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DEVELOPMENT OF AN IN VITRO TOOL TO MEASURE THE FUNCTION OF NEURONS PROTECTED FROM GLUTAMATE-INDUCED EXCITOTOXICITY by MELINDA KAREN KUTZING A dissertation submitted to the Graduate School-New Brunswick Rutgers, The State University of New Jersey and The Graduate School of Biomedical Sciences University of Medicine and Dentistry of New Jersey in partial fulfillment of the requirements for the degree of Doctor of Philosophy Graduate Program in Biomedical Engineering written under the direction of Bonnie L. Firestein, Ph.D and approved by _____________________ _____________________ _____________________ _____________________ _____________________ New Brunswick, New Jersey October, 2011

ABSTRACT OF THE DISSERTATION Development of an in vitro tool to measure the function of neurons protected from glutamate-induced excitotoxicity

By MELINDA KAREN KUTZING Dissertation Director: Bonnie L. Firestein, Ph.D

Traumatic brain injury (TBI) results in devastating neurological damage that affects millions of individuals each year. TBI occurs as a result of an external physical insult that leads to mechanical injury followed by a cascade of secondary chemical insults that develop over a period of hours to days, damaging cells and tissue further removed from the initial site of injury. Often, this secondary injury leads to more significant clinical impairments and may ultimately be the deciding factor in the patient’s recovery. There remains a tremendous need to develop therapeutics that interfere with secondary injury mechanisms to protect neural function. The development of appropriate models of TBI is one of the primary hurdles in this field. Expensive in vivo animal models focus on behavioral endpoints and have resulted in only moderate improvements in patient outcomes. Most of the current in vitro models of TBI rely on endpoints, such as cell morphology or biochemistry that do not necessarily correlate with cell function.

While

preservation of cell number and cell morphology are necessary for a positive outcome after TBI, the cells must also be able to send and receive signals as they did before the

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injury occurred in order for the central nervous system (CNS) to function properly. The objective of this thesis is to develop an in vitro model of TBI that can measure changes in the function of cells following injury. Using microelectrode arrays (MEAs) to study TBI enables one to record the cellular activity before and after injury and with treatment and allows for the measurement of changes in the patterns of electrical activity within the neuronal network, providing a clear picture of not only whether the cells have survived but also how they function. Our results show that we are able to detect changes in neuronal function that are not detected using simpler in vitro models. Additionally, we investigated the ability of memantine, an uncompetitive NMDA receptor inhibitor, to protect against secondary injury mechanisms. Using this tool to screen other potential neuroprotective compounds and identify those that can preserve cell function may translate to better success in animal models.

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Acknowledgements There are a number of people I would like to thank for all of their help and support during my time in graduate school. First, I would like to thank my advisor, Dr. Bonnie Firestein, for her guidance and support throughout my time in her laboratory. She has been an excellent mentor and friend. I would also like to thank my committee members: Dr. Mark Plummer, Dr. Noshir Langrana, Dr. Barclay Morrison, and Dr. Li Cai. Their scientific input has been invaluable to the completion of my thesis. I would also like to acknowledge my funding sources. Much of this work was funded by a grant from the New Jersey Commission on Brain Injury Research, #09-3209BIR-E-2. In addition, I received support from the Biotechnology Fellowship, Grant 5T32GM008339 from the National Institute of General Medical Sciences. The day to day grind of graduate school was made much more interesting and fun thanks to all the past and present members of the Firestein Lab. As a Biomedical Engineer in a Neuroscience and Cell Biology lab, I have often depended on them for their scientific expertise. I appreciate all of their input in helping me to shape my thesis so that it is relevant to both engineers and scientists.

Most of all I am grateful for their

friendship. I would specifically like to thank Vincent Luo for all of his help in grinding through a ton of experiments in the last year and a half. Lastly, I would like to thank my family and friends. I am so incredibly lucky to have such amazing support. Specifically, I would like to thank my mom, Lorraine, and my sisters, Sandy, Lisa and Erica for their love, support and encouragement, and for entertaining me on my long drives to and from the lab. Most of all I would like to thank iv

my fiancé, Dan. I honestly don’t know if I could have completed this without him. He has been my rock throughout graduate school and has helped me in so many ways, including a crash course in Matlab programming, brainstorming sessions on how to analyze and present my data, and supporting me through the many emotional ups and downs of a Ph.D.

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Table of Contents

Abstract .............................................................................................................................. ii Acknowledgements .......................................................................................................... iv Table of Contents ............................................................................................................. vi List of Illustrations........................................................................................................... ix Chapter 1: Introduction ........................................................................................................................1 1.1 Traumatic Brain Injury ...............................................................................................1 1.2 Traditional TBI Models ..............................................................................................4 1.3 Microelectrode Arrays (MEAs) as a model of TBI....................................................8 1.4 Thesis Overview .........................................................................................................8 1.5 References ................................................................................................................10 Chapter 2: Development of a tool to monitor the function of the neuronal network ...................13 2.1 Introduction ..............................................................................................................13 2.2 Methods ....................................................................................................................19 2.2.1 Network preparation .........................................................................................19 2.2.2 Microelectrode array recording ........................................................................19 2.2.3 Data analysis: Overall network activity ...........................................................20 2.2.4 Data analysis: Measurement of synchronous firing behavior .........................21 2.3 Results ......................................................................................................................24 2.3.1 Cortical neurons grown on MEAs exhibit robust spiking behavior .................24 2.3.2 Overall network activity of control cultures .....................................................32 2.3.3 Measurement of synchronized firing of neuronal networks ..............................35

