Cholinergic Effects on Spectral Properties of Spike Trains in Rat Cortical Neurons

Takashi Tateno Yasuhiko Jimbo Hugh P. C. Robinson Department of Physiology Department of Precision Engineering Department of Physiology University of Cambridge University of Tokyo University of Cambridge

Downing Street, Cambridge, 1-3-1 Hongo, Bunkyo-ku, Tokyo Downing Street, Cambridge, CB2 3EG U.K. 113-8656 Japan CB2 3EG U.K.

E-mail: tt259@cam.ac.uk E-mail: jimbo@miki.pe.u-tokyo.ac.jp E-mail: hpcr@cam.ac.uk

Abstrnct- We investigated cholinergic modulation effects on synchronized burst activity of neurons in rat dissociated cortical cultures on electrode arrays, using a cholinergic agonist, Carba- chol (CCh). Application of CCh resulted in a loss of regularity, a less precise synchronization, and a fragmentation of the burst structure. We found that temporal properties of spike trains were well-characterized by a simple Poisson cluster-process model, which provided a precise insight into the temporal structure of spike trains and allowed quantitative fitting of the spectral changes induced by CCh. These results should help to elucidate the complex actions of cholinergic modulation on cortical cells in intact neural networks.

I. INTRODUCTION Cholinergic neurons in the basal forebrain provide a diffuse

innervation to all cortical regions, and their activation modu- lates neuronal activity in the cortex [I]. The operation of this cortical cholinergic system has been implicated in a number of important physiological functions, including arousal [21, sensory processing [31, [4], learning [51, and memory [61, [71. The dementia associated with 'Alzheimer's disease might be related to loss of the cortical cholinergic innervation [SI, [91. In addition, the cortical cholinergic system also plays a critical role in the development of the cortex, since its disruption severely alters the normal development of the cortex [IO]. In order to understand the mechanisms of these phenomena, it will be necessary to understand how cholinergic agonists modulate concerted firing patterns in cortical circuits.

To gain insight into chemical and electrical actions on corti- cal network activity, a useful model system has been network firing in cultures of rat cortical neurons using electrode arrays to monitor the activity of large numbers of neurons simultane- ously (for review, see [ I 11). Such cultures show both evoked and stable spontaneous synchronized burst firing [i2], [I31 whose propagation is mediated through glutamatergic synaptic projections and modulated by GABAergic projections, and which initiates at multiple sites in the network [14]. The strength of this correlated firing is controlled by LTP and LTD in the network [15]. It is also known that at early stages of development in cortical cultures, time series of burst firing are well-described by simple point processes [ 161.

Here, we study the effects of cholinergic activation on the properties of spontaneously synchronized population bursting,

applying the nonhydrolysable cholinergic agonist, carbachol (CCh), to cortical cultures. CCh, mainly acting through mus- carinic receptors, was found, at low concentrations (1-10 p M ) , to slow and to suppress spontaneous synchronized bursting, and at high concentrations (10-50 bM), to induce a switch to asynchronous single-spike firing. To characterize firing properties in the presence and absence of CCh, we especially focused on the temporal structure of spike trains, using the Poisson cluster-process (PCP) model for time series of action potentials. The results show that the model is well-suited to represent the time series of the spike trains, and that the average value ( ~ 1 . 6 9 ) of the power order b of llfb-like spectra in control is shifted to a smaller value ( b x 0.80). implying a change towards a Poisson-process (white) spectrum ( b = 0) [17], after application of CCh.

11. MATERIALS AND METHODS A. Preparations

Neuronal electrical activity was recorded from rat disso- ciated cortical cultures. A standard cell-culture method [i8] was used as in Ref. 1161. Connectivity in the intact cortex is generally high, and it is estimated that each neuron makes several thousands of connections to other neurons [19]. Al- though, unlike in vivo cortex, connections in a cultured cortical network are intrinsically localized, it is not possible to pre- cisely characterize the connectivity of a particular preparation. However, we made a simple estimate of cell connectivity, which indicated that a single neuron in these cultures might connect to approximately 600 neurons [161.

