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Circadian Variations in the Hippocampus of Epileptic Rats

Permanent Link: http://ufdc.ufl.edu/UFE0044749/00001

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Title: Circadian Variations in the Hippocampus of Epileptic Rats
Physical Description: 1 online resource (66 p.)
Language: english
Creator: Re Fraschini, Stefano
Publisher: University of Florida
Place of Publication: Gainesville, Fla.
Publication Date: 2012

Subjects

Subjects / Keywords: circadian -- eeg -- epilepsy -- hippocampus
Biomedical Engineering -- Dissertations, Academic -- UF
Genre: Biomedical Engineering thesis, M.S.
bibliography   ( marcgt )
theses   ( marcgt )
government publication (state, provincial, terriorial, dependent)   ( marcgt )
born-digital   ( sobekcm )
Electronic Thesis or Dissertation

Notes

Abstract: Since the late 19th century it has been observed that epileptic seizures tend to occur at specific times of day, thus suggesting a link between circadian rhythms and epilepsy. However, a thorough understanding of such relationship and its extent is still lacking. This research engages in investigating the circadian variations, in terms of frequency rhythms, in the hippocampus of epileptic rats, during a seizure -free period in the chronic phase of the disease. In-vivo intra-hippocampal recordings were performed on rats, employing 32 microwire electrodes. Following one week of baseline EEG recordings, the animals underwent continuous electrical stimulation, with the purpose of inducing brain injury leading to spontaneous seizures. The animals were kept in a controlled environment with symmetric day/night cycles (lights on at 6 am, lights off at 6 pm) and continuously monitored with a video recording. Spectral analyses was carried out on these recordings, considering the frequency range 0-100 Hz, which includes all the most relevant hippocampal EEG rhythms (theta: 3-11 Hz, beta: 12-29 Hz, gamma 30-70 Hz, high gamma 71-100 Hz). The circadian modulation of the spectral power of these bands was considered and comparisons were 13 drawn between the pre-injury period and the seizure-free period in the chronic phase of the disease. Applying signal processing techniques, differences in the circadian patterns among the healthy and the epileptics were observed and they are presented and discussed in this thesis.
General Note: In the series University of Florida Digital Collections.
General Note: Includes vita.
Bibliography: Includes bibliographical references.
Source of Description: Description based on online resource; title from PDF title page.
Source of Description: This bibliographic record is available under the Creative Commons CC0 public domain dedication. The University of Florida Libraries, as creator of this bibliographic record, has waived all rights to it worldwide under copyright law, including all related and neighboring rights, to the extent allowed by law.
Statement of Responsibility: by Stefano Re Fraschini.
Thesis: Thesis (M.S.)--University of Florida, 2012.
Local: Adviser: Carney, Paul R.

Record Information

Source Institution: UFRGP
Rights Management: Applicable rights reserved.
Classification: lcc - LD1780 2012
System ID: UFE0044749:00001

Permanent Link: http://ufdc.ufl.edu/UFE0044749/00001

Material Information

Title: Circadian Variations in the Hippocampus of Epileptic Rats
Physical Description: 1 online resource (66 p.)
Language: english
Creator: Re Fraschini, Stefano
Publisher: University of Florida
Place of Publication: Gainesville, Fla.
Publication Date: 2012

Subjects

Subjects / Keywords: circadian -- eeg -- epilepsy -- hippocampus
Biomedical Engineering -- Dissertations, Academic -- UF
Genre: Biomedical Engineering thesis, M.S.
bibliography   ( marcgt )
theses   ( marcgt )
government publication (state, provincial, terriorial, dependent)   ( marcgt )
born-digital   ( sobekcm )
Electronic Thesis or Dissertation

Notes

Abstract: Since the late 19th century it has been observed that epileptic seizures tend to occur at specific times of day, thus suggesting a link between circadian rhythms and epilepsy. However, a thorough understanding of such relationship and its extent is still lacking. This research engages in investigating the circadian variations, in terms of frequency rhythms, in the hippocampus of epileptic rats, during a seizure -free period in the chronic phase of the disease. In-vivo intra-hippocampal recordings were performed on rats, employing 32 microwire electrodes. Following one week of baseline EEG recordings, the animals underwent continuous electrical stimulation, with the purpose of inducing brain injury leading to spontaneous seizures. The animals were kept in a controlled environment with symmetric day/night cycles (lights on at 6 am, lights off at 6 pm) and continuously monitored with a video recording. Spectral analyses was carried out on these recordings, considering the frequency range 0-100 Hz, which includes all the most relevant hippocampal EEG rhythms (theta: 3-11 Hz, beta: 12-29 Hz, gamma 30-70 Hz, high gamma 71-100 Hz). The circadian modulation of the spectral power of these bands was considered and comparisons were 13 drawn between the pre-injury period and the seizure-free period in the chronic phase of the disease. Applying signal processing techniques, differences in the circadian patterns among the healthy and the epileptics were observed and they are presented and discussed in this thesis.
General Note: In the series University of Florida Digital Collections.
General Note: Includes vita.
Bibliography: Includes bibliographical references.
Source of Description: Description based on online resource; title from PDF title page.
Source of Description: This bibliographic record is available under the Creative Commons CC0 public domain dedication. The University of Florida Libraries, as creator of this bibliographic record, has waived all rights to it worldwide under copyright law, including all related and neighboring rights, to the extent allowed by law.
Statement of Responsibility: by Stefano Re Fraschini.
Thesis: Thesis (M.S.)--University of Florida, 2012.
Local: Adviser: Carney, Paul R.

Record Information

Source Institution: UFRGP
Rights Management: Applicable rights reserved.
Classification: lcc - LD1780 2012
System ID: UFE0044749:00001


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1 CIRCADIAN VARIATIONS IN THE HIPPOCAMPUS OF EPILEPTIC RATS By STEFANO RE FRASCHINI A THESIS PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTE R OF SCIENCE UNIVERSITY OF FLORIDA 2012

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2 2012 Stefano Re Fraschini

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3 To my parents, who supported me morally and economically during all my years of studying

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4 ACKNOWLEDGMENTS I would like to thank all the people who made it possible for m e to carry out this research. First of all, Dr. Carney, who allowed me to work in his lab and provided me with the resources that I needed for my work. A special mention goes to David Stanley, who gave me precious help that facilitated my job. I also want to express my appreciation for all my lab mates, who made my experience at the Epilepsy Research Lab a pleasant one. I would like to acknowledge the Atlantis CRISP program, which provided me the funding and the opportunity to work at University of Florida. None of this would have been possible without it. Finally, I want to thank all of my friends and family, who never failed to show me their support, even from the other side of the Atlantic Ocean.

