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1Departamentos de Biofísica and 2Neurociencias, Instituto de Fisiología Celular, Universidad Nacional Autónoma de México, Mexico City, Mexico
Submitted 12 October 2007; accepted in final form 8 January 2008
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ABSTRACT |
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INTRODUCTION |
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) and can transform afferent inputs into detailed spatiotemporal outputs (Grillner 2006; Yuste et al. 2005). In vitro experiments with circuits containing CPGs demonstrate that tonic excitation produced by the glutamate agonist N-methyl-D-aspartate (NMDA) can activate the stereotyped electrical behavior that neuronal networks exhibit in more intact preparations (such as fictive locomotion). This is observed as recurrent bursting activity in single neurons, while simultaneous unitary and population recordings demonstrate synchronicity and alternation of cell assemblies activity during bursting (e.g., Gordon and Whelan 2006; Grillner et al. 1981; Guertin and Hounsgaard 1998; Hsiao et al. 1998; Kiehn 2006; Takakusaki et al. 2004b).
Basal ganglia (BG) contain CPGs that activate innate behavioral routines, procedural memories, and learned motor programs (Barnes et al. 2005; Graybiel 1995; Grillner et al. 2005a,b; Takakusaki et al. 2004a). A major component of the BG is the striatum, which receives a widespread input from the cerebral cortex and thalamus. Striatal circuits process cortico-thalamic inputs to produce specific outputs consisting of bursts of action potentials during the execution of motor tasks, probably following voltage transitions to depolarized "up-states" (Hikosaka et al. 2000; Kasanetz et al. 2006; Mahon et al. 2006; Romo et al. 1992; Schultz et al. 1993; Wilson 1993). As in other isolated nervous tissue preparations known to contain CPGs (e.g., Guertin and Hounsgaard 1998), addition of NMDA to neostriatal circuits in vitro (Vergara et al. 2003) and in vivo (Herrling et al. 1983) induces recurrent bursting and pattern generation in single neurons. Moreover, intrastriatal application of NMDA in vivo generates turning behavior when administered unilaterally in freely moving animals (Ossowska and Wolfarth 1995); demonstrating that motor behaviors arise from the striatal processing of enhanced excitatory drives. This evidence suggests that the striatum posses the connectivity and intrinsic mechanisms to orchestrate pattern generation (Grillner 2006).
To test this hypothesis, we used calcium imaging of neuronal populations in a corticostriatal slice preparation to monitor, with single-cell resolution, dozens of cells simultaneously and then discern pattern generation produced at the microcircuit level by the activity of cell assemblies. Simultaneous electrophysiological recordings from striatal neurons demonstrated that calcium transients result from neurons bursting on top of suprathreshold up-states (Kerr and Plenz 2002).
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METHODS |
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Transverse corticostriatal slices (300 µm thickness) were obtained from PD14-29 Wistar rats as previously described (Kawaguchi et al. 1989; Vergara et al. 2003). All procedures conformed to the guidelines of the UNAM's Animals Scientific Procedures Committee. Slices were obtained with ice-cold saline (4°C) containing in mM: 123 NaCl, 3.5 KCl, 1 MgCl2, 1 CaCl2, 26 NaHCO3, and 11 glucose (25°C; saturated with 95% O2-5% CO2; pH = 7.4; 298 mosM/l). Slices were then transferred to saline at room temperature (21–25°C) where they remained for 
1 h before recording. The cationic concentration of this saline favors the appearance of up states in vitro (Vergara et al. 2003). Although the present results were performed mainly in young animals (PD14-21), basically the same network behavior was observed in older animals (PD25-29) (J. Bargas, unpublished data and see RESULTS).
Calcium imaging
Slices were incubated at room temperature in the dark for 20–30 min in the presence of 10–20 µM fluo 4-AM (Tef Labs, Austin, TX) in 0.1% dimethylsulphoxide (35°C), equilibrated with 95% O2-5% CO2. Slices were perfused with control saline (see preceding text) in a perfusion chamber on the stage of an upright microscope equipped with a x10 water-immersion objective (Eclipse E600FN; Nikon, Melville, NY). Excitation at 488 nm was performed with a Lambda LS illuminator (Sutter instruments, Novato CA). Experiments were performed at room temperature.
Images were acquired with a cooled digital camera (SenSys 1401E, Roper Scientific, Tucson, AZ) at 250–500 ms/frame. Imaging software used was RS Image (Photometrics; Roper Scientific). The imaged field was 800 x 600 µm in size. Short movies (100–250 s, 50- to 100-ms exposure, 2–4 image/s) were taken at time intervals of 5–20 min during 1 h.
