents (Correa & Font, 2008). Nicotine dose seems to be quite important in the nature of its effects on anxiety. In mice, low dose nicotine (0.05 mg/kg) caused a significant anxiolytic effect as measured by open arm choice in the elevated plus maze (Kaplan, Greenblatt et al., 1993). This effect was abolished by co-administration of the endocannabinoid (CB1) antagonist rimonabant. In the same study a higher nicotine dose (0.8 mg/kg) caused an anxiogenic effect on the plus maze (Kaplan, Greenblatt et al., 1993). The cholinergic system is emerging as another target for pharmacological modulation of zebrafish anxiety, since N-cholinergic agonist nicotine elicits consistent and very robust anxiolytic responses in clinical (Picciotto, Brunzell et al., 2002) and rodent data (Cohen, 1967) for this drug. Collectively, these findings serve to further validate the zebrafish model of anxiety in its behavioral, pharmacological and endocrine aspects.

5.4. Conclusions From past literature (Egan, Bergner et al., 2009; Norton & Bally-Cuif, 2010; Speedie & Gerlai, 2008) it is known know that zebrafish display robust stress-related behaviors. It is unclear whether zebrafish display common stress-evoked behaviors, or

92 different (e.g., anxiety vs. fear) behaviors in different situations (Stewart, Kadri et al., 2010). While interest in zebrafish models is growing rapidly, the entire catalog of zebrafish behaviors remains unclear, and it is not known when and where these behaviors occur within the zebrafish locomotory path (Stewart, Kadri et al., 2010). Overall, behavioral and physiological endpoints measured in the present study (Figure 29, Figure 30) proved to be sensitive to environmental and pharmacological challenges. The experiments also produced several methodological advancements, for example, using a two zone “top/bottom” variation of the novel tank (Figure 9), differing from traditional versions (Bencan & Levin, 2008; Levin, Bencan et al., 2007), which employ 3 zones (bottom, middle and top). This research results suggest that this simplification does not reduce sensitivity of the novel tank diving paradigm to test a wide spectrum of experimental manipulations, and therefore may be beneficial for highthroughput phenotyping of zebrafish anxiety. Although numerous medications have been developed for anxiety disorders and related neuropsychiatric conditions including phobias, these diseases still represent a large unmet medical need. This may be because despite the concerted research and drug development efforts by pharmaceutical research companies and academic laboratories alike, the mechanisms of these disorders still remain to be fully elucidated. Animal models have been proposed to accelerate research in this area and there is still a large unmet medical need for novel therapeutic treatment of anxiety related disorders. One way zebrafish may be beneficial for such research is to speed up discovery of the

93 biological mechanisms. This may be achieved using, for example, forward genetic screens that identify mutations leading to the isolation of underlying genes. Another completely different approach has been to search for compounds which may alter fear responses. It is thus important to consider what is known about the psychopharmacological properties of zebrafish in the context of fear and anxiety (Gerlai, 2011) Second, this research has substantially modified the cortisol assay to assess zebrafish stress. While previously published studies used serum cortisol kits, and combined 20-25 fish in order to obtain one cortisol sample (Alsop & Vijayan, 2008), this research (Egan, Bergner et al., 2009) used a more sensitive human salivary cortisol kit, measuring cortisol levels in each individual fish. Such marked increase in sensitivity of whole-body cortisol assay appears to be an important methodological advancement, not only reducing the number of animals per group, but also enabling correlational analyses of behavioral and endocrine endpoints for each individual zebrafish. In summary, complex zebrafish behavioral responses to pharmacological modulation support their utility as a new model organism for anxiety research (Stewart, Kadri et al., 2010). As novel zebrafish paradigms continue to be developed, the field may benefit from using this new model species for further conceptual and methodological progress (Egan, Bergner et al., 2009; Kalueff, Wheaton et al., 2007; Laporte, Egan et al., 2010).

94

Chapter 6. Modeling Phenotypes Related to Drugs of Abuse and Withdrawal Several recent studies have demonstrated the potential of zebrafish as a model for drug reward and addiction (Ninkovic & Bally-Cuif, 2006). For example, rewarding properties of different drugs, including amphetamine (Ninkovic & Bally-Cuif, 2006), salvinorin A (Braida, Limonta et al., 2007), cocaine (Darland & Dowling, 2001), morphine and heroin (Bevins, Valone et al., 1995), have been reported in zebrafish. Anatomical homolog, as zebrafish dopaminergic projections to the basal forebrain parallel the mammalian mesolimbic system implicated in drug addiction (Rink & Wullimann, 2002b). Drugs of abuse and potential for dependence typically involve serotoninergic, dopaminergic, GABAergic, and adrenoreceptor signaling, and the zebrafish genome contains homologs to a vast majority of these loci (Klee, Ebbert et al., 2011). Likewise, chronic treatment of zebrafish with ethanol and nicotine alters the expression of multiple CNS genes, some of which have been identified as components of the addiction pathways in mammals (Kily, Cowe et al., 2008). Therefore, there is strong support for the use of zebrafish in addiction and withdrawal research at behavioral, anatomical and genomic levels of analysis. This purpose of modeling phenotypes of drug abuse and withdrawal is aimed to examine the behavioral and physiological responses of zebrafish to drugs of abuse, and

95 drug withdrawal from several psychotropic drugs commonly associated with misuse or abuse, including ethanol, caffeine, and morphine.

6.1. Drugs of Abuse 6.1.1. Cocaine

Figure 31: Effects of Acute Cocaine Treatment in NTT Zebrafish behavior (n = 10) in the novel tank test following acute cocaine treatment (1, 12.5, 25 mg/L, 20 min). Data presented as Mean ± SEM, *p < 0.05, **p < 0.01, ***p < 0.005, vs. controls, ANOVA with Tukey post-hoc analysis (Stewart, Wu et al., 2011).

A one-way ANOVA test (factor: dose) revealed that cocaine (1-25 mg/L) significantly affects the number of top transitions (F (3, 39) = 5.9, P < 0.005) and freezing duration (F(3, 39) = 5.7, P < 0.005).

96

6.1.2. Morphine

Figure 32: Effects of Acute Morphine Treatment in NTT Zebrafish (n = 15) treated acutely with morphine (1, 2 and 5 mg/L, 20 min) were tested in the novel tank test. Data presented as Mean ± SEM, *p < 0.05, vs. controls, ANOVA with Tukey post-hoc analysis (Egan, Bergner et al., 2009).

