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1.  Sleep oscillations in the thalamocortical system induce long-term neuronal plasticity 
Neuron  2012;75(6):1105-1113.
Long-term plasticity contributes to memory formation and sleep plays a critical role in memory consolidation. However, it is unclear whether sleep slow oscillation by itself induces long-term plasticity that contributes to memory retention. Using in vivo pre-thalamic electrical stimulation at 1 Hz which itself does not induce immediate potentiation of evoked responses, we investigated how the cortical evoked response was modulated by different states of vigilance. We found that somatosensory evoked potentials during wake were enhanced after a slow-wave sleep episode (with or without stimulation during sleep) as compared to a previous wake episode. In vitro, we determined that this enhancement has a postsynaptic mechanism that is calcium-dependent, requires hyperpolarization periods (slow waves), and requires a co-activation of both AMPA and NMDA receptors. Our results suggest that long-term potentiation occurs during slow-wave sleep supporting its contribution to memory.
PMCID: PMC3458311  PMID: 22998877
2.  Age dependency of trauma-induced neocortical epileptogenesis 
Trauma and brain infection are the primary sources of acquired epilepsy, which can occur at any age and may account for a high incidence of epilepsy in developing countries. We have explored the hypothesis that penetrating cortical wounds cause deafferentation of the neocortex, which triggers homeostatic plasticity and lead to epileptogenesis (Houweling etal., 2005). In partial deafferentation experiments of adult cats, acute seizures occurred in most preparations and chronic seizures occurred weeks to months after the operation in 65% of the animals (Nita etal., 2006,2007; Nita and Timofeev, 2007). Similar deafferentation of young cats (age 8–12 months) led to some acute seizures, but we never observed chronic seizure activity even though there was enhanced slow-wave activity in the partially deafferented hemisphere during quiet wakefulness. This suggests that despite a major trauma, the homeostatic plasticity in young animals was able to restore normal levels of cortical excitability, but in fully adult cats the mechanisms underlying homeostatic plasticity may lead to an unstable cortical state. To test this hypothesis we made an undercut in the cortex of an elderly cat. After several weeks this animal developed seizure activity. These observations may lead to an intervention after brain trauma that prevents epileptogenesis from occurring in adults.
PMCID: PMC3776140  PMID: 24065884
sleep; wake; trauma; excitability; epileptogenesis; seizure; epilepsy
3.  Long-range correlation of the membrane potential in neocortical neurons during slow oscillation 
Progress in brain research  2011;193:181-199.
Large amplitude slow waves are characteristic for the summary brain activity, recorded as electroencephalogram (EEG) or local field potentials (LFP), during deep stages of sleep and some types of anesthesia. Slow rhythm of the synchronized EEG reflects an alternation of active (depolarized, UP) and silent (hyperpolarized, DOWN) states of neocortical neurons. In neurons, involvement in the generalized slow oscillation results in a long-range synchronization of changes of their membrane potential as well as their firing. Here, we aimed at intracellular analysis of details of this synchronization. We asked which components of neuronal activity exhibit long-range correlations during the synchronized EEG? To answer this question, we made simultaneous intracellular recordings from two to four neocortical neurons in cat neocortex. We studied how correlated is the occurrence of active and silent states, and how correlated are fluctuations of the membrane potential in pairs of neurons located close one to the other or separated by up to 13 mm. We show that strong long-range correlation of the membrane potential was observed only (i) during the slow oscillation but not during periods without the oscillation, (ii) during periods which included transitions between the states but not during within-the-state periods, and (iii) for the low-frequency (<5 Hz) components of membrane potential fluctuations but not for the higher-frequency components (>10 Hz). In contrast to the neurons located several millimeters one from the other, membrane potential fluctuations in neighboring neurons remain strongly correlated during periods without slow oscillation. We conclude that membrane potential correlation in distant neurons is brought about by synchronous transitions between the states, while activity within the states is largely uncorrelated. The lack of the generalized fine-scale synchronization of membrane potential changes in neurons during the active states of slow oscillation may allow individual neurons to selectively engage in short living episodes of correlated activity—a process that may be similar to dynamical formation of neuronal ensembles during activated brain states.
