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Dynamics of epileptiform activity in mouse hippocampal slices.

Filatov G, Krishnan GP, Rulkov NF, Bazhenov M - J Biol Phys (2011)

Bottom Line: In this study, we used multi-electrode recordings from mouse hippocampal slices to explore changes of the network activity during progressive increase of the extracellular K( + ) concentration.Our analysis revealed complex spatio-temporal evolution of epileptiform activity and demonstrated a sequence of state transitions from relatively simple network bursts into complex bursting, with multiple synchronized events within each burst.We describe these transitions as qualitative changes of the state attractors, constructed from experimental data, mediated by elevation of extracellular K( + ) concentration.

View Article: PubMed Central - PubMed

ABSTRACT
Increase of the extracellular K( + ) concentration mediates seizure-like synchronized activities in vitro and was proposed to be one of the main factors underlying epileptogenesis in some types of seizures in vivo. While underlying biophysical mechanisms clearly involve cell depolarization and overall increase in excitability, it remains unknown what qualitative changes of the spatio-temporal network dynamics occur after extracellular K( + ) increase. In this study, we used multi-electrode recordings from mouse hippocampal slices to explore changes of the network activity during progressive increase of the extracellular K( + ) concentration. Our analysis revealed complex spatio-temporal evolution of epileptiform activity and demonstrated a sequence of state transitions from relatively simple network bursts into complex bursting, with multiple synchronized events within each burst. We describe these transitions as qualitative changes of the state attractors, constructed from experimental data, mediated by elevation of extracellular K( + ) concentration.

No MeSH data available.


Related in: MedlinePlus

Power spectrum and principal component analysis of network oscillations. a Time–frequency spectrogram based on Fourier transform for the entire period of activity from one recording channel. The plot on the right shows the averaged power spectrum during the early and late phases of the oscillations. b LFP recording that was used to compute spectrogram in a. Blue and red lines indicate the periods selected for principal component analysis (PCA), as initial and later phases correspondingly. High KCl was applied at the start of the recording shown. c, d Projections of the 32-channel data sets to the first four PCA components. e The cumulative increase of variance. Blue and red lines indicate initial and later phases of the oscillations. f, g Spatial distribution of the PCA weights for the first four components for initial (f) and later (g) phases of the oscillations. Each color-scaled image indicates the interpolated PCA weights on the electrode grid
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Fig3: Power spectrum and principal component analysis of network oscillations. a Time–frequency spectrogram based on Fourier transform for the entire period of activity from one recording channel. The plot on the right shows the averaged power spectrum during the early and late phases of the oscillations. b LFP recording that was used to compute spectrogram in a. Blue and red lines indicate the periods selected for principal component analysis (PCA), as initial and later phases correspondingly. High KCl was applied at the start of the recording shown. c, d Projections of the 32-channel data sets to the first four PCA components. e The cumulative increase of variance. Blue and red lines indicate initial and later phases of the oscillations. f, g Spatial distribution of the PCA weights for the first four components for initial (f) and later (g) phases of the oscillations. Each color-scaled image indicates the interpolated PCA weights on the electrode grid

Mentions: In this study, we recorded simultaneous extracellular activity from CA regions of the mouse hippocampus. We utilized a 32 recording channel array (ALA Scientific Instruments Inc.) to gain insight into spatio-temporal resolution of the hippocampal epileptiform development. Figure 1a illustrates initiation of network synchronization in response to rising [K + ]o from 2.5 mM (normal ACSF) to 10 mM. Bath application of high KCl led to an increase in multiunit activity, followed about 2 min later by appearance of the synchronized network events with progressively increasing amplitude and frequency. This initial phase of oscillations lasted for about 30 to 60 s, after which oscillations reached maximum amplitude. Frequency changes followed a more complex pattern and are summarized in Fig. 3 below. Temporal dynamics of all epileptiform events are summarized in Fig. 1b. The initial phase of oscillations was characterized by burst events at lower amplitude, higher frequency and usually consisted of one intraevent peak (Fig. 1d). As time progressed, amplitude of network bursting increased, frequency decreased, and multiple intraevent peaks appeared. At the end of the initial phase, epileptiform reached steady state, which can be characterized by largest amplitude, lowest frequency, longest interevent intervals and biggest area of the event (Fig. 1e). From that point, the pattern of epileptiform development reversed, showing slow decrease in amplitude, interevent intervals and the area of the event (Fig. 1f). A later phase exhibited fewer intraevent peaks, which, together with decreased event area, reflected a lesser degree of synchronization. Time dependence of the frequency of burst events was inverse to the event duration (Fig. 1c). Duration and frequency of the event can be defined by the set of synaptic and intrinsic properties of the cell and reflects the degree of network synchronization.Fig. 1


Dynamics of epileptiform activity in mouse hippocampal slices.