B. Recording and data analysis In this study, we carried out extracellular recordings of the

activity of neurons in 1 I cultures by using an electrode-array substrate [201, [21] that had 64 recording terminals within a 1 . 6 ~ 1.3-mm area. Each terminal covered an area 30-pm square, and the distance between the centers of adjacent sites was 180 p m [15]. For this experiment we used a measuring system of the same design as was built by Jimbo et al. [22]. Recordings were carried out while perfusing the culture chamber (volume 1 ml) with a solution, at 26°C containing 148 mM NaCI, 2.8 mM KCI, 2 mM CaCI2, 1 mM MgC12,

0-7803-8359-1/04/$20.00 02004 IEEE 3213

I O mM HEPES and 10 mM glucose (pH 7.2). Each recording session lasted for I to 3 hours and took place after the cells had been cultured for 42-5 I days in vitm (DIV). The day before each recording session, the culture medium was exchanged for one without insulin and penicillin-streptomycin. A digital signal processor stored recorded data and signals were filtered at 5 kHz and sampled with 16-bit resolution at 25 kHz.

After the recording, spike sorting was performed in the following way. First, spikes were detected when the extracellu- larly recorded signal exceeded a threshold. The threshold level for detecting spikes was set at 5.0 times the standard deviation of the baseline noise. We then extracted sets of digital samples, each of which included the detected spikes and represented the profile of each spike, using a rectangular window (60- ms window length) centered on the peak of each spike. Next, we used the Wavelet-based Spike Classifier (WSC) method [23]. The WSC method is based on the quantification of energy found in specific frequency bands at specific time locations during each spike profile. To obtain this localized time-frequency information we perform a discrete wavelet analysis on the temporal profile of each detected spike. In the WSC algorithm we used a discrete wavelet transform (DWT) based on a length-8 Daubechies system and computed DWT coefficients by a fast pyramidal algorithm [24]. Vectors consisting of selected coefficients at each of the recording sites were sorted and classified using the hierarchical clustering method [25 ] . The vectors were finally converted into pulse trains, each of which was assumed to represent the response of a single cell. Although we always identitied between 60 and 100 neurons in each network, 5 sets of spike trains from 5 different cells were used as representatives in each culture.

In particular, Griineis er al. proposed a method for estimating the statistical parameters of a cluster process when the primary process is Poisson [27]. In the paper presented here, we have adopted the framework of their model. We apply the parameter estimation method of the Poisson cluster process (PCP) to the experimental data.

Here is a brief explanation of the model. Suppose that NtOt(t) is a stochastic variable that denotes the number of events in a time interval [0, t]. Let < n, > denote the mean rate of the primary process (Poisson process). The cluster consists of N random events where N is a stochastic variable with the distribution function p,, = Prob{N = m}. The model assumes that the clustered distribution is described by

N”

where z is a real number. The function p , is called a clustered distribution and m can take on values in the range 1, 2, . . . , No, where NO is the maximum value of m. The time interval between the kth and (k + 1)th events in the cluster is denoted by XI.. As a definition, the first event in the cluster coincides with the primary event, thus a cluster containing i events contains (i - 1) intervals and the time between i successive events is

i-I

Ai =CAI. (i = 2,3; . . ) . (2) k = l

The distribution of Ai is expressed as

F;-l(t) = ProbfAi 5 t) (i = 2,3; . . ) . (3) Trains of spikes identified in this way exhibited autocorre-

around the origin: lation functions with low values or Here we suppose that in the model the intervals between events this is with their having ken generated by a single cell that becomes refractory after firing. Standard single- cell statistics such as interval means, standard deviations, coefficient of variation, histograms, and average tiring rates were also routinely computed. In this paper, data are given as meanfstandard deviation (SD) and measures such as means were compared using a Student’s t-test. For spike trains obtained from 55 cells in the 11 cultures, the basic statistics were as follows: sample sizes, 3335f437 (spikes); average interspike-intervals, 0.345f0.144 (s); standard devia- tions of intervals, 0.91 lf0.225 (s); and coefficient of variation, 2.7810.69.