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5 TABLE OF CONTENTS page ACKNOWLEDGMENTS ................................ ................................ ................................ .. 4 LIST OF TABLES ................................ ................................ ................................ ............ 7 LIST OF FIGURES ................................ ................................ ................................ .......... 8 LIST OF A BBREVIATIONS ................................ ................................ ........................... 10 ABSTRACT ................................ ................................ ................................ ................... 11 CHAPTER 1 INTRODUCTION ................................ ................................ ................................ .... 13 Epilepsy ................................ ................................ ................................ .................. 13 Circadian Rhythms ................................ ................................ ................................ 15 Temporal Lobe Epilepsy ................................ ................................ ......................... 16 Hippocampus ................................ ................................ ................................ .......... 17 Anatomy ................................ ................................ ................................ ........... 18 Functions ................................ ................................ ................................ .......... 18 EEG Rhythms ................................ ................................ ................................ ... 20 CA1 Region ................................ ................................ ................................ ...... 21 Research Goal ................................ ................................ ................................ ........ 21 Thesis Overview ................................ ................................ ................................ ..... 22 2 EXPERIMENTAL DESIGN ................................ ................................ ..................... 23 Animal Model ................................ ................................ ................................ .......... 23 Protocol Followed ................................ ................................ ................................ ... 23 Surgery and Electrode Implantation ................................ ................................ 24 Electrical Stimulation ................................ ................................ ........................ 26 Data Acquisition ................................ ................................ ............................... 26 High Resolution MRI ................................ ................................ ........................ 27 3 DATA ANALYSIS ................................ ................................ ................................ .... 28 Extraction of the Spectra ................................ ................................ ......................... 28 De trending ................................ ................................ ................................ ............. 31 Averaging ................................ ................................ ................................ ................ 33 Sinusoidal Fitting ................................ ................................ ................................ .... 35 Relative Power ................................ ................................ ................................ ........ 36 Total Power ................................ ................................ ................................ ............. 37

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6 4 RESULTS ................................ ................................ ................................ ............... 38 Peak Times of the Different Frequency Bands ................................ ....................... 38 Relative Power ................................ ................................ ................................ ........ 42 Daily Variations ................................ ................................ ................................ 42 Relative Variations in Relative Power ................................ ............................... 50 Total Power ................................ ................................ ................................ ............. 55 5 CONCLUSIONS ................................ ................................ ................................ ..... 58 6 FUTURE DEVELOPMENTS ................................ ................................ ................... 60 LIST OF REFERENCES ................................ ................................ ............................... 61 BIOGRAPHICAL SKETCH ................................ ................................ ............................ 66

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7 LIST OF TABLES Table page 4 1 Peak times pre injury for all the bands for all the rats and average values ......... 40 4 2 P eak times post injury for all the bands for all the rats and average values ....... 40 4 3 Difference in the peak times for all the bands for all the rats and average values. ................................ ................................ ................................ ................ 41 4 4 Number of daily variations higher than 10% for all the bands of all the rats and average values, pre injury ................................ ................................ ........... 48 4 5 Number of daily variations high er than 10% for all the bands of all the rats and average values, post injury ................................ ................................ .......... 48 4 6 Difference in the daily variations higher than 10% for all the bands of all the rats and average values ................................ ................................ ..................... 48 4 7 Number of daily variations higher than 25% for all the bands of all the rats and average values, pre injury ................................ ................................ ........... 48 4 8 Number of daily variations higher than 25% for all the bands of all the rats and average values, post injury ................................ ................................ .......... 49 4 9 Difference in the daily variations higher than 25% for all the bands of all the rats and average values ................................ ................................ ..................... 49 4 10 Number of variations higher than 10% for all the bands for all the rats and average ................................ ................................ ................................ .............. 54 4 11 Distribut ion pattern of the variations presented in Table 10 ................................ 54 4 12 Number of variations higher than 25% for all the bands for all the rats and average ................................ ................................ ................................ .............. 54 4 13 Distribution pattern of the variations presented in Table 12 ................................ 54 4 14 Peak times of the total spectral power for all the rats and average, pre injury ... 56 4 15 Peak times of the total spectral power for all the rats and average, post injury .. 56

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8 LIST OF FIGURES Figure page 2 1 Placement of the two 16 microwire electrode arrays and of the stimulating electrode. ................................ ................................ ................................ ............ 25 3 1 The shape of a Hamming window and of its transform. ................................ ...... 30 3 2 Example of plot of the hourly spectral values for the four bands considered. ..... 31 3 3 Example of a signal composed by the sum of a straight line and a sinusoid ...... 32 3 4 Example of the sinusoid obtained from the de trending of the signal shown in Figure 3 3, using the technique described above. ................................ .............. 32 3 5 Example of de trended data from this study. ................................ ...................... 33 3 6 Example of de trended 24 hour long data ................................ .......................... 33 3 7 24 hour long segment following the one showed in Figure 3 6 ........................... 34 3 8 Average of the two 24 hour long de trended segments showed in Figures 3 6 and 3 7 ................................ ................................ ................................ ............... 3 4 3 9 Example of the superposition of the fitting sinusoid to the experimental data .... 35 4 1 Comparison of the circadian modulation for the four different frequency bands before i njury (red) and in the chronic phase (blue).. ................................ 38 4 2 Comparison of the circadian modulation for the four different frequency bands before injury (red) and in the chronic phase (blue). ................................ 39 4 3 Comparison of the circadian modulation for the four different frequency bands before injury (red) and in the chronic phase (blue). ................................ 40 4 4 Column graph representing the peak time for each band pre injury and post injury ................................ ................................ ................................ .................. 41 4 5 Distribution during the day of the variations in the relative power pre injury. ...... 42 4 6 Same as Figure 4 5, but after injury ................................ ................................ ... 42 4 7 Distribution during the day of the variations in the relative power pre injury.. ..... 43 4 8 Same as Figure 4 7, but after injury ................................ ................................ ... 43 4 9 Distribution during the day of the variations in the relative power pre injury.. ..... 44

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9 4 10 Same as Figure 4 9, but after injury ................................ ................................ ... 44 4 11 Distribution during the day of the variations in the relative power pre injury. ..... 45 4 12 Same as Figure 4 11, but after injury. ................................ ................................ 45 4 13 Distribution during the day of the variations in the relative power pre i njury. ...... 46 4 14 Same as Figure 4 13, but after injury ................................ ................................ 46 4 15 Distribution during the day of the variations in the relative power pre injury.. ..... 47 4 16 Same as Figure 4 15, but after injury. ................................ ................................ 47 4 17 Column graph representing the number of variations >10% for each band pre injury and post injury.. ................................ ................................ ......................... 49 4 18 Column graph representing the number of variations >25% for each band pre injury and post injury. ................................ ................................ .......................... 50 4 19 Distribution of the variations higher than 10% ................................ ................... 51 4 20 Distribution of the variations higher tha n 25% ................................ ................... 51 4 21 Distribution of the variations higher than 10% .. ................................ .................. 52 4 22 Distribution o f the variations hi gher than 25% ................................ ................... 52 4 23 Distribution of the variations higher than 10% ................................ ................... 53 4 24 Distribution of the variations h igher than 25% ................................ ................... 53 4 25 Circadian modulation of th e total power for rat #1 ................................ ............. 55 4 26 Circadian modulation of th e total power for rat #2 ................................ ............. 55 4 27 Circadian modulation of th e total power for rat #3 ................................ ............. 56 4 28 Column graph representing the peak time for the total power pre injury and post injury. ................................ ................................ ................................ .......... 57

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10 LIST OF ABBREVIATION S DFT Discrete Fourier Transform EEG Electroencephalography EKG Electrocardiography fMRI Functional Magnetic Resonance Imaging ILAE International League Against Epilepsy MRI Magnetic Resonance Imaging PET Positron Emissio n Tomography SCN Suprachiasmatic Nucleus TLE Temporal Lobe Epilepsy

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11 Abstract of Thesis Presented to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Master of Science CIRCADI AN VARIATIONS IN THE HIPPOCAMPUS OF EPILEPTIC RATS By Stefano Re Fraschini August 2012 Chair: Paul R. Carney Major: Biomedical Engineering Since the late 19 th century it has been observed that epileptic seizures tend to occur at specific times of day, t hus suggesting a link between circadian rhythms and epilepsy. However, a thorough understanding of such relationship and its extent is still lacking. This research engages in investigating the circadian variations, in terms of frequency rhythms, in the hip pocampus of epileptic rats, during a seizure free period in the chronic phase of the disease. In vivo intra hippocampal recordings wer e performed on rats, employing 32 microwire electrodes. Following one week of baseline EEG recordings, the animals under went continuous electrical stimulation, with the purpose of inducing brain injury leading to spontaneous seizures. The animals were kept in a controlled environment with symmetric day/night cycles (lights on at 6 am, lights off at 6 pm) and continuously mo nitored with a video recording. Spectral analyses was carried out on these recordings, considering the frequency range 0 100 Hz, which includes all the most relevant hippocampal EEG rhythms (theta: 3 11 Hz, beta: 12 29 Hz, gamma 30 70 Hz, high gamma 71 100 Hz). The circadian modulation of the spectral power of these bands was considered and comparisons were

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12 drawn between the pre injury period and the seizure free period in the chronic phase of the disease. Applying signal processing techniques, differences in the circadian patterns among the healthy and the epileptics were observed and they are presented and discussed in this thesis.