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2.3.4 The effect of distance on the synchronized firing between electrode pairs .....42 2.4 Discussion ................................................................................................................44 2.5 References ...............................................................................................................47 Chapter 3: Measuring the effects of glutamate-induced excitotoxicity on the function of the neuronal network ............................................................................................................50 3.1 Introduction .............................................................................................................50 3.2 Materials and Methods ............................................................................................59 3.2.1 Network preparation .........................................................................................59 3.2.2 Glutamate treatment ........................................................................................59 3.2.3 Evaluation of Neuronal Death ..........................................................................60 3.2.4 Micro-electrode array recording .......................................................................60 3.2.5 Data analysis: Overall network activity ...........................................................60 3.2.6 Data analysis: Measurement of synchronous firing behavior .........................61 3.3 Results .....................................................................................................................66 3.3.1 Glutamate-induced neuronal injury ..................................................................66 3.3.2 The effect of glutamate on the overall electrical activity of neurons ...............68 3.3.3 The effect of glutamate on the synchronized firing of neuronal networks .......71 3.3.4 The effect of distance on the synchronized firing between electrode pairs ......80 3.3.5 The effect of glutamate on the burst duration ..................................................83 3.4 Discussion ...............................................................................................................87 3.5 Acknowledgements .................................................................................................91 3.6 References ...............................................................................................................92

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Chapter 4: Protection from glutamate-induced excitotoxicity by memantine .............................99 4.1 Introduction .............................................................................................................99 4.2 Methods .................................................................................................................103 4.2.1 Network preparation ......................................................................................103 4.2.2 Pharmacological treatments ............................................................................104 4.2.3 Micro-electrode array recording .....................................................................105 4.2.4 Data analysis: Detection of electrical activity ................................................105 4.2.5 Data analysis: Measurement of synchronous firing behavior .......................106 4.3 Results ...................................................................................................................109 4.3.1 The effect of memantine on overall electrical activity ...................................109 4.3.2 The ability of memantine to protect the synchronized firing of neuronal networks ..................................................................................................................111 4.3.3 The ability of memantine to protect against changes in burst duration .........122 4.4 Discussion .............................................................................................................127 4.5 References .............................................................................................................132 Chapter 5: Discussion and Future Directions ................................................................................137 5.1 References .............................................................................................................141 Curriculum Vitae ..........................................................................................................143

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List of Illustrations

Figure 1: Schematic diagram depicting CNS research ........................................................5 Figure 2: Schematic drawing of a theoretical way research could be organized in the CNS field to obtain more positive results .....................................................................................7 Figure 3: Schematic of the method for determining the degree of separation between electrodes ...........................................................................................................................23 Figure 4: E18 cortical neurons grown on a microelectrode array at DIV14 ......................25 Figure 5: Spontaneous electrical activity of cortical culture grown on a MEA.................26 Figure 6: Partial plating pattern of neurons on electrode contacts of a single MEA .........27 Figure 7: Activity of neurons plated on MEA shown in Figure 6 .....................................28 Figure 8: Five minute recording of spontaneous electrical activity of neurons plated on MEA shown in Figure 6 .....................................................................................................29 Figure 9: Synaptic blockers dramatically decrease electrical activity ...............................31 Figure 10: The fraction of MEAs that show an increase, decrease, or no change in measures of overall activity ...............................................................................................33 Figure 11: Burst duration of control cultures at two recording times within two hours ....34 Figure 12: The synchronization index of control cultures at two recordings times within two hours ............................................................................................................................36 Figure 13: The average synchronization index of control cultures at two recordings times within two hours ................................................................................................................38 Figures 14: The synchronization index of the MEAs at the 2nd recording time grouped according to their change in overall activity ......................................................................41 Figure 15: The effect of distance on the strength of synchronization between electrode pairs ....................................................................................................................................43 Figure 16: Flow of glutamate at the synapse .....................................................................52 Figure 17: Mechanisms of glutamate-induced excitotoxicity............................................55 ix

Figure 18: Schematic of the method for determining the degree of separation between electrodes ...........................................................................................................................65 Figure 19: The amount of glutamate-induced LDH released as a measure of cell death ..67 Figure 20: Changes in the overall activity of cortical cultures after glutamate injury ......69 Figure 21: The effect of glutamate treatment on the synchronized firing between electrode pairs ....................................................................................................................72 Figure 22: The effect of glutamate treatment on the average synchronization index value of electrodes .......................................................................................................................74 Figure 23: The effect of glutamate on the synchronized firing of neuronal networks ......77 Figure 24: The initial synchronization between electrode pairs determines their susceptibility to glutamate injury .......................................................................................79 Figure 25: The effect of distance on the change in synchronized firing ............................81 Figure 26: The effect of glutamate treatment on the average duration of SBEs ................84 Figure 27: The effect of glutamate treatment on burst duration ........................................85 Figure 28: Memantine does not interfere with normal synaptic signaling ......................110 Figure 29: The ability of memantine to protect the synchronized firing between electrode pairs ..................................................................................................................................112 Figure 30: The effect of glutamate and memantine treatment on the average synchronization index value of electrodes .......................................................................116 Figure 31: Memantine fully protects neurons from an overall loss in SI ........................118 Figure 32: The initial synchronization between electrode pairs determines the ability of memantine to protect........................................................................................................120 Figure 33: The change in burst duration after glutamate and memantine treatments ......123 Figure 34: The effect of glutamate and memantine treatment on burst duration.............124 Figure 35: Bicuculline increases burst width in uninjured cultures .................................126

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Chapter 1: Introduction 1.1 Traumatic brain injury Traumatic brain injury (TBI) results in devastating neurological damage that affects millions of individuals each year. According to the World Health Organization, central nervous system injury resulted in 5.8 million deaths worldwide in 1998 [1]. In the United States alone, there were an estimated 1.7 million incidences of TBI annually resulting in 52,000 deaths and 275,000 hospitalizations between 2002 and 2006. Unfortunately, these statistics are only getting worse. A Center for Disease Control and Prevention study showed that between 2002 and 2006, virtually all indicators of the number of incidences of TBI increased. There was a 14.4% increase in TBI-related emergency room visits and a 19.5% increase in TBI-related hospitalizations [2]. Because TBI is much more likely to occur in persons between the ages of 15 and 30 years, those that survive the injury could require medical care for 60-80 years [3]. Total medical and rehabilitation costs of TBI patients in the United States are estimated to exceed $40 billion annually [4]. Thus, TBI is not only a major and devastating health problem but also has a strong financial impact on society, leading global health organizations to identify TBI as a “silent” epidemic [5, 6] The most common causes of TBI are falls, automobile accidents, firearms, bicycle accidents, sports and recreational activities, and risky jobs [4]. Traumatic brain injury occurs due to an external mechanical impact to the head [4]. The injury can result from both direct traumas as well as from inertial forces caused by acceleration, deceleration, or rotational forces occurring independently or along with

