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International Journal of Neural Systems, Vol. 17, No. 2 (2007) 87–103 c World Scientific Publishing Company
NETWORK DYNAMICS AND SYNCHRONOUS ACTIVITY IN CULTURED CORTICAL NEURONS MICHELA CHIAPPALONE∗ Neuroengineering and Bio-nanoTechnology Group, Department of Biophysical and Electronic Engineering - DIBE, University of Genova, Via Opera Pia 11A, 16145, Genova, Italy
[email protected] ALESSANDRO VATO Department of Physiology, Northwestern University Medical School, 303 E. Chicago Ave., 60611, Chicago, IL, USA
[email protected] LUCA BERDONDINI Sensors, Actuators and Microsystems Laboratory, Institute of Microtechnology, University of Neuchˆ atel, Rue Jaquet-Droz 1, CH-2007 Neuchˆ atel, Switzerland
[email protected] MILENA KOUDELKA-HEP Sensors, Actuators and Microsystems Laboratory, Institute of Microtechnology, University of Neuchˆ atel, Rue Jaquet-Droz 1, CH-2007 Neuchˆ atel, Switzerland
[email protected] SERGIO MARTINOIA Neuroengineering and Bio-nanoTechnology Group, Department of Biophysical and Electronic Engineering - DIBE, University of Genova, Via Opera Pia 11A, 16145, Genova, Italy
[email protected] Neurons extracted from specific areas of the Central Nervous System (CNS), such as the hippocampus, the cortex and the spinal cord, can be cultured in vitro and coupled with a micro-electrode array (MEA) for months. After a few days, neurons connect each other with functionally active synapses, forming a random network and displaying spontaneous electrophysiological activity. In spite of their simplified level of organization, they represent an useful framework to study general information processing properties and specific basic learning mechanisms in the nervous system. These experimental preparations show patterns of collective rhythmic activity characterized by burst and spike firing. The patterns of electrophysiological activity may change as a consequence of external stimulation (i.e., chemical and/or electrical inputs) and by partly modifying the “randomness” of the network architecture (i.e., confining neuronal sub-populations in clusters with micro-machined barriers). In particular we investigated how the spontaneous rhythmic and synchronous activity can be modulated or drastically changed by focal electrical stimulation, pharmacological manipulation and network segregation. Our results show that burst firing and global synchronization can be enhanced or reduced; and that the degree of synchronous activity in the network can be characterized by simple parameters such as cross-correlation on burst events. Keywords: In-vitro cortical networks; Micro-electrode-array; sub-population segregation; electrical and chemical stimulation; burst event; cross-correlation. ∗
Corresponding author.
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1.
Introduction
Nowadays, nervous tissues can be cultured in-vitro and kept alive for several months, while preserving their adaptive properties.1,2 Furthermore, MicroElectrode Arrays (MEAs), initiated by Pine and Gross,3,4 have become now a reliable interfacing technique capable of establishing a bi-directional communication between a population of connected neurons and the external world.5,6 The functional characteristics of the MEAs permit mid- to longterm recordings of both spontaneous and evoked neuronal network activity patterns and of their spatio-temporal evolution. Under this perspective, large neuronal ensembles coupled to MEAs represent an interesting intermediate level (from in-vitro single cell to in-vivo studies) for investigating information processing and dynamics in neuronal systems under controlled condition and by means of applications of external stimuli and/or physical constraint.7,8 These in-vitro reduced neuronal systems also allow studying basic mechanism of learning and adaptivity and specific understanding of the network functionalities. During development, in-vitro cortical neurons9–11 show highly complex temporal patterns that range from isolated/random spiking to robust and rhythmic bursting behavior. How rhythmic behavior and synchronicity in cortical neurons are implicated in sensory information processing and/or representation is still hotly debated in the literature.12 More recently burst firing as well as tonic spiking has been proven to be involved in sensory information transmission both in in-vivo13,14 and in-vitro systems.15 These studies suggest that the rhythmic and apparently stereotyped behavior of in-vitro neuronal networks, often explained as intrinsic dynamics due to the sensory deprivation,16 can be conveniently exploited for understanding some of the mechanisms of neuronal communication (flow of information) and information processing. In this work we investigate the characteristic of cortical networks under different experimental conditions trying to individuate the main factors affecting the synchronous state of the neuronal system. Synchrony, as general form of temporal relationship between neurons, has been extensively studied and it encompasses a wide spectrum of neuronal behaviors on various spatial and temporal scales. Differently
from what reported in the literature,17,18 here we are only focusing on the bursting activity of large neuronal networks, first quantifying and then analyzing this robust behavior in terms of spatiotemporal correlation. In the following we will show that by using different kinds of stimulation (i.e. electrical stimulation and pharmacological manipulation with specific modulators of neuronal activity) or simply by driving the cell growth towards a desired network topology (e.g. mechanical network segregation), we are able to strongly change the synchronization level of the neuronal population in terms of burst patterns.