The number of fluo 4-loaded neurons in the field was determined at the end of the experiment with a 5-s puff of 50 mM KCl. This maneuver disclosed all fluo-4-labeled neurons (either active or silent during the experiment). Cells active during the experiment were analyzed, and the ratio of active/silent cells was obtained. Spontaneous or evoked calcium transients together with voltage responses were recorded electrophysiologically in some cells, both in control saline and during the application of 5–12 µM NMDA (Sigma-Aldrich-RBI, St. Louis, MO). In some experiments, cortical sensory motor areas were stimulated with a concentric bipolar electrode (12 µm; FHC, Bowdoinham, ME). Stimuli consisted of different trains of 5–10 stimuli at 20 Hz. Each stimulus was 100–200 µs and 50–120 µA. In some experiments, we used the minimal stimulus intensity necessary to evoke peaks of synchrony with amplitudes above chance (P < 0.05). This allowed us to study changes of electrical evoked synchrony under different pharmacological conditions.
Immunohistochemistry
Sections were processed to fix fluo-4 fluorescence—or cells active during experiments—with N-(3-dimethylaminopropyl)-N'-ethylcarbodiimide hydrochloride (EDAC), and to perform conventional immunocytochemistry in fluorescent cells to demonstrate either substance P (SP) or enkephalin (ENK) on fluo-4-labeled cells using commercially available antisera (Peninsula Labs, San Carlos, CA) conjugated to CY3 or CY5. Slices were not processed for both antisera, but one was chosen in each case. Thus in each trial, either SP or ENK positive and negative neurons could be observed. Briefly, sections were rinsed in PBS and incubated for 18–24 h at 4°C with primary rabbit antibody against ENK or SP (diluted 1:200). Sections were mounted in an anti-quenching media (Vectashield, Vector Laboratories) and examined under a confocal microscope (MRC-1024; Bio-Rad, Natford, UK) equipped with a krypton–argon mixed-gas laser. Immunostained cells were studied in either single confocal images or reconstructed sections made by projecting z-series of 10–40 consecutive confocal images 1 µm apart collected throughout the thickness of the section. The background noise was reduced averaging three to six images. Digitized images were transferred to a personal computer (Confocal Assistant, T. C. Brelje). More than 80% of fluo-4-loaded cells were medium spiny neurons.
Drugs
Stock solutions were prepared before each experiment and added to the perfusion solution in the final concentration indicated. NMDA, APV, nicardipine, CNQX, biocytin, and bicuculline methiodide or hydrochloride were obtained from Sigma (St. Louis, MO).
Electrophysiology
Calcium imaging and simultaneous electrophysiological recordings were obtained from areas of the dorsal striatum previously shown as receiving numerous cortical fibers (Vergara et al. 2003).
An Axoclamp 2B amplifier (Axon Instruments, Foster City, CA) was used to perform whole cell current- and voltage-clamp recordings. Signals were filtered at 1–3 kHz and digitized at 3–9 kHz with an AT-MIO-16E4 board (National Instruments, Austin, TX) in a PC computer. Data acquisition used a software designed in the LabView environment (Lemus-Aguilar et al. 2006). Patch pipettes (3–6 M
) were filled with (in mM) 115 KH2PO4, 2 MgCl2, 10 HEPES, 0.5 EGTA, 0.2 Na2ATP, and 0.2 Na3GTP. In some experiments, biocytin 0.5%, and fluo-4 salt (20–30 µM) were added to the recording pipettes.
Image analysis
Image processing was carried out with Image J (v.1.36, National Institutes of Health), Multicell 2.0 (kindly supplied by Robert Froemke), and custom-made programs written in IDL (Cossart et al. 2003; Mao et al. 2001; Schwartz et al. 1998) or MATLAB (The Math-Works, Natick, MA).
All active neurons in a field were semi-automatically identified, and their mean fluorescence was measured as a function of time. Single-pixel noise was discarded using a 5-pixel ratio mean filter. Calcium-dependent fluorescence signals were computed as (Fi –Fo)/Fo, where Fi: fluorescence intensity at any frame and Fo: resting fluorescence, i.e., average fluorescence of the first four frames of the movie.
Calcium signals elicited by action potentials were detected based on a threshold value given by their first time derivative (2.5 times the SD of the noise value). Thus we obtained a C x F binary matrix; were C represents the number of active cells and F the number of frames for each movie. Spike onsets were signaled by ones in the matrix representing transitions to the up states. Recordings were inspected manually to remove artifacts and slow calcium transients which are likely to correspond to glial cells (Ikegaya et al. 2005; Sasaki et al. 2007).
Statistical methods
To determine if calcium transients recorded from different cells were correlated, the numbers of simultaneous activations per trial (onset of signals occurring within 3 frame windows) were detected. To determine the P value of simultaneous transients occurring by chance, the distribution under the null hypothesis of independent transients using Monte Carlo simulations with 1,000 replications were computed (Mao et al. 2001).