2 the direct trauma [7]. Direct trauma results in focal injuries, injuries that occur in a specific location, such as skull fracture, epidural hematoma, coup contusion, and subdural hematoma. Inertial forces result in both focal injuries as well as diffuse brain injuries, injuries that occur over a more widespread area [4]. Inertial forces with only translational acceleration result in focal brain injuries while inertial forces with rotational acceleration produce both focal and diffuse brain injuries [8]. Depending on the type of injury, these forces produce a combination of shear, tensile, and compressive strains on the brain [911]. Because the brain has little internal structural support and is a viscoelastic organ, it does not tolerate these forces well, and the result is deformation of brain tissue [9-11]. The gray matter closest to the brain’s surface is the most susceptible to linear forces, leading to cortical contusions and hemorrhage, the deeper white matter axons are mechanically injured by rotational forces, resulting in diffuse axonal injury, and the gray matter nuclei and axonal tracts in the midbrain and brainstem are susceptible to rotational forces [12]. Brain edema and hematomas that form from the forces caused by the initial trauma often result in prolonged compressive forces, which can further damage brain tissues as a result of elevating intracranial pressure and reduced cerebral blood flow. In addition to the initial mechanical injury, there is a cascade of secondary chemical insults that develop over a period of hours to days that can damage cells and tissue further removed from the initial site of injury [11].

Often, this secondary injury

leads to more significant clinical impairments for patients suffering from TBI [4] and may ultimately be the deciding factor in the patient’s recovery [13]. Excitatory amino acids (EAAs), such as glutamate, have been specifically indicated in this secondary chemical injury [11, 14, 15] and are significant contributors to brain cell death and

3 dysfunction in TBI [15]. The release of glutamate appears to be a self-propagating occurrence, as injured neurons release quantities of glutamate that are sufficient to injure neighboring neurons, resulting in the release of more glutamate [16]. Increased glutamate concentrations can cause changes in extracellular space volume and tortuosity, and can result in neuronal injury and death as a result of the excessive influx of calcium that occurs due to the overstimulation of NMDA and AMPA glutamate receptors [10, 16-18]. The accumulation of calcium in the cells leads to an overstimulation of normal cellular processes through the activation of a number of enzymes and ultimately results in neuronal injury through a disruption of protein phosphorylation and microtubule construction [19], protein breakdown, free-radical formation, and lipid peroxidation [16]. Calcium build-up can also impact the mitochondria, resulting in swelling, depolarization, and functional loss [20], which can lead to cell death directly through apoptosis or indirectly through the loss of oxidative phosphorylation and failure of adenosine triphosphate (ATP) production [21].

The expression of excitatory amino acid

transporters (EAATs) is significantly decreased throughout the brain following TBI due to both the degeneration of astrocytes and the down regulation of EAATs in surviving cells [15, 22]. It is hypothesized that this decrease in glutamate transporters results in a reduction in glutamate uptake by astrocytes, contributing to the increase in extracellular glutamate concentration following TBI [15]. In addition, the overstimulation of both NMDA and AMPA receptors due to the extracellular accumulation of glutamate has been shown to contribute to the progression of other, non-trauma-induced neurodegenerative disorders [9, 10].

4 The unique time course of injury during TBI, in which the primary trauma is often followed by secondary injury mechanisms, provides medical researchers with two points at which to target intervention [1, 5, 23]. Reducing the risk of injury is the most effective way of decreasing the effect of TBI on society. Preventative measures that have lowered the incidences of TBI include requiring the use of seat belts in automobiles and the use of helmets for motorcyclists and bicyclists, lowering speed limits, enhanced enforcement of drunk drivers, and limiting access to firearms [4]. While progress has been made in targeting the injurious event, there remains a tremendous need to develop therapeutics that interfere with secondary injury mechanisms to protect neural function [1, 5, 23]. Because the window of opportunity for treatment in such cases is much longer (potentially hours to days) [23], such agents may provide clinicians with an effective means of improving clinical outcomes and lowering the morbidity associated with such injuries by using pharmaceutical interventions after the initial injury has already occurred.

1.2 Traditional TBI Models The development of appropriate models of TBI remains one of the primary hurdles in this field [1]. Over the past 20 years the emphasis was placed on the quest for a “super” animal model for CNS injury. As a result, a few complex in vivo models dominated the field, as depicted in Figure 1 (modified from [1]). These included the weight-drop injury model, the fluid percussion injury model, and the controlled cortical impact injury model [24]. The use of simpler TBI models was discouraged (through the reviewing process of submitted papers and grants) even though work at this level may

5 have better directed the more complex research. Feedback from basic, simple models has been weak, and the result has been only moderate improvements in patient’s outcomes from these expensive in vivo models [1, 5].

Clinical Trials

Controlled cortical impact injury

Fluid percussion injury

Weight drop injury

Animal models

Simple in vitro models

Figure 1: Schematic diagram depicting CNS research. The arrows show the flow of information. Currently, the emphasis is placed on complex animal models. Therapeutics that have positive results in simple in vitro models are either translated to animal models for further testing, or in some cases, transferred directly to a more applied level (adapted from [1]).

