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Volume 1 | 2007

cornell

synapse

Undergraduate Journal of Neuroscience


cornell

synapse

UNDERGRADUATE JOURNAL OF NEUROSCIENCE

Volume 1 2007 Editors-in-Chief Cina Sasannejad ‘09 Kevin K. Kumar ‘09 Managing Editors Sameer Ahmed ‘07 Aniq Rahman ‘09 Content Editors Vinay Patel ‘10 Caroline Wee ‘10 Cornell Undergraduate Society for Neuroscience President Sameer Ahmed ‘07 Vice President Aniq Rahman ‘09 Faculty Advisor Dr. David P. McCobb

Cover design by Graham Johnson. Original image can be accessed at sciencemag.org.


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LETTER FROM THE EDITORS Fellow Students, Faculty and Members of the Cornell Community: Eric Kandel, a pioneer in the field of Neuroscience, once said, “In a larger sense, the biological study of mind is more than a scientific inquiry of great promise; it is also an important humanistic endeavor. The biology of mind bridges the sciences—concerned with the natural world—and the humanities—concerned with the meaning of human experience. Insights that come from this new synthesis will not only improve our understanding of psychiatric and neurological disorders, but will also lead to a deeper understanding of ourselves.” Neuroscience is truly an interdisciplinary field. In recent years, Neuroscience has captured the interest of many academic fields, including Physics, Chemistry, Biochemistry, Psychology, Computer Science, Mathematics, Philosophy, and an array of other disciplines. The study of the mind is becoming the cornerstone in understanding how we as humans perceive and interpret the world in which we live. Neuroscience explores the physiological and biochemical pathways that mediate cognitive processes. Recent technological advances have permitted scientists to understand and describe the exquisite, yet extremely complex, nature of neurons and neural networks, which result in behavior. It is a great privilege to be part of Cornell University’s research-oriented community. As undergraduates, we have an exceptional opportunity to not only acquire knowledge, but to make significant contributions to scientific knowledge through research. The purpose of the Cornell Undergraduate Society for Neuroscience (CUSN) and its publication, Cornell Synapse, is to encourage undergraduates to become involved in research and promote those undergraduates who are presently involved. CUSN hosted two guest lectures during the course of this academic year. The first talk was given by Dr. Andrew Bass, a Professor in the Department of Neurobiology and Behavior, on his work on the central and peripheral nervous systems of sound-producing teleost fishes. This well-received talk was followed by an engaging discussion between Dr. Bass and the students that attended. In addition, Josh Plotnik, a Cornell alumnus who is currently a graduate student at Emory University studying self-recognition in African elephants, gave a talk for CUSN. The diversity between these talks exemplifies the wide spectrum of research at Cornell, from accomplished professors to recent graduates, which is a testament to the exciting growth in the field of Neuroscience. We hope that this first publication of the Cornell Synapse will raise interest and awareness of Neuroscience research. This issue begins with a series of Reviews, covering the topics of sexual dimorphisms in the brain, the utility of Darwinian medicine, the existence of animal consciousness, and the evolution of language in humans. Next, in the Articles section we have three original research papers. These serve as an example of cutting-edge research by exceptional Cornell undergraduates. The final section of this journal, Proposals, is unique and strives to have students “think outside the box.” The Proposals section is where undergraduates can simply propose a research idea—anything they consider to be academically fascinating and that could be conducted in a laboratory. This section is a good opportunity to search through various types of laboratories on campus and perhaps propose a project to a specific professor or scientist. The goal is simple: to encourage students to think critically about research. We wish to express our sincere gratitude to the Department of Neurobiology and Behavior for their support and advice and we hope that students will continue to support the journal through submissions of original research, reviews, and proposals. Please visit our website, www.cusn.org, for further information or email the editors with questions or comments. Thank you for your time and interest. We hope you enjoy the very first volume of the Cornell Synapse.

Sincerely,

Kevin K. Kumar Neurobiology & Behavior ‘09 kkk25@cornell.edu

©2007 Cornell Synapse | www.cusn.org

Cina Sasannejad Biochemistry ‘09 cs379@cornell.edu

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TABLE OF CONTENTS

REVIEWS 4 6 9 11

Male brain, female brain: Studying sexual dimorphisms Darwinian medicine: A new perspective on classical treatment Animal consciousness and its impact on research methodology The evolution of language in Homo sapiens

ARTICLES 14 19 22

The regulation of protein synthesis in long-term potentiation Time perception and familiar experience: The effect of face recognition on judged duration Courtship signals as advertisers of motor capabilities: A link between flight maneuverability and song repertoire size in birds

PROPOSALS 28 33

Studying the effects of desynchrony of internal oscillators on REM The role of serotonin reuptake transporter in depression and treatment with SSRIs

Š2007 Cornell Synapse | www.cusn.org

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REVIEW Male brain, female brain: Studying sexual dimorphisms Caroline Wee

Cornell University, Department of Neurobiology and Behavior, Ithaca, NY 14853

The scientific world and society at large have regarded that minimal and insignificant differences exist between male and female brains. However, a 2006 Nature Reviews Neuroscience article, “Why Sex Matters for Neuroscience”1, compiles ample evidence to suggest that there are significant sexual dimorphisms in the brain. Substantial research over the years has demonstrated that neuroanatomy, function and chemical composition differ appreciably between males and females. Neural sex differences in neuroscience research can easily be overlooked, simply because similarity in behavioral responses does not necessarily imply similar neural pathways or mechanisms. One such example is from a recent study which examined the neural pathways of retrieval of emotional, autobiographical memories in men and women2. While neither memory performance nor degree of emotion induced by retrieval differed between the sexes, the brain regions associated with such retrieval differed significantly. Although sex differences exist throughout the brain, two important cognitive regions, the hippocampus and amygdala, are of particular interest. The hippocampus is a region of the brain associated with learning and long-term memory. Not only do women have a larger hippocampus (when adjusted for brain size), other functional sex differences related to learning and reaction to chronic stress have been revealed through a number of animal studies3. For example, opposite effects of stressful experience on memory formation have been found in males as compared to females. Exposure to a stressful learning situation increased dendritic spine density and enhanced Pavlovian conditioning performance in male rats, while the same stimuli decreased spine density and impaired such performance in female rats4. These experimental results were further verified in subsequent human studies5. However, research primates also reveal that chronic stress causes much greater damage to the hippocampus of males as compared to females6. This contradicts an existing idea that the higher proportion of women who suffer from post-traumatic stress disorder and clinical depression may be due to increased stress-induced hippocampal cell death6. The amygdala is a region of the brain associated with emotional functions and is significantly larger in men than in women (adjusted for brain size) 3. Furthermore, an interesting collection of findings has consistently demonstrated a sex-related hemispheric lateralization of amygdala function – in short, women left, men right1. In women, the left amygdala is involved to a greater extent in emotional memory7-9, is more active in response to happy ©2007 Cornell Synapse | www.cusn.org

Rate of serotonin synthesis (pmol g-1 min-1) Baseline

Male

Female

120 90 60 30 0

Figure 1: PET scans showing serotonin synthesis rates by gender from Nishizawa et. al (1997). Males demonstrated a higher mean rate of serotonin synthesis compared to females. Reproduced from Ref. 1 (2006) Nature.

faces10 and even shows higher basal level of activity11. In men, the right amygdala shows more activity under similar stimuli7-11. In results that may reflect such lateralization, the left amygdala of women with Turner syndrome (who have only one X chromosome) has also been shown to have a significantly reduced arousal in response to emotional stimuli12. In addition to differences in hemispheric lateralization, important differences between neurochemistry exists between the sexes. Particularly interesting sex-related differences exist in monoamine oxidase (MAO) levels and serotonin synthesis. Levels of MAO, an enzyme associated with monoaminergic neurotransmitter inactivation, have been found to be significantly higher in several brain regions in women than in men13. It is interesting to note that higher levels of MAO may be associated with reduced sensation-seeking tendencies14. The mean rate of serotonin synthesis has been reported to be higher in the brains of healthy males than females, which may be correlated with lower incidences of depression in males15. Sex differences have also been reported in opioid16 and GABA (gamma aminobutyric acid)17 systems. Both the neurochemical and the neurophysiological differences between the sexes have protential application to medicine. From the medical perspective, these neural sexual dimorphisms have considerable implications. Alzheimer’s disease (AD), anxiety disorders, schizophrenia, stroke, autism, addiction, attention deficit hyperactivity disorder (ADHD) and even eating disorders are examples of neurological diseases that show sex differences in their incidence and expression4,18,19. These differences are likely to have their roots in underlying sex-related neural differences. The occurrence of abnormally phosphorylated tau protein in Alzheimer’s disease, for example, is higher in the hypothalamus of older men than older women20. In the nucleus basalis of Meynert however, which provides cortical cholinergic innervation, this observation is reversed20. Brain morphology associated with schizophre04


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nia also differs between men and women. Men with schizophrenia show significantly larger ventricles21 and have a greater size ratio of amygdala to orbitofrontal cortex22. On the other hand, no ventricular enlargement, a decreased amygdala to orbitofrontal cortex ratio are observed in schizophrenic women 21, 22. Though currently poorly understood, the differences in pathology in schizophrenic men and women may have significant implications in treating the illness. In addition to neurological disorders, drug addiction is influenced by the sex of the victim. Previous studies have shown that women are more sensitive than men to the effects of stimulant drugs like amphetamine and cocaine, which may contribute to the increased drug addiction and dependency rates in women as compared to men23. Significant sex differences in levels of dopamine, a neurotransmitter linked to the brain’s reward circuitry have also been found in several brain regions, and the dopamine response to drug stimulation also differs between the sexes24. Even more intriguing is the discovery that drug cues increase activity in the right amygdala of male cocaine addicts, but decrease activity in the right amygdala of female addicts25. Perhaps the hemispheric lateralization trend of ‘women left, men right’, is exhibited again in the amygdalae. Sexual dimorphisms in the brain are indeed influential, and many questions, such as how they exist and how exactly they are linked with observable male-female differences, are still left unanswered. Sex certainly does matter in neuroscience, and ignoring sex differences or treating them as negligible variability will only impede scientific and medical progress in their respective fields. Investigators can benefit by striving to pursue, understand, and appreciate these differences, bringing forth discoveries and expanding the frontiers of neuroscience. References: 1. Cahill, L. Why Sex Matters for Neuroscience. Nature Reviews Neuroscience 7, 477-484 (2006) Primary Literature: 2. Piefke, M., Weiss, P., Markowitsch, H., & Fink, G. Gender differences in the functional neuroanatomy of emotional episodic autobiographical memory. Hum. Brain Mapp. 24, 313-324 (2005) 3. Goldstein, J.M. et al. Normal sexual dimorphism of the adult human brain assessed by in vivo magnetic resonance imaging. Cereb. Cortex 11, 490-497 (2001) 4. Shors, T. Opposite effects of stressful experience on memory formation in males versus females. Dialogues Clin. Neuroscience. 4, 139-147 (2002) 5. Jackson, E.D., Payne, J.D., Nadel, L. and Jacobs, W.J. Stress differentially modulates fear conditioning in healthy men and women. Biol. Psychiatry 59, 516-522 (2005) 6. McEwen, B.S. The neurobiology of stress: from serendipity to clinical relevance. Brain Res. 886, 172-189 (2000) 7. Cahill, L. et al. Sex-related difference in amygdala activity during emotionally influenced memory storage. Neurobiol. Learn. Mem. 75, 1-9 (2001) 8. Canli, T., Desmond, J., Zhao, Z. & Gabrieli, J.D.E. Sex differences in the neural basis of emotional memories. Proc. Natl Acad. Sci. USA 99, 10789-10794 (2002) 9. Cahill, L., Uncapher, M., Kilpatrick, L., Alkire, M.T. & Turner, ©2007 Cornell Synapse | www.cusn.org

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J. Sex-related hemispheric lateralization of amygdala function in emotionally influenced memory: an fMRI investigation. Learn. Mem. 11, 261-266 (2004) 10. Killgore, W. & Yurgelun-Todd, D. Sex differences in amygdala activation during the perception of facial affect. Neuroreport 12, 2543-2547 (2001) 11. Kilpatrick, L.A., Zald, D. H., Pardo, J.V. & Cahill, L.F. Sex-related differences in amygdala functional connectivity during resting conditions. Neuroimage 30, 452-461 (2006) 12. Skuse, D.H., Morris, J.S. & Dolan, R.J. Functional dissociation of amygdale-modulated arousal and cognitive appraisal, in Turner syndrome. Brain 128, 2084-2096 (2005) 13. Robinson, D.S. et al. Monoamine metabolism in the human brain. Arch. Gen. Psychiatry 34, 89-92 (1977) 14. Zuckerman, M. P-impulsive sensation seeking and its behavioral, psychophysical and biochemical correlates. Neuropsychology 28, 30-36 (1993) 15. Nishizawa, S. et al. Differences between males and females in rates of serotonin synthesis in human brain. Proc. Natl Acad. Sci. USA 94, 5308-5313 (1997) 16. Craft, R.M. Sex differences in opioid analgesia: ‘from mouse to man’. Clin. J. Pain 19, 175-186 (2003) 17. Madeira, M.D. & Lieberman, A.R. Sexual dimorphism in the mammalian limbic system. Prog. Neurobiol. 45, 275-333 (1995) 18. Hines, M. Brain Gender (Oxford Univ. Press, New York, 2004) 19. Klein, L.C. & Corwin, E.J. Seeing the unexpected: how sex differences in stress responses may provide a new perspective on the manifestation of psychiatric disorders. Curr. Psychiatry Rep. 4, 441-448 (2002) 20. Swaab, D.F., Chung, W.C., Kruijver, F.P., Hofman, M.A. & Ishunina, T.A. Structural and functional sex differences in the human hypothalamus. Horm. Behav. 40, 93-98 (2001) 21. Noupoulos, P., Flaum, M. & Andreasen, N. Sex differences in brain morphology in schizophrenia. Am. J. Psychiatry 154, 16481654 (1997) 22. Gur, R. E. et al. A sexually dimorphic ratio of orbitofrontal to amygdala volume is altered in schizophrenia. Biol. Psychiatry 55, 512-517 (2004). 23. Lynch, W. J., Roth, M.E. &Carroll, M.E. Biological basis of sex differences in drug abuse: preclinical and clinical studies. Psychopharmacology 164, 121-137 (2002) 24. Becker, J.B. Gender differences in dopaminergic function in striatum and nucleus accumbens. Pharmacol. Biochem. Behav. 64, 803-812 (1999) 25. Kilts, C.D., Gross, R.E., Ely, T.D. & Drexler, K.P. The neural correlates of cue-induced craving in cocaine-dependent women. Am. J. Psychiatry 161, 233-241 (2004)

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REVIEW Darwinian medicine: A new perspective on classical treatment Kevin K. Kumar

Cornell University, Department of Neurobiology and Behavior, Ithaca, NY 14853

The current method through which medical treatment is diagnosed is based completely on alleviating the particular symptoms of a disease. This approach is highly effective and is the method used in almost all forms of modern medicine. However, this classical approach to medicine fails to address the evolutionary cause of an ailment, paying attention only to the biochemical sources of disease. Each pathogen and virus, as well as their subsequent immune response, is a product of natural selection. However, an important but highly neglected perspective on medical treatment is the one espoused by those studying behavior and evolutionary biology, the adaptationist program9. The adaptationist program views all human biological systems in their evolutionary context. In effect, these systems are features that favor survival and reproduction. Recent evidence provided by evolutionary analysis and clinical experimentation has yielded great support for the use of an adaptationist perspective in the combat against modern disease. This nascent field is known as Darwinian Medicine, and it has a wide range of medical applications, particularly for those diseases which are largely promoted by environmental forces, such as infectious disease and physical trauma. The application of Darwinian Medicine is not absolute; it has limitations in its applications to diseases where genetics plays a more influential role than environment, such as mental disorders. However, Darwinian Medicine has great utility in combating some of the most debilitating diseases of the twenty-first century. Evolutionary biology, with a fitness-based perspective of analysis, can be combined with biomedical research in order to provide a better understanding of functions in the human body. The traditional fields of science such as physics and chemistry, focus on proximate mechanisms rather than ultimate causes9. From a biomedical perspective, fever is viewed as a metabolic acceleration which causes an increase in body temperature when the body is infected with a pathogen. Oftentimes, this increase in body temperature has a negative impact on the individual, and as a result, treatment is given to suppress the temperature pharmacologically (Kluger et. al. 1998). However, a critical shortcoming of this perspective is that it fails to address the evolutionary function of fever. Fever is a highly conserved biological process present in both vertebrates and invertebrates. This process serves to escalate the body temperature to a point at which host defense against pathogens is enhanced. However, under certain circumstances, the fever can increase body temperature to a point where it is maladap©2007 Cornell Synapse | www.cusn.org

tive to the organism (Kluger et. al. 1998). In this simple example, classical medicine argues for pharmacological suppression of the fever. However, this action competes directly with the Darwinian approach of targeting the pathogen itself. Through consideration of both perspectives, a physician could carefully decide under which circumstances pharmacological fever suppression is appropriate. This decision should be based on the elevation of body temperature and the identity of the pathogen to which the fever is a response. One could imagine applying this logic to more complex ailments, such as AIDS or Parkinson’s Disease. However, the Darwinian perspective is not an alternative, but rather a complement to traditional medicine from which more effective treatment can be devised. The main premise establishing the utility of evolutionary theory to medical conditions is the fact that the evolutionary mechanisms throughout time have shaped the features of any given organism. These features enhance an organism’s fitness, increasing their genetic representation in subsequent generations. The adaptationist program views all biological phenomena as an aspect of an adaptation. An adaptation can be defined as any biological mechanism which is shaped by natural selection to help solve one or more problems faced by an organism. The observed phenomenon is then considered to be a part of one of three categories: a necessary component of the mechanism, an inherent cost of the mechanism or some incidental manifestation of the mechanism. After determining the nature of the phenomenon, a prediction is formed which states the other necessary components of the mechanism and establishes what appropriate investigation will reveal their presence. The goal of Darwinian Medicine is to reveal adaptive processes which would not be detected by classical medicine. By recognizing these adaptive processes, more appropriate treatment can be used. Darwinian medicine has yielded significant promise in the diagnosis and treatment of infectious disease. Infectious diseases can be viewed as the conflict between the patient and pathogen, a dynamic which Darwinian Medicine is of greatest utility. Infectious agents such as bacteria and viruses have their own unique evolutionary history which maximizes their ability to successfully survive and reproduce against the defenses of a human host. There are a number of methods through which an infectious agent can impact the host. Among these methods are direct damage to host tissues, impairment of host function, evasion of host defenses, attack on host defenses, and the infectious agent’s dispersal methods. In response to an infectious agent, the host can also impact 06