Acute morphine treatment (Figure 32) resulted in behavioral alterations as oneway ANOVA (factor: dose) revealed that significantly affects the latency to enter the top (F(3, 51) = 2.9, P < 0.05) and the number of top transitions (F(3, 51) = 2.8, P < 0.05) with 2 mg/L dose significantly differing from drug-free controls. Chronic morphine treatment (Figure 33) resulted in significant more in upper half exploration, as well as less time spent freezing and a reduction in cortisol concentrations, compared to matched controls.

Figure 33: Effects of Chronic MorphineTreatment in NTT Zebrafish (n = 35) treated with chronic morphine (2 mg/L, 2 wk) were tested in the novel tank test. Data presented as Mean ± SEM, *p < 0.05, **p < 0.01, vs. controls, Mann-Whitney U-test (Cachat, Stewart et al., 2011).

97

98

Figure 34: Representative Trajectory (2D) of Chronic Morphine treatment in NTT The representative 2D swim path following chronic morphine treatment in the novel tank test reflects more exploratory activity, with a lack of predominate bottom dwelling behavior.

The representative 2D swim path of chronic morphine illustrates an overall increase in exploration throughout the NTT, without dominate top dwelling or bottom dwelling behavior (Figure 34).

6.2. Acute and Repeated Withdrawal Paradigms Drug withdrawal is a common problem among both self-medicating abusers and chronically treated clinical patients (Hughes, Higgins et al., 1994; Polizos, Engelhardt et al., 1973; West & Gossop, 1994). Withdrawal syndrome has been reported for many psychoactive drugs, including ethanol (Landolt & Gillin, 2001; Wiese, Shlipak et al., 2000), benzodiazepines (Ashton, 1984), opioids (Himmelsbach, 1941; Koob, Stinus et al., 1989), cocaine (Foltin & Fischman, 1997), nicotine (Shiffman, Paty et al., 1995), caffeine (Hering-Hanit & Gadoth, 2003), phencyclidine (Tennant, Rawson et al., 1981), barbiturates (Essig, 1967) and cannabinoids (Wiesbeck, Schuckit et al., 1996). Clinical symptoms of withdrawal include excessive perspiration, nausea, headache, hallucinations and, most commonly, anxiety (Cooper & Haney, 2009; Cruickshank &

99 Dyer, 2009; Henningfield, Shiffman et al., 2009; Martinotti, Nicola et al., 2008; Prat, Adan et al., 2009; Shoptaw, Kao et al., 2009; Teixeira, 2009; Wu, Pan et al., 2009). In line with clinical findings, published rodent data describe anxiety-like behaviors evoked by acute withdrawal from ethanol (Philibin, Cameron et al., 2008), opioids (Fendt & Mucha, 2001; Harris & Gewirtz, 2004; Rabbani, Hajhashemi et al., 2009) amphetamine (Kokkinidis, Zacharko et al., 1986) and nicotine (Jonkman, Risbrough et al., 2008). In addition to robust behavioral effects of a single period of withdrawal, repeated administration and cessation of a drug treatment evokes strong withdrawal-like effects (Huang, Liang et al., 2009). For example, increased anxiety-like behavior was reported in rodents following repeated withdrawal from ethanol (Wills, Knapp et al., 2009) and morphine (Zelena, Barna et al., 2005). The importance of understanding neurobiological mechanisms requires innovative approaches to modeling withdrawal syndrome, including novel experimental paradigms, new biomarkers and alternative model species (Emmett-Oglesby, Mathis et al., 1990; Negus & Rice, 2009; Rahim, Feng et al., 2004). Some research suggests sensitivity of zebrafish to drug withdrawal. For example, ethanol discontinuation disrupts zebrafish shoaling behavior (Gerlai, Chatterjee et al., 2009), whereas cocaine withdrawal evokes marked alterations in their locomotion (Lopez-Patino, Yu et al., 2008; Lopez Patino, Yu et al., 2008). In addition to behavioral markers of withdrawal syndrome, both clinical and pre-clinical data implicate endocrine dysregulation in drug abuse and withdrawal (Borlikova, Le Merrer et al., 2006; Kiefer, Jahn et al., 2006; Lovallo, 2006; Rabbani,

100 Hajhashemi et al., 2009). Withdrawal-evoked anxiety strongly correlates with elevated blood or salivary cortisol in patients with heroin (Li, Li et al., 2008; Shi, Li et al., 2009), opioid (Bearn, Buntwal et al., 2001; Zhang, Ren et al., 2008), nicotine (Cohen, Al'absi et al., 2004), cocaine (Fox, Jackson et al., 2009) and ethanol (Keedwell, Poon et al., 2001; Nava, Premi et al., 2007) addiction. Similarly, increased levels of brain and plasma corticosterone have been reported in rodents following morphine (Rabbani, Hajhashemi et al., 2009) or ethanol withdrawal (Borlikova, Le Merrer et al., 2006), respectively. Taken together, this indicates that glucocorticoid abnormalities may represent important biological markers of withdrawal syndrome. This research aims to further validate the utility of zebrafish in modeling drug withdrawal syndrome, assessing anxiety-like behavioral and cortisol responses elicited in adult zebrafish by withdrawal from a wide spectrum of psychotropic drugs, including ethanol, caffeine, and morphine.

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6.2.1. Ethanol Withdrawal

Figure 35: Behavioral Effects of Ethanol Withdrawal in NTT Zebrafish (n = 30) were treated with ethanol (0.3% vol/vol, 1 week) followed by 12 hr withdrawal before behavioral and endocrine measures were performed. Data presented as Mean ± SEM, *p < 0.05, **p < 0.01, ***p < 0.005, vs. controls, Mann-Whitney U-test (Cachat, Stewart et al., 2011).

Compared to drug-free control fish, ethanol withdrawal (Figure 35) resulted in a reduction of upper half exploration, reflected by a significant increase in latency to the upper half and less transitions to the upper regions. The bottom dwelling preference was paralleled by an increase in erratic movements, and significantly increased freezing

102 behavior as well as whole body cortisol concentrations. The representative 2D swim path illustrates a strong bottom dwelling profile.

6.2.2. Caffeine Withdrawal

Figure 36: Effects of Caffeine Withdrawal in NTT Zebrafish (n = 15 - 16) were treated with caffeine (50 mg/L, 1 week) followed by 12 hr withdrawal before behavioral and endocrine measures were performed. Data presented as Mean ± SEM, ***p < 0.005, #p = 0.05–0.08 (trend) vs. controls, Mann-Whitney U-test (Cachat, Stewart et al., 2010a).