6 A more integrated approach, in which results from in vitro models are used to design more complex in vitro models, results from these in vitro models are used to design and direct the research in more complex in vivo models, and results from the complex in vivo models are used to design and direct the clinical trials may lead to greater overall success. By taking potential therapeutics through each experimental level, ineffective treatments may be weeded out before a great deal of time and money is wasted (Figure 2). Furthermore, there is a need for better in vitro models that may improve the success of transferring therapeutics from in vitro to in vivo models. Most of the current in vitro models of TBI rely on endpoints, such as cell morphology or biochemistry that do not necessarily correlate with cell function. While preservation of cell number and cell morphology are necessary for a positive outcome after TBI, the cells must also be able to send and receive signals as they did before the injury occurred in order for the central nervous system (CNS) to function properly. Utilizing an in vitro model that measures how the cells are functioning before and after injury may provide a better indication of whether therapeutics are truly protecting against the secondary injury cascade of TBI.

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Clinical Trials

Animal models

Functional in vitro models

Simple in vitro models

Figure 2: Schematic drawing of a theoretical way research could be organized in the CNS field to obtain more positive results. Results from multiple simple in vitro models are used to direct the research of more complex, more functional in vitro models. These results are then used to direct the research of animal models. Finally, the results from animal models are used to direct clinical trials (adapted from [1]).

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1.3 Microelectrode arrays (MEAs) as a model of TBI Microelectrode arrays have been used to study the pharmacological and toxicological response of neuronal networks to numerous compounds [25-28]. The large number of electrodes evenly spaced in a grid provides information on the response of neurons to pharmacological treatments throughout the network. Additionally, neuronal cultures grown on MEAs respond to compounds in the same concentration ranges that result in functional changes in vivo [29]. This similarity to the in vivo condition, along with the ease and accessibility of MEA technology in assaying the effects of various compounds, makes MEAs an attractive tool that could reduce the need for whole animal experiments [26, 30]. Using MEAs to study TBI allows one to record cellular activity before and after injury and with treatment to give a clear picture of not only whether the cells have survived but also how they are functioning. It may be important to understand these functional changes in order to design appropriate therapeutic interventions that can prevent against secondary injury mechanisms [31]. Dissociated cultures grown on MEAs bridge the gap between simple in vitro models, such as biochemical assays, and whole animal experiments [25]. Thus, using MEAs as a more functional in vitro tool may result in a better success rate in the translation of therapeutics from in vitro to in vivo studies.

1.4 Thesis Overview Restructuring the approach to TBI research may translate to a better success rate in identifying therapeutics that can mitigate the effects of secondary injury. However, there remains a need for the development of a more functional in vitro model that can translate the results of biochemical assays and cell counting experiments into success in

9 studies performed in animal models. This thesis focuses on the development of an in vitro model of secondary injury in TBI that can be used as a platform on which to test the ability of potential neuroprotective agents to preserve the functional properties of the neuronal network. The current chapter provides a brief introduction of the secondary injury mechanisms that occur during TBI, a summary of some of the current models of TBI, and the rationale behind the proposed use of MEAs as a tool to study secondary injury mechanisms during TBI. In Chapter 2, we established a system to use MEAs to measure changes in the dynamics of the neuronal network and developed analysis parameters that were stable over short periods of time. In Chapter 3, we studied the effects of glutamate-mediated excitotoxicity on the function of the neuronal network. We determined the analysis parameters developed in Chapter 2 that could detect changes in the communication of the network resulting from glutamate treatment.

Finally, in

Chapter 4, we investigated the use of an uncompetitive NMDA receptor antagonist, memantine, in preventing glutamate-induced excitotoxicity.

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1.5 References 1.

Kazanis, I., CNS injury research; reviewing the last decade: methodological errors and a proposal for a new strategy. Brain Res Brain Res Rev, 2005. 50(2): p. 377-86.

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Faul M, X.L., Wald MM, Coronado VG, Traumatic brain injury in the United States: emergency department visits, hospitalizations, and deaths. Centers for Disease Control and Prevention, National Center for Injury Prevention and Control, 2010.

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Waxweiler, R.J., et al., Monitoring the impact of traumatic brain injury: a review and update. J Neurotrauma, 1995. 12(4): p. 509-16.

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Ray, S.K., C.E. Dixon, and N.L. Banik, Molecular mechanisms in the pathogenesis of traumatic brain injury. Histol Histopathol, 2002. 17(4): p. 113752.

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Morales, D.M., et al., Experimental models of traumatic brain injury: do we really need to build a better mousetrap? Neuroscience, 2005. 136(4): p. 971-89.

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Kelly, D.F. and D.P. Becker, Advances in management of neurosurgical trauma: USA and Canada. World J Surg, 2001. 25(9): p. 1179-85.

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Greve, M.W. and B.J. Zink, Pathophysiology of traumatic brain injury. Mt Sinai J Med, 2009. 76(2): p. 97-104.

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Zhang, L., K.H. Yang, and A.I. King, Biomechanics of neurotrauma. Neurol Res, 2001. 23(2-3): p. 144-56.

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LaPlaca, M.C., et al., CNS injury biomechanics and experimental models. Prog Brain Res, 2007. 161: p. 13-26.

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Smith, D.H., D.F. Meaney, and W.H. Shull, Diffuse axonal injury in head trauma. J Head Trauma Rehabil, 2003. 18(4): p. 307-16.

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Cater, H.L., et al., Stretch-induced injury in organotypic hippocampal slice cultures reproduces in vivo post-traumatic neurodegeneration: role of glutamate receptors and voltage-dependent calcium channels. J Neurochem, 2007. 101(2): p. 434-47.

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Blumbergs, P.C., et al., Staining of amyloid precursor protein to study axonal damage in mild head injury. Lancet, 1994. 344(8929): p. 1055-6.

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Gentry, L.R., Imaging of closed head injury. Radiology, 1994. 191(1): p. 1-17.

11 14.

Lusardi, T.A., et al., Effect of acute calcium influx after mechanical stretch injury in vitro on the viability of hippocampal neurons. J Neurotrauma, 2004. 21(1): p. 61-72.

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van Landeghem, F.K., et al., Decreased expression of glutamate transporters in astrocytes after human traumatic brain injury. J Neurotrauma, 2006. 23(10): p. 1518-28.

