Focal interictal activity in the mouse visual cortex interferes with slow-wave oscillations and visual processing in the contralateral hemisphere

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U

NIVERSITÀ DI

P

ISA

Dipartimento di Biologia

Corso di Laurea in

Biologia Applicata alla Biomedicina

TESI DI LAUREA MAGISTRALE

Focal interictal activity interferes with slow

oscillation and visual processing in the

contralateral hemisphere

Candidato:

Luigi Petrucco Relatore: Prof. Gian Michele Ratto

Correlatori:

Prof. Mario Pellegrino Prof. Maurizio Cammalleri

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A

BSTRACT

Epileptiform activity is associated with impairment of brain function even in absence of seizures, as demonstrated by failures in various testing paradigm in presence of hypersynchronous interictal spikes (ISs). Clinical evidence suggests that cognitive deficits might be directly caused by the anomalous activity rather than by its underlying etiology. Here, I seek to understand whether focal ISs interfere with neuronal processing in connected areas not directly participating in the hypersynchronous activity. I used the GABAA antagonist bicuculline methiodide to cause focal ISs in the visual cortex of anesthetized mice. In this model I determined that, even if IS did not invade the opposite hemisphere, the EEG was subtly disrupted with a modulation of firing probability imposed by the contralateral IS activity. Finally, I found that visual perception was altered depending on the temporal relationship between ISs and stimulus presentation. I conclude that focal ISs interact with normal cortical dynamics far from the epileptic focus, disrupting endogenous oscillatory rhythms and affecting information processing.

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Chapter 1 Introduction

1.1 Excitation and inhibition: the yin and

yang of neural dynamics

The mammalian neocortex is a network composed by a large majority of excitatory pyramidal neurons and a minority of extremely diversified inhibitory cells. Neurons are disposed in organized layers across the cortical depth, and are interconnected by a massive amount of feed-forward and recurrent connections. This ordered structure appears with small variations throughout cortical areas, and the observation of the same circuital model in brain areas performing very different operations inspired early electrophysiologists the concept of the cortical column: a basic structure of excitatory and inhibitory neurons capable to process inputs coming from afferent structures and outputting elaborated information through efferent projections (Harris & Shepherd 2015).

Decades of electrophysiological recordings have provided us with a solid empirical knowledge of the firing statistics of cortical neurons; this has led to some insights about the generative mechanisms of these patterns, although we still lack a clear understanding of the underlying neural dynamics. The distribution of spikes in these cells seems to be sparse and highly irregular, and is often described as following a Poisson statistic distribution (Stevens & Zador 1998). This sparseness play a crucial role in allowing cortical neurons to carry neural computation while remaining in a regime of relative low-frequency firing, but the biophysical details of how the neural network maintains this irregular firing regime has puzzled neuroscientists for a while. Indeed, this variability sharply contrasts with the response of pyramidal cells to steps of depolarizing currents, which elicit sequences of regularly spaced spikes (Softky & Koch 1993), and has been difficult to explain by the simple integration of uncorrelated random excitatory and inhibitory post-synaptic potentials (EPSPs and IPSPs).

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The key element of the first successful attempts to reproduce this irregular statistics in simple integrate-and-fire networks has been the introduction in the model of strictly balanced excitatory and inhibitory inputs (Shadlen & Newsome 1998). This assumption has fond later a confirmation by the experimental observation that excitation and inhibition are tightly coupled events. Every kind of cortical activity, from sensory responses to spontaneous oscillations, is driven by the concomitant occurrence of excitatory and inhibitory potentials. By recording excitatory and inhibitory conductances by voltage clamp methods (Borg-Graham et al. 1998) reported that visual stimulation of anesthetized cats elicit both excitatory and inhibitory currents strikingly similar on a milliseconds timescale. In a subsequent study (Okun & Lampl 2008) reported the same tight coupling between

Fig. 1.1: Excitation/inhibition balance

A) A simple scheme of inhibitory connectivity motives in the cortex. Above: feedforward inhibition, where excitatory afferences fro other cortical or subcortical areas contact both local excitatory and inhibitory cells. Below: recurrent inhibition, with local excitatory cells that, once recruited, contact local interneurons (from (Isaacson & Scanziani 2011)). B) Paired intracellular recordings of spontaneous activity from two nearby neurons in rat somatosensory cortex. Above: cells are hyperpolarized at the reverse potential for inhibition to show the coupling between excitatory inputs to nearby cells. Below: one cell is hyperpolarized and one is depolarized to show respectively excitatory and inhibitory synaptic contributions to cells that are close to each other. Note how closely excitation and inhibition are coupled (from (Okun & Lampl 2008)).

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excitatory and inhibitory inputs in pairs of neurons in rat barrel cortex (Fig. 1.1, B).

Together, these lines of evidences led neuroscientists to the notion of excitation/inhibition balance in the cortex. The recruitment of inhibitory interneurons by feedforward and feedback excitatory connections ensure that local inhibition closely follows excitation in a given cortical area. This mechanism keeps the neural ensemble in an optimal range of firing rate values, and the slightest deviation from this condition can determine pathological outcomes: a slight reduction in the excitatory drive leads to the induction of a comatose state, while the smallest reduction in the inhibitory brakes can result in network hyperexcitability and epileptiform activity (Dudek & Sutula 2007). The maintenance of such a precarious equilibrium requires several homeostatic mechanisms that act on different timescales both at the synaptic and the somatic level to constantly adjust the output firing of the neuron around physiological values (Turrigiano 2011).

1.2 Brain at rest: the slow-wave oscillation

One consequence of this subtle balance in the neuronal activity in the neocortex is the capability to sustain patterns of activation even in the absence of excitatory inputs from afferent projections. One of the most striking examples of this phenomenon is the emergence of slow-wave oscillations in the cortex during anaesthesia or NREM sleep states. In these conditions, the entire cortical network oscillates between a state of global activation – the up state– and a period of relative quiescence – the

down state (Fig. 1.2). 1.2.1 The discovery

This phenomenon was first described by (Wilson & Groves 1981), but the first detailed investigation of its synaptic and cellular mechanisms was published in three detailed papers from the group of Mircea Steriade in 1993 (Steriade, Nuñez, et al. 1993; Steriade, Nunez, et al. 1993; Steriade, Contreras, et al. 1993). By performing EEG and intracellular recordings from neurons in the cortex an thalamus of anaesthetized cats, Steriade and colleagues observed that during this slow wave oscillation cells were oscillating between states of low membrane potential during the down phase to states of relative depolarization and firing activity during the up phase. Barrages of synaptic inputs were observed during the up state, while down states

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were characterized by a reduction in synaptic activity. This oscillation persisted when the thalamic afferents were severed and remained even once cortical slabs were carved out of the rest of the cortex (Timofeev et al. 2000). Later, a completely equivalent dynamic has been observed in cortical slices (Sanchez-Vives & McCormick 2000). Together these observations suggest that the rhythm can be somehow generated by the neocortical circuitry even in the absence of external inputs.

