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Woodpeckers and the biomechanics of concussion Ross, Erica 2014

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WOODPECKERS AND THE BIOMECHANICS OF CONCUSSION  by  Erica Ross  B.Mus., The University of British Columbia, 2011 B.Sc., The University of British Columbia, 2011     A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF   MASTER OF SCIENCE  in  THE FACULTY OF GRADUATE AND POSTDOCTORAL STUDIES (Zoology)   THE UNIVERSITY OF BRITISH COLUMBIA (Vancouver)   October 2014    © Erica Ross, 2014   ! ! ii!!Abstract  Woodpeckers are a remarkable clade of birds commonly known to use forceful blows of their beaks to drill holes in trees while foraging for boring insects or sap, and they also use their beaks to excavate nest cavities and loudly announce their territory by drumming (Short, 1982). They regularly tolerate forces ten times greater than those that would give a human a concussion (Gibson 2006). Human concussions are the focus of a lot of attention and research efforts recently, especially in the world of sports and veteran's affairs where head injury's debilitating effects on immediate and long-term health are becoming more recognised. Despite the significant impact on human health, concussions are still poorly understood. Woodpeckers are good organisms to study to gain insights into concussions. Many factors have been proposed to contribute to the woodpeckers’ ability to withstand the blows to its head. Some hypotheses are more likely than others, but the topic suffers from a lack of data. This thesis addresses the hypothesis that the brain is semi-isolated from the forces experienced by the rest of the head, and the hypothesis that woodpeckers have minimal space between their brains and skulls, and minimal cerebrospinal fluid. We selected these two hypotheses because they are two of the likelier ideas in the literature and there were clear possibilities for supporting or refuting the claims using data instead of theoretical reasoning. We used high-speed video analysis of wild captured Pileated Woodpeckers to evaluate whether there was any evidence that the brain case is semi-isolated from the rest of the woodpecker’s head. The acceleration profiles of points on the head and on the beak were not significantly different, and the distances between the head point and the beak ! ! iii!!point before and after a strike also were not significantly different. We used CT and MRI scans to visualize and measure the space between the brain and skull. The space was quantified and was not smaller than might be expected once scaling between a human’s head and a woodpecker’s head was taken into account.   We conclude that woodpeckers’ resistance to head injury is not likely due to force deflection away from the brain, or especially tight packing of the brain, and hypothesize that it is due to scaling effects, a short impact duration, woodpeckers’ smooth brain, and possible neuroprotective mechanisms.   ! ! iv!!Preface  This dissertation is the original, unpublished work of the author, Erica Ross. The work was carried out under UBC Animal Care Certificate #4981-12, Scientific Permit to Capture and Band Migratory Birds 10844A, and Canadian Wildlife Services Permit to Capture/Kill for Scientific Purposes BC-13-0047.  ! v!Table of Contents !Abstract .............................................................................................................................. ii Preface ............................................................................................................................... iv Table of Contents ...............................................................................................................v List of Tables ................................................................................................................... vii List of Figures ................................................................................................................. viii Acknowledgements .......................................................................................................... xi 1 - Introduction ..................................................................................................................1 1.1 - Woodpeckers ...................................................................................................................... 1 1.1.1 - Ecology ......................................................................................................................... 1 1.1.2 – Biomechanics of drumming ......................................................................................... 2 1.2 - Concussions ........................................................................................................................ 3 1.2.1 - Definition ...................................................................................................................... 3 1.2.2 - Pathophysiology ........................................................................................................... 3 1.2.3 – Chronic Traumatic Encephalopathy ............................................................................ 4 1.2.4 - Biomechanics ............................................................................................................... 5 1.3 – Previous literature ............................................................................................................. 6 1.3.1 - Hypotheses ................................................................................................................... 6 1.3.2 – Goals of this thesis ..................................................................................................... 12 2 - Methods .......................................................................................................................13 2.1 – Permits and bird capture ................................................................................................ 13 2.2 – Video data collection ....................................................................................................... 17 2.3 – Collection and analysis of CT and MRI data ............................................................... 21 2.4 – Analysis of acceleration data .......................................................................................... 22 2.5 – Analysis of data on distance from UB to H ................................................................... 24 3 - Results ..........................................................................................................................25 3.1 – CT and MRI data ............................................................................................................ 25 3.2 – Acceleration data ............................................................................................................. 27 3.3 – Distance from UB to H .................................................................................................... 32 4 - Discussion ....................................................................................................................32 4.1 – CT and MRI data ............................................................................................................ 32 4.2 – Acceleration data ............................................................................................................. 