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itself through its own repair mechanisms, compensatory adjustments, hygienic measures and its own defenses against the infectious agents. Through organization of the various phenomena associated with infection based on their functional significance in the aforementioned categories, infectious diseases can be better understood and controlled. Perhaps the most significant distinction is made when separating the phenomena associated with infections, which are caused by the pathogen, from those caused by the host’s own defenses. Medical treatment can be designed to act upon the pathogen’s adaptations while minimizing the impact on the host’s defenses. In reference to the fever example, those individuals who are allowed to exhibit fever recover more quickly from chicken pox than those who have been treated with acetaminophen which blocks fever (Doran et al. 1989). Another convincing example of classical medicine conflicting with the Darwinian perspective is the use of dietary iron supplements in response to decreased plasma iron levels. Although in classical medicine this deficiency would appear as anemia, which is diagnosed in the developing world, the Darwinian approach would reveal that this is a natural response during the initial stages of infection. The sequestration of iron deprives bacteria of minerals it requires and works alongside fever in fighting infection (Weinberg 1984). In this situation, treatment with iron supplements could actually harm the health of the individual. One of the key perspectives which need to be adopted in order to combat infection is a firm understanding of virulence. For instance, in pathogens with animal vectors, virulence will tend to be higher in the human host and low in the vector. Diseases spread by contact with other individuals are generally less virulent than those spread by the vectors themselves. This largely depends on the mode of transport, those diseases which transmit by inanimate vectors, such as water, will be highly virulent if the immobilized host releases the pathogen near a water source. By recognizing the factors contributing to virulence of a pathogen, effective strategies to combat disease can be undertaken. Great success has been seen in the fight against cholera. Since the mode of transmission is adapted to a particular environment, cholera is spread by personal contact in agrarian society, whereas it is spread by water in an urban community. Therefore the incidence of cholera in an urban community can be reduced by creating a hygienic water supply (Ewald 1988). In addition, the cholera strain will adapt to the hygienic water supply through decreased virulence exhibited in agrarian society. The use of Darwinian Medicine in combating infectious disease can help discover the processes involved in currently uncured diseases such as AIDS. The use of Darwinian Medicine shows tremendous promise in the treatment of mechanical damage and toxins. Mechanical damage and poisoning can be considered a struggle between the patient and the natural environment, an application which Darwinian medicine has significant utility. The categories used to analyze cases of mechanical damage are much simpler than that of the pathogen-host relationship, since there is no competition between two organisms with competing interests (Williams & Nesse 1991). The phenomena observed in an injury is to separate the impact of the damage itself from the repair mechanism and secondary adjustments. Repair consists of the rebuilding of damaged tissue and other mechanisms which directly aid in repair, such as inflammation. Inflammation when combined with pain, acts to discour©2007 Cornell Synapse | www.cusn.org

Cornell Synapse

age use of the damaged area to allow healing. The human body is adapted to Stone Age conditions, not the modern level of technology present today (Williams & Nesse 1991). As a result, in the absence of the forms of artificial restraints such as plaster casts, pain and inflammation serve to limit movement. The application of this perspective to modern medicine is the consideration of the impact of limiting factors such as inflammation and pain during treatment. Furthermore, the role of temperature in the healing process can also be evaluated. This analysis can potentially reveal a particular temperature at which cell growth and repair is optimized. Through analysis of the harmful effects of suppressing these evolved mechanisms in humans, the conditions under which healing is most effective can minimize recovery time. Perhaps one of the best examples of the importance of analyzing the fitness effects of a particular adaptation is treating the impact of toxins. Humans have the ability to discern the presence of naturally occurring toxins in nature. However, humans are limited in this ability with novel substances such as insecticides9. Ideally, humans should be able to have a completely toxin free diet, but this undermines the fitness battle between herbivores and plant life. Plants have evolved toxins to prevent death due to consumption by animals, and humans have co-evolved detoxification metabolic processes. For diagnosing diseases such as morning sickness in pregnant women, the taste aversion from bitter foods is connected with toxins which the embryo is susceptible at that particular stage of development (Sherman & Flaxman 2002). One of the most controversial applications of Darwinian Medicine is in the field of Neuroscience. Mental disorders are a result of genetic or epigenetic factors during the development of the patient. Under these circumstances, Darwinian Medicine has the narrowest application, to only those disorders concerning social environment. One of the most recent and prominent uses of the evolutionary perspective in combating mental disorders is the treatment of psychoactive drug abuse. Psychoactive drugs are unique in the way they operate on the body, because they largely can be regarded as pathogenic, sidestepping the ancient mechanisms within the body which regulate behavior and emotion (Nesse & Berridge 1997). Emotions are defined as coordinated states, shaped by natural selection, that adjust physiological and behavioral responses to take advantage of opportunities and to cope with threats that have recurred over the course of evolution. In a narrow sense, emotions serve an adaptive function of their own: to distinguish the fitness benefit of a particular behavior (Nesse 1964). Drugs which stimulate positive emotions actually give a false signal which indicates fitness benefit to the individual. As a result, these drugs can corrupt the motivation of pleasure, causing continued drug use. Similarly, drugs which impair negative emotions can lower an individual’s defenses. These ancient neurological processes make humans vulnerable to fitness-decreasing incentives associated with substance abuse. The enjoyment of substances is mediated by opioid forebrain systems and by brain-stem systems (Nesse & Berridge 1997). The actual craving for these substances is mediating by the ascending mesolimbic dopamine neurons. When these systems are stimulated by drugs, the craving for drugs increases without a decrease in enjoyment, forming the basis of addiction. Genetic variation makes individuals more or less susceptible to addiction, because most abused substances were not readily available during the evolutionary period. By acknowledging that emotion is the 07


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primary process which instigates addiction, treatment that pays special attention to the variety of factors that influence an individual’s emotions can be devised. For instance, social support, social inequality, discrimination and limited opportunities in life are factors which may contribute to addiction (Nesse & Berridge 1997). However, these influences on addiction are counterweighted by the effect of genetics, which Darwinian Medicine would be unable to provide an alternative perspective for treatment. Modern neurologists now have the ability to block negative emotions through the use of prescription psychoactive drugs. These drugs are commonly prescribed to patients for a variety of mental illnesses associated with negative emotion, yet currently no attention is paid to the fitness effect of these emotions on an individual. For instance, embarrassment and guilt serve to regulate an individual’s position in a hierarchical society. These factors must be taken into consideration when prescribing medication, to decide whether the drug is compensating for a chemical imbalance or suppressing a natural fitness signal. However, the Darwinian view espoused by Nesse is not fully accepted in the field of psychiatry. The main criticism of the Darwinian school of thought is the lack of a viable adaptive purpose to mental suffering (Dubrovsky 2002). Furthermore, one would find it difficult to argue that diseases such as schizophrenia have any functional purpose, and Darwinian Medicine would have little utility under such circumstances. Darwinian medicine provides a unique perspective of analyzing current challenges in medicine in order to come up with novel hypotheses for both research and treatment. Classical medicine merely approaches disease through direct treatment of symptoms, regardless of the evolutionary context of the ailment. By acknowledging the evolutionary history of each pathogen and the defense response of the host, effective strategies of treatment or prevention can be derived. However, the utility of Darwinian medicine is effectively limited to diseases in which there is a host-pathogen or patient-environmental interactions. The adaptationist program will continue to provide benefits in medical fields beyond infectious disease, mechanical injuries, and psychoactive drug abuse. Darwinian medicine has the capacity to revolutionize modern medicine, balancing the proximal causes of illness, addressed by classical medicine, to the ultimate evolutionary causes of disease.

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Evolutionary Perspective. Science. 278, 63-66. 7. Sherman, P. W., Flaxman, S. M. 2002. Nausea and vomiting of pregnancy in an evolutionary perspective. American Journal of Obstetrics and Gynecology. 186, S190-S197. 8. Weinberg, E. D. 1984. Iron withholding: A defense against infection and neoplasia. Physiological Review. 64, 65-102. 9. Williams, G. C. & Nesse, R. M. 1991. The Dawn of Darwinian Medicine. The Quarterly Review of Biology. 66, 1-22.

References 1. Doran, T. F., De Angelis, C., Baumgardner, R. A., Mellits, E. D. 1989. Acetominophen: More harm than good for chicken pox? The Journal of Pediatrics. 114, 1045-1048. 2. Dubrovsky, B. 2002. Evolutionary psychiatry. Adaptationist and nonadaptationist conceptualizations. Progress in Neuro-Psychopharmacology & Biological Psychiatry. 26, 1-19. 3. Ewald, P. W. 1988. Cultural Vectors, virulence, and the emergence of evolutionary epidemiology. Oxford Survey of Evolutionary Biology. 5, 215-245. 4. Kluger, M. J., Kozak, W., Conn, C. A., Leon, L. R., Sosynski, D. 1998. Role of Fever in Disease. Annals of the New York Academy of Sciences. 856, 224-233. 5. Nesse, R. M. 1964. An Evolutionary Perspective on Psychiatry. Comprehensive Psychiatry. 25, 575-580. 6. Nesse, R. M., Berridge, K. C. 1997. Psychoactive Drug Use in ©2007 Cornell Synapse | www.cusn.org

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REVIEW Animal consciousness and its impact on research methodology Yin Tong

Cornell University, Department of Human Development, Ithaca, NY 14853

Anywhere from fifty to one hundred million animals worldwide are used annually and subsequently killed in scientific procedures (1,9). Testing is carried out in a variety of mediums: universities, medical schools, farms, defense research establishments, etc. The subjects range from fruit flies to primates, mostly bred for this purpose in a laboratory, although some are captured from their natural habitat. Those opposed to animal testing range from small groups of pet owners and students to large-scale organizations such as PETA. Regardless, their argument is the same; it is inhumane to test on animals because they are conscious of pain, isolation and all the effects from which humans protect themselves. The opposition perspective is that animals suffer less during experiments than humans, arguing that while all mammals have similar pain receptors and central nervous system pathways, the pain they feel is lessened because of their reduced capacity to remember and anticipate pain. One must acknowledge the paradoxlike situation surrounding this debate – that both sides use the same evidence in support or opposition of the existence of animal consciousness. There are two ordinary senses of consciousness, which garner little debate; the distinction between the awake state and the sleep state and the sense of consciousness implicated in the basic ability of all animals to perceive and respond to their environment. The debate lies in the existence of access consciousness exhibited by humans, the phenomenon whereby information is accessible for verbal report, mental reasoning and novel behavior (2). Although verbal report applies to humans, the aspect of novel behaviors and problem-solving techniques is not exclusive to any animal and its control lies in the realm of brain activity and physiology. The human brain has not produced a neural correlate of consciousness absent in all other species (5). Since the difference is not physical, one must examine the main indication of access consciousness, which is an appropriate response to novel challenges for which an animal has not been previously prepared by experience or genetic programming. This indication is valid because the cognitive ability required to problem solve for novel situations is most effectively organized by a conscious thought process. Animal orientation and navigation serves as a useful example of problem solving novel situations using tools provided by the environment – spiny lobsters for instance, can find their way home from any point as long as they have access to a stable magnetic field (3). Even humans do not posses this ability; instead there are physical ©2007 Cornell Synapse | www.cusn.org

maps, but interpretation directions on a map, the human equivalent of a magnetic field, is considered problem solving novel situations in human behavior. All vertebrates share the same basic brain anatomy; the similarities are even more prevalent in mammals. The case for inferential symmetry rests in evolutionary conservation, the levels of shared biology among members of the animal kingdom (4). At the genomic level, humans and chimpanzees share upwards to 96% of their DNA and this figure, when discussing the coding of proteins, rises to a staggering 99% (4). Since these genes have changed very little in the over 500 million years when the vertebrates diverged from arthropods, it comes as no surprise that similar genes give rise to similar structures. The merging of human and animal brain models give rise to the question of whether or not biological similarities in regions of the brain are valid indicators of equally similar neural ability. The matching of neural blueprints does not always generate complete and consistent responses; variations lie not only in the general size of the specific region, but also their relative size compared to other regions with different functions. Many regions however, do have similar functions across a variety of species. For example, the basal forebrain and extended amygdala meditate the recognition of potential mates and competitors, rituals for mating and the pursuit of mates, aggression and territoriality in many different primates. Genetic modification and transgenic animals have been a long-enduring example of animal testing, especially in the area of genetic research. The transgenic animals have specific genes inserted, modified or removed to emulate the effects of a specific genetic condition, such as albinism or Duchenne muscular dystrophy. These models have been used to approximate the genetic component of many more complex diseases, such as Alzheimer’s disease, the most common form of dementia among older humans which initially involves the parts of the brain that control thought, memory and language. In both humans and treated non-human animals, there is an overall shrinkage of brain tissue and a noticeable widening of the grooves (1). The ventricles containing cerebrospinal fluid are enlarged. The symptoms of the development of Alzheimer’s affect very specific portions of the human brain serving functions which support the concept of consciousness. Other transgenic animals, such as mice, have been used to investigate the effects modifications of specific brain targets, similar in specificity to those affected by Alzheimer’s. The removal of known receptors is often a very useful tool in testing whether or not a particular organism posses the capacity to feel that particular 09


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sensation. In mice, scientists have engineered the genetic removal of a known pain receptor (7). If the genetically altered mice do not respond to a mild paw pressure test, ten the receptor is not only involved in mammalian pain perception, but is also a good target for a new painkiller. Even the smallest and seemingly insignificant animals have come through as important models for the research into testing for pain relief. Drosophila melanogaster, a simple fruit fly, has aided scientists in answering questions of pain receptors and nerve endings. These animals, like humans, react when prodded with a sharp object. By isolating mutant flies which did not have this instinctual response, researchers have been able to look into identifying pain receptors in humans, with special interest to those who exhibit the rare case of pain-insensitivity (10). The majority of American consumers have taken common drugs such as acetaminophen or ibuprofen. The probability of that particular drug having been tested on a type of animal is almost certain. By nature, humans are not inclined to use untested drugs; there is no way to predict how it will react in the overly complex human body. Animal testing solves this problem by providing an alternative to what most would call inhumane analysis on humans. Throughout the years as scientists became more ambitious and diseases more advanced due to changes in the environment, the need for more invasive animal testing arose and individuals in the professional world began conducting experiments that many could not excuse, even on non-humans. The separation of consciousness from body in animals allows many scientists to justify the way that these subjects are treated. If the animal is perceived to experience no cognition of what is about to happen to them and simultaneously not given a chance for escape, then humans are relieved of their moral responsibility. If this were true however, then the basis on which all of their research would be null. Their research is dependent upon the physiological similarities between humans and non-humans; however the discovery of these similarities indicates increasing parallels between sensations and emotions, or at least the potential thereof. The use of animals by researchers to describe, model and find cures for diseases and disorders is testimony in itself of not only the assumption of similar neural physiology, but also the enduring assumption of similar neural function on which this model relies.

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ea-pig to mu- and kappa- opioid receptor agonists.” Glaxo Group Research Ltd. 4:823-32. 8. Home Office (2005). “Statistics of Scientific Procedures on Living Animals.” Home Office. Great Britain 9. Jha, Alok. (2005). “RSPCA outrage as experiments on animals rise to 2.85m”, The Guardian, December 9, 2005. 10. WD Tracey et al.(2003). “Painless, a Drosophilla gene essential for nociception.” Cell. 113:261-273. 11. Varner, G. (1999). “In Nature’s Interests?”. New York: Oxford University Press.

References 1. Iadecola, C. (2004). Neurovascular Regulation in the Normal Brain and in Alzheimer’s Disease. Nature Reviews: Neuroscience 5:347-360. 2. Block, N. (2005). “Two Neural Correlates of Consciousness.” Trends in Cognitive Sciences 9:41-89. 3. Boles LC, and Lohmann, KJ. (2003). True navigation and magnetic maps in spiny lobsters. Nature 421:60-63 4. Bradsaw, GA and Sapolsky, Robert M. (2006). “Mirror, Mirror.” American Scientist. November-December 2006 5. Crick, FC and Koch, C. (1998). “Consciousness and Neuroscience.” Cerebral Cortex. 8:97-107. 6. Bloom, ET. (2001). Xenotransplantation: Regulatory Challenges. Current Opinion in Biotechnology. 12: 312-316. 7. Hayes, AG, Sheehan, MJ and Tyers, MB. (1987). “Differential sensitivity of models of antinociceptin in the rat, mouse and guin©2007 Cornell Synapse | www.cusn.org

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REVIEW The evolution of language in Homo sapiens Kevin K. Kumar

Cornell University, Department of Neurobiology and Behavior, Ithaca, NY 14853

The dawn of language in Homo sapiens is still uncharted territory in the scope of human evolution. Language, the audible, articulate, meaningful sound as produced by the action of the vocal organs, is a complex behavior displayed only in H. sapiens. The power of language is the primary basis of the complex society humans have created, serving as a means of human communication. This form of communication does not exist at the human level of complexity among other organisms, but evidences of similar evolution exist in other species. The role of FOX genes in humans and other vertebrates pinpoints a key moderator in development, and provides insight into this moderator’s evolutionary origin. The selection pressures which favored the evolution of language, likely also favored neural and motor facility growth. Unparalleled in evolutionary influence, language not only allowed a species to express basic observations of the physical world, but also what an individual thinks. The facility to speak imposes a great deal of demands on an organism’s anatomical structures. In order for one to vocalize, syntactic, semantic, phonological and pragmatic representations as well as motor and sensory systems must be coordinated (Fisher and Marcus 2006). The requirements for this level of coordination are stemmed in nervous system development, particularly the cerebral cortex. The cerebral cortex is the center of all language mediating functions. In addition to the focus in the cerebral cortex, language capacity is broadly distributed across cortical and subcortical circuits across the brain (Lieberman 2002). The great demand on the nervous system can be best appreciated through looking at the very complexity of language. Language is composed of a system of syllables and words which can be combined to produce and infinite combination of ideas. The first step in identifying how language emerged in H. sapiens is to identify the regions mediating language in the modern human mind. In particular, two regions of the brain, the Broca’s and Wernicke’s areas, located in the temporal gyrus, are believed to play a significant role in language (Innocenti and Price 2005). Broca’s area is primarily responsible for speech production and grammar, whereas Wernicke’s area is essential to meaning and understanding. Diseases and injuries affecting these areas confirm these roles (Fisher and Marcus 2006). For instance, those suffering from Broca’s aphasia, a legion in the Broca’s area, have difficulty articulating sentences. Conversely, those suffering from Wernicke’s aphasia can speak articulately and rapidly but lack continuity of topic mid-sentence. Although these areas play a vital ©2007 Cornell Synapse | www.cusn.org

role in language, their role is not absolute. Not all legions to these particular regions lead to symptoms of the aphasia, suggesting that language relies on a larger neural network outside the Broca’s and Wernicke’s areas (Fisher and Marcus 2006). These cortical regions share homologues with primates, suggesting that the adaptations which facilitate language are based on the asymmetric brain structure of a recent common ancestor of H. sapiens and apes. One must consider how large scale changes in human evolution left the mechanisms through which language develops intact. No region of the brain is restricted by one particular function; there are critical bodily functions also performed in these areas. Therefore, one can conclude that the development of language facility in the brain must have had flexibility in language mediating centers, while maintaining a level of stability which performs normal functions. One could also note that while it is important to focus on neural structure and development, attention should be paid to the development of the mechanics of speech: vocalization and pronunciation. The general ability to generate simple speech such as grunts and noises was present before his specificity of language. However, there is a widely held belief that specialization in the larynx occurred alongside neural development (Arbib 2005). The dawn of language did not occur independently, certain selection pressures favored language development. Developments in the cerebral cortex, both in size and complexity, were likely caused by selection pressures which favored the development of new functions as well as the maintenance of old ones. Evolutionary cortical adaptations increased behavioral specialization and the level of cognitive thinking, attributes which in effect increased the fitness of those individuals. The two largest ends selection pressures favored were increased motor skills and social behavior (Corballis 1992). The relationship between these two skills is exemplified by the intimate proximity of regions moderating these behaviors within the brain. In addition, both the ability to perform complex motor tasks and complex communication are rooted in brain lateralization (Arbib 2005). Brain lateralization is a highly level neural adaptation which increases both the range and efficiency of tasks which can be performed. The evolutionary mechanism, through which neural adaptations such as lateralization emerged, is based on overproduction and selection of connections in development (Innocenti and Price 2005). This theory, demonstrates that the brain merely did not specialize overnight into a language mediating powerhouse, but gradually through intense selection generated the neural complexity neuron by neuron. The similarities, both genetic and molecular, between humans and their primate cousins reveal a great deal regarding the origin 11


