PLATFORM DEVELOPMENT FOR THE MODULATION OF EPILEPTIC SEIZURES BASED ON INTERICTAL SPIKE RATE

Transcription

1 PLATFORM DEVELOPMENT FOR THE MODULATION OF EPILEPTIC SEIZURES BASED ON INTERICTAL SPIKE RATE By STEPHEN M. MYERS A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY UNIVERSITY OF FLORIDA 2011

2 c 2011 Stephen M. Myers 2

3 To my wife and parents 3

4 ACKNOWLEDGMENTS Numerous people have helped me reach this point. I must first thank my wife, Renee. The road of being married to a graduate student can sometimes be a long and difficult one. Thank you for your patience with me on this journey. You have made the adventure more enjoyable than it would have ever been had it been travelled alone. Everyday with you is a blessing that I am eternally thankful for. Secondly, I would like to thank my parents. You have been amazing role models. I am thankful for your support and love. The last five years would of course not be possible with out my mentors and lab mates. Dr. Carney, thank you the unique opportunity to work in a translational research lab, where biomedical engineers should be. The chance to truly see from bench to bedside in action is something any biomedical engineer would appreciate. My lab mates, you have provided me with many great times. My days will never be the same without you in them. Jason, thank you for keeping the engineering side of my brain entertained with our thought provoking discussions over bowling. Rabia, you were the sister I never had, thank you for keeping me organized and for knowing everything that I didn t. 4

5 TABLE OF CONTENTS page ACKNOWLEDGMENTS LIST OF TABLES LIST OF FIGURES ABSTRACT CHAPTER 1 INTRODUCTION Motivation Terminology Spikes Racine Scale Problem Statement Objective Objective Background Epilepsy Background Temporal Lobe Epilepsy EEG Background Spike Pathology Contribution MODELS OF EPILEPSY Purpose of Epilepsy Models Model Characteristics Model Selection Analysis of the Model for Self Sustaining Status Epilepticus RESEARCH METHODOLOGY Animal Preparation Animal Surgeries Bipolar electrode implantation Microwire implantation EMG implantation Post Surgery Recovery Stimulation Protocol Animal Housing Recording Chambers EEG Recordings

6 4 PLATFORM DEVELOPMENT Recording Systems Electrode Construction and Use Bipolar Twist Electrodes Microelectrodes RELATIONSHIP BETWEEN SPIKE RATE AND SEIZURE Introduction Methods Analysis Discussion and Results Conclusion CONCLUSIONS Final Remarks APPENDIX A FURTHER CONSIDERATIONS Reducing Electrographical Noise Recording Setup Data Conversion Additional Devices B ADDITIONAL EXPERIMENTS Methods for Hybrid Model Preliminary Results for Future Experiments Direct Stimulation to the CA Seizure Intervention With Direct Stimulation VNS Experimental Setup REFERENCES BIOGRAPHICAL SKETCH

7 Table LIST OF TABLES page 5-1 Percentage of preictal distributions that were statically different than interictal distributions

8 Figure LIST OF FIGURES page 1-1 Representative plot of an action potential Interictal epileptiform discharges as seen in recordings from the right CA1 (RCA1) and left CA1 (LCA1) depth electrodes Time line for SE model Schematic of how the brain transitions from normal to epileptic Histogram showing when seizure occurred Circadian nature of seizure occurrence Time difference between seizures Histogram of seizure occurrence Seizures per grade Exposed skull prior to marking the coordinates for electrode placement Stereotaxic frame Placement of Electrodes relative to anatomical landmarks View of headstage immediately post surgery Illustration of headstage Schematic of the location of microwire electrodes for recording from the CA1 and DG EMG implantation technique Screen shot of custom software for data conversion Biopolar electrodes Channel Omnetics connector Jig for microwire construction Setup for TDT system Stellate setup Dynamic states seen in epilepsy

9 5-2 Electrodes placement of bipolar electrodes and stimulating electrode for chronic recording The process taken to extract spikes from EEG data Plot of spike rate per minute vs time Mean firing rates per minute Percent change between windows Average slopes of preictal and interictal windows ROC Curve of preictal and interictal spike rate values Plot of spikes over a multiday period Spikes occurring per minute vs time Normalized spike rate of spikes Comparison of the mean spike rate during nonseizure times of both nonseizing but injured animals and seizing animals Hemisphere comparison of average firing rate A-1 Grass Connections A electrode configuration breakout box A-3 Conversion software A-4 Automated rat feeder through the development process A-5 Output from tracking software B-1 Number of seizure induced with PTZ B-2 Heart rate before and during VNS treatment

10 Abstract of Dissertation Presented to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy PLATFORM DEVELOPMENT FOR THE MODULATION OF EPILEPTIC SEIZURES BASED ON INTERICTAL SPIKE RATE Chair: Paul R. Carney Cochair: Sachin S. Talathi Major: Biomedical Engineering By Stephen M. Myers May 2011 Epilepsy is one the most devastating neurological disorders in existence. Nearly one-third of patients have recurrent spontaneous temporal lobe seizures after pharmacological intervention. More often these patients are turning to types of adjunctive therapy such as vagus nerve stimulation. Current treatments based on vagus nerve stimulation use an open-loop simulation approach. Success of a closed-loop seizure prevention system would require identification of a measurable electroencephalogram (EEG) feature. Spike wave discharges often accompany epilepsy; however, the relationship between interictal spikes and seizure onset is largely unknown. Here, a method for recording, sorting interictal spikes and relating their occurrence with seizure onset in a rat model of temporal lobe epilepsy is presented. To determine this relationship first a study platform was created, which allowed for the chronic recording of highly sampled EEG signals. First a recording platform was created, and then using this system, EEGs containing spontaneous seizures were then recorded. Interictal spikes were extracted from these recordings and grouped into two major groups. When the number of spikes occurring per minute of recording was binned and plotted there was an increase in the firing rate of spikes before the occurrence of seizures. The change in spike rate also increased in the time just before an impending seizure. 10

11 The results presented here show there is a temporal relationship between the rate of CA1 region interictal spikes and seizure onset in an animal model of temporal lobe epilepsy. With this information, a closed-loop seizure prevention system can be developed based on the modulation of interictal spikes. 11

12 CHAPTER 1 INTRODUCTION Motivation Epilepsy is one of the most common chronic neurological diseases in the world [79]. In the United States alone, it affects nearly 1-5% of the population [23, 47]. In addition to these 3 million patients that live with epilepsy, 10% of the population will experience a seizure during their lifetime. It is estimated epilepsy costs Americans over $15 billion each year [24]. While there is no cure for epilepsy, there are a number of treatments. Unfortunately, for those patients affected by epilepsy, nearly a third of them are unresponsive to the pharmacological intervention and never enter into remission [75, 84]. Despite the most advanced medical intervention (antiepileptic medication, surgery, etc) 10% of patients will never be free from seizures. The current failure of our medical knowledge to treat these patients effectively, leaves a large need for improved epilepsy therapy. Not only are one-third of patients not finding the results they seek, but many of those that do enter remission have to deal with a number of impairing side effects. Device driven intervention of epileptic seizure control, is shaping itself to be a promising area of epilepsy treatment. Currently there are two major device based interventions, vagus nerve stimulators and deep brain stimulators. To date these devices have limited feedback control. The major problem with a feedback system is identifying a suitable biomarker that is capable of determining a point of modulation. To combat the short comings in epilepsy treatment the National Institutes of Health has defined a number of benchmarks to motivate the progress of epilepsy research. This project seeks to add knowledge to the field in a number of these newly set standards including: To identify and validate biomarkers to predict the development of epilepsy in at-risk individuals 12

13 To optimize existing therapies and develop new therapies and technologies for curing epilepsy. Develop new approaches for targeted therapies. Initiate clinical trails of new, modified, and combination approaches to enhance cure rates. In conclusion, this project seeks to develop technology, techniques, and initiate the research needed in order to create a fully automated seizure modulation device. Spikes Terminology The electrographical spike can be an ambiguous term. For this purpose it is important to explicitly state the definition that will be used. The term spike often means one of two groups of events. The first, action potentials (Figure 1-1 A), are the all or nothing changes in a cell s electrical membrane potential. These are fast acting events, which typically only last a few a milliseconds. These events are the primary form of neuronal signaling. The action potential represents the activity of a single neuron. In contrast the second type of spike, population spikes (Figure 1-1 B), are the summed activity of synchronous postsynaptic depolarizations. For the purpose of this series of studies dealing with the hippocampus, the primary generators of electrographical activity (seizures, spikes, etc) arises from the pyramidal and interneurons in the CA1 and CA3. These population spikes occur on a much larger time scale than action potentials, but typically last less than 200 milliseconds. There are a number of population subtypes (e.g. interictal epileptiform discharges, sharp waves, after discharges). Between these subgroups, interictal epileptiform discharges (Figure 1-2) are associated with epilepsy at a high enough rate to used for the diagnosis of epilepsy [11]. The sorting methods presented here are unable to distinguish between the different subtypes of spikes. For the purpose of this work, all these subtype events will be the spikes of primary interest when detecting events, and 13

14 will be the type of event referred to when the general term spike is used. These spikes must have the following quantitative and qualitative characteristics [21]: 1. Must be paroxysmal. 2. Must show abrupt change in polarity. 3. Duration must be less than 200 milliseconds. 4. The spike must have a physiological field. Additionally, these spikes are typically followed by a delta slow wave. When these events are extracted from EEG and sorted into groups, the above requirements will be used to classify one cluster as having an ideal spike profile. Each animal s ideal spike will be the spike group followed through the analyzation phase. Racine Scale Throughout this work, the behavioral severity of seizures will be graded based on the Racine Scale. The Racine Scale [74] is a popular scale for the classification of epileptic seizures in rats. The scale ranges from 0 to 5 with 5 being the most severe. Grade 0 Afterdischarges with no physical manifestations. Grade 1 Facial clonus. Grade 2 Head nodding. Grade 3 Forelimb clonus. Grade 4 Bilateral forelimb clonus with rearing. Grade 5 Generalized clonic convulsions with loss of balance. Problem Statement The purpose of this body of work is to build a foundation, both in methods and research, for the study of closed loop seizure modulation. Previous work in the field, focuses on acute studies of the brain, because of the inherent difficulty of long term, highly sampled electroencephalography (EEG) studies. The work also investigates the temporal relationship between seizures and EEG spikes for future use as a feature for closed loop modulation. 14

