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A three-dimensional digital brain atlas and stereotaxic coordinates for the Anna’s Hummingbird, Calypte… Stegeman, Amelia Gwen 2013

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A THREE-DIMENSIONAL DIGITAL BRAIN ATLAS AND STEREOTAXIC COORDINATES FOR THE ANNA’S HUMMINGBIRD, Calypte anna  by Amelia Gwen Stegeman  B.Sc., The University of Victoria, 2010  A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF  MASTER OF SCIENCE in THE FACULTY OF GRADUATE STUDIES (Zoology)  THE UNIVERSITY OF BRITISH COLUMBIA (Vancouver)  June 2013  © Amelia Gwen Stegeman, 2013  Abstract This thesis presents a new three-dimensional atlas for analyzing the neuroanatomical structures of the avian brain. Using magnetic resonance imaging (MRI) and X-ray computed tomography (CT), the brain of an Anna’s Hummingbird (Calypte anna) was scanned. These datasets were coregistered using three-dimensional visualisation software and overlaid with thionin stained slices in the sagittal and coronal view. Results show that previously developed techniques for creating three-dimensional atlases in other birds can be successfully used for representing the anatomical structures of hummingbirds (Güntürkün et al., 2013; Poirier et al., 2008; Vellema et al., 2011). There is no previous atlas, neither two nor three-dimensional, available for hummingbirds or any other wild-caught bird. Studies requiring avian neural recordings have not included hummingbirds, but this combination of imaging protocols along with descriptions from the literature provides the necessary and sufficient information for outlining many of the major functional structures in the Anna’s hummingbird brain. The co-registered datasets are also sufficient for constructing histological data into a three-dimensional representation that is functional in guiding classical tract tracing and electrophysiology experiments. By creating brain area delineations in three dimensions and anchoring these data to a skull, researchers will now be able to reach target structures from outside of the brain. For two regions of the brain, the lentiformis mesencephali and nucleus of the basal optic route, suggested stereotaxic coordinates and angles are provided. Through expanding the collection of atlases to include hummingbirds we introduce a new animal that will be especially useful for future studies of avian motor control.  ii  Preface This project was completed using contributions from many parties. I was the principal person responsible for identification and design of the research program, as well as experimental protocols and analysis of the research data. Data collection was carried out with assistance from various individuals and imaging facilities. The primary magnetic resonance imaging (MRI) data was collected under the initiative of Dr. Robert Donnavon with the staff and facilities provided by Loma Linda Univeristy. Functional MRI scans were made under my initiative at the University of British Columbia (UBC) MRI Research Centre. During this process, I received considerable guidance and logistical support from the researchers there, including supervisory committee member, Dr. Piotr Kozlowski. I was responsible for collecting X-ray computer tomography data from the Centre for Hip Health and Modibility with technical operations led by Danmei Liu. I completed the data analysis using Amira© V5.4.3 software at the UBC Bioimaging Facility with technical assistance from Bradford Ross. I also made images of histological slices using the Olympus SZX10 research stereo microscope with the DP72 digital camera and cellSens® software from the same facility. All experimental procedures were approved by the UBC Animal Care Committee and conducted in accordance with guidelines set forth by the Canadian Council on Animal Care. In all stages I received guidance from my supervisor Dr. Doug Altshuler and my supervisory committee members, Dr. Piotr Kozlowski and Dr. Kiran Soma. Support for histology was given by Dr. Martin Wild. Edits were provided by Dr. Doug Altshuler, Dr. Piotr Kozlowski, Dr. Kiran Soma, and Dr. Andrea Gaede.  iii  Table of Contents Abstract .......................................................................................................................................... ii Preface ........................................................................................................................................... iii Table of Contents ......................................................................................................................... iv List of Tables ................................................................................................................................ vi List of Figures.............................................................................................................................. vii List of Symbols ........................................................................................................................... viii List of Abbreviations ................................................................................................................... ix Acknowledgements ........................................................................................................................x Chapter 1: Introduction ...............................................................................................................1 1.1  Bird Vision ...................................................................................................................... 1  1.2  Motor Control ................................................................................................................. 5  1.3  Hummingbirds ................................................................................................................ 7  1.4  Atlas ................................................................................................................................ 9  Chapter 2: Methods ....................................................................................................................11 2.1  Specimen Prepartion ..................................................................................................... 11  2.2  MRI and CT Data Acquistion ....................................................................................... 11  2.3  Positioning .................................................................................................................... 12  2.4  Histology ....................................................................................................................... 12  2.5  Delineation of Brain Areas and 3D Reconstruction ..................................................... 13  Chapter 3: Results.......................................................................................................................14 3.1  Scanning Protocols and Structural Delineations ........................................................... 14  3.1.1 X-ray Computed Tomography .................................................................................. 15 3.1.2 Magnetic Resonance Imaging ................................................................................... 17 3.1.3 Histology ................................................................................................................... 17 3.2  Data Presentation and Validation.................................................................................. 18  Chapter 4: Discussion .................................................................................................................22 4.1  Three-Dimensional Atlas .............................................................................................. 22  4.2  MRI and CT Applications ............................................................................................. 