The degree of correlation between active cells was calculated with the Jaccard correlation coefficient. Nevertheless the magnitude of the correlations is difficult to discern when many lines are superimposed. Thus we constructed cross-correlation maps of Jaccard correlation coefficients to show the magnitude of the correlations between all cells pairs.
In addition, we identified the sets of cells activated simultaneously over time. To identify peaks of synchronous activity (i.e., that included more cells than those expected by chance), Monte Carlo simulations were also used to estimate the significance of their firing together. The threshold corresponded to a significance level of P < 0.05. We then examined peaks that were significant at P < 0.05. Peaks of synchronous and recurrent bursting activity that remained significant during the experiment were selected for further analysis.
To analyze the dynamics of cell assemblies over time (network dynamics), D x N matrices were constructed, where D represents the number of active neurons in a set of experiments, and N denotes the firing of cells during 250-ms to 1-s time bins (NMDA-induced up states last between 0.5 and 5 s) (Vergara et al. 2003). Peaks of recurrent synchronous activity were vectorized so that bursting over time was associated with different neurons. Each vector element is formed by the sum of the number of calcium spikes displayed by a single neuron during the time bin, where peak derivatives denote the time onset of electrophysiologically recorded up states (see preceding text and RESULTS). Therefore the set of these vectors denote network activity as a function of time (Brown et al. 2005; Sasaki et al. 2007).
To measure the similarity index between the network vectors, we computed the normalized dot product of all possible vector pairs, which is equivalent to the cosine of the angle between the vectors (Sasaki et al. 2006; Schreiber et al. 2003). Then we plotted the similarity indexes as a pseudocolor matrix in which functional states sustained by significantly correlated or synchronized bursting cell assemblies appear as cluster-like structures (Sasaki et al. 2006).
To reduce the dimensionality of the network vectors, locally linear embedding (LLE) was chosen. LLE is an unsupervised learning algorithm that discloses nonlinear structures from multi-dimensional data (Roweis and Saul 2000). Multidimensional scaling (MDS) (Systat, Richmond, CA) gave results quantitatively similar from those obtained with LLE. Nevertheless LLE depicted more effectively the trajectories of the functional states in the network. The trajectories of our high-dimensional data were not well described by linear dimensionality reduction methods (see Brown et al. 2005). To choose the optimal number of states depicted from the LLE reduction, we used hard and fuzzy clustering algorithms taking the Dunn's index as a validity function (Bezdek et al. 1997; Sasaki et al. 2007). To determine the number of neurons in each state, hierarchical cluster analysis was computed using Euclidean distances and the nearest neighbor single linkage method (Systat Software, San Jose, CA).
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RESULTS |
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To study striatal microcircuits in vitro, we used Ca2+ imaging (Fig. 1) to measure electrical activity in many cells simultaneously with single-cell resolution. Activity was measured indirectly, as changes in fluorescence. Fields from the dorsal striatum were imaged in slices loaded with the Ca2+ indicator fluo-4 AM in 174 experiments performed in 86 corticostriatal slices. Figure 1A shows all dye-loaded neurons (see METHODS). Contours of cells both active and inactive during the experiment are depicted in Fig. 1B (filled circles and empty contours, respectively). Only neurons active during an experiment were analyzed. Under control conditions (with no drugs added), only a few cells were active, and their firing was asynchronous (filled circles in Fig. 1B). Records of Ca2+ transients from three spontaneously active neurons are shown (Fig. 1C). These experiments confirm that the striatal circuitry is mostly quiescent (n = 40 slices) under control conditions.
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; Grillner et al. 1981; Guertin and Hounsgaard 1998; Hsiao et al. 1998; Kiehn 2006; Takakusaki et al. 2004b), we were able to generate bistability in striatal neurons after bath application of NMDA (Vergara et al. 2003), a transmitter known to induce motor behavior when administered in the striatum (Fig. 1E) (Ossowska and Wolfarth 1995). This treatment induces persistent bursting behavior in neostriatal neurons (>1 h) without the need of electrical stimulation (spontaneous). Most active cells during a given experiment had the electrophysiological characteristics of medium spiny neurons (Fig. 1D). Ca2+ transients (
F/F) corresponding to up states, had time derivatives [d(F/F)/dt] that matched bursts duration (Cossart et al. 2003; Kerr and Plenz 2002). Clearly membrane potential distribution is bimodal in bursting cells (Fig. 1E4). The current-voltage relationship (I-V plot) measured in voltage clamp at the end of 400- to 500-ms commands shows a negative slope conductance region (NSCR), indicating bistability, in NMDA-treated cells (Fig. 1E5) (e.g., Hsiao et al. 1998; Izhikevich 2007; Vergara et al. 2003). Ca2+ imaging and simultaneous electrophysiological recordings also show that only bursts with two or more action potentials produce detectable Ca2+ transients in striatal neurons (n = 10 neurons; Fig. 2). These experiments confirmed that the striatal neurons can be activated in vitro by NMDA bath application. Therefore the next step was to ask if this activity was correlated and synchronous throughout the network, in which case, it may correspond to a network dynamics capable of producing pattern generation.
