An In vivo Exploration of Cardiac and Pyloric Activity in Cancer borealis. Bachelors s Thesis. Presented to

Transcription

1 An In vivo Exploration of Cardiac and Pyloric Activity in Cancer borealis Bachelors s Thesis Presented to The Faculty of the School of Arts and Sciences Brandeis University Interdepartmental Program in Neuroscience Eve Marder, Advisor In Partial Fulfillment of the Requirements for the Degree Bachelor of Science in Neuroscience by Dahlia Kushinsky May, 2017

2 Acknowledgements I am so grateful for the numerous individuals who took the time to advise me on this work. Many thanks to James Weiss, Sara Haddad, Adriane Otopalik, Jessica Haley, Dr. Markus Frederich, Steve Karel, Matthew McNeely, Hannah Bernstein, the Marder Lab, the Neuroscience Department at Brandeis University, and the Computational Neuroscience Traineeship grant. I would also like to thank Dr. Eve Marder, who never fails to engage, advise, and inspire her students and who has taught me to be a better scientist and a better person. ii

3 Abstract An In vivo Exploration of Cardiac and Pyloric Activity in Cancer borealis A thesis presented to the Interdepartmental Program in Neuroscience Graduate School of Arts and Sciences Brandeis University Waltham, Massachusetts By Dahlia Kushinsky Motor output produced by central pattern generators is reliable, rhythmic, and robust to perturbations that an animal may experience over its lifetime. In the crustacean, Cancer borealis, the activity of the central pattern generators controlling the movement of the stomach and the heart have been studied with a wide range of techniques to understand how these neural circuits respond in to perturbation. In the present study, I explored the interactions of the cardiac and pyloric rhythms in baseline and temperature ramps using in vivo techniques. I also explored the effects of injected neuromodulator in animals. Evidence presented here suggests that while the pyloric and cardiac rhythms behave similarly during most baseline conditions and slight temperature increases, the rhythms decouple in response to more extreme perturbation. This suggests that the rhythms are not tightly controlled by descending modulation or input within the animal, nor by direct action between the heart and the pylorus. The rhythms are likely driven completely separately, or by loose modulatory control which causes them to behave similarly during baseline conditions but allows them to separate when exposed to extreme perturbations. iii

4 Table of Contents Introduction...1 Stomatogastric Nervous System...1 Cardiac Ganglion...5 Anatomy, Physiology, and Circulatory System of Cancer Borealis...6 Modulation...8 Environmental Perturbations...12 Methods...16 Animals...16 Temperature Experiments.. 19 Neuromodulator Experiments Data Acquisition and Analysis.. 20 Spectral Analysis...20 Inhibitory Bouting Analysis...20 Change in Frequency Analysis..21 Q10 Analysis.. 21 Critical Temperature Analysis...21 Results...22 Baseline Cardiac Activity.. 22 Baseline Pyloric Activity...32 Gastric Mill Rhythm.. 35 Temperature...35 Neuromodulation...40 Cardiac vs. Pyloric Activity in vivo...45 Discussion...48 Baseline Functioning of Vital Organs in Cancer borealis...48 iv

5 Temperature Neuromodulation...55 Overarching Control of the Cardiac and Pyloric Rhythms...57 References...59 v

6 List of Figures Figure 1. Connectivity and Motor Output of Two Crustacean Central Pattern Generators...4 Figure 2. Diagrams of PPG System...17 Figure 3. PPG Sensor Placement on Cancer Borealis...18 Figure 4. Raw Waveforms of Cardiac Activity Over 30 Seconds.24 Figure 5. Spectrograms of Cardiac Rhythm over 30 Minute Baseline Recordings Figure 6. Periodic Changes in Long Term Cardiac Rhythm Baseline Recordings Figure 7. Cardiac Inhibitory Bouting Persists over Long Recording Periods...28 Figure 8. Mean Frequencies of the Cardiac Rhythm.29 Figure 9. Inhibitory Bouting Analysis...30 Figure 10. Raw Waveforms of Pyloric Activity Over 30 Seconds 33 Figure 11. Mean Frequencies of the Pyloric Rhythm 34 Figure 12. Spectrograms of Gastric Mill Rhythm over 1 Hour Baseline Recordings...36 Figure 13. Spectrograms from Temperature Ramp Experiments Figure 14. Statistical Analysis of Temperature Ramps on the cardiac and pyloric rhythms.41 Figure 15. GABA Injection Effects on Cardiac and Pyloric Rhythms Figure 16. Statistical Analysis of Neuromodulator Injections...44 Figure 17. Concurrent Cardiac and Pyloric Rhythm Raw Waveforms.46 Figure 18. Concurrent Cardiac and Pyloric Rhythm Spectrograms..47 vi

7 Introduction The neuroscientific community has put a significant focus on exposing the mechanisms of central pattern generation. Scientists have used an array of techniques, ranging from electrophysiology and molecular biology to computational modeling to piece together the workings of these systems and how they may remain robust and active throughout an animal s life. The present study seeks to ask how mechanisms that have been largely described in in vitro work appear in intact animals. Typically, these rhythms are studied in in vitro settings, which lack sensory feedback and hormones that influence these circuits in intact animals. I investigate these rhythms in the face of several perturbations, such as temperature and neuromodulators. Finally, I sought to determine if mechanisms of higher level control exist in the animal regulating both its pyloric and cardiac activity, and if so what this mechanism may be. Stomatogastric Nervous System The stomatogastric nervous system (STNS) has long been used as a model system to study central pattern generation and the mechanisms underlying rhythmic and continuous motor movements in animals. It is an ideal system to work with due to its direct impact on musculature, relatively small number of cells, and ongoing, fictive motor output when removed from the animal. The basic mechanisms of rhythm generation in the STNS are similar to the networks that generate human movement and respiration, making the STNS a powerful tool to study and understand central pattern generation. It is said that even Freud worked on the stomatogastric 1

8 system, using methylene blue stains to help provide evidence of the neural doctrine (Johnson and Hooper, 1992). The STNS is a specialized central pattern generator which controls the crustacean s stomach movements. The STNS is comprised of two commissural ganglia (COGs), the esophageal ganglion (OG), and the stomatogastric ganglion (STG). Descending nerves from these ganglia innervate the muscles of the stomach and control filtering and chewing movements. The STG is found within the ophthalmic artery, which runs from the heart to the brain of the animal and is directly bathed by hemolymph, making it the subject of hormonal modulation. Within the STG, most of the synaptic connections important for the creation of rhythmic muscle movements occur amongst the motor neurons themselves, allowing experimentalists to record both neural activity and neural output simultaneously (Marder and Bucher, 2007). When the STNS is removed from the foregut, it maintains patterns of neuronal activity similar to those seen in vivo. Research over the past few decades has shown that application of a variety of modulators leads to an assortment of behaviors generated by a single network (Beenhakker et al., 2003; Blitz and Nusbaum, 2012; Johnson and Hooper, 1992; Kirby and Nusbaum, 2007; Nagy and Moulins, 2012). The STG produces two motor rhythms: the ongoing pyloric rhythm, and the episodic gastric mill rhythm (Figure 1A). The pyloric rhythm is triphasic, comprised of bursts of action potentials in the PD neurons followed by bursts in the LP neurons and finally bursts in the PY neurons (Figure 1C). The AB neuron is electrically coupled to the PD, and together they comprise the pacemaker kernel. The AB neuron is an intrinsic oscillator which, through its electrical coupling with the PD neurons, drives the circuit. Its rhythmic bursting activity inhibits the LP and PY neurons. The LP neuron rebounds from inhibition faster than the PY neuron and 2