23  4.3  Electrophysiology Applications .................................................................................... 26 iv  4.4  Stereotaxic Coordinates ................................................................................................ 26  References .....................................................................................................................................31  v  List of Tables Table 1. Delineated structures and their associated colours in the images. .................................. 19  vi  List of Figures Figure 1. Schematic of the brain of an Anna's Hummingbird (Calypte anna). .............................. 2 Figure 2. Visual pathways on side-views of a hummingbird brain. ............................................... 3 Figure 3. Schematic summary of the major avian visual pathways. ............................................... 4 Figure 4. Summary of registration and delineation of anatomical structures. .............................. 14 Figure 5. Schematic of a hummingbird skull with stereotaxic coordinates and slicing planes indicated. ....................................................................................................................................... 16 Figure 6. Transparent histological slices laid over orthogonal MRI slices................................... 17 Figure 7. Schematic of a hummingbird skull in default orientation for digital atlas. ................... 18 Figure 8. Highlighted delineations of key areas on 3D brain image. ........................................... 20 Figure 9. Orthogonal slices in the coronal plane showing delineations. ...................................... 21 Figure 10. Threshold specific selection of skeletal regions from CT dataset. .............................. 24 Figure 11. Representation of MRI data showing variation in inclusion and transparency of the whole brain and brain regions. ...................................................................................................... 25 Figure 12. Schematic of the stereotaxic coordinates for the lentiformis mesencephali on CT dataset. ....................................................................................................................................................... 28 Figure 13. Schematic of the stereotaxic coordinates for the nucleus of the basal optic root on CT dataset. .......................................................................................................................................... 29  vii  List of Symbols ®  registered trademark  ©  copyright  τ  tau  ρ  rho  η  eta  viii  List of Abbreviations TeO  optic tectum  AOS  accessory optic system  dLGN dorsal thalamus MRI  magnetic resonance imaging  CT  X-ray computed tomography  2D  two-dimensional  3D  three-dimensional  UBC University of British Columbia Te  telencephalon  BO  bulbus olfactorius  CB  cerebellum  Rt  nucleus rotundus  nBOR nucleus of the basal optic root LM  nucleus lentiformis mesencephali  OPT  principal optic nucleus of the thalamus  Hp  hippocampus  E  entopallium  PFA  paraformaldehyde in 0.1M phosphate buffer  RARE rapid acquisition with relaxation enhancement  ix  Acknowledgements I must first thank my supervisor, Doug Altshuler, for his unyielding support, meticulous labelling of lab supplies, and impressive ability to muster a pep talk with each of my numerous new directions. Thank you to Andrew, Barry and Piotr at the MRI imaging facility for letting me play with your giant magnet, despite that I never did train a hummingbird to hold its head still. To Remy Barois, thank you for creating an impressive and scientific-looking apparatus to hold still the head of a hummingbird. Thanks to Doug Wylie, who tolerated my intrusion on his lab and showed me what is possible when expert instruction is combined with the convenience of a working espresso machine. And thanks to Dave Graham for said expert instruction. Christian, you helped too. To my labmates who showed me that research is never boring; I give extra thanks to Benny for showing me that beer, at any time of the day, underscores this point. Thanks to everyone in biomechanics for drinking with me. Particular thanks to James, for bringing bird calls indoors; Marc, for making cheerful imitations of the calls; and Trisha, for luring me to the swimming pool and Blue Chip when sanity waned. Thanks to Maximillian, Willem von Oranje, Attila the Hum, Montezuma, King Edward, Caesar, Obama, King Tut, and Xerces for animating my world; I am forever in your debt. Thanks to the UBC Bioimaging Facility for giving me access to super software at a time of crisis and then giving me Brad, who proved useful in offsetting my remarkably questionable skills at using super software. Additionally, thank you to my classmates and commiserators for coping with my moods that changed as fast as my research objectives and yielding to my requests to toss disc. Special thanks to Chris and Adam who assured me countless times that I was, in fact, not going to blow anything up. On the home front, thanks Mom and Dad for filling my basement with furniture and my fridge with beer. I would also like to extend thanks to my friends for fueling the writing of the thesis with constructive edits and an endless supply of chocolate. Last but not least, to Jennifer, for being my soul mate and longtime supporter of everything I attempt, this project included.  x  Chapter 1: Introduction 1.1  Bird Vision  To be able to perform at fast speeds, birds need to respond to their surroundings that are quickly changing as they move about in three dimensions. Birds have a large incoming sensory stream of visual information from the eye, proprioception from muscles and joints, mechanical deformations to skin and feathers, and equilibrium from the inner ear, but vision is the primary modality of sensory perception for birds (Brooke et al., 1999; Walls, 1944). Dominating the space within the skull, birds have eyes that take up to 50% of their cranial space. The eyes and associated neural structures are under considerable selective pressure to keep pace with flight control (Burton, 2008; Hughes, 1977). It is common to evaluate organ size based on a functional component; the axial length of the eye is often used, because it dictates the resolving power of the eye (Howland et al., 2004; Hughes, 1977). This distance between the cornea and lens to the retina determines the magnifying power of the eye and consequently the size of the image. However, regardless of how the eye is measured, variables including axial length, diameter, and volume all scale with absolute eye size, which reflects the importance of vision among birds (Howland et al., 2004). Another visibly large structure within the cranial space is the optic tectum (TeO), which is a lobe of the brain dedicated to responding to visual stimuli (Figure 1). As navigating around objects and detecting self-motion is important in flight, many cells in the TeO are sensitive to the movement of objects and self-movement (Kelly et al., 2001; Watanabe and Troje, 2006; Wylie et al., 2009). The avain brain contains three main pathways for collecting and processing visual information: the tectofugal, thalamofugal and accessory optic system (Wylie et al., 2009). Together they receive visual input from light sensitive photoreceptors and project to various processing and integration centres within the brain. Each pathway also exists in mammals with most avian visual centres having a homologous structure in the mammalian brain (Medina and Reiner, 2000).  1  Figure 1. Schematic of the brain of an Anna's Hummingbird (Calypte anna). a Side view illustrating the major regions of the brain. The thalamus is covered by the cerebrum. b Dorsal view. The optic tract and thalamus are located on the ventral surface and not visible here. Scale bar is 2mm.  