2.
Materials and Methods
2.1. Cell culturing Neuronal preparations were obtained from cerebral cortices of Sprague Dawley rats at embryonic day 18 (E18). Cerebral cortices were dissociated by enzymatic digestion in Trypsin 0.125% – 20 min at 37◦ C — and then triturated with a fire-polished Pasteur pipette. Cells were then plated onto poly-D-lysine and laminin coated standard and clustered Micro Electrode Arrays at the final density of 2–6 × 104 cells/device. The cells were incubated with 1% Glutamax, 2% B-27 supplemented Neurobasal medium, in a humidified atmosphere 5% CO2 , 95% air at 37◦ C.19–21 The 50% of the medium was changed every week. Tissue culture media and supplement were purchased from Invitrogen; most of the other chemicals and reagents were supplied from Sigma.
2.2. Micro Electrode Arrays Two types of Micro Electrode arrays (MEAs) constituted of 60 recording/stimulating electrodes were used in our experiments. The first type (Multi Channel Systems — MCS, Reutlingen, Germany, see Fig. 1A) consists of 60 channels planar TiN/SiN MEAs (30 µm electrode diameter, 200 µm spaced) arranged on a 8 × 8 square array (the four electrodes on the corners excluded). On these devices neurons form a “random” network (Fig. 1D, E) on a global surface area of 4 × 4 mm2 (active surface 1.8 × 1.8 mm2 ). The second type (i.e. Cluster MEA) is a custommade MEA device whose main feature relies on
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(F) Fig. 1. Cortical neuronal networks cultured over Micro Electrode Arrays. A. Commercial MEA by Multichannel Systems (Reutlingen, Germany) with 60 electrodes. B. Custom-made “cluster MEA” device. C. Scheme of the cluster MEA, composed by five wells interconned by micromachined channels. D, E. Cortical neurons grow and develop over MEA. F. Electrophysiological signal recorded from a single microelectrode. The big peak corresponds to the external stimulus artifact (1.5 V peak-to-peak), which evokes a “burst” (i.e. a series of densely packed spikes) in the recording channel.
the possibility of segregating five neuronal sub-populations in separated, although interconnected, clusters. Details on the microfabrication and technical features has been already presented in the literature.8 Briefly, as shown in Fig. 1B, the new device is constituted of a micro-machined SU8 structure and Pt-microelectrodes on Pyrex substrate. The MEA also provides a total of 60 microelectrodes with a diameter of 30 µm, which are now distributed as follows: 11 microelectrodes per lateral cluster; 1 microelectrode per open-channel and 12 microelectrodes in the central cluster. The microelectrodes are numbered and the clusters are identified by a letter
(from A to E, see Fig. 1C). The clustering structures define 5 clustering chambers of 3 mm in diameter, connected by open-channels of 500 µm or 800 µm in length and 300 µm in width.