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In corticostriatal slices, electrical stimulation of the cortex evokes long-lasting depolarizations with overriding spikes in medium spiny neurons (Bargas et al. 1991; Vergara et al. 2003). Trains of cortical stimuli (Fig. 3A) also result in prolonged synaptic depolarizations with action potentials (Fig. 3B). Figure 3B illustrates an experiment where whole cell current-clamp recordings (Fig. 3B1) and calcium imaging (Fig. 3B2) were performed simultaneously from a medium spiny neuron (stimulus frequency: 0.1 Hz; stimuli are signaled with arrows at the bottom). A calcium transient accompanies each orthodromic response to the cortical stimulus (Fig. 3B2, arrows at the bottom). The time derivatives of the calcium responses are shown in the third row (Fig. 3B3); note similar duration of derivative positive peaks and voltage responses. Accordingly, each peak from the differentiated calcium signal generates a dot used to build raster plots as that illustrated in Fig. 3D. Each row in the raster plot represents an active neuron (filled circles in Fig. 3C). Filled circles in Fig. 3C indicate fluo-4-loaded neurons within the field of observation that responded to cortical stimulation in one representative experiment. Empty circles indicate loaded neurons that did not respond to cortical stimulation (see METHODS). Responsive cells are a minority of the total number of neurons which are scattered throughout the observational field among many unresponsive neurons (Fig. 3C).
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10–20% of fluo-4-loaded cells. Only a few neurons were active before and after the stimulus train, and these cells present a lack of correlation (Fig. 3, D and E). At each trial, >90% of the responsive neurons followed the cortical stimulus (Fig. 3D, bottom). Some neurons spontaneously active before the stimulus did not follow the electrical stimulation (blue lines). Targeted electrophysiological recordings of cells responsive to cortical stimulation revealed that most active neurons were medium spiny neurons (Fig. 3B). Network dynamics set into action by NMDA
Ca2+ imaging allowed us to observe a population of striatal cells activated by cortical stimulation in control conditions (Fig. 3) and in the bath presence of NMDA (5–12 µM; Fig. 4). Simultaneous voltage recordings of imaged neurons (Fig. 4A) show that voltage transitions (Vergara et al. 2003) were accompanied by Ca2+ transients during NMDA activation (Kerr and Plenz 2002, 2004). Cell activity in the circuit was followed by recording their Ca2+ transients and plotting them as raster plots (Fig. 4B). Similar NMDA-induced electrical activity has been observed in neurons that are part of a CPG (e.g., Grillner et al. 1981; Guertin and Hounsgaard 1998; Hsiao et al. 1998; Takakusaki et al. 2004b). Interestingly, the raster plot (Fig. 4B, top) shows that several neurons are involved in the activity. Moreover, the time histogram (Fig. 4B, bottom; asterisks, red lines) clearly shows periods of increased synchrony and correlated firing that occur spontaneously in different sets of neurons (Cossart et al. 2003; Ikegaya et al. 2004). Statistically significant (threshold P < 0.05) spontaneous peaks of synchrony occurred in sets of neurons from n = 38/45 slices in these conditions. On average, there were 2.5 ± 0.2 peaks of synchronous bursting per 201 ± 10-s time epoch. Peaks of recurrent synchronous activity showed no apparent periodicity, and occurred with a mean interval of 46 ± 10 s. Notably, neurons firing synchronously during NMDA treatment could be hundreds of microns apart intermingled with silent cells (Fig. 4C, filled circles). Spatial correlation maps (Fig. 4D) show the pairs of neurons that exhibit statistically significant (P < 0.05) correlated activity (line thickness is proportional to the degree of correlation). On average, 77 ± 4% of active cells had correlated activity (n = 38 slices) at any given moment. Cells active during synchrony peaks represent most of the correlations between cells. Correlation plots of this activity (Fig. 4E; P < 0.05) show that the degree of synchronization among active neurons is heterogeneous, thus the network activity is not due to chance correlation between any pair of neurons but to the spatiotemporal dynamics of several cells.