Filatov G, Krishnan GP, Rulkov NF, Bazhenov M - J Biol Phys (2011)

Power spectrum and principal component analysis of network oscillations. a Time–frequency spectrogram based on Fourier transform for the entire period of activity from one recording channel. The plot on the right shows the averaged power spectrum during the early and late phases of the oscillations. b LFP recording that was used to compute spectrogram in a. Blue and red lines indicate the periods selected for principal component analysis (PCA), as initial and later phases correspondingly. High KCl was applied at the start of the recording shown. c, d Projections of the 32-channel data sets to the first four PCA components. e The cumulative increase of variance. Blue and red lines indicate initial and later phases of the oscillations. f, g Spatial distribution of the PCA weights for the first four components for initial (f) and later (g) phases of the oscillations. Each color-scaled image indicates the interpolated PCA weights on the electrode grid
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Related In: Results  -  Collection

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getmorefigures.php?uid=PMC3101328&req=5

Fig3: Power spectrum and principal component analysis of network oscillations. a Time–frequency spectrogram based on Fourier transform for the entire period of activity from one recording channel. The plot on the right shows the averaged power spectrum during the early and late phases of the oscillations. b LFP recording that was used to compute spectrogram in a. Blue and red lines indicate the periods selected for principal component analysis (PCA), as initial and later phases correspondingly. High KCl was applied at the start of the recording shown. c, d Projections of the 32-channel data sets to the first four PCA components. e The cumulative increase of variance. Blue and red lines indicate initial and later phases of the oscillations. f, g Spatial distribution of the PCA weights for the first four components for initial (f) and later (g) phases of the oscillations. Each color-scaled image indicates the interpolated PCA weights on the electrode grid
Mentions: In this study, we recorded simultaneous extracellular activity from CA regions of the mouse hippocampus. We utilized a 32 recording channel array (ALA Scientific Instruments Inc.) to gain insight into spatio-temporal resolution of the hippocampal epileptiform development. Figure 1a illustrates initiation of network synchronization in response to rising [K + ]o from 2.5 mM (normal ACSF) to 10 mM. Bath application of high KCl led to an increase in multiunit activity, followed about 2 min later by appearance of the synchronized network events with progressively increasing amplitude and frequency. This initial phase of oscillations lasted for about 30 to 60 s, after which oscillations reached maximum amplitude. Frequency changes followed a more complex pattern and are summarized in Fig. 3 below. Temporal dynamics of all epileptiform events are summarized in Fig. 1b. The initial phase of oscillations was characterized by burst events at lower amplitude, higher frequency and usually consisted of one intraevent peak (Fig. 1d). As time progressed, amplitude of network bursting increased, frequency decreased, and multiple intraevent peaks appeared. At the end of the initial phase, epileptiform reached steady state, which can be characterized by largest amplitude, lowest frequency, longest interevent intervals and biggest area of the event (Fig. 1e). From that point, the pattern of epileptiform development reversed, showing slow decrease in amplitude, interevent intervals and the area of the event (Fig. 1f). A later phase exhibited fewer intraevent peaks, which, together with decreased event area, reflected a lesser degree of synchronization. Time dependence of the frequency of burst events was inverse to the event duration (Fig. 1c). Duration and frequency of the event can be defined by the set of synaptic and intrinsic properties of the cell and reflects the degree of network synchronization.Fig. 1

Bottom Line: In this study, we used multi-electrode recordings from mouse hippocampal slices to explore changes of the network activity during progressive increase of the extracellular K( + ) concentration.Our analysis revealed complex spatio-temporal evolution of epileptiform activity and demonstrated a sequence of state transitions from relatively simple network bursts into complex bursting, with multiple synchronized events within each burst.We describe these transitions as qualitative changes of the state attractors, constructed from experimental data, mediated by elevation of extracellular K( + ) concentration.

View Article: PubMed Central - PubMed

ABSTRACT
Increase of the extracellular K( + ) concentration mediates seizure-like synchronized activities in vitro and was proposed to be one of the main factors underlying epileptogenesis in some types of seizures in vivo. While underlying biophysical mechanisms clearly involve cell depolarization and overall increase in excitability, it remains unknown what qualitative changes of the spatio-temporal network dynamics occur after extracellular K( + ) increase. In this study, we used multi-electrode recordings from mouse hippocampal slices to explore changes of the network activity during progressive increase of the extracellular K( + ) concentration. Our analysis revealed complex spatio-temporal evolution of epileptiform activity and demonstrated a sequence of state transitions from relatively simple network bursts into complex bursting, with multiple synchronized events within each burst. We describe these transitions as qualitative changes of the state attractors, constructed from experimental data, mediated by elevation of extracellular K( + ) concentration.

No MeSH data available.


Related in: MedlinePlus