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13 CHAPTER 1 INTRODUCTION Epilepsy Epilepsy is the most common primary disorder of the brain, and, according to the World Heal th Organization, is one of the leading causes of neuropsychiatric disability worldwide, along with depression [1] The word epilepsy derives from the Greek verb epilamvanein due to the fact that, in ancient times, diseases were often seen as attacks by the gods or evil spirits and seizures were the most vivid examples of demonic possession. For [1] Passing from myth to scie nce, Hippocrates is considered the first one to understand that epilepsy is a brain disease and he proposed that it should have been treated by diet and drugs, rather than by religious practices [1] Epilepsy is not a specific disease, but a broad category of symptom complexes originating from a number of disordered brain functions, whose insurgence may be due to a number of different pathologic processes (such as brain injuries, tumors and genetic factors) [1] The common and fundamental characteristic of epilepsy is the presence of recurrent and usually unprovoked seizures, which are the manifestation that arise from excessive, synchronous, abnormal firing patterns of neurons, predominantly located in the cerebral cortex. Such abnormal activity is usually intermittent and self limited. Seizures are recurrent episodes of brain dysfunction that manifest themselves as stereotyped alterations in behavior such as generalized epilepsy. Patients may seize frequently or infrequently, following a cyclic pattern (for

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14 example seizing at a specific time of the day, such as during sleep or at arousal) or in some other fashion, but, usually, without any intelligible predictability [1] However epilepsy is more than seizures: this condition implies a number of factors, which contribute to disability and impaired quality of life. Examples of these factors are social isolation and unemployment, which result in unsatisfactory life standards For this reason s treatment should focus on more than just preventing seizures in order to be really effective [1] Gowers was the first to recognize, more than a century ago, that is really often present a seizure free interval between a causative brain injury and onset of the process of epileptogenesis which is considered to be a dynamic one during which time progressive changes in neuronal excitability, inhibition a nd recurrent excitation occur [1]. It has been reported since the late 19 th century that epilepsy is affected by endogenous clocks, which produce circadian ( ~ 24 hr period), ultradian (< 24 hr period) and supracircadian (> 24 hr period) rhythms. One of the m ost obvious of those is the sleep wake cycle [2] Three categories of epilepsy are classified according to the timing of generalized seizures with respect to the sleep wake cycle: awakening, sleep and diffuse. The first two categories mean that the seizure s are usually observed at the moment of arousal and during sleep, respectively; the latter means that seizures occur mechanisms underlying these phenomena are poorly understood [2 ].

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15 Circadian Rhythms A series of biological functions following daily rhythms has been observed in a number of different animal species. If this rhythmicity is the result of a homeostatic modulation, such can be considered circadian (from the Latin circa d ies that is implies the existence of a biological clock that can keep track of the time of the day. The existence of such clock, called the circadian pacemaker has been de monstrated by experiments in which the subjects had no environmental clues and so the daily modulation could only be the result of internally generated rhythms [3] As the name suggests, this rhythms are approximately 24 hours long (24.8 hours for humans), which means that some adjusting mechanism is necessary in order to avoid a slow but inexorable drift away of the subjective time with respect to the external which sta tes that the period of the biological clock is not changed and that entrainment is achieved by resetting the pacemaker, according to environmental clues called zeitgebers easiest environm ental clue to think about, it is not surprising that the main zeitgeber is light. Minor ones are temperature, exercise, feeding habits and, according to some researchers, social interactions (non photic zeitgebers, in contrast with the photic zeitgeber tha t is light) [3] Given that the circadian pacemaker exists and is entrained by environmental cues, it is then natural to ask where this structure is located in the human body. It was discovered that the circadian clock is located in the hypothalamus in a r egion called the suprachiasmatic nucleus, so called because it is placed above the optic chiasm, which

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16 is the crossing of the optic nerve. The fact that the clock is positioned in the proximity of nerve bundles carrying visual information is not bewilderin g, given the importance of the photic zeitgeber. In fact, there is a connection between the retinas and the rostral part of the hypothalamus, the retino hypothalamic tract, which ends in the SCN [3]. It has to be said that there are theories that imply the existence of different circadian clocks, however none of them disproved the central role of the SCN as a circadian pacemaker, existence of other circadian clocks in the bo dy [3]. Temporal Lobe Epilepsy Among the many epileptic forms, temporal lobe epilepsy (TLE) is one of the most debilitating, with its diffuse and bizarre psychomotor manifestations [4]. The definition of temporal lobe epilepsy, given in 1985 by the Intern ational League Against Epilepsy, is of a condition characterized by recurrent, unprovoked seizures originating from the medial or lateral temporal lobe. This condition was first described in 1881 by John Hughlings Jackson, who recognized seizures originati type of localization related epilepsy [6]. Two different kinds of seizures are associated with TLE: simple partial seizures, wh ich cause no loss of awareness, and complex partial seizures, which cause loss of awareness. These partial seizures may secondarily generalize, which means that they can spread to a wider portion of the brain [5] and [7]. The features of seizures originat ing from the temporal lobe, even though extremely variegated, have certain common patterns such as a mixture of different feeling,

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17 description of them has been given by t he 19 th century Russian writer Dostoyevsky, who suffered from epilepsy himself, in the novel The Idiot [7]. Some physicians have hypothesized, retrospectively, that Vincent Van Gogh, the famous Dutch painter, suffered from TLE [4]. According to the class ification provided by the International League Against Epilepsy ( ILAE ) TLE etiologies are divided into symptomatic (cause known), idiopathic (genetic) and cryptogenic (cause unknown) [5]. Hippocampal sclerosis is the most common pathologic substrate in ad ult patients with temporal lobe epilepsy. This consists of severe neuron loss in specific regions of the hippocampus, such as CA1, prosubiculum, CA4 and hilus; other regions, such as CA2 and CA3, do not appear to be greatly affected by this kind of d amage. Hippocampal damage is often bilateral, but usually asymmetric, with greater damage in the epileptic side. Surgical studies showed that hippocampal sclerosis is probably the result of previous brain injury and not the result of repeated seizures. Hippocamp al sclerosis is associated with neuronal reorganization, which is typical of epileptic hippocampi and is probably involved in the generation of chronic seizures. The aforementioned reorganization involves excitatory mossy fibers, inhibitory GABAergic axons and fibers with other neurotransmitters, such as somatostatin. It can be said then that a sclerotic hippocampus is not an inactive region, but instead has probably a role in epileptogenesis [8]. Finally, hippocampal sclerosis is the principal structure re sponsible for seizures in TLE [9]. Hippocampus The hippocampus is a subcortical structure that is part of the limbic system, which is considered to be concerned with olfactory perception and, at least in humans,

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18 emotional behavior. The limbic system is an organization of different anatomical units (hippocampus, amygdala, cingulate gyrus, fornix, thalamus and hypothalamus) with common functions and is located on top of the brainstem, under the cortex. The hippocampus bulges into the temporal horn of the late ral ventricle and its shape resembles t he one of a sea horse which explains the name given to this structure. Anatomy The hippocampus consists of the cornu Ammonis (or hippocampus proper) and the gyrus dentatus (or fascia dentata), these two laminae are r olled up in a way that resembles two U shaped structures fitting into one another [10]. The cornu Ammonis can be further divided into four regions, which Lorente de No named CA1, CA2, CA3 and CA4 CA1 continues from the subiculum and its stratum pyramidale is large in humans, but dense and narrow in rats. This field is also said to be the focus for hippocampal damage occurring with epilepsy [10] and [11]. CA2 is compo sed of large, densely packed somata, and so its stratum pyramidale is dense and narrow. CA3 has pyramidal somata similar to those of CA2, but they are less dense; a typical feature of CA3 is the presence of non myelinated fibers, the mossy fibers, which or iginates from the gyrus dentatus; furthermore CA3 is said to be a to hypoxia. Finally, CA4 has few large somata scattered among large myelinated mossy fibers typica l of this region; this is a sector of medium vulnerability or Bratz sector [10]. Functions There are several hypotheses regarding the possible functions of the hippocampus and they can be divided in four categories: (I) learning and memory, (II)

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19 regulation of emotional behavior, (III) aspects of motor control and (IV) regulation of hypothalamic functions [10]. It is generally accepted that the hippocampus has a critical role in learning and memory; in fact, clinical observations show that, in humans, hippoc ampal damage causes memory disorders, in particular short term memory. The hippocampus takes part in all aspects of the declarative memory: semantic memory (memory of concepts), episodic memory (memory of events) and spatial memory (spatial location recogn ition). The hippocampal neurons have a remarkable plasticity that is the cause of long term potentiation: a persistent modification of the neuronal physiological state due to repetitive stimulation. The information to be memorized passes through the hippoc ampus and is then stored in the association cortex; this idea is supported by the fact that it has been shown, using PET and fMRI, that the hippocampus projects to large neocortical areas, including the prefrontal and retrosplenial cortices [10] Starting with the studies of Papez (1937), the regulation of emotional behavior has been considered one of the main functions of the hippocampus; nowadays such mean, however, tha t the hippocampus is not concerned with emotional behavior at all; in fact it is accepted that it has a role in the regulations of some emotional behavior, in particular the one connected to pain [10]. The hippocampus is believed to be involved in the vent ral striatal loop, which may participate in the control of motor behavior, in particular motor reactions to emotion [10]. Finally, the hippocampus is involved in the regulation of the hypothalamo hypophyseal axis [10].

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20 EEG Rhythms Different EEG rhythms can be observed in the hippocampus: theta or rhythmic slow activity (3 11 Hz), beta (12 29 Hz) and gamma (30 70 Hz) [12] and [13]. It is believed that each EEG pattern has specific behavior correlates [14]. Theta rhythms occur during translational movements, arousal and attention and during REM sleep [14]. Three main functions have been hypothesized for theta. The first of which is a global synchronizing mechanism that locks the whole hippocampus into a global processing mode and organizes the activity of each of its region with respect to the others [14]. Second, theta rhythm provides a clocking system for the timing of hippocampal spikes; different responses have been reported, according to the timing at which information reaches the hippocampus with respect to the underlying theta rhythm [14], [15], [16], [17], [18], [19] and [20]. The third function is to provide temporal control over long term potentiation and so over the storage and retrieval of information [14], [15] and [16]. The direct involvement of th eta rhythm in long term storage was indicated by studies showing a severe memory deficit after the selective elimination of theta oscillations, achieved by septal lesions [15]. The behavioral correlates of beta activity are not clear: in the rat, beta wave s can be elicited by olfactory inputs linked to the presence of predators. It was observed that seizures induce an increase in beta activity [14]. It has been also hypothesized that beta rhythm might be used for high level interactions involving distant st directly reached through gamma synchronization [13]. It is thought that gamma synchronization is necessary to bind together elements of complex representation [13] and [14], and it was even hypothesized that such synchronization is n ecessary for the normal functioning of the waking brain [21].

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21 Possible functions of the gamma oscillations are coincidence detection, synaptic plasticity and phase encoding [21]. CA1 Region The CA1 region is of particular interest for several reasons. Firs t, it is the main source of output from the hippocampus and its computational importance is suggested by the anatomy (there are approximately 10 10 synapses projecting from CA3 to CA1) [22] and [23]. Second, for its role in long term potentiation [15] and i n TLE [8], [10]. Third, it is the most frequent location for hippocampal recordings [11, 22, 24]. The CA1 area receives excitatory inputs from the entorhinal cortex and CA3, and inhibitory inputs from basket cells and the oriens alveus/lacunosum moleculare [24]. There are several hypotheses on the functions of CA1. The first hypothesis is that it acts as a relay, ensuring efficient information transfer from CA3 to the neocortex [22], [25] and [26]. The second hypothesis is that it detects novel stimuli by c omparing CA3 output and cortical input [22], [27] and [28]. The third hypothesis is that CA1 acts as a predictor of places and events, based on previous knowledge, by comparing activity in CA3 with cortical input [22] and [29]. The fourth hypothesis claims that CA1 is involved in multiple memory loops [22] and [30]. Research G oal Recent studies carried out in this lab have shown a phase shift in the circadian modulation of EEG features in the CA1 region in an animal model of TLE, during the latent period of the disease [31], [32] and [33]. This study, extending from there, aims at investigating the presence of a similar phase shift during the chronic phase of the disease. In particular, this project focuses on the circadian modulation of the power

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22 spectrum o f frequency bands with physiologic relevance (theta, beta and gamma waves). Thesis O verview In C hapter 2 the experimental design will be discussed, describing the animal model employed, the protocol followed and the electrode placement. In C hapter 3 the me thods employed to analyze the intra hippocampal EEG signal in order to investigate the circadian modulation will be described. Chapter 4 will be dedicated to the presentation of the results of this study. In Chapter 5 the results shown in C hapter 4 will be discussed, providing hypothesis on their possible interpretation and significance. Finally, C hapter 6 will deal with the future developments and improvements of the study described in this thesis.

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23 CHAPTER 2 EXPERIMENTAL DESIGN Animal Model For this stud y, an animal model of chronic limbic epilepsy, which is characterized by recurrent spontaneous seizures, after an episode of limbic status epilepticus [34], [35] and [36], was adopted. The episode of limbic status epilepticus that triggers the development of the disease into the chronic phase is achieved by continuous hippocampal stimulation, which yields a self sustaining limbic status epilepticus [37]. The temporal evolution and the electrographic characteristics of this model have been reported to be co nsistent with the ones observed in human patients affected by temporal lobe epilepsy w ith hippocampal sclerosis [34]. Protocol Followed Nine adult male Sprague Dawley rats of age 63 days and weighing between 200 and 265 grams, were considered for this stud y. The animals were implanted with electrodes bilaterally in the hippocampus and they were housed, each in its own cage, at a room temperature of 25 C in a controlled environment with 24 hour symmetric day night cycle (lights on at 6 am, lights off at 6 pm ), so that environmental factors could be ruled out as a cause of the circadian phase shift, if present. The animals were continuously monitored with both video and electrical activity recordings for an average period of 5 weeks, in order to identify spont aneous seizures. After a week of baseline recordings, the 7 of the 9 animals that survived the surgery have been continuously electrically stimulated (400 A at 50 Hz for 50 70 minutes) with the intent of inducing brain injury that would lead to chronic spo ntaneous seizures. Only 3 of these 7 animals