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) and in vivo (Herrling et al. 1983) induces bursting activity and generates turning behavior in freely moving animals (Ossowska and Wolfarth 1995), suggesting a general mechanism preserved in a broad range of ages. Structured network dynamics with the same characteristics have been shown in the cortex of young mice in vitro (PD13-22) and adult cats in vivo, demonstrating that network activity, intrinsic to specific nuclei, is preserved in slices (Ikegaya et al. 2004). But to address this issue in the striatal microcircuit, we divided our data into age groups to discern possible maturational variables. Figure 5 shows the age independency of NMDA-induced network dynamics. In spite of the previously described reduction in the number of loaded cells as a function of age (PD14-29; Fig. 5, A–C) (Froemke et al. 2002; Peterlin et al. 2000), the number of peaks of synchronous bursting per time epoch was maintained across PD14-29 (Fig. 5D), confirming a robust mechanism, across this age range, for the network behavior described here (Ikegaya et al. 2004).
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Visualizing functional states during network dynamics
We next investigated network dynamics in the striatum over long periods of time (n = 15 slices). Brief movies (100- to 250-s epochs) were taken at different intervals for up to an hour (e.g., Fig. 6C, 1–5, separated by lines). To analyze network dynamics, we binned the raster plots of all the neurons involved in the peaks of synchrony (Fig. 6). Time segments represent vectors coding coactive burst activity of individual neurons (see METHODS). Network dynamics consisted of a set of N vectors (bins) in D dimensions (active neurons). Vectorization allowed us to compare different states of network activity rigorously over time (Brown et al. 2005; Sasaki et al. 2007). The normalized inner product (see METHODS) of all possible vector pairs provides a measure of the similarity among states (Sasaki et al. 2006; Schreiber et al. 2003). To evaluate this similarity along network activity, we plotted all vector pairs as a matrix (Fig. 6A). Notably, abrupt transitions in the similarity index showed the presence of cluster-like structures (Sasaki et al. 2006).
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; Roweis and Saul 2000; Stopfer et al. 2003). The new vectors were projected in two dimensions (Fig. 6B). Clusters of points formed trajectories representing the responses over time to specific stimuli. Trajectories illustrate different subgroups of neurons coactive within each functional state (Brown et al. 2005; Stopfer et al. 2003). Thus changes in the functional state of the network can easily be followed. In Fig. 6B, state 1 represents network response to cortical stimuli in control conditions. State 2 depicts NMDA-induced network activity without electrical stimulation. State 3 represents network response to the same cortical stimuli (given in the state 1) in the bath presence of NMDA. Figure 6C, top, shows the raster plot used to reconstruct network states, whereas bottom shows histograms with the percentage of co-active neurons along time. Cortical stimulation in the presence of NMDA recruited many more striatal neurons (Fig. 6C3, blue dots and peaks) than in control conditions (Fig. 6C1, green dots and peaks) and produced peaks of synchrony much larger than those produced by NMDA only (Fig. 6C, 2, 4, and 5, red dots and peaks). Spontaneous peaks of synchrony induced by bath NMDA were 20% the size of those induced by cortical stimulation (cf. Fig. 6C, 1–5, bottom). Nonetheless the general behavior of the active network after a cortical stimulus (Fig. 6C, 4 and 5) remained essentially comparable to the activity before the stimulus (Fig. 6C2 and see following text). In fact, time histograms showed similar numbers of spontaneous peaks of synchronous activity (Fig. 6C, bottom; P < 0.05) before and after the cortical stimulus. Moreover, cortical stimulation (Fig. 6C3) elicited a trajectory looping back to the NMDA-induced dynamics (Fig. 6B), showing recurrent activation of the same assembly after a perturbation (Stopfer et al. 2003). Figure 6D shows the spatial distribution of the neurons involved in the different states. Hierarchical cluster analysis revealed the existence of different neuronal subsets (assemblies) that underlie network states (Fig. 6F). Each state was basically sustained by a different neuronal assembly. A state transition implies a change of neuronal assembly (alternation). However, some elements are shared by different states (core neurons). Thus using a simple algorithm we could distinguish recursive peaks of synchronization reflecting functional states that involve different sets of neurons. We conclude that spatiotemporal firing patterns codified in multidimensional vectors are sufficient to reconstruct the dynamics of a given network. Pattern generation denoted by functional states sustained by cell assemblies
The preceding analyzed states included some with imposed synchronization, i.e.: with electrical cortical stimulation. If network dynamics induced by NMDA are mediated by cell assemblies, then one may expect the appearance of distinguishable functional states evoked by the excitatory tonic drive brought about by NMDA only (without electrical stimulation). To address this issue, we investigated the relationships between the peaks of synchronous activity induced only by NMDA (similar results were obtained in n = 14/15 slices that presented NMDA-induced peaks of synchrony).
To observe alternation between functional states, we investigated the NMDA-induced network dynamics in the striatum over long periods of time (Fig. 7, n = 15 slices). Brief image series (100–250 s in duration) were obtained at different intervals for 
1 h (Figs. 7C, 1-,3 top, representative image series obtained at different times during the experiment are shown separated by vertical lines).
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). We projected these vectors into two dimensions (Fig. 7B, colored circles) by further reducing vectors dimensionality with LLE (Brown et al. 2005; Roweis and Saul 2000; Stopfer et al. 2003). This projection allowed us to observe a variety of functional states in the network as clusters that follow a series of trajectories in sequence. We found a different set of neurons (cell assemblies) with correlated synchronous firing for each functional state (Brown et al. 2005; Stopfer et al. 2003) (Fig. 7D; blue, red, green). Therefore the method allows us to follow the evolution in time of the functional states within the network. Experiments similar to that illustrated in Fig. 7B demonstrated the existence of robust nonrandom cell assemblies displaying co-active neurons along time with recurrent and alternating activity (Figs. 7, B and C) (Sasaki et al. 2006). States were continuously revisited with no state having a preference or a significantly higher probability of recurrence (Fig. 7B; see percentages of trajectories out of each state including recurrences), demonstrating the existence of various semi-stable network attractors. Time histograms with several synchrony peaks (asterisks; Fig. 7C, bottom) show the alternation of activity between different cell assemblies thus demonstrating multistable dynamics.
Figure 7D shows the spatial distribution of neurons generating the patterns and producing the different functional states. Network states share some elements but most of the neurons are dissimilar (Fig. 7E). Finally, hierarchical cluster analysis confirmed the recruitment of different cell assemblies during specific states (Fig. 7F). Nevertheless there is a small core of cells shared by all the network states (8% of neurons involved in the synchrony peaks).
We concluded that functional states, denoted by spontaneous peaks of synchrony (Barnes et al. 2005), are generated by the coordinated participation of cell assemblies, as it is the case of "unit CPGs" (Grillner 2006). The transitions from one state to the other display long-term network dynamics. The return of the same cell assemblies after net traveling through different trajectories shows that the elements of these modules are robustly associated, allowing alternating participation. Also, a core of neurons is shared by all the states.
Mechanisms underlying network dynamics for pattern generation
For a preliminary investigation into the synaptic and intrinsic requirements for multistable network dynamics, we challenged network activity with both synaptic and intrinsic ion channel antagonists. Fast synaptic inhibition within the striatal circuit (Czubayko and Plenz 2002; Koos et al. 2004; Tepper et al. 2004; Tunstall et al. 2002) was tested with the GABAA receptor antagonist, bicuculline, which was applied once the network became active.
Figure 8 shows that on exposure to 10 µM bicuculline, the number of state transitions is drastically reduced in the NMDA-treated slices (n = 6 slices). The network becomes locked in a preferred state, which is recurrently revisited. Similarity indexes representing network dynamics suggest the absence of diverse cell assemblies (Fig. 8A). Only occasional transitions to another state occurred (Fig. 8B). Paradoxically, the raster plot of network activity showed an increased frequency of recurrent synchrony peaks: from 2.7 ± 0.2 peaks in the NMDA condition to 8.3 ± 2 peaks in the presence of bicuculline (P < 0.01). There was also an increase in the number of coactive cells supporting the preferred state (Fig. 8C). Most synchrony peaks, however, correspond to the same dominant state. Neurons involved in network sates are shown in Figs. 8, D and E. Hierarchical cluster analysis did not reveal cluster-like structures in spite of synchronization (Fig. 8F), meaning that all active neurons were embedded into the same state. Cortical stimulation in the presence of bicuculline recruited even more striatal neurons than those obtained in control conditions or in the presence of NMDA alone (data not shown). Thus fast GABAergic transmission is a necessary requirement for the network to exhibit multistable dynamics and different functional states.
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Active networks were challenged with the AMPA receptor antagonist CNQX (10 µM) (Fig. 9A). As shown in the raster plot of Fig. 9A1, CNQX drastically reduced the number of active cells (cf., left and right) and disrupted the appearance of synchrony peaks in all NMDA-treated slices (Fig. 9A, bottom; n = 6 slices). Interestingly, a few neurons that were active during synchrony peaks maintained spontaneous activity in the absence of fast AMPA transmission (Fig. 9A, 1 and 2, gray circles) (Vergara et al. 2003). Also in the presence of CNQX, cortical stimulation was unable to recruit striatal neurons (data not shown) and all correlated activity was suppressed (Fig. 9A).
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). L-type calcium channels promote the generation of plateau potentials capable of sustaining repetitive firing that outlasts input duration. We therefore tested the effects of blocking L-type calcium channels with the antagonist nicardipine (5 µM). As shown in the raster plot of Fig. 9B1, nicardipine only slightly reduces the number of active neurons (n = 5 slices; cf., left and right panels; n = 41 vs. 35 neurons), it nevertheless reduces the number of peaks with spontaneous synchronous activity. Many cells defining previous functional states continued spontaneously active in the presence of nicardipine (Fig. 9B, 1 and 2, gray circles), but they were rarely synchronized to produce functional states. We conclude that plateau potentials resulting from the activity of voltage-gated L-type calcium channels are required for network synchronization (Yuste et al. 2005) and pattern generation produced by cell assemblies in NMDA-treated slices. Shared core of neurons
In most slices presenting multistable network dynamics (n = 20/25 slices), we found a core of neurons, which were shared by all states. So we aimed at recording electrophysiologically some of these neurons after their identification as a part of a core.
These neurons were 8 ± 2% of all active cells in the network states (n = 20 slices). Some of these core neurons exhibited periodic calcium transients during NMDA treatment or CNQX. Bicuculline disrupted the periodic firing of these cells. Cell-attached (Fig. 10A1) or whole cell current-clamp recordings (Fig. 10, C1 and D1) were performed on some of these cells, and we found that 40% of the core assembly (n = 4/10 cells) showed firing properties characteristic of GABAergic interneurons (Plenz and Aertsen 1996; Tepper et al. 2004), such as high-frequency bursts (Fig. 10A) and periodic pacemaking activity (Fig. 10D, 1–3). However, by fixing fluorescence of cells active during a given experiment (see METHODS), Fig. 11 shows that most active cells in the different assemblies were immunoreactive to either substance P or enkephalines.
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DISCUSSION |
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). Alternation of activity and frequent recurrences of the same states suggest attractor network dynamics.
Ca2+ imaging of neuronal populations (Mao et al. 2001; Sasaki et al. 2006; Schwartz et al. 1998; Stopfer et al. 2003) and simultaneous whole cell recordings show that calcium transients correspond to burst firing in single cells, the synchronization and recurrence of which generates the activity patterns. Pattern generation during network states depend on synaptic and intrinsic properties as well as the activity of a core of active neurons (Berke et al. 2004). In summary, this work shows how pattern generation and correlated activity is generated in the striatal microcircuitry in vitro identifying the neuronal elements involved. The connections and modulation of these elements deserve further study because they may reveal basic general properties of striatal microcircuitry and may further suggest in vivo experiments.
Voltage transitions in striatal neurons
Striatal neurons, both in vivo and in vitro, exhibit spontaneous voltage transitions that sustain burst firing. Transitions occur between a quiescent down state and a recurrent bursting up state (Herrling et al. 1983; Kerr and Plenz 2002, 2004; Mahon et al. 2006; Vergara et al. 2003; Wilson 1993). It is largely unknown how these transitions reflect network activity and how they propagate through the network. It is probable that different classes of bursts exist, for example, during different functional states (e.g., sleep vs. movement) (N. Vautrelle, personal communication). Here we took advantage of the tonic drive provided by NMDA to induce the transitions because in the striatum (Ossowska 1995), as in other circuits (Gordon and Whelan 2006; Grillner et al. 1981; Guertin and Hounsgaard 1998; Hsiao et al. 1998; Kiehn 2006; Takakusaki et al. 2004b), NMDA sets into action patterned activity that generates movement. Accordingly, it was demonstrated that NMDA induces correlated activity that evolves in time. In contrast to control conditions in which the striatum has only a few active cells with no correlated activity, both cortical stimulation and the tonic excitatory drive provided by NMDA exposure (Vergara et al. 2003) recruit neurons spread over a wide area; which, in the case of NMDA, spontaneously express particular spatiotemporal dynamics, indicating that the striatal microcircuit in vitro preserves a set of "unit CPGs" (Grillner 2006), that is, even if most connections present in more intact preparations are severed, remaining sets of neurons, probably belonging to larger CPGs or modules in vivo, preserve some of the properties of these larger modules. In support of this inference, independent evidence in the cortex suggests that cell assemblies activity is preserved along different spatial scales (Plenz and Thiagarajan 2007).
Mechanisms of network dynamics
The conditions that generate circuit dynamics reside in both the synaptic and intrinsic properties of striatal neurons. NMDA induces synchronous peaks of neuronal activity emerging from the co-activation of robust and recurrent cell assemblies that alternate in their patterns displaying a sequence of trajectories. These results suggest the presence of robust mechanisms that maintain and stabilize the cells participating in the assemblies. In particular, AMPA transmission appears to be necessary for turning on network dynamics. In fact, the number of active neurons drastically falls when AMPA transmission is blocked.