34 4.3 – Distance from UB to H .................................................................................................... 35 5 - Conclusions .................................................................................................................36 References .........................................................................................................................39 ! ! vi!List of Tables !Table 1. Example of raw data. X, Y, Z position data of UB point in successive frames calculated by DLT Dataviewer 5 from multiple camera views. Raw position data manipulated as described above .........................................................................................23  !! vii!List of Figures Figure 1. Banders’ grip (left) and photographers’ grip (right) demonstrated. Valuable bird handling experience was gained at Iona Island Banding Station ................14 !Figure 2. Pileated Woodpecker decoy. Although the woodpeckers quickly discovered the decoy was not real, it initially aided in luring them to the area ...................................15 !Figure 3. Cage design. The original wood framed cage (a) was quickly destroyed by the first woodpecker (b). Later iterations of cage design used PVC pipe as a frame (c) .........17 !Figure 4. Set up for high-speed video recording. Woodpecker spontaneously pecked at 2 x 4 clamped to acrylic box. Later iterations used a shorter acrylic box to minimize birds’ vertical deviation from the point at which the cameras were focussed ...................19 !Figure 5. Birds were marked with a white dot on beaks and head. Red circles highlight the point on the birds’ head behind the eye (H), the point on the birds’ upper beak (UB), and the point on the bird’s lower beak (LB). These points were later tracked semi-automatically using a digitization program ...............................................................20 !Figure 6. Video analysis with DLT Dataviewer 5. Points on bird were tracked in 4 simultaneous video feeds taken from different angles (a). Calibration of video feeds accomplished by using 3D Lego calibration object (b) .....................................................21 !! viii!Figure 7. Example of a linear regression through 10 points between peak and lowest velocity during a strike. Regression line is in dashed red. Equation of the line is displayed on the chart, with -6143.2 being the slope of the line (acceleration in m/s2) ....24 !Figure 8. 3D reconstruction of brain and skull. (a) brain reconstructed from MRI data (b) brain coregistered with 3D reconstruction of skeletal elements from CT data ............25 !Figure 9. Measurements of space between brain and skull. (a) coronal plane (b) sagittal plane. Measurements taken from MRI data slices using Amira ............................26 !Figure 10. Progression of stills from a woodpecker’s strike. Stills taken from slow motion video feed at a rate of 1600 frames/sec .................................................................27 !Figure 11. Effect of different filters on velocity trace. Noise is greatly reduced by both a running average filter and a cubic spline filter (above). The difference between the two filters is most evident at the peak velocity (right, a) and lowest velocity (right, b), and the running average filter was determined to be most accurate when compared to the original data and video feed ............................................................................................................29 !Figure 12. A typical trace. Peak velocity is at the moment the woodpecker’s beak first contacts the wood (marked on the graph as !) and then rapidly decelerates. This trace illustrates 3 marked points (H, LB, and UB) through a series of 2 strikes. The dashed grey line marks zero velocity .....................................................................................................30 !! ix!Figure 13. Mean acceleration profiles for UB, LB, and H. Error bars represent 95% confidence intervals ...........................................................................................................31   !!! ! x!!  Acknowledgements   First, I would like to thank Bob Shadwick and his enthusiasm for all things Science – nothing inspires like enthusiasm. Many thanks to Doug Altshuler and John Gosline, for non-threatening committee meetings where I felt comfortable asking questions and throwing out ideas.    To the Beak and Blowhole for being the best place for lab meetings, filled with lab mates who were always interested in helping. Special thanks to Dimitri and Paolo for getting me off the ground with data collection and analysis.   I could not have accomplished any of this without the generous assistance of WildResearch staff and volunteers, Hip Health and Mobility’s Danmei Liu, Andrew Yung from the UBC MRI lab, Brad Ross and the bioimaging staff, Gordon and the UBC Animal Care staff, and my field assistants.     Perhaps most importantly, I could not have done this without my office mates in the Hen House. Your easygoing ability to work in chaos and noise was pivotal to keeping my spirits up throughout the grad school process.   And lastly, to Mom and Dad who let me put up mist nets everywhere and supported me through dramatic ups and downs, to Sabine who especially understood, and to Ray who married me in the middle of it all: I love you.     ! 1! 1 – Introduction  1.1 – Woodpeckers 1.1.1 - Ecology Woodpeckers (Picidae) are a widely distributed family with representative species on every continent except Australia and Antarctica (Webb and Moore, 2005). “True” woodpeckers (Picinae), though, are a subfamily of the Picidae. There are currently 24 genera recognised within Picinae containing 183 species (Winkler and Christie, 2002). All species of Picinae engage in their distinctive namesake behaviour: wood pecking. Woodpeckers are commonly known to use forceful blows of their beaks to drill holes in trees while foraging for boring insects or sap. They do not only peck at trees in search of food, though: they also use their beaks to excavate nest cavities and loudly drum (Short, 1982).   Drumming, and drilling and excavating are separate behaviours. Drilling and excavating use chisel-like, forceful blows with a more varied rhythm to make holes in the trees for foraging for food or creating nesting cavities (Shaw, 2002). Drumming is less forceful, but blows are repeated at high frequencies and produce loud sounds. Drumming does not create deep holes in the wood, because drumming is not for the purpose of foraging or nesting: it is to announce a woodpecker’s presence for mating and territorial purposes.   Woodpeckers are aggressively territorial. When a woodpecker is seen pecking at a telephone pole or a chimney, for example, it is not mistaking the pole or chimney for an ! ! 2!!inset-rich tree. It has found a platform that allows it to be heard by other woodpeckers. It is claiming its territory and announcing its presence to potential mates (Short, 1982).   