within the clusters are assumed to be exponentially distributed with a distribution function expressed as

(4) 1

w l ( X ) = -exp -- <A> ( <:>>,

where <X>=E[X] and E[.] represents expectation. We thus have the probability distribution function of the intervals t of i successive events

i-2 <A>-’ wi-l(t) = ~

( i = 2 , 3 ; . . ) . ( i - Z ) ! (27)

(5)

C. The Poisson clusrer pmcess

In this study, we used “cluster” process analysis [261 to characterize spike trains time series. Here, clusters imply a tendency to tire synchronously. In each spike train individually we identified clusters in the way explained below and each was taken as an individual event; the series of clusters was considered to be a realization of a point process-with timing described by the sequences of inter-cluster intervals. In such a cluster process, a primary process determines the timing of the clusters. Each of the primary events triggers a secondary series.

The distribution of Ai is given by

1-1

Fi-l(t) = l - E w k ( t ) (2=2 ,3 ; . . ) . (7) k = l

We then obtain .

Prob{NtOt(t) 2 i} = = RN(i - l )Fi- l ( t )

Prob{NtOt(t) > i - l}Prob{Ai 5 t }

(C= 2 , 3 , . . . ) , (8)

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B1 Control a-

b-

I n L U U U e I i n n - n t t t

I

2 s B2 Carbachol 20 pM a '1 T,-

_.

b

i I I

2 s

Fig. 1. A. Experimental parameters for estimating the Poisson cluster-process model and a comparison of Fana factor curves before and aher application of 20 pM CCh. The two solid curves show the ratio (variancelmean) for numben of spikes N ( t ) versus period of observation time t (s) on a log YS. log scale. The dotted lines indicate slopes ( p e ) of the corresponding EUTVIS. saturating valucs (VMo) , and cutoffs (Tmcn and TmaZ). The four experimental parameten (i.e. pE. !-'MO, T,,.jn, and T,,.,,) that are derived from the curves are used to estimate the parameten of the PCP model as described in the [ext. The culture had her" 42 days in v i m . B. Extracellular recordings of spontaneously active conical cells at 5 different Sites (labeled as Site a to e) in a cultured cortical network in control (Bl) and in the presence of 20 pM CCh (82). In control. spontaneous synchronized bunting discharges were observed and remained stable. In the presence of 20 pM CCh, we observed a loss of regularity. a less precise synchronization. and a fragmentation of the burst SlNCture, with more isolated single spikes.

where NO

R N ( ~ ) = Prob{NtOt(t) > i} = p,. (9) ,=if1

Since the cluster constitutes a renewal process (Poisson pro- cess), for the probability density of Nt,,(t) , we obtain

Prob{N,,,(t) = i} = Prob(N,,,(t) 2 i} -Prob(N,,,(t) 2 i + 1)

= R N ( ~ - l)P+i(t) - RN(i)Fi(t) (i = 2,3, ...). (10)

Next we briefly explain the procedure we used in order to fit the model to data. The PCP is sufficiently described by four parameters I, NO, <n,>, and <A> when the intervals X are exponentially distributed. Although we assumed the intervals were exponentially distributed, the distribution functions of the intervals in our recorded data could in fact be approximated by exponential functions [16]. We thus have to derive the four parameters from the experimental data. For this purpose, we used counting statistics on the PCP. That is, the number of events (NtOt(t)) counted in a time interval t is used as a random variable for the estimate. Then the variancelmean curve, which is also referred to as the Fano factor curve [28], [29], of NtOt( t ) was calculated and transformed into a log-log plot. The parameters T,j,, T,,,, and V M O can be obtained from such a plot as shown in Fig. IA. In addition, a redundant parameter

is used to fit the slope p of the variancehean curve under the condition that E[N2]/E[N] = VMo. If the condition is satisfied, the p depends on the two parameters NO and t. Best- fit values for No and z are thus obtained for p FZ f ie . In practice, it is convenient to find No first and then obtain z because NO is an integer and z is a real number. Comparing either T,c, or T,,, with the abscissa of the variance/mean curve we obtain <A>. Finally, we derive <n, > from < n, >= E[NtOt]/E[N], where the expectation E[N] of N is calculated with known values No and z . To interpret the average duration of the primary process, a variable denoted by TO(E No.<X>) is used later and is hereafter referred to as a maximum cluster duration period.

In practice, after estimating the four PCP parameters, we calculated b from several thousands of spikes. Since the analysis stated above involves the assumption that the process is stationary, we therefore tested the stationarity using Kendall rank correlation tests [30], [16]. In a few cases, a time series of interspike intervals actually had an increasing or decreasing trend and such series were discarded from this analysis.

111. RESULTS

A. Cholinergic effects on spontaneous activiry In the absence of CCh, spontaneous synchronized bursting

discharges were observed and remained stable in the cultures

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over 40 DIV. Figure IBI shows typical examples of sponta- neous synchronized bursting of cells in a culture at 42 DIV. At this mature stage, the average firing rate (< N,,, >) was 4.05i2.66 (meanfSD) spikes/s (n=l I cells) over 45 minutes.

With respect to the spatial characteristics of spontaneous activity, recorded through an electrode array, continuous ex- posure to 20 pM CCh resulted in a loss of regularity, a less precise synchronization, and a fragmentation of the burst structure, with many more isolated single spikes, as shown in Fig. IB2. This is easily characterized in the auto- and cross-correlation functions of spike trains which clearly show oscillatory peaks of correlation, while in the presence of carbachol, these peaks are abolished, and the central peak is widened (not shown here). The lack of clear peripheral peaks indicates a loss of regularity, while the reduction in size of the central peak of the cross-correlation function indicates a reduction in the strength of synchronized firing. This pattern of firing induced by CCh was stable over prolonged periods (> 40 min), but terminated rapidly upon washing out CCh, which resulted in a recovery of synchronized bursting activity (not shown here). Reapplying CCh to the cultures (n=11) generally elicited changes which were not as reliable as during the first application.

The carbachol-induced changes in spontaneous spike pattern were reflected in the synaptic input received by cells. Applying CCh at 10-50 p M caused a decrease in the amplitude of the large, massed spontaneous postsynaptic currents in all I O cells recorded in whole-cell voltage-clamp (number of cultures n=X). This was accompanied by an increase in the number of small, unitary synaptic currents. These effects were reversible upon washout (data not shown here).

The main effects of CCh on the network activity were mediated by muscarinic receptors, since both the selective muscarinic agonists, muscarine (10-20 #M; n=6, data not shown here) and acetyl-beta-methylcholine (10-20 pM; n=10, not shown here) mimicked the suppression of synchronized bursting and the rise in ongoing spontaneous discharge rate which were produced by CCh. These effects were stable for prolonged periods (more than 45 min).

In contrast, the selective nicotinic agonist nicotine (20 pM) had a different set of effects on network firing (n=5, data not shown here). Application of nicotine did not lead to asynchronous single spike firing patterns. Instead, immediately after application of nicotine, the synchronized bursting activity increased in frequency and overall strength.

B. Parameter estimation of the PCP model

The results of PCP estimation show that in control the mean rate (<n,>) of the primary process in 55 cortical cells was 0.744i0.255 clustersls. All the results of the PCP parameter estimation in the 55 cortical cells are listed in Table 1. As mentioned above, application of CCh caused a change in the pattern of spontaneous activity. At higher concentrations of CCh (15-40 pM), an increased spontaneous discharge was observed at 20 pM, the average firing rate was 4.77i4.00

Number of cells

<A> (msj <a,> (ciustemisj

No (spikes) e b

clusters/s (n=30). At still higher concentrations of CCh (50- 100 pM, n=30), there were no apparent changes in the pattern of activity, but the average firing rate increased further. Whole- cell recordings of intracellular membrane potential revealed that spike bursts are associated with synaptic potentials which increase in frequency during CCh application (not shown here).

A typical example of the comparison of variance-to-mean curves before and after application of 20 pM CCh is shown in Fig. IA. Figure IA indicates that after application of 20 pM CCh the saturating value V M o and the slope of the variancehean curve decreased. The estimated power value b in control was 1.69f0.81, while the value at 20 pM CCh was 0.80i0.55. Compared with the control case, in summary, the parameter <n,> increased ( P < 0.01). <A> increased ( P < 0.05), No decreased ( P < 0.05). I decreased ( P < 0.01), b decreased ( P < 0.01), and To increased ( P < 0.01).

TABLE I RESULTS OF PCPESTIMATION

In control 20 p CCh 5 p CCh 55 30 25

9.01f4.06 27.7f13.0 18.5f11.4

-1.2010.68 -2.20+0.55 -1.633~0.57 1.6910.81 0.8010.55 1.28f0.59

n . 7 u i n . 2 ~ 5 4.77f4.00 o.910+0.450

36.oi45.0 32.5f8.6 62.sf78.2

Although, in contrast, low concentrations (1-5 pM) of CCh slightly suppressed spontaneous activity (n=5, data not shown here), a clear change of spontaneous activity could not be observed by visual inspection. The results of PCP parameter estimation in the 25 cortical cells after application of 5 pM CCh are also listed in Table 1. The estimated power value b was 1.2Xf0.59. Compared with the control case, for the five parameters of <n,>, <A>, NO, z, and b we could not reject the null hypotheses by t-tests at the 0.05 significance level. Similarly, although the average value of To(=963i890 ms) after application of 5 pM CCh was much larger than the control case (in control, To=617i794 ms for n=25 cells), we could not reject the null hypothesis at the 0.05 significance level. The result thus supported the observation of no clear change in the spontaneous activity after application of 5 pM CCh.

IV. DISCUSSION A. Cholinergic acrions on cortical cells

In a wide range of cortical preparations, acetylcholine (ACh) has been shown to suppress the normal adaptation of pyramidal cell firing in response to sustained current injection or synap- tic inputs. In cortical pyramidal cells in slice preparations, for instance, activation of muscarinic receptors blocks “ M - type potassium current, increasing the effect of depolarizing current and reducing adaptation of spike frequency. For other

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known examples of cholinergic effects on transmembrane ionic currents, we refer the reader to Ref. [31]. The other well- documented effect of cholinergic activation in the cortex is a suppression of excitatory and inhibitory synaptic transmission [SI, [321. The strength of this effect depends on the cell type, the origin of the presynaptic fibers involved, and on the layer of cortex. Local or columnar excitatory connections are suppressed more powerfully than long-range, horizontal connections [33]. The close similarity of cholinergic actions on excitability and synaptic transmission observed here with those observed in slice preparations suggests that the cultured network preparation provides a reasonable model system for examining the network effects of cholinergic activation.

As noted in the Results section, a subset of dissociated cortical neurons in cultures at over 40 DIV intrinsically generates synchronized rhythmic bursts of action potentials, at rest. Application of a relatively high concentration of CCh (10-50 pM) resulted in a switch from synchronized bursting to a tonic, asynchronous single spike mode of spontaneous tiring, through muscarinic receptor activation. We also showed that applying ACh itself (10-50 pM) to cultures both suppressed adaptation to current steps in cells (n=IO) and inhibited syn- chronized bursting (n=5) (data not shown). This characteristic effect of cholinergic activation on spontaneous activity can be understood from its two cellular effects. The synaptic depression, which is strong even at low CCh concentrations (1- 5 JAM) would be expected to disrupt synchronous firing, while the increase in single-spike firing (c.f., Fig. lB2) requires higher concentrations of agonist (up to 100 JAM), matching the concentration-dependence of the reduction in spike frequency adaptation at the single-cell level.

It has also been reported that ACh excites cortical pyramidal cells slowly and with low sensitivity (20-300 pM) but excites inhibitory interneurons more rapidly and at lower concentra- tions (less than 10 JAM), through perhaps different types of muscarinic receptor [34], [35]. Thus, the two different ranges of sensitivity reported in the previous literature and observed in this study as well could also reflect such differential sensitivity between cell-types, with stimulation of inhibition resulting in a slowing and disruption of synchronized bursting, and exci- tation of pyramidal cells producing the rise in asynchronous firing rate at higher agonist concentrations.