15 Objective 1 The first objective of this study was to develop the methods and equipment required to build a system to test a closed loop seizure modulation device. This primarily involves the creation of methods that were used for recording long term and highly sampled EEG signals. Using these methods, a seizure database was then created, which provided a resource to investigate EEG time series as they progressed towards seizure. Objective 2 The second aim of the study was to take the seizure databases and quantify the relationship between interictal epileptiform discharges and seizures. To date these spikes are believed to be highly correlated to diseased tissue; however, their temporal correlation to impending seizures is still highly debatable. Background Epilepsy Background Epilepsy is one of the most common chronic neurological diseases. It affects nearly 1% of the world s population [21, 35, 78] with nearly a third of epileptic patients never gaining full remission of seizures. Epilepsy is characterized by its recurrent, spontaneous seizures [78]. The large number of affected patients can be linked to the disease s many causes, which include both genetic defects and physical insults. This makes epilepsy not a single disease, but rather a class of symptoms that arises from a number of pathological states. This in itself makes epilepsy a difficult disease to study. In almost all cases the recurrent, spontaneous seizure is a mainstay of an epileptic disorder. The spontaneous nature of the seizure also makes epilepsy a difficult disease to study. Patients are relatively normal when not seizing, but seizures themselves can have extreme morbidity. The transient nature of the events correlates to few occurring in a research or hospital setting. Seizures can be categorized into many types; however, they typically result in synchronization of a large number of neurons, which results from an imbalance of 15

16 inhibitory and excitatory neurons [7]. The seizures resulting from these imbalances are classified into two major categorized, partial and generalized [13, 27]. During partial seizures, the seizure has a primary focus that gives rise to the seizure. Conversely, generalized seizures encompass the entire cortex. The many manifestations of epilepsy make it a highly dynamic disease, with seemly random transitions between seizure and normal states. The unpredictability of epilepsy is very debilitating for those it affects. The paroxysmal nature of epilepsy not only causes pathological and neuropsychiatric issues, but also social. The occurrence of seizures without warning makes epilepsy the leading cause of neuropsychiatric disability worldwide [21]. Studies have shown the sociological cost of epilepsy also affects the patient s loved ones as patients experience tremendous emotional, financial, self-esteem, and family issues [10]. The social effects, combined with decreases in cognitive function, adverse effects of anti-epileptic medications and the shear unpredictability of seizures all culminate in an impaired quality of life. To date, the most prolific treatment for epilepsy is the use of antiepileptic drugs (AED). It is important to note, all drugs classified as an AED are not antiepileptic but rather antiseizure medication. None of these drugs are capable of altering the course of the disease that is epilepsy [72], but rather only server to suppress the occurrence of seizures. If a patient was responsive to AED medication, suspension of the medication would result in the reappearance of the seizures. The development of AEDs is often slow with long periods of no new drugs to market being seen [49], again giving rise for the need of new treatments. AEDs operate under three basic principles: (1) inhibit excitation, (2) enhance inhibition, or (3) modify cell excitability by the modulation of voltage dependent ion channels [72]. Unfortunately, for nearly a third of the patients suffering with epilepsy, they will continue to have spontaneous seizures despite the best medicine has to offer in terms of pharmacological intervention. Many of these patients are seeking new forms of 16

17 treatment. For these patients surgical resection of the epileptic focus, or a vagus nerve stimulator may be their only hope. While the use of surgical intervention for the treatment of epilepsy is not a new concept [17], it is still thought of as radical by most patients. It has been reported that less than 2% of patients that are candidates for surgical resection have the surgery [20]. If patients do not have a seizure focus or have a focus that is an area that can not be resected then they must seek another form of augmented therapy. The newest augmented therapy for these patients is vagus nerve stimulation. Vagus nerve stimulation was first used for the control of seizures in 1988 [69] and became FDA approved in The treatment consists of implanting a pacemaker like device under the collar bone. Leads are then connected to the left vagus nerve. The vagus nerve has wide spread inhibitory projections to many parts of the brain [58, 60, 76, 77]. Studies have shown vagus nerve stimulation has positive results, with over half of patients seeing a 50% decrease in seizure frequency [26]. Temporal Lobe Epilepsy The most advanced therapies still leave millions of epileptic patients with reoccurring seizures. Even with these advances, the number of patients still unresponsive to treatment shows new therapies are still desperately needed. Of particular interest for this study is temporal lobe epilepsy (TLE). TLE is a particularly devastating incarnation of epilepsy. It often begins in childhood and continues throughout the patient s life. Of the patients with TLE, fewer than 20% will become completely seizure free [34, 42, 46]. This study chooses to study TLE because it is widespread among the epileptic population and is one of the forms of epilepsy that is most commonly resistant to anti-epileptic drugs [8]. TLE occurs in the structures that comprise the temporal lobe, specifically the hippocampus, parahippocampal gyrus and amygdala [1]. TLE occurs when these areas experience degeneration and sclerosis[28]. TLE is subdivided based on the region it 17

18 affects into mesial temporal lobe epilepsy (MTLE) and lateral temporal lobe epilepsy (LTLE). Of these MTLE is the most common, and the form that will be modeled and studied throughout these experiments. Patients with MTLE suffer from partial seizures, which cause cognitive, motor and sensory impairments. Severe partial seizures can cause altered mental states and convulsive movement seizures. General seizures are more severe than partial seizure and are associated with altered conscious and tonic-clonic motor seizures. EEG Background The body and electricity have been linked since the late 18 th century, when Luigi Galvani discovered electricity could cause a muscle contraction in a frog s leg. Nearly 100 years later it was discovered that stimulation of the body s sensory system resulted in electrical activity in the brain. Then in 1912, the first electrographical seizure was witnessed. In 1929, Hans Berger recorded the first scalp EEG, changing the diagnosis and treatment of epilepsy forever. The EEG is generated from the summation of synaptic potentials (excitatory postsynaptic potentials and inhibitory postsynaptic potentials). As ions flow through the neuron s permeable membrane, charge flows in the opposite direction. Thus a region of the neuron is charged compared to the rest of the cell. This separation of charge is termed a dipole, and it is the projection of the dipole to the EEG electrode which is recorded in the EEG. The placement of the electrode can have a great effect on the waveform which it records. The scalp electrodes that are typically used in clinical setting primarily receive signals from the cortex directly beneath them. Since this study seeks to understand epilepsy in tissue deep within the brain, intracranial electrodes are required to be placed in or near these regions of interest. The EEG is unique in that it can provide a high level of temporal resolution, samples can be recorded quickly enough to capture the most basic element of neuronal communication - the action potential, and it can also provide a high amount of spatial 18

19 resolution through the addition of multiple electrodes. The electrodes can be placed superficially or intracranial. Intracranial electrodes introduce a new subclass of EEG called electrocorticography (ECoG). ECoG electrodes can be placed in specific regions of interest of the brain. Being closer to the source of the electrographical generation, the electrodes record signals 10 times greater in amplitude compared to those seen in scalp recordings [33]. These recordings have been shown to record the same electrical potentials as seen in scalp electrodes [82]; however, higher frequencies are seen with ECoG recordings [33]. Additionally, the use of invasive ECoG electrodes allows for a smaller population of neurons to be monitored [83]. The number, type and placement of electrodes must be dictated by the experimental question. Spike Pathology The normal and abnormal brain is capable of generating a number of unique events [33]. One subclass of this electrophysiological activity is the population spike (spike). While not all spikes are pathological [11], interictal spikes are a unique form of spike correlated to epilepsy [11, 21, 39]. Their correlation to epilepsy is largely undebated; however, their temporal relationship to seizures is highly debatable. Currently, studies have negative[1, 85], no [11, 31, 32] or unclear[2, 80] relationship between seizures and spikes. The spike is the result of a synchronous excitation of a population of epileptic neurons. Individually these neurons generate action potentials. If there is enough synchrony between the neurons, the summation of their depolarizations is large enough to be detected by extracellular recording electrodes. A number of studies have shown that spikes and seizures are generated through different cellular and network mechanisms [11]; however, spikes have been shown to arise from the damaged areas of the brain known as the irritative zone [30, 53]. The spike generating zone does not have to be the seizure focus, but they are generally in the same hemisphere. The correlation between seizure focus and spike presence is high enough to use spikes 19

20 as an electrographical marker for the epileptogenic zone. These spikes are seen in a number of models of epilepsy, and in a number of regions of the brain in these models. While the methods presented in this study are not able to distinguish between pathological spike types, pathological spikes do differ from normal spikes by having a more regular shape, higher amplitude, and shorter duration [11]. The hippocampus, and specifically the CA3, has been shown to be a generator of spikes [3, 9, 40]. Since the population spikes arising from the trisynaptic loop have been shown to be inhibitory in nature and important in minimizing seizure occurrence [4, 18, 44], this study will record spikes from the hippocampus in order to truly decipher the relationship between seizures and spikes. To date, the study of spikes has been hindered by several key issues. Primarily among them are a non-uniform definition of spikes, lack of continuous recordings to provide a true window into the relationship, and differences between cortical interictal spikes and interictal spikes recorded at a seizures focus [30]. Contribution Success of a closed-loop seizure prevention system would require identification of a measurable EEG feature that has a direct correlation to seizures. Spikes often accompany epilepsy; however, the relationship between spikes and seizure onset is largely unknown and highly debated. Here a method for recording highly sampled, long term EEG signals and then showing the temporal correlation between seizures and spikes is presented. This information can then be used as the first steps in building a closed loop system for the control of epileptic seizure based on interictal spike modulation. 20

21 A Action Potential B Population Spike Figure 1-1. Representative schematic plot of an action potential (A) and a population spike (B). Note the vast difference in the scale between the two plots. Figure 1-2. Interictal epileptiform discharges as seen in recordings from the right CA1 (RCA1) and left CA1 (LCA1) depth electrodes. 21

22 CHAPTER 2 MODELS OF EPILEPSY Purpose of Epilepsy Models The transient nature of epilepsy makes it a difficult disease to study. The average epileptic patient is, for all intents and purposes, normal when not experiencing a seizure. The spontaneity of the episodes means patients would have to be monitored continuously for long periods of time to garner meaningful results. In addition to the high cost associated with human studies, there are a number of ethical questions a researcher must face. A major concern is the balance of good for the patient vs the good of the greater collective whole the research might yield. These concerns lessened by using animal models. Animal models allow researchers to greatly increase their participant pool without having to involve human patients. Supplementing the benefit of cost reduction, animal models often allow for a more thorough study of a disease state. Animal models allow for a more invasive study of the epileptic brain. When investigating with an animal model, an investigator can study cellular mechanisms and other aspects of the brain that could never be studied in a human. In a disease as dynamic as epilepsy, there have been studies showing that diet and environment can have an effect on seizures [15, 25]. The use of animals also allows for many of variables to be controlled. Epilepsy is a complicated and dynamic disease. For this reason, a number of different models exist, which allows for the study of various aspects of the epileptic state. There are two primary groups of model [19]. The first is the acute model. In these models of epilepsy, some aspect of epilepsy is studied in a relatively normal brain. Seizures are manifest in an acute manor through one of a number of stimulants. These stimulants can be pharmacological, electrical, or audiogenic in nature [70, 71]. When exposed to the stimulant, an acute ictal event is generated. While there is often a disconnect between these seizures and seizures that would be seen in epileptic 22

23 patients, the acute model does allow for the study of a number of unique features of the epileptic brain. Primary amongst these is the study of the cellular and molecular basis of a seizure [71]. Through the use of these models, investigators have been able to study changes about the intrinsic properties of neurons. The second type of model is the chronic model of epilepsy. The most important aspect of the chronic model is its ability to let investigators study the latent and interictal periods. This provides the opportunity to study more clinically relevant mechanisms. As mentioned in section 1, temporal lobe epilepsy is a particularly devastating incarnation of epilepsy because of its resistant to many modern epilepsy treatments. Chronic models are most often used to study this form of epilepsy [86]. The various types of chronic models can generate damage in the limbic structures of the brain similar to sclerosis seen in human patients suffering from MTLE. One type of chronic model is the kindling model. In this model, stimulating electrodes are placed into the amygdala and stimulated for 2 seconds twice a day until grade 5 seizures are seen [29, 57]. These repetitive stimulations will damage the brain, eventually causing the rat to seize when stimulated with another pulse of electricity. This is a popular model for the study of new drugs as it (1) causes on demand seizures and (2) causes seizures that are very repeatable from trial to trial. A second subclass of chronic model is the self sustaining status epilepticus (SSSE) model. Status can be induced through either medication or electrical stimulation of the brain. When using electrical stimulation, stimulating electrodes are placed into the ventral hippocampus. In this model the hippocampus is stimulated for roughly 1.5 hours. This induces status epileptics in the animal, which continues after the stimulation has been stopped and is thus termed self sustaining status epileptics. After recovering, the animal will develop seizures four to eight weeks post stimulus. These seizures, like their human equivalent, are spontaneous. 23

24 Model Characteristics Choosing the proper model depends on the experiments to be done and the particular facet of epilepsy that one wishes to study. The electroencephalogram is the most widely used device for the diagnosis and study of epilepsy. Therefore, when studying many clinical questions it is important that a model generates seizures that are electrographically similar to their human counterparts. For this series of studies, electrographical biomarkers are very important. Thus, it is critical to pick a model that not only produces seizures but can also produce electrographical events, such as spikes and high frequency oscillations, that might be capable of acting as a biomarker for seizures. Chronic models also yield the investigator the unique opportunity to study the entire disease state and not just the seizure itself. The different stages the epileptic evolution are: Latent Period - the time from the initial insult that causes epilepsy to the first seizure. Interictal - the time between seizures. Preictal - the time just prior to a seizure. Postictal - the time just after a seizure. Preictal and postictal are nebulous terms because the transition into and out of a seizure can be slow and difficult to detect. For this purpose when analyzing data in this study, these variables will be set to concrete time limits as supported by the literature on the subject. In the EEG, there are also several other characteristics that can be measured. The most important of these is the seizure itself. Seizures can be broken down into two major categories, partial and generalized. Animal behavior is also another metric that can monitored. Models may either produce full onset seizures, which always present physically as a full body clonus of the animals, or as partial seizures. The advent of noninvasive imaging techniques (e.g. MRI, fmri) has increased the interest in models 24

25 with accurate representation of the structural changes that are associated with epilepsy. Structural changes are often of special interest to studies involving MTLE because those it affects often experience lesions in the brain. Model Selection For the purpose of these studies a model needs to be chosen that is: Chronic Produces spontaneous seizures with enough interictal time to allow the animal model to return to baseline between seizures. Closely mimic the MTLE condition in structural changes and response to treatment. After reviewing the literature it was determine to work with the a self sustaining status epilepticus model. This type of model was chosen because its damage to the hippocampus mimics the focal pathologies in patients with MTLE [5]. The full method of inducing an animal into self sustaining status epilepticus will be described later in section 3, but a general mechanism and overview will be discussed here. Pathologically, the model recreates the severe hippocampal sclerosis caused by the hilar and CA1 neuronal loss [54, 55]. Studies have shown that following the electrical insult, there is a reduction in GABA A receptor mediated synaptic inhibition [51, 52]. Since GABA is the main inhibitor in the central nervous system [64] the lack of inhibition then causes the pyramidal cells in that CA1 region of the hippocampus to become overly excitable. The stimulation appears to be indiscriminate in its reduction of GABA receptors, as studies have shown it also reduces GABA B receptors [52]. The reduction in inhibition seems to play a major role in the development of seizures; however, the complete mechanism for seizure development is not yet completely understood. The major benefit for this series of studies is the electrographic morphology the model generates. The seizures and their response to current epilepsy treatments are very similar to those seen in humans. Like human MTLE, the seizures have a 25

26 similar focus and are typically pharmacoresistant. The model gives a sufficiently long latent period (Figure 2-1), which is beneficial for studies that wish to develop curative treatments for epilepsy or wish to understand how epilepsy develops. Studies have shown seizures can begin between 7 days and 3 months after status epilepticus [56]. The seizures produced by this model are ideal to study because they also mimic the severity and interictal time of MTLE. Status epilepticus is defined as a state of continuous seizure activity lasting at least 30 minutes [48]. The continuous hippocampal model of self sustaining status epileptics that was used in these studies generates status epilepticus by electrical stimulation of the CA3 portion of the hippocampus. The electrical stimulation of the hippocampus continues for 30 to 90 minutes. After being discontinued, if the animal continues to seize then the animal is said to be a state of self sustaining status epilepticus. The amplitude of electrical stimulation was initially chosen based on the work done in kindling models of epilepsy. However, the current stimulation was later refined as being twice the current required, on average, to generate afterdischarges in the animal [56]. For a normal animal, afterdischarges being at 150µA. If an animal does not experience afterdischarges until 300µA then the likelihood the animal will enter into self sustaining status is low. Hybrid model of status epilepticus. The main draw back to selecting the self sustaining status epilepticus model is when trying to investigate therapeutic intervention. While deemed beneficial for most aspects of the study, the spontaneous seizure causes a problem when investigating various intervention schemes. With seizures occurring between one and seven times a week, it would take a considerable amount of time to generate enough data to statistically prove a therapy s benefits. In light of this deficit, several experiments were performed in order to create a hybrid model. The purpose of this hybrid model is to create a model with all the characteristics of the self sustaining SE model yet have the capability to initiate an on demand seizure. A model that simply 26

27 creates on demand seizures was not used because they generate seizures that are not similar to those found in temporal lobe epilepsy. The theory behind this model is that when the animal is in its interictal state it is predisposed to having a seizure. The impetus has been severely heightened and only a slight perturbation in the brain could cause the brain to cross a threshold into a seizure (see Figure 2-2). For the hybrid model, Pentylenetetrazol (PTZ) was the chosen stimulus to perturb the brain into seizure. PTZ is a common proconvulsant that has been shown to work in a wide range of animals. PTZ works by acting on the GABA receptors in the brain to impair GABA-mediated inhibition [66]. PTZ can be used to create both acute and chronic models of epilepsy. In acute situations, PTZ is used to generate generalized seizures for the study of antiepileptic drugs [43]. After the delivery of a large bolus of PTZ, seizures typically begin less than 30 minutes after injection [71]. The seizures generated can included: freezing, myclonic twitches, clonic seizures, and tonic-clonic seizures [71]. Based to the desired reaction, the dosage can be titrated to generate mild, electrographic only seizures, to severe, tonic-clonic seizures. When used to create a chronic model, whether through kindling or the induction of status epilepticus [63], PTZ creates generalized tonic-clonic seizures. To generate a chronic PTZ model of epilepsy, roughly half the does (or 40 mg/kg) is given as compared to the amount administered for an acute study over several days [36]. Analysis of the Model for Self Sustaining Status Epilepticus A primary thrust of this body of work was to generate EEG data containing spontaneously occurring seizures in order to characterize seizure dynamics from long term, chronic recordings. The primary model used in these experiments was the self sustaining status epilepticus model. In total, nearly 900 spontaneous seizures have been recorded in the past five years. These seizures include multichannel, highly sample (1000 Hz) recordings, with time locked EEG. To date not much work has been 27

28 done on the long term seizure statistics of the SSSE model as they would apply to a study on seizure prediction. This study seeks to investigate seizure occurrence (the time of day a seizure is most likely to occur), seizure interval, and the distribution of seizure grade. Using the log files generated by the recording systems, each seizure s occurrence was plotted onto a 24 hour day. A histogram was then performed on this dataset to give the number of seizure per hour of the day (Figure 2-3). In Figure 2-3 (n = 699 seizures), the light cycle information is shown with the shaded background. In humans, it is known that seizures are more likely to occur when is patient is just entering or exiting a sleep state. Rats are nocturnal, therefore the pattern of seizure occurrence should be inverted for their inverted sleep cycle. The animals showed the highest seizure rates in the hours just before the lights turned off. This would equate to the final stages of their sleep cycle and correlates to what is seen in human patients. Using a cosinor test, the data in Figure 2-3 was fit to a circadian rhythm (p.001) (Figure 2-4). Because of its spontaneous nature, the frequency of this model s seizures has not been studied in detail. If the model contains a dominate frequency of seizure interval, then when tests are made to compare a fully closed loop seizure controller with the standard, tonically firing seizure controllers used in today s VNS devices, the tonic firing may have an advantage in such a situation that would need to be accounted for. After the recordings were made, animals (n = 4) that had more than 10 seizures were used to determine the frequency and distribution of seizure severity. Seizure interval (SI) times were calculated for all the seizures generated by these animals (SI = t(seizure n+1 ) t(seizure n )). Figure 2-5 shows normalized time interval between neighboring seizures. The median time interval varied highly between animals, but the purpose of this study was to determine if, once the interval stabilized, there was a seizure frequency most often encountered. It was determined to normalize the intervals to see if a common frequency presented itself when comparing various animals. If the 28

29 results were consistent then this normalized average could be used to estimate the next seizure occurrence. Figure 2-5 A-C show animals in which recordings began shortly after the completion of the model induction. Doing so allowed for the capture of the first seizure. The figure shows each animal initially started out with a high seizure interval before it quickly leveled off after several seizures. The animal shown in Figure 2-5 D does not contain this flat lining because its recordings did not being until one month after the induction of the model. All four animals show the progression towards a steady state of seizure interval. Plotting this same data as a histogram of the seizure intervals (Figure 2-6), it become apparent the seizures are not randomly distributed. The animals showed a consistent average seizure interval (ASI) between animals (0.1339± , ± , ± , and ± ). Comparing the seizure intervals only one animal had a mean that was significantly different the the other animals. The distribution of seizure grade was also important to understand. Humans also experience a range of seizures grades and it was important to determine if a range of seizure grades were present in the recordings. Seizure grades were scored using the Racine scale. Plotting the number of each seizure grade found, it was discovered that higher grade seizures are more likely to occur (Figure 2-7). As the inter-seizure interval reached a steady rate, the animal also settled on a seizure severity that would comprise most of its seizures. Early seizures were generally grade 2 or 3 seizures while individual animals would plateau and generate predominately grade 4 or 5 seizures. For this study, 43 rats entered the study and progressed through surgery and model induction. All 43 animals survived surgery and model induction. Of those 43 animals only 16 (37%) remained in the study to the point of initial EEG recordings (1 month post model induction). The main reason for removal from the study was due to dislodgment of the headstage containing the electrodes. Of the 16 animals that were recorded and 29

30 screened for seizure only 9 produced recorded seizures. The 7 that did not seize fell into two groups, the first, and most common, were animals that under the stress of their first seizure dislodged their headstage. The smaller subset (n = 3) were animals that never seized after several weeks of continuous recordings. Effectively, 1 in 5 animals (20.9%) completed the study with spontaneous seizures. 30

31 Figure 2-1. Time line for self sustaining status epilepticus model of epilepsy. On day 0 surgery is performed. After allowing the animal to recover for one week baseline recordings begin. One week later self sustaining SE is induced in the animal. Roughly 4 weeks later spontaneous seizures will begin. Figure 2-2. It was hypothesized that SE puts the animal in a hyper excited state. In such a state it is easier for the brain to transition into seizure. This cartoon schematic shows how the brain becomes more prone to seizures as its normal state nears the threshold for seizures. 31

32 Figure 2-3. Histogram showing when seizure occurred for all animals (n=699). The gray background indicates when the lights were off in the recordings area. The occurrence of seizures was shown to be circadian in nature. The animals are more likely to experience a seizure just prior to the lights out condition in the housing units. 32

33 Figure 2-4. Plot showing occurrence of seizures during a 24 hour day. Using a cosinor test, the signal was shown to be circadian (p < 0.001, n = 699 seizures) 33

34 Figure 2-5. Plots representing the normalized time difference between neighboring seizures. With time the interval between seizures levels to a relatively constant rate. Animals A - C were recorded immediately after SE induction and therefore their first seizures were captured. Animal D did not begin recordings until 4 weeks post SE induction, and therefore its first seizures were not recorded. This is why its seizure interval has already reached steady state. 34

35 Figure 2-6. After normalizing the histograms of seizure interval both in terms of interval and count per interval, the histograms from all the animals were averaged together. While actual seizure interval was varied among animals, when the interval was normalized, all animals showed to have a similar clustering of inter seizure intervals. 35

36 Figure 2-7. Number of seizures in each grade. In the studied animals, high grade seizures were more likely to occur than low grade seizures. 36

37 CHAPTER 3 RESEARCH METHODOLOGY Animal Preparation All experiments were performed on 2 month old, male Sprague Dawley rats weighing between grams. All experiments and procedures were approved by the Institutional Animal Care and Use Committee of the University of Florida. Animal Surgeries Initial surgery preparation for all surgeries follow the same general methods. Animals are brought to the surgery room. The room was segregated into three distinct areas: surgery preparation area, surgery area, and recovery area. In the preparation area the animal was placed in an anesthesia induction box and exposed to 4% isoflurane at a rate of 1 L per minute. Once induction was complete the animal was removed from the induction box. Isoflurane was then reduced to 1% at 0.6 L per minute and delivered through a nose cone. 20 mg of xylazine was administered subcutaneously to act as the primary analgesic for the duration of the surgery. Isoflurane administration was continued throughout the surgery to smooth the effects of the xylazine. The animal s toe pinch reflex was checked to ensure the animal was correctly sedated before the surgery proceeded. Once completely unresponsive, the top of the animal s head was shaved. The head was then chemically sterilized with an alternating bath of iodine and alcohol. From here the animal was moved to the surgery area and placed in a stereotaxic frame. A stereotaxic frame was used in conjunction with a rat atlas [68] to precisely position electrodes. The top of the skull was exposed via a midsagittal incision that extended from between the eyes to the to ear level. The skull s fascia was separated from the skull to expose the anatomical landmarks, lambda and bregma. Four curved halsted mosquito forceps were used to retract the skin and fascia, allowing for ample workspace atop the skull (Figure 3-1). A peroxide wash was applied to the surface of the skull to remove any 37

38 remaining soft tissue. The stereotaxic arm was used to denote the bregma land mark as (0, 0). It was from this position that all future positions were calculated. Bipolar electrode implantation The common surgery for bipolar electrode implantation consists of contralateral electrode placement accompanied by the implantation of a ground and reference screw electrode. The contralateral bipolar electrodes can be placed at the investigators desired location. For the purpose of this study, the bipolar electrodes were placed in the CA1 of each hippocampus. Once the skull has been exposed and all bleeding stopped, electrode placement can begin. When using a stereotaxic instrument with its arm on the left (Figure 3-2), electrode placement should proceeded top to bottom and right to left. This methodology should be followed to insure the stereotaxic arm does not interfere with later placement of electrodes. Using the stereotaxic arm, the following positions were marked on the skull surface: 1. Right CA1: AP -4.3 mm, Lateral 2 mm 2. Left CA1: AP -4.3 mm, Lateral -2 mm 3. Ground Electrode: AP 2 mm 4. Reference Electrode: AP -6 mm Using a 0-80 stainless steal drill bit, pilot holes were drilled for the ground and reference electrodes at their respective locations with a hand drill. Additional holes for anchoring screws were then also made (Figure 3-3). These holes were drilled 3 mm anterior to the recording electrodes. Working from front to back, the ground electrode was screwed into its pilot hole. A proper screw depth is one that is deep enough to secure the screw to the skull without causing damage to the brain. The electrode wire should then be bent out of the working surgical field. Next, using a trephine bit and Dremel hand tool, a 2 mm wide section of skull was removed at the locations marked for the bipolar electrodes. A bipolar electrode was placed in the stereotaxic 38

39 arm and positioned to determine the (0, 0) point. The proper electrode position was then recalculated based on the (0, 0) point. The electrode was then moved over the exposed brain tissue and slowly lowered to a depth of 2.8 mm. A small amount of super glue was used to temporally hold each electrode in place before a permanent glue was applied. After the super glue had set, the electrode was released from the stereotaxic arm. This procedure was repeated for each remaining bipolar electrode. Finally, the reference electrode was screwed into place. A small amount of dental cement was mixed to the consistency that would allow it to flow around the electrodes and screws. The cavity created by retracting the skin (Figure 3-1) was filled with the dental cement. After allowing the cement to completely cure, each electrode pin was inserted into the pin holder (Figure 3-5). The pin configuration was kept standard amongst all animals. The default settings was: 1. Ground Pin 2. Empty Pin 3. Right CA1 Pin Right CA1 Pin Left CA1 Pin Left CA2 Pin Empty Pin 8. Reference Pin Once the electrodes were in the holder, a skull cap was built up around the holder to secure it in place. This was done by mixing dental cement to a putty like consistency and applying it around the pin strip. A default bilateral CA1 recording headstage can be seen in Figure

40 Microwire implantation The general technique for microwire implantation did not vary from that of any other surgery. The only difference between the surgery for a bipolar twist and microwire surgery, was the requirement of a larger window of the brain to be exposed. In order to lower the array a rectangular section of skull had to be removed. Based on the desired location of the array, the stereotaxic arm was used to map out the center and then the four corners of the array (Figure 4-3). Using a 1/16 th inch ball drill bit, the outline of the array was marked on the skull according to its coordinates. The outline was repeatedly traced until the bone fragment became loose. The fragment was then removed with tweezers. The small size of the microwire electrodes makes them highly susceptible to deflection during the implantation processes. In order to reduce the chances of the the electrodes flexing, the dura was then removed from the brain with a set of fine tweezers. The array was the lowered into the brain using the stereotaxic arm as described in Section 3. A typical configuration can be seen in Figure 4-3. EMG implantation For many studies it is important to determine the sleep state of the animal. This is most often done with video monitoring and analysis of the animal s electromyography (EMG) trace. Before beginning the surgery a 2 inch piece of 120 micron wire was cut. 5 mm of insulation was stripped from one end and 2 mm of insulation from the other end. After exposing the skull of the animal as described in Section 3, a small incision (1 cm long) in the skin was made about 2 cm posterior to the initial incision. The wire was threaded through an 18 gauge needle and then the 5 mm of expose wire was folded back on the needle shaft to create a barb (Figure 3-7). The needle was then inserted into the neck muscle. Pressure was applied to the muscle and the needle was slowly removed. 40

41 Next, a large bore needle was subcutaneously burrowed from the initial incision to the second incision. Once the needle was present at the second incision, the EMG wire was feed towards the skull. After removing the needle the EMG wire was left under the skin surface. A male pin was crimped to the exposed end of the wire. The EEG electrodes were then implanted as described in the previous sections. Followed by the creation of the headstage as described previously. Post Surgery Recovery Following all surgeries animals were monitored continuously until they regained their righting reflex and monitored once an hour for the rest of the day. Immediately post surgery, Marcaine was delivered around the wound to provide pain relief. After removal from the stereotaxic frame, the animal was placed in a recovery chamber, which kept the animal warm during recovery via a temperature controlled heating pad. Stimulation Protocol After recovering for 1 week, status epilepticus was induced. The animal was quickly anesthetized with isoflurane (4% in 1 L per minute oxygen). Once immobile, a stimulating cable that connected the animal to an A-M Systems current stimulator was connected to the rat. The rat was allowed to come to full conscious in the stimulating chamber for 15 minutes. The delivered stimulus consisted of a 10 second train of 1 millisecond, 50 Hz biphasic square wave pulses. Each 10 second train was separated by 2 seconds. The stimulation amplitude averaged 300 microamps; however, the current was titrated on an animal by animal basis if more current was required to elicit a response. During the stimulation protocol, the animal would elicit each grade of the Racine scale, culminating in grade 5 seizures. The physical manifestations would begin as wet-dog shakes, and after 30 minutes convulsive seizures would begin. Stimulation would continue until grade five seizures were detected every 5-10 minutes (typically after minutes of continual stimulation). At the end of stimulation, afterdischarges were 41

42 detected for the next 12 hours. In addition to the hyperexcited electrographical activity, seizures would continue over the next four hours. Animals that never achieved grade 5 seizures or did not maintain an electrographical hyperexcited state had a low chance of developing spontaneous seizures and were therefore not included in any studies. Animal Housing At all times the animals were kept in a controlled environment. Temperature and humidity were kept constant and the environment had a consistent 12-hr light-dark cycle. Animals were given free access to food and water at all times. Recording Chambers During times of experimentation, all animals were moved into specially constructed housing that was designed to accommodate long term, continuous EEG and video monitoring. While any number of other designs would work for long term housing, several key components must be considered: Housing must allow for the passage of all cables required for recording an EEG signal Housing should be designed in such a way to minimize the likelihood of catching the cable on the housing. Housing should be clear to allow for unobstructed viewing during video monitoring Some electronics are sensitive to the bedding material getting caught in female connectors. Therefore, the best practice is to design a cage which does not use bedding and allows all dropping to fall through the bottom of the cage. A design that meets these requirements was initially described by Bertram et al. [6]. In the design a 12 inch tall x 10 inch wide clear acrylic tube is affixed to a stainless steal mesh with openings between 1/4 and 1/2 inches. Silicon sealant was used to affix the mesh to the tube. Care was taken to make the seal complete in order to reduce the chances of the animal becoming stuck between the tube and wiring. The wiring extended 3 inches past the perimeter of the tube and then was bent downward on the two opposing sides; creating a space for a bedding tray below the cage. A 1/4 hole was 42

43 drilled in the side of the acrylic tube 3 inches from the bottom to allow the passage of a water bottle s nozzle into the cage. EEG Recordings EEG recordings are the primary source of analyzed data. Unfortunately, once EEG recordings have begun it is typically only a matter of time before the animal s headstage becomes dislodged. Because the SSSE model of epilepsy typically does not produce spontaneous seizures until four weeks post stimulus, recordings did not begin until four weeks post stimulus. Doing so provided the greatest possible chance of recording spontaneous seizures before the animal was unusable because of headstage dislodgment. Four weeks post stimulus, an animal would be induced in 4% isoflurane in 1 L per minute of oxygen. Once the animal was immobile, it was removed from the induction box and a specially designed recording cable was attached to its headstage. The animal was then placed in a recording chamber and connected to the recording system. After the animal regained its righting reflex, EEG recordings would begin. Each recording session would begin with a calibration of the EEG recording until. During this time a standard 50 microamp peak to peak voltage would be passed into the recording until. This signal was recorded by the system and then used to calibrate the EEG. After the EEG was calibrated, the recording system s input was switched to the animal s EEG. The animal was then left to record for the remainder of the experiment. 43

44 Figure 3-1. Exposed skull prior to marking the coordinates for electrode placement Figure 3-2. Stereotaxic frame 44

45 Figure 3-3. Placement of Electrodes relative to anatomical landmarks Figure 3-4. View of headstage immediately post surgery. The 1 designates the first pin. 45

46 Figure 3-5. Illustration of how headstage looks when electrodes are being inserted into the appropriate holding locations Figure 3-6. Schematic of the location of microwire electrodes for recording from the CA1 and DG. The right recording array often had to be rotated for placement of the stimulating electrode. 46

47 Figure 3-7. For EMG implantation surgeries, an EMG electrode was threaded through a sterile need and then a barb was created to secure the electrode into place. 47

48 CHAPTER 4 PLATFORM DEVELOPMENT Recording Systems The recording of electrical activity of the brain is not a new concept [33]. Advances in computation power and digital storage space has allowed for EEG data recordings to progress from analogue traces recorded on paper to highly sampled digital EEGs saved on terabyte sized hard drives. This has provided the investigator with the ability to study long term changes in the epileptic brain. A system for the long term studies involving EEG activity needs to contain several key capabilities. Primary amongst these are: The ability to highly sample data simultaneously from multiple animals. The ability to capture time locked video. There are a number of EEG recording systems on the market [6, 12], unfortunately most of these systems will not fully meet the requirements of long term data acquisition without some modification. In these studies, three primary recording systems were used. For highly sampled, microwire recordings a Tucker-Davis Technologies (TDT) RX5 was used. The system was capable of collecting 32 channels of data simultaneously from two rats. It contained 5 DSP chips capable of 16 bit data collection at a maximum sampling rate of 50 khz. The system s two major downfalls were its lack of time locked video and the fact that it was not designed for long term studies. To solve the problem of time locked video, a separate computer was used to capture digital video. Custom software was used to output a time stamp via a serial port, which was overlaid onto the video stream with an on screen display module. This allowed EEGs to be monitored and then the corresponding video could be found via the time stamp. The problem of the system not being intend for long term studies was again fixed with custom software. Initially, the TDT s recording and data management system was used and the files were converted and stitched together in software written in MATLAB. Eventually TDT s software was 48

49 completely replaced with custom software that handled data management and recorded all data in 16 bit binary files. The other systems used were more clinical in their background and were used to record EEG signals that would be more likely to be seen in a clinical setting (e.g. 1 khz sampling rate, macroscopic recordings). The first of these systems was the Stellate Harmonie package. This system worked by first amplifying the animals EEG with a Grass Technologies analogue amplifier. The output of the amplifier was then passed to a National Instruments (NI) digital acquisition card. Stellate software on the recording computer handled the digitization done on the NI card. The Stellate system was capable of recording highly sampled EEG data with time locked video; however, coming from the clinical world, it was intended to only work with one patient at a time. This problem was solved by recording multiple rats under one patient. Custom software (Figure 4-1) was then used to read the Stellate proprietary format, separate it into multiple subjects and write it as a universally readable int16 binary format. Cables and commutators. Through experience it was learned that cables and commutators can be the largest source of noise introduced into an EEG recording. For this purpose special care was taken when constructing the cables and commutators required for recordings. There are a number of commutators on the market [12]. The preferred commutator during these experiments was the 6 channel Dragonfly slip ring commutator. These commutators provided very low torque and injected very little noise into the recording when rotating. The Dragonfly commutators come with long leads with Dale connectors for input into the system. Unfortunately these leads are too long and will be destroyed in experiments by the animals. Before experimentation could begin these leads and the Dale connector were replaced with a shorter lead which contained a connector more suitable to our animals headstage. The press fit top of each commutator was removed, and the wires inside the commutator were snipped. The top was then heated with a hot 49

50 air gun. This made it possible to remove the epoxy that held the wiring in place. The commutator was then rebuilt with new wiring and a more compact connector. Electrode Construction and Use Experimental questions should dictate which electrode is used, what metal it is constructed from, and where it is implanted. After weighing all the possible options electrodes can be assembled in house or purchased. All electrodes used in these experiments were constructed in house. Bipolar Twist Electrodes Bipolar twist electrodes, whether intended for a rat or a mouse, follow the same general methods for construction. The only difference is that a mouse electrodes is typically made from a 125 micrometer diameter Teflon coated, stainless steal wire, while rat electrodes use 330 micrometer diameter wire. In either instance, construction began with a 3 inch piece of wire. With a sharp knife, 1.5 mm of insulation was removed from the tip of each end. The wire was then connected to a male pin by either crimping or soldering the wire and pin together. A U was then created with the wire by bending the wire and placing both pins together. A hemostat was then clamped to the bottom portion of the U. While holding the two pins tightly, the hemostat was twisted until the junction of the wires was 1/4 inch from the pins (Figure 4-2). Prior to surgery the electrodes were cut to length, typically 5 mm from the junction. Microelectrodes In the initial phase of these studies, many of the recordings dealt with microwire array recordings. In these experiments, the TDT system was used. The standard connection for electrodes using this system is the 18 channel Omnetics connector (Figure 4-3). Before beginning construction of the array, a jig was created to aid in keeping standard spacing between the electrodes. This was done by aligning three alligator clips 50

51 in a PCB mini vise. Each clip was separated by about 1 inch. Each alligator clip was then used to hold a piece of 105 micron mesh (Figure 4-4). After making the jig, the standard soldering points on the Omnetics connector were first cut in half. The connector was positioned below the three mesh screens with a third-hand. Care was then taken to level the three mesh screens and the Omnetics connector. Sixteen, 2-inch gold-plated tungsten wires were then cut. Approximately 2 mm of insulation was removed from one end of each wire. Next, the exposed end of wire was carefully lowered down through the three levels of mesh. Using a microscope, the wire was soldered to the solder point on the connector. Once set, the next wire was lowered through the mesh and soldered. Each electrode was separated by 4 holes in the mesh, creating a electrode spacing of 420 microns. After all the wires were soldered to the connector, they were checked for electrical shorts. If no shorts were found, the bottom piece of mesh was trimmed and then lowered towards the connector. A small amount of Cranioplast was used to then secure the wires and connector together while also insuring the small pins would not flex and create a short. Once the Cranioplast set, a 3 inch piece of teflon-coated 330 micron steel wire was connected to the ground and reference pin. 51

52 Figure 4-1. Custom software to extract and then separate multiple subject s data form the proprietary Stellate data format. Figure 4-2. Top: Ground and reference electrode. Bottom: Bipolar twist electrode before being cut to length. 52

53 Figure Channel Omnetics connector used for the construction of microwire array electrodes Figure 4-4. Vise holding three alligator clip. Each clip is holding a small piece of mesh. Below the jig an Omnetics connector can be seen 53

54 Figure 4-5. Setup for TDT system 54

55 Figure 4-6. Stellate setup 55

56 CHAPTER 5 RELATIONSHIP BETWEEN SPIKE RATE AND SEIZURE Introduction Numerous studies have classified epilepsy as a dynamic disease [50, 59]. It is believed that studying these changes in dynamics could give an investigator enough insight into the system to allow for closed loop modulation of the system. The EEG produced by an epileptic brain transitions through many states, such as from normal to ictal and back to normal through some form of hysteresis [45] (Figure 5-1). The dynamics likely arise from the degradation of the the brain s natural control mechanisms. It is the hope of this study to determine a measure which could be used in the artificial recreation of this control. In order to act on the brain, a marker is needed to be investigated that could be easily measured, and then monitored as feedback input was administered. To date determining what to control in the EEG has been a significant problem. The EEG does not present itself as having a set point which can easily be compared, such as can be seen in control systems to maintain a set temperature. In this example, the desired temperature is the set point the system can compare itself to a make actionable decisions. Unfortunately, when dealing with EEG, the decision making processes is not as black and white. When analyzing an EEG for biomarkers of seizures, two classes of marker can be investigated. The EEG can be read as a time series, and numerous measures from signal analysis theory can be applied to the signal to extract a value for the EEG over time. Patterns can then be inferred in these time signals and possible relationships to seizures can be made. The second type of markers are electrographical events. These can include the occurrence of action potentials, population spikes, high frequency oscillations or other events clinical neurologists have been trained to detect. Once 56

57 detected these events can be counted, converted to a rate, or compared in many other ways to determine a relationship to seizures. Methods Male g Sprague-Dawley rats (n = 43) were prepared as described in detail in section 3. Briefly, bilateral, bipolar twist electrodes were implanted into the left and right CA1 (AP -4.3 mm, ML ±2 mm, DV -2.8 mm) and a stimulating electrode was implanted into the ventral hippocampus (AP -5.3 mm, ML ±4.9 mm, DV -5.0 mm) (Figure 5-2). The animals were allowed to recover for 1 week. After the recovery period the animals were inducted into status epilepticus (section 3). Following a 4 week recovery period, continuous EEG recordings began. Data was recorded at 1 khz with time locked video. Recordings continued until the animal removed it s headstage or it was apparent the animal would not seize. During the recording period, data was screened weekly for seizures. Using the time locked video, each detected seizure was catalogued and graded based on the Racine scale. Following the completion of recording phase, all data was processes for population spikes. The process began by band pass filtering the data from Hz. Then any event that exceeded a threshold set 5σ deviations above the mean of the absolute value of the EEG was extracted from the continuous data set. The threshold value was calculated every hour to accommodate for changes in the signal that occurred over time. The use of 5σ was based on previous work done in-house for spike detection [81]. When a threshold crossing was encountered, 0.5 seconds of data before and 1.0 seconds after the crossing was extracted. After the detection of a spike, the detector was put into a refractory period for 1.5 seconds. If another spike occurred within 1.5 seconds of the previous spike it was ignored. This refractory period helped to reduce noise from being detected as a spike. To cluster the spikes and remove artifact events, a well know clustering algorithm was employed [22]. First, the peak of each window was determined. To accomplish 57

58 this, the data above the threshold crossing was approximated with a polynomial fit. The peak of this fit was then used as the peak of the spike. Principle component analysis will be used to sort the spikes. It is crucial to have proper alignment of the spikes and to subtract the waveforms mean. If not, the analysis might represent this variance rather than the intended variance between waveforms. Without using the peak as the zero time point, the initial 1.5 second waveform was trimmed such that the peak of the waveform occurred 0.15 seconds into a new 0.45 second, trimmed window. Due to natural artifacts that are common in long term recordings, many events will be chewing or movement that can often dominate an EEG signal. These large artifacts can often cause problems later in the sorting routine. Their removal is done with a k-means outlier function. After the outliers have been removed then the initial clustering step can begin. In the first clustering phase, the spikes were highly overclustered. The first clustering size was normally ten times the number of clusters that were expected to be seen. In most instances, all true spikes were expected to eventually be clustered into two groups. The initial clusters are formed by dividing the spikes into two groups. The mean of one half is taken. A second mean was then made by taking the first mean and adding a small amount of noise. The spikes are then classified as belonging to the mean which it is the closest. As the spikes are assigned to the nearest mean, the mean is updated with weight given to the number of spikes assigned to that mean. The means were updated three times before another bisection was made. This process of moving and recalculating the means and bisecting the data, was repeated until the desired number of clusters were generated. After the initial clusters have been set, the clusters were then combined into larger groups. The aggregation of the clusters continued until only clusters remained that corresponded to a specific spike type. The decision to combine clusters was made if the two clusters had a high interface energy, or number of waveforms near the border that separated the clusters. A graphical overview of this process is shown in Figure

59 Analysis Of the 43 animals in this study, 16 remained in the study to yield chronic recordings. No fatalities were caused by surgery or the model induction process. Most animals were removed from the study due to a dislodged headstage that occurred during the four week recovery period after model induction. Of the 16 animals, 8 eventually produced 765 spontaneous seizures. The spike detection and clustering algorithm was computed on this database of seizures. After spike clusters were generated for each animal s files, spike fell into one of three primary categories: an upward spike, downward spike or noise. The upward inflecting spike was seen in all animals and in both recording channels. The download spike was only seen in some animals, and therefore it was not as closely scrutinized as the upward spike. For each animal an ideal upward spike cluster was determined by visual inspection. As the analysis progressed, the clusters in each file were compared to the ideal spike. If the spike cluster being viewed had a 90% correlation to the ideal cluster then time points of it s spikes were recorded for later rate analysis. This was done for each spike cluster in every file. The resulting sorted spikes from the EEG were binned into 1 minute segments and counted. The count in these 1 minute segments is called the spike rate (R). Based on the the methods used here, it is impossible to classify the spikes into their clinical subgroups. For this reason all population spikes are detected and sorted and simply classified as spikes. The purpose of this study is to determine if there are changes in occurrence of spikes rate prior to seizure. Fundamentally, the analysis will try to determine if spikes can be used to distinguish between the preictal and interictal state. This distinction will be made by comparing the amplitude distributions of interictal and preictal spike rates. Through this technique, increases or decreases in spike rate can be characterized. In addition to spike rate comparisons, comparisons will also be made between the trends during a preictal and interictal time period. 59

60 A seizure precursor could occur at any time scale, and therefore, numerous preictal time periods should be analyzed. Based on literature, the preictal time (s) was constrained to four durations: s = {5, 30, 60, 120} min [14, 16, 37, 38, 62, 73]. Because there could possibly be postictal effects from the seizure on spike occurrence, the 30 minutes of data after a seizure was not analyzed [61]. For each s, the spike rate was binned into non overlapping windows of equal length to s. If an interictal-preictal time period was not at least s + 30 minutes long, then it was discarded. To determine the separability of the interictal and preictal time periods, receiver operating characteristics (ROC) were employed. This showed the sensitivity and specificity based on varying thresholds. The ROC curve is a plot of the sensitivity (S) vs the false predictive rate (FPR). FPR is also 1 - specificity. When conducting the ROC analysis, the hypothesis that spike rate increased during the preictal time periods was used. S = FPR = True Positive True Positive + False Negative False Positive False Positive + True Negative (5 1) (5 2) The spike rate in each dataset was then normalized from 0 to 1 to account for variability and thus to allow for a direct comparison between animal datasets. It was also investigated if spike rate could be used to make inferences into the location and extent of damage in the brain. Evaluations. Several different approaches were taken to evaluate the spike rate data and try to distinguish between interictal and preictal time periods. Two evaluation types were used, with each evaluations having various schemes. The first type of evaluation was spike rate analysis, which had three evaluation schemes. In the first scheme, the distribution of R for all the preictal periods of length s were compared to the distribution of R for the interictal time periods. This will result in a single ROC curve for 60

61 each s. The next scheme employed a threshold to try to determine a difference between interictal and preictal times. Here the R for all the interictal time periods are combined to create a single interictal distribution of R. A ROC curve is then generated for each preictal time period by comparing it to the constant interictal distribution. The area under the curve (AUC), is then recorded for each ROC curve as a performance parameter. Because changes may happen in the brain that cause the baseline firing rate to change, the third scheme will use an adaptive threshold. In this final scheme the distribution of the preictal R is compared to the distribution of the interictal R immediately preceding it. Again, the AUC value is then recorded as a performance parameter. The next evaluation type is to compare the change in firing rate between the interictal and preictal time periods. This evaluation type will have two schemes. In the first, the percent change in the mean firing rates will be compared. For this analysis, the preictal mean (PE) is the mean of R over the preictal time period of length s. This will be compared to the total interictal mean (TIM), which is the mean of R for each complete interictal time period and to the previous interictal mean (PIM). PIM is the mean of R over the window of length s just prior to the preictal period. For this analysis, all windows of s length will be non-overlapping. The percent changes between neighboring windows will then be compared to their respective controls via a student t-test to determine if they are statistically different. The control for the TIM group will be the percent changes seen between adjacent interictal time segments. For the PIM group, the interictal time periods will be further broken into windows of size s and then the percent change in the mean of each of these window s R will be calculated. The next scheme for this evaluation type is to analyze the trend seen in preictal windows. Here the slopes of trend lines for preictal time segments will be compared to the slopes of trend lines for interictal segments. Preictal windows of length s will be extracted and the remaining interictal data will be windowed into segments of length s. A 61

62 student s t-test will then be conducted to determine if a statical difference exists between the trends seen in interictal and preictal time periods. Discussion and Results When examining the ability for spike rate to yield information about seizures, the first evaluation conducted looked at the distributions of the amplitude in the spikes per minute vs time plots (Figure 5-4) for the interictal periods and preictal periods of length s. Figure 5-5 shows that the means of these distributions were significantly different. Using a student s t-test, the 5 minute preictal time period had the highest, but still significant, p value of All other values for s had a p value < In each case the preictal time showed an increased firing rate when compared to the corresponding interictal time. Conducting an ROC curve analysis on these values did show the distributions were highly overlapping. For each value of s, the area under the curve never deviated from 0.5 by more than 0.1. This indicates that while the groups are statically different from one another, a simple thresholding operation is not sufficient to separate the distributions. Next, the distribution of each preictal window was compared to the distribution of all interictal time periods and the distribution of the interictal time period immediately preceding the preictal window. These distributions were used to generate ROC curves. This yielded a AUC and p value for comparison. Table 5-1 shows what percentage of preictal distributions was statically different than the interictal distributions. Increasing the size of s also increased the likelihood of generating a preictal window that was significantly different than the interictal period. Generating a constant threshold by grouping all interictal time periods together also showed better results in every s when compared to trying to distinguish a preictal distribution from its preceding interictal distribution. Seeking to investigate the importance in different spans in time on spike rate peaks and minimums, mean firing rates were also compared over the preictal windows. First 62

63 in evaluating the trends seen during the preictal window and immediately preceding interictal window of the same size, the slope of a linear fit through the window s spike rate vs time plot was recorded. Plotting the mean of these values, no statistical difference was seen in the slope of any s (Figure 5-7). The smallest p value was 0.59 in the 5 minute preictal window. Next, the percent change seen between mean firing rates of adjacent windows was compared. Here the change between the mean of the firing rate of an entire preceding interictal time period was compared with the mean of the firing rate for the preictal window. Additionally, the change in the mean between the preictal window and a interictal window also of length s immediately preceding the preictal window was studied. The percent change in these values was compared to a control that was generated by measuring the percent change in the mean firing rate over windows from the interictal time periods. Essentially, all seizure data, postictal data, and preictal data was removed from the recording. Then the remaining signal, which now only contained interictal data, was windowed into bins of size s and the percent change in the mean firing rate between adjacent windows was calculated. Figure 5-6 shows that for almost all s there was a significant difference between the percent change seen when entering the preictal window and the change between the interictal windows. The difference between the average change between the preictal window and entire interictal window and the average change between the interictal windows for s = 5 was the only relationship whose differences were not significant (p = 0.12). All other comparisons had p values less than Spikes are a marker for damage. The study contained a number of animals that were electrographically stimulated to induce seizure, but failed to develop seizures. When comparing these nonseizing animals to animals that did develop seizures (Figure 5-11), it was determined that nonseizing, but injured brain, had a significantly higher normalized spike rate in the damaged hemisphere when compared to the seizing 63