1.1.2 - Biomechanics of drumming  Woodpeckers’ drumming behaviour is remarkable. They reach high velocities over very short distances and yet manage to avoid knocking themselves off the trees on impact (Vincent et al., 2007). A woodpecker may drum up to 28 beats per second (Stark et al., 1998). Even more impressively, Acorn Woodpeckers have been recorded decelerating at 600g-1500g for 1 ms (May et al., 1979), and Great Spotted Woodpeckers at 1000g (Wang et al., 2011) with no immediately apparent evidence of even mild brain damage. In contrast, humans can easily get concussions below 100 g (Pellman et al., 2003). Woodpeckers reach terminal velocities of 6-7.5 m/s (May et al., 1976, Wang et al., 2011) – and the bird may do this 500-600 times every day (Sielmann, 1959).    Woodpeckers apparently sustain no damage at 6-15 times the human injury threshold. Although it is not true, as Yoon and Park (2011) claim, that natural selection produces creatures that are perfectly adapted to their lifestyle and that therefore woodpeckers must be perfectly adapted, and it is not necessarily true that if a woodpecker got a headache it would stop pecking, as Wang et al. (2011) claim, the high forces sustained without immediately evident injury do suggest potential adaptations. Compellingly, adult woodpecker brains do not show any changes compared to juvenile woodpecker brains indicative of cumulative damage or neurodegeneration that one would expect if humans engaged in the same behavior (Iwaniuk, personal communication, ! ! 3!!2014). Certainly they easily sustain these forces without loss of consciousness. How do they avoid brain injury?   1.2 - Concussions Concussions are a hot topic of research, as awareness increases about their immediate and long-term effects on human health. War veterans and amateur and professional athletes of all ages are populations of special concern.  However, concussions remain a confusing field.   1.2.1 - Definition To begin with, the definition of a concussion, or mild traumatic brain injury (mTBI), is nebulous and tends to be defined by each study individually (Iverson, 2005). Typically diagnosis of head injury severity is based on length of loss of consciousness and post-traumatic amnesia, and a physical and neurological examination (Borg et al., 2004). mTBI is generally defined as a blow to the head that results in an alteration or loss of consciousness of less than 30 minutes, post-traumatic amnesia of less than 24 hours, and a Glasgow Coma Scale rating of 13-15, but does not result in macroscopic damage such as skull fracture (Borg et al., 2004).     1.2.2 - Pathophysiology Microinjury to neurons is the most well-studied of possible areas contributing to concussion (Meany and Smith, 2011). Technological advances such as computed tomography (CT) and magnetic resonance imaging (MRI) scans have revealed that less ! ! 4!!than half of mTBI victims who come into hospital have observable structural abnormalities (Borg et al., 2004). In a study of college athletes with mTBI, functional magnetic resonance imaging (fMRI) scans show differences in neural functioning between baseline scans and scans one week post concussion (Jantzen et al., 2004). Diffusion tensor imaging is another emerging investigative technique. It is likely that normal CT and MRI scans in patients with mTBI symptoms are due to technology not yet being developed enough to pick up microscopic abnormalities that may be extremely subtle (Inglese et al., 2005).   There are also complex physiological changes that happen after mTBI at a cellular level, including massive ionic flux, problems with energy metabolism, reduced cerebral blood flow, and abnormal neurotransmission (see review by Iverson, 2005). Cells usually recover from these changes after a period of time (Iverson, 2005).  1.2.3 - Chronic Traumatic Encephalopathy Furthermore, concussions, or even repeated subconcussive impacts, can result progressive neurodegeneration known as chronic traumatic encephalopathy (CTE), but originally described as “punch drunk syndrome” (Martland, 1928). CTE is characterized by tau protein depositions around blood vessels, mostly found in the depths of sulci, problems with microglia and astrocytes, and problems with myelinated axons, as well as psychological problems such as depression, cognitive deficits, and dementia (Goldstein et al., 2012). Frustratingly, there is no reliable way to predict who will develop CTE and who will not, and no predictable progression of symptoms (McCrory, 2011).  ! ! 5!! Of course, the greatest predictor of CTE is exposure to blows to the head. In addition, a person’s APOE genotype plays a role (McCrory, 2011). APOE codes for Apolipoprotein (Apoe) (Wenham et al., 1991), which, among other things, is involved in recovery after neurological injury (Lynch et al., 2002).  Allele APOE4 produces Apoe that binds more weakly to tau protein (Huang et al., 1995), and as a result tau protein gets hyperphosphorylated, does not bind as it should to microtubules, and instead binds to itself and builds neurofibrillary tangles that form deposits in the brain (Goedert, 1995).   1.2.4 - Biomechanics From a biomechanics perspective, there are so many factors involved in concussion that description is difficult (Shaw, 2002). Concussion is due to inertial forces resulting from high acceleration, rather than focal forces (Meany and Smith, 2011). It is difficult of course to measure brain dynamics directly, but head acceleration is easily measured and acceleration’s relationship to brain injury has been considered and tested for more than 50 years (Guskiewicz and Mialik, 2011). Large accelerations or declerations of the head can cause several types of injury to the brain. Coup and contre-coup are two of the most common; coup injuries happen when the brain contacts the inside of the skull directly beneath the point of head impact, while contre-coup injuries happen when the brain rebounds and contacts the inside of the skull opposite to the point of head impact (Shaw, 2002). Injury can also result from sudden rotation of the cerebrum about the fixed brainstem, sudden rotation of the brain about the craniospinal junction, or ! ! 6!!skull compression resulting in an increase in intracranial pressure and brain compression (Shaw, 2002).   Even the question of what magnitudes of accelerations give concussions remains complicated. Many different injury thresholds have been proposed under many different conditions (Guskiewicz and Mialik, 2011). Pellman et al. (2003) used crash-test dummies to recreate 182 concussion-causing impacts seen in NFL games and found that the average concussed player experienced an acceleration of 98 +/- 28 g, with concussion occurring at accelerations as low as 48 g.   1.3 - Previous literature  So why don’t woodpeckers get concussions? Studies have used various methods to address this question, including theoretical models such as finite element modeling and mathematical models (Gibson, 2006; Oda et al., 2006; Yoon and Park, 2011, Wang et al., 2011, Wang and Fan, 2013; Zhu et al., 2012), observations of CT scans and cross sections of skulls (May et al., 1979; Oda et al., 2006; Wang et al., 2011; Wang et al., 2013b; Yoon and Park, 2011; Zhu et al., 2012), high-speed video analysis (May et al., 1979; Wang et al., 2011), and comparative analyses (May et al., 1979; Wang et al., 2011).   1.3.1 - Hypotheses Hypotheses of varying degrees of credibility abound about woodpeckers’ ability to withstand the blows to its head. Many of these hypotheses have been convincingly ! ! 7!!refuted in the literature yet persist, but some hypotheses are compelling and deserve further investigation. Presented here are the hypotheses found in the literature.  