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NEUROSCIENCE |
1 Departamento de Fisiología, Facultad de Medicina, Universidad de Buenos Aires, Buenos Aires, Argentina
2 Center for Neuropharmacology and Neuroscience, Albany Medical College, Albany, NY 12208, USA
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Abstract |
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(Received 8 May 2006;
accepted after revision 23 August 2006;
first published online 24 August 2006)
Corresponding author F. Kasanetz: Departamento de Fisiología, Facultad de Medicina, Universidad de Buenos Aires, Argentina. Paraguay 2155 7°, (1121) Buenos Aires, Argentina. Email: ferkasa{at}fmed.uba.ar
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Introduction |
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The above studies established that subthreshold MSN oscillations are modulated by global brain dynamics. However, the precise temporal pattern of cortical control of MSN Up states remains to be clarified. An electrical pulse applied to the cerebral cortex in vivo evokes a stereotyped MSN response consisting of a depolarizing postsynaptic potential (dPSP) followed by a long-lasting hyperpolarization (LLH) and a late depolarization (LD). The LLH is thought to be caused by a reduction of cortical input (Wilson et al. 1983), but this has never been conclusively demonstrated. As recent studies show that cortical persistent activity may be turned on and off by local electrical stimulation in slices (Shu et al. 2003), it is possible that cortical stimulation in vivo induces a similar phenomenon. Here we explored the temporal and spatial dynamics of the cortical control of striatal Up states by recording for the first time cortical field potentials and cortical spiking activity through multichannel electrodes together with the membrane potential of MSN that receive inputs from specific cortical locations. We further examined whether brisk phase perturbations of ongoing cortical rhythms induced by local electrical stimulation affected cortical and striatal network synchrony.
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Methods |
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Adult male Sprague-Dawley rats (n = 22) were maintained on a 12: 12 h light–dark cycle with food and water available ad libitum, and were cared for in accordance with local institutional regulations on the use of laboratory animals (Servicio Nacional de Sanidad y Calidad Agroalimentaria, RS 617/2002, Argentina). On the day of the experiment, rats (weight, 300–450 g) were anaesthetized with urethane (1.2–1.5 g kg–1, I.P.), treated with a local anaesthetic on the scalp (bupivacaine hydrochlorate solution, 5% w/v, Duracaine, AstraZeneca S.A. Argentina, 0.1–0.3 ml, S.C.) and pressure points (lidocaine hydrochlorate gel, 2% w/w, Denver Farma S.A. Argentina), and secured to a stereotaxic frame (Stoelting, Wood Dale, IL, USA). Temperature was maintained at 36–37°C with a servo-controlled heating pad (Fine Science Tools, Vancouver, Canada). Additional urethane was administered throughout the experiment as necessary in order to maintain a constant level of anaesthesia, as determined from cortical field potential recordings and evaluation of the hindlimb withdrawal reflex (customarily, supplements of 0.3–0.4 g kg–1 S.C. every 3–4 h) (Kasanetz et al. 2002). At the end of the recording session, rats received a lethal dose of urethane and were transcardially perfused with cold saline followed by 4% paraformaldehyde in phosphate buffered saline (PBS). Brains were removed, stored overnight in the same fixative, and then incubated in 0.1 M PBS containing 15% sucrose for 24–48 h. The localization of extracellular recording and stimulation sites was determined from Nissl-stained sections.
Cortical recording and stimulation
In most rats (n
= 17), three concentric bipolar electrodes (SNE-100, Better Hospital Equipment, New York, USA; outer contact diameter 0.25 mm, exposure 0.25 mm; central contact protrudes 0.75 mm from outer contact, has 0.1 mm in diameter and 0.25 mm tip exposure) were used to obtain differential electrocorticogram (ECoG) recordings from separate cortical regions: the medial frontal prelimbic cortex (3.5 mm anterior to bregma, 0.5 mm lateral to midline and 4 mm below the cortical surface, 20 deg angle in the sagittal plane; Paxinos & Watson, 1997), motor cortex (3.5 mm anterior to bregma, 2.5 mm lateral and 2.5 mm below cortical surface, 20 deg angle in the sagittal plane) and primary somatosensory cortex (2.8 mm posterior to bregma, 6.5 mm lateral and 2 mm below cortical surface, positioned with a 20 deg angle in the coronal plane; Fig. 1A and C). Three additional bipolar electrodes each consisting of two Teflon-coated tungsten wires (50 µm tips; vertical tip separation of 
0.5 mm) were located at a distance of 0.5 mm from each ECoG recording site. Stimulation consisted of 0.5 mA square wave pulses of 0.3 ms duration at 0.5 Hz or 0.16–0.25 Hz. The cortical field potential was amplified (ER-98, NeuroData, Delaware Water Gap, PA, USA; Lab1, Akonik, Argentina), band-pass filtered (0.1–300 Hz), and sent to an analog-to-digital converter (DigiData 1322A, Axon Instruments, Union City, CA, USA). In some rats (n
= 5), cortical recordings were obtained with a 16-channel, four-shank silicon probe (200 µm vertical site spacing and 200 µm horizontal shank spacing, kindly provided by The University of Michigan Center for Neural Communication Technology). The array was orientated in the coronal plane with a 20 deg lateral angle and positioned 0.5 mm posterior to the motor cortex ECoG recording/stimulation site (no electrodes were located in the prelimbic or somatosensory cortices in these experiments). These signals were amplified, filtered (0.3–3 kHz), and digitized at 10 kHz (DigiData 1200, Axon Instruments).