Fig. 1.2: Slow-wave oscillation at the network and neuronal level

Above: the local field potential (LFP) recorded from deep cortical layers during spontaneous non-REM sleep in a rat (top row), and a raster plot of the corresponding neuronal spiking activity of five individual neurons (middle row) are shown. Note that LFP slow waves are associated with generalized population silence (off periods), which alternates with periods of raised spiking activity (on periods). Bottom: a schematic representation of a membrane potential expected in one individual neuron within the network. Note that LFP slow waves and extracellular off periods are associated with a prominent membrane hyperpolarization, which alternate regularly with periods of depolarization, when spiking propensity is increased (from (Vyazovskiy & Harris 2013)).

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1.2.2 Cellular and network mechanisms

Initiation

During the down phase of the slow-wave oscillation the cortex is in a state of quiescence: neurons maintain a low membrane potential and remain silent, and synaptic inputs are scarce. How can the network transit quickly from this condition to the phase of global network activation of the up state? Responses to this answer can be divided in two categories: models that require some kind of specialized cells that act as pacemakers for the initiation of the oscillations, and models that predict that the transition could be lead by the stochastic activation of sparse cells whose excitatory effect could be enhanced by recurrent connections in the network. The two models are not mutually exclusive, and mechanisms of both types could concur in the rising phase of the slow oscillation.

There are several evidences pointing toward the involvement of neurons of cortical layer 5 (L5) in the initiation of up states. Current source density analysis don on multi-electrode recordings (Steriade & Amzica 1996) and in vitro recordings of multi-unit activity (Sanchez-Vives & McCormick 2000) have shown that activity at the beginning of the up state seems to propagate from L5 neurons. This observation is strengthen by the fact that severing connections between L5 and more superficial layers abolished slow-waves in the superficial network preserving the oscillatory behaviour of neurons in L5. Moreover, up states can be optogenetically evoked by the stimulation of neurons in L5, but they cannot be elicited when the channelrhodopsin expression is restricted to L2/3 (Beltramo et al. 2013).

Some experimental evidences suggest that a specific class of L5 pyramidal neurons is a good candidate for the role of up state initiator. These neurons have intrinsic bursting properties, and they express resonant firing bursts at low frequencies (< 15 Hz) upon depolarization (Silva et al. 1991). Moreover, they have widespread horizontal connections with other neurons of the same layer that may be well suited for the propagation of the IS. Anyway, those neurons are silent like the rest of the network during the down state. How can they activate to trigger the oscillation in the rest of the network? A plausible module postulate that excitatory synaptic potentials released in the absence of action potentials (mini EPSP, or mEPSP, (Fatt, & Katz 1952)) may summate over a threshold in a critical number of L5 neurons, which in turn amplify this small input and ignite the up phase. Accordingly to this hypothesis, a gentle ramp of EPSP has been recorded in L5 neurons, while in the rest of the neurons the rise phase of the up

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state is to steep to distinguish single potentials (Chauvette et al. 2010). This could explain why the slow wave oscillation observed in slice has a longer period, since a large amount of the recurrent connections have been severed in the preparation.

Maintenance

Once initiated, the neural activity that characterizes the up state is maintained for hundred of milliseconds, up to one second or more. Such a prolonged period of activation require active mechanisms that keep the cell near to the depolarization threshold after the initial rising phase. This bistable behaviour of the neuronal potential seems not to be generated by intrinsic membrane properties of the cells, since evidences for intrinsically sustained activity in neocortical neurons are lacking. On the contrary, the capability of maintaining the depolarization period seems to be an emergent property of the cortical network whose electrophysiological underpinnings are the recurrent excitatory and inhibitory synapses formed among cortical pyramids and interneurons.

Two important characteristics of the intracellularly recorded up states support this view. Firstly, injection of depolarizing or hyperpolarizing currents in the recorded cells does not alter in any way the frequency and the duration of the up state in the cell, which remain synchronous with the rest of the network (Steriade, Nunez, et al. 1993; Shu, Hasenstaub, Badoual, et al. 2003). Secondly, irregularity of the membrane potential is bigger during the up state, suggesting that barrages of synaptic potentials are constantly shaping the level of depolarization during the up state. Thirdly, excitatory and inhibitory conductances are both increased. Therefore, neuronal sustained depolarization seems to be mediated by excitatory potentials that drive the cell nearer to the threshold potential for action potential triggering.

Obviously inhibition levels are of paramount importance in this process. Maintain the neuron near the threshold potential require adequate levels of control to stabilize the membrane potential dynamics. This have been confirmed by the observation that in up states inhibitory and excitatory conductances vary concomitantly (Shu, Hasenstaub, Badoual, et al. 2003; Haider et al. 2006).

Termination

As for up state initiation, both intrinsic and extrinsic mechanisms have been proposed for its termination. Up phases could be closed by an

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enhanced recruitment of inhibitory cells toward the end of the active state or by a reduction in excitability arising from changes in the channels activation, or both.

Experimental evidences strongly suggest that the main mechanism leading to the up state termination. In their first characterization of the slow wave oscillation Steriade and colleagues (Steriade, Nunez, et al. 1993) showed that the up-to-down state transition was abolished by arousing cholinergic inputs from the brainstem, and that the blockade of muscarinic (and not nicotinic) receptors eliminated this effect. Since muscarinic receptors were known to inhibit KCa channels (McCormick &

Williamson 1989) they postulated that during the up state activity-dependent KCa conductances were progressively activated producing

long lasting hyperpolarizing currents. These currents in turn would reduce neuronal excitability, reducing firing rates and resulting in generalized disfacilitation. More recent pharmacological studies have proposed a role for other classes of K+_{conductances, such as K}+_channels

activated by Na+_{, intracellular ATP or extracellular adenosine}

(Contreras & Steriade 1995; Sanchez-Vives & McCormick 2000; Cunningham et al. 2006). A second intrinsic mechanism that could account for the decrease in neuronal excitability is short-term synaptic depression, a phenomenon observed in neocortical neurons (Thomson & Deuchars 1994). However, this second contribution remains scarcely investigated, and computational models suggesting that its relative contribution should be weak compared to K+_{currents (Hill & Tononi}

2004).

A different line of experimental results seems to exclude that inhibitory currents play a role in the termination of the up state. First of all, the progressive pharmacological blockade of GABAA receptors by low

doses of bicuculline results in a progressive decrease of the duration of the up state, contrarily to what would be expected if inhibition were responsible for its termination (Sanchez-Vives et al. 2010). The shortening of the up states could instead be easily understood if their termination is mediate on activity dependent K+_{channels, that could be}

recruited faster if inhibition is impaired. Moreover, recordings of GABAergic aspiny neurons showed that their firing probability was unaltered in the up-to-down state transition, excluding their involvement in the phenomenon (Contreras & Steriade 1995). Finally, experiments estimating excitatory and inhibitory conductances during the up states showed that both monotonically decreased from the beginning to the end of the up state (Shu, Hasenstaub & McCormick 2003). This strengthen the idea of a parallel withdrawal of both excitatory and inhibitory synaptic inputs, coherent with the model of a progressive disfacilitation during the maintenance of the up state that

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could be mediated by slow hyperpolarizing currents. While the firing of GABAergic neurons seems not to be generally boosted at the end of the up state, slow GABAB receptors could flank K+ currents in

hyperpolarizing the cell. This is suggested by the fact that progressive blockade of GABAB receptors seems to increase the up state duration

(Mann et al. 2009).

1.2.3 Functions of the slow wave oscillation

In deep sleep, the cortex is almost entirely disconnected from its sensory inputs, and early neurophysiologists and psychologists considered sleep only as a state of behavioural inhibition and suppression of brain activity. For this reason, it could seem surprising that so much spontaneous activity is maintained in the sleeping brain.

The simple observation of slow wave oscillation does not demonstrate by itself its functional role. This activation pattern could arise simply as the result of the network architecture and wiring once suitable conditions (e.g., the absence of tonic thalamic inputs) are established, and may come with no functional implications. Still, it is clear that sleep has to play a key role in brain function, as sleep deprivation has serious consequences on the physiological equilibrium of the organism that could eventually lead to death. Slow-wave oscillation is one of the most prominent states of the brain during sleep, and it massively involves neural circuits throughout cortical and subcortical areas. It is therefore a good candidate to provide for at least some of the necessary functions that sleep exerts in mammals. Among the hypothesis about the role of sleep states in cortical maintenance two have been found particularly promising: basic biochemical maintenance of neural cells during down states and the consolidation of memory traces formed during the awake state.

Memory consolidation

One of the most promising theories about the function of sleep suggests its involvement in the formation of long-term memories. The suggestion that synaptic plasticity and sleep could somehow be related with each other have been firstly proposed by Moruzzi (Moruzzi 1966), but experimental evidences supporting the hypothesis came several years later. (Karni et al. 1994) were the first to prove that sleep deprivation affected the consolidation of acquired skills in a visual task administered to human volunteers.

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The first mechanistic explanation for the consolidation of memories during sleep came from (Wilson & McNaughton 1993), which observed that place cells recorded from the hippocampus of rats replayed awake patterns of firing during the slow wave oscillation. This suggested an elegant model about the involvement of sleep in learning and synaptic plasticity: the neuronal population may replay multiple times trajectories in the activity space previously encountered during the wake state. This provides a way to strengthen the synaptic connections that characterize those activity sequences even in the absence of the correlated behavioral states. Moreover, the entrainment of the hippocampus in the cortical up states suggested the involvement of this electrophysiological rhythm in conveying newly formed short-term engrams in the hippocampus to long-term storage in neocortical circuits. Synchronization of hippocampal and neocortical neurons has been shown to occur during hippocampal ripples (oscillations at about 200 Hz induced after the onset of an up state), and simultaneous replay of awake patterns of activity in hippocampus and sensory cortex have been reported (Ji & Wilson 2007).

Biochemical cellular maintenance

The role of slow wave oscillation in brain metabolism regulation has been firstly suggested by the homeostatic nature of sleep regulation. The need for sleep increases proportionally to the duration of the previous sleep episode, and is dissipated by subsequent sleep in proportion to its duration and intensity. Of all sleep phases, NREM3/4 stage (during which the slow oscillation occurs) seems to have the highest correlation with sleep pressure: it is high in early sleep and decreases in late sleep, and it has a longer duration after sleep deprivation.

In general, “restorative” theories of sleep function suggest that the sleeping phases could be necessary for a range of cellular processes, from the synthesis of macromolecules (Mackiewicz et al. 2007) and the replenishment of energetic storages (Benington & Heller 1995) to the clearance from oxidative stress products and other toxins accumulated during the wake stage (Reimund 1994). Several studies have reported an increment in the expression of proteins involved in the cellular response to stress after sleep deprivation in mice (Kim et al. 2010). Moreover, in healthy animals sustained sleep deprivation can eventually lead to death (Everson et al. 1989).

Although there are several evidences for the restorative role of sleep, the question of why the biochemical processes carried out by in the single cells require a global oscillation in the network to be efficiently

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executed is still unsolved. Recently, (Vyazovskiy & Harris 2013) proposed that the key feature of the slow wave oscillation in orchestrating such a global restoration process could be the down state period. Indeed, while the period of up state strongly resembles the condition of sustained activity in the awake animal (for neuronal membrane potential, number of synaptic inputs, and firing statistics), the down phase is the true peculiarity of the slow oscillation. Down states are characterized not only by the interruption of action potential firing, but also by the almost complete absence of synaptic inputs. Therefore, they could provide a crucial time window of cellular resting that could assist the implementation of the biochemical processes that provide neuronal restoration.

1.3 Interictal discharges in epilepsy

1.3.1 Swinging the E/I balance: epileptic seizures

The most striking examples of what may result from alterations of the excitation/inhibition balance can be found in the large group of pathologic manifestation that goes under the definition of epileptic seizures. Epileptic seizures are brief episodes of symptoms that arise from excessive or synchronous neuronal activity in the brain (Noachtar et al. 1999). They are a diverse group of clinical events that goes from brief and nearly undetectable losses of consciousness to vigorous episodes of muscular shaking that can result in physical injuries. Epileptic seizures can arise from an enduring disposition to develop this abnormal activity (a condition named epilepsy) or be transiently induced by brain trauma, injuries, drugs, temperature, hypoxia and other deviations from normal brain homeostasis.