1: An unusual hyoid bone that loops around the skull (May et al., 1976; Oda et al., 2011; Turner et al., 2012; Wang et al., 2011; Yoon and Park, 2011; Zhou et al., 2009; Zhu et al., 2012). It has been hypothesized that the hyoid evenly distributes force throughout the skull (Yoon and Park, 2011), that it minimizes potential shearing forces (May et al., 1976), that it is a protective sling or seat belt for the brain (Wang et al., 2011), or that it limits neck motion (Turner et al., 2012). Zhu et al. (2012) go so far as to say that most of the stress from the blow to the tree is absorbed by the hyoid. Outside of theoretical models, there is nothing to support this hypothesis. There is no doubt that a woodpecker’s tongue and hyoid have remarkable mechanical properties (Zhou et al., 2009) and a very unusual way of wrapping around the head, but this should not be taken as proof of the hyoid’s role in protection against concussion. In fact, Bock (1988) describes elongated hyoid horns not only in woodpeckers, but also in hummingbirds and other birds that extend their tongues far beyond their beaks as part of their feeding strategies. In woodpeckers that are predominantly ground feeders, and therefore drum as a display but do not use their tongues to extract insects from tree trunks, the hyoid horns are much shorter and the tongue does not project as far from the mouth (Livingston, 1956). Since the tongue is not extended at impact, and therefore the hyoid is loose around the outside of the woodpecker’s skull (Waller, 1716), it is difficult to visualize how the hyoid would act as a shock absorber (Gordon, 1976).   ! ! 8!!2: The distribution of spongy bone in the skull and mechanical properties of skull bone (May et al., 1976; Oda et al., 2006; Wang et al., 2011; Wang and Fan, 2013; Wang et al., 2013b; Yoon and Park, 2010; Zhu et al., 2012). Higher proportions of spongy bone are found in the woodpecker’s skull in the coup and contre-coup position (May et al., 1976; Wang et al., 2011). Spongy bone could evenly distribute mechanical excitation so that it doesn’t damage the brain (Oda et al., 2006; Yoon and Park, 2011), although Gordon (1976) protests that comparative studies have found that birds taking strong impacts to the skull generally have denser, not more pneumatisized skulls. Unique mechanical properties of woodpecker cranial bone could also aid in shock absorption (Wang et al., 2013b).    3: A smooth brain positioned to maximize surface area in contact with the skull in the plane of impact (Gibson, 2006). This would allow the brain to contact skull such that force would be spread as diffusely and evenly as possible, although Wang et al. (2013) say that the majority of bird brains are positioned in the skull essentially like a woodpecker’s. Goldstein et al., (2012) note that tau plaques are concentrated in the depths of brain sulci, which is suggestive of force amplification at the blind ends of the narrow folds of the brain. A smooth brain would eliminate this problem.   4: Scaling effects (Ommaya et al., 1967; Gibson, 2006). A woodpecker’s brain has a lower ratio of mass to surface area than a human’s brain does (May et al., 1979). Therefore, the accelerative force of a woodpecker’s impact with a tree is spread over a relatively larger surface area, which decreases the force on each unit area of the brain. ! ! 9!!However, it is entirely possible for small animals to be concussed. Small birds often stun themselves by flying into windows, for example, and later fly away again proving a broken neck wasn’t responsible for loss of consciousness (May et al., 1979).    Ommaya et al. (1967) cited an unpublished equation by Holburn outlining a scaling relationship between brain mass and tolerable rotational acceleration, which  May et al. (1979) used to say that woodpeckers with brains between 1.25 and 3.95 g should be able to withstand rotational accelerative forces 50-100 times greater than humans with brain masses of 1400g can.  Gibson (2006) then derived a scaling relationship for tolerable translational acceleration in keeping with the observed linear trajectory of some woodpeckers’ pecking. Gibson concluded that a woodpecker with a brain mass of 2.5g could withstand translational accelerations 16 times greater than a human can.  With such large theoretical tolerances of rotational and translational acceleration, it is likely that scaling effects are indeed an important consideration. However, Wang et al., (2013) say that Holbourn’s reasoning only holds for sustained accelerations and isn’t applicable for short impact durations.   5: A short impact duration (Gibson, 2006). The maximum tolerable acceleration a body can sustain depends on the duration of that acceleration (Gibson, 2006). Yoon and Park (2011) calculated that taking scaling effects into consideration, a woodpecker should theoretically lose consciousness at 65.5 g, based on the fact that humans lose consciousness at 4-5 g. However, this again is for sustained accelerations. In fact, for a 1 ! ! 10!!millisecond impact duration (typical of woodpeckers), a human could withstand 300 g (Gibson, 2006).   6: A pecking trajectory that minimizes rotational impact (May et al., 1979; Wang et al., 2011). Early research into concussions suggested that brains are more easily injured by rotational forces than by translational forces (Holbourn, 1943; Ommaya et al., 1967), and high-speed video analysis suggests that woodpeckers peck mostly in a linear trajectory (May et al., 1979; Wang et al., 2011). However, it is entirely possible for linear acceleration to produce concussions (Pellman et al., 2003), and Wang et al. (2011) did observe rotational components to birds’ pecking trajectories.   7: A narrow subdural space with relatively little cerebrospinal fluid (CSF) (May et al., 1976; Oda et al., 2011; Schwab 2002, Shaw, 2002; Wang et al., 2011; Yoon and Park, 2011). Tight packing of the brain inside the skull would reduce focal impacts such as coup and contre-coup injuries. Adolescent human athletes are more prone to concussion because they have a larger subarachnoid space (Borich, 2012). Yoon and Park (2011) suggest that a narrow subdural space would reduce shock wave transmission through the CSF. However, there is very little actual data on the size of woodpeckers’ subdural space or CSF volume. May et al., (1976) and Oda et al., (2006) refer to qualitative observations of cross sections of skulls. Yoon and Park (2011) refer to CT scans, but the soft tissue of the brain does not show up well on CT scans.   ! ! 11!!8: A cartilaginous or muscular cushion at the base of the beak that would act as a shock absorber (Beecher, 1953; Beecher, 1962; Schwab, 2002; Sielmann, 1959). This idea has long since been refuted by authors who pointed out that shock absorption would stop the blow from making an impact on the tree and could not therefore explain how woodpeckers are able to protect their brains and also excavate large holes (Bock, 1966; May, 1979; Spring, 1965). A number of similar hypotheses exist that suggest that various aspects of the skull musculature stretch or contract to absorb the blow, holding the skull in a state of “resilient rigidity” (Beecher, 1953) or that some aspect of cranial kinesis or skull or beak shape could direct most of the force generated by a woodpecker’s blows away from the brain into the more posterior region of the skull where it could be absorbed by the neck (Bock 1964, 1966, 1999a, b; May, 1976; Schwab, 2002; Shaw, 2002; Spring, 1965; Oda et al., 2006; Vincent, 2007; Wang et al., 2011; Yoon and Park, 2011; Zhu et al., 2012). Yoon and Park (2011) incorporated the beak into their model as a tough outer layer that protects the brain rather like a helmet in the direction of impact. Wang et al., (2011) hypothesized that the difference in length between the upper and lower halves of the beak could result in force being directed through the lower beak to the neck and the brain, although some of the assumptions were flawed, such as the relative modulus of the keratin and bone of the beak (Zhu et al., 2012).  