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Lipton, S.A. and P.A. Rosenberg, Excitatory amino acids as a final common pathway for neurologic disorders. N Engl J Med, 1994. 330(9): p. 613-22.

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Kwak, S. and R. Nakamura, Acute and late neurotoxicity in the rat spinal cord in vivo induced by glutamate receptor agonists. J Neurol Sci, 1995. 129 Suppl: p. 99-103.

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Vargova, L., et al., Glutamate, NMDA, and AMPA induced changes in extracellular space volume and tortuosity in the rat spinal cord. J Cereb Blood Flow Metab, 2001. 21(9): p. 1077-89.

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Hayes, R.L. and C.E. Dixon, Neurochemical changes in mild head injury. Semin Neurol, 1994. 14(1): p. 25-31.

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Verweij, B.H., et al., Impaired cerebral mitochondrial function after traumatic brain injury in humans. J Neurosurg, 2000. 93(5): p. 815-20.

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Kim, J.S., L. He, and J.J. Lemasters, Mitochondrial permeability transition: a common pathway to necrosis and apoptosis. Biochem Biophys Res Commun, 2003. 304(3): p. 463-70.

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Beschorner, R., et al., Expression of EAAT1 reflects a possible neuroprotective function of reactive astrocytes and activated microglia following human traumatic brain injury. Histol Histopathol, 2007. 22(5): p. 515-26.

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Rossignol, S., et al., Spinal cord injury: time to move? J Neurosci, 2007. 27(44): p. 11782-92.

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Albert-Weissenberger, C. and A.L. Siren, Experimental traumatic brain injury. Exp Transl Stroke Med, 2010. 2(1): p. 16.

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Keefer, E.W., et al., Characterization of acute neurotoxic effects of trimethylolpropane phosphate via neuronal network biosensors. Biosens Bioelectron, 2001. 16(7-8): p. 513-25.

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Morefield, S.I., et al., Drug evaluations using neuronal networks cultured on microelectrode arrays. Biosens Bioelectron, 2000. 15(7-8): p. 383-96.

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Parviz, M. and G.W. Gross, Quantification of zinc toxicity using neuronal networks on microelectrode arrays. Neurotoxicology, 2007. 28(3): p. 520-31.

12 28.

Gopal, K.V., Neurotoxic effects of mercury on auditory cortex networks growing on microelectrode arrays: a preliminary analysis. Neurotoxicol Teratol, 2003. 25(1): p. 69-76.

29.

Xia, Y., K.V. Gopal, and G.W. Gross, Differential acute effects of fluoxetine on frontal and auditory cortex networks in vitro. Brain Res, 2003. 973(2): p. 151-60.

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Chiappalone, M., et al., Networks of neurons coupled to microelectrode arrays: a neuronal sensory system for pharmacological applications. Biosens Bioelectron, 2003. 18(5-6): p. 627-34.

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Kutzing, M.K., V. Luo, and B.L. Firestein, Measurement of synchronous activity by microelectrode arrays uncovers differential effects of sublethal and lethal glutamate concentrations on cortical neurons. Ann Biomed Eng, 2011. 39(8): p. 2252-62.

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Chapter 2: Development of a tool to monitor the function of the neuronal network 2.1 Introduction Microelectrode arrays have been used as a tool to study the electrical activity of neuronal networks for more than 20 years.

Dissociated cultures grown on MEAs

represent an intermediate level between in vitro single cell recordings and in vivo studies and are a valuable tool for investigating information processing and dynamics in neuronal systems under controlled conditions [1]. It is well-established that neuronal primary cell cultures retain the major receptor, synaptic, and cellular mechanisms responsible for basic pattern generation in in vivo CNS tissues [2]. Likewise, neuronal networks grown on MEAs have been found to retain many of the properties of neurons in vivo [3, 4]. Many of the functional characteristics of in vitro cortical networks are similar to those observed in vivo, including connectivity, inhibition-excitation ratio, overall activity, and plasticity [4].

In addition, changes in the network in response to pharmacological

treatments are often in the same concentration ranges that alter functions of intact mammalian nervous system [5-8]. Using MEAs to study neuronal network dynamics offers a number of benefits over simpler in vitro techniques, such as cell counting and biochemistry. Using MEAs allows investigators to study not only the survival of neurons but also how they are communicating with one another before and after injury and drug treatments.

It is

possible that cells may survive after injury but may not function as they did before the

14 injury. In addition, some studies suggest that MEAs may be able to detect more subtle changes in the neuronal network. One study, for example, found that changes in the network activity occurred before detectable morphological changes, suggesting that monitoring the electrical activity of neuronal networks may be a more sensitive method than traditional in vitro studies as electrophysiological deficits may occur before major morphological changes are manifested [2]. Experiments using MEAs also offer some benefits over traditional single-cell experiments. Unlike patch-clamp techniques, MEA recordings are non-invasive, which permits multiple and long-term recordings from the same culture, allowing one to monitor both acute and chronic exposure to toxins and pharmacologic compounds [9]. The location of neurons grown on MEA surfaces is very stable [10], making it possible to gather information from the same cells at multiple time points. Furthermore, MEAs gather data from multiple locations in the neuronal network at a single time (up to tens of channels) [9]. This allows one to monitor the interactions between neurons at multiple locations in the network [11]. In vitro studies using MEAs also offer some benefits over animal models as they generate a greater amount of more specific information in a shorter period of time. In vivo electrophysiological studies on awake animals are limited to only a few electrodes [12], while MEAs contain many more. Furthermore, in vitro experiments allow more control over concentrations of pharmacological agents as digestion and metabolism are less of a concern [4]. Investigators have used dissociated cultures grown on MEAs to study a variety of compounds as well as various aspects of the neuronal network.