Epileptic seizures are a violent clinical manifestation and they have been known from centuries1_{. Their link with anomalous electric activity}

1_{The first description of epileptic seizures and is found in an ancient Akkadian text}

(2000 B.C.), from the region of Mesopotamia (magiorkinis2010). The author described the symptoms of an epileptic patient explaining the infirmity as a curse of the god of the Moon. Other descriptions can be found in Egyptian papyri, and also in the Hammurabi code (as a condition under which a bought slave could be returned to the seller, magiorkinis2010). The word epilepsy come from the Greek verb επιλµβυειυ,

(epilambanein) i.e. “to posses, to afflict”, and in ancient Greece it was commonly named

sacred disease. In fact in the thought of ancient Greeks the illness was some kind of miasma, a divine punishment affecting bodies and minds. The first recognition of

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in the brain, however, had to wait the work of Fritsch and Hitzigh, who observed epileptic convulsions in dogs under electrical stimulation of the cortex (Magiorkinis et al. 2014). The first clear observation of the electric anomalies that come with the condition came with the invention of the electroencephalography (EEG) from Hans Berger, which allowed for the first time direct measurement of electrical activity from the scalp of human subjects. One of the most important discoveries of the German scientist was the occurrence of large deflections in the electroencephalographic trace together with the symptoms of epileptic seizures (Berger 1934). This tool opened the exploration of these “epileptic brain disorders” that resulted in the organization of a vast taxonomy of different EEG anomalies.

A broad classification of epileptic seizures distinguishes partial from generalized seizures:

• In partial seizures the pathological activity is localized in one region of the brain. It is often accompanied by the so auras, sensory, psychic, motor or autonomous phenomena such as hallucinations, deja-vu or localized jerking activity. Partial seizures can remain localized or spread to other areas, resulting in secondary generalized seizures.

• In generalized seizures the paroxysmal discharges affect the whole brain and usually result in complete loss of consciousness of the subject. This can be the only outcome of the event (as in absence seizure) or be also accompanied by motor manifestations, from loss of muscular tone (atonic seizures) to excessive contraction (tonic seizures) and spasmodic shaking (clonic seizures) of the limbs.

This is only a superficial distinction, and more subtle characterizations of each typology of epileptic seizures are still a matter of active research, since they require a more accurate understanding of the underling etiopathogenic mechanisms in order to give a precise interpretation to each clinical manifestation.

Epileptic anomalies that accompany epileptic syndromes are not restricted to ictal discharges. Indeed, by monitoring of the EEG of patients with recurrent seizures epileptologists have discovered several other abnormal events in the electric traces recorded in between seizures, which have been named interictal discharges (Fig. 1.4).

entire tractatus On the Sacred Disease. Here, he clearly contested the sacred origin of the pathology.

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1.3.2 Definitions of interictal discharges

The International Federation of Societies for Electroencephalography and Clinical Neurophysiology defines interictal discharges as a subcategory of epileptiform patterns, in turn defined as

“[…] Distinctive waves or complexes, distinguished from background activity, and resembling those recorded in a proportion of human subjects suffering from epileptic disorders.” (Noachtar et

al. 1999)

In turn, interictal discharges are commonly divided into four major morphological categories: sharp waves, spikes, spikes-and-slow-waves, and multiple spikes-and-slow-waves.

This is a somewhat circular definition as it relies only on the empirical observation of the phenomenon. Still, a clinical elettroencelphalographist can reliably detect interictal events in patient recordings, and automatic algorithms detecting these electrophysiological hallmarks on the basis of their temporal and spectral feature are being described (see Fig. 1.4 A for an example of EEG with interictal spikes). Indeed, tracing the interictal patterns in the recorded EEG has great clinical relevance. Seizures are infrequent in the majority of patients, and their detection by EEG is painstaking and time-intensive; for this reason, the main diagnosis method remains the detection of interictal events. Moreover, several characteristics of interictal spikes provide the clinician with insights into the nature of the associated epileptic pathology. For example, the generalized or focal nature of the epileptic syndrome of one patient is often associated with similar generalized or focal interictal events, and the localization of the epileptogenic locus in an epileptic patient can be inferred by the position of the recorded interictal.

1.3.3 Investigating interictal events: the bicuculline

model of interictal discharges

Besides analysing the brain of epileptic subjects by electrophysiological recordings and brain imaging tools, epileptologists have developed a series of animal models that recapitulate the features of different types of epilepsy. A wide variety of lesions, electrical stimulations, pharmacological treatments and, more recently, genetic models have been shown to induce various types of epileptiform activity. Given the range of conditions subsumed under the term epilepsy, this variety comes with not surprise (Fisher 1989).

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Bicuculline induction of IS

Bicuculline (Fig. 1.3 A) is a competitive antagonist of GABAA

receptors, originally isolated in 1932 in alkaloid extracts of Dicentra

cucullaria (Manske 1932) and characterized in 1933 (Manske 1933). Its

specificity as ionotropic GABA receptors antagonist was first suggested by (Curtis et al. 1970) and it proved itself a fundamental tool in the investigation of GABAergic inhibition in the following years. It remains the most commonly used GABAA antagonist in modern

electrophysiological studies (Fig. 1.3 B).

The first observations about the convulsant properties of bicuculline came from observation of seizures in frogs and rabbits injected with the substance (Welch & Henderson 1934). However, a more exhaustive characterization of its pharmacological effects had to wait until the discovery of the role of γ-aminobutyric acid as a fundamental mediator of central inhibition ((Krnjević & Schwartz 1967), for a review see (Bowery & Smart 2006)) and the characterization of bicuculline as a GABAergic

Fig. 1.3: The GABAA receptor antagonist

A) The chemical structure of bicuculline, an alkaloid isolated from Dicentra

cucullaria. B) The effect of bicuculline on inhibitory conductance. Traces are the

responses of cultured neurons to puffs of GABA before (left) and after (right) the perfusion with bicuculline. Note that the amplitude of the hyperpolarizing current is strongly reduced, but not completely suppressed because of the presence of bicuculline-insensitive GABAC receptor-mediated currents (from

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inhibitor (Curtis et al. 1970). In 1971 Meldrum and Boldrum (Meldrum 1971) investigated its convulsive properties in baboons and macaques, comparing its activity with pyridoxine antagonist convulsants.

In the seventies, the most common model for generating interictal activity was penicillin2_{. Collins and colleagues (Collins & Caston 1979)}

used for the first time topical applications of bicuculline to generate non-convulsive spikes (i.e., interictal events) in rats, observing that it was more efficient than penicillin in generating interictal events. Recently, bicuculline has been applied to produce a variety of different in vivo and

in vitro models that recapitulate several of the features of interictal

spikes.

1.3.4 Anatomy of interictal spikes

Several intracellular studies carried out during induced interictal events have been performed in the early years of electrophysiology. The first recordings from cortical neurons during EEG “epileptiform” discharges have been done by (Goldenshohn & Purpura 1962) in the

cerveau isolé preparation from cats. After this first linking of EEG

epileptic manifestations to intracellular phenomena, (Matsumoto & Marsan 1964) provided the first systematic investigation of the neuronal activity pattern during ictal and interictal events (Fig. 1.4 B). In the description of penicillin-induced interictal patterns they introduced the term paroxysmal depolarization shift (PDS) to describe the stereotypical dynamics observed at the cellular level.