9 therefore bursts and inhibits PY. Eventually, PY escapes from this inhibition and stops the LP from bursting. In this circuit, the rhythm frequency is dependent upon the AB/PD neurons while the phase is dependent on factors such as the inhibitory neurotransmitters released in the synapses (acetylcholine or glutamate), or currents such as hyperpolarization-activated inward (Ih) and transient outward (Ia) that determine when the LP and PY neurons rebound from inhibition (Marder and Bucher, 2007, Tang et al. 2010). Often, the IC neurons fire in time with the LP and VD neurons fire with PY (Marder and Bucher, 2007). The gastric mill is the dorsal posterior region of the stomach made of three specialized ossicles referred to as the teeth. It controls the grinding of the food in the animal s stomach and has many muscles responsible for a variety of actions. The gm3 muscle is responsible for pulling the lateral teeth apart while the contractions of gm5, gm6, and cpv3 muscles close the lateral teeth. The medial tooth is moved forward by contraction of gm1, gm2, and gm3 muscles. The tooth is pulled backwards by the contraction of gm4, c6 and c7 (Johnson and Hooper, 1992). The gastric mill rhythm has a more varied pattern of firing than that of the pyloric rhythm, with alternations of many different cells. The DG and GM neurons, which control the medial tooth movements, fire in alternation. The LG/MG neurons and LPG neurons, which control the movements of the lateral teeth, also fire in alternation (Marder and Bucher, 2007). Many projection neurons from the OGs and COGs, such as MCN1, influence both the gastric and pyloric rhythms. Additionally, gastric neurons in the absence of the gastric mill rhythm may fire in time with the pyloric rhythm. These neurons can entrain and reset the pyloric rhythm (Weimann and Marder, 1994). When the gastric mill rhythm is active, these neurons may switch from firing with the pyloric rhythm to firing with the gastric mill rhythm, having the ability to influence both CPGs (Marder and Bucher, 2007). 3

10 Taken from: Otopalik AG, Lane BJ, Marder E, Schulz DJ. (2017; in Review) "Innexin expression in electrically coupled motor circuits 4

11 Cardiac Ganglion Similarly, the crustacean cardiac ganglion (CG) contains intrinsically oscillating pacemaker cells (Figure 1D). The heart is innervated by three distinct neuronal elements: the intrinsic fibers (comprising the CG), the extrinsic fibers (one inhibitory and two excitatory which may modify heart rate in relation to behavioral demands), and the nerves innervating the ligaments, valves, and ostia. In C. borealis, the CG is comprised of nine neurons, wrapped in glia and connective tissue. Within the CG, the most posterior four neurons are smaller, referred to as small cells (SC). The axons of the five anterior motor neurons, called the large cells (LC), innervate the muscle fibers of the heart (Cooke, 2002). The SC pacemakers of the CG produce excitatory synaptic inputs onto the LCs, eliciting large cell bursting activity (Figure 1E) (Cooke, 2002). The general excitability of the circuit is shared among all neurons due to electrical coupling. Therefore, when a hyperpolarizing or depolarizing current is injected into the soma of a large cell, the whole ganglion will respond. This may also come from reciprocal excitatory synaptic interactions among the cells (Cooke, 2002). The CG has the ability to produce patterned bursts of impulses in response to simple stimuli such as excitation from stretch sensitive dendrites allowing for the adjustment of the heart rate and strength of contraction (Cooke, 2002). This small number of cells creates coordinated, patterned activity eliciting reliable and synchronous muscle contraction. Each neuron of the system has intrinsic burst-organizing mechanisms resulting in a patterned output onto the muscle fibers (Cooke, 2002). Both heart rate and contraction strength are sensitive to the intraburst frequency, rate, and patterning of impulses of the LCs. A number of neurohormones may affect these burst features, transported from the pericardial organ to the heart via the circulating hemolymph. These neurohormones include serotonin, proctolin, dopamine, octopamine, crustacean cardioactive 5

12 peptide (CCAP), FMRFamide like peptides (FLPs), and many others (McGaw et al., 1995; Cruz- Bermudez and Marder, 2007). Each of these modulators has different sites and mechanisms of action in changing heart performance. The modulators may act on the valves to modify the distribution of hemolymph, on alary muscles affecting the refilling of the heart, on the neuromuscular junctions, or directly on the CG. In general, these hormones create slow and long lasting increases in heart rate and strength of contraction (McGaw et al., 1995; Cooke, 2002). Anatomy, Physiology and Circulatory System of Cancer borealis The foregut of the crustacean is derived from the ectoderm and is composed of striatal muscles (Johnson and Hooper, 1992). It has three distinct regions including the cardiac sac, gastric mill, and pylorus. The movement of the foregut allows for crustacean feeding behavior; including swallowing, chewing, and processing of waste. Peristaltic waves of muscle contractions, initiated by alternating movements of dorsal and ventral extrinsic muscles which dilate and then constrict the esophagus, move food dorsally into the cardiac sac of the stomach (Johnson and Hooper, 1992). Cancer borealis, like other invertebrates, has an open circulatory system which allows for the animal s hemolymph to diffuse out into the tissues of the animal and later return to the heart. The circulatory system has elaborate capillary beds in many of the animal s tissues paired with valves that allow for selective distribution of hemolymph to separate arterial systems (McMahon and Burnett, 1990). The cardiac output (calculated as stroke volume by heart rate) produced by the heart is larger than that of a closed system, while the output pressure and flow is lower. In this system the fine control over its flow rate is regulated by the cardiac ganglion (CG). In Cancer magister, seven arteries arise from the ventricle. At the origin of each, there is a cardio 6