Zebrafish undergoing caffeine withdrawal (Figure 36) displayed significantly more erratic movements (compared to matched controls), as well as a trending decrease in freezing bouts. Caffeine withdrawal did not significantly alter cortisol concentrations (data not shown).

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6.2.3. Morphine Withdrawal

Figure 37: Effects of Repeated Morphine Withdrawal in NTT Zebrafish (n = 30) tested in novel tank test following two 3 h withdrawal periods daily for 1 week from chronic morphine (1.0 mg/L, 1 week) in zebrafish tested in the novel tank diving test. Data presented as Mean ± SEM, *p < 0.05, ***p < 0.001, #p = 0.05–0.08 (trend) vs. controls, Mann-Whitney U-test (Cachat, Canavello et al., 2010a).

Although single morphine withdrawal did not evoke anxiety-like behaviors (data not shown), repeated morphine withdrawal produced robust anxiogenic responses. As can be seen in Figure 37, the repeated withdrawal cohort exhibited a

104 significantly longer latency to enter the upper half, had fewer transitions to the upper half and spent less time there. Significantly increased erratic movements, freezing behavior and whole body cortisol concentration was also observed in zebrafish following repeated morphine withdrawal. The representative 2D swim path illustrates bottom dwelling behavior; with bursts vertical movements with sharp turn angles never crossing into the upper half of the novel tank (Figure 37).

6.3. Discussion and Translational Value

Figure 38: Summary of Drugs of Abuse and Withdrawal Treatments in NTT Effects of drugs of abuse and withdrawal treatments on behavior and cortisol included in this research. Green upward arrows represent statistically significant increase relative to matched controls, red downward arrows reflect significant decrease in the respective endpoint vs. matched controls and yellow slanted arrows represent a trend (p = 0.05-0.08) upwards or downwards compared to the control cohort. Dash lined (“-“) represents non-significant effects, “na” reflects that test was not performed for given experiments.

105 A summary of behavioral and physiological effects evoked by hallucinogenic drug treatments used in this study prior to the NTT is given below (Figure 38). Cocaine: Cocaine dose-dependently inhibited zebrafish behavior, evoking longer freezing and fewer top transitions (Figure 31). This profile parallels cocaine’s known anxiogenic profile in rodents and humans (Blanchard & Blanchard, 1999; Blanchard, Hebert et al., 1998; Blanchard, Kaawaloa et al., 1999; Costall, Kelly et al., 1989; DazaLosada, Rodriguez-Arias et al., 2009; Fontana & Commissaris, 1989; Salas-Ramirez, Frankfurt et al., 2010; Simon, Dupuis et al., 1994; Sobrian, Marr et al., 2003), but is not consistent with previous zebrafish studies showing the lack of anxiety in a wide range of systemic doses (Lopez-Patino, Yu et al., 2008). It is possible that the inbred AB zebrafish strain (hyperactive in anxiety-evoking situations (Norton & Bally-Cuif, 2010)) used in these studies was less sensitive to the anxiogenic effects, compared to the outbred wild type short-fin strain used here. A similar situation has been reported in rodents, where cocaine was anxiogenic in non-anxious strains, but failed to affect the behavior of selectively-bred anxious rats (Rogerio & Takahashi, 1992). Likewise, zebrafish strains may be differentially sensitive to cocaine (similar to their strain-specific sensitivity to ethanol (Dlugos & Rabin, 2003)) or treatments (0.0045-45 mg/L cocaine for 75 min (Lopez-Patino, Yu et al., 2008) vs. 1-25 mg/L for 20 min here). Caffeine, Ethanol and Caffeine Withdrawal: Withdrawal from chronic ethanol produced robust behavioral effects indicating increased anxiety in zebrafish, accompanied by elevated cortisol levels. This research’s experiments showed increased

106 anxiety elicited by repeated withdrawal from morphine, generally consistent with known anxiogenic-like effects of withdrawal from these drugs. The increase in certain anxiety-like behaviors, demonstrated by caffeine withdrawal, also resembles caffeine withdrawal-evoked anxiety in humans (Evans & Griffiths, 1999) and rodents (Kaplan, 1993). With a strongly significant increase in erratic movements, these fish rapidly darted around the novel tank during the six minute observation period. It is believed this accounts for the apparent, but non-significant increase in total transitions to the upper half and also for the trend towards less total freezing bouts. Morphine Withdrawal. Although single session morphine withdrawal did not produce significant data, all anxiety endpoints were higher compared to controls, indicating the possibility of slight anxiety in morphine withdrawal zebrafish. It is believed the lack of significance can be attributed to a tolerance effect. The zebrafish were administered the same dose of morphine each day for 1 week. It is plausible that tolerance to morphine’s effects developed during the week and any withdrawal response was gradually mitigated by morphine distributed throughout the tank water. In future experiments, the dose of daily morphine treatment ought to increase across the administration phase, in order to prevent tolerance from developing. In the repeated withdrawal paradigm, the effects of tolerance were minimized because the experimental cohort was removed from treated water twice a day for three hours. Moving from exposure to fresh water and back to exposure twice a day, for one week prior to behavioral observation.

107 In humans, chronic drug abuse represents a cyclical process of repeated reward and withdrawal. Therefore, repeated withdrawal models are needed in addition to acute withdrawal studies in order to more accurately model clinical withdrawal phenomena. Repeated drug withdrawal paradigms have been recently developed for rodents, showing that both the rat and human share common triggers of relapse (such as the drug of abuse, stress, stimuli, or the environment conditioned to the drug of abuse), and that withdrawal selectively potentiates responses to anxiogenic stimuli (Fendt & Mucha, 2001; Harris & Aston-Jones, 2003; Jonkman, Risbrough et al., 2008; Miczek & Vivian, 1993; Vorel, Liu et al., 2001). The genomic profiling of zebrafish withdrawal also provides further insights, including altered gene expression in the zebrafish brain following chronic drug treatment and withdrawal (Gerlai, Chatterjee et al., 2009; Gerlai, Lee et al., 2006; Kily, Cowe et al., 2008). Sex differences have been reported for zebrafish withdrawal-related behaviors (Lopez Patino, Yu et al., 2008) paralleling sexual dimorphism in human (Fox, Garcia et al., 2006) and rodent (Alves, Magalhaes et al., 2008; Butler, Smith et al., 2009; Strong, Kaufman et al., 2009; Taylor, Tio et al., 2009) withdrawal responses, therefore increasing population and construct validity of these models.