The PDS consists in a large, positive deflection in the membrane potential lasting between 100 and 400 ms and resulting in a sequence of action potentials that may show a progressive reduction in their amplitudes (Fig. 1.4). This happen because of the excessive depolarization of the neuron, which interferes with the reactivation phase of NaV channels and thereby causes a depolarization block. At the

end of each PDS, neurons frequently showed a long lasting hyperpolarizing wave. PDSs appeared only in correspondence to interictal events recorded in the surface EEG, and far from these events neurons were almost completely silent. As for the mechanisms generating these abnormal events, these first investigations proposed both abnormal synaptic inputs and alterations in the properties of the

2_{Penicillin, a well-known antibiotic, in the central nervous system can act as a}

GABAA receptor antagonist (Tsuda et al. 1994). It has been used by epileptologists

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neuronal membrane, without being able to support one hypothesis or the other.

The synaptic origin of PDS

In the following years a wealth of evidences coming from similar experimental setups (penicillin-induced paroxysmal events in cerveau

isolé or anaesthetized cats, reviewed in (Ayala et al. 1973)) confuted the

view that the PDS could be generated by alteration of the physiological membrane properties in neurons interested by the epileptiform event. Indeed, neurons within a cortical focus were able to respond in a normal fashion to both orthodromic and antidromic stimulations, and the normal action potential generating mechanism was intact.

As for the origin of the PDS, it was not determined by the initial barrage of action potentials, since keeping the cell at a voltage that preclude its firing does not prevent the PDS to occur. Finally, a number of features of the PDS associated it with excitatory post-synaptic potentials (EPSP, Fig. 1.4 C):

• It is associated with a conductance increase;

• Its amplitude changes systematically in amplitude with alterations in background membrane potential;

• It has a reversal potential (Prince 1969);

• It is not possible to generate the PDS by applied currents. Similar electrophysiological properties point toward a synaptic nature also for the after-discharge hyperpolarization. It can be graded; it changes in size by varying the membrane potential, and could be reversed by inward current; it is accompanied by a reduction of resistance. The most convincing evidence for identifying the hyperpolarization as an inhibitory postsynaptic potential (IPSP) is the fact that it could be reversed by injecting Cl-_{ions intracellularly (Prince}

1969).

Since the IPSP can begin at the same time, or even precede the start of the depolarizing potential, it is not directly produced by the preceding PDS of a given neuron, but it seems to be the synaptic manifestation of the recruitment of inhibitory interneurons (presumably also displaying PDSs) in the epileptiform event. This hyperpolarization event may be even more spatially spread that the PDS, an therefore give rise to what has been described as a ring of surrounding inhibition that encircle the epileptogenic focus (Prince & Wilder 1967).

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Fig. 1.4: EEG and intracellular recordings of interictal spikes.

A). Scalp EEG from a 29-year-old woman with history of complex partial seizures. Note the sharp waves phase reversing Sphenoidal SP1 electrodes (from (Javidan 2012)). B) One of the first intracellular recordings of paroxysmal depolarizing shift in penicillin-induced focus in cat cortex. The line above: surface EEG; lower line: intracellular recording from a putative pyramidal neuron. In the first phase the depolarization is crowned by a group of spikes of decreasing amplitude (Calibrations: 1 mV surface tracing, 10 mV microelectrode tracing, 100 cycles/sec; from (Matsumoto & Marsan 1964)) C) Evidences for the IS as a paroxysmal EPSP. 1-Triggering of 'graded' PDS. Two IS were induced at different time intervals. At the longest one a full PDS was triggered, but the PDSs decreased in amplitude and duration upon reducing the interval. 2- Resistance changes during the PDS. An inward current pulse of the same intensity was applied at various times during the PDS. Note the reduction of the test membrane displacement during the depolarization. 3-C, Current was passed after a VL shock (indicated by a triangle), which failed to evoke an EPSP, but triggered a PDS of long latency. (1, control, 2, outward current; 3/4, inward currents). D, Record of a current injection during PDS. 1, 2, and 3,

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Those are examples of basic physiological phenomena (EPSP and IPSP) that, once being recruited abnormally, could give rise to the pathological interictal discharge. However, other mechanisms could contribute to the phenomenon. For example, accumulation in the interneuronal cleft of ions such as K+_{could play a critical role in the}

genesis and control of the discharge. Other candidates that may be involved in the synchronization are inhibitory interneurons and the neuronal interactions with astrocytes, and hundred of studies have depicted a much more complex scheme of dependences resulting in the generation of epileptiform activity. However, the simple description of PDS as a paroxysmal EPSP hold valid as a first approximation.

1.3.5 Effects of interictal spikes on epileptogenesis and

ictal events

Experimental studies have suggested that interictal events and ictal events are characterized by intracellular events that may share the same fundamental characteristics, except for their duration. In fact, the recording of interictal events is usually considered a reliable way to localize anomalous brain circuits that trigger ictal events. However, the causal connection that links the two phenomena is still unclear: is the occurrence of ictal events that foster the developing of interictal spikes, or interictal spikes that make the local circuitry more prone to ictal discharges? This seems to be a hard “chicken-and-egg” problem, and is made more difficult by the obvious lack of information about the frequency of interictal events before an ictal crisis that reveals the presence of some kind of epileptic syndrome in the patient.

Why has been postulated that interictal spikes can affect epileptogenesis? The paroxysmal depolarizing shift described above may have several implications at the level of synaptic homeostasis that normally ensure a correct balance of excitatory and inhibitory forces. During this huge synaptic events, the glutamate release from excitatory neurons produce a sustained opening of ionotropic AMPA glutamate receptors, and the prolonged depolarization that follows can recruit an abnormal number of NMDA receptors, usually blocked by Mg2+_ions

(Mayer & Westbrook 1987). NMDA receptors are particularly permeable to calcium, which is the key trigger in mechanisms processes of synaptic plasticity. Therefore, the outcome of the paroxysmal event may be the abnormal strengthening of recurrent excitatory synapses in the

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network, which can lead to hyperexcitability of the circuit (Debanne et al. 2006). Other plasticity mechanisms may also be hijacked by this pathological rhythm, including misguided axonal sprouting to the epileptic network (Miles et al. 1988) and metabotropic glutamate receptor-induced overexpression of slowly inactivating cationic channels, which may result in the progressive development of long lasting seizure-like events (Stoop et al. 2003). Several of these hypotheses have been tested only in in vitro preparations, a condition that may differ in many aspects from the physiology observed in vivo. However, some observations have been made in the kainate model for acquired epilepsy, where repeated low doses of the AMPA antagonist kainate result on a prolonged episode of status epilepticus followed after a couple of weeks by the onset of spontaneous seizures. In this time it is possible to observe changes in the circuitry that lead to epileptogenesis. Notably the interval that precedes ictogenesis is characterized by the continuous presence of interictal discharges (Hellier et al. 1999), strengthening the idea that this subclinical manifestation could herald the appearance of acquired epilepsy. However, these lines of evidences come from experimental models that, especially for pathologies whose etiogenesis is not fully understood, can offer only an incomplete picture of what happen in human patients. Systematic studies of the EEG activity that precede the appearance of acquired epilepsy in human subjects may help in clarifying the relationship between interictal events and ictogenesis.