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24 developed the chronic condition, with seizure of Racine grade 3 or higher; the other 4 stages. Once the recording session was over, the animals were sacrificed and the ir brains extracted and imaged in order to confirm the location of the electrodes. Surgery and Electrode Implantation All the procedures have been approved by the Institutional Animal Care and Use Committee of the University of Florida (IACUC protocol D710 ). The animals were completely anesthetized with the injection of 10 mg/Kg (0.1 mL by volume) of xylazine and kept in that state with the inhalation of isoflorane (1.5%). The skull was then exposed by a mid sagittal incision and a 16 microwire electrode ( 50 m, T ucker D avis Technologies, Alachua, FL) array, consisting of two rows separated by 500 m and with a spacing of 250 m between two adjacent electrodes, was placed bilaterally over the CA1 CA2 and dentate gyrus. The furthest left microwire was 4.4 mm caudal to bregma, 4.6 mm left of midline suture and at a depth of 3.1 mm from the dura. The second microwire array was placed to the right of midl ine suture in a diagonal way The furthest right microwire was 3.2 mm caudal to bregma, 2.2 mm to the right o f midline suture. The close s t right microwire was 5.2 mm caudal to bregma, 1.7 mm to the right of midline suture and at a depth of 3.1 mm from the dura. A bipolar, twisted, Teflon coated, stainless steel electrode (330 m) was implanted in the right poster ior ventral hippocampus (5.3mm caudal to bregma, 4.9 mm right of midline suture and at a depth of 5 mm from the dura) to provide the electrical stimulation necessary to induce the brain injury (Figure 2 1 ). To anchor the electrode arrays to the skull four 0.8 mm stainless steel screws (two for each electrode array) were used. Of each pair of screws one ( 2 mm to the bregma and bilaterally 2 mm ) served as the ground electrode and the

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25 other ( 2 mm to the lambdoidal suture and bilaterally 2 mm ) as the reference electrode. The whole surgical area was finally closed and secured with cranioplast cement and the animals were allowed to recover for a week before the starting of the recording session. Figure 2 1 Placement of the two 16 microwire electrode arrays a nd of the stimulating electrode.

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26 Electrical Stimulation To induce status epilepticus, 7 of the 9 rats (the ones which survived the surgery) underwent continuous electrical stimulation. A biphasic square wave stimulus at a frequency of 50 Hz and with a puls e width of 1 millisecond and intensities of 300 400 A was applied continuously for 50 70 minutes with a duty cycle of 10 seconds on and 2 seconds off. After 20 30 minutes of stimulation, the animals showed convulsive seizures, minutes. After the end of the stimulation continuous EEG recordings were observed for electrographic evidence of seizures in the following hours. When the animals stabilized showing no more signs of abn ormal electrographic activity, they were brought back to the vivarium and kept there for 4 6 weeks, during which 3 of the 7 animals developed spontaneous seizures of a minimum Racine grade of 3. So, all the data considered for this study comes from this 3 rats that entered the chronic phase of the disease. Data Acquisition Each rat was connected to a 32 channel commutator for the continuous EEG recordings. The output of such device was fed into the recording system, which consisted of two 16 channel pre am plifiers and a 16 bit A/D converter with a sampling rate of 12 KHz. The digitized signal was then band pass filtered between 1.5 Hz and 7.5 KHz by a Pentusa RX 5 data acquisition board (Tucker Davis Technologies Alachua, FL ) The band passed digital strea m was finally stored in binar y process for later processing.

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27 High Resolution MRI The animals were perfused with formalin (10% concentration) and their brains were extracted and soaked in phosphate buffer solution for 24 hours in order to remove any residua l fixative. After this, the brain was kept in a tube containing fluorinated oil (Fluorinert, 3M Corp., Saint Paul, MN) and imaged at 17.6 T with a Bruker (Bilerica, MA) Avance MRI machine in order to verify the correct positioning of the electrodes. Images were acquired with a 3D gradient echo pulse sequence with a repetition time of 150 m illi s econds gradient echo time of 15 m illisecond s The field of view of the image was 30 mm 15 mm 15 mm in a matrix of 400200200 pixels, with a resolution of 75 7 5 3 A tri dimensional image was reconstructed applying a 3D Fourier transform.

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28 CHAPTE R 3 DATA ANALYSIS This study focused on spectral features of the intra hippocampal EEG signal, acquired from the electrode placed in the CA1 region. After having extracted the spectrum of the signal, this was de trended and divided in 24 hour long cycles, which were then averaged. The 24 hour cycle resulting from this operation was then fitted to a sinusoid of period 24 hours and the time at which the positive pea k was reached was identified manually from the generated plots. In addition to that, the relative power of each band was calculated as the ratio of the power of the considered band with respect to the sum of the power of all the bands. Furthermore, the rel ative variation in the relative power post injury with respect to pre injury was calculated as the ratio of the power of the considered band after in the chronic phase with respect to the power of the same band before injury. Finally the total power was co nsidered. The details of each of these operations wil l be described Extraction of the Spectra To evaluate the frequencies present in the signal the discrete Fourier transform was employed. Shortly, the Fourier transform decomposes a waveform into a sum o f sinusoids of different period and amplitude. The discrete Fourier transform is the implementation of the Fourier transform in discrete time, which means with a finite number of points rather than with a continuous function. The discrete Fourier transform maps a sequence of points x(n) in the time domain into the frequency domain. The mathematical definition of the DFT is given in Equation 3 1 [38].

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29 ( 3 1) 1, is a uniformly sampled sequence, with sampling interval T. XF(k) is the k th DFT coefficient. The DFT has many applications, among which are the processing of biological signals such as EKG and EEG [38]. So, the analysis performed in this study employs techniques which are well established in the field. The Fourier transform assumes that the signal considered is stationary and that the signal in the sample repeats infinitely [39]. However, the EEG signal is non stationary, in general; to solve this issue it is possible to divide the EEG signal in short chunks over which the hypothesis of stationarit y can be assumed to be true [40], [41] and [42]. In this study the signal has been broken down in 10 seconds long sequences [43] and [44] with a 50% overlap. Before the application of the DFT a Hamming window was applied. The Hamming window is designed in order to minimize the maximum side lobe [45], the reason for doing this is the reduction of the artifacts arising from the abrupt transitions from the end of a chunk to the beginning of the same chunk, due to the hypothesis of periodicity of the signal ass umed by the Fourier transform. The mathematical definition of the Hamming window is given in Equation 3 2 [46] and an example of its shape is given in Figure 3 1 [45]. (3 2)

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30 Figure 3 1 The shape of a Hamming window and of its transform. M = 21 A) Hamming window. B) transform. The reason for the 50% overlapping of the 10 seconds windows is to compensate for the fact that the Hamming windows attenuates the amplitude of the time samples at the extremities of the chunk considered. The spectra of the 1 0 seconds long chunks were averaged over an hour, yielding the hourly estimate of the spectral power of each band considered. This was done over a length of 4 6 days, yielding a series of points each of which represents the hourly value of the spectral pow er in the considered band (Figure 3 2 ). This procedure was A B

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31 done both before the injury and in a seizure free period during the chronic phase of the disease. Figure 3 2 Example of plot of the hourly spectral values for the four bands considered. De trend ing To remove the effects of the supra circadian rhythms, the data obtained applying the procedure described above has been de trended. To do so from each point was subtracted the average of the following 48 points, which means 48 hour. This decision is ju stified by the fact that, considering two complete periods, the average of the circadian modulation over those 48 hours is equal to zero, assuming a sinusoidal circadian modulation, and so the result of the performed averaging is the average supra circadia n variations in the 48 hours following the time point considered. Numerical example s to prove the efficacy of such an approach, under the given assumptions are presented as follows:

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32 5) and a sinusoid of period 50 (y=sin(2 x/50)), shown in Figure 3 3 Subtracting from each point the average of the following 100 (that is 2 periods) a sinusoid of period 50 with zero average is obta ined, as can be seen in Figure 3 4 This showed that the approach followed is able to remove the effects of the drift of the baseline, due to supra circadian modulation. An example of de trended data used in thi s study is provided in Figure 3 5 Since, in order to de trend, it is necessary to have data for the f ollowing 48 hours, the length of the de trended data is 2 4 days. Figure 3 3 Example of a signal composed by the sum of a straight line and a sinusoid Figure 3 4 Example of the sinusoid obtained from the de trending of the signal shown in Figure 3 3 using the tec hnique described above