6.4. Conclusions Overall, chronic ethanol and repeated morphine withdrawal treatments evoked the strongest anxiogenic profiles as evaluated in the NTT, strongly resembling that of acute alarm pheromone exposure (Figure 29). In addition, acute and chronic exposure to

108 these drugs resulted in behavioral patterns suggestive of an anxiolytic phenotype. This collectively demonstrates the capability of the NTT to discern robust modulation of affective domains by drug treatments previously established or predicted to result in high or low anxiety phenotypes. Moreover, the range of experimental treatments, doses and exposure times evaluated in these NTT trials provides a comprehensive tabulation indicating the ways affective domains can be modulated in adult zebrafish for future studies (Figure 39).

Figure 39: Overview of Treatments Modulating Affective Domains in Zebrafish A summary of different forms of stress used in zebrafish neurobehavioral research. Fear-like responses are more likely to occur following alarm pheromone and predator exposure, anticipatory generalized “trait” anxiety is more likely to occur following anxiogenic drug treatment or novelty stress, whereas chronic long-term “state” anxiety can be seen following withdrawal, or in more anxious zebrafish strains (genetic differences) (Cachat, Canavello et al., 2010c).

109 For example, “trait” and “state” anxiety interplay could be investigated in adult zebrafish NTT models by examining genetic or neurodevelopmental changes induced by early exposure to alarm pheromone, and throughout the zebrafish lifespan.

110

Chapter 7. Modeling Phenotypes of Hallucinogenic Drug Action Hallucinogenic drugs are well-known for their ability to exert profound effects on the behavior and cognition of both human and animal subjects (Castellano, 1979; Geyer, 1998; Grossman, Utterback et al., 2010; Kyzar, Collins et al., 2012; Leikin, Krantz et al., 1989; Mayer-Gross, 1951; Mayer-Gross, Mc et al., 1951; Navarro & Maldonado, 1999). There are three classes of hallucinogens: psychedelics that include ‘classical’ serotonergic tryptamines and phenethylamines; dissociatives which are predominately NDMA antagonists and κ–opioid agonists; and deliriants that possess anticholinergic activity (Freedman, 1969 #3418}. Despite structural differences, these drugs all cause significant alterations in sensation and perception, as well as marked changes in mood and affect (Nichols, 2004). Since their discovery, these drugs have been tested in a variety of model organisms, including non-human primates (Frederick, Gillam et al., 1997; Murnane, Fantegrossi et al., 2010; Schenk, 2009), rodents (Audet, Goulet et al., 2006; Krall, Richards et al., 2008; Nakama, Ochiai et al., 1972; Schoenfeld, 1976) and various fish species (Abramson, Gettner et al., 1979; Abramson, Gettner et al., 1963; Braida, Limonta et al., 2007; Riehl, Kyzar et al., 2011, pp. 658-667; Zakhary, Ayubcha et al., 2011), revealing complex actions on multiple neurotransmitter systems and brain circuits (Bennett,

111 Bernard et al., 1988; Gonzalez-Maeso, Yuen et al., 2003; Gudelsky & Yamamoto, 2008; Halberstadt, Van Der Heijden et al., 2009; Marona-Lewicka, Chemel et al., 2009; Meltzer, Horiguchi et al., 2011; Nawata, Hiranita et al., 2010; Perera, Lichy et al., 2008; Seeman, Guan et al., 2009; Willetts, Balster et al., 1990). A recent interest in the therapeutic applications, particular in a clinical therapy setting, of hallucinogenic drugs requires increased understanding of the behavioral and molecular correlates of the hallucinogeninduced states (Alper, Lotsof et al., 2008; Geyer, 1998; Halberstadt & Geyer, 2011; Halberstadt, Van Der Heijden et al., 2009; Passie, Halpern et al., 2008; Reissig, Rabin et al., 2008; Schenk, 2009; Sessa, 2008; Sigafoos, Green et al., 2007). A comparative approach to study the effects of these compounds in vivo during the following experiments represents the first analysis of the effects of these compounds on adult zebrafish and may reveal significant differences and similarities in behavioral and physiological responses, reflecting integral but micro-level differences in the mechanistic profile of each compound. This research investigated the effects of lysergic acid diethylamide (LSD), 3,4-Methylenedioxymethamphetamine (MDMA) and ibogaine on affect and social domains in adult zebrafish. It was hypothesized that the effects of these drugs on adult zebrafish would resemble those observed in rodent models, and therefore support translational relevance to clinical profiles.

7.1. Lysergic acid diethylamide (LSD) LSD is the most potent known hallucinogenic drug (Cohen, 1967; Nichols, 2004; Passie, Halpern et al., 2008; Siegel, 1978). Despite an established history of LSD research,

112 the mechanisms of its action are complex and remain poorly understood (Backstrom, Chang et al., 1999; Gonzalez-Maeso & Sealfon, 2009; Nichols, 2004; Passie, Halpern et al., 2008). LSD acts on several neurotransmitter systems, modulating various serotonin (Backstrom, Chang et al., 1999; Grailhe, Waeber et al., 1999; Gresch, Strickland et al., 2002; Halberstadt & Geyer; Mittman & Geyer, 1989; Palenicek, Hlinak et al.; Reissig, Eckler et al., 2005) and dopamine (Gonzalez-Maeso, Weisstaub et al., 2007; Jerome, 2008; MaronaLewicka, Chemel et al., 2009; Marona-Lewicka & Nichols, 1995; Marona-Lewicka, Thisted et al., 2005; Passie, Halpern et al., 2008; Seeman, Guan et al., 2009) receptor subtypes. The clinical profile of LSD is also complex, ranging from anxiety/panic and mood swings to hyperactivity/euphoria, depersonalization, hallucinations (Cohen, 1967; Eveloff, 1968; Gonzalez-Maeso & Sealfon, 2009; Jerome, 2008; Levy, 1971; Passie, Halpern et al., 2008), as well as perceptually altered social behavior and memory (Passie, Halpern et al., 2008; Siegel, 1971; Sigafoos, Green et al., 2007; Simmons, Leiken et al., 1966). In rodents, LSD treatment has been shown to affecting social behaviors (Geyer & Light, 1979; Krsiak, 1975; Silverman, 1966; Uyeno & Benson, 1965), sensorimotor gating (Halberstadt & Geyer), exploration and locomotion (Backstrom, Chang et al., 1999; Castellano, 1979; Krebs-Thomson, Paulus et al., 1998; Mittman & Geyer, 1991). LSD exerts complex context-specific effects on animal social behaviors and cognition, including social aggression (Krsiak, 1979; Silverman, 1966; Uyeno & Benson, 1965), memory and learning (Castellano, 1979; Chaplygina, 1975; Frederick, Gillam et al., 1997).