A second relationship has been a matter of more deep investigation in human subjects: the relationship on a short temporal scale between periods of interictal discharges and ictal crisis in subjects whose status

epilepticus is already known. Two main hypothesis dominated the field:

on one side, some authors proposed that interictal discharges may drive abnormal synchronization in the network and facilitate the onset of ictal crisis (Ayala et al. 1973); on the other side, other scientists suggested that interictal spikes may interfere with the developing of a full ictal event, thereby exerting a protective effect. Even if some evidences from

in vitro and in vivo models have partially confirmed this second idea

(Swartzwelder et al. 1987; Librizzi & De Curtis 2003) the observation of interictal spikes frequencies in patients with temporal lobe epilepsy has shown that before a seizure the number of events was neither increased or decreased (Lange et al. 1983; Gotman 1991). Therefore, the short-term relationship between ictal and interictal activity remains ambiguous.

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1.4 Perception and action in a synchronous

brain: the effects of interictal spikes on

cognition

Information processing in the brain crucially depends on the capability of neural ensembles to encode information, from sensory updating about environmental conditions to motor patterns that generate the desired behaviour. In the current interpretation of neural coding in the neocortex, ensembles of cells represent information as patterns of action potential firing in a multidimensional space descripted by the spiking rate of each neuron. Indeed, one of the most striking features of the mammalian neocortex is its suitability to generate an astronomical number of possible representations by the combinatorial combination of the activity of millions of neurons.

In order to exploit this huge number of possible configurations it is crucial to maintain the sparseness of neuronal firing. As described in this introduction, this feature of pyramidal cells spiking statistics is maintained by the persistent equilibrium between excitatory and inhibitory drives. Deviations from this homeostatic point have immediate repercussions on the coding capacity of neural cells: an excessive inhibition leads to a situation of progressive abolishment of firing, while excessive excitation cause an excessive activation of every cell in the ensemble in a synchronous, periodic way. Both these alterations dramatically reduce the number of possible representations, by generating a condition of constant silence or stereotyped activity (Trevelyan et al. 2013).

From this simple consideration immediately follows that every kind of electroencephalographic abnormality should impact in some way brain computations. This appears clearly in the phenotypic manifestations of large ictal events characterized by lose of consciousness, abnormal perceptions and alteration of motor activity, but can be less obvious for interictal discharges, whose clinical symptoms are much more subtle.

1.4.1 Clinical impacts of interictal discharges

Even if we are used to associate the pathological features of epilepsy only with ictal manifestations and seizures, we can distinguish between two types of influences:

• A transitory effect, that briefly impact on the cognitive processes at the time of the discharge event;

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• A long term effect, related to the effects of the abnormal EEG pattern on cortical plasticity and memory.

1.4.2 Long-term effects of interictal activity

Whereas in some patients psychomotor deterioration appears to occur independently of the etiology of the seizures, there is a diagnosis of epileptic encephalopathy. The conditions of these patients have been referred to as the epileptic encephalopathies. The key feature of the epileptic encephalopathies is that the slowing or regression of development is due primarily to seizures, abnormal interictal cortical and subcortical activity as reflected in the EEG, or both, and is not due to the underlying cause of the seizures. Those patients, when successful treated with drugs or surgery, can regain normal brain function (Holmes & Lenck-Santini 2006).

Observations in the continuous spike-waves of sleep syndrome (CSWS)

CSWS is a disorder that appears during childhood, characterized by the presence of continuous generalized spike-wave complexes during 85% of slow-wave sleep (a pattern described as Electrical Status Epilepticus during Sleep, or ESES). This electrophysiological hallmark is followed by a continued reduction in neuropsychological functions, reduced IQ, reduction of language, memory and motor impairment. During puberty the ESES pattern progressively reduces, the neuropsychological assessments generally improve, but some of the acquired deficits often remain. Up to date, there are not standard defined lines for the management of this rare disease, and a variety of therapeutic agents have been used (the most common of which are valproate, levitiracetam, diazepam). Two studies on patients treated with benzodiazepines (De Negri et al. 1995; Yan Liu & Wong 2000) have shown an improvement in cognitive functions in the patients successfully responding to the treatment. This suggests that the EEG pathological pattern, abolished by the drug, is the primary cause for the onset of the cognitive symptoms.

The idea that interictal EEG abnormalities can interfere with cognitive function extends far beyond uncommon conditions such as CSWS, and could apply to many patients with epilepsy, abnormal EEGs, and cognitive deficits.

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1.4.3 Interictal

spikes

and

transient

cognitive

impairment

The extent of the cognitive deficit resulting from single discharge patterns is obviously related to the location, the dimensions, and the propagation pattern of the epileptiform event.

General bursts of spike-wave pattern are usually related to a slowing of reaction times for several seconds, and are often followed by total amnesia for events occurred during the EEG alteration (Porter et al. 1973; Holmes et al. 1987). This is not surprising, given the extension of the cortical surface involved in the paroxysmal event. What appears to be more surprising is that even single focal interictal events can result in cognitive impairment (D. a Shewmon & Erwin 1988; Siebelink et al. 1988; Binnie 2003; Aarts et al. 1984; Kasteleijn-Nolst Trenité & Vermeiren 2005; Kasteleijn-Nolst Trenité 1995; Binnie et al. 1987; D. A. Shewmon & Erwin 1988; Shewmon & Erwin 1989).

(Aarts et al. 1984) developed two short-term memory tasks, one using verbal and the other non-verbal material presented in the form of television games. Forty-six patients (age range, 10–46) with EEG discharges were studied. Transitory cognitive impairment was demonstrable in 11 of 22 patients with focal or asymmetrical discharges and 13 of 24 with symmetrical generalized epileptiform activity. A significant association was observed between the laterality of focal or asymmetrical generalized discharges and impairment on the task, left-sided discharges being associated with errors in the verbal task and right-sided discharges with impairment in the nonverbal test. This study was the first to show that the cognitive effects of discharges are location dependent.