64 animal s interictal dataset (p value = ). In a hemisphere comparison, the hemisphere which received the model inducing stimulation had significantly average higher spike rates (Figure 5-12). When comparing these groups, all but one animal experienced a p value of less than Conclusion This study represents the first long term study conducted to show the relationship between interictal spikes and seizure onset. The study was able to drastically increase the number of seizures analyzed by improving on the techniques of long term recordings. This study yielded several results. The first is there is an increase in spike rate during the preictal time period when compared to the interictal time period. This was seen across all values of s; however, the separation between the groups was the greatest for the window size of 30 minutes and greater. As determined by the performance parameter, AUC, the 120 minute window showed the greatest separation between groups with an AUC of This was only a minimal improvement over the AUC of 0.59 seen with the 60 minute window. The AUC values from the ROC curves do indicate that spike rate is a poor classifier between the interictal and preictal periods when all preictal time periods are compared to all interictal time periods. When comparing each preictal and interictal periods individually, better results were seen. A steady increase was seen in the percent of preictal periods that were able to be distinguished from interictal periods as s was increased. The lowest percentage see, was barely above chance, at 58.13% for the 5 minute window, and peaked at 85.0% for the 120 minute window. In a system that is highly applicable to seizure prediction, when the mean of windows were taken, there was a statically significant change in the mean of windows when entering a preictal time period. For windows greater than 30 minutes in length, the change between neighboring interictal windows was less than 30%; however, the change in mean firing rate when entering a preictal window was always statistically 64

65 greater than this for window sizes of 30 minutes or greater. This indicates that seizures may be predicted by looking for abnormally large variations in mean spike rate over time. When comparing the mean normalized spike in the hour prior to a seizure to that of animals that were stimulated but never seized, the animals with stimulations but no seizures showed a higher normalized mean spike rate (Figure 5-11). Analyzing average spike rate also revealed information about spike rate and possible pathology. Almost all animals experienced a greater number of spikes, and higher spike rate, in the stimulated hemisphere, regardless if they seized or not. These spikes are generated in pathological tissue. Only one animal showed a higher spike rate in its contralateral hemisphere (Figure 5-12), and this was present in the animal that generated the greatest number of seizures. When all mean firing rates were normalized and divided into stimulated and contralateral groupings, there was shown to be a 25.5% reduction in average firing rate between the two hemispheres (Figure 5-13 p < ). In conclusion, spikes provide useful information into the pending occurrence of a seizure. This study showed there was a significant change in spike rate before a seizure, when compared to interictal intervals. The study also showed there is a change in mean spike rate when transitioning into a preictal time period. The spike count could also be used to determine epilepsy severity since animals experienced greater spike counts in their damaged hemispheres, and conversely, animals with elevated spike counts in both hemispheres experienced the greatest number of seizures with the highest grade seizures. 65

66 Figure 5-1. Dynamic states seen in epilepsy 66

67 Figure 5-2. Electrodes placement of bipolar electrodes and stimulating electrode for chronic recording 67

68 Figure 5-3. The process taken to extract spikes from EEG data. (A) Events crossing a set threshold are extracted for analysis. The events marked with the closed circle will eventually become the upwards spikes, the squared events will become downward spikes and the event marked with a start will eventually be classified as noise. (B) Plot of all events detected after an initial clustering. (C) PCA plot of events after events categorized as outliers have been removed. (D) Plot of the average shape of the two groups of spikes that were detected. 68

69 Figure 5-4. Figure showing the regions of interest for the spikes/minute vs time plot. The vertical red bars indicate seizures. The 30 minute postictal time period is discarded from analysis. The preictal period has a length of s minutes, here shown to be 60. The interictal time period is of length (t n+1 t n ) (30 + s), where t is the location in time of a seizure. 69

70 Figure 5-5. Mean firing rates per minute for different preictal window sizes (s) compared to their respective interictal times. The figure shows the average, normalized firing rate per minute for each preictal window size. Each preictal average is compared to its corresponding interictal average. The interictal time was 30 minutes after the previous seizure to s minutes before the next seizure. Each preictal mean was significantly different than its interictal counterpart. s = 5 min had the largest p value of 0.045, while all other pairs had p values less than In all circumstances, the preictal period experienced an increase in firing rate. 70

71 Table 5-1. Percentage of preictal distributions that were statically different than interictal distributions. s (min) Threshold Type Percentage Different from Control 5 constant adaptive constant adaptive constant adaptive constant 85.0 adaptive

72 Figure 5-6. Plot in the percent change of the mean of neighboring windows. The percent change between the mean in the firing rate was calculated between the entire interictal period and the preictal period and between the last s sized window of the interictal period and the preictal period. For each window size of s, the change between neighboring windows of size s during the interictal period acted as the control to which the percent change when entering the preictal time zone was compared. Results are grouped based on the size of s along the x axis. The figure shows that the percent change in mean firing rate during the transition into the preictal window was statically greater than the percent change seen between interictal windows for all except s = 5 min. 72

73 Figure 5-7. Average of slopes from trend lines for spike rate over a window of size s. When comparing the slopes seen in the preictal time period to the slope seen in the window of the same length immediately preceding the preictal window with a student s t-test, no significant change was detected. 73

74 Figure 5-8. ROC curve calculated from the distribution of interictal and preictal firing rates. For this figure s = 60 minutes is shown. The AUC for this ROC curve is 0.59, with a p value < This indicates the distributions are significantly different from one another; however, the low value for the AUC indicates there would be a significant number of false positives if this scheme was used to detect seizures. In this example, a preictal window will have a greater spiking rate then 59% of interictal windows. 74

75 Figure 5-9. Plot of spikes over a 7.5 day period. Spike representation remained constant during recordings. Each plotted spike in this figure is the average of all the upward spikes found during a 4 hour recording session. Figure Plot of spikes counted per minute over the entirety of one animal s recording. The yellow lines denote seizures. 75

76 Figure Normalized spike rate in animals that were electrographically stimulated. The animals were then grouped into seizing and nonseizing animals. Animals that were stimulated but never experienced a seizure had higher firing rates when compared to seizing animals. The figure shows there is a statical difference in the normalized firing rate when comparing data from stimulated but nonseizing animals to interictal data of seizing animals. Figure Comparison of the mean spike rate spike during nonseizure times of both nonseizing but injured animals and seizing animals. The model was induced in the hemisphere denoted by the shaded bar. R1 - R6 are hemisphere pairs from 6 individual animals. The figure shows in both types of animals, the damaged side of the brain typically contained a higher number of spikes per minute. Significantly different groups are denoted with a line above the grouping. 76

77 Figure Normalized average firing rate comparing the stimulated and contralateral hemispheres. The figure shows there a statically higher average firing rate seen in the stimulated hemisphere. 77

78 CHAPTER 6 CONCLUSIONS This work was able to construct a number of systems, and supporting infrastructure for the recording and analysis of long term, highly sampled data. In doing so it has been able to add knowledge to a each of the fields is sought to improve. The first was to identify a biomarker that could potentially be used for the control and modulation of seizures. This work showed there is a correlation between spikes and seizures. Using ROC curves it was shown the distribution of spike rate amplitude is statically different between interictal and preictal periods. Additionally, the percent change in mean spike rate over windows greater than 30 minutes is higher when transitioning into preictal time periods as compared to the transition from one interictal window to another. This study has also shown that spike rate is also correlated to tissue damage, by showing the spike rate was higher in the hemisphere of the brain that was stimulated to induce the model of status epilepticus. Additionally, this work has characterized the patterns of seizure occurrence in this model of epilepsy. This information and infrastructure, in turn, can be used to optimize current technologies by improving the current state of the art in electrically stimulating the brain for seizure control. This study could lead to the development of a closed loop seizure modulator that acts on detected spike rate. The detection of spikes is a minimally computationally endeavor which could be implemented in today s pacing technology. Additionally, this information could be used to trigger any means of therapy (e.g. genetic, pharmacological, or thermal). Final Remarks In conclusion, this work present a complete system for the chronic recording of EEG in freely moving animals. The work then determined a biomarker which was proven to have a direct relationship with impending seizures. Using methods put forth to cluster interictal spikes, it was determined that interictal spikes increased in rate prior 78

79 to an impending seizure. This information can be used to create a closed loop seizure prevention system based on the modulation of interictal spikes. 79

80 APPENDIX A FURTHER CONSIDERATIONS The proper setup of a recording station is paramount to proper EEG signal capture. Past experiments have often focused on short term, low sampled recordings. In the past decade the most cost prohibitive barrier to long term, highly sampled data recordings was the cost of high capacity hard drives. During the initial phase of this body of work, a single rat s multi-month recording would require approximately $5000 in hard drives. The rapid drop decrease in the price to store data has facilitated the development of long term recording stations. Due to the inherent costs question posed by most research, recording stations are designed to record for short term sessions. To over come these limitations, custom software was required that was capable of accessing the hardware and saving it s output independently of the software intended to be used by the manufacture of the recording equipment. The primary bulk on the recordings done for this work was recorded on either a completely custom recording system employing a Nation Instruments data acquisition board or the Stellate Harmonie. These systems were both capable of recording long term data sets and storing them into propitiatory file formats. Time locked video was a crucial necessity that was required for the grading of seizure severity. Because no two crystals put out the exact same frequency (and therefore no two computers can be independently time locked with one another), video recording has to be done on the same computer as the EEG recording itself or done on a second computer that receives time information from the first. Because of the intense computational requirements to record video and EEG at the same time, video monitoring was often done on a second computer. When this was done, a time stamp was generated by the first computer and then overlaid onto the video before it was recorded and stored by the second computer. This overlay was generated by a program running on the first computer which output time information via a serial port. The time 80

81 stamp was then overlaid on the video feed as ASCII text with a Decade Engineering video overlay system. Reducing Electrographical Noise Experience has shown the most technically challenging aspect of recording EEG activity is getting it from the animal to a data server without introducing artifact. There is a considerable amount of noise that will be present in the signal no matter what precautions are taken. This includes noise induced by the animal chewing, scratching and moving. Some inherent noise can be mitigated by proper montage selection; however, biological data is always noisy. Conversely, external noise can be controlled. This noise can include 60 Hz noise from power sources, vibration noise and noise induced by cable movement. In order to remove 60 Hz noise, all computers and other electrical devices should, including cellphones, should be kept away from the animal. If possible, recordings should be done in a Faraday cage. Computers and other other devices that have moving parts such as fans should not be stored on the same table the animals will reside. The electrode sensitivity is capable of detecting these slight vibrations. When purchasing a commutator, high quality commutators such as Dragonfly, will not introduce noise into the signal when the commutator swivels. Lower quality commutators can often induce noise or skip when they rotate. Recording Setup Most of the recording systems used in this study were intended to be used with humans. Figure A-1 gives an overview of how captured signals progress from the animal to the recording computer. An animal with three recording sites (RS) will have three recording electrode pins extruding from its headstage, long with a ground and reference electrode. These pins will be connected to a commutator s inputs via a custom built cable. The data will exit the commutator and interface with a standard electrode configuration breakout box (Figure A-2) via another custom cable. At the breakout box, multiple animal s EEG will feed into the recording system. It is best practice to have each 81