113 LSD effects are reported to possess biphasic action on rodent behavior, which includes initial anxiety and behavioral inhibition followed by hyper-locomotion, characteristic of behavioral excitation (Adams & Geyer, 1982, 1985; Gupta, 1971; Krebs-Thomson & Geyer, 1996; Marona-Lewicka, Thisted et al., 2005; Mittman & Geyer, 1991; Palenicek, Hlinak et al.; Uyeno & Benson, 1965). Soon after its discovery, LSD was tested in fish, evoking surface swimming, nose-up/tail down position and hypolocomotion in beta splenders, guppies, neons, carps, minnows and goldfish (Abramson & Evans, 1954; Abramson, Gettner et al., 1979; Abramson, Gettner et al., 1962; Abramson, Gettner et al., 1963; Arbit, 1957; Chessick, Kronholm et al., 1964; Chessick, Kronholm et al., 1963; Gettner, Rolo et al., 1964; Siegel, 1971; Trout, 1957). These early studies focused on general assessment of fish locomotion, and did not evaluate other behavioral domains. A recent interest in LSD research (Dyck, 2005; Geyer, 1998; Gonzalez-Maeso & Sealfon, 2009; Passie, Halpern et al., 2008; Sessa, 2008; Sigafoos, Green et al., 2007) requires novel approaches, tools and animal models to better understand the effects of this drug on the brain and behavior. Since LSD effects have not yet been reported in this a zebrafish model, this research will examine in-depth the behavioral and physiological effects of LSD on adult zebrafish (Grossman, Utterback et al., 2010).

114

Figure 40: Effects of LSD Treatment in NTT Adult zebrafish (n = 10 - 16 per group) were treated acutely with lysergic acid diethylamide (25, 50 or 250 µg/L, 20 min). Data are presented as Mean ± SEM, **p < 0.01, ***p < 0.001; ANOVA with Tukey post hoc, vs. control (Stewart, 2011), also see (Grossman, 2010).

In the NTT (Figure 40), pilot studies with LSD (25-250 µg/L) significantly affected the latency to enter the top (F(3, 49) = 10.3, p < 0.005), number of top transitions (F(3, 49) = 8.7, p < 0.005), time spent in top (F(3, 49) = 9.7, p < 0.005), and freezing bouts (F(3, 49) = 13.8, p < 0.005) in adult wild type (short-fin) zebrafish. In the 6-min NTT (Figure 41), 250 µg/L LSD produced significantly shorter latency to enter the top, less freezing, and markedly more transitions, time spent in top and longer average entry duration. The distance traveled and velocity was unaffected in this study.

115

Figure 41: Effects of LSD Treatment in NTT and on Swim Path Adult zebrafish (n = 10 - 12 per group) were treated acutely with lysergic acid diethylamide. Data presented as Mean ± SEM, **p < 0.01, ***p < 0.001, vs. controls, Mann-Whitney U-test (Grossman, Utterback et al., 2010).

Representative traces (Figure 41, bottom) clearly demonstrate active top swimming in LSD-treated fish, compared to the initial bottom dwelling followed by exploratory top half excursions in wild-type controls. In the light-dark box, the LSD-treated zebrafish spent more time (trend) and had significantly higher average entry durations to the light half (Figure 42). Similar trends were observed for higher light:total time spent ratio. Representative traces, shown in Figure 42, further illustrate higher light activity in LSD group in this test (Grossman, Utterback et al., 2010).

116

Figure 42: Effects of LSD Treatment in Light Dark Box Zebrafish (n = 12) treated with LSD (250 µg/L, 20 min) prior to the 6 min light-dark box test. Data presented as Mean ± SEM, *p < 0.05, #p = 0.05–0.08 (trend) vs. controls, Mann-Whitney U-test (Grossman, Utterback et al., 2010).

Figure 43: Effects of LSD Treatment in Open-Field Test Zebrafish (n = 15) treated with LSD (250 µg/L, 20 min) prior to 20-min open field test. Data presented as Mean ± SEM, *p < 0.05 vs. controls, Mann-Whitney U-test (Grossman, Utterback et al., 2010).

117 Figure 43 shows behavioral effects of LSD in the open field test. While the drug did not affect distance traveled or velocity, it evoked thigmotaxis, significantly reducing center dwelling. Representative traces also confirm higher peripheral activity in LSDtreated fish (Grossman, Utterback et al., 2010).

Figure 44: Effects of LSD Treatment in Social Prefence and Shoaling Tests In the social preference test (B, n = 10) and the shoaling test (C, n = 16) were performed in following LSD treatment (250 µg/L, 20 min). Data presented as Mean ± SEM, *p < 0.05, ***p < 0.001, #p = 0.05– 0.08 (trend) vs. controls, Mann-Whitney U-test (Grossman, Utterback et al., 2010).

In the social preference test, LSD significantly reduced the number of total arm entries, center, con-conspecific and empty entries, but did not influence zebrafish social preference ratios (Figure 44). In the shoaling test, LSD disrupted normal shoaling behavior by significantly increasing the average inter-fish distance (Figure 44).

118

Figure 45: Effects of LSD on whole-body cortisol concentrations Measures reflect cohorts from previously described behavioral tests, in which LSD (250 ug/L, 20 min) was administered prior to the novel tank test (n = 10, 16), light dark box test (n = 12) and open field test (n = 15). Data presented as Mean ± SEM, *p < 0.05, #p = 0.05–0.08 (trend) vs. controls, MannWhitney U-test (Grossman, Utterback et al., 2010).

In the NTT (Figure 41) and light dark box (Figure 42) there was a trend for elevated cortisol concentrations in LSD treated cohorts, with those tested in the open field test (Figure 43) showing significantly increase cortisol concentration (Figure 45).

7.2. 3, 4-Methylenedioxymethylamphetamine (MDMA) MDMA is a popular recreational drug that modulates brain monoamines by inhibiting their reuptake (De La Torre, Farre et al., 2004; Doly, Bertran-Gonzalez et al., 2009; Kalant, 2001; Nagai, Nonaka et al., 2007; White, Obradovic et al., 1996) and degradation (Leonardi & Azmitia, 1994). The serotonergic system appears to be the primary target of MDMA action, although dopamine also plays an important role (Benturquia, Courtin et al., 2008; Nida, 2010; Stove, De Letter et al., 2010). This research examined MDMA behavioral effects on this species for the first time. In the novel tank test, it was hypothesized that adult zebrafish would also show signs of behavioral activation along with mixed effects suggestive of elevated anxiety.

119

Figure 46: Effects of Acute MDMA Treatment in NTT Zebrafish (n = 27 (controls), 28 (10 mg/L), 12 (40 mg/L), 27 (80 mg/L) and 12 (120 mg/L)) tested in the standard 6-min novel tank test following 20 min pretreatment with MDMA. Data are presented as Mean ± SEM, *p < 0.05, ***p < 0.001, #p = 0.05–0.08 (trend); ANOVA with Tukey post hoc, vs. controls (Stewart, Riehl et al., 2011).

As hypothesized, acute MDMA exposure dose-dependently produced behavioral profiles indicative of behavioral excitation (Figure 46). This is reflected by a decrease in the latency to the upper half (control vs 120 mg/L, p < 0.005), less transitions to but more time spent within the upper half (control vs 80 and 120 mg/L, p < 0.005) as well as significantly less erratic movements (control vs 80 mg/L, p < 0.005) (Stewart, Riehl et al., 2011). Across the doses tested, 10 mg/L resulted in a significant elevation of whole-body cortisol concentrations while 40, 80 and 120 mg/L did not cause alterations in this physiological biomarker (data not shown) (Stewart, Riehl et al., 2011). Moreover,

120 treatment with 80 mg/L of MDMA increased the average inter-fish distance in a recent shoaling analysis (Green, Collins et al., 2012).

Figure 47: Representative (2D) Swim Paths of MDMA Treatment in NTT Representative 2D swim paths of wild-type controls, 10 mg/L, 40 mg/L, 80 mg/L and 120 mg/L of MDMA administration for 20 min prior to novel tank test (Stewart, Riehl et al., 2011).

These dose-dependent effects are clearly illustrated in representative 2D swim paths for each treatment group of MDMA (Figure 47).

7.3. Ibogaine Ibogaine is an indole alkaloid with hallucinogenic properties that is isolated from the African shrub Tabernanthe iboga (Alper, Stajic et al., 2012; Bulling, Schicker et al.,

121 2012). Despite potential therapeutic applications, particularly in the treatment of opiate and alcohol addiction (Maciulaitis, Kontrimaviciute et al., 2008; Mash, Kovera et al., 1998), the mechanisms of ibogaine action remain poorly understood. The pharmacological profile of ibogaine is very complex and involves multiple neurotransmitter systems. Structurally resembling serotonin (5HT), ibogaine inhibits serotonin and dopamine transporters (Bulling, Schicker et al., 2012) and activates serotonin (e.g., 5HT2a, 5HT2c) (Helsley, Fiorella et al., 1998; Helsley, Rabin et al., 1999), opioid (mu and kappa) (Codd, 1995; Sershen, Hashim et al., 1995) and sigma-1 and 2 receptor subtypes (Helsley, Rabin et al., 2001; Itzhak & Ali, 1998; Sweetnam, Lancaster et al., 1995). The drug also acts on NMDA (N-methyl-D-aspartate) receptors as a glutamatergic antagonist (Chen, Kokate et al., 1996; Glick, Maisonneuve et al., 1997; Itzhak & Ali, 1998), in addition to weak antagonistic properties at cholinergic muscarinic and nicotinic receptors (Glick & Maisonneuve, 1998), also see (Cachat, Kyzar et al., 2013) for review. While ibogaine is a controlled substance in various countries, including the United States (Schedule I), the drug does not appear to be commonly abused and is administered in medical settings in South Africa and Mexico (Alper, Stajic et al., 2012). In humans, ibogaine produces intense dream-like hallucinations which subjectively differ from those caused by classic serotonergic psychedelics (Alper, Lotsof et al., 2008; Alper, Stajic et al., 2012) and includes a vivid ‘visual’ phase followed by a longer ‘introspective’ phase (Alper, Lotsof et al., 2008; Alper, Stajic et al., 2012). Ibogaine can occasionally cause

122 acute psychoses (Houenou, Homri et al., 2011), and its anti-addictive properties have also been reported in the literature (Maciulaitis, Kontrimaviciute et al., 2008; Mash, Kovera et al., 1998), including lasting anti-craving effects after a single ibogaine dose (Alper, Lotsof et al., 2000; Sheppard, 1994). Further supporting the complex nature of ibogaine action are the prolonged effects of ibogaine in attenuating addiction and depressive symptoms (Mash, Kovera et al., 2000). The pharmacological profile of ibogaine includes receptor targets that are shared with serotonergic psychedelic hallucinogens (e.g., LSD, mescaline, psilocybin), dissociative glutamatergic hallucinogens (e.g., ketamine) and hallucinogenic drugs acting via opioidergic systems (e.g., salvinorin A) (Chen, Kokate et al., 1996; Helsley, Rabin et al., 2001; Sweetnam, Lancaster et al., 1995). The unique aspect of ibogaine action is that it affects all these targets simultaneously, most likely resulting in a complex profile that may theoretically include the actions of LSD, mescaline, psilocybin, MDMA, ketamine, PCP and salvinorin A combined. The present study aimed to evaluate the potential effects of ibogaine in several behavioral paradigms in adult zebrafish. In the NTT, ibogaine induced robust behavioral responses, significantly affecting the latency to top, erratic movements and freezing bouts (F2,90 = 12.1, 14.8 and 10.8, p 0) && (deltaY12 >= 0)) heading1 = alpha1; elseif ((deltaX12 >= 0) && (deltaY12 < 0)) heading1 = -alpha1; elseif ((deltaX12 < 0) && (deltaY12 0) && (deltaY23 >= 0)) heading2 = alpha2; elseif ((deltaX23 >= 0) && (deltaY23 < 0)) heading2 = -alpha2; elseif ((deltaX23 < 0) && (deltaY23 = pi turnAngle = heading - 2*pi; else turnAngle = heading; end turnAngleValues(i,1) = turnAngle; %% Calculating meander values if dist(2)~=0 meanderValues(i,1)=turnAngleValues(i,1)/dist(2); else meanderValues(i,1)=0; end %% Calculating angular velocity values angularVelocityValues(i,1)=turnAngleValues(i,1)/timeStep; drugTypeValues{i,1}=drugType; end nofp=(nofp/(nofp+nofn))*100; nofn=100-nofp; %% Calculating global features % Percentage of freezing and swimming ix=find(strcmp(behaviorValues,'freezing')); countFreezing=length(ix); freezingPercentage=(countFreezing/length(rawData))*100; swimmingPercentage=100-freezingPercentage; behaviorChange=0; for s=1:length(behaviorValues)-1 if strcmp(behaviorValues{s},behaviorValues{s+1})==0 behaviorChange=behaviorChange+1; end end % %

191 gFeatures=[behaviorChange nofp nofn freezingPercentage swimmingPercentage]; %% Writing to Excel sheet outputLables = {'Time','X','Y','Distance from Bottom','InTop','Sinuosity','Velocity','Acceleration','Turn Angle','Angular Velocity','Meander','Behavior','Drug'}; movementParameters=cell(length(rawData)+1,length(outputLables)); for j=1:length(outputLables) movementParameters{1,j}=outputLables{1,j}; end; variables=[tCoord xyCoord distancefromBottomValues inTopValues sinuosityValues velocityValues accelerationValues turnAngleValues angularVelocityValues meanderValues]; for k=2:length(movementParameters) for h=1:length(variables(1,:)) movementParameters{k,h}=variables(k-1,h); end movementParameters{k,h+1}=behaviorValues{k1,1};movementParameters{k,h+2}=drugTypeValues{k-1,1}; end xlswrite (inputFile,movementParameters,4); globalLabels={'Behavior Change','TopPer','BottomPer','FreezPer','SwimmPer'}; globalFeatures=cell(2,length(globalLabels)); for t=1:length(globalLabels) globalFeatures{1,t}=globalLabels{1,t}; globalFeatures{2,t}=gFeatures(1,t); end; xlswrite (inputFile,globalFeatures,3); end

readCSV-openrow.py – This python script opens raw spatiotemporal data exported from EthoVision as a comma separate value file and prepares it into an array for further calculations. # Read CSV # Jonathan Cachat - Nov 2012 import sys, csv # set & open CVS track file if (len(sys.argv) != 2): print "usage: %s " % sys.argv[0] exit() else: cr = csv.reader(open(sys.argv[1], 'rU')) #rU universal-newline mode data = [] # show each row in CSV file for row in cr: # print row data.append( (float(row[0]), float(row[1]), float(row[2]))) print data print "\n%d time points." % len(data)

192 getDistanceVelocity.py – This python script processes raw spatiotemporal data exported from EthoVision as a comma separate value file as input and calculates the total distance traveled and average velocity for that subject. # Calculate Distance and Velocity # Jonathan Cachat - Nov 2012 import sys, csv, math # take two time series points and compute 2D distance def dist(p, q): x1 = p[1] y1 = p[2] x2 = q[1] y2 = q[2] return math.sqrt((x1 - x2) * (x1 - x2) + (y1 - y2) * (y1 - y2)) # take two time series points compute time difference def time(p, q): t1 = p[0] t2 = q[0] return t2 - t1 # set & open CVS track file if (len(sys.argv) != 2): print "usage: %s " % sys.argv[0] exit() else: cr = csv.reader(open(sys.argv[1], 'rU')) #rU universal-newline mode # show each row in CSV file data = [] for row in cr: data.append((float(row[0]), float(row[1]), float(row[2]))) # calculate distance traveled distance = 0.0 for i in range(len(data)-1): distance += dist(data[i], data[i+1]) # calculate average velocity instanVelo = 0.0 for i in range(len(data)-1): instanVelo += ((dist(data[i], data[i+1])) / (time(data[i], data[i+1]))) duration = len(data) velocity = (instanVelo/duration) # show results print 'total distance traveled = %f' % distance print 'average velocity = %f' % velocity

193 trackFeatures.m – This MatLab script processes spatiotemporal track data exported from EthoVision and formatted with formatZFishDataName.m, to calculate various measures of trajectory curvature (at a user chosen window length) including number of squared steps, heading direction, angular change, straightness (or path curvature), bending, track asymmetry, skewness, kurtosis which are then exported into an excel sheet for statistical analysis. %************************************************************************* %******* @Author Jonathan Cachat ********* %******* @Version 1.0, 11.13.12 ********* %******* ********* %******* Modified from trackFeaturesTest.m ********* %******* Jo Helmuth last change: May 23, 2007 ********* %************************************************************************* function features = trackFeatures(inputFile,windowWidth) %reading the input raw file [rawData]=xlsread(inputFile); time=[rawData(:,1)]; x=[rawData(:,2)]; y=[rawData(:,3)]; % get number of points nPoints = length(x); % check if request is valid if(~(nPoints > windowWidth)) warning('track too short to compute properties, I quit with empty output!'); features = []; return; end % allocate some memory squaredDisp = zeros(nPoints-windowWidth,1); sumSquaredStep = zeros(nPoints-windowWidth,1); straightness = zeros(nPoints-windowWidth,1); Asym = zeros(nPoints-windowWidth,1); bending = %angleSum pointSkew pointKurt

zeros(nPoints-windowWidth,1); = zeros(nPoints-windowWidth,1); = zeros(nPoints-windowWidth,1); = zeros(nPoints-windowWidth,1);

alpha = zeros(nPoints-1,1); % calculate squared step lengths dx = diff(x); dy = diff(y); squaredSteps = (dx.*dx + dy.*dy); % loop over the points for j = 1:(nPoints-windowWidth) disp_x = x(j+windowWidth) - x(j); disp_y = y(j+windowWidth) - y(j); % squared displacement within "frameshift" steps squaredDisp(j) = disp_x*disp_x + disp_y*disp_y; % sum of the squared steps that lead to the displacement sumSquaredStep(j) = sum(squaredSteps(j:j+windowWidth-1));