Several other works have strengthened those observations. (D. A. Shewmon & Erwin 1988) developed a setup that allowed them to observe the effect of single interictal discharges on the perception of a visual stimulus, and found that the presentation of stimuli at the time of a in interictal event in visual cortex resulted in missed or delayed perception. A more detailed characterization of the timing of this alteration (Shewmon & Erwin 1989) showed that the effect of the spikes started immediately before the deflection in the EEG trace and terminated at the end of the slow wave, leading the authors to the conclusion that the long lasting slow wave, and not only the paroxysmal spike, can affect the perception of the stimulus. Other investigators has described the effect of interictal spikes on short term memory in children (Kasteleijn-Nolst Trenité, Smit, et al. 1990), their effect on scholastic performances (Kasteleijn-Nolst Trenité, Siebelink, et al.

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1990), and even the impact of this events on driving behaviour (Kasteleijn-Nolst Trenité et al. 1987). Together, these studies confirm that even if usually those events are considered as “subclinical”, nonetheless they may significantly impact on brain computations even in sporadic occurrences (Binnie 2003).

1.5 Aim of the thesis

Even if several studies have investigated short and long term effects of interictal discharges in the brain area where they occur, the interactions of the abnormal focus with unaffected regions of the cortex are still poorly understood. Aim of this thesis was to characterize of these interactions by local field potential and single unit recordings, in a model of bicuculline-induced interictal discharges in the visual cortex of anesthetized mice. Firstly, I explored whether and how the activity patterns in the cortex of the animal generate discharges in the localized focus. Secondly, I described the long-range effects of this localized abnormal activity on the contralateral cortex, showing how it interacts with the slow oscillation at the level of the single neurons and the entire population. Finally, I assessed the effect of these alterations on physiological brain activity using their effect on visual evoked potentials as readout for their impact on local computing.

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Chapter 2 Materials and Methods

2.1 Materials

2.1.1 Mice

Adult (age > postnatal day 60) C57BL/6J mice were used (N = 24). Animals were reared in a 12 h light/dark cycle, with food and water available ad libitum. All the experimental procedures were conformed to the European Communities Council Directive n°86/609/EEC and were approved by the Italian Ministry of Health.

2.1.2 Solutions

• Urethane solution: a 20% solution of urethane (cat#U2500-100g, Sigma-Aldrich) was prepared by dissolving the reagent in 9% saline solution.

• Artificial Cerebrospinal Fluid (ACSF): The ACSF solution was prepared as follows: NaCl 132.8 mM, KCl 3.1 mM, CaCl2 2 mM,

MgCl2 1 mM, K2HPO4 1 mM, HEPES 10 mM, NaHCO3 4 mM,

glucose 5 mM, ascorbic acid 1 mM, Myo inositol 0.5 mM, pyruvic acid 2 mM. The pH was adjusted at 7.4 (De Vivo et al., 2013).

• Bicuculline Methiodide (BMI): BMI powder (cat#14343-50mg, Sigma-Aldrich) was dissolved in ACSF solution to obtain a final concentration of 100 µM.

2.1.3 The experimental setup

Local Field Potential (LFP) recordings

A thick-wall borosilicate micropipette with filament with a outer diameter of 1 mm, inner diameter of 0.58 mm and length of 10c m (BF-100-58-10, Sutter Instruments) was pulled with a Sutter Instruments puller to a tip diameter of about 1.5 µm (resistance 1-3 M ). It was inserted into an electrode holder connected to an AgCl filament. The

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holder was inserted into a headstage (NPI Electronics) connected to a reference electrode bathing in the recording chamber obtained in correspondence of the craniotomy. The recorded signal was routed to a multichannel differential amplifier (EXT-02F, NPI Electronics), amplified 1000-fold (EXT-02F, NPI) and band pass filtered (0.1–1000 Hz). A HumBug active filter (Quest Scientific) was used to filter the 50 Hz signal from the power line as well as other periodic electric noise. Cleaned signal was digitized at 10 kHz with 16 bit precision by a National Instruments (NI-usb6251) screw terminal controlled by a custom made LabView software on a dedicated computer.

Loose-patch recordings

An AgCl electrode was inserted in a borosilicate micropipette filled with ACSF (cat#Z611255-250EA Hirschmann micropipettes); by using a Sutter instruments puller, the final tip diameter was 2 µm and the resistance 4-8 M . Pipettes were backfilled with ACSF solution. Holders were inserted into a headstage specific for patch-clamp recordings, both connected to a common reference electrode bathing in the cranial chamber solution. The signal was routed to an amplifier (Axopatch 1-D). A HumBug active filter (Quest Scientific) was used to filter the 50 Hz signal from the power line as well as other periodic electric noise. Cleaned signal was digitized at 10 kHz with 16 bit precision by a National Instruments (NI-usb6251) screw terminal controlled by a custom made LabView software on a dedicated computer.

Visual stimulation

Visual stimulation was provided via LCD monitor (15’’, 60 Hz refresh rate) controlled by a dedicated computer with MATLAB 2008a and the Psychophysics Toolbox (Brainard 1997). An in-house Graphical User Interface (GUI) was used to create all the visual stimuli used in the experiments. Levels of contrast produced were quantified by means of a photometer (Konica Minolta); the luminance at maximum contrast was assessed to be 3 cd/m2_{. The screen was positioned 30 cm from the right}

eye (contralateral to the stimulated hemisphere), roughly at 45 degrees to the medial sagittal plane of the animal. The Data Terminal Ready (DTR) pin of the computer serial port was used to generate a synchronization signal from the MATLAB program. This signal was routed to the main digitizer board and acquired together with the electrophysiological traces.

2.1.4 Data analysis - Rosetta

To facilitate data navigation and analysis, I developed a dedicated GUI, Rosetta, to import in MATLAB all the information from the

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laboratory notebook together with data from the electrophysiological recordings. Rosetta has an exploratory mode that allows the user to navigate all the recordings acquired in an experimental session, discard traces presenting corrupted signals, and to change metadata of each track (Fig. 2.1). At the end of each Rosetta session, a MATLAB file (.mat extension) containing the electrophysiology recordings and the associated data is created as a variable of the “structure” type, and it can be saved for further analysis.

This standard format is very useful for data analysis, as each function (e.g, a function for the calculation of the mean evoked potentials shape) can easily iterate through the elements of the structure, check all of its attributes (e.g., controls that a stimulation was present in a given recording), and perform its operations (averages in phase with the stimulus onset signal). All the functions for the data analysis were scripted for this common data format.

Fig. 2.1: Rosetta main window.

The figure depicts a screenshot of Rosetta, the GUI for the upload of electrophysiological data and their metadata insertion in MATLAB. The program has three windows for the active exploration of up to three electrophysiological recordings (left), as well as a list of the uploaded files (down right), windows to change values for each metadata (bottom).