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33 Figure 3 5 Example of de trended data from this study. Averaging The de trended data has been divided in 24 hour long segments and they have been averaged, in order to enhance the underlying circadian modulation b y attenuating the effects of transitory activity and noise. An example of such an operation done with data used in this study is provided below in Figures 3 6 3 7 and 3 8 Note that the values on the time axes do not correspond to the time of the day they refer to. Figure 3 6 Example of de trended 24 hour long data

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34 Figure 3 7 24 hour long segment following the one showed in Figure 3 6 Figure 3 8 Average of the two 24 hour long de trend ed segments showed in Figures 3 6 and 3 7

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35 Sinusoidal Fitting Since it was hypothesized a sinusoidal modulation of the circadian rhythms, the de trended and averaged data has been fitted with a sinusoid of period 24 hours. This was done employing an algorithm that minimizes the squared error between the experimental data and the fitting function. The fitting function used is given in Equation 3 3 An example of the fitting sinusoid superimposed to the experim ental data is shown in Figure 3 9 (3 3) W here x1 and x2 are the free parameters (amplitude and phase of the sinusoid) and x data is the experimental data. Figure 3 9 Example of the superposition of the fitting sinusoid to the experimental data

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36 Relative P ower In addition to the analysis of the power spectra of the four bands described above, the relative power of each band with respect to the sum of all the bands has been calculated and its variations during the day have been considered. Furthermore the variations in the relative power for a giv en time of the day between the pre injury and chronic phase have been considered. The calculation performed to evaluate the relative power is described in Equation 3 4. (3 4) Where RP is relative powe r, i=1,2,3,4 is the index relative to the band considered, power. The relative power has been evaluated using Equation 3 4 on 3 5 days of data, each yielding a 4x24 matr ix containing the values for each band at each time of the day. The data contained in these matrices has been averaged, giving a 4x24 matrix containing the averaged relative power values. The relative variation of the relative power for a given band at a g iven time of the day between the pre injury and chronic phase has be en calculated using Equation 3 5. (3 5) Where RVRP is relative variation of relative power, indexes i,k are the same as the ones described for Equation 3 4 and RP is the averaged relative power. To make the data more intelligible only the variations of a certain entity were taken into account. In particular, only the ones who were higher than 10 and 25 percent o f the mean relative power for a given band, when evaluating the daily variations of relative power, and the

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37 ones who were higher than 10 and 25 percent of the pre injury value, when evaluating the variations in relative power between pre injury and chronic phases. The results with both these two threshold values (10% and 25%) will be prov ided in C hapter 4 Total P ower Finally, the total spectral power has been taken into account. This was calculated as the sum, hour by hour, of the averaged spectral power of all the bands (Equation 3 6). (3 6) Where TP is total power, P is spectral power and indexes i, k are the same as before. In this way the total power consists of a vector of 24 points, which was then fitted to a sinusoid of period 24 hours as done before for the power of the single frequency bands.

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38 CHAPTER 4 RESULTS Here the results of this study will be shown and briefly described. All the comments about their relevance and interpretation will be done in C hapter 5 Peak Times of t he Different Freq uency Bands The results shown below refer to rat #1. Figure 4 1 Comparison of the circadian modulation for the four different frequency bands before injury (red) and in the chronic phase (blue). The time of day is expressed so that 1 corresponds to 1 am and 24 to midnight. A ) Low B) B eta C) Gamma D) High Before injury the low band peaks at 10, the beta at 11, the gamma at 21 and the high at 23. After injury the low band peaks at 10, the beta at 17, the gamma at 21 and the high at 19. A C B D

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39 The results shown below refer to rat #2. Figure 4 2 Comparison of the circadian modulation for the four different frequency bands before injury (red) and in the chronic phase (blue). The time of day is expressed so that 1 corresponds to 1 am and 24 to midnight. A) Low B) Beta C) Gamma D) High Before injury the low band peaks at 11, th e beta at 10, the gamma at 21 and the high at 23. After injury the low band peaks at 16, the beta at 19, the gamma at 21 and the high at 22. The results shown below refer to rat #3. Before injury the low band peaks at 10, the beta at 13, the gamma at 16 an d the high at 20. After injury the low band peaks at 11, the beta at 16, the gamma at 18 and the high at 21. A C B D

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40 Figure 4 3 Comparison of the circadian modulation for the four different frequency bands before injury (red) and in the chronic phase (blue). Th e time of day is expressed so that 1 corresponds to 1 am and 24 to midnight. A) Low B) Beta C) Gamma D) High A summary of the results is given in Ta bles 1, 2 and 3 and in Figure 4 4 Table 4 1. Peak times pre injury for all the bands for all the rats and average values Pre Rat #1 Rat #2 Rat #3 Avg pre Low 10 11 10 10.33 Beta 11 10 13 11.33 Gamma 21 21 16 19.33 High 23 23 20 22.00 The low and beta bands peak in the morning on average, while the gamma and high bands peak in the evening. Table 4 2. Peak times post injury for all the bands for all the rats and average values Post Rat #1 Rat #2 Rat #3 Avg post Low 10 16 11 12.33 Beta 17 19 16 17.33 Gamma 21 21 18 20.00 High 19 22 21 20.67 A B C D

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4 1 The low band peaks around noon, while the beta, gamma and high bands peak i n the evening. Table 4 3. Difference in the peak times for all the bands for all the rats and average values. Post Pre Rat #1 Rat #2 Rat #3 Avg post Low 0 5 1 2.00 Beta 6 9 3 6.00 Gamma 0 0 2 0.67 High 4 1 1 1.33 Figure 4 4 Column gr aph representing the peak time for each band pre injury and post injury. On the y axis time of the day, where 1 is 1 am and 24 is midnight. The error bars are expressed in terms of standard deviation. It can be seen that only in the beta band there is a c hange bigger than 3 hours (that corresponds to a phase shift of /4). Furthermore, only the difference in peak time of the beta band is statistically significant (p<0.05, evaluated through a paired t test).

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42 Relative Power Daily Variations In this paragraph the variations in the relative power during the day are presen ted for all the bands for all the rats both pre injury and in the chronic phase. The results shown below refer to rat #1. In Figures 4 5 and 4 6 the variations in relative power higher than 10% are shown. Figure 4 5 Distribution during the day of the v ariations in the relative power pre injury. In red increases of more than 10%, in blue decreases of more than 10%. On the y axis is the time of day, expressed so that 1 corresponds to 1 am and 24 to midnight. Figure 4 6. Same as Figure 4 5 but after inj ury

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43 In Figures 4 7 and 4 8 the variations in relative power higher than 25% are shown. Figure 4 7 Distribution during the day of the variations in the relative power pre injury. In red increases of more than 25%. On the y axis is the time of day, expre ssed so that 1 corresponds to 1 am and 24 to midnight. Figure 4 8. Same as Figure 4 7 but after injury

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44 The results shown below refer to rat #2. In Figures 4 9 and 4 10 the variations in relative power higher than 10% are shown, while in Figures 4 11 an d 4 12 variations higher than 25% are considered. Figure 4 9 Distribution during the day of the variations in the relative power pre injury. In red increases of more than 10%, in blue decreases. On the y axis is the time of day, expressed so that 1 co rresponds to 1 am and 24 to midnight. Figure 4 10 Same as Figure 4 9 but after injury

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45 Figure 4 11 Distribution during the day of the variations in the relative power pre injury. In red increases of more than 25%. On the y axis is the time of day, ex pressed so that 1 corresponds to 1 am and 24 to midnight. Figure 4 12 Same as Figure 4 11 but after injury.

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46 Referring to rat #3, in Figures 4 13 and 4 14 the variations in relative power higher than 10% are shown, while in Figures 4 15 and 4 16 var iations higher than 25% are considered. Figure 4 13 Distribution during the day of the variations in the relative power pre injury. In red increases of more than 10%, in blue decreases. On the y axis is the time of day, expressed so that 1 corresponds to 1 am and 24 to midnight. Figure 4 14 Same as Figure 4 13 but after injury

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47 Figure 4 15 Distribution during the day of the variations in the relative power pre injury. In red increases of more than 25%, in blue decreases. On the y axis is the time of day, expressed so that 1 corresponds to 1 am and 24 to midnight. Figure 4 16. Same as Figure 4 15 but after injury. In all the circumstances it can be observed that the daily variations in relative power follow the same pattern that is increases duri ng the evening/night and decreases during the day. A summary of the results is given in Tables 4,5,6,7,8 and 9 and in Figures 4 17 and 4 18

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48 Table 4 4. Number of daily variations higher than 10% for all the bands of all the rats and average values, pre inj ury Pre Rat #1 Rat #2 Rat #3 Avg pre Low 0 0 0 0.00 Beta 3 2 2 2.33 Gamma 15 9 15 13.00 High 15 7 21 14.33 Total 33 18 38 29.67 Table 4 5. Number of daily variations higher than 10% for all the bands of all the rats and average values, po st injury Post Rat #1 Rat #2 Rat #3 Avg post Low 0 0 1 0.33 Beta 15 4 8 9.00 Gamma 15 12 13 13.33 High 14 18 20 17.33 Total 44 34 42 40.00 Table 4 6. Difference in the daily variations higher than 10% for all the bands of all the rats and average values Post Pre Rat #1 Rat #2 Rat #3 Avg post Low 0 0 1 0.33 Beta 12 2 6 6.67 Gamma 0 3 2 0.33 High 1 11 1 3.00 Total 11 16 4 10.33 Table 4 7. Number of daily variations higher than 25% for all the bands of all the rats and av erage values, pre injury Pre Rat #1 Rat #2 Rat #3 Avg pre Low 0 0 0 0.00 Beta 0 0 0 0.00 Gamma 2 2 2 2.00 High 1 1 9 3.67 Total 3 3 11 5.67

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49 Table 4 8. Number of daily variations higher than 25% for all the bands of all the rats and averag e values, post injury Post Rat #1 Rat #2 Rat #3 Avg post Low 0 0 0 0.00 Beta 1 0 0 0.33 Gamma 7 0 2 3.00 High 5 4 11 6.67 Total 13 4 13 10.00 Table 4 9. Difference in the daily variations higher than 25% for all the bands of all the rats and average values Post Pre Rat #1 Rat #2 Rat #3 Avg post Low 0 0 0 0.00 Beta 1 0 0 0.33 Gamma 5 2 0 1.00 High 4 3 2 3.00 Total 10 1 2 4.33 Figure 4 17 Column graph representing the number of variations >10% for each band pre injury a nd post injury. The error bars are expressed in terms of standard deviation.

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50 Figure 4 18 Column graph representing the number of variations >25% for each band pre injury and post injury. The error bars are expressed in terms of standard deviation. Apply ing a paired t test, it appeared that the increase in the number of variations higher than 10% for the beta band is statistically significant p<0.1. An increase in the number of variations in the beta band is consistent with the phase shift observed, since the behavior of such band appears to become more similar to the one of the gamma and high bands, which have a higher number of such variations pre injury than beta. Considering variations higher than 25%, no statistically relevant difference is observed. Relative Variations i n Relative Power In this paragraph the relative variations in relative power in the chronic phase with respect to the pre injury phase are presented. The results below refer to rat #1. Variations higher than 10% are shown in Figure 4 1 9 while variations higher than 25% are shown in Figure 4 20

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51 Figure 4 19 Distribution of the variations higher than 10%; in red increases, in blue decreases. On the y axis is the time of day, expressed so that 1 corresponds to 1 am and 24 to midnight. Figure 4 20 Distribution of the variations higher than 25%; in red increases, in blue decreases. On the y axis is the time of day, expressed so that 1 corresponds to 1 am and 24 to midnight. A consistent decrease in the beta and gamma bands can be obse rved. Some less relevant increases are seen in the low and high bands. The results referring to rat #2 are presented below; in Figure 4 21 variations higher than 10%, in Figure 4 22 variations higher than 25%.

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52 Figure 4 21 Distribution of the variations higher than 10%; in red increases, in blue decreases. On the y axis is the time of day, expressed so that 1 corresponds to 1 am and 24 to midnight. Figure 4 22 Distribution of the variations higher than 25%; in blue decreases. On the y axis is the time of day, expressed so that 1 corresponds to 1 am and 24 to midnight. Some decreases in the gamma and high bands can be observed, but only few of them are higher than 25%. The results referring to rat #3 are presented below; in Figure 4 23 variations higher than 10%, in Figure 4 24 variations higher than 25%.

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53 Figure 4 23 Distribution of the variations higher than 10%; in red increases, in blue decreases. On the y axis is the time of day, expressed so that 1 corresponds to 1 am and 24 to midnight. Figure 4 24 Distribution of the variations higher than 25%; in red increases. On the y axis is the time of day, expressed so that 1 corresponds to 1 am and 24 to midnight. In this case decreases in the beta and increases in the gamma and high can be observed. A summary of the results is given in Tables 10,11,12 and 13.

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54 Table 4 10. Number of variations higher than 10% for all the bands for all the rats and average; (+) increases, ( ) decreases Rat #1 Rat #2 Rat #3 Avg Low 13 (+) 0 0 4.33 Beta 23 ( ) 2 (+) 13 ( ) 12.67 Gamma 24 ( ) 13 ( ) 13 (+) 16.67 High 14 (+) 16 ( ) 22 (+) 17.33 Total 74 31 48 51.00 Table 4 11. Distribution pattern of the variations presented in Table 10; (+) increases, ( ) decreases D during the day, N during the night, ( T ) throughout Rat #1 Rat #2 Rat #3 Majority Low DN ( + T) Beta DN ( T) N (+) DN ( T) DN ( T) Gamma DN ( T) D ( ) DN ( + T) D ( ) High DN( + ) D ( ) DN ( + T) DN (+) Table 4 12. Number of variations higher than 25% for all the bands for all the rats and average; (+) incr eases, ( ) decreases, Rat #1 Rat #2 Rat #3 Avg Low 0 0 0 0.00 Beta 13 ( ) 0 0 4.33 Gamma 18 ( ) 1 ( ) 4 (+) 7.67 High 4 (+) 3 ( ) 12 (+) 6.33 Total 35 4 16 18.33 Table 4 13. Distribution pattern of the variations presented in Table 12; in red increases, in blue decreases, D during the day, N during the night, (T) throughout, (S) sparse Rat #1 Rat #2 Rat #3 Majority Low Beta DN ( T) Gamma DN ( T) D ( ) DN ( + S) D ( ) High DN( + ) D ( ) N (+) N (+) It can be seen that a clear pattern in this variations is lacking. This means that

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55 differences observed might be justified as the result of longer term modulation (supra circadian rhythms) or other factors that we Total Power The daily variations in the total spectral power before and after injury are presented in this paragraph. The results referring to rats #1, #2 and #3 are given in Figures 4 25 4 26 and 4 27 respectivel y. Figure 4 25 Circadian modulation of the total power for rat #1; in red pre injury, in blue post injury. On the x axis is the time of day, expressed so that 1 corresponds to 1 am and 24 to midnight. It can be observed that the total power still peaks at the same time. Figure 4 26 Circadian modulation of the total power for rat #2; in red pre injury, in blue post injury. On the x axis is the time of day, expressed so that 1 corresponds to 1 am and 24 to midnight.

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56 It can be observed that before injury the peak is at 10, while in the chronic phase it is around 16. It has to be said however that the sinusoid for the chronic phase has a relatively low amplitude and so, the accuracy in the estimation of the peak time is not extremely reliable. Figure 4 2 7 Circadian modulation of the total power for rat #3; in red pre injury, in blue post injury. On the x axis is the time of day, expressed so that 1 corresponds to 1 am and 24 to midnight. It can be observed that the total spectral power peaks at 11 both b efore and after the injury. A summary of the results is given in Tables 14 and 15 and in Figure 4 2 8 Table 4 14. Peak times of the total spectral power for all the rats and average, pre injury Pre Rat #1 Rat #2 Rat #3 Avg pre Total pow 10 10 1 1 10.33 Table 4 15. Peak times of the total spectral power for all the rats and average, post injury Post Rat #1 Rat #2 Rat #3 Avg post Total pow 10 16 11 12.33

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57 Figure 4 28 Column graph representing the peak time for the total power pre injury and post injury. On the y axis time of the day, where 1 is 1 am and 24 is midnight. The error bars are expressed in terms of standard deviation. It can be said, after applying a paired t test, that there is no statistically relevant difference in t he peak time of the total spectral power before and after injury. This is consistent with the fact that the low band, where most of the spectral power is shift.

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58 CHAPTER 5 CONCLUSIONS It clearly emerges from this study th e fact that a phase shift in the beta band occurs in the chronic phase of TLE during the interictal period. Furthermore, the phase shift observed is in accordance to the one observed during the latent epileptogenic period [33], meaning that such a conditio n is not transient, but instead permanent. In [32] was shown that structural damage to the fimbria fornix developed during the latency phase, as a result of the initial injury; it can be hypothesized that such structural damage causes the destruction of th e path that allows the transmission of the circadian drive to the CA1 region, causing a disruption of the normal circadian modulation [31], [32] and [33]. Since it has been proposed that beta rhythms might be used for high level interactions between distan synchronization [13], it can be hypothesized that the phase shift in the beta rhythms shown in this study might have some negative effects on cognition and memory. It is known, in fact, that jet lag causes a temporary reduction in intellectual performances [3] and the observed phase shift might have the same effect, with the difference that this is sustained and not a transitory condition. TLE is frequently associated with cognitive and memory impair ments, which are the result of a number of different factors such as the primary epileptogenic condition, the damaged caused by the seizures and the effects of the treatment [47], [48], [49] and [50] and it can be thought that the observed phase shift in t he circadian modulation of beta rhythms might be one of the causes of the aforementioned cognitive impairments.

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59 It has also been showed that seizures induce phase shifts of rat circadian rhythms [51]. From this it can be hypothesized that seizures might a ct as a mechanism to reset the normal circadian modulation that has been disrupted by the brain injury; judging from [31], [32] and [33], one might even hypothesize that the circadian modulation disruption is the driving factor leading to the development o f TLE, at least in the cases that present hippocampal sclerosis, since epilepsy shows some symptoms similar to jet lag [52] and jet lag can induce seizures [53] and [54]. It could be then be hypothesized that each seizure resets the normal circadian rhythm s, but, due to the structural damage that prevent the circadian input to reach the CA1 region, the phase shift observed will re appear, leading to a new seizure and so on.

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60 CHAPTER 6 FUTURE DEVELOPMENTS This study led to a series of fascinating hypothesis, all of which would need further investigation in order to be tested.To start with, it would be interested to prove if provoking the structural damage to the fimbria fornix observed in [32] would lead to the same phase shift in the beta rhythm observed in this study and/or seizures with characteristics comparable to the ones observed in TLE. It would also be interesting to see if it is possible to induce seizures by forcing a sustained jet lag effect for several days or weeks (the temporal window of the lat ent epileptogenic phase). Finally it would be interesting to study the circadian dynamics between a series of spikes that occur at a distance of some days one to the other, in order to verify if the proposed hypothesis of the seizures as a reset mechanism for circadian modulation is consistent. Last but not least, it would be of great interest to extend the results of this study to humans This in order to verify if the observed phase shift or other similar disruptions in circadian rhythms can be observed i n human patients affect ed with TLE with hippocampal sclerosis.

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61 LIST OF REFERENCES [1 ] Epilepsy: a C omprehensive T extbook vol. 1, Ed. Philadelphia: Lippincott Raven, 1998, ch. 1, pp. 1 7. [2 ] Epilepsy: a C omprehensive T extbook vol. 2, J. Engel and T. A. Pedley, Ed. Philadelphia: Lippincott Raven, 1998, ch. 181, pp. 1917 1928. [3 ] R. Refinetti, Circadian Physiology Ed. Boca Raton: CRC Press 2000. [4 ] Int. Colloq. Temporal Lobe Epilepsy Bethesda, MD, 1958, pp. 7 12. [5 ] D. Y. Ko S. Sahai Srivastava, S. R. Benbadis, J.E. Cavazos et al. (2012, Mar. 12), Temporal Lo be Epilepsy [Online]. Available: http://emedicine.medscape.com/article/1184509 overview#a0101 [6 ] Can. J. Neurol. Sci ., vol. 1, no.1, pp. 6 10, May 2000. [7 ] G. L. Holmes (2006, Oct. 21), Temporal Lobe Epilepsy [Online]. Available: http://www.epilepsy.com/EPILEPSY/epilepsy_temporallobe [8 ] G. W. Mathern C. L. Wilson and H. Beck Epilepsy: a C omprehensive T extbook vol. 1, Ed. Philadelphia: Lippincott Raven, 1998, ch. 13, p p. 133 155. [9 ] Temporal Lobe Epilepsy with Hippocampal Sclerosis Epilepsia vol. 45, no. 6, pp. 695 714, May 2004. [10 ] The Human Hippocampus: Functional Anatomy, Vasculariza tion and Serial Sections with MRI Ed. New York: Springer, 2005, ch. 3, pp. 5 37. [11 ] The Hippocampus vol.1, Ed. New York: Plenum Press, 1975, ch.1, pp. 9 39. [12 ] G. Buzs The Hippocampus vol. 3, Ed. New York: Plenum Press, 1975, ch. 5, pp. 137 167. [13 ] N. Kopell G. B. Ermentrout, M. A. Whittington and R. D. Traub mma Rhythms Proc. Nat. Academy Sci. vol. 97, no. 4, pp. 1867 1872, Feb. 2000.

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66 BIOGRAPHICAL SKETCH Stefano Re Fraschini was born in Legnano, Italy, in 1988, the only son of Enza Stefano graduated from Galileo Galilei High School, Legnano, Italy, in 2007 with a score of 100/100. The same year he enrolled in Politecnico di Milano, Milan, Italy, pursuing a Bachelor of Science in Biomedical Engineering, which he obtained in July of 2010, with a score of 108/110. In September of the same year Stefano got into graduate school at Politecnico di Milano, Milan, Italy, aiming at a Master of Science in Biomedical Engineering. In November Stefano took part in the Athens Programme Fall Sessio n at ENSTA Paristech, Paris, France for a week long course on medical imaging. In M arch of 2011 Stefano was selected to be one of the three students from his University to take part in the Atlantis Program, a double degree exchange agreement between the E U and the US, that would have given him the chance to study at University of Florida, Gainesville, FL, USA. In June of the same year Stefano participated to a summer school on medical imaging and robotics in Strasbourg, France, in partial fulfillment of th e requirements of the Atlantis Program. Stefano received his Master of Science degree in Biomedical Engineering at University of Florida in August 2012.