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of language. There is a significant amount of genetic difference between humans and Pan troglodytes, there exists a 1.23% substitution rate in single copy genomic regions (The Chimpanzee Sequencing and Analysis Consortium 2005). The consequence of such variation is that it clouds the role of specific genes in language development. However, primates and humans display an accelerated rate of neural development, compared to that of other mammals. Research thus far has been unable to identify a set of genes in humans which specifically regulates language development. However, a particular gene FOXP2 is critical in a disorder called Specific Language Impairment (SLI), which involves difficulty in both learning and mouth movements (Cooper 2006). Therefore, one hypothesizes that the FOXP2 gene is critical in language development, based on the symptoms which dramatically impact speech. In addition, language development is independent of intelligence to a certain degree, increasing evidence regarding the impact of FOXP2 on the development of speech. Interestingly, the FOXP2 gene is not only present in humans, but is a present in other vertebrates (Cooper 2006). This type gene also is indicative of an organism’s complexity, there is a direct correlation between the number of different FOX genes and a species complexity. These genes effect the formation of proteins critical in the development of the cerebral cortex. Particularly, the gene FOXP2 creates a protein which dictates the development of Layer V of the Broca’s area (Arbib 2005). Further confirming FOXP2’s role in language, the gene is also expressed in songbirds. The gene encodes in subpillial areas critical for songbirds learning cycle. There is an upregulation of FOXP2 during the critical learning period for songbirds (Teramitsu et al. 2004). The genetic similarities between humans and other vertebrate in language production identify primary selection pressures to have acted in favor of communication. Through analysis of the research preformed on the evolutionary role of language, certain concepts are consistent despite currently conflicting between hypotheses. For example, the evolution of the Wernicke’s and Broca’s areas preceded the emergence of language, suggested by the homologous structures in great apes (Cooper 2006). This chronology of the development of language mediating structures indicates that the selection pressures which generated these structures were selecting on another function of these regions. As a result, the Broca’s area did not develop for the specific function of language, but as a brain region which performs a necessary bodily function. The lack of Broca’s and Wernicke’s areas in other vertebrates, such as songbirds (Teramitsu et al. 2004), provides evidence that these regions developed relatively recently in the evolutionary past of H. sapiens. Further substantiating the hypothesized chronology of language development is the role of FOX genes. The presence of the language critical FOXP2 gene in primates identifies language to be a part of a greater evolutionary trend favoring complex social behavior. Through examination of apes, social behavior clearly enhances survival and overall fitness (i.e. social hierarchy of gorillas) (Robbins et al. 2004). Even in modern human society, the interdependence between individuals reflects the complexity of human language and communication. Language permits the highest level of social behavior providing the ability to communicate instructions and thoughts. As a result, the higher number of FOX genes in humans is directly correlated with species complexity. The similarity in FOX expression between humans and songbirds provides ©2007 Cornell Synapse | www.cusn.org

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a great deal of evidence regarding the role of historical period in which language related genes developed. Although Cooper ascribes too much significance to one of the potentially thousands of genes mediating language, the presence of FOXP1 and FOXP2 in species of songbirds (Teramitsu et al. 2004) which lack the Broca’s area provides evidence that these genes originated further back in the vertebrate phylogeny than the Broca’s and Wernicke’s areas. The functional differences between the origin of FOX genes and the Broca’s area suggest that these two elements of language development were once independent of one another. The cortical circuitry which permits correct speech in cases of Broca’s aphasia, also accounts for this independence. If both FOX genes and the cerebral cortex evolved together originally, those with Broca’s aphasia would always lose speech abilities. Since this case does not exist, selection pressures favoring social behavior likely integrated these two systems during the period of the common ancestor between H. sapiens and primates. The series of adaptations resulting in the advent of language seem to be intrinsically tied to a selection force favoring complex behavior and effective communication. The evolution of language is still not fully understood by the scientific world. Through combining methods which pinpoint language development in the nervous system and genome, hypotheses can be generated regarding the evolutionary origin. Language, a highly complex method of communication, is not limited to a particular gene or a particular portion of the brain. Selection pressures over a long period of time selected for adaptations which favored brain lateralization, cortical development and larynx specialization. Current research has provided information which has aided theories regarding language specialization, however a great deal of critical information is unknown. Until further genomic and molecular advances concerning language development are made, the gift of speech will be left shrouded in the evolutionary fog. References 1. Arbib, M. A. (2005). “From monkey-like action recognition to human language: an evolutionary framework for neurolinguistics.” Behav Brain Sci 28(2): 105-24; discussion 125-67. 2. Cooper, D. L. (2006). “Broca’s arrow: Evolution, prediction, and language in the brain.” Anat Rec B New Anat 289(1): 9-24. 3. Corballis, M. C. (1992). “On the evolution of language and generativity.” Cognition 44(3): 197-26. 4. Fisher, S. E. and G. F. Marcus (2006). “The eloquent ape: genes, brains and the evolution of language.” Nat Rev Genet 7(1): 9-20. 5. Hauser, 1997 M.D. Hauser. (1997). “Artifactual kinds and functional design features: What a primate understands without language.” Cognition 64: 285–308. 6. Innocenti, G. M. and D. J. Price (2005). “Exuberance in the development of cortical networks.” Nat Rev Neurosci 6(12): 95565. 7. Lieberman, P. (2002). “On the nature and evolution of the neural bases of human language.” Am J Phys Anthropol Suppl 35: 36-62. 8. Robbins, M. M., Bermejo, M., Cipolletta, C., Magliocca, F., Parnell, R. J., and Stokes, E. (2004). “Social structure and life history patterns in Western gorillas (Gorilla gorilla gorilla)”. Am. J. Primatol 64(2): 145-159. 12


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9. Teramitsu I., Kudo L. C., London S. E., Geschwind D. H., White S. A. (2004). “Parallel FoxP1 and FoxP2 expression in songbird and human brain predicts functional interaction.” J Neurosci 24:3152–3163. 10. The Chimpanzee Sequencing and Analysis Consortium. (2005) “Initial sequence of the chimpanzee genome and comparison with the human genome.” Nature 437: 69–87.

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ARTICLE The regulation of protein synthesis in long-term potentiation Vinay L. Patel1,2, Tao Ma1, Robert D. Blitzer1

Mount Sinai School of Medicine, Department of Pharmacology and Biological Chemistry, New York, NY 10029 Cornell University, Department of Neurobiology and Behavior, Ithaca, NY 14853

1 2

Synapses have the ability to encode memories through an increase in signaling efficiency; this is also known as synaptic plasticity. Long-term potentiation (LTP) is a long lasting form of this increase and is believed to underlie the molecular mechanisms of memory storage within the hippocampus. The maintenance of LTP requires a period of accelerated protein synthesis, but the exact mechanism regulating protein synthesis is still not well understood. Here , various experimental methods are employed to identify the mRNAs that enhance translational capacity within the hippocampus as well as proteins that are synthesized as a result of this increase. We show that LTP inducing stimulation causes the increase of three feature terminal oligopyrimidine tract (TOP) mRNAs, ribosomal protein S6 (rpS6), poly(A)-binding protein (PABP), and elongation factor 2 (eEF2). This increase is blocked by rapamycin, demonstrating that the increase in expression of these TOP mRNAs is mediated by the mammalian target of rapamycin (mTOR) pathway. After implicating TOP mRNAs in LTP, we have also confirmed that the activity of the GluR2/3 subunit of the AMPA receptor functions in a rapamycin sensitive manner. These results suggest that the mechanism of memory consolidation lies in a “two-wave” model which first increases the translational capacity, and then allows for synthesis of LTP related proteins. Introduction The brain is an integral component of life, regulating thousands of processes each day. Being a complex structure, it has inherent regulatory capacities for the formation and maintenance of memory, one of the most integral functions of the brain. Many aspects of human behavior affect how the brain changes and functions to record experience-based thoughts. The brain consists of billions of neurons, each signaling through chemical and electrical processes across a large number of neuronal pathways. The properties of these pathways have been explored, establishing what is thought to underlie memory at the cellular level: the ability of synapses to increase or decrease their signaling efficiency, also known as synaptic plasticity.5 The synapse is studied as the point of communication between neurons with roughly 1014 synapses representing an enormous computational capacity. Recent studies have elucidated many aspects of how the synapse encodes memories with a particular focus on a long-lasting form of plasticity called long-term potentiation (LTP).5 ©2007 Cornell Synapse | www.cusn.org

The LTP of synaptic signaling is studied chiefly within the hippocampus, an elongated C-shaped structure. The intrinsic neuronal circuitry of the hippocampus extends from the entorhinal cortex to the CA1 subregion of the hippocampus through a trisynaptic pathway utilizing glutamate as the main neurotransmitter at each synapse.12 The hippocampus is used in LTP research for two main reasons. First is the fact that it has been implicated in the formation and storage of memories through several experiments and clinical studies. Secondly, this structure is ideal for research in synaptic plasticity as it provides convenient access to a set of synapses, which each exhibit LTP. Through the use of electrophysiological methods, LTP can be induced and examined in control and experimental conditions.5 LTP can be used to explain the molecular mechanisms of memory and learning, which depend on the fluctuation of synaptic transmission. The association of learning and memory with synaptic enhancement stems from the striking resemblances between the two. In many cases, learning and memory are based in transient events which are somehow “stored” in the brain. LTP features the same long term effect after a brief synaptic event.4 One question which has been a dominant part in LTP research is whether the modification following LTP inducing stimulation takes place in the pre- or post-synaptic region. While this information is still not completely agreed upon, the initial steps which establish LTP are well recognized as being post-synaptic in location. In response to LTP-inducing stimulation and the release of glutamate into the synapse, the alpha-amino-3-hydroxy-5-methyl-4-isoxazole propionate (AMPA) and N-methyl-D-aspartate (NMDA) glutamate receptors are activated, and Ca2+ enters the postsynaptic cell.13 The influx of Ca2+ is mediated by the NMDA receptors, which require concurrent binding of glutamate as well as a depolarizing event to release a Mg2+ ion which regulates its activity. From this point, the initiation of LTP primarily depends on the Ca2+-dependent activation of enzymes, including Ca2+/Calmodulin-Dependent Kinase Type II (CaMKII). CaMKII has been shown to act by taking a signal and splitting it to multiple targets, leading to further downstream events, including the insertion of additional AMPA-type receptors into the synapse.5 One of the most intriguing aspects of LTP, however, lies in the mechanisms that are responsible for the persistence of the potentiation. Various studies have shown that the maintenance of LTP for more than 1-3 hours requires ongoing protein synthesis. The main mechanism thought to regulate protein synthesis in LTP maintenance is activation of the mammalian target of rapamycin (mTOR) 14


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pathway.15 Studies have shown that mTOR (named for sensitivity to the drug rapamycin which inhibits its activity), which is widely known as a regulator of cell cycle progression, is also necessary for stable LTP. This discovery came through the study of two forms of LTP, one which lasts roughly one to three hours (named EarlyLTP) and one which can last for many hours to months (named Late-LTP).5 Both Early-LTP (E-LTP) and Late-LTP (L-LTP) require the same initiation which allows the subsequent synaptic response measured by the excitatory post-synaptic potential (EPSP). In ELTP, EPSPs show an initial increase, but return to baseline within a few hours after stimulation. L-LTP on the other hand, can last for many hours in acute brain slices, and months in vivo. It is L-LTP that requires protein synthesis, while E-LTP is supported solely by post-translational mechanisms including protein phosphorylation.5 In our lab, we are exploring the hypothesis that the maintenance of LTP requires activation of the mTOR pathway and a consequent increase in translational machinery to provide for an increased rate of protein synthesis. This would increase the levels of various molecules necessary for synaptic plasticity, for example CaMKII, the glutamate receptors, and other proteins that play a role in the process. Since the dendrites of hippocampal neurons contain many components of the translational machinery, including ribosomes, membranes that resemble the rough endoplasmic reticulum, and many encoding mRNAs, they are likely to be involved in local protein synthesis. These proteins are then subsequently transported to the vicinity of the synapse.5 Recent work has established that such local translation does in fact occur, providing the stimulated synapses with proteins that are involved in synaptic plasticity. This paper will focus on the theory of how synaptic transmission is enhanced. In essence, the mTOR pathway is activated in the dendrites of the hippocampus in response to high-frequency stimulation (HFS; usually 100Hz) which induces LTP and regulates synthesis of the aforementioned translational machinery. This involves translation of a select group of mRNAs which feature terminal oligopyrimidine tracts at their 5’ ends (TOP mRNAs). These transcripts encode various proteins including ribosomal proteins

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Figure 1: Immunohistochemistry and Western blot experiments confirmed the increase of TOP mRNAs in a rapamycin sensitive manner. 1A: Immunoblot images for rpS6 showed an increase in levels relative to the controls within the dendrites after HFS. The robust effect is blocked by the application of rapamycin. In all cases tubulin is used as a control for the three conditions. 1B: Immunoblot images for PABP showed the same effect between control, HFS, and HFS + rapamycin slices. 1C: Slices were pretreated with anisomycin (Ani), DMSO (Veh), or Rapamycin (Rapa). In CA1 regions frozen 30 minutes after HFS, immunoreactivity for eEF2, rpS6, and PABP were elevated relative to the controls. All of these effects were mediated by protein synthesis and the mTOR pathway as application of anisomycin, an inhibitor of protein synthesis, and rapamycin, an inhibitor of the mTOR pathway, blocked the elevated levels of expression. Representative gel images are shown to the right. All results are significant to a P value < .05. Š2007 Cornell Synapse | www.cusn.org

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and elongation factors. Rapid synthesis of these proteins following HFS recapitulates processes of the cell cycle during the early G phase, when the developing cell synthesizes the translational machinery necessary for accelerated protein synthesis and progression to the S phase.2 The increase in TOP mRNA translation thus produces a “first wave” of protein synthesis, which provides a platform for a “second wave” of synthesis during which plasticityrelated proteins are made. In earlier work from our lab, it was shown that the protein encoded by one of the TOP mRNAs, elongation factor 1A (eEF1A), increased in dendrites of hippocampal neurons within 5 minutes after HFS.17 The present project addresses two research questions: (1) Is the increase in TOP mRNA translation by HFS a general phenomenon (i.e. do other TOP-mRNA encoded proteins also increase in LTP); and (2) is it possible to detect a “second wave” increase in proteins known to be involved in synaptic function? The potential second wave proteins examined were the AMPA receptor subunits GluR1 and GluR2/3. Materials and Methods Microarray: Methods used for dissection to obtain 500 µm slices of rat hippocampus, involved a deep anesthetization with halothane, removal of the brain, rapid dissection of the hippocampus, and section preparation. Sections were maintained in an interface chamber at room temperature for two hours to recover from the dissection. Slices were then used for various experiments, with 30 slices being flash-frozen on microscope slides using dry ice, for microdissection of the stratum-radiatum layer of area CA1 which is enriched in dendrites. The slices were microdissected using a dissecting microscope with the glass plate chilled on dry ice in an RNase free environment at a temperature of 4oC to provide minimal thawing of the tissue. The microdissected stratum-radiatum regions were then placed in tubes which were chilled on dry ice. Twenty-eight successfully dissected slices were then pooled in one tube and kept in a -80oC freezer overnight. The pooled tissue was then transferred into a dry-ice filled container and taken to the shared Microarray Facility to run a full genome gene-chip analysis. Preparation included a quality checkup using the Agilent BioAnalyzer, double-stranded cDNA synthesis, transcription/target labeling (in vitro), hybridization, control mixture preparation, and chip-hybridization. The analysis was run in full by the facility on the Rat Genome U34 Chip, with results analyzed using the Affymetrix Gene-Chip Operating System software. Data were provided on cd-rom and analyzed using the Affymetrix Gene-Chip Operating System software. The raw data was presented in spreadsheet form and listed thousands of components which were detected in the genechip analysis. Of greatest relevance to this study were the TOP mRNAs. The spreadsheet was cross-referenced for all ribosomal protein mRNAs and other TOP mRNAs. Western Immunoblotting: Methods used were described previously.15 Briefly, slices were removed from the recording chamber and were immediately frozen on microscopic slides using dry ice. The CA1 region was microdissected out using similar procedures as in the microarray experiment. The tissue was placed in microcentrifuge tubes and stored in -80oC for no more than two days. The tissue was homogenized using a motorized Potter-Elvehjem ©2007 Cornell Synapse | www.cusn.org

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homogenizer with Kontes Reusable Pellet Pestle in lysis buffer containing detergent and a cocktail of protease inhibitors. After incubation and centrifugation at 4oC, supernatants were transferred to pre-chilled microtubes. Protein determination was performed using the RC-DC Protein Assay Kit from Bio-Rad. Appropriate amounts of 4x NuPage LDS sample buffer, and Beta-mercaptoethanol were added to calculated amounts of the homogenates. The contents were then boiled for 5 minutes at 100oC. The samples, which ranged from 10-35µg per gel, were loaded in equal quantities into a 12% SDS-PAGE gel and run using standard electrophoresis methods. The proteins were transferred electrophoretically onto nitrocellulose membranes with 0.2 µm pores from Invitrogen. After transferring, the membranes were stained with Ponceau S Solution to ensure successful protein transfer. The membranes were then washed with 0.1% Tween 20 in Tris Buffered Saline (TBS-T), and blocked for 30 minutes with blocking buffer (5% nonfat dry milk in TBS-T) and then probed overnight with primary antibodies dissolved in blocking buffer. Antibodies used included: (first membrane) Beta-actin (mouse monoclonal; 1:10,000; Cell Signaling Technology) and GluR1 (rabbit polyclonal; 1:1,000; Chemicon International) and (second membrane) Beta-actin (1:10,000) and GluR2/3 (rabbit polyclonal; 1:5,000; Chemicon International). The membranes were washed three times for five minutes each in TBS-T. Horseradish peroxidase-conjugated anti-mouse and anti-rabbit IgG secondary antibodies were applied respectively (Pierce Biotechnology; 1:5,000 in blocking buffer) for 30 minutes. After washing, the membrane was visualized using chemiluminescence (ECL Western blotting analysis system; Amersham Biosciences) and developed on film. Prior experience with the antibodies revealed that neither interfered with the corresponding signals and thus were used concurrently. Densitometric analysis was performed on the signals and was further analyzed using Origin 6.1 software. Immunohistochemistry: Methods used were described previously15. Briefly, after electrophysiology, the sections were placed in ice-cold 4% paraformaldehyde/0.1% glutaraldehyde in PBS and fixed overnight. After washing, the slices were cut into 40µm sections using a Leica VT 1000S vibratome. These sections were blocked for two hours in 10% normal goat serum, 1% BSA, and 0.01% sodium azide in PBS. They were then incubated overnight with primary antibody (1:200) at 4oC. After washing in PBS, sections were incubated in 1% BSA with secondary antibodies (1:1000) complexed to either Alexa Fluor 568 or Alexa Fluor 488. After washing, the slices were mounted and imaged using a Zeiss LSM meta-510 confocal microscope. Electrophysiology: Methods of dissection as previously mentioned were used to obtain hippocampal slices. The slices were removed from the interface chamber and transferred to a submersion chamber preheated to 30-31oC. The stimulating electrode was placed in the CA3 region to activate synapses in the CA1 region where the recording electrode was placed. After delivering pulses at low frequency to establish a baseline, two 1s long trains of 100Hz stimulation, separated by 20 seconds, were delivered to induce LTP. This stimulation has been shown to induce protein synthesis-dependent LTP in similar conditions. Control slices were placed in the submersion chamber concurrently with experimental 16