For example, MEAs

have been used to study the response of the neuronal network to zinc [2], mercury [13], NMDA, MK801, NBQX [9], and dopamine [4]. MEAs have also been used to study the

15 development of the neuronal network by monitoring the electrophysiological activity from plating until up to five weeks in culture [3, 14]. The majority of MEA studies focus on the spontaneous electrical activity of neurons. Typically, the activity pattern of the network takes the form of spontaneous bursting events (SBEs): periods of high electrical activity during which numerous action potentials are observed on multiple electrodes of the MEA within a short period of time [15]. It is well-established that brain processing relies on the synchronized interaction between groups of neurons [3]. Previous studies have shown that synchronous network oscillations are crucial for brain functionality [16, 17]. Functions, such as motor control and information processing, depend on the ability of neuronal networks to generate synchronized sequences of action potentials [17]. Synchronization has also been studied in other areas of neuroscience [18]. Multiple studies have found synchronization of multiple neurons in the vision centers of cats presented with visual stimuli [19-21]. Similar studies found enhanced synchronization in EEG signals from humans listening to music [22]. Synchronized bursts have also been found to play an important role in learning and memory [23]. Previous work has shown that dissociated neurons are capable of responding to external stimuli [3, 23-25]. Chao and colleagues found an increase in both the synchrony and the strength of the spontaneous electrical activity of the network in response to learning stimulations [25]. In a similar study, spontaneous bursts, periods of high activity throughout the culture, increased in response to closed-loop low frequency stimulation [3].

Another study found a marked change in the synchrony of the

spontaneous activity in response to low frequency stimulation. Prior to stimulation, the spontaneous activity consisted of random spikes and bursts. Following stimulation, the

16 electrical activity consisted of organized burst sequences. In addition, overall firing frequency also increased [23]. While the parameters used in in vitro MEA studies can vary with each experiment, there are a small number of parameters that are repeated throughout the majority of studies.

The overall activity rate of spikes (action potentials) and

spontaneous bursting events is used in many studies to describe the overall activity level of the network [2, 8, 9, 13, 14, 26, 27]. In some studies, the coefficient of variation (CV) of these parameters is used in order to measure the dispersion of the parameters [14]. In addition, other bursting properties, such as the burst duration, the number of spikes per burst, and the interburst interval (the time between the bursts), are sometimes reported [9, 13, 26]. These direct data analysis methods have been shown to be quite effective in describing major events that result in major changes in the electrical activity of the neuronal network and are therefore useful in the rapid screening of compounds [8]. Some of these parameters may not be reliable, however, when looking at more subtle changes in the neuronal network. The rate of overall activity, as measured by the spike rate, for example, has been shown to fluctuate greatly over short periods of time, possibly due to the variability in culture preparations [9, 28]. To observe subtle changes, the parameters must be stable in normal circumstances over the timescale of the experiment [29]. Subtle changes due to injury or pharmacological treatment may be better detected by looking at the dynamics of the network in terms of changes in the correlation of activity among channels [9]. Previous studies have investigated changes in the synchronization of the electrical activity in neuronal networks grown on MEAs.

Chiappalone and Eytan and their

17 colleagues computed cross-correlograms between a single electrode channel and every other channel [4, 14], generating a graph that displays how the activity of one electrode is distributed in time with respect to the activity of a second electrode. While this analysis method resulted in an informative representation of the correlation profile, the data from different electrodes or even different experiments cannot be combined. Thus, it is more of a qualitative rather than a quantitative look at correlation changes. Selinger and colleagues developed a method to calculate the mean correlation coefficient that represented the overall level of synchronization in the network and then showed how that parameter changed in response to pharmacologic compounds. While this method is quantitative, it took into consideration all of the activity within a given recording period, including both random spikes and coordinated bursting events [27].

We are more

interested in measuring the involvement of electrode pairs only within SBEs since SBEs represent periods of organized activity that are responsible for multiple brain functions, such as motor control and information processing [17]. Our objective in this chapter was to identify and develop analysis parameters that provide insight into changes in the dynamics of the neuronal network and that are consistent over short periods of time so that these changes can be detected.

We

investigated the most commonly used parameters of overall electrical activity and determined that the temporal variability was too high for our experiments as this variability may mask actual changes in the network. We found a few commonly used parameters to be stable over short periods of time (burst duration and the number of active electrodes) that may provide some useful information into the network behavior. In order to look more in depth at the dynamics of the network, we built off of previous

18 work and developed a measure of culture synchronization [27] as a representative measure of correlation between activity in different areas of the culture. Once the correct analysis parameters are developed, we will be able to investigate the effects of glutamate toxicity on the network activity and test whether treatment with various pharmacological compounds can prevent against these effects.

19

2.2 Methods 2.2.1 Network preparation Cortices were dissected from rat embryos at embryonic day 18 (E18) gestation. Meninges were removed, and the cells were dissociated with gentle trituration. Cells were plated on microelectrode arrays (MEA; Multi Channel Systems, Germany) at a density of 3,500 cells/mm2. The MEAs consist of 60 electrodes (59 active electrodes and 1 reference electrode), each with a diameter of 30 µm, arranged in a square grid with a spacing of 200 µm between them. Prior to plating, the MEAs were coated overnight at 37°C with a solution of 0.3% polyethylene imine (PEI; Sigma 03880, USA) dissolved in borate buffer. The cultures were grown in glutamate-depleted serum containing media (SCM; 84.4% MEM (Minimum Essential Medium; Gibco 42360, USA), 10% horse serum, 0.6% glucose, supplemented with 5% penicillin and streptomycin) for 14 days at 37°C and 5% CO2 prior to recording and treatments. Prior to use, the SCM was depleted of glutamate in mature astrocyte tissue-culture flasks for at least 6 hours and then filtered [30]. Medium was replaced every other day. All animals were cared for using ethical practices in accordance with IACUC standards.