The tectofugal pathway receives retinal signals to the TeO (mammalian homologue: Superior Colliculus) and projects to the nucleus rotundus of the thalamus (Rt; mammalian homologue: pulvinar complex) and to lesser amount to the nucleus triangularis, on to the entopallium (mammalian homologue: extrastriate visual areas (Güntürkün et al., 2013; Mpodozis et al., 1996; Nguyen et al., 2004)). The entopallium is a telencephalic structure that projects to the hyperpallium (or Wulst) through the arcopallium, which is thought to be involved in higher level integration in both birds and mammals (Butler and Hodos, 2005; Karten and Shimizu, 1989; Nguyen et al., 2004). This is considered the primary route that visual stimuli travel to the telencephalon. The thalamofugal pathway provides a secondary route from the retina to the telencephalon, but through the principal optic nucleus of the thalamus, rather than the optic tectum and other structures of the tectal path. In the thalamofugal pathway, the retina projects to the principal optic nucleus of the thalamus (OPT; mammalian homologue: lateral geniculate nucleus) which then projects to the hyperpallium (Medina and Reiner, 2000; Reiner et al., 2005).  2  Figure 2. Visual pathways on side-views of a hummingbird brain. Arrows indicate projections from the retina and subsequent nuclei; red for mossy fibres, green for climbing fibres. Cb, cerebellum; Rt, nucleus rotundus; TeO, optic tectum; LM, lentiformis mesencephali; nBOR, nucleus of the basal optic route; IO, inferior olive.  3  The accessory optic system (AOS) is an important system for responding to optic flow. The AOS receives retinorecipent signals at the nucleus of the basal optic root (nBOR; mammalian homologue: medial terminal nucleus) and the pretectal nucleus lentiformis mesencephali (LM; mammalian homologue: nucleus of the optic tract) (Karten et al., 1977). These two centers send signals to the pre-motor strucutures such as the inferior olive and directly to the cerebellum (Azevedo et al., 1983; Brecha and Karten, 1979). The LM and nBOR project directly to the vestibulocerebellum (folia IXcd and X) as mossy fibers, and indirectly as climbing fibers from the inferior olive. The inferior olive is a pre-motor integration centre that plays a role in detecting selfrotation and optic flow (Winship and Wylie, 2003; Wylie et al., 1999). The LM also projects to the oculomotor folia of the cerebellum (folia VI-VIII) (Brecha et al., 1980; Pakan and Wylie, 2006; Winship and Wylie, 2003; Wylie, 2013). Other projections, particularly from the LM, have not been well explored (Brecha and Karten, 1979; Giolli et al., 2006; Wylie et al., 1999; Wylie et al., 2009). The AOS is also important in other behaviours, such as postural control, speed and direction selectivity and changes in eye position, particularly in the vertical plane (Crowder et al., 2003; Simpson, 1984; Winship et al., 2006). The LM is known to play a role in perceiving optic flow from self-motion and generating the optokinetic response (Winterson and Brauth, 1985; Wylie, 2013). The optokinetic response is responsible for stabilizing the eye during movement (Gioanni et al., 1981).  Figure 3. Schematic summary of the major avian visual pathways. In parentheses, the region of the brain where the structure is located. Adapted from Wylie et al. (2009).  4  1.2  Motor Control  Birds have many avenues of sensory perception, including vision, hearing, and proprioception. All of this must be processed and converted into a set of coherent motor commands within a short period of time. Muscles are innervated by motor nerves that receive signals that extend down the spinal cord from the brain. At the level of the brain, we know that sensory input modulates motor control with the product accumulating in actions such as controlled flight. However, even though much is known about the visual centres in the avian brain and studies have explored neuromuscular anatomy in birds (Donovan et al., 2013; Gaunt and Gans, 1993; Sokoloff et al., 1998), senorimotor integration and motor control for avian flight at the level of the brain is largely unexplored. Many prior studies of animal flight have been performed with insects and contribute to our knowledge of flight control. These studies have focused on specific flight control algorithms including altitude control (Baird et al., 2006), velocity determination (Fry et al., 2009), landing versus avoidance responses (Tammero and Dickinson, 2002), the presence of certain control nuclei (Reiser and Dickinson, 2010), and numerous other components of flight control (Srinivasan et al., 1999; Wegener et al., 1991). These studies provide a good background for comprehending flight control, while analagous behavioural experiments in hummingibrds increase our understanding of vertebrate motor control. These studies are already underway, including in our lab, and will present an opportunity to understand how flight control is organized anatomically in the vertebrate brain. Studies have been done to explore the anatomy of the avian peripheral nervous system, in the context of motor control through muscle innervations. Retrograde tracers have revealed the location of nerve pools in the spinal cords by following the source of muscle contractions back to the motor pools (Donovan et al., 2013; Sokoloff et al., 1989). Also, many studies have looked at the arrangement of motor endplates, the place to which motor neurons project (Donovan et al., 2013; Gaunt and Gans, 1993; Rosser et al., 1987; Torrella et al., 1993). John Steeves et al. (1987) explored the descending projections of the spinal cord with geese and ducks. They identified locomotor centres in the pontomedullary reticular formation, an ancestral somatic motor centre common to verterates which projects to tracts in the spinal cord and cerebellum (Sholomenko and  5  Steeves, 1987; Steeves et al., 1987; Webster and Steeves, 1988). In the mid-and fore-brain, essentially nothing is known about flight control. The cerebellum is strongly associated with coordination and movement in vertebrates. The avian cerebellum is a clearly foliated structure with a combination of climbing fibers and mossy fiber cell-types projecting to the Purkinje cells, which are arranged in a parasagittal pattern within the folds (Pakan et al., 2007; Wylie, 2013). It receives singals from motor regions in the reticular formation and integrates these signals with visual, auditory, and vestibular stimuli for motor coordination (Paulin, 1993). However, reviews by Paulin (1993) and Bower (1997) challenge the primary function of the cerebellum, suggesting that it functions primarily as a tracking system which follows movements of other objects in addition to and with consideration to controlling is own movements. The AOS does play an important role in sensory preception as it receives visual infromation from nuclei of the AOS and tecto pontine (Wylie, 2013). For more information on the precise interaction of the these nuclei with the cerebellum see Wylie (2013). Another region proposed to be a key structure for flight control is the nucleus rotundus, which also functions in visual tracking. It has a collection of cells that respond to various properties of looming visual motion and has been studied in pigeons (Sun and Frost, 1998). This is important for guiding motor behaviours such as ‘aerial docking,’ or landing in insects and birds (Hatsopoulos et al., 1995; Sun and Frost, 1998). By taking extracellular recordings from cells in response to expanding images, which simulates an object looming directly toward the bird, three types of cells in the nucleus rotundus were identified from the different firing patterns. The first group, known as τ (tau), are sensitive to the size of the object (Lee, 1976; Sun and Frost, 1998). The second group, ρ (rho), are sensitive to the rate of expansion. And the third group, η (eta), are sensitive to proportional changes in velocity and size. Rho and eta are absolute values, while tau is relative, in that they detect absolute or relative changes to the stimuli, respectively. Tau and eta together are able to predict time to collision information, which is of obvious ecological importance in birds for both docking, and eliciting a collision avoidance response to approaching objects. The role of tau and the other cells illustrates a pattern for understanding flight control. The motor behaviours of birds must be very closely controlled by visual signals, as they travel freely in a 3D space with fast changes to the environment. This demonstrates the importance of vision in flight and the importance of understanding the connection between vision and motor control.  