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Figure 2: GluR2/3. 2A: In CA1 regions frozen 30 minutes after HFS, immunoreactivity for GluR2/3 was elevated relative to the controls. An average effect of 386.54% above control was viewed in the 30’ slices. The effects were mediated by the mTOR pathway as the increase is blocked by application of rapamycin. The amount of protein expressed in the rapamycin treated slice was slightly less than the control tissue (99.16% of control). 2B: Representative immunoblots. The dashed line represents the normalized control value. The results for the HFS experiment are significant to a P value of < .05.

slices and prepared by applying only test stimuli (sham-stimulation). Treatment of slices with the mTOR inhibitor rapamycin was performed at room temperature in a submersion maintenance chamber followed by transfer to the recording chamber. Results A microarray analysis of the stratum radiatum region of the hippocampus was carried out to provide a complete analysis of the mRNA composition in this dendritic region of rat hippocampus. Results showed that 49 possible TOP mRNAs were highly expressed within unstimulated stratum radiatum tissue. With the exception of ribosomal protein L39 and mitochondrial ribosomal protein L2, all were significant at p-Values of .01. The two exceptions were significant to p-Values of .05. Having identified numerous TOP mRNAs in stratum radiatum, the next step was to determine if these transcripts were translated in LTP. Of the four TOP mRNAs that were tested, three were verified to be present and sensitive to LTP inducing stimulation: poly(A)-binding protein (PABP), ribosomal protein S6 (rpS6), and elongation factor 2 (eEF2). Increases were induced by HFS and challenged by the application of rapamycin prior to stimulation (Figure 1). With evidence that select 5’TOPs were abundant and increased in a rapamycin sensitive manner, the focus was turned towards identifying which, if any, second wave proteins increased after HFS, and at what timepoint(s) the increases took place. Two subunits of the AMPA receptor, GluR1 and GluR2/3 were studied. Because the synthesis and insertion of these receptors play an integral role in LTP, we hypothesized that GluR1 and GluR2/3 would increase following HFS, and in a rapamycin-sensitive manner. Initial tests aimed at identifying which time point would provide the greatest increase in the two proteins. CA1 regions were ©2007 Cornell Synapse | www.cusn.org

used to compare normal and stimulated sections after being frozen 30 and 60 minutes after stimulation. Western blots were performed to determine the levels of GluR1 and GluR2/3 in this tissue. The results showed a 396% increase in GluR2/3 expression 30 minutes after HFS, but no effect on GluR1. The slices frozen after 60 minutes followed the same general trend although the GluR2/3 expression increased 138%. The results were representative of six experiments (three for 30’ slices; three for 60’ slices). The increase of GluR2/3 expression in the 30’ experiments were 287.7% greater than the increase of GluR2/3 expression in the 60’ experiments, and thus chosen for further experimentation. Further experiments confirmed that the increase in GluR2/3 30’ after stimulation was in fact significant between control and HFS tissue as LTP inducing stimulation caused a 396 percent increase in GluR2/3. A later experiment utilized three conditions (Sham stimulation, HFS, HFS treated with rapamycin) to test whether the increase was dependent on the activation of the mTOR pathway. Results displayed an increase of GluR2/3 in HFS tissue, and this effect was blocked by rapamycin (Figure 2) Discussion The experiments provide mounting evidence supporting the “two wave” mechanism of protein synthesis to provide for LTP consolidation within synapses in the hippocampus (Figure 3). The initial experiments showed that the same pathway used in the cell cycle to regulate proliferation (the mTOR pathway) was activated in the dendrites by protein-synthesis dependent LTP stimulation.5,15 The primary consideration in choosing TOP mRNA encoded proteins for subsequent western blotting and immunohistochemistry analysis was the abundance of each detected TOP mRNA. The results were sorted based on signal values and then screened based on biological process description until the three mRNAs [ribosomal protein S6 (rpS6), poly(A)-binding protein (PABP), and elongation factor 2 (eEF2)] remained. They each functioned to increase protein biosynthesis and ribosome biogenesis, mRNA polyadenylation, and regulate translational initiation respectively. The rapamycin-sensitive translation of multiple TOP 5’TOP-encoded translational machinery

Growth/Plasticityrelated proteins

Figure 3: Proposed two-wave model of synaptic plasticity: compared with the process of cell growth. The growing cell, when provided with serum, increases TOP mRNA translation which creates the machinery for synthesis of additional proteins. The proteins aid in cell growth. (Bruce et al., 2002) The neuronal dendrite, when stimulated, increases TOP mRNA translation which creates the machinery for synthesis of plasticity related proteins. The proteins aid in the maintenance of LTP/LTD. 17


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mRNAs indicates that protein synthesis dependent LTP relies on the up-regulation of TOP mRNA encoded proteins to create the necessary components to support increased signaling efficiency. Of the other TOP mRNAs, a majority aided in protein biosynthesis (19/49 mRNAs), both protein biosynthesis and ribosome biogenesis (17/49 mRNAs) and other processes, including translational and transcriptional regulation, that are necessary for the creation of proteins (13/49 mRNAs). A limiting factor for our approach, however, is that the stratum radiatum region though enriched in dendrites also contains some other cell types and axons, which could have contributed to certain levels TOP mRNAs.2 To confirm the dendritic localization for the mRNAs that we detected, in situ hybridization would have to be utilized. Synaptic protein synthesis may be able to create the molecules necessary for the increased transmission. However, increased efficiency requires more than just one protein and is more than likely a very complex interaction between many molecules. One major part to the process may be the insertion of the AMPA receptor, which has a significant central role in initiating Ca2+ influx, into the post-synaptic membrane.5 Since mRNAs for the GluR1 and GluR2/3 were shown to be locally synthesized in the dendrites10, the experiments display a rapamycin-sensitive regulation of the AMPAR subunit GluR2/3. The sensitivity to rapamycin establishes the involvement of the mTOR pathway, which was previously noted to regulate cell cycle progression through the increase of the translational machinery. This agrees with the proposed model of AMPAR insertion into the membrane.5 The increase in GluR2/3 provides the first confirmation of the “second wave” proteins which increase in response to stimulation to support increased efficiency. The introduction of second wave proteins provides another cornerstone to understanding the exact molecular mechanism involved in LTP, which may be important in understanding disorders relating to memory function, such as Alzheimer’s disease. This information may be able to aid the development of treatments or drugs that would target specific molecular disorders elucidated by further research. At this point, our understanding of Alzheimer’s disease is too basic to create any such treatment. Further experimentation will also be required to identify other second wave proteins that play a significant role in LTP. The increase of GluR2/3 in response to LTP inducing stimulation is probably only a small part of a complicated pathway. Furthermore, the depolarization of the post synaptic area through the insertion of AMPA receptors is probably only one of the many other protein interactions that are likely to drive LTP expression.

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5. Blitzer R, Iyengar R, Landau E. Postsynaptic signaling networks: Cellular cogwheels underlying Long-Term Potentiation. Biol Psychiatry. 57(2):113-9 (2005). 6. Fingar D, Blenis J. Target of rapamycin (TOR): an integrator of nutrient and growth factor signals and coordinator of cell growth and cell cycle progression. Oncogene 23(18):3151-71 (2004). 7. Giovannini M, Blitzer R, Wong T, Asoma K, Tsokas P, Morrison J, Iyengar R, Landau E. Mitogen-Activated protein kinase regulated early phosphorylation and delayed expression of Ca2+/ Calmodulin-Dependent Kinase II in Long-Term Potentiation. J Neurosci. 21(18):7053-62 (2001). 8. Hay N, Sonenberg N. Upstream and downstream of mTOR. Genes Dev. 18(16):1926-45 (2004). 9. Hayashi Y, Shi S, Esteban J, Piccini A, Ponder J, Malinow R. Driving AMPA receptors into synapses by LTP and CamKII: Requirement for GluR1 and PSZ Domain Interaction. Science 287(5461):2262-7 (2000). 10. Ju W, Morishita W, Tsui J, Gaietta G, Deerinck T, Adams S, Garner C, Tsien R, Ellisman M, Malenka R. Activity-dependent regulation of dendritic synthesis and trafficking of AMPA receptors. Nature Neuroscience 7(3):244-53 (2004). 11. Li Y, Corradetti M, Inoki K, Guan K. TSC2: filling the GAP in the mTOR signaling pathway. Trends Biochem Sci. 29(1):32-8 (2004). 12. Malenka R, Nicoll R. Long-term potentiation-A decade of progress? Science 285(5435):1870-4 (1999). 13. Malinow R, Mainen Z, Hayashi Y. LTP mechanisms: from silence to four-lane traffic. Curr Opin Neurobiol. 10(3):352-7 (2000). 14. Meng Y, Zhang Y, Jia Z. Synaptic transmission and plasticity in the absence of AMPA Glutamate Receptor GluR2 and GluR3. Neuron 39(1):163-76 (2003). 15. Meyuhas O. Synthesis of the translational apparatus is regulated at the translational level. Eur J Biochem 267(21):6321-30 (2000). 16. Muddashetty R, Khanam T, Kondrashov A, Bundman M, Iacoangeli A, Kremerskothen J, Duning K, Barnekow A, Huttenhofer A, Tiedge H, Brosius J. Poly(A)-binding Protein is associated with neuronal BC1 and BC200 ribonucleoprotein particles. Journal of Molecular Biology 321:433-445 (2002). 17. Tsokas P, Grace E, Chan P, Ma T, Sealfon S, Iyengar R, Landau E, Blitzer R. Local protein synthesis mediates a rapid increase in dendritic Elongation Factor 1A after induction of Late Long-Term Potentiation. Journal of Neuroscience 25(24):5833-5843 (2005).

References 1. Abbott C, Proud C. Translation factors: in sickness and in health. Trends Biochem Sci. 29(1):25-31 (2004). 2. Alberts, Bruce, et al. Molecular Biology of The Cell. New York: Garland Publishing, 2002. 3. Blitzer R. Genetics of childhood disorders: LIV. Learning and Memory, part 7: Maintenance of Long-Term Potentiation. J Am Acad Child Adolesc Psychiatry 42(9):1131-3 (2003). 4. Blitzer R, Lombroso P. Genetics of childhood disorders: LIII. Learning and Memory, part 6: Induction of Long-Term Potentiation.J Am Acad Child Adolesc Psychiatry 42(8):998-1001 (2003). ©2007 Cornell Synapse | www.cusn.org

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ARTICLE Time perception and familiar experience: The effect of face recognition on judged duration Frank A. Fetterolf, Ulric Neiser, Bruce P. Halpern

Cornell University, Department of Neurobiology and Behavior, Ithaca, NY 14853

This experiment sought to investigate the time perception of objects and faces. More specifically, the variable of familiarity among faces was crucial to the goal of this experiment. Nine university undergraduates participated in a relative time estimation test where their task was to determine the longer duration of two visually presented stimuli. The effects of image type and familiarity of the images was measured by comparing accuracy in distinguishing duration. Results demonstrated that the estimation of time using disks is more accurate than faces confirming the notion that our ability to keep track of time is strongly related to attentional resources. It was also found that in trials where plain disks are used, the initial presentation time affects performance. However, no significant evidence was found to account for any effects of familiarity on time perception. The social nature of human beings requires two things, a high level of sensitivity toward the facial features of our own species and the ability to keep track of time. Many models have been hypothesized to describe the “special” nature of faces and how we perceive them. Evidence for both a feature-by-feature and template-matching method (Tanaka and Fara, 1993) provides that faces are dynamic sets of information, processed much differently than other objects. Additionally, it has been found that familiar and unfamiliar faces are processed differently (Bruce and Young, 1986). Using data from diverse sources, a functional modal was proposed of face perception based on distinct types of information derived from the face. Bruce and Young put forth that recognition of familiar faces results when these faces include features that could be matched with products of structural coding of those previously seen (Fig 1.). Brain scans have confirmed this hypothesis (Dubois et al., 1998). Researchers sought to find the effects of familiarity on the visual processing of faces. Activation of different brain structures revealed that processing of known and unknown faces is indeed not the same. Most research that has united the concepts of time perception and familiarity has dealt with auditory perception. There is evidence that perceived time is shorter for familiar auditory signals than it is for unfamiliar signals. This suggests that perceived time is not absolute for auditory signals but maybe influenced by the content of the perceived signal (Kowal, 1984). Kreitler and Kreitler (1980) suggested that the cognitive orientation of a listener during a familiar melody does not satisfy their drive to explore and generates no expectations. Complex tasks on the other hand, lead to cognitive overload. So far our understanding of time perception presents us with ©2007 Cornell Synapse | www.cusn.org

two explanations for our ability to keep track of temporal experiences (McCone, 1997). Perception is either governed by a specialized clock mechanism inside the brain or a by-product of more general cognitive faculties, namely memory and visual awareness. Much work done on the perception of time has supported the latter theory attributing any disparity in estimates of duration to attention. More specifically, cortically mediated attention has had the greatest affect on our temporal estimation ability (Chaston & Kingston, 2004). This study used two search tasks of varying levels of difficulty. Afterward, participants were asked to judge how long these tasks took them to complete. They consistently overestimated duration directly proportional to the task’s complexity.

OR

View-centered descriptions

Expression Analysis

Structural Encoding Facial Speech Analysis

Directed Visual Processing

ExpressionIndependent descriptions

Face Recognition Units

Person Identity Nodes

Cognitive System

Name Generation

Figure 1: Bruce & Young’s (1986) model of face processing. 19


The subjective expansion of time during experiences of brief traumatic duration also has been attributed to the influence of the amount of perceptual information processed (TSE et. al., 2004). In this experiment an expanding object or oddball’s duration was subjectively measured to the point that it would correspond with a stationary object. It was found that, to the participants, the low probability stimulus seemed to last longer even when both oddball and stationary object were presented for the same duration. Studies on the early stages of visual processing have found that judgments of apparent duration are related to variations in familiarity (Avant et. al., 1975). Using tachistrophic flashes of letters, researchers indexed the influence of stimulus familiarity upon judged durations (each 30 ms) when the stimulus area was controlled and the ease of recognition was manipulated by noise mask vs. no-mask conditions. The mask condition was used to deter stimulus recognition. The purpose of the present study was to examine participants’ ability to correctly perceive durations of disks and faces (both familiar and unfamiliar). The current experiment, modeled slightly after Avant et. al (1975) used relative duration estimation with visual stimuli of varying durations separated by a blank screen. After the presentation, participants were asked whether or not the second stimulus lasted longer or shorter than the first. In the trials containing faces, familiarity was either positive (famous face) or negative (novel face). There were two major differences between the methods used from the Avant (1975) study. The first being that there were no varying degrees of familiarity and that duration of the faces presented were not the same. In this experiment it was predicted that participants’ ability to estimate time would be worse for faces than disks. The reason being that a greater amount of information contained in the stimulus needed to be processed, thus supporting a model of time estimation which depends on cognitive faculties. This diverted attention will cause participants to underestimate duration length. Results of visual perception are hypothesized to reflect those found by Kowal (1984) in auditory perception. Moreover, within face trials, familiar faces should be perceived shorter than unfamiliar faces.

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A

B

C

Figure 2: Examples of visual stimuli used in the relative duration task. 2A: Disks. 2B: Unfamiliar faces. 2C: Familiar faces.

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Disks Trial Type

Faces

Figure 3: Mean accuracy scores for disk and face trials. Answers correspond to the participant’s ability to discern the longer duration of a set of disks or faces.

Methods

270 by 270 pixels, were used as stimuli for the first block and various faces for the second block. Combinations of faces included twenty familiar (famous) faces and fifty-five unfamiliar faces each about 184 by 225 pixels in size (Fig. 2b-c). Face trials consisted of three types: unfamiliar then familiar (U-F), familiar then unfamiliar (F-U), and unfamiliar then unfamiliar (U-U). Data was measured by computing the accuracy of differentiating between durations (correct/total exposures).

Participants: The participants for this study were nine undergraduate students from Cornell University. They spoke English as their first language and did not have a history of developmental disorder, learning disability, or neurological disorder. They were proficient computer users. Apparatus Cornell University’s Cognitive Studies lab was used to test students via a computer running SuperLab experimental software. The monitor’s refresh rate was set to seventy-five hertz. The keyboard was used as the interface between the participant and program. Keys ‘L’, ‘S’, and the space bar were used for the corresponding responses of ‘longer’, ‘shorter’, and screen advances, respectively. Design This experiment consisted of two blocks both of a within subject-design. One trial consisted of two visually presented stimuli differing in certain durations. Durations were 27 ms, 40 ms, 53 ms, 67 ms, 80 ms, 93 ms, 106 ms, and 120 ms. For each trial, the two stimuli differed in presentation time by either 26 ms, 40 ms, or 53 ms. Thirty trials consisting of equal long-short and short-long conditions were administered in both blocks. Black disks (Fig. 2a),

Experimental design: The design and procedure were suggested by Ornstein’s (1969) contention that, to investigate the experience of duration, the appropriate measure should index relative experienced duration of two intervals. Each trial began with the participant seated comfortably, approximately fifty-seven centimeters away from the computer screen. Before each trial, a prompt signaled the participant to anticipate the next set of stimuli. Two thousand milliseconds later, two visually presented stimuli appeared in the center of the screen, separated by a blank screen lasting one thousand milliseconds. Following the second of the two stimuli was another blank screen, this time lasting only seven hundred milliseconds. The participant was then prompted with a question regarding the nature of the two observed durations. They were required to indicate whether or not the second stimulus was presented longer or shorter than the first by pressing ‘L’ for longer or ‘S’ for shorter. After the first block participants took a short break lasting about two minutes. Participants then immediately proceeded to the second block of the experiment, following exactly the same directions as in the first block.

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Results Participant’s correct responses for the particular relative duration time differences (26 ms, 40 ms, and 53 ms) between both disks and faces were found to be insignificant. Trial order (long vs. short and short vs. long) was also found insignificant. For these reasons data was pooled across these variable factors in order to provide optimal power. Participant’s responses in regard to the time duration estimation task were affected by stimulus type. Accuracy for disk trials was a mean 5.3 percent higher than in the face trials X2(1) = 5.3, p < 0.02 (Fig. 4). However, the variable of familiarity was found not to be significant within the face trials. When disks were used as the visual stimulus, participant’s estimations were affected by the initial presentation time X2(7) = 16.4, p < 0.02 (Fig. 3). On face trial initial presentation times did not significantly affect responses. Discussion

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The goal of the present study was to determine whether the familiarity of an image affects a person’s perception of time. The use of this experiment failed to yield any significant results that could provide evidence in support of the initial hypotheses that the processing of a familiar event, in this a case face, causes one to underestimate the passage of time. The type of processing involved in the perception of familiar and unfamiliar faces may be different but the results provide no conclusive evidence as to how or to what extent this may affect time perception. However, image type (disk vs. face) caused disparity is participant’s accuracy of time estimation. When presented with faces, the ability to discern relative duration differences was worse when compared to disks. More robust results could have been obtained if conditions would have allowed for a greater internal and external validity. First, the degree of familiarity among the images may not have been constant with all viewers. Faces termed as familiar were not controlled in respect to quality the same way as the unfamiliar faces (i.e. celebrities usually wear makeup and have more dynamic hairstyles). One way to improve this inconsistency would be to repeatedly expose participants to novel faces, so as to control for the condition of familiarity. Second, size of the stimuli may have been an issue. Pictures may have not been able to effectively represent the extent of the complexity of a face at such a small size. To

27

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53 67 80 93 106 Initial Presentation Time (ms)

120

Figure 4: Mean accuracy scores for varying initial presentation times. Answers correspond to the participant’s ability discern the longer duration of two disks.

©2007 Cornell Synapse | www.cusn.org

improve ecological validity, faces could be resized to compare to what participants would encounter in everyday communal contact. Third, time differences were most likely too distinct to observe any significant effects among them. People were more sensitive than previously thought before experimenting. Lastly, faces may have been a poor choice of representing familiarity. It might be the case that a cross-modal sensory experience may be stronger than a strictly visual identification of familiarity. An experimental schema that can control for these possible problems may do a better job of isolating the variables of time and familiarity. Significant results of this experiment uphold the model of time perception that predicts that time underestimation should be greater when attention is engaged. Face processing must divert enough attention to affect our cognitive ability to time events. It has been thought that attention is a resource limited system correlated with mental effort (Kahneman, 1973). Evidence provided by this experiment supports a view that some sort of attentional engagement in face processing may constrain an individual’s cognitive ability to estimate time. An important area for future research studies would be to study the perception of longer durations of time involving a more controlled familiar environment. The task involved in this experiment used intervals so brief in duration that they may have been too easy to distinguish. Additionally, I would like to test time perception in an experience that does not involve as much attentional processing as face discrimination because as this study indicated, certain general effects may prevent or outweigh more subtle ones from being observed. References 1. Avant, L. L., Lyman, P. J., & Antes, J. R. (1975). Effects of stimulus familiarity upon judged visual duration. Perception & Psychophysics, 17, 253-262. 2. Bruce, V., & Young, A. W. (1986). Understanding face recognition. British Journal of Psychology, 77, 305-327. 3. Chaston, A. & Kingstone, A. (2004). Time estimation: The effect of cortically mediated attention. Brain and Cognition, 55(2), 286-289. 4. McCrone, John (1997). When a Second Lasts Forever (how the brain measures the passing of time), New Scientist, 156, 52-56. 5. Kahneman, A. (1973). Attention and Effort. Englewood Cliffs, New Jersey: Prentice-Hall. 6. Dubois, S., Rossion, B., Schiltz, C., Bodart, J. M., Michel, C., Bruyer, R., & Crommelinck, M. (1999). Effect of familiarity on the processing of human faces. Neuroimage, 9, 278-289. 7. Kowal, K. (1984). Familiar melodies seem shorter, not longer, when played backwards. Annals of the New York Academy of Sciences, 423, 610-611. 8. Kreitler, H., & Kreitler, S. (1980). Psychologie der Kunst. Stuttgart: Kohlhammer. 9. Ornstein, R.E. (1969). On the Experience of Time, Harmondsworth: Penguin. Tse, P., Intriligator, J., Rivest, J., & Cavanagh, P. (2004). Attention and the subjective expansion of time. Perception & Psychophysics, 66, 1171-1189. 21


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ARTICLE Courtship signals as advertisers of motor capabilities: A link between flight maneuverability and song repertoire size in birds Kristen Aliano, H. Kern Reeve

Cornell University, Department of Neurobiology and Behavior, Ithaca, NY 14853

This paper presents a new evolutionary explanation for the function and rhythmic nature of courtship signals. We propose that nervous systems are optimal function approximators; that is, they add different combinations of rhythmic neural outputs to efficiently build complex and dynamic motor responses. This idea is motivated by a recent proof that mathematical functions are most economically approximated by additively combining wavelet outputs from neurons in a feedfoward neural network. In accordance with our “one-at-a-time system test” hypothesis, courtship signal, function to reveal, one at a time, the underlying rhythmic neural outputs from which complex motor outputs are fabricated. Thus, the signals reveal the overall motor competence of the signaler. Our hypothesis explains in an evolutionary sense why organisms with rhythmic courtship displays tend to exhibit complex locomotive capabilities in three dimensions (i.e. these rhythmic-signaling organisms, such as birds, many orthopterans, and cetaceans, have the ability to fly, jump, or swim, respectively). The hypothesis also explains why organisms with the largest signal repertoires tend to be the ones with complex locomotor outputs. After conducting a comparative analysis of the association between flight maneuverability, as measured by wing aspect ratios, and song repertoire size for a variety of passerine and non-passerine species, I found that our hypothesis was upheld. The analysis indicated that avian species with greater flight maneuverability (i.e. those species with lower aspect ratio, or more elliptical, wings) had greater song repertoire sizes. Introduction At the present time, there is no general theory to explain the precise forms of animal signaling, aside from the known fact that all bioacoustic signals are constrained by the need for efficient propagation and clarity; studies of animal signaling are rapidly increasing and such an all-encompassing explanation for such exhibitions is needed (Guilford and Dawkins 1991). In this paper, I provide a novel hypothesis regarding the function of courtship signals based on the premise that the function of courtship signals is to allow an individual to demonstrate its motor capabilities and thus the quality of its genes to a prospective mate. Our hypothesis is based upon Andersson’s (1994) observation that courtship ©2007 Cornell Synapse | www.cusn.org

signals are often both visual and auditory and that most organisms demonstrating elaborate visual and acoustical courtship signals are usually able to perform intricate and complex locomotion behaviors in three dimensions, such as flying (such as in avian species, bats, and flies), jumping (such as in frogs, toads, crickets, grasshoppers, and jumping spiders), or swimming (as in cetaceans and fish). Anurans are an archetype of this idea; the most complex vocalizations occur in anurans that not only must move along the ground, but also climb trees (Narins 2000). Additionally, spiders that are highly mobile predators also tend to have elaborate courtship signals. In these aforementioned organisms, both survival and reproduction are dependent upon motor versatility, or the ability to quickly change movement patterns. We believe that there is a deep mathematical connection between an organism’s motor abilities and the form of its courtship signaling. This connection is provided by Kreinovich et al. (1994), who recently proved that single-variable mathematical functions are most efficiently approximated by a three-layer, feedforward neural network in which the component neurons produce simple wavelet functions. Since we view nervous systems as flexible output generators, we believe that Kreinovich et al.’s (1994) proof gives a rule for the optimal organization of nervous systems. The mathematical functions generated by nervous systems are the motor outputs of motor circuits. The function is an optimal motor dynamic output F(t) (where t = time) and can be approximated by the actual output of the motor network. The different outputs produced are stimulus-dependent, meaning that the different signaling contexts can lead to different output functions. The proof provided by Kreinovich et al. (1994) is that the vast array of motor outputs, each of which is an “approximator” of its corresponding optimal motor output, can be generated by adding together the outputs from neurons that individually produce wavelets, a type of wave form (Figure 1). Wavelets are rhythmic functions that have a finite domain (i.e. definite endpoints) and a mean value of zero. Networks designed in such a fashion generate multiple motor outputs in a “cost-effective” manner; this is because such networks minimize the amount of information that must be stored to specify any function (i.e. the motor output in this case) with a given level of precision (Kreinovich et al 1994). (Note that precision is measured by minimizing the quantity ∫ab |F(t)-F*(t)|2 dt 22


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where F*(t) is the optimal output function, F(t) is the actual output function, and a and b are the beginning and end times of the response, respectively). If the weights and thresholds of the communicating neurons are changed to reflect the stimulus context (i.e. are context-dependent), a single network can efficiently generate multiple, near-optimal outputs. Our “optimal function approximator” view of the nervous system may explain why there are a great deal of rhythmically bursting neural elements in the nervous system; such rhythmic neural components produce wavelet outputs from which extremely complex motor outputs can be constructed. Neural rhythmicity is central to motor activity and perception, as growing evidence from neurobiological research indicates (Singer 1993, Farmer 1998, Bevan et al. 2002, Farmer 2002). In the vertebrate brainstem, rhythm-generating circuits combine their outputs to create or control a variety of behaviors, including locomotion and vocalization (Nishikawa et al. 1992 and Dubbledam 1998). The weighted outputs of these premotor circuits are malleable and appear to direct the operation of motor neurons and lower-level pattern generators (Steeves et al 1987, Nishikawa et al. (1992), Dubbledam 1998). Our function approximator model of the nervous system is in accordance with the increasing amounts of evidence that brain rhythms and the organization of the neural networks from which they come (such as the basal ganglia: Brown and Marsden 1998) are an essential part of motor coordination, perception coordination, and the conscious experience as a whole (Farmer 1998 and Farmer 2002). As an example, consider the pathologically rhythmic motor behavior of Parkinson’s disease, as well as other neurological ailments, may reflect a pathological unveiling of the fundamental wavelet basis functions from which normal motor outputs are constructed. Patients who have damaged their basal ganglia, and thus their neural network organization, are unable to integrate all of their rhythmic basis functions; as such, some of the wavelet functions get expressed individually and are manifested in characteristic symptoms of the disease, such as a shaking hand. We propose that an organism may closely approximate complex optimal motor output functions by adding together the neural effects of rhythmically bursting individual or populations of neurons in a motor-controlling circuit (Figure 1). Therefore, a signaling organism’s command of it underlying, simple rhythms may reflect its ability to produce many different higher-level motor behaviors. During courtship, a signal-receiving individual may want a prospective mate to advertise the precision of its underlying rhythms before accepting the prospective mating partner. The individual’s command of its underlying rhythms is an indication of the individual’s genetic and phenotypic quality. Thus, receivers may not only want their mates to demonstrate rhythmic signals, but they may also want their potential mating partners to produce signals that specific and predictable types of rhythmicity appropriate to the kinds of motor capabilities being signaled. We hypothesize that courtship signals have evolved to demonstrate the extent to which the signaler is an able function approximator and is, as a consequence, capable of performing a variety of different motor behaviors. Signaling of motor network competence: an economic model for the evolution of courtship signals The function approximator model predicts that if an organism is selected to evaluate a potential mate’s ability to survive and reproduce, an organism may demand that the prospective mate sig©2007 Cornell Synapse | www.cusn.org

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time

time

Figure 1: The All-At-Once System Test Signal. Shown is a three-layer, feedforward neural network than efficiently approximates multiple motor outputs. In the nervous system, each “neuron” may actually correspond to populations of biological neurons. In the AST, all hidden layer neurons are turned on, each producing a wavelet output, as illustrated by the bottom graph on the left. These rhythmic outputs are added together with variable weights to produce a complex motor, e.g., locomotor output. The graph on the bottom right is a composite of the wavelet functions; it exemplifies the behavioral output as a function of time. An individual performs the AST when a prospective mate demands direct demonstrations of different motor behaviors. This form of courtship signaling is a high-cost behavior.

time

time

Figure 2: The One-At-A-Time System Test Signal. In the OST, only one hidden layer neuron (or subsets of these neurons)—indicated by the arrow--is turned on at a time. This hidden layer neuron produces a wavelet. Thus, the organism is able to demonstrate its command of its underlying rhythmic outputs from which more complex outputs and behaviors are synthesized. The signal is rhythmic in nature.

time

time

Figure 3: OST in a low-cost motor pathway. An organism can either perform highcost outputs, such as performing flight maneuvers, or relatively low-cost outputs, such as singing a song, as a means of courtship signaling. In this diagram, the organism only has one hidden layer neuron activated, as in OST. This one rhythmic output via a low-cost motor pathway demonstrates its neural capabilities while minimizing energetic costs. 23


nal its ability to generate the rhythmic outputs, one at a time, from which its various optimal motor outputs are to be synthesized. We refer to this situation as the “one-at-a-time system test signal”, abbreviated OST (Figure 2). Thus, our idea comprises a unique hypothesis for why courtship signals involve sequences of simple rhythms. In some contexts, the receiver may want the prospective mate to demonstrate all of the complex motor behaviors, such as locomotion. We call this the “all-at-once system test signal”, abbreviated AST (Figure 1). In certain situations, OST will be more beneficial than AST. When is this the case? Let L equal the number of all of the behaviors critical for survival and reproduction that must be performed by the advertiser in the AST and let C equal the average cost of performing each of these behaviors. In contrast, the OST signaler has to signal n different network components at an average cost of c per signaling event. In OST, the signaling is through a lowercost motor system, i.e. the organism might produce a vocalization instead of an energetically costly locomotion behavior such as limb movements; in the OST C > c. An organism discriminates between the AST and OST and performs the low-cost behavior by only turning on only the low-cost neural circuit, as opposed to producing the high-cost, complex motor output (Figure 3). L increases as the motor versatility of a species increases; as such, the number of network components n would increase as well (dn/dL > 0). A need for greater motor versatility should select for greater ability to approximate many outputs. By expanding the size of the neural network, approximation precision can be improved. Therefore, we can conclude that AST will be favored over OST if, LC > n(L) c where n is written as an increasing function of L. We can extract three main predictions from this model. Prediction 1: As L, the required motor versatility of a species, increases, OST and sequential rhythmic signaling in a low-cost motor pathway is more likely to be favored over AST and a full behavioral performance. This idea follows from inequality (1), provided that dn/dL < C/c. This second inequality is true since C/c >1 and rapid convergence of an approximated function to the optimal function as network size increases implies that dn/dL < <1. Prediction 2: OST and sequential signaling in a low-cost pathway is favored over AST and a complete motor demonstration as the required motor behaviors become more costly to perform (i.e. as C increases). Locomotion behaviors are extremely costly for an organism to perform, so we project that OST would function as a means of indicating locomotion versatility. Prediction 3: Under the AST system, the number of rhythmically distinct signals in a sequence, such as signal repertoire size, is expected to increase as L increases. This statement is upheld because dn/dL > 0 and as the number of network components increases, so does the number of separate rhythmic signals of network competence. We tested these three aforementioned predictions by analyzing the relationship between flight maneuverability and song repertoire size in birds. Flight maneuverability was measured indirectly as wing aspect ratio, since it is well-known on engineering grounds that flight maneuverability is promoted by lower aspect ratio wings, i.e. wings that are elliptical as opposed to long and narrow (e.g., Warrick 1998). Low aspect ratio wings tend to be

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shorter and stubbier than higher aspect ratio wings and thus the former have lower inertia (Warrick 1998). Methods Comparative analysis of association between flight maneuverability and song repertoire size: We conducted an empirical, comparative analysis of the association between flight maneuverability, as measured by wing aspect ratio, and song repertoire size. Firstly, a database containing aspect ratios (AR) for almost 700 species was compiled. Aspect ratios were obtained by two means. The first method involved scouring the literature for reviews or compilations of aspect ratios; a particularly useful paper was Hartman (1961). Aspect ratio was measured by Hartman (1961) as the length of one wing divided by width (mean chord length) of one wing. The second method of gathering aspect ratios involved analysis of wing photographs from the University of Puget Sound’s Slater Museum of Natural History’s Wing Image Collection. The ImageJ computer program was utilized to find the actual area of the wings using tag size standards present in the photographs. Then, aspect ratios were calculated as wingspan squared divided by total wing area, using published wingspans and the computermeasured wing areas. A second database containing information on song repertoire sizes was blindly compiled through literature research. We used published reports of the number of song types per male (the repertoire size) without defining strict criteria for what types of vocalizations can be considered song or how song should be broken up into song types. We did this because it is extremely difficult to define what a song or a song type is, and there is currently much debate in the literature about the proper formal definition of song. Despite the lack of agreement on the precise definition of song or song type, we found considerable (at least rough) agreement among different investigators in estimates of song repertoire size for a given species. Afterward, the aspect ratio and song repertoire size databases were merged together; species that were in one database, but not

Wing Aspect Ratio

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Non-passerines

Passerines

Figure 4: Wing aspect ratios in passerine versus non-passerine birds. The wing aspect ratio for non-passerines is 18.45, with a standard deviation of 10.1. The wing aspect ratio for paserines is 2.0, with a standard deviation of 0.4.

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the other were eliminated so that the final database contained species for which both aspect ratios and song repertoire sizes were known. Existing avian phylogenic reconstructions based on DNA sequence data were then examined to identify the maximum number of independent clades into which the species in our merged database could be classified such that there were at least two species per clade. Within each clade, a coefficient of correlation between aspect ratio and song repertoire size was calculated using the Spearman rank correlation coefficient test. Each such correlation, one per clade, counted as one data point in our overall analysis. Since the coevolution of aspect ratio and song repertoire size within a clade must have occurred independently in independent clades, we then performed a Wilcoxon one-sample test on the sample of independent correlation coefficients, testing the hypothesis that these correlations were drawn from a distribution with a mean different from zero. This test for an association between wing aspect ratio and song repertoire size thus controls for phylogenetic non-independence. Results Comparison of songbird and non-songbird wing aspect ratios: As seen in Figure 4, wing aspect ratios of songbirds tend to be markedly smaller than that for non-songbirds. The average passerine aspect ratio for those species included in this study is 1.77 (standard deviation= 0.361). The average aspect ratio for nonpasserine species is 18.45 (standard deviation = 10.06). Phylogenetic non-independence within these two groups prevents a statistical test here, but many other authors have noted the relatively great flight maneuverability of passerine birds, likely reflecting an adaptation to forested habitats (Welty 1979). Correlation between song repertoire size and wing aspect ratio measured as winglength divided by mean chord length: In the first analysis, we rank-correlated Hartman’s (1961) aspect ratio measure with repertoire size in each of thirteen independent avian clades. We grouped the species into clades for which existing data suggest probable phylogenetic independence from each other. The relative abundance of repertoire size data for Parulid species allowed us to break this rather unresolved (and possibly paraphyletic) family into three genera-level clades (Dendroica, Oporomis, Vermivora). Nuclear DNA sequence data suggest that Certhia, Sitta, and Troglodytes belong to a single clade (Barker et al., 2002). Thraupidae and Cardenalidae were grouped into a single clade, also as indicated by DNA sequence data (Bledsoe 1988; Barker et al. 2002, 2004; Yuri and Mindell 2002). The mean Spearman correlation coefficient across these clades was -0.540 (S.E. = 0.176), which is significantly less than 0 (P = 0.022, Wilcoxon signed-ranks, one sample test). Correlation between song repertoire size and wing aspect ratio measured as wingspan squared divided by total wing area: In the second analysis, we rank-correlated the second aspect ratio measure with repertoire size in each of nine independent avian clades. We grouped the species into clades for which existing data suggest probable phylogenetic independence from each other. Nuclear DNA sequence data suggest that Sturnidae and Mimidae are ©2007 Cornell Synapse | www.cusn.org

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sister groups (Cibois and Cacraft, 2004), and we merge them into a single clade to generate an additional independent sample unit. The mean Spearman correlation coefficient across these clades was -0.375 (S.E. = 0.106), which is significantly less than 0 (P = 0.016, Wilcoxon signed-ranks, one sample test). Discussion In accordance with prediction 1, flight maneuverability in birds, as measured inversely by wing aspect ratio, is higher in songbirds (Passeriformes) than in non-songbirds, which typically lack elaborate courtship songs (although they may have simple courtship calls) (Fig. 4). Indeed, the elliptical wings of most songbirds are designed for high flight maneuverability at low speeds, probably an adaptation to forest habitats (Welty 1979), as birds living in such environments would need to change their trajectories very quickly to fly around and above trees. This suggests that complex song and locomotor versatility evolved in concert in birds. (In general, most non-passerine birds do not live in heavily wooded habitats; instead, they tend to live more frequently in open areas. Their higher aspect ratio wings, which are long and narrow, enable them to fly efficiently over large distances in a relatively strait trajectory (Welty, 1979). In support of predictions 2 and 3, song repertoire size significantly negatively correlates with wing aspect ratio and thus positively with flight maneuverability. Thus, the data strongly support the economic model of courtship song viewed as a “one-at-a-time system test” (OST) signal. The OST model may explain not only why courtship signals are rhythmic and why they tend to be produced by animals with pronounced locomotor agility, but also why there is frequently a morphological or neuromuscular tie between the mechanisms of signal production and the generation of elaborate movements, e.g., as exhibited in the cricket by the overlap in the muscles involved in flight, stridulation (sound production), and leg movements (Wilson 1962; Huber 1963; Ramirez & Pearson 1988; Wang & Robertson 1989). The origin of rhythmic circuits in vertebrates provides intriguing evidence supporting a high-order neural connection between the generation of acoustical signals and the generation of complex motor patterns. Bass and Baker (1997) hypothesize that the pattern generating circuits for vocal, electromotor, and extraocular systems all have the same genetic origin in specified parts of the vertebrate embryonic hindbrain. The pontine nuclei that function in the control of skilled movements (Schwarz & Their 1999) in vertebrates are closely anatomically associated with song-producing neural systems (Bottjer et al. 2000). In fact, recent evidence has indicated that the anterior forebrain pathway, which controls the avian song system and is crucial to vocal plasticity and song learning, is a songbird basal ganglia pathway (Perkel 2004). This conjecture further supports our OST model. Vertebrate brain organization is highly conserved; there are connectional, molecular, and developmental similarities among distantly related vertebrates (Perkel 2004). Intriguingly, in songbirds, the forebrain systems involved in song learning and generating song rhythms are either homologous or functionally analogous to the basal ganglia that are known to organize locomotion in mammals (Konishi 1994, Carr 2000). An intriguing speculation is 25