2.3. Experimental set-up The experimental set-up is based on the MEA60 System, (Multi Channel Systems - MCS, Reutlingen, Germany) and it is routinely used for conducting experiments on neurons extracted from the embryo’s rat cortex. The system consists of the following main
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components: • a Faraday cage and an anti-vibration table; • an inverted microscope, connected to a TVcamera, to visually monitor the cells during the experiment; • a Micro Electrode Arrays (as described in the previous paragraph); • a mounting support (MEA 1060) with integrated 60 channels pre- and filter amplifier with a gain equal to 1200×; • the STG 1008 Stimulus Generator, with 8 channels to deliver both current and voltage desired stimulating signals; • a PC equipped with the MC Card, a PCI A/D — D/A board with a maximum of 128 recording channels, 12 bit of resolution and a maximum sampling frequency of 50 kHz/channel (sampling frequency is set to 10 kHz/channel in our case). Signals are monitored and recorded by using the commercial software MCRack (MultiChannelSystems, Reutlingen, Germany); then data are processed by using specifically developed software tools (cf. Sec. 2.5). 2.4. Experimental protocols Electrophysiological signals were recorded during the third week in culture (22–24 Days In Vitro, DIV). During each recording session, the MEA was maintained at 37◦ C. Spontaneous activity in culture medium (i.e. basal condition) was used as control condition for each experiment (10 minutes). Then, the network was exposed to chemical or to electrical stimulation, with the aim to suppress or enhance the emergence of synchronized network bursts. 2.4.1. Drug addition One of the major substances that affects sleep-wake alternations in-vivo, is acetylcholine (Ach). In the intact animal it is involved in cognitive functions, attention, plasticity and sleep-wake alternations.22,23 Since the frequency of the network bursting in our cultures is in the range of the slow-wave sleep frequencies,24 we added Ach to our culture medium to disrupt the slow-wave-sleep-like synchronous bursting. Ach was added to our cultures at low concentrations, namely 10µM, inspired by a study of Liu.25
Bicuculline (BIC, antagonist of the inhibitory pathways mediated by the receptor GABAa,) was added at the final concentration of 30 µM.26–28 Administration of the chemicals to the culture medium was made by pipetting in directly to the medium. 2.4.2. Electrical stimulation The applied electrical stimulation protocol consisted of delivering trains of 50 stimuli to six/eight specific MEA sites, in a serial way, and recording the activity from the (up to 59) remaining electrodes.7,29 The sites of stimulation were selected randomly, and changed if no observables response was detected. Stimuli consisted of trains of biphasic pulses at low frequency (0.2 Hz, ±750 mV). 2.4.3. Protocol details Specifically, utilized:
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• Basal (10 minutes) + Bicuculline (BIC) 30 µM (10 minutes) – MCS MEA. • Basal (10 minutes) + Acetylcholine (Ach) 10 µM (10 minutes) – MCS MEA. • Basal (10 minutes) + electrical stimulation from 8 microelectrodes (serially) – MCS MEA. • Basal (10 minutes) + electrical stimulation from 5 microelectrodes, one for each cluster (serially) - Cluster MEA. 2.5. Data analysis Neuronal networks process information by generating action potentials (i.e. spikes) and propagating them, forming spatio-temporal patterns of electrical activity. In particular, in in-vitro neuronal networks, spikes are arranged in bursts and pauses.9,30 Bursts represent an essential aspect of the neuronal code.15 Then, phenomena like synchronization and oscillations, which play an essential role in the evaluation of complex neuronal patterns, must be investigated not only at the spike but also at the burst level. 2.5.1. Spike detection Extracellularly recorded spikes (Fig. 1F) are usually embedded in biological and thermal noise of about 10–20 µV peak-to-peak and they can be detected
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using a threshold based algorithm.31,32 Briefly, a sliding window, sized to contain at most one single-unit spike (i.e. of 2–3 msec in duration),33,34 is shifted over the signal until the difference between the maximum and the minimum within the window is below the peak-to-peak threshold and, when the difference exceeds the threshold, a spike is found and its time-stamp is saved. The threshold, calculated as a multiple of the standard deviation (8*SD) of the biological noise, is separately defined for each recording channel.7 Signals recorded on each channel can reflect the activity of more than one neuron35 ; however, we did not use spike sorting to distinguish the contributions of different neurons on each recording channel.15,29 2.5.2. Burst detection A population burst consists of episodes of activity (i.e. densely packed spikes) occurring simultaneously at many channels, spread over the entire network. The spikes belonging to a burst are time spaced in a range of a few milliseconds; these packages generally last from hundreds of milliseconds up to seconds (burst duration) with long quiescent periods (Inter Burst Interval — IBI). An algorithm was developed to detect the presence of a burst.36,37 : it is based on setting the minimum number of spikes within a single burst (i.e. minSpikes = 10) and the maximum delay (i.e., inter spike interval) between two consecutive spikes belonging to the same burst (i.e. maxISI = 100 msec). Based on this definition, from each spike train we obtained a new time series for analysis, the Burst Event train (i.e. BE train), containing only the initial spikes of each burst.15 The channels showing a bursting rate less than 0.4 burst/min (i.e. at least 2 bursts during an acquisition trial of 300 sec) were not included in the analysis. 2.5.3. Cross-correlation of burst events We built the cross-correlation histograms Cxy (τ )38,39 according to the method of the activity pairs described by Eytan40 : given two ‘peak’ trains (i.e. X and Y), recorded from two electrodes of the MEA, we counted the number of ‘peaks’ in the Y train within a time frame around the spikes of the X train of ±T (T = 150 msec), using bins ∆τ = 5 msec. The correct Cxy (τ ) was obtained by means of a normalization procedure, by dividing each element of the array by
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the number of ‘peaks’ in the X train.40 In our specific case, each ‘peak’ represents a ‘burst event’ and the considered train is actually the BE train (cf. Burst Detection section). To get qualitative information on how the burst activity of an electrode x is correlated to the burst activity of all the other electrodes y (with y = x), the mean correlogram was defined and calculated as follows: n 1 Cxy (τ ) (1) Cx (τ ) = n − 1 y=1 for x = y and 1 ≤ x ≤ 60, n = 60. To better quantify the possible changes in burst synchronization, we used a parameter called Coincidence Index (i.e. CI).7,41 The CI was calculated as the ratio of the integral of a cross-correlation function at a specified electrode around time zero (multiple of the bin size) of the total time, according to the definition below: k∗(∆τ /2) τ =−k∗(∆τ /2) Cxy (τ ) (2) CI = T τ =−T Cxy (τ ) where T = 150 msec, ∆τ = 5 msec, k = 3. Defined in the previous way, the numerator of the fraction represents the cumulative correlogram within the three central bins [−15; +15] msec and the denominator represents the cumulative one in the time window [−150; +150] msec.
2.5.4. Evoked response To investigate the neural activity evoked by stimulation, we computed the post-stimulus time histogram (i.e. PSTH), which represents the impulse response of each site of the neural preparation to electrical stimulation. The PSTHs were calculated by taking 600-msec time windows from the recordings that follow each stimulus. We then counted the number of spikes occurring in a 2–4 msec bin and divided this measure by the number of stimuli.39 For our cultures, typical PSTHs show an ‘early’ (less than 50 msec) and a ‘late’ (50–250 msec) component.1,15,29 3.
Results
The aim of these experiments was to study if and how specific external stimuli (i.e. both chemical and electrical) can affect network behavior and change its spontaneous dynamics.
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Fig. 2. Raster plots of the BIC experiment (top) and Raster plots of the Ach experiment (bottom). A. Culturing medium for the BIC experiment. The spontaneous activity is characterized by the presence of evident burst activity, in the form of network bursts, involving all the recording channels. B. Addition of 30 µM BIC. The number of bursting channels increases, together with the duration of each burst. Burst activity appears more highly synchronized among the recording channels. C. Culturing medium for the Ach experiment (very similar to A). D. Addition of 10 µM Ach. The network becomes more active, both in terms of burst and spike rate, but the network burst phenomena are less evident. Time frame: 50 sec for each raster.
We decided to measure networks in the third week of culture, since this is the age of the complete maturation of the connections, with a good balance between excitatory and inhibitory synapses.9,42,43 The typical electrophysiological activity in this age is a mixture of random spiking and collective strong bursting, usually spread over the entire network.11,30,33,44,45 We will show that this pattern of activity can be modulated and the level of synchronization can be enhanced/depressed by using specific
chemicals, a focused electrical stimulation and/or by confining the network into separated clusters. 3.1. Drug treatment The application of BIC and Ach produces a significant change in the synchronization level of cortical networks, even if in a completely opposite directions. The raster panels in Fig. 2 show the electrophysiological activity in two different experiments: a network
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Fig. 3. Histograms for the BIC experiment (A – burst duration, B – Inter Burst Interval, IBI) and of the Ach experiment (C burst duration, D – Inter Burst Interval, IBI).
under exposure of BIC (top panels, Fig. 2A, B) and a network under exposure of Ach (bottom panels, Fig. 2C, D). In both experiments, the initial behavior during spontaneous activity (i.e. no drug addition) is similar. The network presents a typical burst pattern, spread over the entire array, called ‘network burst’.24,33 When a chemical is added, the behavior completely changes its initial appearance. Under the effect of 30 µM BIC (Fig. 2B), the network burst phenomenon increases: more bursts are generated, involving almost all the recording channels and showing a high level of synchronization. On the contrary, during the application of 10 µM Ach (Fig. 2D) to the culturing medium the activity still remains sustained in terms of burst pattern but the network appears
more de-synchronized and a clear random spiking is generated. 3.1.1. Burst duration and IBI histogram Bursting behavior can be quantitatively characterized by two main parameters: the burst duration, BD (i.e. temporal length of the burst [msec]) and the Inter-Burst-Interval, IBI (i.e. temporal distance between two consecutive burst events [sec]). Figure 3 presents the histograms for BD and IBI when the network is exposed to BIC and to Ach. The left side of Fig. 3 shows the histogram of the burst duration in the case of BIC (Fig. 3A) and in the case of Ach (Fig. 3C). While the histogram
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in the initial condition is very similar between the two experiments (concentrated in the [50–400 msec] region, the mean values in the BIC and Ach conditions are different: 629.6 ± 8.5 msec in the case of BIC and 362.8 ± 3.3 msec in the case of Ach (i.e. mean ± se). The analysis of the IBI histogram shows that also the period of the burst changes as a consequence of the added chemical (BIC: 8.9 ± 0.3 sec; Ach: 3.9 ± 0.1 sec). The reported results suggest that in both cases, even with different features, we were able to modulate the burst patterns with respect to the initial spontaneous activity of the network (baseline state). 3.1.2. Cross correlation on burst events The main changes between the two experiments occur in terms of event synchronization. While bursts are maintained following different temporal patterns, the timing of the burst events (i.e. as defined by the timing of the first spike in each burst, cf. Materials and Methods section) is strongly affected by the input (e.g. presence of a specific compound.17 ) A synchronization analysis among bursts between different electrodes was conducted by using the cross-correlograms of the activity recorded in pairs of bursting electrodes (59 × 59, excluding selfcorrelations). Panels A and B of Fig. 4 show the correlograms, obtained during an experimental session, between a specific channel (ch 15, corresponding to electrode 31-third column, first row — in the MEA layout) and all the others. In this case, also the auto-correlogram is included and it corresponds to the maximum peak. For each couple (a, b), where a = el 31 and b anyone of the 59 other electrodes, the correlograms depict the spike count (Z-axis), in which electrode ‘a’ and ‘b’ fire a spike with a precise time delay (‘Time (msec)’-axis). On the other axis, the electrode number (i.e. ‘Electrode index’) is depicted. The shapes of the obtained functions clearly change from the basal condition, in which small spread peaks around time zero denote a good level of synchronized bursts, and the BIC phase, when an evident single central peak is shown, denoting maximum synchronization of the network. Figure 4 (bottom panels) shows the graphs of the mean cross-correlation (cf. Materials and Methods section) between each recording channel and
all the others, in the baseline phase (Fig. 4C) and Ach condition (Fig. 4D). The mean (spatial) crosscorrelograms are depicted over an 8 × 8 grid, in the MEA layout. It is worth to note that during the BIC phase, not only the number of synchronized channels increase (i.e. more channels are recruited for generating the network burst) but also the level of synchronization reaches its maximum values in most of the bursting channels. In Fig. 5 (top panels) we report the crosscorrelograms obtained from a single channel (electrode 13, as an example) versus all the others in two experimental conditions: spontaneous baseline activity (Fig. 5A) and activity under the effect of 10 µM Ach (Fig. 5B). The only visible peak refers to the autocorrelation function for the considered channel. While the level of synchronization is good in the control condition, a depression can be clearly noted in the Ach phase (i.e. absence of peaks around zero). It is worth to note that the lack of synchronization in the second condition is due to the presence of the drug and not to the absence of activity, which is actually strong, as demonstrated by the previous results (cf. Burst Duration and IBI histogram section). It is worth noting that the shapes of the reported functions change critically in the transition from the baseline phase (Fig. 5C) to the Ach phase (Fig. 5D); an evident decrease of the general level of synchronization is given by the low amplitude of the central peak, which almost disappears during the Ach phase. To quantify the differences in the crosscorrelograms presented in Figs. 4 and 5, we used a parameter called Coincidence Index (CI, cf. Materials and Methods section), which represents the level of synchronization between pairs of active bursting channels. The histograms, calculated for all bursting pairs in two representative experiments, are presented in Fig. 6. Figure 6A shows the CI histogram in the BIC experiment: note that during the drug treatment the parameter reaches the maximum value of 1 for many pairs, indicating that all the activity is concentrated in the central peak (± 15 msec) and is zero elsewhere in time (± 150 msec). Figure 6B presents the histogram in the case of Ach administration. In this condition, the natural synchronization of the network, denoted by a CI distributed mainly between the 0.1–0.5 interval, is completely lost and confined to the lowest possible values (0.1–0.2 interval), still indicating that the presence of Ach in the culture
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Fig. 4. Cross-correlograms for the BIC experiment. A. Cross-correlograms between one sample channel (ch 31) and all the others in the control phase. The autocorrelation is included and corresponds to the highest peak. B. Cross-correlograms between one sample channel (ch 31) and all the others in the BIC phase. Bin size is 5 msec. C. Mean correlograms for the control phase depicted over an 8 × 8 grid, in the MEA layout. Autocorrelation is excluded. D. Mean correlograms for the BIC phase. Time-axis range: [−150, +150] ms; Y-axis range: [0, 1]. Autocorrelation is excluded.