6  1.3  Hummingbirds  Hummingbirds provide a unique opportunity to study vision and flight in a vertebrate system. Hummingbirds achieve flight with insect-like motions within a vertebrate cognitive system. Shifting flight studies to a vertebrate has both comparative and applied benefits. Hummingbirds are used as a model for bird flight as they possess behaviours that suit them well for scientific investigation and are incredible fliers. They are achieve vision stabilisation and collision avoidance while maneoevering in three dimensions. They hover in mid-air and depend on frequent feeding. It is these features combined with avian cognitive powers that makes them trainable. Already hummingbirds have been the focus of several fight experiments including studies on aerodynamics (Warrick et al., 2005), biomechanics (Hedrick et al., 2012), muscle anatomy (Donovan et al., 2013), and even visual control (Goller, 2011), but rarely have they been used as a model for studies of the brain in the context of motor control. Avian neuroscience with hummingbirds has largely been for the purpose of expanding evolutionary studies on songbirds, particularly they are used in comparative species models (Gahr, 2000; Jarvis and Vielliardk, 2000). Despite their extraordinary flying abilities and ease of training in the laboratory, hummingbirds have not yet been used to study how the avain brain controls flight. They possess hypertrophied regions that suggest strong specialisation for flight control and navigation. Specifically, the LM and hippocampus are hypertrophied. The LM, as mentioned earlier, relays sensory messages in the accessory optic tract to the cerebellum and inferior olive (Ambrosiani et al., 1996; Wild, 1989). These regions have a function in coordination and movement, as well as perception of movement of self and objects. Likewise the hippocampus, the other region known to be hypertrophied in hummingbirds, is generally in vertebrates responsible for memory and location in space (Ward et al., 2012). O’Keef and Dostrovsky (1971) were the first to demonsterate that the hippocampus creates a two-dimensional (2D) spatial map which can be used to navigate space. The volume of the hippocampus has been shown to be related to spatial memory in birds (Basil et al., 2008). We expect that these regions of hypertrophy can teach us more about motor control. Larsell (1967) suggests that more agile animals require brains that are larger and more complicated. Hummingbirds; although very small animals, have brains that are more than half the size of birds that are an order of magnitude larger. Given they have relatively large brains and are capable of 7  complicated manoevers, we expect that the regions of the brain that are hypertrophied in hummingbirds will play a role in motor control. Evidence for this can be collected by electrophysiology and tract tracing studies that show projections from the hypertrophied regions in the hummingbird brain. We predict that in hummingbirds we will see either novel projections to motor centres from the LM and hippocampus, or disproportionate projections to known premotor and motor centres. This would provide support for the theory the hypertrophied neural regions have ecological functions for animals. Alternatively, it is possible that any hypertrophy seen in hummingbirds is a consequence of a minimum size requirement. They are the smallest vertebrates with the bee clades weighing in at as low as 2g. Structures of the brain and the brain itself may not be so much hypertrophied, as unable to scale to such a small body size. Reducing the size further may result in loss of function. Larger regions have more cells and it is clear that hummingbirds have larger LMs than birds with similar sized brains (Iwaniuk and Wylie, 2007). Thus, they have to have more cells in the LM. If the same proportion of cells projecting to other nuclei in both hummingbirds and birds with similar sized brains, then we might conclude that this region could not be reduced. If, on the other hand, we see a disproportionate number of cells projecting to a specific nucleus or to a novel circuit, then this suggests a specialization for hovering. Given that hummingbirds are so highly adapted for the maneouvers required for nectavrious living in other ways, it would not be surprising to uncover that their brains are likewise adapted for extreme flying. These regions of hypertrophy may be centres for the higher processing necessary for hummingbird flight. Setting out to uncover the function of these pathways, we discovered a major gap in the neuroanatomical studies of hummingbirds. There are no published live recordings taken from a hummingbird brain, in the context of motor control or in any other context. Electrophysiology is not typically done on free ranging small animals (Jarvis et al., 2005). The previous studies done on hummingbirds were completed on fixed brain tissues (Feenders et al., 2008; Terpstra et al., 2005; Ward et al., 2012). By performing classical electrophysiology experiments, we gain access to information on a new avian family. Before this can be done, however, the brain needs to be mapped out in such a way that information can be collected from within the brain from the surface through electrode readings and tract tracing injections. Given that such a map is not published, we  8  set out to establish our own neuroanatomical representation or ‘atlas’ of the brain, using a wild caught Anna’s Hummingbird (Calypte anna).  1.4  Atlas  The objective of a neurotanatomical atlas is to provide its user with information about the location of brain regions relative to landmarks such as the external surface. Classically, careful histology was used to separate the brain into a coordinate system, operating from interaural zero (the precise point between the ears, indicated on the skull as bregma in birds such as pigeons); down from the surface of the brain; and laterally away from the rostro-caudal midlines (Karten and Hodos, 1967). Early atlases were constructed from serial sections of the whole brain in sagittal and coronal view. However; the most recent atlases have liberated neuroscientists from applying tools only straight down the vertical axis in a Cartesian coordinate system. With the introduction of high resolution magnetic resonance imaging (MRI) and X-ray computer tomography (CT), three-dimeionsal (3D) models of brains can be constructed within the context of the cranium. MRI atlases utilise high resolution images of the brain collected in three dimensions to create a digital representation of the brain. Investigation of the hummingbird brain centres for sensory input, motor control, and sensorimotor integration require precise identification and localisation. With a 3D digital brain, one can utilise what are essentially an infinite number of angles and translations to reach every part of the brain, selecting a minimally invasive path for recording and injecting pipettes. For example, it was previously necessary to pull back muscles attached to the caudal point of the skull