Because all glutamatergic synapses in the striatum originate from cortical or thalamic afferents, cortico-thalamic afferents are likely to be the source of tonic excitatory drive underlying synchronous activity. NMDA presumably generates an increase in the tonic excitatory drive conveyed by cortico-thalamic afferents (Grillner 2006; Grillner et al. 1981), and although some neurons maintain spontaneous activity in the presence of CNQX—or after dissecting away the cerebral cortex (Vergara et al. 2003)—the analysis shows that this behavior is asynchronous so that the emergence of striatal cell assemblies appears to require fast AMPA glutamatergic transmission, presumably originating from cortical and thalamic neurons (Kasanetz et al. 2006; Yuste et al. 2005). The most parsimonious interpretation of these results is that cortico-thalamic afferents are essential for the appearance of multistate network dynamics. That is, network dynamics are commanded by the cortex and/or the thalamus (Kasanetz et al. 2006; Magill et al. 2001). However, the actual relationship between cortical or thalamic activation and cell assembly activity in the striatum needs further investigation. It is known that activation of NMDA receptors is enough to induce bistability in striatal neurons, amplifying the excitatory drive conveyed by cortical or thalamic inputs (Grillner 2006; Grillner et al. 1981) so that activity (e.g., from the cortex or thalamus) can be relied on. And although cell assembly activity has been recorded in the cortex using NMDA plus dopamine, it has not been recorded after NMDA only (Tseng and O'Donnell 2005). Finally, striatal assemblies receive the convergence of widespread cortical areas, and slices cannot contain all these areas (Parthasarathy and Graybiel 1997). However, cortical cell assemblies driving striatal cell assemblies are not excluded and may be possible in several conditions (e.g., with added dopamine receptor agonists). Therefore striatal assemblies may represent the coordinated activity between cortical areas, that once formed by development and learning, only need a cortical trigger or a subset of the original stimuli for activation.
In addition, our data show that the striatum does not simply follow cortical commands. The role of the striatal circuit proper in the management of cortical drives is revealed by the result of inhibiting GABAA receptors. Blockade of GABAA inhibition not only increases the peaks of synchronous activity and recruits more cells during synchrony peaks but also shifts the network toward a preferred state that recruits most active neurons. Without inhibition, the striatal circuit gets tied up in a preferred state, from which alternation and selection among different cell assemblies becomes unlikely. Therefore we conclude that inhibitory circuits in the striatum are responsible for transforming cortical commands into a sequential activity of striatal cell assemblies (Ossowska 1995; Wickens and Oorschot 2000). In addition, the blockade of L-type calcium channels disrupts the peaks of synchrony. This result confirms that intrinsic conductances, such as voltage-gated calcium channels, by generating plateau potentials may work as synchronization enablers of neuronal networks (Kiehn 2006; Yuste et al. 2005). Thus NMDA-induced network dynamics in the striatum requires the participation of both synaptic and intrinsic mechanisms.
Finally, electrophysiological recordings of neurons exhibiting Ca2+ transients showed that most of the active neurons in cell assemblies were medium spiny projection neurons. This was confirmed by immunocitochemistry (Fig. 11). Nevertheless, a subpopulation of neurons shared by all functional states (see Parthasarathy and Graybiel 1997) exhibited periodic Ca2+ transients and the firing properties of pacemaking GABAergic interneurons (e.g., Berke et al. 2004; Tepper et al. 2004). The fact that striatal GABAergic interneurons comprise 2–5% of striatal cells and yet make up 40% of the neurons active in multiple assemblies suggests that striatal interneurons participate in the orchestration of multistate dynamics (Berke et al. 2004). Activity of pacemaker neurons is a frequent finding in CPGs (Grillner 2006; Yuste et al. 2005).
Functional implications and perspectives
Recurrent and alternating bursting is characteristic of cell assemblies included in CPGs in vivo and in vitro (Barnes et al. 2005; Grillner 2006; Ikegaya et al. 2004). Synchronized activity of these modules exhibit attractor dynamics (Cossart et al. 2003), providing a unified description for circuits encoding the storage and retrieval of long-term and working memory. Attractors are seen as memory traces retrieved through excitatory tonic driving or partial cues useful for executing motor programs. The persistence of cell assemblies along time is due to recurrent connectivity (Barnes et al. 2005; Tsodyks 2005). The alternation of their activity is under neuromodulatory control (Yuste et al. 2005). Apparently, the properties of these microcircuits are at the interface between small networks and global brain functions (Plenz and Thiagarajan 2007). Their disturbance provokes abnormal processes of synchrony, associated with different disorders such as schizophrenia and Parkinson Disease (Schnitzler and Gross 2005; Uhlhaas and Singer 2006). We show that an isolated circuit set into action, in vitro, can exhibit synchronized states in specific cell assemblies emerging and returning during relatively long periods of time.
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ACKNOWLEDGMENTS |
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FOOTNOTES |
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Address for reprint requests and other correspondence: J. Bargas, Instituto de Fisiología Celular UNAM, PO Box 70-253, Mexico City, DF 04510 Mexico (E-mail: jbargas{at}ifc.unam.mx)
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ABSTRACT
INTRODUCTION
, 1 brain slice. —, best linear fit. Note that although the number of loaded cells decreases with age, the network dynamics remains constant.