2.2.2 Microelectrode array recording Microelectrode array recordings were taken from each MEA twice within a two hour period on day in vitro (DIV) 14. Prior to each recording, recording media (NaCl 144mM, KCl 10mM, MgCl2 1mM, CaCl2 2mM, HEPES 10mM, Na-pyruvate 2mM,

20 glucose 10mM, pH 7.4) was added to the MEAs for 10 minutes to allow the cultures to reach equilibrium. The recording media formulation was chosen so as to regularize the bursting behavior between the electrodes. During recording episodes, the MEAs were covered with a semipermeable membrane (ALA MEA-MEM; ALA Scientific, USA) that is selectively permeable to gases but prevents contamination from airborne pathogens. This greatly reduced the risk of contamination as our MEA recording equipment is not in a sterile environment. After each recording, the MEAs were washed three times with growth media. 2.2.3 Data analysis: Overall network activity The electrical signals were monitored and recorded using the data acquisition software, MCRack (Multi Channel Systems, Germany). The data were imported into Matlab using MEA-Tools, an open-source toolbox. The data were then filtered through both a highpass and a lowpass filter to remove any recording artifacts. Spikes were defined as any signal whose voltage surpassed a specified positive or negative threshold [13, 23]. The threshold was chosen to be at least two standard deviations above the level of the background noise that the signals were embedded in. Spikes were only counted at their maximum value to ensure that spikes were not counted multiple times on a single electrode. Electrodes often recorded signals from more than one neuron. However, no attempt was made to sort the signals collected from a single channel. Instead, a “whole channel analysis approach” was chosen in order to gather data from „micro‟ networks within the area of each electrode [8, 9, 13]. Spontaneous bursting events (SBEs), or bursts, were defined as periods of high spike activity recorded simultaneously on multiple electrodes [1]. Spikes were identified as belonging to a specific SBE if they occurred

21 within a specified period of time of a minimum number of other spikes [1]. existence of each burst was verified by visual inspection.

The

The burst duration was

calculated by subtracting the time point at which the first spike occurred in the burst from the time point at which the last spike occurred in each burst. The number of active electrodes was calculated by counting the number of electrodes that were involved in at least 5% of the SBEs.

2.2.4 Data analysis: Measurement of synchronous firing behavior In order to measure the synchronous behavior of the network, the synchronization index (SI) between all pairs of electrodes was calculated. This analysis is similar to the method used by Selinger and colleagues [27] that allows the results from multiple MEAs within multiple experiments to be compared quantitatively. Our analysis differs in that we measured the involvement of electrode pairs only within SBEs since SBEs represent periods of organized activity that are responsible for multiple brain functions, such as motor control and information processing [17]. Given two electrodes, x and y, the synchrony of firing (SF) was defined as the number of times that the two electrodes recorded a spike within the same burst (Bxy) normalized to the total possible number of times either of the two electrodes recorded a spike within the same burst (Bx|y):

These values were then weighted by the frequency by which the two electrodes recorded a spike within the same burst (Nxy) compared with the number of bursts that occurred on

22 that MEA during that recording period (NB) as a function of the maximum frequency of bursts that occurred on that MEA during either recording period (MB).

(

) (

)

The values were weighted in order to minimize the effect that occasionally occurred when two electrodes that were otherwise inactive each recorded a spike within the same single burst. Without weighting, this phenomenon would generate a value of 1. The SI values at the earlier recording time were subtracted from the SI values at the later recording time in order to find the change in synchronization (CS) of action potential firing that occurred in control cultures between the two recording periods. The SI value was calculated between every pair of electrodes such that each electrode had 58 SI values.

These 58 values were then averaged in order to obtain the average

synchronization index (ASI) for each electrode. Lastly, in order to determine whether the distance between neurons in a network affects the strength of the synchronicity of the electrical activity of the neurons, we designated three measurement zones based on the distance between the electrodes (Figure 3). For each electrode, all electrodes that were immediate neighbors to it were considered within the first zone. All electrodes that were immediate neighbors to electrodes within the first zone, but were not themselves contained in the first zone, comprised the second zone. All remaining electrodes made up the third zone.

23

Figure 3: Schematic of the method for determining the degree of separation between electrodes. In this example, the red electrode is the master electrode.

All electrodes

contained within the blue square are direct neighbors with the master electrode and are identified as 1st degree connections. All electrodes that are contained within the green square, but outside of the blue square, are identified as 2nd degree connections. All electrodes that are outside of both squares are identified as 3rd degree connections. The blue and green squares will change location depending on the location of the master electrode.

24

2.3 Results 2.3.1 Cortical neurons grown on MEAs exhibit robust spiking behavior Using our plating protocol, we found that mixed cortical cultures plated on MEAs adhere and survive (Figure 4). Additionally, we are able to successfully record from the cortical cultures grown on MEAs.

We observed sporadic action potential spiking,

beginning at approximately DIV 7 (Figure 5A). At DIV14, we consistently observed rich coordinated bursting behavior (Figure 5B). Experiments where cells did not adhere to all of the electrodes indicated that the cell body of the neurons must actually be touching the electrodes in order for the electrode to record a signal. Figure 6 shows the plating pattern that occurred on one MEA. In this experiment, no cells attached to the left side of the MEA. This phenomenon occasionally occurred due to an air bubble present in the media at plating. Figure 7 is a snapshot of the electrical activity of a single SBE occurring on this MEA and shows that no activity was observed on the electrodes that had no cell coverage. Furthermore, a rastor plot representing five minutes of continuous recording of the electrical activity on this MEA confirms that the electrodes that did not have direct contact with neurons did not record any action potentials during the entire recording period (Figure 8). The electrodes highlighted in Figure 8 correspond to the electrodes numbered in Figure 6.

25

Figure 4: E18 cortical neurons grown on a microelectrode array at DIV14. A) Image of the entire recording area. B) Zoomed in image of neurons on a few electrode contacts.

26

Figure 5: Spontaneous electrical activity of cortical culture grown on a MEA. A) Activity at DIV7 consists of isolated spikes.

B) Activity at DIV14 consists of

spontaneous bursting events where multiple spikes are detected on multiple electrodes simultaneously.

27

Figure 6: Partial plating pattern of neurons on electrode contacts of a single MEA. Due to an air bubble in the media, neurons did not adhere to the majority of the left side of the recording area. The electrodes that are not covered with neurons have been numbered according to their corresponding number in the Matlab analysis.