Gibson (2006) noted convincingly, however, that this reasoning ignores the fact that the brain itself is accelerating and therefore force deflection cannot explain why the brain is spared. We reason that the only way the brain could be isolated from an impact force would be if the braincase is moving semi-independently of the rest of the woodpecker.  ! ! 12!! 1.3.2 – Goals of this thesis  It is difficult to tease apart the effects of potential protective factors to show how they either do or do not contribute to concussion tolerance. For example, it is not possible to remove the hyoid bone and evaluate a hyoid bone-less woodpecker pecking to conclusively prove that the hyoid is not a brain seatbelt. Furthermore, with such a complex system as a brain and skull, the explanation is more likely to involve several interconnected factors rather than one simple answer.  However, it was immediately clear that more theoretical reasoning and additional hypotheses are not what are needed in this field.    The purpose of this thesis was to investigate hypotheses 7 and 8: the idea that woodpeckers have a narrow subdural space with relatively little CSF, and the idea that the brain is somehow isolated from the impact forces of pecking. We selected hypotheses 7 and 8 because they are two of the likelier ideas in the literature and there were clear possibilities for supporting or refuting the claims using data instead of theoretical reasoning. We used CT and MRI scans to image the brain and the skull to gain qualitative and quantitative data on the space in between the two, and high-speed video and tracking technology to analyze the movement of both the beak and the braincase to evaluate whether there is evidence they move semi-independently. We used Pileated Woodpeckers (Dryocopus pileatus) because they are a local species of true woodpecker that drums, drills, and excavates even hard, live trees, and we reasoned that their large size would make high-speed video tracking easier.  ! ! 13!! 2 - Methods 2.1 – Permits and bird capture  The major obstacle to our research, surprisingly, turned out to be obtaining the required permits. When we were first exploring options for measuring space between the brain and skull we obtained a salvage permit to possess dead birds. Next we obtained the certificates and protocol approval to work with live animals at UBC. In order to then obtain the Canadian Wildlife Services (CWS) Permit to Capture/Kill for Scientific Purposes we had to first get a Scientific Permit to Capture and Band Migratory Birds, which required a substantial amount of banding experience. To gain the necessary banding experience, I joined WildResearch, a Vancouver-based ornithological research group, and spent over 100 hours at Iona Island Banding Station banding birds over approximately 15 trips during a period of 8 months during fall and spring migrations. I gained valuable skills in bird handling (see figure 1), and learned how to capture birds in flight by using mist nets. Once the banding permit was obtained, we were able to apply for the CWS permit, a process that took several months.  ! ! 14!! Figure 1. Banders’ grip (left) and photographers’ grip (right) demonstrated. Valuable bird handling experience was gained at Iona Island Banding Station.  Having obtained the required permits, we began the process of searching for locations to capture Pileated Woodpeckers. Such locations were limited, as capture within 50 km of urban centres, capture in National, Provincial, or Municipal Parks (including UBC’s Pacific Spirit Park), and capture on private property without landowner permission were prohibited. Seasonal restrictions were also in place: capture during nesting season (March-July) was prohibited. The seasonal restriction was especially limiting, as the birds’ territorial behavior that we used to attract birds to the capture location is strongest during nesting season. After submitting a project proposal, attempts to capture in the UBC Research Forest were made with no success. Similarly unsuccessful attempts were made to capture on a private farm with landowner’s permission. Eventually it became clear that we would need to capture in areas that were unquestionably confirmed by multiple sightings to be inside the territory of at least one ! ! 15!!Pileated Woodpecker. The birds captured in the end were from two separate sites in British Columbia, both on private property with landowner permission. Mist nets, ordered online from Avinet, were set up using 3m or 6m tall conduit pipe slipped over rebar that had been driven into the ground and braced with rope tied to pegs in the ground. Although we caught one bird using a 38 mm mesh net, several woodpeckers escaped from the 38 mm net before we switched to the 60 mm mesh nets, which were much more successful. Suet feeders, decoys (see figure 2), and recordings of Pileated Woodpecker calls were used to attract birds to the area.    Figure 2. Pileated Woodpecker decoy. Although the woodpeckers quickly discovered the decoy was not real, it initially aided in luring them to the area.  Once captured, birds were transported in dog kennels and housed at the University of British Columbia’s Centre for Comparative Medicine. Design of holding ! ! 16!!cages was done in collaboration with the UBC Zoology workshop. The first cage, a 3ft x 3ft x 4ft wood framed structure covered in metal netting, was unsurprisingly demolished in short order by its occupant, so subsequent cages of the same dimensions were made with a frame of PVC pipe (see figure 3). Cages were fitted with nesting boxes, perches, and rotting logs to keep the birds occupied. The woodpeckers were fed a daily diet of suet, egg yolks, berries, live meal worms, a mixture of chick starter feed and wet dog food, and mixed nuts. Butterfly nets were used to snag birds out of large cage for transport to the lab for filming sessions.  ! ! 17!!     2.2 – Video data collection  One adult male and two adult female birds were used in data collection. One bird at a time was put in a transparent, mesh-topped acrylic box that had a 2 x 4 clamped inside (see figure 4).  The bird instinctively pecked at the 2 x 4 and its movements were captured by 3 to 4 synchronised Miro cameras (model numbers 120 or 4). The multiple ! !!Figure!3.!Cage%design.%The!original!wood!framed!cage!(a)!was!quickly!destroyed!by!the!first!woodpecker!(b).!Later!iterations!of!cage!design!used!PVC!pipe!as!a!frame!(c).!!! ! 