13 arterial valve innervated by the CNS which allows for modification of hemolymph flow within the animal (McGaw et al., 1995). At low contraction frequencies, supplemental cardiac contractions occur, enhancing cardiac output at low rates (Wilkens and McMahon, 1994). While the heart rate of the crustacean is usually stable, hemolymph flow rate and stroke volume are subject to wide variations both within and across animals (McGaw et al. 1995). These flow rate changes are likely due to modifications in stroke volume or valve activation. The cyclic changes in cardiac output and hemolymph delivery to tissues within the animal may occur in response to local tissue demands, with the green gland (excretory organ), stomach, and nervous tissue receiving the greatest amounts of hemolymph (Wilkens and McMahon, 1995). These changes in cardiac output may also be the result of spontaneous firing events in the CNS, CG, or cardioarterial valves of the heart (Wilkens and McMahon, 1995). The pericardial organ (PO) serves as a secretory organ to deliver neurohormones into the animal throughout its life. The PO contains modulators that have the ability to influence the activity of the heart (Alexandrowicz and Carlisle, 1953). In Cancer pagarus, the release of these modulatory substances from the PO increases the frequency and amplitude of the heart beat with a high resemblance of activity of adrenaline and noradrenaline (Alexandrowicz and Carlisle, 1953). The movement of the pylorus is continuous throughout the animal s life and therefore must remain stable and reliable despite perturbations the animal may experience. The activity of the pylorus begins in the muscles that dilate the pyloric chamber, followed by activity in the initial group of pyloric constrictor muscles, and finishing with activity in the second group of pyloric contractor muscles. The ossicles serve both as supports for the stomach walls and as a 7

14 mechanism to cut or grind food. Many muscles control the cardio-pyloric valve (CPV) and pylorus with either direct or indirect effects on its movements. The gastric mill is the dorsal posterior region of the stomach made of three specialized ossicles referred to as the teeth. It controls the grinding of the food in the stomach of the animal and has many muscles responsible for a variety of actions. The gastric mill rhythm has a more varied pattern of firing than that of the pyloric rhythm, with alternations of many different cells. Heinzel and colleagues (1993) and later Stein and Diehl (2014) correlated behaviorally relevant movements (visualized through endoscopy) of the gastric mill with neuronal firing patterns previously characterized in vitro. They identified two types of teeth movements: squeeze where the teeth are positioned outward and then simultaneously converge to squeeze the food, and cut and grind where the serrated edges of the lateral teeth grind posteriorly along the medial tooth. Govind and colleagues (1975) studied the foregut in Callinectes sapidus and Panulirus argus. In C. sapidus, the gastric mill muscles are singly motor neuron innervated, while many of the pyloric muscles are multiply motor neuron innervated with up to three cells innervating a single muscle. This multiple innervation allows for fast depolarization of the muscles through summation of a low frequency of impulses arriving from each neuron. Modulation Neuromodulation adds complexity and depth to network dynamics, and can be found in almost any neural circuit. Neuromodulators can alter neuronal intrinsic properties, action potential waveforms, and membrane currents. They may also modify the strength of synaptic interactions among the units of a central pattern generating circuit (Dickinson, 2006). 8

15 Neuromodulators may change circuit activity through the regulation of properties or densities of subsets of ion channels at both transcript and protein levels in a cell-type-dependent fashion (Temporal et al., 2012). Exogenous application of neuropeptides and other neuromodulators may increase or decrease the amplitude of many voltage dependent currents and strength of synapses (Marder, 2012). Modulation may be intrinsic or extrinsic depending on the modulators release site and method of diffusion to its target receptors. In the STG, many substances are released by descending modulatory neurons and neurosecretory structures. When these descending modulatory projection neurons are removed, the pyloric rhythm stops or slows. A triphasic motor pattern may be elicited when a specific neuromodulator is bath-applied. Each substance may cause a different variation of the same rhythm, indicating that a large number of neuromodulators may activate or stabilize the network (Marder, 2012). Work has shown that electrically coupled neurons may respond differently to the same modulatory substance and a single neuron may respond to many modulators (Marder and Eisen, 1984; Swensen et al., 2000). Additionally, multiple circuits may be the target of one modulator, or one modulatory input may cause the reconfiguration of several CPGs (Harris-Warrick and Johnson, 2010; Dickinson, 2006). Many neuromodulators are both released into the hemolymph from the PO and delivered directly from nerve terminals onto the STG by modulatory projections from the COGs. How can circuit dynamics be maintained in the animal with the activity of a wide range of neuromodulators constantly acting upon the system? Some modulators act both upon inward and outward currents, or other opposing processes (Harris-Warrick and Johnson, 2010). Others have voltage-dependent effects helping to maintain the burst generating mechanism in the pyloric pacemaker cell (Marder and Eisen, 1984). For example, bath application of dopamine, octopamine, or serotonin modifies Ih in isolated pyloric neurons in different ways, suggesting 9

16 that modulators may elicit some effects using the same mechanisms and some using different mechanisms. Dickinson et al. (2015) showed that the actions of two peptides on contraction amplitude and frequency of isolated whole hearts was concentration-dependent. Additionally, many neuropeptides may act on the same current in a given cell at the same time, occluding each other (Swensen and Marder, 2000). Coordinated modulation of muscles, neuromuscular junctions, and the CPG ensures that activity in the motor neurons are matched to their muscle fiber targets allowing for a stable output (Brezina et al., 2005). Moreover, studies have shown that some neuromodulators have activity at many sites and influence many factors of pattern generation. (Stevens et al., 2009; Swensen at al., 2000). Crustacean cardio active peptide (CCAP) is a relevant and conserved neurohormone (McGaw 1995, Cruz-Bermudez and Marder 2007). Bath applications of CCAP in vitro increase the amplitude and frequency of heart contractions. CCAP modulates both gastric mill and pyloric rhythms through the activation of IMI, a voltage dependent inward current mediated by extracellular Ca 2+, in many of the stomatogastric neurons (Golowasch and Marder, CCAP has not been found to be present in the STG neuropil but does localize in the pericardial organ and thus must be acting hormonally (Fort et al., 2007). In the CG, CCAP is likely working at the level of the premotor neurons (Fort et al., 2007). Proctolin, another neurohormone, activates both the cardiac and stomatogastric ganglia through IMI (Swensen and Marder 2001). In the STG, proctolin strongly excites the lateral pyloric (LP) and inferior cardiac (IC) neurons and causes them to fire long, high frequency bursts of action potentials (Hooper and Marder 1987; Golowasch and Marder, 1992). Proctolin also increases the amplitude and frequency of bursts produced by the AB pacemaker neurons (Hooper and Marder, 1987). Proctolin is able to depolarize the cells at membrane potentials close to 10

17 action potential threshold through activation of an inward current, partially carried by sodium (Golowasch and Marder, 1992). In the cardiac ganglion, proctolin causes similar depolarization effects, with a reversal potential between 0 and +20mV (Freschi, 1989). When applied to the CG, proctolin rapidly excites the small premotor cells while slowly exciting the large motorneuron cells with a longer lasting effect on the order of minutes (Sullivan and Miller, 1984). Red pigment concentrating hormone (RPCH) is an additional peptide which acts upon both the CG and STG and is spread diffusely through the animal s hemolymph. The period of the pyloric rhythm increases only transiently with the application of RPCH, even when LP is hyperpolarized (Thirumalai et al 2006). Thirumalai and colleagues concluded that the effects of RPCH on synaptic strength have a small role in modulating the pyloric rhythm when the rhythm behaves normally, but may help stabilize the rhythm when the period is too slow or too fast. In the isolated CG, RPCH increases burst frequency and the number of large cell spikes. It causes significant increases in burst duration, duty cycle, number of spikes per burst and spike frequency (Cruz-Bermudez and Marder, 2007). In the STG, gamma-aminobutyric acid (GABA) is released by two projections neurons (MCN1 and MPN) onto the neuropil (Swensen et al., 2010). Application of GABA onto the isolated STNS evoked responses in all STG neurons, some depolarizing with reversal potentials of -40mV while other hyperpolarizing with K + and Cl - components (Swensen et al., 2000). GABA application has been shown to inhibit heartbeat frequency through activation of Cl - components (Ando and Kuwasawa, 2004). 11

18 Environmental Perturbations Because crustaceans are poikilotherms and experience fluctuations in body temperature due to changes in the environment, it is pertinent to study the effect of temperature on the activity of the STNS and CG. Temperature changes simultaneously affect many cellular processes and therefore could disrupt neuronal function. Crustaceans experience both short temperature fluctuations due to changing tidal patterns, with shifts as large as 12 C in one day, as well as long temperature shifts due to seasonal temperature changes. Despite these temperature changes on multiple timescales, animals must maintain the rhythmicity and performance of the foregut and heart. Temperature is useful, therefore, as a perturbation of the neuronal circuit to study network homeostasis and animal-to-animal variability in their responses to these perturbations. Much work has been done in both crabs and lobsters investigating the crustacean heart s ability to maintain activity over wide temperature ranges. Studies show that the strength of the heartbeat decreases as the temperature increases, while the heart rate increases as temperature increases (Worden et al., 2005). Therefore, the increase in heart rate partially compensates for the decrease of stroke volume, establishing a relationship between overall cardiac output and temperature. The cardiac output, dependent on stroke volume and frequency, is therefore constant as the temperature increases. At colder temperatures, the contraction and relaxation phases of each heartbeat go slowly but maintain the same phase relationships becoming shorter with increasing temperatures. A useful measurement of temperature dependence of biological processes is the calculation of Q10, defined as the measure of the temperature sensitivity of a biological process over a 10 C increase. Many biological processes have a Q10 between two and three (Marder et 12

19 al., 2015). Some ion channels, which are thought to be important for temperature sensitivity, have Q10s as high as 50 or 100. If all Q10s are the same in a biological process, this process will be temperature-compensated and phases will remain relatively similar. However, a small difference in Q10s across multiple processes indicates a significant change in response to even a small temperature perturbation (Marder et al., 2015). Worden and colleagues (2005) found that the Q10 values were between 1 and 3.5, suggesting that the heart rate is highly sensitive to changes in temperature. Interestingly, between the temperature ranges of 2 C and 22 C, the heart rate increased four times in vitro while only increasing 2.5 times in vivo, suggesting homeostatic control mechanisms in the animal (Worden et al., 2005). The pyloric rhythm also maintains its triphasic rhythm across a wide range of temperatures. In vivo, this maintenance is achieved between the temperatures of 7 C and 23 C. This rhythm increases in frequency as temperature increases, with Q10s between 2 and 2.5, with relatively conserved phase relationships (Soofi et al., 2014; Tang et al., 2010). Like the effects found in the heart, the maximum frequency attained at highest temperatures in vivo are lower than those of in vitro conditions, likely due to the presence of sensory feedback and neurohormonal input. The voltage dependent gating dynamics of several ion channels, controlling the synaptic and intrinsic membrane currents, show varying degrees of temperature dependence and therefore different Q10s. Temperature sensitivity of the maximal conductances also critically contributes to temperature robustness (Caplan et al., 2014). Because of this variation between different channel dynamics, precise compensations must exist to maintain the triphasic rhythm (Soofi et al., 2014). Models have suggested that there must be tight correlations in channel expression in those neurons which are temperature robust (O Leary and Marder, 2016). However, it is still unclear how robustness to one perturbation (such as temperature) may 13

20 coexist with robustness to others, with each perturbation imposing a new constraint on the underlying circuit s parameters (O Leary and Marder, 2016). Temperature increases beyond 23 C lead to severely disrupted motor patterns in both the cardiac and pyloric rhythms. Interestingly, individual preparations respond similarly to temperature changes in the permissive range but crash differently at extreme temperatures. This highlights the circuit differences in conductance parameters as the pacemaker kernel (AB and PD cells) loses its ability to maintain oscillations and hold regular inhibition on the LP and PY cells (Marder et al., 2015). There is a high amount of variability between animals and their responses to temperatures (Soofi et al., 2014). None of the burst phases showed a Q10 significantly different than 1, suggesting that these phases are independent of temperature. It was noted that at faster pyloric frequencies (above 1Hz), the temporal dynamics of some muscles were too slow, preventing them from fully relaxing between cycles, which is another possible limiting factor during in vivo trials (Soofi et al., 2014). In vitro, the isolated pacemaker kernel (the AB and PD cells) causes the oscillations to increase as temperature increases. This explains much of the way that the phase is maintained due to its rhythmic inhibition. The response of the LP and PY neurons depend on strength and timing of inhibition from the pacemaker kernel, as well as the hyperpolarization-activated inward (Ih) and transient outward (Ia) currents in the neurons, which may also be temperature dependent (Marder et al., 2015). Effects of long-term temperature changes upon both the CG and STG have been investigated in several studies. Camacho and colleagues (2006) showed that warm-acclimated animals could withstand higher temperatures before crashing in vivo compared to 11 C (control) or cold acclimated animals. However, warm acclimation impairs the animal s ability to maintain regular heart rate at lower temperatures (<5 C). It is therefore advantageous for an animal to 14

21 adapt to slow seasonal temperature changes, allowing the animal to maintain semi-regular rhythms in a temperature range around normally occurring water temperatures. In the pyloric rhythm, warm temperature acclimation produces little change in the response to acute temperature ramps but allows for the maintenance of stability at higher temperatures (Marder et al. 2015; Camacho et al., 2006; Tang et al. 2012). Temperature acclimation may be achieved by tuning maximal conductance parameters through homeostatic pathways (Caplan et al. 2014; O Leary and Marder, 2014). The current study seeks to explore the cardiac and pyloric rhythms in an intact animal. I sought to learn how many of the mechanisms of the CG and STG, largely described in vitro, present themselves in an intact animal. As a part of this question, I also asked how rhythmic motor patterns, like the cardiac and pyloric rhythms, remain robust in an animal despite perturbations. Lastly, I sought to determine if there are tightly controlled descending modulatory inputs affecting both the cardiac and pyloric rhythms during the animal s life. 15

22 Materials and Methods Animals Adult male Jonah crabs (Cancer borealis) weighing between 400 and 700 grams were obtained from Commercial Lobster (Boston, MA). Before experimentation, all animals were housed in tanks with flowing artificial seawater (Instant Ocean) between 10 C and 13 C on a 12- hour light/dark cycle without food. Animals were kept in tanks for a maximum of 10 days. During experiments, animals were housed in a 25-liter tank filled with approximately 10 liters of artificial seawater and maintained inside an incubator at 10 C to 12 C. Prior to each experiment, crabs were weighed and anesthetized on ice for 10 minutes. Photoplesmogram (PPG) sensors (Vishay CNY70331) (Figure 2, as described in Depledge, 1983) were then placed on the carapace above the heart, pyloric, and gastric mill 1 (GM1) muscles to record the cardiac, pyloric, and gastric mill rhythms, respectively (Figure 3). Sensors were secured to the carapace using dental wax and cyanoacrylate glue (Starbond, EM-2000) and covered in Marine Adhesive Sealant (3M, Fast Cure 5200) to waterproof and ensure stability of the sensors over time. For experiments involving neuromodulatory injection, cannula (Metcal, Plastic Needle 22 GA) were implanted into the animal near the pericardial organ by drilling a small hole using a 20G needle (BD PrecisionGlide Needle). Cannula were secured with dental cement (Prime-Dent, Glass Ionomer Cement) and covered with Marine Adhesive Sealant. For a minimum of 12 hours prior to experimental recordings, animals were not handled and the door to the incubator was not opened. 16

23 PPG sensor PPG sensor Figure 2. Diagrams of PPG system used to non-invasively record muscle movement of animals. A. PPG sensor system, indicating light source and photosensor on device. Devices make use of infrared light to shine and detect changes in refraction to measure muscle movement. B. Modified from Depiction of PPG use above animal s carapace. Infrared light (depicted here as black arrows) is emitted from PPG system through carapace onto muscle and refracted light is detected by sensor with signal sent to PPG amplifier and digitizer. 17

24 Figure 3. PPG sensor placement on Cancer borealis. A. Dorsal view of the animal. Placement of the heart, pyloric, and gastric mill sensors are specified. Placement of cannula is also shown. B. Lateral view of the animal, with carapace and claws removed. Placement of the heart, pyloric, and gastric mill sensors are specified. Placement of cannula is also shown. 18

25 Temperature Experiments After a period of baseline (10 C to 12 C) recording, water temperature was manipulated by flowing either cold or warm saline into the tank through a tube inserted through the door of the incubator. A vacuum line was used to pump water out of the tank to maintain a constant volume. Temperature was slowly ramped from 11 C to 32 C over 1.5 to 2 hours. Heart rate was closely monitored to ensure health during the temperature changes and ramps were halted once heart rate developed an arrhythmia or decreased to baseline frequencies, indicating that a critical temperature had been reached. Neuromodulator Experiments Neuromodulator solutions were made by adding concentrated modulator to Cancer borealis physiological saline (440mM NaCl, 11 mm KCl, 26 mm MgCl2, 13 mm CaCl2, 11 mm Trizma base, 5 mm maleic acid, ph ) stored at 4 C. The concentration of injected neuromodulator was adjusted to the animal s weight, to achieve 10-6 M neuromodulator (RPCH, Proctolin, or CCAP) or 10-3 M (GABA) circulating in the animal s hemolymph (Cruz-Bermudez and Marder, 2007) according to findings that the volume of hemolymph is 30 percent of animals by weight. Experimental protocol included one to two hours of baseline recordings, followed by sham injections of physiological saline and recording of one to two hours, and injection of neuromodulator with subsequent recording. 19

26 Data Acquisition and Analysis PPG data were acquired through the PPG amplifier (Newshift AMP03) and recorded digitally through a digitizer (Axon Digidata 1550B) into computer software (AxoScope 10.6) Data were analyzed through code written for MATLAB. Spectral Analysis Waveforms collected by PPG amplifiers were analyzed through short term Fourier transform (STFT). This analysis determines sinusoidal frequency and calculates the power of each frequency from 0 to 3 Hz. Data were analyzed with either 5 second, 10 second, or 1 minute time windows (indicated in figures) and half window overlap. STFT was then plotted in a spectrogram using MATLAB software, with frequencies of highest power (and therefore those best matching best frequency of waveform) appearing brightest in spectrograms, according to color legend. These points of highest power were extracted for further analysis. Inhibitory Bouting Analysis Cardiac inhibitory bouts were defined as spontaneous (non-perturbation induced) events in animals, with a 33% decrease in frequency and amplitude of cardiac muscle movement. Inhibitory bouts occurring during baseline conditions of experiments were analyzed for period, duration, and change in amplitude following bout. Change in amplitude was calculated by subtracting the mean amplitude 5 seconds after the end of a bout from the mean amplitude 5 seconds prior to the beginning of a bout. 20

27 Change in Frequency Analysis Both temperature and neuromodulator experiments were evaluated for changes in frequency following perturbation. For temperature experiments, change in frequency was the difference between mean frequency 30 minutes prior to ramp initiation and max frequency during temperature ramp. For neuromodulator experiments, change in frequency was calculated as mean frequency 10 minutes after injection subtracted from mean frequency 10 minutes before injection. Changes in frequency in these experiments were calculated both for sham and neuromodulator injections. Q10 Analysis Q10 analysis was performed to evaluate the temperature dependence of the cardiac and pyloric rhythms in vivo. Q10 is a measure of the change in rate of a biological process over a 10 C temperature change (Tang et al. 2010). Frequency data (P) was plotted against temperature (T) in a semilog plot and the Q10 was extracted from the slope of the linear regression (m), following the equation: Critical Temperature Analysis Q10 = 10 10M M = dlogp dt Critical temperatures were defined as temperatures (in C) where the cardiac or pyloric muscle movement was lost for at least 15 seconds with a subsequent drop in contraction frequency to near-baseline values (Worden, 2005). Critical temperatures of the pyloric rhythm were often impossible to determine due to global irregularity in pyloric rhythm signals. 21

28 Results Baseline Cardiac Activity All animals tested (n = 54) showed clear cardiac motor patterns (raw traces shown in Figure 4) at variable speeds. Cardiac contraction PPG waveforms displayed large amplitude slow waves. Many recordings also exhibited artifacts with higher frequency components (Figure 4, animal 1 and 2). Within a single animal, waveform frequency, amplitude, and shape remain stable for long periods of time with slight differences between each peak. Some animals displayed cardiac rhythms at low frequencies (Figure 4 animal 1 and 2; 0.7 and 0.6 Hz, respectively) while others displayed cardiac rhythms at higher frequencies (Figure 4 animal 3 and 4; 1.4 and 1.3 Hz, respectively). Those with higher cardiac rhythm frequencies had fewer occurrences of secondary peaks in waveform as compared with animals with lower cardiac rhythm frequencies, likely due to PPG amplifier resolution. These waveforms are defined as complex because they are not made of pure signal frequencies. Fourier transform analysis was completed for all animals to determine dominant cardiac rhythm frequencies. Baseline cardiac rhythm frequencies were often stable over long periods of time (Figure 5A) with minimal drift in frequency or changes in shape of waveform. Ten second traces (Figure 5A) show little change in frequency, amplitude, or shape of muscle movement waveform. Some animals displayed shifting cardiac rhythm frequencies which increased or decreased during baseline conditions (Figure 5B). These changes occurred gradually over time 22

29 with no abrupt changes. Some animals displayed inhibitory bouts during baseline recordings (Figure 5C). These bouts occurred spontaneously and followed broadband increases in frequency seen as clouds of red/orange in spectrograms. Ten second recordings from these animals display changes in frequency and amplitude before, during, and after inhibitory bouts. Multiple bands appear during inhibitory bouting phases due to complex signal frequencies. Long-term baseline recordings (Figure 6) occurred over 24 hour periods beginning at 10 AM and ending the following day at 10 AM. These recordings occurred after a minimum 12- hour rest period where animals experienced no perturbations or stress. Long-term baseline recordings reveal gradual shifts in frequency of cardiac rhythm over time (Figure 6). For one animal, changes in frequency and variability occurred in a regular and predictable pattern, with frequency changes occurring every three hours. One animal displayed inhibitory bouting episodes throughout the 24 hours (Figure 7) with a regular pattern of many inhibitory bouts close together in time, followed by a long period where no bouts occurred. Cardiac frequencies between inhibitory bouts were stable at 1.5 Hz. This inter-bout frequency did not drift over the 24-hour period. Mean cardiac rhythm frequencies of the first 30 minutes of experiments were collected (Figure 8A) for each animal (n = 54). Mean cardiac rhythm frequencies ranged between 0.5 Hz and 2 Hz with a mean of 1.3 Hz and standard deviation of Cardiac rhythm frequency distribution was bimodal (Figure 8B), suggesting two distinct subgroups (n = 36 for high, n = 18 for low, as in Figure 8B) The cardiac rhythm of animals with low frequency had a median of 0.7 Hz while animals with high frequency had a median of 1.3 Hz. 23

30 Figure 4. Raw waveforms of cardiac activity over 30 seconds. Slow waveforms appear with different phases of movement. Both animals 1 and 2 exhibit low frequency muscle movement (0.7 Hz and 0.6 Hz, respectively), while animals 3 and 4 exhibit higher frequency muscle movement (1.4 Hz and 1.3 Hz, respectively). All waveforms shown are from animals at baseline conditions. 24

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32 Figure 5. Spectrograms over 30 minutes of animal s cardiac rhythms at baseline conditions. Spectrograms calculated with 5 second windows. Each part in figure shows three 10 second traces of cardiac muscle movement, muscle movement over full 30 minutes, and spectrogram of these waveforms. A. Animal s cardiac rhythm is stable over 30-minute time window. 10 second waveforms also appear regular in both frequency and amplitude over 30 minutes. Spectrogram shows frequency at 1.3 Hz with no significant change in frequency or variability over time. B. Animal s cardiac rhythm changes over 30-minute time window. Ten second raw traces show no significant visible change in shape of waveform of muscle recordings even with increase in frequency. Raw 30 minute traces show slight changes in amplitude. Spectrogram shows significant changes in both cardiac rhythm and variability over 30-minute baseline condition. C. Animal s cardiac rhythm exhibits inhibitory bouting over 30-minute time window. Ten second raw traces show changes in frequency and amplitude at stable and inhibitory bout times. Raw 30-minute trace shows occurrences of several bouts over time, including changes in amplitude before, during, and following bouts. Spectrogram indicates inhibitory bouting episodes as places of increased variability in cardiac rhythm frequency, with stable frequencies between these bouting episodes. 26

33 Figure 6. Periodic Changes in Long Term Cardiac Rhythm Baseline Recordings. Animal s heart rate changes in a periodic manner over 24-hour period. Average heart rate was binned into 10 minute windows and plotted over the recording period. 20 second raw traces are plotted and labelled at time of recording in military time. 27

34 Figure 7. Cardiac Inhibitory Bouting Persists over Long Recording Periods. Animal experienced cardiac inhibitory bouts throughout the 24-hour long recording. Occurrence of inhibitory bouts over the 24-hour period is plotted against time. Inter-inhibitory bout intervals were calculated and plotted over the 24-hour period. A pattern appears with groupings of several short inter-inhibitory bout intervals followed by a single long inter-inhibitory bout interval. 28

35 Figure 8. Mean frequencies of cardiac rhythm activity over 30-minute time windows during baseline conditions. A. Mean frequencies of each animal s cardiac rhythm (n = 54) over the first 30 minutes of recording. Mean frequencies ranged between 0.5 Hz and 2.1 Hz with a mean of 1.3 Hz. B. Frequencies of baseline cardiac rhythms were bimodal with normal distributions. Peaks were at 0.7 Hz and 1.3 Hz. 29

36 30 Figure 9. Analysis of inhibitory bouting in animals during baseline conditions. Each color indicates a specific animal. A. Number of inhibitory bouts were calculated per hour. Twelve animals out of 53 tested experienced inhibitory bouting during baseline conditions. B. Inter-inhibitory bout interval was calculated for each animal. In some animals, periods were regular with little variability. In other animals, periods were highly varied. C. Duration of inhibitory bouts was calculated for each animal. Duration of episodes were more varied than periods both inter and intra-animal. D. Change in amplitude was calculated for each animal. Change in amplitude was calculated as difference between mean amplitude 5 seconds after end of inhibitory bout and mean amplitude 5 seconds before inhibitory bout. Some animals experienced positive changes in amplitude (amplitude increased following inhibitory bouting events) while others experienced negative changes in amplitude (amplitude decreased following inhibitory bouting events). E. 90 second traces are shown of one animal s inhibitory bouting episodes over time. Inhibitory bouts occur with both decreases in amplitude and frequency. Both inhibitory bout durations and periods in this animal are highly variable.

37 Many animals (12 out of 54) experienced inhibitory bouting of the heart, defined as a 33% drop in frequency and amplitude lasting for 10 or more seconds. Inhibitory bouting episodes were analyzed for inter-inhibitory bout interval, event duration, and change in amplitude (Figure 9). No significant difference was found between baseline cardiac frequencies of animals experiencing inhibitory bouts and those not experiencing bouting episodes. Number of inhibitory bouts per hour (Figure 9A) varied highly between animals experiencing bouting during baseline conditions. Inter-inhibitory bout interval, or time between inhibitory bout episodes, varied both within and between animals (Figure 9B) (mean = s, CV = 1.96). Some animals experienced regular inhibitory bouts with little variability in inter-inhibitory bout interval (animal 1, mean = s, s.d. = 84.8), while others experienced large variations in interinhibitory bout interval (animal 10, mean = s, s.d. = 180.7). Duration of inhibitory bouting was also examined and found to have high variability both between and within animals (mean = 67.2 s, CV = 1.35). Interestingly, animals with highly variable inter-inhibitory bout intervals also had highly variable durations of inhibitory bouts. Lastly, changes in amplitude following inhibitory bouts were calculated (Figure 9D) as the difference between mean amplitude 5 seconds following an inhibitory bout and mean amplitude five seconds before onset of an inhibitory bout. Differences in amplitudes ranged from V to V, and both negative and positive changes following inhibitory bouts could be seen in the same animal. Change in amplitude displays the largest spread of any bouting measurement presented, as can be seen in Figure 9E. Inhibitory bouts, in this animal, include low frequency muscle movements which are also smaller in amplitude and last variable lengths of time. The frequency, amplitude, and waveform after the inhibitory bouting event eventually returns to baseline levels. 31

38 Baseline Pyloric Activity PPG recordings of the pylorus were not as consistent across animals as cardiac rhythm recordings. Figure 10 displays three different varieties of pyloric rhythm muscle recordings, including regular, complex, and dynamic movements. Regular recordings (animal 1) show consistent frequency and movement waveform. Like cardiac PPG waveforms, pyloric PPG waveforms have higher frequency components. Complex recordings (Figure10, animal 2) are regular and stable in frequency but have multipart waveforms, with peaks followed by plateaus. Peak amplitude and plateau length are variable within the same animal. Dynamic waveforms (Figure 10, animal 3) drastically change over time with fast transitions between semi-regular rhythms to semi-dynamic rhythm. Because of the difficulty of analysis for these dynamic waveforms, dynamic waveforms were left out of quantitative analysis. Of the 54 total animals tested, 28 had distinct pyloric rhythms which could be statistically analyzed. Mean pyloric rhythm frequencies of the first 30 minutes were collected from these animals (Figure 11). Unlike the cardiac rhythm, frequencies of the pyloric rhythm were not bimodal, but were spread in a normal distribution. Pyloric rhythm frequencies ranged from 0.4 Hz to 1.8 Hz, with a mean of 0.9 Hz (s.d. = 0.4). 32

39 Figure 10. Raw Waveforms of pyloric activity over 30 seconds. Each animal shows a different form of muscle movement rhythm, representative of the overall experiment population. Animal 1 displays a regular, stable rhythm with normal frequency over time. Secondary peaks can be seen in the waveform. Animal 2 displays a complex, with distinct peaks followed by longer plateaus. Each movement is slightly different, but occur at regular frequencies and amplitudes. Animal 3 displays a dynamic rhythm, where the muscle movement transitions quickly from a semi-regular rhythm (similar to animal 1) to dynamic rhythm and returns to semi-regular. 33

40 Figure 11. Mean frequencies of pyloric rhythm activity. A. Mean frequencies of each animal s pyloric rhythm (n = 29) over the first 30 minutes of recording. Mean frequencies ranged between 0.42 Hz and 1.8 Hz with a mean of 0.85 Hz. B. Frequencies of baseline pyloric rhythms were not bimodal and were spread in a normal distribution. 34

41 Gastric Mill Rhythm Gastric mill rhythm was recorded by placing PPG sensors above the GM1 muscle of the animal. Gastric mill rhythm was episodic in vivo, with short bursts of activity, often at regular intervals (Figure 12A). Raw traces of this gastric mill activity shows quick repetition of muscle movement for short periods of time, followed by silence of the muscle. Other animals showed tonically active GM1 recordings (Figure 12B), with short bursts of muscle movement at highly variable intervals. These muscle movements occurred in one short contraction, lacking the repetitive movement seen in Figure 12A. Gastric mill rhythms were not further explored as they were found to have different activity in each animal and did not change activity with any global perturbation. Temperature Temperatures effects on the cardiac and pyloric rhythms were tested on 13 animals while recording both heart and pyloric rhythms. Temperature fluctuations in an animal s environment elicited significant changes in both cardiac and pyloric frequencies (Figure 13). Temperature increases caused linear increases and subsequent decreases with decreasing temperature in both cardiac and pyloric rhythms (Figure 13A). Animals experienced crashing at high temperatures of both pyloric and cardiac rhythms, seen broadband increases in frequency on spectrograms. While frequency of beats increased with increases in temperature, cardiac rhythm waveform characteristics remained similar throughout experiments (Figure 13A). This is not the case with pyloric rhythm waveform, as many pyloric muscle movement recordings seem to change over temperature. 35

42 Figure 12. Spectrograms of gastric mill rhythm over 1 hour baseline recordings. 1 hour of gastric mill rhythm baseline activity (calculated with 1 minute windows). A. Animal s gastric mill rhythm is episodically on and off, with short bursts of activity. 30 second raw traces of muscle movement indicate quickly moving GM1 muscles at various times during baseline recordings, with almost no outside muscle movement or noise recorded. They show the occurrence of muscle movements in repetition for several seconds. One hour long traces show regular GM1 muscle movements. Spectrogram shows increases in power (shown in red) with bursts of activity. B. Animal s gastric mill rhythm is turned on for a longer period, with short bursts of muscle movement at highly variable intervals. 60 second traces show occurrence of single muscle movements, different than seen in part A. 1 hour long raw traces and corresponding spectrogram show activity of GM1 and occurrence of muscle movement throughout hour long traces. 36

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44 Figure 13. Spectrograms (calculated with 1 minute windows) and from temperature ramp experiments. 30 second raw traces of PPG recordings from heart and pylorus are shown at various time points through the experiment. Left side of figure shows cardiac rhythm data, while right portion of figure shows corresponding pyloric rhythm data. Letter markings on spectrogram indicate location in experiment where corresponding raw traces are shown. Temperature across experiment are plotted below spectrogram. A. Temperature ramp experiment with clear increases and decreases in frequency due to changing temperature. An immediate increase in cardiac rhythm is seen with increase in temperature, and rises linearly with increase in temperature. Cardiac rhythm reaches a clear crashing point at its critical temperature, and once temperature is reduced, slows its frequency until returning at original frequency. Pyloric rhythm frequency also increases with temperature before crashing, and returning to near baseline frequencies when temperature is reduced. B. Cardiac rhythm increases linearly with increase of temperature, with a faster ramp. Heart crashes at its critical temperature, and is followed by a return in normal activity following temperature reduction. Pyloric rhythm can be seen to crash much earlier and retain high variability throughout the experiment, returning in an upper harmonic as temperature is reduced. C. Animal shows clear linear relationship with temperature in both cardiac and pyloric rhythms. Crash points are reached in both heart and pylorus at different temperatures. Distinct changes can be seen in pyloric waveform at different temperature points, as shown. 38

45 Both cardiac and pyloric rhythms increased in frequency with increases in temperature, and decreased in frequency when temperature was lowered, as in vitro experiments suggest. Change in frequency (Figure 14B) was determined by subtracting maximum frequency from average frequency 30 minutes before temperature ramp initiation. Change in frequency of cardiac and pyloric rhythms were not significantly different with an average increase of 1.64 Hz in cardiac rhythm frequency and average increase of 1.63 Hz in pyloric rhythm frequency. Frequency increases in both cardiac and pyloric rhythms appeared to be linearly related to temperature. Q10, a measurement of rate of change of a biological process in response to change in temperature was calculated for each temperature experiment (Figure 14B). No significant difference was found between Q10 values of pyloric and cardiac rhythms (paired t- test, p = ), Average cardiac rhythm Q10 was 1.97 while average pyloric rhythm Q10 was Critical temperatures, defined as the temperature where cardiac or pyloric muscle movement stability was lost for at least 15 seconds with a subsequent drop in contraction frequency, were measured. Critical temperatures for the heart had a mean of 29.2 C (s.d. = 2.2). Critical temperatures of the pyloric rhythm were more challenging to establish as their movements were complex and could not be determined for all temperature tested animals. Critical temperatures for the pyloric rhythm that were defined (n = 8) were significantly lower than those of the heart, with a mean of 22.2 C (s.d. = 3.8). 39

46 Neuromodulation Four neuromodulators (GABA, proctolin, CCAP, and RPCH) were tested to determine their effects on both the cardiac and pyloric rhythms. Effects of neuromodulators were quantified by finding changes in muscle movement frequency in response to sham and neuromodulator injections. None of the traditionally excitatory neuromodulators tested (RPCH, CCAP, and proctolin) elicited statistically significant changes in frequency on cardiac or pyloric rhythms (Figure 16). GABA caused significant inhibition of the cardiac rhythm while not significantly affecting the pyloric rhythm (Figure 15). GABA injection acts quickly on the cardiac rhythm, significantly decreasing frequency only a few minutes after injection (Figure 15A), with residual inhibitory activity often appearing in waves of increases and decreases in frequency after initial recovery. GABA did not appear to cause significant changes in the pyloric rhythm frequency (Figure 15, Figure 16). GABA injections significantly decreased cardiac rhythm frequencies (n = 4, mean change following GABA injection = -.308Hz, s.d. = 0.194). I found a statistically different response between the cardiac and pyloric rhythm frequencies following GABA injection (p = , paired t-test). 40

47 Figure 14. Statistical analysis of temperature ramps on the cardiac and pyloric rhythms. A. Change in frequency of cardiac and pyloric rhythms was defined as difference between maximum frequency attained due to increases in temperature and baseline frequency 30 minutes before beginning temperature ramp. Frequency changes between the cardiac and pyloric rhythm were not significantly different. B. Q10 values calculated for both the cardiac and pyloric rhythms, as described in methods. Because pyloric rhythm was often unstable across increase in temperature, Q10 could not be calculated for many animals tested. Q10 values of cardiac and pyloric rhythms were not significantly different. C. Critical temperatures marked for each experiment, defined as temperatures (in C) where the cardiac or pyloric muscle movement was lost for at least 15 seconds with subsequent drop in contraction frequency to near-baseline values. Cardiac rhythm critical temperatures were significantly higher than pyloric rhythm critical temperatures. 41

48 rhythm (Figure 17). The pyloric rhythm does persist during cardiac inhibitory bouts, but displays drops in frequency at the same times as those in the cardiac rhythm. 42

49 Figure 15. GABA (10-3 M) injection effects on cardiac and pyloric rhythms. Effects of sham injection are shown in top panels while effects of neuromodulator injection are shown in bottom panel. Spectrograms calculated with 1 minute windows. Cardiac rhythm activity is shown on right, pyloric rhythm activity on left. Injection times of sham and RPCH injection are matched, with 30 minutes before injection and 90 minutes following injections, with injections occurring at time second raw traces are shown before, immediately after, 45 minutes after, and 75 minutes after injection. A. Animal displays significant decrease in cardiac rhythm following GABA injection. Pyloric rhythm has high variability throughout experiment. GABA injection does not seem to cause significant effects on pyloric rhythm frequency, but does increase variability immediately after injection. B. While sham injection has no effect on the pyloric or cardiac rhythms, GABA injection induces quick inhibition on the cardiac rhythm with no significant long term changes on the activity. Pyloric rhythm has high variability throughout experiment, and does not display a significant change in activity following GABA injection. 43

50 Figure 15. Statistical analysis of neuromodulator injections. A. Responses to RPCH injections. Neither cardiac nor pyloric rhythm had significant changes in frequency between sham and RPCH injections. B. Responses to GABA injections. In the cardiac rhythm, GABA injection caused a significantly different change in frequency compared to sham injection responses. In the pyloric rhythm, GABA injection caused no significant change in frequency compared to sham injection responses. C. Responses to CCAP injection. Neither cardiac nor pyloric rhythm had significant changes in frequency between sham and CCAP injections. D. Responses to proctolin injections. Neither cardiac nor pyloric rhythm had significant changes in frequency between sham and proctolin injections. 44

51 Cardiac vs. Pyloric Activity in vivo Concurrent recordings of pyloric and cardiac rhythms indicate no cycle-to-cycle phase locking behavior (Figure 17). Animals show different combinations of cardiac and pyloric rhythm variations. Spectrograms often show general shifts in frequency of both cardiac and pyloric rhythms (Figure 18). In many animals during times of cardiac inhibitory bouts, the pyloric rhythm frequency increases and decreases with near-identical timing of the cardiac 45

52 Figure 17. Concurrent cardiac and pyloric rhythm raw waveforms. Each animal has a different cardiac and pyloric waveform. Animal 1 shows a complex cardiac rhythm with a regular pyloric rhythm. Animal 2 shows a tri-peaked cardiac rhythm with a matching complex pyloric rhythm. Animal 3 shows a tri-peaked cardiac rhythm with a dynamic pyloric rhythm. 46

53 Figure 18. Concurrent cardiac and pyloric rhythm spectrograms. Cardiac rhythm spectrograms (10 second windows) are shown in upper panels of section while pyloric rhythm spectrograms are shown in lower panels. A. Pyloric rhythm follows increases and decreases of cardiac rhythm in time with inhibitory bouting intervals. Pyloric rhythm frequencies do not drop to as low frequencies as those seen in cardiac inhibitory bouting episodes, but do decrease on the same time scales. B. Pyloric rhythm variability increases during long of inhibitory bouting episodes. 47