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that the basal ganglia degeneration leading to Parkinson’s disease in effect reveals the rhythmic elements out of which complex locomotor outputs are synthesized, in accordance with the “optimal function approximator” view of the nervous systems. The above neuoranatomical studies provide some support for the requirement of the OST model that either (1) the mechanisms underlying courtship song are mechanistically connected to those underlying high-level control of locomotion or (2) that the two mechanisms are developmentally connected, such that competence of the song production system provides a “read-out” on the competence of the locomotor system, given their shared developmental history. The OST hypothesis applied to birds may explain other sources of variation in song repertoire size. Birds found in forested habitats tend to exhibit high maneuverability (i.e., more frequent alteration in trajectory) and have been shown to have more versatile singing than birds found in open habitats (Read & Weary 1992). Warrick (1998) showed that pure coursing insectivores such as swifts are built for less maneuverability than are pure hawking insectivores such as flycatchers, with hawking coursers such as swallows lying in between. Intriguingly, our repertoire data indicate that song repertoire size (RS) in these groups mirrors flight maneuverability: The mean RS for swifts (2.0, N = 2 species) is markedly less than that for swallows (34.5, N = 3 species), which in turn is less than that for the highly maneuverable flycatchers (410.8, N = 6 species), although phylogenetic non-independence and small sample sizes preclude statistical inference. The OST hypothesis may apply widely to non-avian taxa. In lizards, the species with the largest signal repertoires tend to be arboreal, swimming, or possessing some other unique form of locomotion (Ord & Blumstein 2002). Furthermore, in anurans, the most complex calls occur in arboreal species (Narins 2000; Feng et al. 2002). Moreover, all frogs that have protrusible tongues have vocal sacs, suggesting that the vocalizations may in part serve to signal ability to generate the complex tongue movements used to capture mobile prey (frogs that have vocal sacs but lack protrusible tongues nevertheless exhibit elaborate lunging behavior during predation) (Nishikawa et al. 1992). Frog songs may serve to demonstrate their control over the protrusible tongue, which likely requires fine motor control and is linked intricately to foraging success. Our data suggest that future studies of a possible link between motor versatility and courtship signal complexity in other taxa are likely to be fruitful. The deepest tests of the OST model introduced here will involve quantitatively relating the properties of acoustical and visual courtship displays to the precise motor dynamics of particular species. In addition, the model predicts that males with the most attractive courtship songs should also tend to perform better in complex locomotor tasks. Thus, the model suggests entirely new lines of research at the intersection of evolutionary biology, biomechanics, neurobiology, and behavioral biology. References 1. Alberts SC, Altmann J, Wilson ML 1996 Mate guarding constrains foraging activity of male baboons. Anim Behav 51:1269– 1277 ©2007 Cornell Synapse | www.cusn.org

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2. Andersson, M. 1994 Sexual Selection. Princeton University Press, Princeton, NJ. 3. Barker, F.K. , G. F. Barrowclough, and J. G. Groth. 2002. A phylogenetic hypothesis for passerine birds; Taxonomic and biogeographic implications of an analysis of nuclear DNA sequence data. Proc. R. Soc. Lond. B 269:295-308. 4. Barker, F.K., A. Cibois, P. Schikler, J. Feinstein, and J. Cracraft. 2004. Phylogeny and diversification of the largest avian radiation. Proc. Natl. Acad. Sci. USA 101:11040 11045. 5. Bass, A.H., and R. Baker. 1997 Phenotypic specification of hindbrain phombomeres and the origins of rhythmic circuits in vertebrates. Brain Behav Evol 50, 3-16. 6. Bevan, M.D., P.J. Magill, D. Terman, P.J. Bolam, and C.J. Wilson. 2002 Move to the rhythm: oscillations in the subthalamic nucleus-external globus pallidus network. Trends in Neurosci 25: 525-531. 7. Bledsoe, A. H. 1987. DNA evolutionary rates in nine-primaried passerine birds. Mol. Biol. Evol. 4:559-571. 8. Bottjer, S. W., J.D.Brady, J. D., & B. Cribbs. 2000 Connections of a motor cortical region in zebra finches: Relation to pathways for vocal learning. J Comp Neurol 420, 244-260. 9. Brown, P. and C.D. Marsden. 1998 What do the basal ganglia do? Lancet. 351: 18011804. 10. Calmaestra R.G. & Moreno E. (2001) A phylogeneticallybased analysis on the relationship between wing morphology and migratory behaviour in passeriformes. ARDEA 89 (2): 407-416. 11. Carr, C. 2000 Locating an error correction signal for adult birdsong. Nature Neuroscience 3, 419-421. 12. Catchpole, C.K. and P.J.B. Slater. 1995. Bird Song: Biological Themes and Variations. Cambridge: Cambridge University Press. 13. Cibois A, J. Cracaft. 2004. Assessing the passerine “Tapestry”: phylogenetic relationships of the Muscicapoidea inferred from nuclear DNA sequences Phylogenet. Evol. 32: 264-73. 14. Delacour, J and E. Mayr. 1945. I Subfamily Anserinae. Wilson Bulletin 57:8-11. 15. Devoogd T.J., J.R. Krebs, S.D. Healy, A. Purvis. 1993. Relations between song repertoire size and the volume of brain nuclei related to song: comparative evolutionary analyses amongst oscine birds. Proc R Soc Lond B Biol Sci 254: 75-82. 16. Dubbeldam, J. L. 1998 The neural substrate for ‘learned’ and ‘nonlearned’ activities in birds: a discussion of the organization of bulbar reticular premotor systems with side lights on the mammalian situation. Acta Anatom 163, 157-172. 17. Farmer, S.F. 1998 Rhythmicity, synchronization and binding in human and primate motor systems. J Physiol 509, 3-14. 18. Farmer, S.F. 2002 Neural rhythms in Parkinson’s disease. Brain 125, 1175-1176. 19. Feng, A.S., P.M. Narins, and C.H. Xu. 2002 Vocal acrobatics in a Chinese frog, Amolops tormotus. Naturwissenschaften 89, 352-356. 20. Guilford, T. M.S. Dawkins.1991 Receiver psychology and the evolution of animal signals. Anim Behav 42, 1-14. 21. Hartman, Frank A. 1961. Locomotor mechanisms of birds. Smith Misc Col 143: 1-91. 22. Hedenstrom, A. 1998 The relationship between wing area and raggedness during molt in the willow warbler and other passerines. J. Field Ornithol. 69(1):103-10. 23. Hedenstrom, A. and Mikael Rosen. Predator versus prey: on 26


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aerial hunting and escape strategies in birds. 2001. Behavioral Ecology 12: 150-156. 24. Huber, F. 1963 The role of the central nervous system in Orthoptera during the coordination and control of stridulation. Acoustic behavior of animals. Elsevier, Amsterdam, London, New York, pp 440-488. 25. Ince, S. A., and P.J.B. Slater. 1985. Versatility and on manipulation, p. 7 l-86. In R. W. Mitchell and Continuity in the songs of thrushes turdus spp. N. S. Thompson, eds. deception: perspectives. Ibis 127:355-364. 26. Konishi, M. 1994 An outline of recent advances in birdsong neurobiology. Brain Behav Evol 44, 279-285. 27. Kreinovich, V., Sirisaengtaksin, and S. Cabrera. 1994. Wavelet neural networks are asymptotically optimal approximators for functions of one variable. IEEE World Congress on Computational Intelligence. 1: 299-304. 28. Kroodsma, D. 2005. The Singing Life of Birds. Boston: Houghton Mifflin. Macdougall-Shackleton, S.A. 1997. Sexual selection and the evolution of song repertoires. Cur Ornith 14: 81124. 29. Moller, A.P., P.Y. Henry, J. Wrritzoe. 2000. The evolution of song repertoires and immune defense in birds. Proc R Soc Lond B 267: 165-167. 30. Narins, P.M., E.R. Lewis, and B.E. McClelland. 2000 Hyperextended call note repertoire of the endemic Madasgascar treefrog Boophis madagascariensis (Rhacophoridae). J Zool Lond 250, 283-298. 31. Nishikawa, K. C., C.W. Anderson, S.M. Deban, J.C. O’Reilly.1992 The evolution of neural circuits controlling feeding behavior in frogs. Brain Behavior and Evolution 40,125-140. 32. Ord, T.J., D.T. Blumstein, and C.S. Evans. 2002 Ecology and signal evolution in lizards. Bio J Linn Soc 77, 127-148. 33. Perkel, D.J. 2004. Origin of the anterior forebrain pathway. Ann. N.Y. Acad. Sci. 1016: 738-748. 34. Poole, E.L. 1938. Weights and wing areas in North American birds. Auk 55: 511–517. 35. Poole, A. (Editor). 2005. The Birds of North American Online. Cornell Laboratory of Ornithology, Ithaca, NY. 36. Ramirez, J.M., and K.G. Pearson. 1988 Generation of motor patterns for walking and flight in motoneurons supplying bifunctional muscles in the locust. J Neurobiol 19, 257-282. 37. Read, A.F., D.M. Weary.1992. The evolution of bird song: comparative analyses. Phil Trans R Soc Lond B 338: 165-187. 38. Schwarz, C., and P. Their. 1999 Binding of signals relevant for action: Towards a hypothesis of the functional role of pontine nuclei. Trends Neurosci 22, 443-451. 39. Singer, W. 1993 Synchronization of cortical activity and its putative role in information processing and learning. Ann Rev Physiol 55, 349-74. 40. Spedding, G. R., M. Rosen & A. Hedenstrom. 2003. A family of vortex wakes generated by a Thrush Nightingale in free flight in a wind tunnel over its entire natural range of flight speeds. J Exp Biol 206: 2313-2344. 41. Steeves, J. D., G.N., Sholomenko, & D.M.S.Webster. 1987 Stimulation of the pontomedullary reticular formation initiates locomotion in decerebrate birds. Brain Research 401, 205-212. 42. Tatner, P. and D. M. Bryant. 1986. Flight cost of a small passerine measured using doubly labeled water: Implication for en©2007 Cornell Synapse | www.cusn.org

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ergetic studies. Auk 103: 169-180. “University of Puget Sound Slater Museum of Natural History Wing Image Collection”. 15 November 2006. 43. Wang, S. and R.M. Robertson. 1989 Morphological study of flight motor neurons in the cricket. J Exp Biol 129, 303-316. 44. Warrick, DR. 1998. The turning-and linear-maneuvering performance of birds: the cost of efficiency for coursing insectivores. Can. J. Zool. 76, 1063-1079. 45. Welty, Joel Carl. 1979. The Life of Birds. Philadelphia: Saunders College Publishing. “What Bird The Ultimate Bird Guide”. 15 November 2006. 46. Wilson, D.M. 1962 Bifunctional muscles in the thorax of grasshoppers. J Exp Biol 39, 669-677. 47. Yuri, T., and D. P. Mindell. 2002. Molecular phylogenetic analysis of Fringillidae, “NewWorld nine-primaried oscines” (Aves: Passeriformes). Mol. Phylogen. Evol. 23:229

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PROPOSAL Studying the effects of desynchrony of internal oscillators on REM Sameer Ahmed

Cornell University, Department of Neurobiology and Behavior, Ithaca, NY 14853

Sleep rhythms (REM and non-REM stages) are regulated by the interaction of two distinct internal oscillators: 1) the circadian pacemaker in the SCN, and 2) the sleep homeostat. REM sleep quality is important for mental performance, especially memory function. Optimal REM sleep quality requires proper synchronization between the two oscillators. This study will analyze the effects of forced desynchrony in human subjects on REM sleep quality and mental performance (sustained vigilance and memory function). We expect to see changes in REM quality and memory performance as a result of forced phase differences between the internal oscillators. Introduction Circadian pacemaker and sleep homeostat are distinct internal oscillators that interact in a complex manner to regulate sleep rhythms: Current models of sleep regulation point towards the interaction of two distinct internal oscillators: 1) the circadian hypothalamic oscillator in the suprachiasmatic nucleus (SCN), and 2) the sleep-control mechanism, which is also referred to as the sleep homeostat or hourglass oscillator (Dijk et al., 2005; Dijk et al., 2002). The amplitude and phase relationship of these two oscillators affect various aspects of sleep and performance during wakefulness (Dijk et al., 2005). The circadian oscillator is responsible for modulation of sleep tendencies during particular phases of the 24-hr cycle and the consolidation of sleep and wake into distinct episodes (Dijk et al., 2005). The sleep homeostat monitors and allows the body to react to sleep tendencies, which causes the need for sleep to be dependent upon prior amounts of sleep or wakefulness (Dijk et al., 2005). Essentially, the sleep homeostat monitors the history of sleep-wake behavior, so its rhythm runs according to this behavior (i.e. its rhythm is synchronized with sleep-wake behavior). It is known that light-dark cycles regulate the circadian oscillator (Fig. 1, Dijk et al., 2005; Boivin et al., 1997). When an individual is removed from external light-dark cycles, the sleepwake cycle still remains consolidated, but it desynchronizes from the 24-hr day (Dijk et al., 2005; Boivin et al., 1997). This loss of entrainment leads to changes in the internal phase relationship between the sleep-wake cycle and various indicators of the circadian rhythm, like core body temperature and plasma melatonin (Dijk et al., 2005). During entrainment, the circadian oscillator and sleep homeostat are synchronized due to light input to one or both os©2007 Cornell Synapse | www.cusn.org

cillators (Dijk et al., 2005). For example, an individual entrained to the natural light-dark cycle will sleep accordingly for about 8 hours. If the light cues are removed, the individual will still go to sleep because the sleep history is tracked by the sleep homeostat, but the timing will be altered due to the lack of synchronization with the circadian pacemaker. Social factors, like going to work at different shift times or studying all night for a test, can alter the timing of sleep-wake behavior, but do not directly alter the circadian oscillator or the sleep homeostat (Fig. 1, Dijk et al., 2005). This can lead to a conflict between socially controlled sleep behavior and internal oscillators. The circadian pacemaker oscillates nearly independent of sleepwake behavior (Dijk, et al., 2005). For example, a sudden shift in the sleep-wake cycle due to air travel will not lead to an immediate change in the phase of the circadian pacemaker, which results in “jet lag”. The sleep homeostat is driven primarily by sleep-wake behavior because it tracks the amount of sleep that one receives during the course of the day (i.e. it follows the history of sleep/ wakefulness). The sleep-wake cycle creates a strong feedback loop with the sleep homeostat and it also feeds back onto the circadian pacemaker (primarily due to the light-sensitivity of the SCN) (Fig. 1, Dijk et al, 2005). Overall, complex interactions are taking place, but it is important to note the distinction between the two oscillators. REM sleep is important for mental performance and desynchronization of the circadian pacemaker and sleep homeostat leads to deterioration of REM sleep quality: Sleep is divided into two states: non-REM and REM. The circadian pacemaker and sleep homeostat work together in a fixed phase relationship (i.e. they are synchronized) to provide proper state transitions during sleep. Evidence suggests that the rapid eye movement (REM) phase plays an important role in learning and memory (Kim et al., 2005; Davis et al., 2003). REM sleep increases significantly following certain learning tasks and it seems to be important in memory consolidation (Kim et al., 2005). REM-sleep deprivation impairs mental ability and memory performance and can also result in negative mood changes (Marks et al., 2005). In this study, we will focus on the synchronization of the two oscillators for proper REM sleep and the effects of desynchronization on mental performance. Very few studies have attempted to understand the circadian and homeostatic variation in performance within this dual oscillator model (Dijk et al., 2005). This study will be the first to look at the effects of desynchronization on mental performance in relation to REM sleep. 28


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Significance In our fast-paced society, sleep-wake behavior is often altered due to social factors. For example, college students occasionally stay up late into the night studying for tests. Although they may still sleep for an adequate amount of time during the course of the day (about 8 hours consolidated), their sleep-wake cycle is not synchronized with the circadian pacemaker, which is running according to the light-dark cycle. The sleep homeostat shifts according to sleep-wake behavior because it tracks the amount of sleep (i.e. sleep history) and will promote sleep after a certain period of wakefulness (perhaps 16 hours). The desynchronization of the circadian oscillator and sleep homeostat leads to altered REM sleep, which may cause a decrease in mental performance (e.g. memory performance), despite getting an adequate amount of sleep. We already know that limited sleep (shorter than optimal length) leads to poor mental performance, but this study could potentially show that not only length, but also timing of sleep affects mental performance. Specific Aims First, we will study the effects of forced desynchronization of the two oscillators on REM sleep structure using polysomnography. This will provide an understanding of how REM sleep structure is changing as the phase difference between the two oscillators changes. Second, we will test mental performance during desychrony by looking at two elements: sustained attention/vigilance and memory. Finally, we will compare changes in mental performance to the degree of desynchronization and REM sleep structure. Methods 50 healthy males of ~25 years of age are used for this study. Subjects will be carefully selected through initial trials which confirm similarity in natural circadian rhythms (i.e. close to a 24-hr rhythm) between the subjects. All subjects will be free of sleep disorders and any unexplainable circadian abnormalities. 25 males (experimental group) are subjected to 20 cycles of a 28-hr forced desynchrony protocol. The bedtime of each subject is to occur 4 hours later each day for ~3.5 weeks (i.e. 4-hr shift each day). The 28-hr day consists of 18 h 40 min of scheduled wakefulness in dim light (~10-20 lux) and 9 h 20 min of scheduled sleep in darkness (~0.03 lux). During scheduled sleep times, subjects are confined to bed and instructed to attempt to sleep. The 28-hr day length allows for desynchrony because it is far enough outside the range of entrainment of the human circadian pacemaker so as to minimize the influence of the imposed schedule on the observed circadian period (Khalsa et al., 2002; Czeisel et al., 1999). Dim lighting also prevents the synchronization and resetting of the two oscillators (Khalsa et al., 2002; Czeisel et al., 1999). In other words, the circadian phase will not shift during the course of the experiment, but the sleep-wake behavior will change, which also forces the sleep homeostat to shift along with the behavior due to a strong feedback loop. 25 males will be used as controls with 24-hr sleepwake cycles (control group). This protocol is adapted from Khalsa et al., 2002. Š2007 Cornell Synapse | www.cusn.org

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Core body temperature and plasma melatonin levels are monitored to estimate the endogenous circadian period and rhythm (Khalsa et al., 2002; Czeisel et al., 1999). Hourly blood samples are collected during intermittent segments throughout the study and assayed for plasma melatonin concentration using radioimmunoassay with a sensitivity of 5 pg mL-1. Core body temperature is also recorded throughout the course of the day. Endogenous circadian period is estimated using a non-orthogonal spectral analysis (NOSA) technique, in which these two sets of data are fitted simultaneously to a dual-harmonic regression model (Brown et al., 1992). Polysomnography is used to observe REM sleep structure. A Nicolet Ultrasom Sun 386I sleep system is used to record central and occipital electroencephalogram (EEG), right and left electrooculogram (EOG), chin electromyogram (EMG), and electrocardiogram (ECG). Sleep stage is scored in 30-s epochs using standard criteria (Khalsa et al., 2002) and rapid eye movements during REM sleep epochs are scored visually by displaying each epoch in 15-s segments on a CRT screen. REM duration and density are both measured. This protocol is adapted from Khalsa et al., 2002. To measure mental performance, sustained vigilance and memory are tested after two hours of waking up (to control for fatigue effects of the schedules). First, the psychomotor vigilance test (PVT) is given to test sustained vigilance. It assesses simple reaction time by measuring the amount of time taken by the subject to respond to a visual cue comprised of blinking numbers. The subject is asked to press a response button on a remote control as soon as the stimulus is perceived, thus stopping the counter and displaying the reaction time. The stimulus interval on the task varies from 2 to 10 seconds. The entire PVT lasts 10 minutes, with 80 reaction times recorded per trial. PVT parameters include the following: 1) frequency of lapses, the number of times the subject failed to respond in less than 400 ms; 2) duration of lapse domain, a shift in lapse duration calculated from the slowest 10% of reaction times; 3) optimum response times, the average of the fastest 10% reaction times; 4) fatigability function, the extent to which the subject maintained performance across time; and 5) false response frequency, the number of responses initiated when no stimulus was present. These are standard parameters used in nearly all PVT studies (Adam et al., 2006). This protocol is adapted from Dijk et al., 1992, and Adam et al., 2006. Following PVT, both working and explicit memory are tested. Immediate recall involves the central executive component of working memory and delayed recall is a function of explicit memory (Bennett et al., 2006). Subjects are given a word list consisting of 20 nouns, based on norms for concreteness, imagery, and meaningfulness. They are additionally matched for number of syllables. Subjects are allowed 2 minutes exposure and learning time to examine each list, immediately followed by a 2-min period in which they must write down as many of the words as they can recall (function of working memory). After approximately 30 min has elapsed (from the learning period), subjects are given a further 2 minutes to write down as many words as they can recall (function of explicit memory). Scores are calculated as the number of words recalled correctly. This protocol is adapted from Bennett et al., 2006. PVT and memory tests are conducted only once during each 29


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Social Time Light-Dark Cycle Circadian Photoreception

Sleep-Wake Cycle

Circadian Pacemaker (SCN)

Homeostat (Hourglass Oscillator)

Figure 1: This shows the distinction between the two oscillators and their connections to the sleep-wake cycle through various feedback loops, reproduced from Ref. 8 (2005) Dijk et.al.

wakefulness period, two hours after waking up. The tests are separated by a 30 min break. Statistical analyses are used to compare degree of desynchrony of the two oscillators, REM sleep structure, and the effects on mental performance. Results and Expected Outcomes The expected results of this experiment should show that REM sleep is disrupted by desychronization of the circadian pacemaker and sleep homeostat. There should be a decrease in both REM density and duration as the degree of desynchrony increases. Also, REM sleep quality should oscillate in this experiment. As the phase difference between the two internal oscillators increases, the REM sleep quality should deteriorate, reaching its lowest quality at maximum phase difference. As the phase difference decreases, the REM sleep quality should improve, reaching peak quality at complete synchronization of the two oscillators. Mental performance should also oscillate with phase difference. As the phase difference increases between the two oscillators, performance scores should decrease on PVT and memory tests. As the phase difference decreases, scores should increase on the tests, reaching maximum scores at complete synchronization. Since we will perform 20 cycles of 28-hr days (4 hr shift each “day”), there should be a total of 3.8 cycles of phase difference, which should show any oscillation in the data. In other words, the circadian pacemaker and sleep homeostat should be resynchronized 3 times during the course of the experiment, but the 4th synchronization event (0¼ phase difference) will not be reached. We expect a deterioration of mental performance, especially memory function (working, explicit, or both), with increasing phase difference because REM sleep is disrupted. REM sleep is critical for mental performance, especially memory function. Since desynchronization has shown to disrupt REM sleep quality, albeit not in great detail in previous studies, it should exacerbate mental performance. This study will work towards establishing a relationship between desynchrony of the two oscillators, REM sleep quality, and mental performance. Discussion This study is important for today’s society because we have ©2007 Cornell Synapse | www.cusn.org

a tendency to change our sleep-wake behavior quite frequently. Sometimes we stay up late working on an assignment for school or work. College students are frequently staying up late into the night (sometimes all night) to prepare for exams. Our study asks: Is getting the right amount of sleep good enough for proper mental performance or is synchronization of the circadian pacemaker and sleep homeostat also necessary (i.e. synchronization with the lightdark cycle)? For example, if a student at Cornell University sleeps at 3 am (studying for a test) and gets up at 11 am (8 hours of sleep), will the student have sub-optimal mental performance for the test at 1 pm that same day? The student has received the proper length of sleep, but the sleep schedule is not synchronized with the lightdark cycle, which leads to desynchrony of the two internal oscillators. Our study will work towards answering questions about desynchrony and mental performance, which are quite important for our daily life, as illustrated in the example. Perhaps it is better to adjust our social/work schedules in accordance to the light-dark cycle to optimize mental performance. In the example of the Cornell student, perhaps it is better for him/her to sleep at the proper time (assuming 3 am is later than the usual bedtime) and wake up early in the morning to study, which allows for synchrony between the two oscillators, and thus optimal mental performance. This study is using an artificial condition, forced desynchrony, to explain elements of our daily behavior. This creates a problem because artificial conditions do not work exactly like natural conditions. For example, the experimental group is forced to be in a 28hr day, which is unnatural, and then compared to the control group, which is in a 24-hr day. Being in a 28-hr day may add confounding variables, like stress due to the unnatural schedule, which may alter mental performance scores. Although this is a potential pitfall of this experiment, several studies have used the 28-hr day forced desynchrony protocol with great success in explaining human behavior (Dijk et al., 2005). It is the best known method for desynchronization of the circadian pacemaker and sleep homeostat. Secondly, since this is a human study, it is nearly impossible to have a homogenous group of subjects. There are always some differences between humans, which can add confounding variables to the study. In order to account for differences between humans, mental performance scores and REM sleep patterns will be normalized to individual baseline data. By normalizing each individual’s scores, it allows for direct comparisons between individuals. Despite potential pitfalls, as discussed above, the protocols of this study are adapted from successful experiments, so their validity is already confirmed. Forced desynchrony has been used for many years and it is considered to be an excellent approach to studying the circadian pacemaker and sleep homeostat. References 1. Adam M, Retey JV, Khatami R and Landolt HP (2006) Agerelated changes in the time course of vigilant attention during 40 hours without sleep in men. Sleep 29(1):55-57. 2. Bennett IJ, Golob EJ, Parker ES and Starr A (2006) Memory evaluation in mild cognitive impairment using recall and recognition tests. J Clin Exp Neuropsychol 28(8):1408-1422. 3. Boivin DB, Czeisler CA, Dijk D-J, Duffy JF, Folkard S, Minors DS, Totterdell P, and Waterhouse JM (1997) Complex interaction of the sleep-wake cycle and circadian phase modulates mood in 30


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healthy subjects. Arch Gen Psychiatry 54:145-152. 4. Brown EN and Czeisler CA (1992) The statistical analysis of circadian phase and amplitude in constant-routine core-temperature data. J Biol Rhythms 7(3):177-202. 5. Czeisler CA, Duffy JF, Shanahan TL, Brown EN, Mitchell JF, Rimmer DW, Ronda JM, Silva EJ, Allan JS, Emens JS, Dijk D-J and Kronauer RE (1999) Stability, precision, and near-24-hour period of the human circadian pacemaker. Science 284(5423):21772181. 6. Davis CJ, Harding JW and Wright JW (2003) REM sleep deprivation-induced deficits in the latency-to-peak induction and maintenance of long-term potentiation within the CA1 region of the hippocampus. Brain Research 973:293-297. 7. Dijk D-J and Lockley SW (2002) Integration of human sleepwake regulation and circadian rhythmicity. J Appl Physiol 92(2):852-862. 8. Dijk D-J and von Shantz M (2005) Timing and consolidation of human sleep, wakefulness, and performance by a symphony of oscillators. J Biol Rhythms 20(4):279-290. 9. Khalsa SB, Conroy DA, Duffy CA and Dijk D-J (2002) Sleepand circadian-dependent modulation of REM density. J Sleep Res 11(1):53-59. 10. Kim EY, Mahmoud GS and Grover LM (2005) REM sleep deprivation inhibits LTP in vivo in area CA1 of rat hippocampus. Neuroscience Letters 388:163-167. 11. Marks CA and Wayner MJ (2005) Effects of sleep disruption of rat dentate granule cell LTP in vivo. Brain Research Bulletin 66:114-119.

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PROPOSAL The role of serotonin reuptake transporter in depression and treatment with SSRIs Sameer Ahmed

Cornell University, Department of Neurobiology and Behavior, Ithaca, NY 14853

Depression is one of the most important global health issues. It the leading cause of disability in the US and it costs the US economy nearly 43 billion dollars each year [1,2]. Antidepressants are taken by patients to reverse the symptoms of depression, but unfortunately, millions of people continue to suffer from ineffective treatment. Selective serotonin reuptake inhibitors (SSRIs) are the most commonly prescribed antidepressants. Since the beginning of treatment with SSRIs in the 1980s, there has been an increased incidence of suicide in children and adolescents taking these drugs [1]. Numerous patients have reported their ineffectiveness, yet doctors continue to prescribe SSRIs to most patients without any prescreening—a “hit or miss” approach. It is hypothesized that malfunctions in the breakdown or reuptake of serotonin causes a dramatic decrease in its levels in the synapses of the brain. This leads to symptoms of depression. SSRIs are blockers of the serotonin reuptake transporter (5-HTT) and they allow for an increase in serotonin levels, which reverses the symptoms of depression. Individuals have different capacities for serotonin reuptake according to their 5-HTT genotype. Certain genotypes are linked with an increased risk of depression. In this study, I will compare 5-HTT genotype and SSRI effectiveness in mice. I will also test for age dependence of SSRI effectiveness, since SSRIs are least effective in children and adolescents. I will be using two different SSRIs, fluoxetine and sertraline, in order to compare their interaction with 5-HTT genotype and age. Fluoxetine is the only FDA approved SSRI for children under the age of 18 and sertraline was recently banned in the UK due its link with adolescent suicide. There may be an interesting link between 5-HTT genotype, age, and SSRI treatment. It is important to explore this area of depression because it may lead to more effective methods of treatment, particularly in those not responsive to SSRIs. Introduction Depression is one of the most important health issues in the world Major depressive disorder (MDD) is the most common form of depression in the United States. It affects approximately 10% of men and 20% of women in the US over the lifetime. Other forms of clinical depression are dysthymia, bipolar I, and bipolar II disorders. A study by the World Health Organization and the World Bank showed that the burden of disease caused by depression is the second highest in developed nations and the fourth highest in ©2007 Cornell Synapse | www.cusn.org

developing nations. It is also the leading cause of disability in the US and several other countries [1]. Depression costs the US economy nearly 43 billion dollars each year [2]. Depression is an extremely important global health issue and it needs to be researched in greater detail. The neurobiology of depression: Depression is commonly characterized by low mood, anhedonia—a loss of interest or pleasure in almost all activities—and fatigue. Depression is not clearly understood, but there is consistent evidence indicating it is a product of both genetic and environmental factors. The monoamine hypothesis was developed early and it correlates depression with an imbalance of noradrenaline and serotonin [2]. It is based on the alleviation of symptoms through antidepressant drugs that increase synaptic levels of these two molecules. It is also observed that catecholamine-depleting drugs can induce depression-like symptoms. However, this hypothesis does not explain why antidepressant effects only occur after several weeks of treatment, since reuptake blockers increase levels of monoamines much rapidly. Scientists now believe that the effects of antidepressants are caused by alteration of brain gene expression after chronic treatment [2]. Anxiety, stress, and depression are closely linked on the chemical level. Stress responses are mediated by the sympathetic nervous system and the hypothalamus-pituitary-adrenal (HPA) axis. Depression is identified with sustained activation of the sympathetic nervous system and hypercortisolaemia [3]. Hypercortisolaemia is the result of elevated cortisol levels which fail to adequately suppress corticotrophin-releasing hormone (CRH) secretion in depressive states. An elevated CRH level in patients suffering from depression is confirmed by clinical studies [2]. Depression is a syndrome with many facets. It has been linked to dysfunctions in growth hormone, in the thyroid axis, in opioid receptors, and in substance P, which is expressed in the central nervous system (CNS) and plays an important role is affective behavior [4,5]. Depression also has detrimental effects on circadian rhythms [2]. Abnormal sleep, temperature, and activity cycles are found in patients. A short latency to the onset of rapid-eye-movement sleep and awakening in early hours of daytime are common symptoms seen in depression [6]. There is also an observed correlation between some infectious agents and depression. The most widely reported case is with Borna-disease virus (BDV), which is a neurotrophic, single-stranded enveloped RNA virus. Patients suffering from recurrent depres32


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sion have shown expression of BDV antibodies and viral genomic transcripts [2,7]. Infectious BDV cells and the BDV antigen are also present in these patients [2]. On the other hand, there are some reports that show no association between BDV and depression [2]. Tsuji et. al. showed that groups of BDV patients in Japan did not exhibit symptoms of depression [16]. The correlation between BDV and depression is still unclear. The neurobiology of depression is both complex and interesting, and requires further study. The genetics of depression: Depression is a heritable disorder. First-degree relatives of an individual suffering from depression are three times more likely than the general population to have depressive disorder [8]. Since depression is a genetically complex disorder, its inheritance is difficult to predict. Linkage and association patterns are still unclear [8]. MDD has an estimated heritability of 31-42% [8]. Schizophrenia and bipolar disorder have a heritance of nearly 70% [8]. Several studies have shown that depression is a product of gene and environment. An individual with a genetic risk of depression is more likely to enter this state under stressful circumstances. The role of serotonin and the serotonin reuptake transporter in depression: Serotonin (5-hydroxytryptamine, or 5-HT) is an important signaling neurotransmitter in the CNS. It is known to play a critical role in regulation of mood, sleep, hunger, and sexual drive. Serotonin is also identified as one of the major role players in clinical depression. It is hypothesized that malfunctions in the breakdown or reuptake of serotonin causes a dramatic decrease in its levels in the synapses of the brain. This leads to symptoms of depression. Exact mechanisms of serotonin imbalance are unknown, but several theories point to the serotonin receptor and transporter. Svenningsson et. al. (2006) have identified a powerful interaction between a brain protein called p11 and a serotonin receptor (5-HT1b subtype) that has been previously associated with mood regulation [9]. The rodent 5-HT1b receptor is an excellent candidate for research because it is remarkably similar in structure, functional characteristics, and distribution to the human homolog. p11 is a member of the S100 family of proteins that translocate their binding partners to the plasma membrane. p11 makes more 5-HT1b receptors available at the cell surface, while leaving the dynamics other G protein-coupled receptors unchanged [9]. Serotonin is able to bind its receptors more readily when a greater number of receptors are available at the cell surface. It is shown that a shortage of p11 is linked to depression and an increase in p11 is linked to the relief of its symptoms [9]. The most commonly used antidepressants are blockers of the serotonin reuptake transporter (5-HTT), which lead to an increase in serotonin levels in synapses of the brain. My study will focus on the role of 5-HTT in depression. Several studies on humans and mice have shown the essential role of 5-HTT in producing symptoms of depression. Caspi et. al. (2003) conducted a landmark study on the influence of life stress on depression. This study focused on a polymorphism in the 5-HTT gene. They asked the question: Why do stressful experiences lead to depression in some people, but not in others? They found that a functional polymorphism in the promoter region ©2007 Cornell Synapse | www.cusn.org

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of a 5-HTT gene, SLC6A4, moderates the influence of stressful life events on depression [10]. The promoter activity of the 5HTT gene is modified by sequence elements within the proximal 5’ regulatory region, called the 5-HTT gene-linked polymorphic region (5-HTTLPR). There are two alleles for 5-HTTLPR. The short, “S”, allele is associated with lower transcriptional efficiency of the promoter compared with the long, “L”, allele. Murphy et. al. showed mice that are either homozygous or heterozygous for S allele (S/S or S/L) exhibit more fearful behavior, symptoms of depression, and greater levels of adrenocorticotropin in response to stress compared to homozygous control (L/L) [11]. Interestingly, in the absence of stress, there were no observable differences between the mice. Bennet et. al. (2002) showed that in rhesus monkeys, who have great similarity in length variation of the 5-HTTLPR with humans, the short allele is associated with decreased serotonergic function in monkeys reared in stressful conditions but not normally reared monkeys [12]. Hariri et. al. (2002) used neuroimaging to show that the stress response in humans is mediated by variations in 5-HTTLPR [13]. Caspi et. al. (2003) showed convincingly that humans homozygous or heterozygous for S allele (S/S or S/L) have a greatly increased risk of suffering from depression when placed in stressful circumstances as compared to homozygous control (L/L) [10]. Lesch et. al. (1996) and Mazzanti et. al.(1998) have shown very similar results from their research on humans [14]. From these studies, one can see a correlation between abnormal levels of 5-HTT and depressive behavior. Several studies have been conducted on serotonin reuptake transporter knockout mice. Knockouts have increased anxiety-like behavior, depressed response to inescapable stress (e.g. increased bouts of immobility in the forced swim test), increased anxiety, neuroticism, harm avoidance, impaired coping response to stress, low energy, and psychomotor retardation [14]. Knockouts also show increased reward-related responses to cocaine and substance abuse [14]. It is interesting to note that there is only a modest indication of compensatory alterations in dopamine and norepinephrine function in these knockouts [14]. It is clear from these studies that knockout mice have increased symptoms of anxiety and depression and greater risk of addiction to drugs, like cocaine. It can be concluded that serotonin is playing an extremely important role in depression. Decreased levels of serotonin in the synapse or decreased response to its binding induce symptoms of depression. Interestingly, those who have abnormally low levels of the serotonin reuptake transporter (S/L and S/S genotype) and knockouts exhibit an increased risk of depression. This is paradoxical because one would predict that a reduced ability to reuptake serotonin from the synapse would also reduce the risk of depression, which is usually linked with decreased levels of serotonin in the synapses of the brain. In fact, the most common medications for depression are selective serotonin reuptake inhibitors (SSRIs), which specifically block serotonin reuptake transporters. This leads to the important question: Are SSRIs an effective treatment for those who have abnormal levels of the serotonin transporter? If an individual already has a reduced ability to reuptake serotonin from the synapse, will SSRIs alleviate his/her depression? I will address these questions in my study. Treatment for depression and its failures: The most common antidepressants are selective serotonin reuptake inhibitors (SS33


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RIs). Some well-known SSRI antidepressants are fluoxetine (Prozac), sertraline (Zoloft), escitalopram (Lexapro), and paroxetine (Paxil). Monoamine oxidase inhibitors (MAOIs), which block the degradation of neurotransmitters by monoamine oxidases, and tricyclic antidepressants (TCAs), which prevent the reuptake of various neurotransmitters (serotonin, norepinephrine, and dopamine), are also used as antidepressants. These are less common now with the increasing use of SSRIs. In recent years, there has been much media attention directed towards SSRIs and suicidality. Since the beginning of treatment with SSRIs in the 1980s, there has been an increased incidence of suicide in children and adolescents taking these drugs [1]. Numerous depression patients have reported the ineffectiveness of SSRIs [1]. How can we account for the ineffectiveness of treatment with SSRIs in some patients? I hypothesize that the answer may lie in the gene coding for the serotonin transporter. Specifically, individuals with reduced serotonin reuptake capacity may have a reduced sensitivity to block off that reuptake function. It is also plausible that the reverse is true, namely that those patients with a particularly high capacity for reuptake maybe less sensitive to inhibition of that function as a result. While they are not as likely to get depressed, if they do, exceptionally high levels of reuptake inhibitors (SSRIs) may be required to alleviate the symptoms. It is necessary to compare the effectiveness of SSRI treatment in those with high and low levels of 5-HTT. It is also possible that the effectiveness of treatment is age dependent. SSRIs have been least effective alleviating the symptoms of depression in younger patients. It is necessary to compare the effectiveness of SSRI treatment across various ages. In this study, both fluoxetine and sertraline will be tested. Fluoxetine is the only SSRI officially endorsed by the FDA for the treatment of depression in minors. In June 2003, Britain banned the use of sertraline for children under 18 after studies showed a link to increasing suicidal rates. These two SSRIs are of particular interest for this study because they show variability in effectiveness in younger patients. Modeling depression in mice: The mouse has become a popular subject in depression research. Depression is usually induced by a stressful environment. Many models for assessing depressionlike behavior in mice involve exposure to stressful situations. Mice are tested for depression level in the forced swim (FST) and tail suspension tests (TST) [15]. These are used to quantify a mouse’s ability to cope with stress. Mice that have a reduced ability to cope with stress (i.e. they show greater bouts of immobility in FST and TST) are identified as depressed. Mice can be additionally stressed (prior to FST and TST) through various techniques, like wet bedding, constant lighting, food deprivation, or drug-withdrawal, and then tested for depression level through FST and TST. The forced swim test is the most widely used test for depression [15]. In this test, mice are placed in an enclosed (inescapable) cylinder filled with tepid water. They will initially engage in vigorous activity in an attempt to escape. After a period of time, the mouse will exhibit increasing bouts of immobility. This act of “giving up” or inability to cope with stress is a sign of depression. The depression is reversed through administration of antidepressants, like SSRIs. Following the administration of an antidepres©2007 Cornell Synapse | www.cusn.org

Cornell Synapse

sant, a mouse will take much longer to exhibit bouts of immobility. Also, for a given period, a mouse will have lower total time in an immobile state. FST also induces neurochemical, neuroimmune, and neuroendocrine alterations that resemble those observed in depressed patients [15]. The tail suspension test is also used to test for depression [15]. In this test, mice are hung upside-down by their tail and exhibit passive immobility after a period of struggling. Following the administration of antidepressants, mice display increased struggling times (less total time in an immobile state). The validity of FST and TST has been tested through years of research and most of the previously described studies use these tests for modeling depression in mice. Signficance In this project, I will ask two specific questions. First, is the effectiveness of SSRI treatment of depression dependent on one’s natural level of 5-HTT? Second, is the effectiveness of SSRI treatment age dependent? I will test the reversal of depression with SSRIs—fluoxetine and sertraline. SSRIs are not effective in alleviating the symptoms of depression in many patients. Is there any explanation for this problem? I hypothesize that SSRIs are relatively ineffective in treating depression in people with abnormally low levels of serotonin reuptake transporters and this will be tested in mice. The logic behind this hypothesis is rather simple. If the 5-HT reuptake transporter levels are low (inefficient transcription with the S allele) or lacking completely (knockouts), then the body will may have a reduced sensitivity to block off that reuptake. It is also plausible that the reverse is true, which raises another hypothesis. Perhaps patients with a particularly high capacity for reuptake maybe less sensitive to inhibition of that function as a result. While they are not as likely to get depressed, if they do, exceptionally high levels of reuptake inhibitors (SSRIs) may be required to alleviate the symptoms. I will also test the reversal of depression with SSRIs in various age groups. SSRIs are least effective at alleviating the symptoms of depression in younger patients, which suggests age dependence in treatment. First, this study will do some work towards understanding the role of 5-HTT in depression and its treatment with SSRIs. Second, it will also test for the age dependence of SSRI treatment. Finally, it will compare the effectiveness of two SSRIs, fluoxetine and sertraline. The overall goal is to develop more effective methods of treatment for depression, particularly in those not responsive to SSRIs. Since millions are dying from this terrible illness each year, it is crucial to conduct further research on depression. Specific Aims Treatment of depression with SSRIs has failed in many patients, especially in children and adolescents. The variability in SSRI effectiveness is not clearly understood. The purpose of this study is to compare the effectiveness of SSRI treatment between different genotypes and age groups. It is known that the S allele in the serotonin reuptake transporter gene-linked polymorphic region (5-HTTLPR) is associated with lower trascriptonal efficiency of 34


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the 5-HTT gene promotor compared with the L allele. Mice that are either homozygous or heterozygous for S allele (S/S or S/L) exhibit more fearful behavior, symptoms of depression, and greater levels of adrenocorticotropin in response to stress compared to homozygous control (L/L). This suggests that the S allele is linked with depression. It also suggests that effectiveness of treatment with SSRIs, which block the serotonin reuptake transporter in order to increase synaptic levels of serotonin, is dependent on the 5-HTT genotype. SSRIs are less effective at alleviating the symptoms of depression in children and adolescents. This suggests that the effectiveness of SSRI treatment is age dependent. The study will work towards understanding the variability in SSRI effectiveness. The first goal is to determine the effectiveness of SSRI treatment in mice with varying levels of the serotonin reuptake transporter. I will test the following genotypes: S/S, S/L, L/L, and knockout. Level of depression and recovery with SSRI treatment will be tested using the forced swim test (FST) and tail suspension test (TST). The percent changes in immobility time will be compared. Two types of SSRIs, fluoxetine and sertraline, will be used. The second goal is to test the age dependence of SSRI treatment. Mice with different levels of 5-HTT (S/S, S/L, L/L, and KO) will be tested at three different ages. The first group will be tested at 4 weeks of age (prepubescent). The second group will be tested at 6 weeks of age (pubescent). The third group will be tested at 12 weeks of age (post pubescent). Level of depression and recovery with SSRI treatment will be tested using the forced swim test (FST) and tail suspension test (TST). The percent changes in immobility time will be compared. Two types of SSRIs, fluoxetine and sertraline, will be used. The third goal is to compare the effectiveness of sertraline and fluoxetine. Both SSRIs will be used in this study. Experimental Design Forced Swim Test: The forced swim test is conducted as previously described by Sanchez et. al. (1997) [17]. Mice are placed in a transparent Plexiglas cylinder (20 cm in diameter) filled with water (25 ± 2°C). Filling the cylinder to a depth of 12 cm prevents mice from using their tails to support themselves in the water. A camera is placed over the cylinder, pointing downward, and it records the session for later scoring. Immobility is defined as the cessation of limb movements, except minor movement necessary to keep the mouse afloat. Immobility is sampled during the last 4 min of a 6min test session by an observer who is blind to genotype. Tail Suspension Test: The tail suspension test was conducted as previously described by Mayorga et. al. (2001) [18]. Mice are fastened by the distal end of the tail to a flat metallic surface and suspended in a visually isolated area (40 × 40 × 40 cm white Plexiglas box). A camera is used to record the session for later scoring. The presence or absence of immobility, defined as the absence of limb movement, is sampled during the last 5 min over a 6-min test session by an observer who is blind to genotype. Age/Genotype Groups: There are three age groups of C57BL/6J mice. The groups are 4-, 6-, and 12-week-old mice. The 4-week group is prepubescent, the 6-week group is pubescent, and the ©2007 Cornell Synapse | www.cusn.org

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Baseline FST (6 min) 24 hr waiting period

Baseline TST (6 min)

Return to cage for 72 hr waiting period SSRI or Saline injection 30 min waiting period FST (6 min) 24 hr waiting period SSRI or Saline injection 30 min waiting period TST (6 min) Figure 1: Mouse Schedule. The total testing period is 6 days.

Modeling depression in mice: The mouse has become a popular subject in depression research. Depression is usually induced by a stressful environment. Many models for assessing depressionlike behavior in mice involve exposure to stressful situations. Mice are tested for depression level in the forced swim (FST) and tail suspension tests (TST) [15]. These are used to quantify a mouse’s ability to cope with stress. Mice that have a reduced ability to cope with stress (i.e. they show greater bouts of immobility in FST and TST) are identified as depressed. Mice can be additionally stressed (prior to FST and TST) through various techniques, like wet bedding, constant lighting, food deprivation, or drug-withdrawal, and then tested for depression level through FST and TST. The forced swim test is the most widely used test for depression [15]. In this test, mice are placed in an enclosed (inescapable) cylinder filled with tepid water. They will initially engage in vigorous activity in an attempt to escape. After a period of time, the mouse will exhibit increasing bouts of immobility. This act of “giving up” or inability to cope with stress is a sign of depression. The depression is reversed through administration of antidepressants, like SSRIs. Following the administration of an antidepressant, a mouse will take much longer to exhibit bouts of immobility. Also, for a given period, a mouse will have lower total time in an immobile state. FST also induces neurochemical, neuroimmune, and neuroendocrine alterations that resemble those observed in depressed patients [15]. The tail suspension test is also used to test for depression [15]. In this test, mice are hung upside-down by their tail and exhibit passive immobility after a period of struggling. Following the administration of antidepressants, mice display increased struggling times (less total time in an immobile state). The validity of FST and TST has been tested through years of research and most of the 35


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12-week group is adult. Each group is subdivided into genotypes of S/S, S/L, L/L, and KO. S/S, S/L, and L/L mice are obtained through breeding. 5-HTT knockouts are generated by replacing the second exon of the 5-HTT gene with a phosphoglycerine kinaseneo gene cassette, as previously described by Bengel et. al. (1998) [19]. There will be 60 mice for each age group. SSRI Treatment: Injections of SSRIs are given as previously described by Sanchez et. al. (1997) [17]. Injection concentration is based on body weight (kg). Fluoxetine is given at 500 μmol/kg and sertraline at 250 μmol/kg. Total injection volume is 5 mL/kg. These concentrations have been shown in previous studies to reduce immobility times in FST and TST. SSRI injections are given 30 min prior to FST. Please refer to Fig 1. Mouse Schedule: Mice are weighed prior to each testing session. During the baseline session, a mouse is first subjected to the forced swim test for 6 min. The final 4 min are scored for immobility. After the FST session, there is a 24 hr waiting period. Then the mouse is subjected to the tail suspension test for another 6 min baseline session. The final 5 min are scored for immobility. Following a 72 hour waiting period, the mouse is injected (5 mL/kg) with an SSRI (appropriate concentration) or saline (vehicle-control). There is a 30 min waiting period after the injection. Then the mouse is subjected to FST for 6 min. The final 4 min are scored for immobility. After the FST session, there is a 24 hr waiting period. The mouse is again injected with an SSRI or saline and a 30 min waiting period follows. Then the mouse is subjected to the tail suspension test for another 6 min session. The final 5 min are scored for immobility. After each testing session, the mouse is returned to its home cage (with littermates). This 6-day schedule applies to all mice. Please refer to Fig 1. The purpose of the waiting periods is to reduce confounding variables. The period between FST and TST sessions is only 24 hours due to time limitations. Mice are adolescents for a very short period. Contingency Plan: If early results show that SSRIs are less effective in mice with a high capacity for serotonin reuptake (L/L), then FST and TST session times will be increased in order to sufficiently depress all mice. While mice with a high capacity for serotonin reuptake are not as likely to get depressed, if they do, exceptionally high levels of reuptake inhibitors (SSRIs) may be required to alleviate the symptoms. The goal is to see pronounced differences in SSRI treatment between different genotypes. If feasible, different doses of treatment can also be tested to see differences between genotypes. Predicted Outcomes There are three variables in this experiment: age (4-, 6-, or 12week old), genotype (S/S, S/L, L/L, or KO), and SSRI treatment (fluoxetine or sertraline). This allows for numerous patterns in the results. It is difficult to predict the complex interaction between these variables, but some general results are expected. In terms of genotype, I hypothesize that mice with the S allele and 5-HTT knockouts should be less responsive to SSRI treatment. Mice with reduced serotonin reuptake capacity may have a reduced sensitiv©2007 Cornell Synapse | www.cusn.org

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ity to block off that reuptake function. Therefore, they will be less responsive to SSRI blockage of the serotonin reuptake transporter. On the other hand, it is also plausible that the reverse is true, which raises another hypothesis. Perhaps patients with a particularly high capacity for reuptake may be less sensitive to inhibition of that function as a result. While they are not as likely to get depressed, if they do, exceptionally high levels of reuptake inhibitors (SSRIs) may be required to alleviate the symptoms. SSRI effectiveness may also vary with age. For example, SSRIs may be ineffective in younger mice (4 and 6 weeks of age) with the L/L genotype, but more effective in older mice with the same genotype. This would explain the ineffectiveness of SSRI treatment in children and adolescents. In general, it is predicted that SSRIs will be least effective in younger mice (4 and 6 weeks of age) and most effective in older mice (12 weeks of age). Fluoxetine is expected to be more effective than sertraline at reducing depression in younger mice. Differences in fluoxetine and sertraline should not be seen in adults. Fluoxetine is the only FDA approved SSRI for children under 18, and sertraline is known to be less effective in children and adolescents. It is possible that sertraline is ineffective in all young mice, while fluoxetine is more effective in young mice with the L allele than the S allele. Clearly, there are many possibilities for these results and it is necessary to conduct these experiments to elucidate any interesting patterns. Summary This study will work towards understanding the link between 5-HTT genotype and SSRI treatment. It will also test for the age dependence of SSRI treatment and compare the effectiveness of two specific SSRIs, fluoxetine and sertraline. Two hypotheses are equally plausible for the expected link between 5-HTT and genotype. First, mice with reduced serotonin reuptake capacity may have a reduced sensitivity to block off that reuptake function. Therefore, they will be less responsive to SSRI blockage of the serotonin reuptake transporter. Conversely, mice with a particularly high capacity for reuptake may be less sensitive to inhibition of that function as a result. While they are not as likely to get depressed, if they do, exceptionally high levels of reuptake inhibitors (SSRIs) may be required to alleviate the symptoms. In either case, treatment of depression can be improved by gaining a better understanding of this link. In humans, SSRIs are least effective in children and adolescents. This study will test the age dependence of SSRI treatment. It is hypothesized that SSRI treatment will be least effective in younger mice. It is plausible that the effectiveness of SSRI treatment is dependent on a complex interaction between 5-HTT genotype and age. This study will also compare the effectiveness of fluoxetine and sertraline. It is hypothesized that sertraline will be less effective than fluoxetine at reversing the symptoms of depression in younger mice. In adult mice, a difference in effectiveness is not expected. Perhaps there is an interesting pattern linking genotype, age, and SSRI treatment. The overall purpose is to develop more effective methods of treatment for depression, particularly in those not responsive to SSRIs. Depression is an important world health issue and it needs to be researched in greater detail. 36


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References 1. Licinio J. et. al. (2005) Depression, antidepressants, and suicidality: a critical appraisal. Nat Rev Drug Discov. 4(2):165-171. 2. Wong M.L. et. al. (2001) Research and treatment approaches to depression. Nat Rev Neurosci. 2(5):343-351. 3. Wong M.L. et. al. (2000) Pronounced and sustained central hypernoradenergic function in major depression with melancholic features: relation to hypercortisolism and corticotropin-releasing hormone. Proc Natl Acad Sci. 97(1):325-330. 4. Whybrow P.C. et. al. (1981) A hypothesis of thyroid-catecholamine-receptor interaction. Its relevance to affective illness. Arch Gen Psychiatry 38(1):106-113. 5. Gold P.W. et. al. (1999) The endocrinology of melancholic and atypical depression: relation to neurocircuitry and somatic consequences. Proc Assoc Am Physicians. 111(1):22-34. 6. Kupfer D.J. et. al. (1982) Application of automated REM analysis in depression. Arch Gen Psychiatry. 39(5):569-573. 7. Yamaguchi K. et. al. (1999) Detection of borna disease virusreactive antibodies from patients with psychiatric disorders and from horses by electrochemiluminescence immunoassay. Clin Diagn Lab Immunol. 6(5):696-700. 8. Sullivan P.F. et. al. (2000) Epidemiology of major depression: review and meta-analysis. Am J Psychiatry. 157(10):1552-1562. 9. Svenningsson P. et. al. (2006) Alterations in 5-HT1B receptor function by p11 in depression-like states. Science. 311(5757):7780. 10. Caspi A. et. al. (2003) Influence of life stress on depression: moderation by a polymorphism in the 5-HTT gene. Science. 301(5631):386-389. 11. Murphy D.L. et. al. (2001) Genetic perspectives on the serotonin transporter. Brain Res Bull. 56(5):487-494. 12. Bennett A.J. et. al. (2002) Early experience and serotonin transporter gene variation interact to influence primate CNS function. Mol Psychiatry. 7(1):118-122. 13. Hariri A.R. et. al. (2002) Serotonin transporter genetic variation and response of the human amygdala. Science. 297(5580):319. 14. Holmes A. et. al. (2003) Abnormal behavorial phenotypes of serotonin transporter knockout mice: parallels with human anxiety and depression. Biol Psychiatry. 54(10):953-959. 15. Cryan J.F. et. al. (2005) The ascent of mouse: advances in modeling human depression and anxiety. Nat Rev Drug Discov. 4(9):775-790. 16. Tsuji K. et. al. (2000) No association of borna disease virus with psychiatric disorders among patients in northern Kyushu, Japan. J. Med. Virol. 61(3):336-340. 17. Sanchez C. et. al. (1997) Behavioral profiles of SSRIs in animal models of depression, anxiety and aggression. Psychopharmacology. 129: 197-205. 18. Mayorga A.J. et. al. (2001) Antidepressant-like behavioral effects in 5-hydroxytryptamine(1a) and 5-hydroxytryptamine(1b) receptor mutant mice. J. Pharmacol. Exp. Ther. 298:1101-1107. 19. Bengel D. et. al. (1998) Altered brain serotonin homeostasis and locomotor insensitivity to 3, 4-methylenedioxymethamphetamine in serotonin transporter-deficient mice. Mol. Pharmacol. 53:649-655.

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ACKNOWLEDGEMENTS This publication was made possible with generous contributions and guidance from the following: Dr. Kraig Adler

Professor, Department of Neurobiology and Behavior

Dr. Andrew H. Bass

Professor, Department of Neurobiology and Behavior

Dr. Ronald Booker

Professor, Department of Neurobiology and Behavior

Ms. Bonnie Comella

Director of Undergraduate Advising, Office of Undergraduate Biology

Dr. Thomas Eisner

Professor, Department of Neurobiology and Behavior

Dr. Gerald W. Feigenson

Professor, Department of Biochemistry, Molecular and Cell Biology

Dr. Joseph R. Fetcho

Professor, Department of Neurobiology and Behavior

Dr. Carl D. Hopkins

Professor, Department of Neurobiology and Behavior

Dr. David P. McCobb

Associate Professor, Department of Neurobiology and Behavior

Dr. Michele Moody-Adams

Vice Provost of Undergraduate Education and Professor, Department of Ethics and Public Life

Dr. Thomas D. Seeley

Chairman and Professor, Department of Neurobiology and Behavior

Dr. Laurel Southard

Director of Undergraduate Research and Outreach, Office of Undergraduate Biology

Student Assembly Finance Commission (SAFC)

If you are interested in contributing your support (financial or otherwise) to Cornell Synapse and the Cornell Undergraduate Society for Neuroscience, please email us at synapse@cusn.org

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