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2.2 Methods

2.2.1 Surgery

Mice were anesthetized by intraperitoneal injection of urethane (0.8ml/hg in 0.9% NaCl; Sigma). Additional doses (10% of initial load) were intraperitoneally administered to maintain the anesthetic level when necessary. Body temperature during the experiments was constantly monitored with a rectal probe and maintained at 37°C with a heating blanket (Harvard Instruments). The depth of anesthesia was evaluated by monitoring pinch withdrawal reflex and other physical signs (respiratory and heart rate). Head was shaved and a 2.5 % lidocain gel was applied on the scalp. Eyes were protected by the application of an ophthalmic gel (Lacrigel, Bracco). The head skin was cut over the parietal and occipital hemisphere with surgical scissors and the exposed bone was cleaned with saline solution to improve glue adhesion. A custom made metal headpost was glued to the fronto-parietal part of the skull and cemented with a synthetic resin (Paladur, Heraeus Kulzer GmbH & Co.). A double chamber was created with a thin layer of a synthetic resin around the edges of the craniotomy. A circular portion of the skull overlying the visual cortex (0.0 mm antero-posterior and 2.7 mm lateral to the lambda suture, diameter 2-3 mm, (Franklin, K.B., Paxinos 1997)) was carefully drilled on both the hemispheric sides, while the dura mater was left intact (Fig. 2.2). During the operation bleeding was kept to the minimum and, once exposed, the cortex was constantly

Fig. 2.2: Scheme of the craniotomies.

The picture represents the mouse skull with its craniometric points. V1 is situated (black and red dashed circles) in the occipital portion of the cortex, laterally with respect to lambda. In correspondence of bregma a metal bar was glued to the cranial surface in order to head- fix the animal in front of the monitor.

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bathed in ACSF. For the electrophysiological recordings the animal was head fixed in front of the monitor in order to stimulate the eye contralateral to the recorded visual cortex.

2.2.2 Local Field Potential recordings

Two Ag-Cl electrodes in borosilicate pipettes (see Materials section) were positioned into the visual cortex at a depth of about 250 µm (II/III layer) with a motorized micromanipulator (MPI electronic), at an angle of 60° from the horizontal axis in both hemispheres.

Visual stimuli consisted of a baseline of 4 seconds of a gray screen (0.09 cd/m2_{), followed by a sequence of reversals of a checkerboard}

(temporal frequency: 0.5 Hz, spatial frequency: 0.04 cycles/degree) of different contrast levels (100%, 31%, 13%, 5%, 4%, 3%). For each animal, responsiveness was verified by the visual inspection of the average response in a trial with stimuli of maximum contrast. Experiments were performed in the dark, and the response to a blank stimulus (0 % contrast) was also recorded to estimate noise.

2.2.3 Loose-patch recordings

For the loose-patch recordings the pipette (see Materials section) was inserted through the cortex by applying about 300 mbar of positive pressure until II/III layer was reached (as in (Perkins 2006)). Cells were searched in voltage-clamp mode with the positive pressure lowered to 30 mbar while monitoring the tip resistance with a square-wave current pulse (test stimulus 20 mV). On approaching a cell, the resistance increased: at this point pressure was relieved and light suction was applied. Responses were recorded in current clamp mode with a 10x gain (Axopatch 1-D amplifier). During the experiments LFP was always recorded in the same or the contralateral cortex.

2.2.4 Data processing

Up states detection

Collected data were visually inspected, and traces presenting drift or other artefacts were excluded (~1% of the data). All the subsequent analysis were performed on MATLAB (The MathWorks Inc.). In order to discriminate up and down states a custom algorithm based on spectral power in the gamma band was designed. (Bragin et al. 2012; Sanchez-Vives et al. 2010). The entire pipeline is depicted in Fig. 2.3.

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In brief, Short-time Fast Fourier Transform STFT of the raw traced was calculated in the 40-90 Hz frequency band with an overlapped Blackman window of 0.2 s. Since LFP power tends to decay following a 1/f rule (Buzsáki & Mizuseki 2014), spectrograms were then normalized using the average spectrum powers at each frequency. In this way, the

Fig. 2.3: Up state detection pipeline.

In order to detect up states I calculate the power of the raw track (top) in gamma frequency over time by short-time fast-Fourier transform (STFT). Red trace represents the signal filtered in the considered frequencies (gamma range), and orange line below indicates power over time as calculated by the described algorithm. Histogram of power values is reported below (blue line of the histogram) together with the double Gaussian fit, the detected peaks and the calculated threshold. Graph on the right shows the normalized power in gamma frequency separated by the calculated threshold, and the last track on the bottom is the final track with the detected up states in yellow and the down states in blue.

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components at different frequencies have similar orders of magnitude (Sanchez-Vives et al. 2010). Gamma-band activity (GBA, 40-90 Hz) was estimated as the sum of the normalized STFT components in the considered frequency range. GBA was then smoothed with a sliding window of 80 ms and logarithmically scaled. The time histogram of log(GBA) was bimodal (Fig. 2.3), reflecting the distribution of gamma band-activity during up and down states. The threshold for the discrimination of up states was set to 60% of the peak-to-peak interval on the histogram. A cut-off in the minimum up (down) states duration was set to 200 ms, and up (down) states shorter than the cut-off were recursively assigned to the ongoing down (up) state.

Half-width time of the interictal spike

IS half-width time was used to align contralateral LFP and spikes, and to detect the distance of the IS event from the visual stimulus. To find the half-width of the IS, the field recording was high-pass filtered at 5 Hz and a threshold was set as 3 standard deviations (SDs) below the mean voltage of the track. Every time the trace crossed the threshold, it had to remain below it for a minimum time of 100 ms to be counted as an interictal. Half-with point was defined as the point closest to the mean between the maximum and the minimum voltage in a window of 200 ms around the threshold crossing point.

Spike detection

Spike detection on loose-patch recordings was performed with a dedicated GUI by high-pass filtering (200 Hz) the track and fixing a 3 SDs threshold. Detected spikes were displayed and visually inspected to remove high frequency drifts and electronic artefacts. Cell recorded for less than 100 IS events were excluded from subsequent analysis.

Visual Evoked Potentials calculation

Tracks where a visual stimulation was administered, were a visual stimulation was administered were cropped based on the acquired DTR signal from the stimulation computer. Mean Visual Evoked Potentials (VEPs) were calculated by subtracting the onset from each event (the first 100 ms after the stimulation) and averaging across all the events from a given contrast condition.

Focal interictal activity in the mouse visual cortex interferes with slow-wave oscillations and visual processing in the contralateral hemisphere

U

P

Biologia Applicata alla Biomedicina

Focal interictal activity interferes with slow

oscillation and visual processing in the

contralateral hemisphere

A

Table of Contents

1 Introduction

...

9

2 Materials and Methods

...

31

4 Conclusions

...

63

Chapter 1

Introduction

1.1

Excitation and inhibition: the yin and