medium strongly affects the level of synchronization of the network in the burst generation process.
the Materials and Methods section, is depicted in Fig. 7B.
3.2. Electrical stimulation
3.2.1. Site-dependent responses
By using localized extracellular voltage stimulation through a single electrode of the array, it is possible to control the rhythm of bursting activity.30 The spontaneous bursting activity recorded from all the channels of a 21 DIV cortical culture is reported in the raster plot of Fig. 7A. The bursting activity invoked by the electrical stimulation (i.e. stimulus delivered through electrode 28), obtained by applying the experimental protocol described in
In Fig. 8 the Post Stimulus Time Histograms (i.e. PSTHs), obtained from signals recorded from all the array electrodes, are shown. Samples from the responding microelectrodes and occurring in the 600 msec-window after the stimulus were used to generate the PSTHs. The histograms are arranged over an 8 × 8 grid, in the MEA layout, in two different conditions: under the stimulation from site 17 (first column, seventh row, Fig. 8A) and under the
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Fig. 5. Cross-correlograms for the Ach experiment. A. Cross-correlograms between one sample channel (ch 13) and all the others in the control phase. The autocorrelation is included and corresponds to the highest peak. B. Cross-correlograms between one sample channel (ch 13) and all the others in the Ach phase. Bin size is 5 msec. C. Mean correlograms for the control phase depicted over an 8 × 8 grid, in the MEA layout. Autocorrelation is excluded. D. Mean correlograms for the Ach phase. Time-axis range: [−150, +150] ms; Y-axis range: [0, 1]. Autocorrelation is excluded.
stimulation from site 28 (second column, eighth row, Fig. 8B). As it clearly emerges from the figure, the network responds to the stimulus in two different ways. In one case (Fig. 8A) the network shows only the so called “delayed response”, at about 50–100 ms after the stimulus and consisting of a burst lasting about 300 msec, related by delayed to the stimulus at site 17. The obtained response could be interpreted as the propagation of the electrically-induced activity from the stimulating site to neurons located far from it. By changing the stimulating site (i.e. 28 instead of 17, Fig. 8B), only an “early response” is observed.
In general, in different preparations it is always possible to obtain different responses, from the early response (the majority of the obtained responses) up to the delayed response, with a multitude of intermediate combinations of these two extremes (Fig. 8C). Additionally, it should be noted that, to each stimulating channel and the same tissue, very much one kind of response is observed.
3.2.2. Phase locking of burst cycle to extra cellular stimulation The burst duration histogram is not affected by the presence of an external stimulus which drives
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Fig. 6. Coincidence Index – CI histogram. A. CI histogram for the BIC experiment (control and BIC phase). Note that, in case of BIC addition, most sites have a CI equal to 1. B. CI histogram for the Ach experiment (control and Ach phase): during the Ach phase, most sites present CI values in the range [0, 0.2], indicating that the natural synchronization of the network is almost completely lost.