194 wx = x(j:j+windowWidth); wy = y(j:j+windowWidth); m_x = sum(wx)/(windowWidth+1); m_y = sum(wy)/(windowWidth+1); wx_mx = wx - m_x; wy_my = wy - m_y; mwxsq = mean(wx_mx.^2); mwysq = mean(wy_my.^2); mwxwy = mean(wx_mx.*wy_my); % tensor of gyration (uses shifted positions) R = [mwxsq mwxwy; mwxwy mwysq]; [EigVecR,EigR] = eig(R); % asymetry measure of Huet2006 paper Asym(j) = -log( 1 - ((EigR(1) - EigR(4))^2) / (2 * (EigR(1) + EigR(4))^2) ); % get the right EV if(EigR(1)>EigR(4)) mainEVInd = 1; else mainEVInd = 2; end mainEV = EigVecR(:,mainEVInd); % normalize mainEV = mainEV/norm(mainEV); % compute distances parallel to it Proj = [wx_mx, wy_my]*mainEV; % get abs of skewness: seventh property pointSkew(j) = abs(skewness(Proj)); % get kurtosis : eighth property pointKurt(j) = kurtosis(Proj); % consider kurtosis and skewness of point distances with resp to % lines through center of gravity in direction of eigenvectors end % first property netDisp = sqrt(squaredDisp); squaredDispPerStep = squaredDisp./windowWidth; % second property efficiency = squaredDispPerStep./sumSquaredStep; % calculate direction angles dx_eq_0 = find(dx==0); dx_gt_0 = find(dx>0); dx_lt_0 = find(dx0); dy_lt_0 = find(dy pi); beta(ind) = beta(ind) - 2*pi; cosBeta = cos(beta); sinBeta = sin(beta); for j=1:nPoints-windowWidth straightness(j) = sum(cosBeta(j:j+(windowWidth-2))); bending(j) = abs(sum(sinBeta(j:j+(windowWidth-2)))); end % third property straightness = straightness/(windowWidth-1); % fifth bending = bending/(windowWidth-1); % assemble in one matrix features = [netDisp efficiency straightness Asym bending pointSkew pointKurt]; %% Writing to Excel sheet outputLables = {'time','x','y','netDisp','Efficiency','Straightness','Asym','Bending','PointSkew','Point Kurt'}; % movementParameters=cell(features)+1,length(outputLabels)); movementParameters=cell(length(features)+1,length(outputLables)); for j=1:length(outputLables) movementParameters{1,j}=outputLables{1,j}; end; for h=1:length(outputLables) for k=2:length(movementParameters) movementParameters{k,h}=features(k-1,h); end end xlswrite (inputFile,movementParameters,2); end

196 ArenaPartitioning.m – This MatLab script processes spatiotemporal track data exported from EthoVision and formatted with formatZFishDataName.m, to calculate the average distance traveled and velocity within segmented regions of the novel tank. It is designed to work with any zebrafish testing arena, and the percentage of outer/periphery can be customized for each test (use for results in Figure 70). %% This function segments the tank arena in the following manner: %%%%%-----------------------------------%%%% %C4% E1 %C1| %%%-%---------------------------------%%%| % % % | % E4% M %E2| % % % | %%%%%-----------------------------%%%| %C3% E3 %C2| %%%-----------------------------%%%% function ArenaPartitioning (inputFile, waterLevel, bottomLength, tankHeight, waterTopDist, percentage) %% Input parameters % input file % waterLevel: Length of water level at the top of the tank % bottomLength: Length of the bottom of the tank % tankHeight: Height of the tank % waterTopDist: Vertical distance between water level and top of the tank % percentage: Percentage of areas of all corner zones + edge zones %reading the input raw file [rawData]=xlsread(inputFile); tCoord=rawData(:,1); xyCoord=100*[rawData(:,2) rawData(:,3)]; zoneValues=cell(length(rawData),1); l=waterLevel; b=bottomLength; h=tankHeight; d=waterTopDist; c1=[b/2,h/2-d]; c2=[b/2,-h/2]; c3=[-b/2,-h/2]; c4=[b/2-l,h/2-d]; % C1 Zone c1Edge=sqrt(0.039*percentage/100*(l+b)*(h-d)/2); c11=[b/2,h/2-d]; c12=[b/2,h/2-d-c1Edge]; c13=[b/2-c1Edge,h/2-d-c1Edge]; c14=[b/2-c1Edge,h/2-d]; % C2 Zone c2Edge=c1Edge; c21=[b/2,-h/2+c2Edge]; c22=[b/2,-h/2]; c23=[b/2-c2Edge,-h/2]; c24=[b/2-c2Edge,-h/2+c2Edge]; % Zone C3 c3Height=c1Edge; c33=[-b/2,-h/2]; % Line equation on the left edge of the tank syms x y; m=(c3(2)-c4(2))/(c3(1)-c4(1)); %% slope

197 y = m*x - m*c4(1) + c4(2); %% line equation x = (y + m*c4(1) -c4(2))/m; %% inverse function c34=[(-h/2+c3Height+m*c4(1)-c4(2))/m,-h/2+c3Height]; b1=c33(1)-c34(1); b2=c3Height-b1/2; c32=[b2-b/2,-h/2]; c31=[b2-b/2,-h/2+c3Height]; % Zone C4 c4Height=c3Height; c44=[b/2-l,h/2-d]; c41=[b1+b2+b/2-l,h/2-d]; c42=[b1+b2+b/2-l,h/2-d-c4Height]; c43=[(h/2-d-c4Height+m*c4(1)-c4(2))/m,h/2-d-c4Height]; % % % % % % % % % % % % % % % %

disp(['c11 disp(['c12 disp(['c13 disp(['c14 disp(['c21 disp(['c22 disp(['c23 disp(['c24 disp(['c31 disp(['c32 disp(['c33 disp(['c34 disp(['c41 disp(['c42 disp(['c43 disp(['c44

coordinates= coordinates= coordinates= coordinates= coordinates= coordinates= coordinates= coordinates= coordinates= coordinates= coordinates= coordinates= coordinates= coordinates= coordinates= coordinates=

', ', ', ', ', ', ', ', ', ', ', ', ', ', ', ',

num2str(c11)]); num2str(c12)]); num2str(c13)]); num2str(c14)]); num2str(c21)]); num2str(c22)]); num2str(c23)]); num2str(c24)]); num2str(c31)]); num2str(c32)]); num2str(c33)]); num2str(c34)]); num2str(c41)]); num2str(c42)]); num2str(c43)]); num2str(c44)]);

for i=1:length(xyCoord) % defining belonging of points to zones %% Zone C1 if (xyCoord(i,1) >= c14(1)) && (xyCoord(i,1) = c13(2)) && (xyCoord(i,2) = c24(1)) && (xyCoord(i,1) = c23(2)) && (xyCoord(i,2) = c34(1)) && (xyCoord(i,1) = c33(2)) && (xyCoord(i,2) = c44(1)) && (xyCoord(i,1) = c43(2)) && (xyCoord(i,2) c41(1)) && (xyCoord(i,1) < c14(1)) && (xyCoord(i,2) >= c43(2)) && (xyCoord(i,2) = c13(1)) && (xyCoord(i,1) c24(2)) zoneValues{i,1}='E2'; %% E3 elseif (xyCoord(i,1) > c31(1)) && (xyCoord(i,1) < c24(1)) && (xyCoord(i,2) >= c23(2)) && (xyCoord(i,2)