to gain access to posterior-caudal structures of the brain. Coming in at an angle from the surface of the brain would reduce the complexity and invasiveness of this procedure. However, for the hummingbird, there is no published atlas, histolgical, 3D, or otherwise. For the canary, zebra finch and pigeon, there are stereotaxic 3D MRI atlases available online in digital archives (Güntürkün et al., 2013; Poirier et al., 2008; Vellema et al., 2011). These are the only birds to be documented in this way. Forty-five years ago the pigeon atlas (Karten and Hodos, 1967) became the standard for its clarity, ease of use, and the popularity of the pigeon (Columbia livia) as a study organism. Again, with the generation of the MRI atlases, the new pigeon atlas looks to be the standard. As the third 9  3D atlas to be published, it has a more streamlined approach for building the data needed to localize and identify brain centres (Güntürkün et al., 2013). Here we attempt to follow the steps outlined by Güntürkün et al. (2013) to create a 3D digital MRI atlas for hummingbirds. In the same way the pigeon is the model for so many avian neural studies, we used the 3D atlas of the pigeon as a methodological model for creating an atlas. It has been by the careful work of the previous atlases that we have been able to investigate visual pathways and other functional anatomical brain structures. Already, the hummingbird is seeing an increasing presence in laboratory investigations of flight (Elimelech and Ellington, 2013; Pournazeri et al., 2012; Sapir and Dudley, 2013). Furthermore, this study represents the first known atlas for a wild caught, non-domestic animal. It is setting the stage for live recordings and tract tracing injections into an animal that is a important model for flight. Also, Anna’s hummingbird provides information on a whole new family of birds that with a few exceptions (Feenders et al., 2008; Gahr, 2000; Iwaniuk and Wylie, 2007; Iwaniuk et al., 2009; Jarvis and Vielliardk, 2000; Terpstra et al., 2005; Ward et al., 2012) have been underrepresented in avian neuroscience.  10  Chapter 2: Methods  2.1  Specimen Prepartion We used one wild caught adult Anna’s hummingbird (Calypte anna) for the MRI and CT  scans. This primary bird was captured near the University of California, Riverside and housed for 4 days. We also used one wild caught adult C. anna from the Univeristy of British Columbia (UBC) for collecting histological data. We housed this bird for 12 days in a vivarium room at UBC. We fed both the birds a diet of sugar water and fortified Nektar-Plus. They were both male and weighed between 3.5 grams and 4.5 grams at time of death. The birds were both transcardially perfused to fix the tissues. Before the perfusion, birds were first sedated with isoflourane and then anesthetized with a mixture of Ketamine (65µg/g) and Xylazine (9µg/g), delivered as one intramuscular injection. The birds were then perfused with saline solution (0.9%), followed by paraformaldehyde in 0.1M phosphate buffer (PFA, 4%). We removed the feathers and soaked the heads in PFA (4%). For the primary bird, twenty-four hours prior to the MRI scan, we soaked the head in gadolinium paramagnetic contrast agent. After the MRI scan, the head was immediately returned to PFA (4%) until it was again temporarily removed for the duration of the CT scan.  2.2  MRI and CT Data Acquistion  To make a clear model of the cranium, we used HR-pQCT (XtremeCT, Scanco Medical) to scan the bone structure around the head, including the beak and ears at the Centre for Hip Health and Mobility (Vancouver, Canada). To minimize the space scanned, a preliminary scan was completed to produce a scout image. The field of view was 38.9mm, which corresponds to the maximum bore size for this operation. The start and end positions of the scan were selected to be just outside the tip of the beak and the back of the head, as interpretted from the scout image. We acquired the images at a high isotropic voxel resolution of 19µm. There were a total of 1778 slices, each with a 19µm thickness. We carried out the scans at an energy level of 55kVp with a current of 142mA. The total integration time was 300 ms and the total acquisition time was 120 minutes. 11  For magnetic resonance imaging (MRI), we made 3D rapid acquisition with relaxation enhancement (RARE) scans using Brucker MRI 11.7T (500MHz) with a 8.9 cm bore magnet at the Non-Invasive Imaging Laboratory at Loma Linda University (Loma Linda, United States of America). The scans produced T2 weighted images (field of view = 2x2x2cm; repetition time = 2937.75ms; echo spacing = 8.3ms; RARE factor = 8). We made scans that produced images with 3D 256x256x256 slice resolution and an isotropic spatial resolution of 78.125µm. The data was taken using one acquisition (number of excitations = 1) and total acquisition time 13 hours.  2.3  Positioning  For both the HR-pQCT scan and the MRI scan, the bird head was suspended in the middle of the scanning tube. The CT scan was done with the neck of the fixed head inserted into a hole in a single Styrofoam block. The bill was tapped into a hollow Styrofoam chip that was secured to the main Styrofoam block with tape. In this set up, the head was immobilized in place so that it would not shift or move during the scan. The Styrofoam has a comparably much lower density relative to the hummingbird head, so as not to interfere with data acquisition. The block was then compressed to fit securely into a transparent plastic cylinder. The head was aligned carefully along the major axis of the scanner. The whole apparatus was inserted and used for both the scout view scan and the data acquisition scans. For the MRI scans, a small apparatus was designed to immobilize the bird head. Motion creates artifacts in high resolution MRI data, so a secure device is used to prevent any movement of the head, such as drifting. We secured the head between two adjustable plastic ear bars and an acrylic beak clamp that gently pressed the beak against a ledge.  2.4  Histology  We extracted the brain from the skull and soaked it in 4% PFA + 30% sucrose for 2 h followed by a cryoprotectant, 0.1 M PBS + 30% sucrose for 48 h, at 4oC. Then, we sliced the brain into two even halves along the mid-sagittal line. We sectioned one half in the sagittal plane and the other in the coronal plane using a freezing microtome. Slices were made at 30 µm in three parallel series. We mounted one slice from every third series and stained with a nissl stain, Thionin. 12  Then, we coverslipped and photographed slices with Olympus SZX10 research stereo microscope with DP72 digital camera with 17.28 Megapixel resolution and cellSens® software. We stacked the 2D images and registered them onto the scans from the MRI using Amira© V5.4.3 software. These images were then used to confirm anatomical structures on the MRI dataset. We used descriptions of regions from previous publication to identify strucutes on the histology for the Anna’s hummingbird histology and MRI dataset (Gamlin and Cohen, 1988a; Gamlin and Cohen, 1988b; Güntürkün et al., 2013; Iwaniuk and Wylie, 2007; Karten and Hodos, 1967).  