18!!slightly different camera angles were important in order to reconstruct 3D movements of the points being tracked. The slight differences in what each camera “sees” of the movement of each point in 2D space can be used by a computer to calculate where each point is in 3D space – much like how humans’ brains interpret the slight differences between our two eyes to give us depth perception.   A large, 3D object built from Lego™ was used to calibrate the multiple camera feeds (see figure 6). A simple ruler was not adequate in this situation because the several camera angles were combined to recreate the 3D movement of marked points on the bird and therefore an object of precisely known dimensions occupying 3D space was needed. Furthermore, minute variations in the camera lenses could possibly have resulted in large variations when calculating derivative parameters (for example acceleration), and an object that covered as much of the viewing frame as possible was needed to minimize this source of error. Filming was carried out at 1600 frames/second.     ! ! 19!!  Figure 4. Set up for high-speed video recording. Woodpecker spontaneously pecked at 2 x 4 clamped to acrylic box.    Birds were marked with a white dot on the right side of their heads behind the eye after feathers were clipped down to the skin, and on their upper beaks in front of the nares. The second two birds were also marked on their lower beaks (see figure 5).    After filming, two of the birds were released back into the wild with the approval of a UBC veterinarian, while one was euthanised for CT and MRI scans as described below in section 2.3.  ! ! 20!! Figure 5. Birds were marked with a white dot on beaks and head. Red circles highlight the point on the birds’ head behind the eye (H), the point on the birds’ upper beak (UB), and the point on the bird’s lower beak (LB). These points were later tracked semi-automatically using a digitization program.   Once several videos of each bird were successfully recorded, the videos were calibrated and digitsed using Ty Hedrick’s DLT Dataviewer 5 program (downloadable from http://www.unc.edu/~thedrick/software1.html), which comes pre-programmed to work in conjunction with Matlab to semi-automatically track the markings on head and beak (see figure 6). Once calibrated, DLT Dataviewer calculates an X, Y, and Z coordinate for each point selected in each frame.   ! ! 21!! Figure 6. Video analysis with DLT Dataviewer 5. Points on bird were tracked in 4 simultaneous video feeds taken from different angles (a). Calibration of video feeds accomplished by using 3D Lego calibration object (b).   X, Y, Z position data were smoothed using a running average filter with a subset size of 5. See figure 11 for comparison of filtering methods and table 1 for example of raw position data.   2.3 - Collection and analysis of CT and MRI data  After obtaining video data, one bird was euthanised and immediately taken to the UBC MRI Research Centre for the head and beak to be scanned using the Bruker Biospec 7 Tesla MRI scanner. The bird was then taken to the Centre for Hip Health and Mobility located on the Vancouver General Hospital campus and scanned using the High Resolution Peripheral Quantitative Computed Tomography scanner (Scanco-XtremeCT model). Previous attempts to scan a woodpecker head using a normal CT scanner for whole-body human use showed that the resolution of a microCT scanner would be necessary to obtain the level of detail needed from a scan.   !! ! 22!! The CT and MRI data were viewed and analysed using Amira©  V5.4.3 software. A 3D model of the MRI data was co-registered with a 3D model of the CT data to create an accurate model of both the soft and hard tissue in the woodpecker’s skull.    Measurements of space between brain and skull were taken directly from coronal and sagittal MRI slice data using Amira. Measurements were taken from frames 25%, 50%, and 75% along the length of the brain. In each of the three frames in the coronal plane, one measurement was taken from the superior sagittal sinus, and six measurements from the cerebrum to the skull. In each of the three frames in the sagittal plane, one measurement was taken from the anterior-most portion of the cerebrum.  2.4 - Analysis of acceleration data The smoothed X, Y, Z position data were converted to velocity data using a Pythagoras equation for three dimensions in orthogonal planes X, Y, Z:  ∆!!"#$%$"&/!"# = !! − !! ! + !! − !! ! + !! − !! !!×!1600!!"#$%&/ sec !!!!(!)   Excel formulas were used to restore the directional component of the velocity data, which was lost by squaring each difference in the above formula. The velocity data were then smoothed using a running average filter with a subset size of 7. See table 1 for example of data and figure 12 for a representative trace.    ! ! 23!!   Table 1. Example of raw data. X, Y, Z position data of UB point in successive frames calculated by DLT Dataviewer 5 from multiple camera views. Raw position data manipulated as described above.   The acceleration of each blow was then determined by taking the slope of the velocity graph using a linear regression through 10 points between the peak velocity and lowest velocity recorded from each blow analysed.  X position raw (cm) X running average Y position raw (cm) Y running average Z position raw (cm) Z running average upper beak           2.279159 2.270143 10.704558 10.744273 -0.063687 -0.047435 2.271102 2.265391 10.727997 10.762742 -0.057716 -0.034171 2.281577 2.261964 10.755939 10.780372 -0.069088 -0.021236 2.258044 2.258774 10.752909 10.797131 -0.009687 -0.006119 2.260833 2.260071 10.779963 10.816832 -0.036995 -0.004145 Change in X position (cm/frame) Change in Y position (cm/frame) Change in Z position (cm/frame) Velocity (m/s) Velocity running average (m/s) upper beak              0.004752 -0.018468 -0.013263 -3.557775 -3.251508 0.003426 -0.017630 -0.012934 -3.455299 -3.200206 0.003190 -0.016758 -0.015117 -3.574879 -3.154499 -0.001297 -0.019701 -0.001974 -3.174788 -3.141812 ! ! 24!! Figure 7. Example of a linear regression through 10 points between peak and lowest velocity during a strike. Regression line is in dashed red through the 10 points used to calculate. Equation of the line is displayed on the chart, with -6143.2 being the slope of the line (acceleration in m/s2).   A Two Way ANOVA was performed, with strike number and point (UB, LB, or H) included as factors with no interactions.  2.5 - Analysis of data on distance from UB to H  Again using a Pythagoras equation, smoothed X, Y, and Z position data were used to calculate the distance from the upper beak point to the head point.   !"#$%&'(! = (!!" − !!)! + (!!" − !!)! + (!!" − !!)!!      (2)  The distance data were smoothed using a running average filter with a subset size of 7. 15 distance data points leading up to beak contact with the wood and 15 data points ! ! 25!!immediately after contact were averaged. A paired t test was used to compare distance from upper beak to head before and after (or during) a strike.      3 - Results 3.1 - CT and MRI data   Figure 8. 3D reconstruction of brain and skull. (a) brain reconstructed from MRI data (b) brain coregistered with 3D reconstruction of skeletal elements from CT data.    Some slight inconsistencies in the coregistration of the MRI and CT data led us to question the precise accuracy of using the 3D reconstruction for measurement, but it was helpful as a model for visualization of the brain’s positioning and fit into the skull (see figure 8). We found that the MRI data were more useful for measurement of the space between the brain and the skull, and so quantitative data were taken directly from the MRI slides (see figure 9). The resolution of the MRI scans was 150 microns (isotropic).    !! ! 26!! Figure 9. Measurements of space between brain and skull. (a) coronal plane (b) sagittal plane. Measurements taken from MRI data slices using Amira.     Coronal plane: the average distance from brain to skull in the superior sagittal sinus was 1.8 mm, and around the cerebral cortex was 0.4 mm.   Sagittal plane: the average distance from brain to skull at the anterior-most portion of the cerebral cortex was 1.3 mm. This is an important measurement, as the anterior-most portion of the cerebral cortex is the part of the brain that could reasonably be expected to be at highest risk during a woodpecker’s strike.          !! ! 27!!3.2 - Acceleration data    Figure 10. Progression of stills from a woodpecker’s strike. Stills taken from slow motion video feed at a rate of 1600 frames/sec. White time markers are in milliseconds.   A visual analysis of slow motion video showed a shockwave that travels through the birds’ head and neck feathers (see figure 10).   A value calculated for a marker on a bird’s upper beak will be referred to as a UB point, lower beak as an LB point, and head as an H point. The purposeful blow of a bird’s beak on the wood will be referred to as a “strike”, and successive strikes recorded and analysed in a single video segment will be strike 1, strike 2, or strike 3.   0.0! 43.8! 59.4!61.9! 63.1! 63.8!66.2! 68.1! 87.5!! ! 28!!In total data were collected from 27 strikes, with 2-3 points (UB, LB, and H) tracked through each strike (67 data points total). No data were collected on LB from bird 1, as it was decided to add the LB point only after analysis of the results from bird 1. No data were collected on strike 3 by bird 3, as bird 3 was uncooperative at best.   As mentioned above, a running average filter was applied to both the raw position data and to the calculated velocity trace. Minute variations in the tracking of points in the digitization program resulted in substantial high frequency noise on the unfiltered velocity trace once the differences in position were squared to calculated velocity.  A cubic spline filter applied to position data was considered, but it was discovered that the differences in the resulting filtered trace at the peak and lowest velocity were enough to substantially alter the calculated accelerations. A running average filter applied to position data and velocity data resulted in a workably smooth trace that still closely followed the unfiltered data trace and video feed timing of strikes (see figure 11).   ! ! 29!! Figure 11. Effect of different filters on velocity trace. The grey trace is an unfiltered velocity trace calculated from unfiltered position data. Noise is greatly reduced by both a running average filter applied to position and velocity data (red) and a cubic spline filter applied to just position data  (black) (see a). The difference between the two filters is most evident at the peak velocity (b) and lowest velocity (c), and the running average filter was determined to be most appropriate when compared to the original data and video feed.   a!b c ! ! 30!!   Figure 12. A representative trace. Peak velocity is at the moment the woodpecker’s beak first contacts the wood (marked on the graph as !) and then rapidly decelerates. This trace illustrates 3 marked points (H, LB, and UB) through a series of 2 strikes. The dashed grey line marks zero velocity.         ! ! 31!!    Figure 13. Mean acceleration profiles for UB, LB, and H. Error bars represent 95% confidence intervals.   Accelerations calculated ranged from 321.1 g from a UB point to 904.1 g from an H point, with the overall mean value calculated being 639.3 g. The mean acceleration for UB was 645.6 g, for LB was 586.7 g, and for H was 655.0 g (see figure 13).   The trend was for successive strikes to have increasing accelerations. Strike 1 averaged 566.3 g, strike 2 averaged 663.0 g, and strike 3 averaged 783.1 g.   ! ! 32!!The difference in accelerations between strike number was statistically significant (p<0.001), while the difference in accelerations between point locations was not statistically significant (p=0.634).   3.3 - Distance from UB to H  The average change distance from UB to H from immediately before a strike was to immediately after was 0.2 mm.   A paired t-test showed that the change in distance from before to after was not statistically significant (p=0.05).   4 – Discussion  We did not find any support for either the hypothesis that woodpeckers are resistant to concussion due to a narrow subdural space and relatively little CSF or the hypothesis that the braincase is somehow semi-isolated from the rest of the woodpecker’s head and body, and thus protected from the full force of the impact of the woodpecker’s head and beak.  4.1 - CT and MRI data The 3D reconstruction of the brain and skull, and measurements made from the MRI slices show that there is indeed some space between the brain and the skull. But how much space is enough space to matter?   ! ! 33!! In humans, MRI studies looking at the intensity of stimulus needed to reach the brain have reported the distances from scalp to brain cortex. The distance from scalp to motor cortex can vary between 11mm and 24.5 mm (McConnell et al., 2001; Stokes et al., 2005), or a mean of 12.7 +/- 2.6 mm. The distance from the scalp to the prefrontal cortex can vary from 12.5 mm – 21 mm (McConnell et al., 2001), or a mean of 14.4 +/- 2.7 mm (Kozel et al., 2000).   Li et al. (2007) used CT to measure the skull thickness of 3000 human patients. The average thicknesses of the frontal, parietal, and occipital bones were respectively 6.58 mm, 5.37 mm, and 7.56 mm in males, and 7.48 mm, 5.58 mm, and 8.17 mm in females. If we average these values together to get 6.8 mm, and use that to get a rough idea of the thickness of the skull overall and subtract it from the mean scalp to cortex distances above, then we get 5.9 mm from inside of the skull to the motor cortex (roughly analogous to measurements taken in the coronal plane in this study, which averaged 0.4 mm), and 7.6 mm from the inside of the skull to the prefrontal cortex (roughly analogous to the measurements taken from the woodpecker at the anterior-most portion of the cerebral cortex, which averaged 1.3 mm).    An average human brain is 1336 g (male), and 1198 g (female) (Haartman et al., 1994) and a woodpecker!brains!range!in!mass!from!1.2!g!(Dendrocopos*minor),!to!7.7!g!(Drycocopus*martius)!(Mlikovsky,!1989), which is a difference of three orders of magnitude. However, the difference between skull to brain measurements are only increased by 1 order of magnitude at most in humans compared to woodpeckers. The ! ! 34!!differences between 5.9 mm (human) and 0.4 mm (woodpecker), and 7.6 mm (human) and 1.3 (woodpecker) mm are in fact smaller than might be expected by scaling: humans do not have a surprisingly larger endocranial space than a woodpecker. It is possible that the woodpecker’s values of 0.4 mm and 1.3 mm are small enough to have passed some critical threshold for tolerable movement of brain within the skull, but this is difficult to evaluate. It is not likely that an unusually small amount of space between the brain and the skull accounts for the difference between humans’ and woodpeckers’ concussion risk.   4.2 – Acceleration data If a woodpecker is recorded on an accelerometer at 1000 g, as in Wang et al. (2011), then unless there is a shock absorbing system built into the skull, the brain itself must be decelerating at 1000 g. Hypotheses that depend on forces being deflected away from the brain, cannot explain how the brain avoids injury while itself decelerating at 1000 g. The only way, then, that the brain can be spared from experiencing 1000 g is for the brain case to not be decelerating at 1000 g. We did not find support for the idea that the braincase is experiencing different accelerations than the rest of the woodpecker’s head.   The mean acceleration calculated was actually higher for the H point, although the differences were not significant between the UB, LB, and H groups. This suggests that the braincase and therefore the brain are experiencing the same accelerations as the rest of the woodpecker’s head and beak.   ! ! 35!!4.3 – UB to H We also did not find support for semi-isolation of the braincase when we looked at the distance from UB to H data. Even if the changes in distance from UB-H were found to be significant, the difference of 0.2 mm is minute and would not likely provide any protection against concussion through any dampening effect. It is possible that cranial kinesis at the nasofrontal hinge could be involved in a semi-isolation mechanism for the braincase, and that a rotation at that hinge would not be picked up in measuring the linear distance between UB and H, but our data do not allow for evaluation of this possibility. Furthermore, Burt (1930) describes a mechanical stop for potential rotation at the nasofrontal hinge in the form of an overhang of the skull, which would make cranial kinesis an unlikely contributor to any isolation of the brain.   A potential insight into woodpeckers’ concussion resistance seen in our data is that the forces we calculated were not as high as previously reported. Wang et al. (2011) used a force plate and recorded accelerations as high as 9790 m/s2 (998.3 g). Our calculated accelerations were between 321.1 g and 904.1 g, with the mean acceleration being 639.3 g. A force plate can be more reliable than our method of calculating acceleration, because any slight deviations in tracking accuracy in our method become hugely magnified as differences between successive position data points are squared in the subsequent velocity calculation (see formula 1). We did attempt to mitigate this by filtering the position data using a running average filter and filtering the velocity data with another running average filter before using a regression to calculate average acceleration, instead of again squaring the differences in successive velocity data points ! ! 36!!to calculate instantaneous acceleration. Force plates can measure acceleration directly and are more reliable. However, Wang et al.’s measurement was for a smaller woodpecker than a Pileated Woodpecker and scaling effects could be contributing to a higher tolerable acceleration, as impact forces are theoretically spread over a relatively larger surface area in a smaller brain because of the higher surface area to mass ratio. Wang et al. cite May et al. (1979) as saying they calculated a Pileated Woodpecker decelerating at 13680 m/s2 (1395 g). However May et al. did not calculate the acceleration of a Pileated Woodpecker, but rather an Acorn Woodpecker, which is also much smaller than a Pileated Woodpecker. Secondly, video technology in the 1970’s was not as sophisticated as it is now. The calculated acceleration for one strike was 763 g to 2170 g - a very wide range, and therefore values that are not reliable. These are the only papers with quantitative data on the forces a woodpecker’s brain may experience. More direct measurements using force plates or accelerometers and involving woodpeckers of various sizes would be valuable.   5 - Conclusions  The topic of woodpeckers and concussions is an engaging topic, but the interest that the topic generates should not stand in for actual data and proper literature reviews in further papers. In addition, while sometimes a theoretical model that shows how a hypothesis could be true is useful, the model itself is not evidence that the hypothesis is true.    ! ! 37!!Our data and a review of the literature produce the following as the likeliest explanations for woodpeckers’ resistance to concussions, and should be considered in further research:  1: Scaling effects. Scaling effects may not provide a complete explanation, given that small birds have been seen to knock themselves out, then recover and fly away. This suggests that even very small brains are vulnerable to concussion (May et al., 1979), and that a woodpecker should be able to get a concussion despite the small size of its brain compared to humans. The fact that woodpeckers do not knock themselves out while drumming or excavating suggests there is more going on to protect their brains during these activities than scaling effects alone can account for. However, the large differences in theoretical tolerances of brains of different sizes that have been proposed by Ommaya et al. (1967) and Gibson (2006) could explain a lot of the apparently wide gap in acceleration tolerance between a woodpecker and a human even if Wang et al. (2013) say that Holbourn’s reasoning cited by Ommaya et al. (1967) has more to do with sustained rather than impact accelerations. Further research should be done into the way impact forces spread over the surfaces of viscoelastic surfaces of various sizes. It would also be interesting to know more about even smaller animals, such as insects, and whether or not they get concussions.   2: Short impact durations. In a well-reasoned paper from 2006, Gibson believes that a short impact duration is one of three explanations for concussion resistance in woodpeckers. A human can withstand 4-5 g sustained, but up to 300 g for the typical 1 ! ! 38!!ms duration of woodpecker impacts. Measurement of impact durations of various woodpeckers could provide insight into whether different impact durations correlate to any increase or decrease in other potential protective features. For example, it would be very interesting to know whether smaller woodpeckers can withstand longer impact durations.   3: Smooth brain. A brain without sulci and gyri, like a bird’s, would even out the distribution of force across the surface of the brain and avoid the focal forces that Goldstein et al. (2012) suggest are responsible for the concentration of tau plaques in the depths of brain sulci in human brains. Knowledge of how forces act on smooth and folded viscoelastic surfaces would be valuable in evaluating this hypothesis. For example, what are the maximum forces experienced by a point on a smooth surface vs. a point at the blind end of a narrow fold?  4: Neuroprotection. More research is needed into neuroprotective mechanisms in woodpecker brains. It could be that something intrinsic about the neurons and glia themselves are protective, in addition to any external factors. There are methods available for staining neuronal cells and proteins and a visualization technique like staining could be a profitable first step in learning more about woodpeckers’ brains.   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