PMCID: PMC3397925  PMID: 21854963
intracellular recording; cat; sleep; synchrony
4.  Properties of slow oscillation during slow-wave sleep and anesthesia in cats 
Deep anesthesia is commonly used as a model of slow-wave sleep (SWS). Ketamine-xylazine anesthesia reproduces the main features of sleep slow oscillation: slow, large amplitude waves in field potential, which are generated by the alternation of hyperpolarized and depolarized states of cortical neurons. However, direct quantitative comparison of field potential and membrane potential fluctuations during natural sleep and anesthesia is lacking, so it remains unclear how well the properties of sleep slow oscillation are reproduced by the ketamine-xylazine anesthesia model. Here, we used field potential and intracellular recordings in different cortical areas in the cat, to directly compare properties of slow oscillation during natural sleep and ketamine-xylazine anesthesia. During SWS cortical activity showed higher power in the slow/delta (0.1-4 Hz) and spindle (8-14 Hz) frequency range, while under anesthesia the power in the gamma band (30-100 Hz) was higher. During anesthesia, slow waves were more rhythmic and more synchronous across the cortex. Intracellular recordings revealed that silent states were longer and the amplitude of membrane potential around transition between active and silent states was bigger under anesthesia. Slow waves were largely uniform across cortical areas under anesthesia, but in SWS they were most pronounced in associative and visual areas, but smaller and less regular in somatosensory and motor cortices. We conclude that although the main features of the slow oscillation in sleep and anesthesia appear similar, multiple cellular and network features are differently expressed during natural SWS as compared to ketamine-xylazine anesthesia.
PMCID: PMC3209581  PMID: 22016533
Sleep; oscillations; synchrony; intracellular; anesthesia; ketamine-Xylazine
5.  Origin of Active States in Local Neocortical Networks during Slow Sleep Oscillation 
Cerebral Cortex (New York, NY)  2010;20(11):2660-2674.
Slow-wave sleep is characterized by spontaneous alternations of activity and silence in corticothalamic networks, but the causes of transition from silence to activity remain unknown. We investigated local mechanisms underlying initiation of activity, using simultaneous multisite field potential, multiunit recordings, and intracellular recordings from 2 to 4 nearby neurons in naturally sleeping or anesthetized cats. We demonstrate that activity may start in any neuron or recording location, with tens of milliseconds delay in other cells and sites. Typically, however, activity originated at deep locations, then involved some superficial cells, but appeared later in the middle of the cortex. Neuronal firing was also found to begin, after the onset of active states, at depths that correspond to cortical layer V. These results support the hypothesis that switch from silence to activity is mediated by spontaneous synaptic events, whereby any neuron may become active first. Due to probabilistic nature of activity onset, the large pyramidal cells from deep cortical layers, which are equipped with the most numerous synaptic inputs and large projection fields, are best suited for switching the whole network into active state.
PMCID: PMC2951844  PMID: 20200108
intracellular; intrinsic; oscillations; sleep; synaptic; synchronization
Epilepsia  2008;49(11):1925-1940.
A cortically generated Lennox-Gastaut type seizure is associated with spike-wave/polyspike-wave discharges at 1.0–2.5 Hz and fast runs at 7–16 Hz. Here we studied the patterns of synchronization during runs of paroxysmal fast spikes.
Electrographic activities were recorded using multisite intracellular and field potential recordings in vivo from cats anesthetized with ketamine-xylazine. In different experiments, the recording electrodes were located either at short distances (<1 mm) or at longer distances (up to 12 mm). The main experimental findings were tested in computational models.
In the majority of cases, the onset and the offset of fast runs occurred almost simultaneously in different recording sites. The amplitude and duration of fast runs could vary by orders of magnitude. Within the fast runs, the patterns of synchronization recorded in different electrodes were as following: (i) synchronous, in phase, (ii) synchronous, with phase shift, (iii) patchy, repeated in phase/phase shift transitions and (iv) non-synchronous, slightly different frequencies in different recording sites or absence of oscillatory activity in one of the recording sites; the synchronous patterns (in phase or with phase shifts) were most common. All these patterns could be recorded in the same pair of electrodes during different seizures and they were reproduced in a computational network model. Intrinsically-bursting (IB) neurons fired more spikes per cycle than any other neurons suggesting their leading role in the fast run generation.
Once started, the fast runs are generated locally with variable correlations between neighboring cortical foci.
PMCID: PMC2629507  PMID: 18616553
Synchronization; cortex; electrographic seizures; EEG; intracellular; in vivo; computational model

Results 1-6 (6)