82 rat feed into a separate column on the breakout box. The Grass amplifier will generate the desired montages from the signals input into the breakout box as single ended signals. For these studies, the montages used were each channel with the signal from the reference channel subtracted. The output from the of the Grass amplifier is the fed into the channels on the Stellate s data acquisition board. Data Conversion All data recorded with these systems was saved into a propitiatory file format. In order to analyze the data it must first be extracted and converted into a usable file format. Additionally, these systems were intend to be used with a single patient. Therefore, what the recording system viewed as a single patient actually contained the recorded EEG from 4 animals. INT16 binary was chosen for the file format of choice. Lacking the precision to store the data in its natural state, each data point was multiplied by a large scaling factor in order to bring it into the range of INT16 s precision. Doing so saved half the hard drive space when compared to using a FLOAT32 binary format with no scaling factor. To convert the data, a custom GUI (Figure A-3) was created to convert and separate the animals into their own files. Upon selecting the files to be convert. The GUI would prompt the user to assign channels to each rat. After channel assignment was complete the software would convert the data by first gathering the time stamp information and sorting the files into the correct chronological order. It would then read through the files header information to check which montage the the recordings were made. This was a method of error catching to ensure that animals were not moved between stations during recording sessions, and therein changing the channel they appeared when viewing the Stellate system. After converting all the data, the program would also generate a log file that contained information such as: old and new file names, recording start date for each file, recording length, and sampling frequency. It was also capable of extracting flagged information if it was present in the data files. 82

83 Flags could be used when reviewing the data with the Stellate software to indicate interesting events such as large amount or noise or seizures. Additional Devices In addition to custom cables and experimental techniques, a number of custom tools were created during these experiments. The first of these was for a side project studying place cell activity in rat hippocampus. For this experiment an automated food pellet disperser (Figure A-4) was required. The device was modelled in SolidWorks 2010 and then constructed with a 3D printer. The device was designed with a replaceable flywheel that could be exchanged based on what size food pellet was being used for the experiment. Once in place the flywheel was rotated with a stepper motor. The stepper motor was controlled with a Atmega328 microcontroller. The microcontroller also controlled an LCD where the experimenter could see the experimental settings such as experiment length and delivery rate of the pellets. When pellets were released from the device they would pass through a photogate. The gate would determine if a pellet was released. If a pellet failed to load into the flywheel and therefore failed to be released, the system would catch the error and continue to attempt to release pellets until one was released. Animal Movement Tracking. One possible biomarker investigated was high frequency oscillations. After the studies began, it became apparent that many of the high frequency oscillations the system was detecting were possibly motion artifact. Because the studies were being conducted over long periods of time, a rat tracking system was developed that possessed the capability to quantify the animal s movements. After reviewing the state of the art in tracking systems [41, 65, 67], it was decided the simplest means to detect if the animal moved was through the use of custom software written around an infrared camera. A more accurate system could have been build around a Hall effect or multiple camera system; however, it was deemed that a 83

84 single camera system would provide the needed results. The Hall effect system was rejected over worries of inducing artifacts into the EEG signal with the use of high magnetic fields, and the multi camera system was rejected based on the fact movement detection was required but not precise location data. The motion detection system worked by first attaching a small fiducial marker to the headstage of the animal. The marker was positioned in such a way that it was not hidden as the animal moved about the cage. The infrared camera was then positioned in front of the cage to capture the movement of the animal throughout its environment. The custom software then read in the data from the infrared camera at 30 frame/sec. From the received images, the x, y, and size of the marker was calculated and then the data was time locked to the EEG data and saved. The resulting output can be seen in Figure A-5. In the figure the green vertical lines represent high frequency oscillations that were found in the data. As the figure shows, man of them correspond to motion artifact. 84

85 Figure A-1. Grass Connections 85

86 Figure A electrode configuration breakout box 86

87 Figure A-3. Conversion software 87

88 Figure A-4. Automated rat feeder through the development process. A) Initial sketch, B) 3D CAD rendering and C) final device (with flywheel removed) 88

89 Figure A second plot of the area of a fiducial marker as determined by tracking software. The area of the marker, along with its x and y position were used to determine when a rat was moving. 89

90 APPENDIX B ADDITIONAL EXPERIMENTS Methods for Hybrid Model Animals (n = 5) in this experimental hybrid model were first made to have spontaneous seizures by following the protocol set forth by Lothman et al. and described in section 3. After spontaneous seizures were detected, a titration was begun to determine the lowest dose of PTZ that was required to initiate a seizure. PTZ was dissolved in sterile saline and administered intraperitoneally in doses of 10, 25, or 50 mg/kg. Each concentration was administered once a day for three consecutive days. If, during this time, a seizure never occurred within the hour immediately following the injection of PTZ the next highest concentration of PTZ was used for three day intervals until a dosage that caused seizures was discovered. In all five animals no seizures were seen when administering a subconvulsant dosage (Figure B-1) of PTZ. All animals reliably experienced seizures after a 50 mg/kg dose of PTZ. These findings were in line with the results seen in the work of Holmes et al. In that study, when trying to produce a chronic model of epilepsy, animals that were repeatedly exposed to less then 50 mg/kg of PTZ never produced consistent seizures [36]. For the purpose of trying to develop a hybrid model, while 50 mg/kg is below the dosage used for many acute models, the dose required to initiate a seizure was higher then expected. Additionally, it was impossible to titrate the dosage in order to generate grade 4 or 5 seizures. The PTZ quickly went from having no effect to causing severe, generalized tonic-clonic seizures. The seizures were severe enough to cause the only seizure induced mortality seen in this body of work. 90

91 Preliminary Results for Future Experiments Direct Stimulation to the CA1 To explore this possibility, several preliminary experiments were conducted. In the first, animals were prepared as they were in section 5. These animals were implanted with biploar electrodes in each CA1; however, they were never induced into status epilepticus. After they were fully recovered from the implantation surgery, acute experiments were conducted in which they were recorded daily during which time a single, lower frequency stimulation was delivered to the right CA1 for 10 seconds. The current was increased each day from 10 to 25 µa by 5µA steps. Comparing the spectral analysis of the signals directly before and prior to the stimulation injection, there was little change in power of the signal until the stimulation reached 25 µa. The 25 µa stimulation drastically increased the power of the signal below 10 Hz, including the injection of signals that could emulate interictal spikes. Based on the prior knowledge this series of studies has yielded, direct stimulation of the CA1 may provide a promising avenue for the modulation of seizure by mimicking the occurrence of interictal spikes. Seizure Intervention With Direct Stimulation Furthering this work, a preliminary study was conducted to determine if seizures could be aborted using direct stimulation to the CA1. The animals (n = 4) were prepared as detailed in section 5. After recovery animals were induced into status epileptics and then allowed to recover until spontaneous seizures began. In order to create on demand seizures, the hybrid model (section 2) was used. Briefly, animals were intraperitoneally injected with PTZ at the dose that was titrated to be the least concentration capable of generating a seizure. After injection of PTZ, the animal was then immediately connected to the recording and stimulating equipment. The EEG was continually monitored until the first appearance of an electrographical 91

92 seizure. At this time the control stimulation was manually started. The stimulation was a 30 seconds, 120 Hz biphasic square wave with an amplitude of 20 or 100µA. The results of this preliminary study showed the stimulation was unable to stop, reduce the severity, or duration of seizures. The animals seizures did not vary from those of control animals, which received PTZ but no control stimulation. While the results of this experiment were contrary to what was expected to happen, the results were not unexpected after working with the hybrid model involving PTZ. When creating the model, it was impossible to titrate the dosage to create partial seizures. Unfortunately, because of the already hyperexcited state of the animal s neurological condition all seizures became generalized. It is hypothesized that due to the generalized nature of the seizures they would be impossible to prevent by local stimulation just prior to the start of a seizure. Once the seizures became generalized, their control could probably only be done with a generalized treatment that would spread to the entire brain such as an AED or vagus nerve stimulation. This experiment does lead credence to the need for a control system that works by modulating an EEG biomarker rather then trying to intervene directly on a seizure. It is believed this experiment also shows it would be more efficient to modulate brain activity rather then try to stop a seizure after it has began. VNS Experimental Setup Vagus nerve stimulation is a promising new therapy for epilepsy. Several animals were tested to see if it was possible to mimic a vagus nerve stimulation setup with equipment already available in the lab. For such a setup, the most difficult item to make is an electrode to connect to the vagus nerve. In human vagus nerve stimulators the development of an acceptable electrode has been one of the most studied aspects of vagus nerve stimulation research. After preparing animals with electrodes in the each CA1, and additional electrode was also placed in the abdomen to detect heart rate. After the placement of these 92

93 electrodes, the vagus nerve was dissected. An electrode containing a barbed anode and cathode was created to allow for the passage of current into the vagus never. The electrode was attached to the vagus nerve and then tunneled to the back of the skull where it was inserted into the animals headstage. Because the vagus nerve innervates the heart, it can be used to determine if current is being delivered to the vagus nerve. After the animal recovered from the implantation surgery for one week, the animal was connected to the stimulator and recording system. The animals heart rate and EEG were recorded while different amounts of current were delivered to the vagus nerve. Unfortunately, the electrode design failed and the heart rate was unchanged throughout the experiment (Figure B-2). Vagus nerve stimulation is an interesting area of future study, unfortunately we were unable to make in house electrodes capable of delivering current to the vagus nerve. This is not an end point for the study of vagus nerve stimulation in this lab, as there are manufactures of electrodes that have been shown to work on the rat vagus nerve. 93

94 Figure B-1. Average number of seizures seen when administering 10, 25, or 50 mg/kg of PTZ. The figure shows that no seizures were produced with 10 or 25 mg/kg of PTZ. The only concentration that produced any seizures, 50 mg/kg, is reported to do the same in normal animals. This proves the hybrid model is not viable. 94

95 Figure B-2. Heart heart before and during the delivery of stimulation through the vagus nerve. No change is seen in the heart rate. It can therefore be assumed that no current is being delivered to the vagus nerve. 95