28

Figure 7: Activity of neurons plated on MEA shown in Figure 6. Due to incomplete neuronal plating on all electrode contacts, electrodes that were not in direct contact with neurons were unable to record neuronal depolarizations. The area of the MEA that did not contain any cells is circled in blue and corresponds to the numbered electrodes in Figure 6.

29

Figure 8: Five minute recording of spontaneous electrical activity of neurons plated on MEA shown in Figure 6. Each dot in the graph represents a recorded spike. The rows highlighted in yellow correspond to the electrodes in Figure 6 that were not in contact with any neurons. As a result, these electrodes did not record any neuronal depolarizations.

30 To determine whether the activity recorded by the electrodes was transmitted synaptically, we recorded the electrical activity of the cultures in the presence of synaptic blockers. Exposure to a mixture of synaptic blockers (5 µM 6-cyano-7-nitroquinoxaline2,3-dione (CNQX), 20 µM bicuculline, and 20 µM (2R)-amino-5-phosphonovaleric acid (APV)) resulted in a dramatic decrease in spiking activity (Figure 9), suggesting that electrical activity of the cultures is a result of action potentials evoked by synaptic transmission rather than from intrinsically generated action potentials.

31

Figure 9: Synaptic blockers dramatically decrease electrical activity.

Electrical

activity of a representative MEA prior to and during treatment with synaptic blockers (5 µM CNQX, 20 µM bicuculline, and 20 µM APV). Each dot in the graph represents a recorded spike. (A) Prior to treatment with synaptic blockers, the culture showed high levels of spiking and SBEs. (B) Application of synaptic blockers dramatically decreased the amount of spiking and completely eliminated SBEs.

32 2.3.2 Overall network activity of control cultures We investigated the overall activity of the network by measuring the number of spikes, the number of SBEs, and the number of active electrodes. In order to determine the stability of these parameters, we compared the values obtained from the second recording to those obtained from the first recording. For each parameter, we counted the number of MEAs that showed a large increase (>20%), the number of MEAs that showed a large decrease (>20%), and the number of MEAs that showed only minor changes (20% decrease 0.6

>20% increase 20% change in the number of spikes, approximately 50% of the MEAs experienced a >20% change in the number of SBEs, and less than 10% of the MEAs experienced a >20% change in the number of active electrodes. n=30 MEAs.

34

Figure 11: Burst duration of control cultures at two recording times within two hours. The average burst duration between the two recording times was similar. No significant differences were observed between the two groups as determined by twotailed unpaired Student t-test.

35 2.3.3 Measurement of synchronized firing of neuronal networks In addition to measuring the overall activity of the network, we analyzed how the pattern of action potential firing changed in control cultures over short periods of time to determine how stable the pattern of action potential firing is. The synchronization index was calculated between all pairs of electrodes at each time point in order to measure changes in the synchronization of neuronal firing throughout the neuronal network. This resulted in a matrix in which each electrode is plotted against all other electrodes, yielding a 59 x 59 matrix (Figure 12, top). The SI values at the earlier recording time were subtracted from the SI values at the later recording time in order to find the change in synchronization (CS) of action potential firing that occurred in control cultures between the two recording periods (Figure 12, bottom). A majority of the electrode pairs of these control cultures showed no change or only a minor change in the SI, as is reflected in this example MEA. The 58 SI values for each electrode were averaged in order to calculate an average synchronization index (ASI) for each electrode. The SI value of the electrode with itself and with the reference electrode was excluded. This resulted in a matrix where the locations of the ASI values represent the actual physical location of each electrode (Figure 13).

The change in ASI grids for each of the

experimental groups is similar to those seen in the SI grids (Figure 12) except that changes in ASI values are reduced because in this analysis, the individual SI values were averaged. In our cultures, the ASI values of the electrodes did not change much within the two hour time period. Interestingly, the MEA used in Figures 12 and 13 had a greater than 30% increase in the number of bursts between recording times even though the majority of the changes in the SI between electrodes were small.

36

Figure 12

37 Figure 12: The synchronization index of control cultures at two recordings times within two hours.

SI=synchronization index.

The upper left picture shows the

synchronization grid during the first recording, the upper right picture shows the synchronization grid during the second recording, and the bottom picture shows the change in synchronization (CS) between the two recordings. In the top two pictures, a red color designates that the two electrodes have a high SI, indicating that both electrodes frequently record a spike within the same burst. A dark blue color designates little to no synchronization between the neurons whose action potentials are recorded on those electrodes. In the bottom picture, a red or yellow color indicates an increase in the SI (positive CS), a pale green color represents no change in the SI, and a blue color represents a decrease in the SI (negative CS).

38

Figure 13

39 Figure 13: The average synchronization index of control cultures at two recordings times within two hours. ASI = average synchronization index. The location of the ASI value for each electrode represents the actual physical location of the electrode on the MEA. The top left picture shows the ASI values during the first recording, the top right picture shows the ASI values during the second recording, and the bottom picture represents the change in the average synchronization of each electrode between the two recordings. In the top two pictures, a red color designates that that electrode has a high ASI, indicating that the neurons on that electrode fire in synchrony with neurons on other electrodes. A dark blue color designates that the neurons whose action potentials are recorded on that electrode do not fire with a high degree of synchrony with many neurons on other electrodes. In the bottom picture, a yellow color indicates an increase in the ASI, a pale green color represents no change in the ASI, and a blue color represents a decrease in the ASI.

40 To determine the stability of the synchronization index as a parameter that could potentially detect changes in the network activity, we calculated the overall SI values for each MEA at each recording time (Figure 14).

To compare this parameter to the

measures of overall activity that we found to be variable (spikes and SBEs), we grouped the MEAs into three groups; those that showed a greater than 20% increase in either spikes or SBEs (group 1), those that showed a greater than 20% decrease in spikes or SBEs (group 2), and those that changed 20% increase in the number of spikes or SBEs, Group 2: >20% decrease in the number of spikes or SBEs, Group 3: