Dolgozat címe: A lassú alvási oszcillációk intrakortikális generátorainak elemzése krónikusan beépített multielektróddal epilepsziás páciensekben

Átírás

1 DIPLOMATERV PPKE-ITK MŰ SZAKI INFORMATIKA Dolgozat címe: A lassú alvási oszcillációk intrakortikális generátorainak elemzése krónikusan beépített multielektróddal epilepsziás páciensekben Szerző neve: Csercsa Richárd Konzulensek neve: Dr. Ulbert István Dr. Karmos György Külső cég neve: Magyar Tudományos Akadémia Pszichológiai Kutatóintézete Külső cég címe: 1068 Budapest, Szondi u Leadás dátuma: január 8. Pázmány Péter Katolikus Egyetem Információs Technológiai Kar

2 NYILATKOZAT Alulírott Csercsa Richárd, a Pázmány Péter Katolikus Egyetem Információs Technológiai Karának hallgatója kijelentem, hogy ezt a diplomatervet meg nem engedett segítség nélkül, saját magam készítettem, és a diplomamunkában csak a megadott forrásokat használtam fel. Minden olyan részt, melyet szó szerint, vagy azonos értelemben, de átfogalmazva más forrásból átvettem, egyértelműen a forrás megadásával megjelöltem... Csercsa Richárd Budapest, január 8. 2

3 Table of Contents A feladat rövid össszefoglalása... 4 Abstract Biological introduction Electrophysiological background Cortical layers Anatomical division of the cerebral cortex Importance of electrophysiology Potential recording types Electrode properties Stages of sleep Slow Wave Sleep About epilepsy Information technological introduction State-of-the-art of IT in neural sciences Software engineering Methods of recording Patients Electrophysiological recordings Methods of analysis State detection Current Source Density Multiunit analysis Biological results and discussion Conclusion Publications Acknowledgements References

4 A feladat rövid össszefoglalása Macskában és rágcsálóban különböző anesztetikumok és természetes lassú hullámú alvás alatt tapasztalható lassú oszcillációk ritmusosan visszatérő kérgi hiperpolarizációs és depolarizációs áramokat váltanak ki. A felszíni EEG-n a hiperpolarizációs fázis (down-state) negativitást a depolarizációs fázis (up-state) pedig pozitivitást mutat. Állatokban az up-state során a kérgi neuronok többsége depolarizált és akciós potenciálokat tüzel, míg down-state alatt a legtöbb neuron viszonylagosan hiperpolarizált és az akciós potenciál generálási tevékenysége jelentősen lecsökken. A lassú oszcilláció funkcionális jelentősége a memórianyomok hosszútávú megőrzésében nyilvánul meg, valamint sejthetően szoros összefüggés áll fenn az epilepsziás aktivitás létrejöttével is. Az emberi lassú alvási oszcilláció alatti agykérgi neuronális mechanizmusok tisztázására rétegelektródák kerültek krónikus beültetésre gyógyszerrezisztens epilepsziás betegek agykérgébe az epilepsziás roham fókuszának meghatározása közben. Intrakortikális mezőpotenciálokat, áramforrás sűrűség (CSD), sok- és egysejt aktivitást rögzítettünk lassú hullámú alvás alatt. A lassú oszcilláció vizsgálatához szükséges módszereket és szoftvert sikeresen kifejlesztettem. Állapotdetekciós módszereket használtam, hogy az agykérgi neuronok mély alvás alatti viselkedését vizsgáljam. Depolarizációs periódus (upstate) alatt felszíni pozitív és mély negatív mezőpotenciálokat, gyors (gamma) oszcillációt, CSD forrást a középső rétegekben és megnövekedett tüzelési gyakoriságot figyeltem meg. Hiperpolarizációs periódus (down-state) alatt felszíni negatív és mély pozitív mezőpotenciálokat, valamint a gyors oszcillációk hiányát tapasztaltam, a kérgi neuronok aktivitása pedig visszaesett a zaj szintjére. Elsőként sikerült kimutatnom, hogy a lassú alvási oszcilláció emberben hasonlóságot mutat az állatmodellekben tapasztaltakhoz. 4

5 Abstract Slow oscillations in the cat, and rodent under various anesthetics and in natural slow wave sleep (SWS) non-rapid eye movement (non-rem) stage exhibit rhythmically recurring phases of widespread cortical hyperpolarizing and depolarizing currents. The hyperpolarizing phase (down-state) appears to be a negative deflection and the depolarizing phase (up-state) as a positive deflection in the surface recorded electroencephalogram (EEG), with polarity inversion in the depth of the cortex. Similar patterns were found in the human sleep surface EEG during non-rem sleep, which appeared as propagating waves. In the up-state in animals, the majority of cortical neurons is depolarized and fire action potentials, whereas in the down-state most of the neurons are relatively hyperpolarized and their action potential generating activity is highly decreased. The functional significance of slow oscillation lies in the long term preservation of memory traces, and is also believed to have strong linkages to the generation of epileptic activity. To elucidate the intracortical neuronal mechanisms of the sleep slow oscillations in humans, laminar multielectrodes were chronically implanted into the cortex of patients with drug resistant epilepsy undergoing cortical mapping for seizure focus identification. Intracortical laminar local field potentials, current source density (CSD), multiple and single unit activity (MUA, SUA) was recorded during quiet slow wave sleep, non-rapid eye movement periods. My work was built up of two parts: software engineering that enabled me to process recorded data, and biological analysis to reveal the functioning of the brain during slow wave sleep in human and the underlying physiology. My main aim was to show that slow oscillation in humans is essentially similar to the animal models. There are no reports up to the present date on human SUA, MUA and CSD data related to slow oscillation, so my work is essential in proving, that similar mechanisms are in effect in humans and in animals during slow sleep oscillations. The methods and software necessary to examine slow oscillations were successfully developed. I applied previously published, and also developed some own state detection methods. I used state detection methods to analyze the behavior of cortical neurons during slow oscillation in deep, non-rem sleep. During depolarization periods (up-state) I observed positive field potentials on the surface and negative potentials in deeper layers, fast (gamma) oscillations, CSD sink in the 5

6 middle layers and increased firing rate. During hyperpolarization periods (downstate) negative field potentials appeared on the surface and positive potentials deeper, current sources were present in the middle layers, there were no fast oscillations, and cortical neurons remained silent, generated no action potentials. My results show for the first time, that the behavior of slow oscillation in human is similar to cat and other animal models. 6

7 1. Biological introduction For a better understanding of brain functions and relations between different subdivisions of the central nervous system we need well established, sophisticated methods which can provide us with appropriate, useable, further processable information from which we can draw certain conclusions. Ever since the first realization of the role of bioelectricity, researchers have sought answers to questions like How does the brain work?, How are the constituting parts connected to each other?, How can we observe its activities?, etc. Nowadays the questions have changed, and as we dive deeper and deeper into the acquaintance of operation some of the questions become answered, others remain unsolved, and at the same time new questions pop up too. As time elapses newer and newer methods are devised which prove to be efficient and fruitful, others turn out to be less useful, but the research never stops. There exist problems having different approaches that can lead us to the combination of diverse alternatives for a better performance. The task of neural science is to explain behavior in terms of the activities of the brain. How does the brain marshal its millions of individual nerve cells to produce behavior, and how are these cells influenced by the environment, which includes the actions of other people? Like all science, neural science must continually confront certain fundamental questions. Are the relationships between the anatomy and physiology of one region and its specific function in perception, thought or movement more likely to be revealed by examining the region as a whole or by studying its individual nerve cells? Our current views about nerve cells, the brain, and behavior have emerged over the last century from a convergence of five experimental traditions: anatomy, embryology, physiology, pharmacology and psychology. The microscope revealed the true structure of the cells of nervous tissue. Even so, nervous tissue did not become a subject of a special science until the late 1800s, when the first detailed descriptions of nerve cells were undertaken by Camillo Golgi and Santiago Ramón y Cajal, who developed some of the key concepts and much of the early evidence for the neuron doctrine the principle that individual neurons are the elementary signaling elements of the nervous system [1]. 7

8 1.1. Electrophysiological background According to Neumann [2] brains do in "very few short steps" what computers do with "exquisite numerical precision over many logical steps". The challenge is to characterize those few steps as neural operations by which spatiotemporal patterns emerge from interactions of cortical neurons over broadly distributed synaptic connections. The patterns are observed with electrophysiological recordings from different types of neurons of the cortex. In the cerebral cortex the principal neurons are the so-called pyramidal cells (fig. 1). These cells have a triangularly shaped soma (cell body), a single apical dendrite extending Fig. 1. Pyramidal cell towards the pial surface, multiple basal dendrites, and a single axon. Pyramidal neurons compose approximately 80% of the neurons of the cortex, and release glutamate as their neurotransmitter, making them the major excitatory component of the cortex. The major inhibitory cells in the central nervous system are the so-called interneurons. They are a group of small, locally projecting neurons using the neurotransmitter GABA (gamma-aminobutyric acid). The pyramidal cells and interneurons are organized in cortical columns (fig. 2.) which are composed of six layers. Each layer receives and sends signals to different parts of the brain. The cerebral cortex is roughly 2-3 mm thick. The columnar functional organization states that neurons that are horizontally more than a half mm from each other do not have overlapping sensory receptive fields. An important distinction is that this rule is functional in origin, and reflects the local connectivity of the cerebral cortex. Connections "up" and "down" within the thickness of the cortex are dramatically denser than connections that spread from side to side. 8

9 Fig. 2. Columnar structure of pyramidal cells with tetrode multielectrode [3] Cortical layers I. The molecular layer contains few scattered neurons and consists mainly of extensions of apical dendrites and horizontally oriented axons. II. The external granular layer contains small pyramidal neurons and numerous stellate neurons. III. The external pyramidal layer contains predominantly small and medium sized pyramidal neurons, as well as non-pyramidal neurons with vertically-oriented intracortical axons. Layers I through III are the main target of interhemispheric corticocortical afferents, and layer III is the principal source of corticocortical efferents. IV. The internal granular layer contains different types of stellate and pyramidal neurons, and is the main target of thalamocortical afferents as well as intrahemispheric corticocortical afferents. V. The internal pyramidal layer contains large pyramidal neurons (as the Betz cells in the primary motor cortex), as well as interneurons, and it is the principal source of efferent for all the motor-related subcortical structures. VI. The multiform layer contains few large pyramidal neurons and many small spindle-like pyramidal and multiform neurons [4]. 9

10 Anatomical division of the cerebral cortex The main anatomical parts of the cortex which can still be determined without the use of a microscope are called lobes. In human we distinguish four cerebral lobes: frontal, parietal, occipital and temporal lobes (fig.3.). Fig. 3. Cerebral lobes The frontal lobe is most commonly associated with thinking, personality, and creativity; temporal lobe is responsible for learning and memory; the processing of visual information takes place in the occipital lobe; while the motor cortex is situated on the border of the frontal and parietal lobes Importance of electrophysiology According to the narrow definition of electrophysiology it is the research of the bioelectric phenomena of the peripheral and central nervous system. This springs from the fact that each and every cell is excitable, produces response to electric, chemical and other stimuli, moreover, it holds direct or alternating current, as well as voltage. As it is well known, in case of typical nerve and muscle cells the electric voltage difference evolves between intra- and extracellular regions. When stimulated, mostly quick running impulse (spike) arises and spreads, then typically by chemical transmitters further electric signals are evoked in other cells. Also important factor is the proportion of the energy reserved in the external stimulus and the response (trigger phenomenon). The energy of the adequate stimulus is small compared to that 10

11 of the response. The charge carriers of bioelectricity (+ and ) are ions and not electrons. In the maintenance of potentials the nearly molecular cell membrane plays a critical role Potential recording types We measure the potential-difference between two electrodes in the following manners: bipolar: both electrodes are on active surface or in tissue. uni(mono-)polar: one of the electrodes is in indifferent, non active place with 0 potential. Other classification approach (localization, fig. 4.): intracellular: glass capillary microelectrode observation of cell activity extracellular: local field potential: any of the above in the frequency range of Hz multi unit activity (MUA) in the range of Hz Fig. 4. Recording techniques (EEG: Electroencephalogram, AEP: Auditory Evoked Potential, EcoG: Electrocorticogram, EP: Evoked Potential, FP: Field Potential, RP: Resting Potential, PSP: Post-synaptic Potential) 11

12 Electrode properties "There exists no ideal electrode" because the electrode, as a part of the measuring device, intervenes in the system. "The living tissue is a chemically aggressive medium" it is capable of shattering unfamiliar substance, moreover of consuming them. This is why the biocompatibility of the electrode bears so much importance. electrode impedance the input impedance of the electrode depends on the size and the material. electrode stability and biocompatibility (fig.5.) The optimal biopotential recording metal electrode is constituted of Ag/AgCl. The transmission characteristics are nearly identical to the original signal, while we can get only approximate results by using other metal electrodes. Voltage Current Ag/AgCl Platinum Silver Copper Gold Stainless steel Fig. 5. Transmission characteristics of different electrode materials As far as frequency dependence is concerned, again, the Ag/AgCl gives the best performance from 0.5 Hz 100% of the signal is transmitted, while in case of stainless steel with 1 mm tip the same characteristics can be achieved only from 6 Hz; and with 0.5 mm tip only 85% of the signal can be transmitted, but only above 15 Hz. The characteristics described above are valid only by applying 750kΩ input impedance, however, the devices used nowadays have much bigger value (10 12 Ω), thus in case of almost all of the materials we can achieve similarly acceptable results. 12

13 1.2. Stages of sleep Sleep is a process during which deep physiological changes can be observed in humans [1] affecting both the peripheral and the central nervous system [5]. This research is based upon the analysis of the intracortical field potentials during slow wave sleep in human. Sleep seems to be one of the last complex integrated behaviors for which the adaptive advantage remains unknown. There is, however, no shortage of theories explaining the functions of sleep: energy conservation and protection against energetic exhaustion, restoration of tissue integrity, neuronal plasticity, processing of memory traces. The situation becomes more complicated because of the presence of two special types of sleep (slow wave sleep, SWS, and rapid eye movement, REM), which may have different and independent functions [6]. Theories to explain sleep often rely on EEG patterns recorded during sleep sessions. Historically five major types of continuous rhythmic sinusoidal EEG activity are recognized (delta, theta, alpha, beta and gamma). There is no precise agreement on the frequency ranges for each type. Delta is the frequency range up to 4 Hz and when awake it is often associated with the very young and certain encephalopathies and underlying lesions. It is seen in stage 3 and 4 sleep. Theta is the frequency range from 4 Hz to 8 Hz and is associated with drowsiness, childhood, adolescence and young adulthood. This EEG frequency can sometimes be produced by hyperventilation. Theta waves can be seen during hypnagogic states such as trances, hypnosis, deep day dreams, lucid dreaming and light sleep and the preconscious state just upon waking, and just before falling asleep. Alpha (Berger's wave) is the frequency range from 8 Hz to 12 Hz. It is characteristic of a relaxed, alert state of consciousness. Alpha rhythms are best detected with the eyes closed. Alpha attenuates with drowsiness and open eyes, and is best seen over the occipital (visual) cortex. An alpha-like normal variant called mu is sometimes seen over the motor cortex (central scalp) and attenuates with movement, or rather with the intention to move. Beta is the frequency range above 12 Hz. Low amplitude beta with multiple and varying frequencies is often associated with active, busy or anxious thinking and 13

14 active concentration. Rhythmic beta with a dominant set of frequencies is associated with various pathologies and drug effects, especially benzodiazepines. Gamma is the frequency range approximately Hz. Gamma rhythms appear to be involved in higher mental activity, including perception, problem solving, fear, and consciousness. Rhythmic slow activity in wakefulness is common in young children, but is abnormal in adults. In addition to the above types of rhythmic activity, individual transient waveforms such as sharp waves, spikes, spike-and-wave complexes occur in epilepsy, and other types of transients occur during sleep. The sleeping cycle is constituted of recurring stages, through which the depth of the sleep shows a non-uniform pattern. Presently, scientists determine sleep categories of two general types: REM (Rapid Eye Movement) and NREM (non-rem). Statistically speaking the NREM accounts for 75-80% of total sleep time: Stage 1, characterized by near-disappearance of the alpha waves seen occipitally in awake states, and by the first appearance of theta waves. This stage is often referred to as "drowsy sleep". It shows at the beginning of sleep (for mostly being a transition state into Stage 2) and is related with sudden twitches or jerks many people experience when falling asleep. Throughout this period, people lose muscle tone, as well as conscious awareness of the external environment: Stage 1 can be thought of as a gateway state between wake and sleep. Stage 2 is characterized by "sleep spindles" (12 16 Hz) and "K-complexes". Electromyographic recordings show lowering power, while conscious awareness of the external environment evaporates. This stage occupies 45 55% of total sleep. Stage 3, with delta waves, also called delta rhythms (0.5 4 Hz), is thought to be part of slow-wave sleep (SWS) and acts principally as a transition into stage four. In general it occupies 3 8% of total sleep time. Stage 4 is the deep sleep, where slow sleep oscillations (SO, 0.1-1Hz) appear. It governs the first third of the night and accounts for 10 15% of total sleep time. This stage is usually depicted as the deepest stage of sleep, for it is exceptionally difficult to wake someone in this state. Also, this is considered to be the stage in which night terror and sleepwalking occur. We have analyzed this stage in epileptic humans with laminar multielectrodes. 14

15 REM sleep is commonly identified with dreaming. This stage is predominant in the final third of a complete sleep period; its timing is linked to circadian rhythm and body temperature. During this stage, the activity of the cortical neurons is quite similar to that during waking hours; for this reason, the phenomenon is often called paradoxical sleep. Electroencephalography (EEG) shows arousal and similarities to stage 1, at times including beta waves too. Motor neurons are not stimulated and thus the body's muscles do not move (REM atonia), except for sudden clonic twitches. Lack of such REM atonia causes REM Behavior Disorder; sufferers act out the movements occurring in their dreams. In hippocampus theta oscillation can be observed. A newborn baby spends more than 80% of total sleep time in REM mode, while people over 70 years old spend less than 10%. If REM sleep is repeatedly interrupted, the person will make up for it with longer REM sleep at the next opportunity. Acute REM sleep deprivation can improve certain types of depression. Most antidepressants selectively inhibit REM sleep. This stage is also known as Stage 5 sleep. Sleep proceeds in cycles of NREM and REM phases. In humans, the cycle of REM and NREM takes about 90 minutes. Every stage may have a separate physiological role. Drugs, alcohol and sleeping pills may restrain sleep stages, which might be more than just unpleasant. This can lead to a situation where sleep does not fulfill its physiological functions. Sleep stages are not necessarily uniform. This means, that in a given stage, a cyclical alternating pattern may be observed (fig. 6). Fig. 6. Cyclical alternation of stages during sleep, grey vertical lines indicate the occurrences of an EEG graphoelement strongly connected to slow wave sleep 15

16 1.3. Slow Wave Sleep In 1993 Mircea Steriade and his colleagues described a slow (<1 Hz) oscillation of intracellularly recorded neocortical and thalamic neurons and proposed that this oscillation observed in anesthetized and naturally sleeping cats is the emergent activity of a synchronized network [7]. The concepts of synchrony and of an antinomy between synchronized and desynchronized electroencephalographic (EEG) patterns were widely used over the past six decades to distinguish the high-amplitude slow waves during sleep from the low-amplitude fast waves during wakefulness. Probably the first to suggest that the amplitude of EEG waves is related to the degree of neuronal synchrony were Adrian and Matthews [8]. However, when inferring the process of synchrony among neurons, one has to rely on simultaneous recordings from multiple sites. Steriade and his colleagues showed with intracellular recordings that in the sleeping cat during the slow oscillation both in ketamine-xylazine anesthesia and natural sleep the cortical neurons produce rhythmic hyperpolarization and depolarization periods [9-21]. In the hyperpolarization period the neurons are inactive for a few hundred milliseconds, while in the depolarization period the membrane potential can exceed firing threshold and neurons generate action potentials. In vivo, in vitro findings in animals and computational models [22] showed, that synaptic, and non-synaptic network and intrinsic membrane currents are involved in the production of the slow oscillation. The slow oscillation can be generated and sustained by the cerebral cortex alone, it is resistant to thalamic lesion, but disrupted by the sectioning of intracortical pathways, though restored a few hours later. This is supported by the observation that up-state as well as down-state appears in the cortex earlier than in the thalamus [22-24]. There is a critical mass of cortical tissue, which is needed to exhibit this type of activity, however under certain circumstances, in vitro cortical slices can also show similar pattern. During up-state a window is opened so that thalamic input is able to reach the cortex, while during down-state this window is closed. This refers to a subtle strategy of stimulus processing that enables the incoming auditory, tactile or olfactory stimuli from the outer world to reach the cortical level even during slow wave sleep. This strategy needs to fit the physiological circumstances of slow wave sleep, that are the phases of 16

17 the slow oscillation. Furthermore, it needs to ensure the sufficient amount of sleep (sleep protecting effect), on the other hand the stimulus processing requirements for awakening should be met (awakening effect). As mentioned earlier, the cortex oscillates between active (up-state) and inactive (down-state) states during deep sleep. Hyperpolarization during down-state does not provide adequate conditions for stimulus processing, since neurons are in a less excitable state. The opposite applies for up-state. In active state (that is similar to the awake state) there is stimulus processing, this state is responsible for the awakening effect. The inactive state (in which stimulus processing is reduced) is responsible for the sleep protecting effect. For example when a zebra is sleeping and hears a faint noise of a lion footstep, it remains asleep, but when the noise is accompanied by a roar, the smell of the lion, or further footsteps, the window opens for the thalamic input, the cortex reacts and the zebra wakes up. Emerging evidences suggest that non-rem period, especially SO sleep plays an important role in declarative memory consolidation in animal models as well as in humans. The basic substrate of these processes are identified as theta/gamma interactions, spindle and numerous other, including gamma frequency band oscillations linked to the up-state of the slow oscillation. Recent findings of coalescing neuronal rhythms orchestrated by the cortical slow oscillation also strengthen the role of sleep in memory consolidation [23,25-26]. It has been proposed that synaptic plasticity associated with slow and delta oscillations could contribute to the consolidation of memory traces acquired during wakefulness [27]. Based on the analysis of multiple extracellular recordings of slow oscillations during natural sleep, it was suggested that fast oscillations during active states of slow-wave sleeps could reflect recalled events experienced previously; these events are "imprinted" in the network via synchronized network events that appear as slow-wave complexes in the EEG [28]. It is well known, that sleep modulates epileptic activity [29]. Human studies show, that slow wave sleep (SWS) enhances paroxysmal activity and nocturnal seizures are often observed in patients with frontal lobe epilepsy. In animal models, extensive intra- and extracellular observations revealed, that seizures also often develop from the cortical slow oscillation (SO). In these models, the epileptiform activity and seizures consist of several well defined electrical patterns of intracortical events, such as spike-wave, polyspike-wave complexes and fast runs [24]. Similar 17

18 building blocks were found in the human disease too with scalp and intracranial field potential recordings. Destexhe [28] shows that slow oscillation is significantly different from REM sleep and wake state in its spatiotemporal properties. In cat he experienced that slow wave sleep is correlated over large cortical distances (7 mm), while REM sleep and awake state are not (fig. 7). Fig. 7. Correlation of cortical areas during SWS, REM and awake state in cat Several physiological consequences can be drawn from these findings. One of them indicates that recurrent fast oscillations reflect recalled events experienced previously, which are imprinted in the system via synchronized networks that appear as slow-wave complexes [28]. Another interpretation of the slow oscillations is a fluctuation in gross cortical excitability [30]. This conclusion is derived from the finding that the phase of slow oscillation was robustly correlated with the magnitude of faster EEG oscillation, as well as with the occurrence of interictal epileptiform events and K-complexes [24]. It could mean that slow oscillations predispose brain to epileptic activity About epilepsy Epilepsy, as a disease, is the earliest discovered disorder in the nervous system. Hundreds of years B.C. it was known as loss of consciousness in India and in Babylon. According to the definition by the World Health Organization, epilepsy is a chronic brain inconvenience in function with diverse etiology characterized by returning seizures, in the background of which stands the excessive discharge of 18

19 neurons, and which may have various clinical and laboratory manifestations. Its incidence in the population is between 2-5 %. An epileptic bout can be caused by birth anomalies, brain injuries, tumors, pressure in the brain, head traumas, infection in the nervous system, developmental disorder in the blood-vessel or in the nervous system, genetic defections, intoxication (alcohol, medicines, drugs, herbicides, etc). Certain epilepsies can not be cured. The treatment aims at the prevention of seizures. Children may grow out of it, or there exist various antiepileptic medications, or in special cases epilepsy surgery. Surgical success depends on the type of surgery, but most patients experience a substantial improvement with regard to seizure intensity, seizure frequency, and seizure duration. Surgery is preceded by the mapping of the brain in order to discover the focus of the epilepsy where seizures start. This area needs to be removed in case of drug resistant epilepsy. The procedure cannot be carried out if the epileptic focus coincides with cortical areas responsible for essential functions such as speech, movement, etc. 19

20 2. Information technological introduction 2.1. State-of-the-art of IT in neural sciences In the laboratories of the Comparative Psychophysiological Group of the Institute for Psychology, Hungarian Academy of Sciences, we employ the products of Neuroscan, the world leading developer of software and hardware for neuroscience applications. Primarily, we use Neuroscan Edit, a software which is appropriate for most offline data analysis methods [31]. I have gained experiences in other software products as well, such as DataView (by Dr. W. J. Heitler, University of St Andrews, Scotland [32]) for event clustering (spike sorting) with 3D visualisation of principal components, wavelets and other parameters, EEGLab (by Arnaud Delorme, Scott Makeig, University of California San Diego [33-34]) an open source Matlab toolbox for electrophysiological research, MCFA (by Balázs Dombovári, László Grand) a Multiple Channel Frequency Analysis tool for calculating the Fourier or wavelet analysis of multiple channel recordings at the same time. For data acquisition we use AcqUnit and AcqMUA, LabView-based customized systems created by Dr. István Ulbert. After having evaluated the above-mentioned products I concluded that developing a brand new software is needed in order to execute some specific functions I need for my research. These customized functions include PSTH analysis, Hilbert transform, CSD maps, coherence maps, different types of spike sorting (based on PCA, CEM, k-means). With the help of Andor Magony, we created a Matlab-based software, named SpikeSolution [35]. To make its use easier we applied some other functions as well. For example different types of data display, raster plots, applications of the Fast Fourier Transform, etc. A very important part of SpikeSolution is the state detection. This feature is essential for the analysis of slow wave sleep and cannot be found in any other programs. We applied different state detection methods, those previously published and those developed by us also. 20

21 We also created two other Matlab-based programs, named HistPlot and WavePlot (latter with the help of László Grand), to gain further information on the data we dealt with. HistPlot displays the histogram of the values of a waveform (essential in state detection), while WavePlot uses wavelet transform to display the timefrequency map of the signal. During the development of our software, we paid special attention to the compatibility with other neuroscientific data analysis programs. Since the world s leading and most widely-used product is Neuroscan, most other products use its file structure too, thus we decided to adopt it. There are three main file formats in Neuroscan: CNT, containing continuous data of multiple channels, EEG, consisting of epochs (data slices) of multiple channels, and AVG, containing the average of epochs of multiple channels (fig.8.). Fig. 8. Neuroscan file structures, CNT (left), EEG/AVG (right) 21

22 3. Software engineering The demand for an efficient data processing software is of utmost importance. Since we can draw conclusions based only on precise results the significance of the correct application of methods is primary. In order to gain appropriate outcome, a new software has been developed. The program (SpikeSolution, fig. 9) is a result of a 30-month-long work which proved to be lucrative. Since many of the existing bioelectric signal processing software are implemented in MatLab, due to its relative simplicity and effectiveness we also used this platform to carry out the project [36-38]. Naturally, we could choose one of the great variety of commercial data processing software, but on the one hand they cost immensely much, on the other hand they are not equipped with special features we require for our research. Fig.9. Main screen of the SpikeSolution sotware 22

23 SpikeSolution is a multichannel processing system running on graphical user interface (GUI) with built-in calculation and evaluation procedures, equipped with the following major features: Opening NeuroScan CNT files: in the current phase of the development the program works exclusively with data stored in CNT files produced by NeuroScan. This is a binary file with a header part at the beginning, an event table at the end, and data in-between. The header part contains information on e.g. number of channels, length, sampling rate, etc. Sample values are stored in 2-byte representation, symbolizing signal amplitude. Multichannel view: this is the default view option in which all the channels are displayed at the same time, on the same screen, from a certain starting time, in a specific view time (window length). Single channel view: this is the other view option where only one specified channel can be seen with its actual threshold values (green (+) and red (-) lines). Again, starting time and view time can be determined. Clustered multichannel view: similar to multichannel view, additionally displaying the cluster events under each channel marked by red vertical line. Clustered single channel view: similar to single channel view, additionally displaying the clusters belonging to that channel, with the events marked by red vertical lines (fig. 10). Fig. 10. Clustered single channel view 23

24 Sorted channel view: the user can choose the channels to view (fig. 11). Fig. 11. Sorted channel view Manual threshold setting: to reduce the amount of information and concentrate only on important activity we are expected to specify two threshold levels above which we take signals into account: a positive and a negative threshold. In manual case these threshold levels can be determined either with mouse or with a specific numerical value. The actual positive threshold is represented by a green line, while the negative threshold is represented by a red line in single channel view. Dynamic threshold setting: it might be a time- and work-consuming task to specify each threshold value by hand. Thus the software is equipped with a dynamic threshold determiner, based on standard deviation, and only a deviation coefficient is needed to be declared. In this way we calculate the mean value of the signal to which we add the standard deviation weighed with the coefficient. 24

25 PSTH (Peristimulus Time Histogram) of two channels: A peri-stimulus time histogram (PSTH) forms a frequency histogram of events on one channel around a stimulus event on another trigger channel. One of the cells (channel) will be the reference cell - the spikes of this cell's spike train will provide the reference marker. To make the PSTH we proceed as follows: line up the 2 spike trains (so that their time markers are vertically aligned); for each spike in the reference spike train center a window broken into small segments of time called bins; now examine the target spike train within the window, and increment the bins in which we find any spikes (fig. 12). Fig. 12. Spike trains constituting a PSTH The PSTH thus shows a count of the spikes of the target cell at specific time delays with respect to the spikes of the reference cell. The time delay is given by the distance along the horizontal axis, and both "positive" and Fig. 13. Examples of PSTHs "negative" delays are recorded. Bin counts on the positive side - counts that occur after time 0 which is the window center - mean that the target cell spike came after the reference cell spike; conversely for the negative side. By the way, if we compute the PSTH using neuron A as reference and B as the target, and then compute the PSTH using B as reference and A as the target, we'll get the same result, only 25

26 reversed in time (i.e., the horizontal axis). PSTHs give some measure of the firing rate or firing probability of the target neuron around the time that the reference neuron fires. Therefore, the PSTH provides some indication of the dependence, or lack thereof, of the two neurons. If, for instance, the output of neuron A is completely unrelated to neuron B, then (by definition) the spike times of A are just random instants of time from B's point of view. On the other hand, if there is a higher probability that the target cell fires immediately following the reference cell's spike (or marker), then we would see a peak in the PSTH at that time (fig. 13). PSTH of all channels: this option performs a PSTH described above on every possible pairing, thus creating a triangle of NUMBER OF CHANNELS NUMBER OF CHANNELS NUMBER OF CHANNELS (fig. 14). With this feature we can draw conclusions of channel relations and dependencies. Fig. 14. PSTH of all channels. One of them is enlarged in the top right corner. 26

27 Population PSTH: another spectacular representation of a peristimulus time histogram is a 3D vision of correlation between one and all the other channels (the dependencies of one channel to all the others, fig. 15). Fig. 15. Population PSTH Creating epochs: epochs with a given time window can be assigned to certain events of a channel (fig. 16). This event can be a spike over a threshold, or, it is also possible to create epochs on a channel by a trigger signal source in which case the trigger is the event. By averaging all the epochs we can get acquainted with spike characteristics of a channel in question. This feature is mostly efficient when we try to analyze the effects of a trigger signal (e.g. acoustic stimulus) on channel activities this way we can get to know how spikes emerge, how inhibition takes place, how stimuli spread, and in general, how the nervous system reacts. By adjusting the time window of the epoch we can set the accuracy of the analysis. 27

28 Fig. 16. Epoch screen. The average of epochs is enlarged in a new window. Raster plot: this feature displays the spiking frequency of a certain channel related to a trigger event against time (fig. 17). We can specify the window size (before and after trigger). Fig. 17. Raster plot 28

29 Spike sorting: Basically what we have after our electrophysiological recordings is "raw" data which consists of a large set of waveforms from each electrode. Since each electrode samples an area - not necessarily small with respect to the size of neurons - the waveforms from any of the electrodes may consist of spikes (i.e. "action potentials") from numerous cells. Spike sorting is distinguishing between spikes of different neurons. It is generally accepted that the action potentials of most neurons have basically the same shape (it may change during bursting). However, the action potential of different cells will be distinguishable due to differences in size and shape of the neurons as well as their varying orientation with respect to the electrode. This means that different neurons will produce at least slightly different recorded waveforms when they fire an action potential. Spike sorting involves finding some sort of measurements, or variables that can help us distinguish between different waveforms. After we have identified these variables, we plot each waveform using the variables as axes, and what we hope is to see clusters of points, representing waveforms that are similar with respect to the variables. We are then justified in taking a cluster as representing a single cell, if the cluster is obviously distinct from the other waveforms in the sample. Thus, each waveform in the cluster is labeled as coming from the same cell. In our software we realized four different methods for spike sorting, explained in the following: Spike sorting manually: the amplitudes are displayed on a graph, where the user can manually classify the different partitions (fig. 18). The software then places a Gaussian curve onto the histogram of amplitudes in one cluster, determining the mean and standard deviation of the class. After that the signal is sorted into those clusters allowing the user to examine and analyze the behavior of single neurons. 29

30 Fig. 18. Spike sorting screen Spike sorting CEM: The CEM algorithm uses probabilistic properties to determine which elements belong to which cluster. The number of clusters needs to be specified. An opportunity to evade this drawback is that we run the algorithm first for one cluster, then for two, three, and so on. In the end we can compare the results by the complete-data likelihoods [39-42]. o o o o Step 0. We have labeled data in each cluster and unlabeled data waiting to be labeled. Step 1. We determine the expected value and the covariance matrix for each cluster. Step 2. We determine the distance of each unlabeled data from each cluster and classify the data to the cluster that is the closest. Step 3. We check the stopping criterion. If converged, stop, else repeat from step 1. 30

31 Spike sorting k-means: a simple approach, such as in nearest-neighbor or k- means clustering, is to define the cluster locations as the mean of data within that cluster. A spike is classified to whichever cluster has the closest mean, using Euclidean distance. This defines a set of implicit decision boundaries that separate the clusters. Classifying in this manner uses only the information about the means and ignores the distribution of data within the cluster. This approach is adequate when the clusters are well separated, but breaks down when clusters significantly overlap or when the cluster shapes differ significantly from a spherical distribution. Algorithm: given a finite number of points in a real linear space. The number of clusters (k) is specified. 0) Initialization: we partition the points into k non-empty classes. 1) We calculate the center points (bar centric points) of the actual partition s classes. 2) We assign each of the points to the closest center point. Thus we get a partition with k classes. Return to step #1 if the partition has changed or stop if it has not. Spike sorting PCA: Choosing features based on an intuitive idea of what might be useful is an ad hoc approach and, although simple, it can often yield poor cluster separation. Might there be a way of choosing the features automatically? One method for choosing features automatically is with principal component analysis. The idea behind principal component analysis (PCA) is to find an ordered set of orthogonal basis vectors that capture the directions in the data of the largest variation. The data are the original spikes from the recorded waveform. A sample from this data set and the gained principal components are shown in fig. 19. Each waveform is centered in the spike maximum to minimize the variability of the spike shapes. 31

32 Fig. 19. Principal component analysis. All waveforms (left) and first three principal components (right) The first three principal components for the spikes are shown in fig. 19. The first component is the direction of the largest variation in the data and has a generic spike-like shape. The second component also has a spike-like shape, but is offset with respect to the first component. The third component has no obvious interpretation and is the beginning of components that represent variability due to background noise. The effectiveness of the sorting can be verified by autocorrelograms (interspike interval, fig. 20). The software enables this by calculating the PSTH of two same clusters. If there is a sufficient refractory period (no spikes in the middle of the histogram for at least 2 milliseconds), the sorting can be regarded satisfactory. Fig. 20. Autocorrelogram 32

33 Hilbert transform: Brainwave recordings are noisy and nonstationary. Though Hilbert transform is a linear operator, it is useful for analyzing nonstationary signals by expressing frequency as a rate of change in phase, so that the frequency can vary with time. Typically multiple time-varying frequencies coexist in raw recordings, so in biological usage it is recommended to use a filter before the transformation in order to remove the irrelevant components (e.g. frequencies out of the 4-7 Hz range in case of examination of theta activity in the hippocampus). Fast Fourier Transform gives high frequency resolution, while Hilbert transform gives better temporal resolution of rapid changes in analytic state variables for frequency, phase and amplitude, since the temporal resolution of wavelets and FFT is bounded by the Nyquist criterion: the digitizing rate must be at least twice and preferably three times the highest component frequency [43-46]. The definition of the Hilbert transform is as follows: where. During down-state detection we first filter the signal recorded with the desired electrode in the frequency range of the slow oscillation (0.5 3 Hz). Thus we get a sinus-like signal on which we can apply the Hilbert transform that specifies the phase in every sample point. The result is a sawtooth curve with small increments of phase lag along a diagonal from the lower bound to the upper bound, and a downward fall in one step to the lower bound on reaching the upper bound. This happens once in each cycle. We defined the valley of the sinus signal as the down-state and the peak as the up-state of the slow oscillation. (fig. 21). 33

34 A B C Fig. 21. Hilbert transform. A: original signal, B: filtered signal (4-7 Hz), C: Hilbert transform Current Source Density map: CSD is the summation of a neuron population s transmembrane currents in a certain region. Diverse neurons may generate diverse currents in size and polarity. The positive and negative membrane currents can counteract each other, thus CSD is a macroscopic (field level) instead of microscopic (single cell) description. Far field signals are eliminated so that CSD depicts local macroscopic membrane currents [47-48]. Calculation of one dimensional CSD: - 3 point spatial gradient (more local, more frequently used), - 5 point spatial gradient (used in case of high noise level). CSD of all channels can be displayed on a colour map (fig. 22) 34

35 Fig. 22. Current source density (CSD) map. Sink in this map is indicated by blue, source by red Coherence map: the program features a map that illustrates the coherence values of all the possible channel-pairs in a given frequency band (fig. 23). Colours from blue to red correspond to coherence values from low to high. This method describes borders of cortical areas well, because the intra-areal coherence is much bigger than the inter-areal coherence. (The border is shown by a blue cross.) Fig. 23. Coherence map of two different cortical areas on 22 channels 35

36 FFT Display: this function transforms the signal into frequency spectrum and displays it in the desired frequency range (fig. 24). Fig. 24. Fast Fourier Transform (FFT) Display. Axis x: frequency (Hz). Axis y: power Simple filter: transforms the signal into frequency spectrum, where the unnecessary frequency ranges are set to zero, then transforms back to time domain (fig. 25). Fig. 25. Simple filter (bandpass filtering in the range of Hz) 36

37 State detection from MUA: given a threshold value, this function determines up and down states from MUA recordings during slow wave sleep with certain criteria (fig. 26). Levels above threshold are considered as up-states, while levels below threshold as down-states. The user may specify the minimum duration of up- and down-states. In case the detected states do not last long enough, they are classified as the opposite state. Fig. 26. State detection from MUA. Original MUA signal (top, green) and detected states (bottom, blue). Up-states are up, down-states are down. State detection from field potential: this function detects up and down states from field potential recordings during slow wave sleep (fig. 27). The steps of the algorithm are [49-50]: 1. Filtering the signal in the frequency range of Hz (preferably multiplying the Fast Fourier Transform (FFT) of the signal with one in the region between 20 and 100 Hz and with zero elsewhere, then transforming back to time domain using inverse FFT) in order to emphasize the gamma oscillation that appears in up-states. 2. Calculating the root mean square (RMS) with a 50 ms moving window to determine the power of the gamma oscillation. 3. Displaying the histogram of the sampled values with the extreme values (by a user-specified percentage) cut off, which is ideally expected to be bimodal. 37

38 4. Setting a threshold level at the trough between the two heaps of the histogram. It can be determined either automatically or manually, by a user-given value, percentile, or a mouse click. 5. Considering values above the threshold level as up-states and below that as down-states with the criteria that no up-state or down-state can be shorter than 50 ms. Fig. 27. State detection from field potential. Original signal (green), filtered signal ( Hz, blue), root mean square (red) and detected states (black). There is an essential criterion if we want to use this method for state detection: the bimodality of the histogram. If the histogram of gamma oscillation is not bimodal, we cannot set a threshold level at the trough between the two heaps, since there is only one heap. In order to enable ourselves to inspect the bimodality, we created HistPlot, a Matlab-based software, that displays the original waveform loaded from the Neuroscan CNT format file and its histogram (fig. 28). 38

39 Fig. 28. HistPlot. The original signal in the bottom window, the histogram in the top window. Several properties can be set in order to achieve the best result: o o o the length of the window (Start time, Stop time in milliseconds), the bin number (number of columns in histogram), the scaling of the histogram (X Scale, Y Scale). There are additional buttons to move between channels and back and forth in time. Phase lag: in a window, a Hilbert channel is subtracted from another Hilbert channel, then the mean values are calculated of the differences. Hilbert phase difference: the Hilbert channels are unwrapped, subtracted, then quantized with a given range. This way we get a step function of the difference, showing the phase difference and the frequency difference of the original signals. 39

40 State detection density and delay map: NeuroScan has an option that creates a.dat file containing the information of previously selected events. Our function processes these data and displays the results as a 2-D colour-coded map. First we need to pick the events relevant in our case. There are numerous methods implemented in our software to complete this task. We can keep events that have higher value than the average plus standard deviation weighed with a coefficient, or the ones that have values higher than the median or a user-defined percentile. Another method to sort events is the CEM algorithm. The result of sorting is displayed on the detection map (fig. 29). The values of the map represent the number of events picked in that electrode. Fig. 29. Detection density map When we sorted out our events, we can determine the delay of each event to the first one (fig. 30). After averaging the delays we get the delay map that represents the time when the events appeared on each electrode, thus shows us the direction of propagation. Events propagate from blue (small delays) to red (large delays). Fig. 30. Delay map 40

41 State simulation: this function simulates chains of up- and down-states by normal distribution (with given length mean, standard deviation, and transition length), and calculates in which state triggers with arbitrary interval can be found. This algorithm runs in a pre-defined number of cycles, giving us statistics of a slow oscillation with certain parameters (fig. 31) % % % % Fig. 31. State simulation. Red line represents states (up-, down-states and transitions), blue bars indicate number of events as well as their percentage compared to all events. Phase PSTH: it calculates in which phase of the slow oscillation the events (e.g. multiunit activity) occurred. The resulting figure shows the phase of the slow oscillation with a red curve, and the histogram of the phase of events (fig. 32) Fig. 32. Phase PSTH. Axis x: phase of slow oscillation, bins: degrees of phase, axis y: number of events occurring in that phase 41

42 WavePlot displays the current time-frequency map of a continuous signal stored in a Neuroscan CNT format file. It is suitable for the visualization of the signal s power in different frequencies in a time window. The signal appears in the top window, the time-frequency map in the bottom window (fig. 33). Fig. 33. WavePlot Several properties can be set in order to achieve the desired performance: o the window in which the calculation is carried out (Start time, Stop time, in milliseconds), o the minimum and maximum values of the heat map (ERSP min, ERSP max), o the minimum and maximum frequency to display (Freq min, Freq max, in Hz), o the length of the baseline (Baseline min, Baseline max, in milliseconds), o the level of significance (Bootstrap), o the quality of resolution (Padratio). Furthermore, the user can choose whether they want to use Fast Fourier Transform or wavelet transform during the calculation and set the number of cycles in case of wavelet analysis, and switch between display modes: power or relative (db). There are additional buttons to navigate through the continuous signal in time and from channel to channel. 42

43 4. Methods of recording 4.1. Patients I have analyzed data from four patients with medically intractable epilepsy underwent chronic subdural grid or strip electrode implantation for localization and removal of their seizure focus. Grids and strips usually consisted of 8-32 contacts, each ~8mm in diameter and spaced at 10mm centers (fig. 34). The MR reconstruction of the grid and multielectrode placement is shown in fig. 35. One or two multielectrodes (fig. 36) were placed underneath the grid spanning across the gray matter. Care was taken to insert the multielectrode perpendicular to the cortical surface in the middle of the selected gyrus. The choice of patients for intracranial studies, the location of the clinical electrodes, and the duration of implantation were all determined entirely by clinical considerations unrelated to the experimental protocol. The patients consented to the experimental procedure after a complete explanation of the risks, under procedures monitored and approved by the National Institute for Neurosurgery (OITI). At the time of resection, the multielectrode site was removed en bloc [51-52]. Serial 40µm sections were stained with NeuN (neuron marker) to localize the electrode position to the respective cortical lamina. Fig. 34. Electrode grid locations in the frontal lobe. Position of multielectrodes with black numbers (29, 30). 43

44 Fig. 35. MRI images of the electrode grid s position on the brain. Grid electrode locations (left) and peeled surface of the brain with the gyri visible (right). Yellow arrows point to the position of multielectrodes. Fig. 36. Thumbtack multielectrode 44

45 4.2. Electrophysiological recordings Each multielectrode [47] spanned a cortical column, with its base in layer I and its tip in layer VI, with 24 individual recording sites spaced evenly at 150µm (fig. 37). Output cable Cortical layers Body Histological slice Recording contact Penetration track Tip Fig. 37. Position of multielectrode after histology Low frequency-band spatial Potential Gradient (PG) (bandwidth of Hz, sampled with an analog to digital (A/D) conversion card at 2 khz/channel sampling rate, 16bit conversion accuracy) and high frequency-band Multiple (MUA) and occasionally Single Unit Activity (SUA) (bandwidth of Hz, at 20 khz/channel sampling rate, 12bit conversion accuracy) were recorded from the multielectrodes simultaneously, while the patients were sleeping in the epilepsy intensive care unit after electrode implantation. The lower resolution of MUA conversion is sufficient because the MUA band biopotentials show much lower amplitude dynamics than the EEG band potentials. Conventional clinical grid recordings (1 70 Hz, sampled at 200 Hz/channel on the clinical video-eeg system) were co-registered with multielectrode recordings using a common marker. The LabView 6.0 Full Development System for WinXP was used for data acquisition. The software contained on-line display and data storage functionality; it allowed us to continuously store the data on the hard drive at high speed. The software is capable of setting the gain and sampling rate of the converters and can synchronizes up to 4 A/D cards (64 channels each), so a total of 256 channels can be sampled simultaneously. The data was acquired in Neuroscan format. 45

46 5. Methods of analysis 5.1. State detection In order to enable ourselves to analyze the behavior of the brain the awareness of the actual state of the cortex is vital. The state determination is not self-evident, so we need to apply diverse methods for disambiguation, and also to compare it with human expert identification. I have employed the three different approaches with different efficacy. Comparing the three state detection methods, I have found the Hilbert transformation as the most robust one. It was the most stable method if compared with human expert state detection outcome. MUA method was sometimes unusable, because of the lack of large action potentials. The gamma band power method was also ambiguous sometimes, if the signal contained contaminations from movement and muscle activity Current Source Density In order to understand the underlying physiology, we need to check the current source density (CSD) in the observed cortical structures. CSD (as described earlier in Software engineering section) is the local summation of a neuron population s transmembrane currents that gives the spatiotemporal pattern of current sinks (positive charges flowing across the plasma membrane to the inside of the cell) and sources (outflowing current to the extracellular space). The physiological relevance of CSD lies in the fact that current sinks can be interpreted either as a synaptic depolarization of the membrane or a return current source while source could mean hyperpolarization (synaptic or non-synaptic) or a return current sink. The awereness of these is vital since up-states are depolarization periods and down-states are hyperpolarization periods. It is still under discussion whether this hyperpolarization is of synaptic or non-synaptic origin. I calculated CSD using the second derivative of field potential, and first derivate of the field potential gradient (practically a spatial convolution with the mask [-1 2-1] and [-1 1] ) that is essentially the rate of change in amplitude gradient 46

47 and smoothing with Hamming window (also a convolution with mask [ ]). The second derivative of field potential approximates the depth distribution of the current sources in laminated structures, such as the cerebral neocortex. Inhomogeneous conductivity was not taken into account Multiunit analysis Filters were used to split the recorded wide band data into signals that sample either the EEG (0.2 Hz 500 Hz) or a continuous estimate of the population cellular activity (multiunit activity, MUA, 100 Hz 5 khz) range. MUA was derived by additional band pass filtering (zero phase shift, Hz, 48dB/oct), full wave rectifying. MUA shows several nearby neuron s action potentials, but no post-synaptic potentials below firing threshold such as excitatory post-synaptic potentials (EPSP) or inhibitory post-synaptic potentials (IPSP). MUA can be clustered to single unit activity (SUA) showing an individual neuron s firing. This process is called spike sorting. The comparison of MUA and CSD is important to explore the mechanisms underlying the cortical generators of field potentials, thus understanding the background physiology (e.g. sink in CSD and firing in MUA means excitation through the synapses). 47

48 6. Biological results and discussion In my research I recorded field potential and multiunit data (fig. 38). Apparently, they show periodicity according to the slow oscillation. In field potential recordings, positivity on the surface indicates depolarization phases, negativity on the surface represents hyperpolarization phases. In muliunit recordings series of spikes refer to depolarization, silent periods stand for hyperpolarization. These phases coincide in time µv +4 µv 2 s 2 s Figure 38. Field potential (left) and MUA (right) recordings. Axis x: time, Axis y: amplitude. I determined states by using the method with Hilbert transform, because the other two approaches appeared to be inappropriate. I averaged field potential and MUA signals to up-states (fig. 39). During up-states, an increase in MUA signals can be observed referring to an increase in action potentials locally around the recording area. During down-states MUA signal decreases to noise level, showing that no action potentials occurred. Fig. 39. Field potential (green) and MUA (red) signals averaged to up-states recorded from the upper layers (positive field potential means up-state). Axis x: time, Axis y: amplitude. 48

49 I calculated current source density map averaged to up-states (fig. 40). Sink refers to depolarization, source to hyperpolarization. During up-state, sink can be observed in the middle layers (input area) with source on the surface (presumably return source), while during down-state, source can be noticed in the middle layers and a supposedly return sink on the surface Fig. 40. Current-source density map averaged to up-states. Red: sink, blue: source. Axis x: time, axis y: electrode channels (cortical depth). Multiunit map was also created (fig. 41), averaged to up-states. During upstate it shows intensification in MUA activity in nearly all of the layers (referring to the increase in the number of action potentials generated by neurons in those layers), and attenuation during down-state (indicating that the number of action potentials decreased heavily) Fig. 41. Multiunit map averaged to up-states. Colors indicate amplitude. Axis x: time, axis y: electrode channels (cortical depth). 49