2.5  Delineation of Brain Areas and 3D Reconstruction We viewed and analyzed CT and MRI images using Amira© V5.4.3 software. We also used  this program to co-register a 3D model of the MRI onto the 3D CT data. The delineation of the skull from the skin were based on the CT scans. We selected the skull automatically using a signal intensity high-pass threshold. We then auto-registered the MRI dataset to the skull delineation. A rigid registration of the normalized mutual information was done through an affine transformation of the 2 data sets with the default settings from Amira© V5.4.3. After the auto-registration, we made fine tuned adjustments to the registration using landmarks. To make these adjustments, we viewed the MRI data as transparent orthogonal slices laid over top of the solid CT data also represented in corresponding othogonal slices. We manually subdivided the brain surface and regions in both hemispheres of the brain based on the MRI signal intensity differences that are visible in the orthogonal slice views. We made the delineations first in the coronal plane and then validated them and made minor adjustments in the frontal and saggital planes. All delineations were made onto the MRI scans using Amira© V5.4.3 software in reference to previous literature (Gamlin and Cohen, 1988a; Gamlin and Cohen, 1988b; Güntürkün et al., 2013; Iwaniuk and Wylie, 2007; Karten and Hodos, 1967). Histological stains of a male C. anna were also registered to confirm the location of regions on the MRI dataset.  13  Chapter 3: Results 3.1  Scanning Protocols and Structural Delineations  A combination of imaging protocols provides the necessary and sufficient information for outlining major functional structures in the hummingbird brain related to visual flight control. Whereas MRI data provide detail about the anatomical structures of the brain, X-ray CT data shows exactly how the brain sits in relation to the skull and traditional histology provides high resolution slices that reveal cyto-structural differentiation (Figure 4).  Figure 4. Summary of registration and delineation of anatomical structures. a Transparent representation of the skull isolated from a threshold range of CT data. b Three-dimensional representation of the brain isolated from delineations of MRI orthogonal slices. c Representation of the skull (a) registered with the brain (b). d Threedimensional representation of the anatomical regions of the brain isolated from delineations of MRI orthogonal slices after histological confirmation.  14  3.1.1  X-ray Computed Tomography  To construct a stereotaxic atlas, the information about the brain must be represented in the context of the skull. The CT dataset shows precisely the fine detail of the skull, the location of the skull relative to the brain, and how the brain is anchored within the skull. Traditional reference targets are visible with the CT dataset. The location of the ear canals and the shape of the beak along its entire length are visible from the CT data. The coronal plane of the brain is taken as a slice perpendicular to the brain when it is at rest on its ventral surface. This has been adopted in absence of a set beak angle, as is used in other birds (Karten and Hodos, 1967). In the specimen investigated, the coronal slice was found to match as if a vertical slice were taken when the beak is 73.75o below the horizontal plane. The horizontal plane was established as when the ear bars are level with the underside of the beak 22 mm away. The distance of 22mm was selected as it represents the distance from the centre of ear bars to the beak clamp on our stereotaxic apparatus (Figure 5). Structures within the brain are not easily revealed by the CT and are better visualized by MRI and histology techniques.  15  Figure 5. Schematic of a hummingbird skull with stereotaxic coordinates and slicing planes indicated. The circle indicates the location of the ear. 73.75o is the angle between the beak clamp and the horizontal plane of the ear bars for a coronal slice to be in the vertical plane. The line descending from the ear represents 22 mm, the distance between the centre of the ear bars and the beak clamp of the stereotaxic apparatus. Sagittal plane is also indicated.  16  3.1.2  Magnetic Resonance Imaging  T2-weighted magnetic resonance images show the anatomical details within the brain through varying signal intensities. T2-weighted images produce a contrast that is influenced by both the proton density (water content) and T2 (spin-spin relaxation time). Myelinated areas are darker due to the shorter T2 of myelin and grey matter appears light on the other end of the spectrum. The MRI data can be used to delineate many neural anatomical structures (Error! Reference source not found.). Subdivions of the brain can be identified manually in both hemispheres of the brain based on the MRI signal intensity differences that are visible in the orthogonal slice views. 3.1.3  Histology  Prepared slides of coronal and sagittal sections help visualize the detailed architecture of the brain (Figure 6). This information used in conjunction with the MRI data gives the precise location of the regions of interest. Previous literature on histological information was used to accurately identify these regions and confirm the structural information from the MRI dataset.  Figure 6. Transparent histological slices laid over orthogonal MRI slices. a Coronal slice b Sagittal slice; green line indicates the sectioning plane for coronal slices, red line indicates the sectioning plane for sagittal slices, and blue line indicates the sectioning plane for frontal slices (not shown).  17  3.2  Data Presentation and Validation  The images collected from the CT, MRI, and histological data sets are co-registered and available as a compilation or separately to be downloaded. Free access to these files is available by email request to stegeman@zoology.ubc.ca or doug@zoology.ubc.ca. The default orientation for this data set is with the head at angle slightly above the horizontal plane. In this angle, a coronal slice would appear in the horizontal plane or parallel to a plane perpendicular to the extended ear bar axis (Figure 7). Sagittal slices will therefore appear in the vertical plane, perpendicular to the coronal slice. Based on the location of the ear bars and a distance of 22 mm from the centre of the ears to the centre of the beak clamp, the beak must be placed 73.75o below the horizontal plane when the head is properly fitted in the hummingbird stereotaxic device (Figure 5). Depending on which region is being investigated, variations in the stereotaxic angle can be made. Our stereotaxic apparatus allows movement in one dimension, above and below a horizontal plane, for instruments coming in from a vertical direction. Therefore, consideration of angles will be made only with reference to how the head would rotate on the axis of the centre of the ears; however, in reality the head is not restricted to moving in this plane.  Figure 7. Schematic of a hummingbird skull in default orientation for digital atlas. Coronal plane indicates that a hypothetical coronal slice would be aligned with horizontal plane.  18  The delineations can be both individually mapped on a brain or synchronously mapped with other regions. This provides opportunities to compare relative location and volumes of different regions. It also assists in visualising the circuitry of the brain as it connects from one region to the next.  Abbreviation  Structure  Te  telencephalon  BO  bulbus olfactorius  TeO  optic tectum  CB  Cerebellum  Rt  nucleus rotundus  nBOR  nucleus of the basal optic root  LM  nucleus lentiformis mesencephali  OPT  principal optic nucleus of the thalamus  Hp  hippocampus  E  Entopallium whole brain  Table 1. Delineated structures and their associated colours in the images.  Many structures were identified using the collected datsets (Figure 8). The whole brain was was selected using a threshold selection tool on the MRI dataset and manually adjusted to exclude non-brain regions. Of the nucelei in the tectofugal pathway, we were able to identify the nucleus rotundus and entopallium using data from the T2 MRI with comparison to the pigeon dataset (Güntürkün et al., 2013). The optic tectum was visible using all the imaging protocols. In the thalamofugal pathway, the principle optic nucleus of the thalamus (also known as the lateral geniculate nucleus, pars doralis) was visible in the histology and to a lesser extent on the T2 MRI dataset. The key nuclei of the accessory optic system, the LM and nBOR, were identified on the MRI dataset with support from the histological data and literature (Gamlin and Cohen, 1988a; Gamlin and Cohen, 1988b; Güntürkün et al., 2013; Karten and Hodos, 1967). Additionally, some 19  larger anatomical structures were labelled using the MRI data set, including the telencephalon, hippocampus, bulbus olfactorius, and cerebellum (Figure 8b; Figure 9).  a|  b|  Rt  nBOR  Te  BO  LM TeO  OPT CB  E Hp  t  Figure 8. Highlighted delineations of key areas on 3D brain image. a 3D representation of the brain, opaque and transparent, from the MRI data set with colours showing delineations of key visual nuclei, excluding the optic tectum. b 3D representations of the brain, opaque and transparent, with delineated regions shown in different colours. See Table 1 for abbreviations.  20  Figure 9. Orthogonal slices in the coronal plane showing delineations. Black and white squares indicate 100µm scale bars. Slices are marked according to how posterior they are from the most anterior horizontal plane of the brain. Orange boxes are borders of the delineation dataset. Rt CB  Hp  nBOR  LM  OPT  E  Te  BO  TeO  See Table 1 for abbreviations.  21  Chapter 4: Discussion 4.1  Three-Dimensional Atlas Karten and Hodos’ pigeon atlas (1967) has become a seminal reference work in avian  neuroscience with 1149 citations listed on Google Scholar to date. The extensive citing of the pigeon atlas can be explained by the importance of its practical role in avian research. For decades, it provided the most comprehensive source for navigating the avian brain, and has done so in an easy-to-use format. The construction of atlases has moved away from this histologically based 2D format and the rotationally rigid framework (Karten and Hodos, 1967). For avian 3D atlases to gain prevalent use, they also need to provide clear functions in an accessible format. In the production of this fourth 3D avian atlas, we attempt to emphasize the applications of the atlas for complementing both future MRI studies and studies using classical electrophysiological and tract tracing techniques. In assembling this atlas from the 3D datasets and histological sections, we repeated the steps until a simple and replicable methodology was found. We constructed the atlas as suggested in the methods outlined by previous 3D avian atlases, but using only one analytical software, Amira© V5.4.3 (Güntürkün et al., 2013; Poirier et al., 2008; Vellema et al., 2011). Non-skeletal CT data was excluded (Figure 10), simple auto-registrations were completed (with only minor manual adjustments; Figure 4; Figure 6), and a single brain was sliced for the histology. Furthermore, all the data are bundled together into layers that viewers can pull away or increase transparency depending on their needs (Figure 11). The data is publically available so that any interested person can access it for free and contribute to the development of the regional delineations. One novel contribution of this 3D avian atlas is the inclusion of scale bars within the 3D representations, such that coordinates can be calculated. There are coordinates provided along the bottom and side of the viewing panel. These interactive scale bars can be used as viewers zoom in and out while examining a section. However, caution should be used in interpreting distances in 3D. The scale bars are sensitive only to the centre of the dataset and should be readjusted for each slide. For the most accurate result, view images in orthographic projection, recalibrate scale bars (78.125µm/pixel for MRI data), and save the camera status. The default setting for the data 22  provides an accurate scale for the mid-sagittal slice of the MRI dataset. Aligning the scale bar against oblique slices to convert internal viewing software measurements into meaningful values best uses the functionality of this tool. Relative to histological samples, the MRI data set has a low resolution, which is particularly problematic for identifying small regions. This resolution could be improved with a longer acquisition time and a stronger magnetic field. This problem is amplified when investigating brains that are very small, where each voxel represents a larger proportion of the brain. However, for our CT data collection, our small sample was scanned at a very high resolution to produce meaningful high quality images. Also, the representations created from MRI and CT data may be superior to histological data because they can be collected from the specimen with the brain still encased in the skull. Removal of the brain and removal of the dura layer for histological techniques results in at least some minor deformation of the brain. Also, through slicing, mounting and staining for gel-embedded samples, there is some change in the shape of the slices. While wax embedded samples are better at preserving the structure of the slices, slices are often restricted to a thickness that leads to information loss about the cytoarchitecture (Johnson et al. 1997; Dhenain et al. 2001).  4.2  MRI and CT Applications  With much recent advancement in MRI technology and 3D imaging in general, more small animal researchers are adopting advance imaging protocols into their standard operations. Included in this evolution are many studies on birds, such as the functional MRI study of a fully alert pigeon responding to visual stimuli (De Groof et al., 2013). A 3D atlas supports these datasets by providing a framework for visualizing the anatomical structures being studied in techniques such as functional MRI and manganese enhanced MRI (Boumans et al., 2007; Van Meir et al., 2005). These techniques typically produce images that are either distorted or have a disruptively low resolution or both. A 3D MRI atlas with high quality images can be used to correct these errors. Distortions can be mathematically corrected to increase resemblance to a real brain. Low resolution images can be overlaid onto higher resolution MRI scans to increase information that is available for a particular area. Also, future studies may take advantage of manganese enhanced MRI techniques in ex vivo specimens to improve imaging quality for neuroanatomical studies. 23  In addition to providing this new framework for studying the brain, 3D atlases increase our anatomical knowledge of avian brains at a precise level and expand our global understanding of previously characterized structures. With an established MRI database, detailed anatomical information can be easily collected to uncover variation in structures across and between species. In conjunction with well-characterized circuitry, 3D imaging expands how we can think about and visualize interactions among active nuclei.  Figure 10. Threshold specific selection of skeletal regions from CT dataset. a Sagittal slice. b Coronal slice. c Frontal slice. Green line indicates the sectioning plane for frontal slices, red line indicates the sectioning plane for coronal slices, and blue line indicates the sectioning plane for sagittal slices. d 3D representation of the delineated region from threshold selection.  Contrary to our traditional understanding of neural pathways, Alexander et al. (2013) has argued that brain activity is not the product of interactions between concentrated stable regions with defined borders in the brain, but rather that brain activity moves between regions that are plastic over time and disperse signals locally in addition to key areas. While 3D histological 24  techniques are possible, they are labour intensive and time consuming. Usually histological techniques are carried out in one plane; typically sagittal, coronal, or frontal; and they are more effective at monitoring changes that occur across slices rather than between slices. Collecting data in three dimensions may strengthen support for such a hypothesis, and would more accurately illustrate how a signal can spread in more than two dimensions. An established atlas can act as a rigid-space-model on which to overlay dynamic data and monitor changes.  Figure 11. Representation of MRI data showing variation in inclusion and transparency of the whole brain and brain regions. Beige for whole brain, pink for Te, violet for BO, green for TeO, light blue for CB, dark blue for RT, yellow for nBOR, and red for LM. See Table 1 for abbreviations.  25  4.3  Electrophysiology Applications  A reliable 3D atlas can also complement histological representations of the brain as a tool for selecting the optimal angle for slicing. Without 3D imaging, the task of selecting nonstandard slicing angles is difficult to extrapolate from previous 2D studies. The 3D atlas allows for slices taken at any angle to be laid onto a framework, so that standard and non-standard angles can be understood together. More diverse angle options are helpful for accessing unusual areas of the brain with electrodes and injectable tract tracers, such as the posterior cerebellum. Classically, coordinates for electrophysiology recordings, tracer injections, and lesioning experiments were determined using information from 2D atlases. Datasets from MRI and CT technology have since been used for constructing 3D atlases that are in use for mice (e.g. MacKenzie-Graham et al., 2004; Kovacevic et al., 2005; Ma et al., 2005), macaque monkeys (e.g. Saleem & Logothetis, 2012), and even humans (e.g. Duvernoy & Parratte, 1999). Already we have seen three atlases produced for birds: pigeons, zebra finches, and canaries (Güntürkün et al., 2013; Poirier et al., 2008; Vellema et al., 2011). The Anna’s Hummingbird provides an interesting addition to this group as a member of a unique group of animals that are important for studies in motor control. This atlas presents a possibile method for navigating hummingbird brains. It provides the organization of information into a representation of the brain so that regions inside of the brain can be determined from the surface. Using established software for registering datasets, the production of this atlas provides the coordinates necessary for identifying many neuroanatomical sturcutures. Access to reliable nueuranatomical coordinates may proivde the information necessary for the first neural recordings to be made from a live hummingbird.  4.4  Stereotaxic Coordinates  No atlas, 2D or 3D, is available for hummingbirds. Studies requiring avian neural recordings do not include hummingbirds. Some studies have been completed using ZENK, but there are no known live recordings (Feenders et al., 2008; Terpstra, Bolhuis, Den Boer-Visser, & Ten Cate, 2005). Here we provide more information about the organization of the hummingbird  26  brain, through establishing a 3D coordinate system. We also suggest angles for accessing two small regions, the LM and nBOR. When performing stereotaxic studies of Anna’s hummingbirds, it is helpful to establish a standard angle for placing the head in the stereotaxic apparatus. We found this to be 73.75o below the horizontal plane (between the ear bars and the beak attachment point), in order for a coronal slice to fall in the vertical plane (Figure 5). However, for many regions, the optimum angle for investiagation is depenendent on where the region is located within the brain. The shortest distance between the surface of the brain and the region of interest should be considered. When reaching a specfic region, in addition to adjusting the angle, the instruments can be moved medial-laterally (x-axis), rostral-caudally (y-axis), and down to a depth (z-axis). This creates a courtesian coordinate system. For the x-axis, one can move laterally away from the midline, which is the zero line. The midline is visible along the skull. To determine the y-axis, we are no longer restricted to setting bregma as zero. The interaural line can still make a suitable starting point, but humminbird skulls do not have a clear bregma point or any other clear markings on there skull in this axis. Therefore, we propose that one extends a line vertically from the centre of the ears and set that as the zero point. The y-axis zero point will then change for every angle. The z-axis is set as zero at the surface of the brain and continues to depth with a positive value. With the lentiformis mesencephali (LM) the shortest distance from the dorsal surface is in line with the coronal plane. However at 73.75o below the horizontal, the beak is facing the bench top. When investigating this region, a visual stimulus is typically used to elicit neuronal activity to accurately identify the nucleus. For such a test, it would be more suitable to have the head at a posture that is looking forward. Therefore, we suggest an alternate angle of 43.15 o above the horizontal be used (Figure 12). When the beak is 43.15o above the horizontal, the eyes are looking forward and an electrode may detect extracellular activity. Instruments should enter the brain 2148 µm lateral to the midline (x-axis) and 2525 µm caudal from the centre of the ear bars. The centre of the LM will be at a depth of 4262 µm, or z-axis. Placing the beak 73.75o below the horizontal gives access to the LM with the minimum distance from the surface and with the largest target in the z-axis; while placing the beak 43.15o below the horizontal plane gives clean axis to instruments in the vertical plane and maximizes the visual field available to the bird. The nucleus of the basal optic root (nBOR) is also responsive to visual stimulus, so again the optimum angle is selected with consideration for both the shortest distance from the surface of 27  the brain and at an angle suitable for presenting the bird with a visual stimulus. This angle was determined to be 10.75o. To reach the centre of nBOR with an instrument, the instrument would need to be moved 1132 µm laterally from the midline, 450 µm caudally from the vertical axis determined by the centre of the ear bars, and down 4371 µm from the surface of the brain.  Figure 12. Schematic of the stereotaxic coordinates for the lentiformis mesencephali on CT dataset. Beak attachment point, vertical axis of instrument, and lentiformis mesencepahli indicated. Circle indicates location of ear bar in the stereotaxic apparatus.  28  Figure 13. Schematic of the stereotaxic coordinates for the nucleus of the basal optic root on CT dataset. Beak attachment point, vertical axis of instrument, and nucleus of the basal optic root indicated. Circle indicates location of ear bar in the stereotaxic apparatus. Inset shows the MRI matched slice with a depth to surface indicated (4.371mm).  29  Each neurotanatomical atlas provides new information about the location of brain regions within the cranium. Also, with the increase in availability of such representations of data, comparison of structures is more straightforward. For example, the structures of the accessory optic system of the pigeon can be downloaded, scaled, and overlaid onto the framework of the hummingbird brain. This provides a new method for comparing and contrasting the organization of brains across species; however, future studies might choose to compare individuals from within the same species. For instance, it would be interesting to compare changes in neuroanatomy and circuitry between seasons for breeding male Anna’s hummingbirds, or to contrast song learning regions in males and females at the same time of year. With the increase in prevalence of hummingbirds as subjects for studying flight, it is important to connect information about muscle activity to control at the level of the brain (Elimelech and Ellington, 2013; Pournazeri et al., 2012; Sapir and Dudley, 2013). Electrophysiology experiments using Anna’s hummingbirds will have profound implications for future studies on motor control and sensori-motor integration. 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