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Fig. 7. A 2 sec raster plot of bursting activity from a 21 DIV cortical culture. A. Spontaneous burst. B. Burst after electrical stimulation delivered through channel 28.
the network, as demonstrated by the histogram of Fig. 9A. On the contrary, the inter burst frequency (Inter Burst Interval, IBI) is strongly influenced by an electrical stimulation. Figure 9B shows the IBI histogram calculated during spontaneous and evoked activity by stimulation for the active bursting electrodes of a representative tissue. It is worth to note that during spontaneous activity there is no preferred value for inter-burst period and the IBIs are spread over the entire time scale (i.e. from a few seconds up to 20 sec). When a stimulus is applied to any of the stimulating electrodes, the IBI values generate
a compact histogram, clustered around the 5 sec and 10 sec interval, which corresponds exactly to the stimulation frequency of 0.2 Hz (single and doubled). In addition to the above observations, an external electrical stimulus does not only change the burst rhythm generation but also the synchronization level among the active channels, as demonstrated by the cross correlation analysis of Figs. 10 and 11. Under the effect of an electrical stimulation, the network becomes more synchronized, as denoted by the graphs of Fig. 10A and B. The same result is confirmed by the mean cross-correlograms between
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(C) Fig. 8. Post-Stimulus Time Histograms (PSTH) of the neural preparation. A, B PSTHs for each recording site, arranged according to the actual topography of the MEA electrodes, in a typical experiment. The evoked response changes in both shape and magnitude when stimulation is delivered from two different sites: site 17 (panel A) and site 28 (panel B). Empty squares indicate sites that are not responding or used for stimulation. Bin size: 4 ms. C. Modulation of the mean responses obtained from the active electrodes in a sample experiment. Different shapes of the mean PSTH correspond to different stimulating electrodes, while the same recording electrodes show a reproducible response.
each recording channel and all the others (Fig. 10C, D), in baseline phase versus the under electrical stimulation phase at a selected channel. It is worth to note that in the presence of the stimulus the number of synchronized channels increases (i.e. more channels are recruited) and the level of synchronization reaches higher values. This last result is quantitatively presented in Fig. 11, where the CI histogram is reported. Note that in the stimulation phase the CI values are close to 1, even if they hardly reach their maximum as in the case of BIC addition. This means that, even if the evoked bursts by the stimulus are spatially synchronized, a spontaneous activity, unrelated to the external stimulation, is still present.
(B) Fig. 9. Burst duration and IBI histogram from an electrical stimulation experiment. Note that while the burst duration (panel A) histogram follow more or less the same shape in both experimental phases (i.e. baseline and stimulation), the number of bursts has doubled under the effects of the electrical stimulation. B. Under the effect of the electrical stimulation, the inter-burst period is very similar to the period of the stimulus (i.e. 5 sec, equal to a 0.2 Hz stimulation).
3.3. Sub-population segregation Spontaneous activity in random neural networks tends to exhibit complex activity patterns synchronized over the whole network and this behavior can make difficult to recognize internal mechanisms of adaptation and to exploit the possible interactions among sub-network circuits. For this reason a new device was built based on physical barriers in order
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Fig. 10. Cross-correlograms for the electrical stimulation experiment. A. Cross-correlograms between one sample channel (ch 24) and all the others in the control phase. The autocorrelation is included and corresponds to the highest peak. B. Cross-correlograms between one sample channel (ch 24) during the effects of an electrical stimulation delivered from site 28: most activity is centered in zero. Bin size is 5 msec. C. Mean correlograms for the control phase depicted over an 8 × 8 grid, reproducing the MEA layout. Autocorrelation is excluded. D. Mean correlograms during the electrical stimulation from site 28: new channels are recruited with respect to the control phase and the amplitude of the peaks is higher than the previous phase. Time-axis range: [−150, +150] ms; Y-axis range: [0, 1].
to constrain the network into interconnected (via integrate microchannels) cell assemblies. In this way the network is self-organized into sub-populations (each of one related to a cluster, cf, Fig. 1C) constituting a global connected network. This configuration is a good compromise between a completely random network and a patterned network allowing us to better understand the neural connection within and among the clusters. In Fig. 12A the raster plot of the burst event shows the spontaneous activity in 5 min of recording. There is a synchronous activity within the clusters
and the global behavior of the network is influenced by the neural connections among the clusters. The Inter Burst Interval histogram (Fig. 12B) for each cluster shows the differences in term of burst activity (i.e. preferred burst frequency for each cluster). The Cross Correlogram histogram of burst events using two different electrodes (Fig. 12C, D) shows that some electrodes (i.e. group of neurons) have strong connections only within the cluster they belong to (Fig. 12C), while others can be correlated (with lower peaks) also with the electrodes placed in the other clusters (Fig. 12D).
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(B)
Fig. 11. Histogram for the Coincidence Index — CI during spontaneous activity and electrical stimulation delivered from site 28. Not that, under the effects of an electrical stimulation, the CI values tend to be higher and close to 1.