B. Spectral properties of spike trains Gruneis er al. investigated the statistical characteristics of

the single cell activity in the cat mesencephalic reticular for- mation (MRF) during two activated states: paradoxical sleep (PS) and a state in which the animal is watching birds (BW) [36]. The authors reported that I l f spectra were observed for both PS and BW states, being more pronounced for the PS state. Moreover, they found that MRF neuronal activities in the two states were closely fitted by the. PCP model which allowed a clear demonstration of this result.

In this study, we have also applied parameter estimation for a PCP model of neural spike trains in the presence and absence of CCh in order to-'reveal detailed information

,300

v)

c- 51 10 100 to00 10000

Frequency (HI)

Fig. 2. Estimated power spectra of the Poisson cluster-pracess model before and after application of 20 pM CCh. Four parameters (<n,>, <A>, I, and No) are listed in Table 1.

about the temporal structure of the neural activity from the statistics of the model parameters. Since estimated parameters (particularly, < A > and <TI,>) correspond well with values obtained by visual inspection of the burst firing (c.f., Figs. 1BI and 1B2), the PCP model is well-suited to characterize the sets of spike trains presented here.

Furthermore, because it has been shown that PCPs have 11 fb-like spectra [27], the present analysis is indirect evidence that the spike trains recorded in this study could have such spectra. However, we did not use power-spectrum plots to directly show l/ fb-like behaviour, because interspike intervals are not a time-series equally sampled on the time axis and a lot more data would be needed to calculate a power spectrum from an instantaneous firing rate. The results in this study also indicate that after the application of 20 pM CCh the value of b significantly decreased. This implies that the slope of the corresponding spectrum was decreased as shown in Fig. 2. At the same time, this shallower spectrum in the CCb application causes a shift to lower frequencies and a narrowing of the frequency range of firing. In contrast, after the application of a lower concentration of CCh (5 pM), we could observe no significant change of the power order b.

In addition, the PCP model suggests that a cluster- generation mechanism was described by an underlying pri- mary process which randomly triggers clusters of events. As reported in Griineis et al. [27], an interpretation of such a generation mechanism may be considered as follows: cultured single neurons gather distributed inputs from the surrounding adjacent neurons and transmit synaptic outputs to them. The synchronized cluster process suggests that the meaningful units of input-triggered activity are the clusters themselves. This would imply that the output could hold a short-time memory about the input for a period which corresponds to the duration of the clusters. It is the case that the period of the memory (c.f., TO in the Results section) differs from DIV to DIV, from culture to culture, and even from cell to cell. However, the results we found here could be interpreted as suggesting that the neural spike trains lend to hold a longer lasting memory of the input in the presence of high concentrations of CCh (2 20 pM) than at lower concentra-

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/

tions. Moreover, since low concentrations of CCh induced no significant change of TO, the result indicates that the change is a strongly concentration-dependent phenomenon.

C. Implications for corjical functions

There are many suggestions in the literature that cholinergic activation is associated with sensory processing 141, learning [ 5 ] , and memory [6], 171 in the cortex. With respect to the control of memory consolidation, for example, experimental and clinical evidence has led to the hypothesis that while high levels of ACh are appropriate for encoding new information in the hippocampus, low levels of ACh, as during slow-wave sleep, allow a stronger spread of activity within the hippocam- pus and from the hippocampus to the entorhinal cortex 1371. Thus, many observations support the idea of an important function for cholinergic activation in focusing or refinement of associative firing in the cortex, controlling information flow during population firing in networks of cortical neurons. Our results also support such a role through a quantitative analysis of ACh action on cortical network firing.

ACKNOWLEDGMENT

T.T. thanks Prof. S. Sat0 and Prof. T. Nomura (Osaka University) for their support. This work was in part supported by the Postdoctoral Fellowships for Research Abroad from the Japan Society for the Promotion of Science and grants from the BBSRC and EC.

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