(C)
(A) Fig. 12. Behaviors of a cortical network cultured over a cluster MEA. A. Raster plot of a 60 sec acquisition. Different colors correspond to the five different clusters of the array. Note that the electrodes belonging to the same cluster tend to fire simultaneously. B. Histograms of the IBI for the electrodes belonging to the five different clusters. The shapes of the histograms clearly changes among the clusters. C, D. Comparison between cross correlogram obtained from the burst events. The highest peak is the autocorrelation. An electrode from cluster C correlated to all the others (left panel); an electrode from cluster D correlated to all the others (right panel). The synchronization level is high (i.e. big peak around zero) only among the electrodes belonging to the same cluster. Lower synchronization levels are observed between electrodes of different clusters.
(D) Fig. 12.
(Continued)
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4. Discussion Cultured neuronal networks are a widely used and accepted experimental model in neuroscience research. They have been successfully utilized for studying pharmacological effects,28,46,47 for investigating the synapse formation and change in the connectivity during development and for studying learning and plasticity mechanisms at the network level.29,48 In-vitro cortical networks are fundamentally different from in-vivo preparations in terms of anatomy and synaptic connections; nonetheless they provide a rich environment in which pharmacological manipulation, external electrical stimulation and chronic recordings allow to investigate in controllable and reliable conditions some aspects of the nervous system. There is a large number of published papers addressing the role of synchrony, rhythmic activity and burst firing in in-vivo systems (see for a review12 ) while few works are related to the characterization of such behaviors in in-vitro studies.45,49 As mentioned the roles of rhythmic oscillation and synchrony are still debated in the literature. The formation of a cell assembly (in terms of correlated activity) and the possible formation of coherent cell assemblies are investigated in the context of sensory processing, attention, cross-modal and sensorimotor integration, working memory and conscious awareness.50,51 Cultured neuronal systems coupled to MEA devices offer the unique opportunity to bi-directionally interact (record from and stimulate at) with the neuronal population and to address specific and basic questions about synchrony and its relationship with the processing of information and sensory coding. In this work we characterized the firing properties of cortex tissue by internal pharmacological manipulation and by external conditioning (electrical stimulation and/or physical confinement). Particularly we showed that spontaneous spike and burst firing obtained by an intrinsic balance of excitatory and inhibitory connections can be modulated towards a more correlated rhythmic activity or to a more un-correlated activity. If the inhibitory connections are partly blocked (by Bicucculine application in the bath medium), the patterns of activity show a robust synchronous behavior at population level. This means that the global coherent formation of a cell assembly is modulated by
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the balance between excitatory and inhibitory connections and enhancing the excitatory components results in an increase in the synchronization at the level of population burst. On the contrary, neuromodulators such as Acetylcholine partly suppress the synchronization of the rhythmic population bursting behavior. Additionally, by electrically stimulating from specific channels, the burst firing is almost locked around the stimulating frequency (at least for a very-low frequency stimulation). This result demonstrate that rare stimulation (that it might be related to specific sensory stimulus — coding for rare input-event) is linked to the bursting behavior, and that synchronous burst event may code for some sensory input carrying out even more information than single spike event.13 Finally we also characterized the dynamics of interconnected cell assemblies clustered in MEAs. The presented results showed that synchrony is almost confined within the same cluster, opening the possibility of disrupting synchronization by separation of large neuronal population into clusters. This new experimental framework can be applied to homogeneous neuronal population (e.g., from cortical areas) or to different neuronal populations (e.g., hippocampal, thalamic, cortical) allowing to study how different dynamics are combined (tuned) in a neuronal orchestra. Acknowledgements This work was partly supported by the NeuroBit project IST-2001-33564, “A bioartificial brain with an artificial body: training a cultured neural tissue to support the purposive behavior of an artificial body” and by the Italian National Project FIRB (RBAU012KF8). The authors whish to thank Dr Mariateresa Tedesco (Brunella) for cell culture preparation. References 1. S. Marom and G. Shahaf, Development, learning and memory in large random networks of cortical neurons: Lessons beyond anatomy, Q. Rev. Biophys. 35 (2002) 63–87. 2. G. Q. Bi and M. M. Poo, Synaptic modifications in cultured hippocampal neurons: Dependence on spike timing, synaptic strengthand postsynaptic cell type, J. Neurosci. 18 (1998) 10464–10472.
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