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Intracellular recordings were obtained as previously described (Tseng et al. 2001; Kasanetz et al. 2002) from one of the following striatal regions (ipsilateral to the ECoG recordings): anterior dorsolateral striatum (0.5–1 mm anterior to bregma, 3–4 mm lateral, 3–5 mm below the cortical surface; 8 recorded neurons), medial striatum (+0.4 to –0.2 mm relative to bregma, 2–2.5 mm lateral and 3–5 mm below the cortical surface; 8 neurons) or posterior dorsal striatum (1.4–2 mm posterior to bregma, 4–5 mm lateral and 3–5 mm below the cortical surface; 5 neurons) (Fig. 1B). These striatal regions were paired with the three cortical stimulation/recording sites (anterior dorsolateral striatum with motor cortex, medial striatum with prelimbic cortex and posterior dorsal striatum with somatosensory cortex; Voorn et al. 2004). In addition, another nine neurons were recorded in the anterior dorsolateral striatum together with MU activity in the motor cortex. In order to ascertain that the recorded striatal neuron received inputs from the matched cortical region, cortical stimulation was used to verify an evoked monosynaptic dPSP in the striatal neuron. All ECoG–MSN pairs analysed in the present work met these anatomical and physiological criteria. Intracellular microelectrodes were filled with 2 M potassium acetate and 2% Neurobiotin (RBI, Natick, MA, USA), and had a resistance ranging from 60 to 130 M
. The signal was sent to a bridge amplifier (Axoclamp 2B, Axon Instruments) and digitized at 10 kHz (DigiData 1322A, Axon Instruments). Microelectrodes were slowly advanced through the striatum with a hydraulic micromanipulator until a neuron was impaled. After completion of experimental procedures, neurons were labelled with Neurobiotin (Kita & Armstrong, 1991).
Signal analysis
Cortical and striatal activities evoked by a single cortical stimulus can be divided into early and late components. The early components include an abrupt positive wave in the ECoG and a dPSP in the intracellular MSN recordings. The late components include a slow wave with an initial negative phase in the ECoG and a LLH–LD sequence in striatal MSN. Through this report we will solely consider the late components and their relation to cortico-basal ganglia network dynamics. For all subsequent analyses, recordings were grouped according to the ongoing cortical network activity state at the time of electrical stimulation, as shown by the ECoG. Epochs with obvious ongoing cortical slow waves or notorious activation were selected by careful visual inspection of the signals. During spontaneous activity, cortical activation results in a marked drop of the ratio between relative powers in the low (
2 Hz) and high (> 2 Hz) frequency ranges of the ECoG power spectrum (see Kasanetz et al. 2002 for more details). Throughout periods of electrical stimulation, the presence of ongoing slow waves or cortical activation could be visually discerned during pre-stimulus periods. Ongoing cortical activation was evident as a significant decrease of the power of low frequency ECoG components in pre-stimulus epochs compared with epochs of similar length recorded during spontaneous slow wave activity in the same neuron.
Concomitant MSN and ECoG activities were studied in recording epochs that included the 1 s preceding and the 1 s following the stimulus (25–90 consecutive trials). To simplify the analysis, each successive group of N points in the signals was averaged to yield a single point that was retained, allowing sampling reduction to 1000 Hz (Axoscope). A discrete wavelet transformation of the signals (Meyer, 1992), was performed by means of a finite impulse response (FIR) digital filter approximation of the Meyer wavelet function (MatLab, The MathWorks, Inc., Natick, MA, USA). The procedure is based on an iterative algorithm that filters and down-samples the signal during each iteration. Down-sampling is performed by dyadic decimation, so the maximal number of iterations, or decomposition levels, is given by log2(N), with N being the length of the signal (number of sampled points). Briefly, the signal is first convoluted with the FIR filter and then down sampled, providing N/2 approximation coefficients (cAi; a representation equivalent to low pass filtering the signal) and N/2 detail coefficients (cDi; retaining information of frequencies above the filter cut-off frequency). In the next iteration, the cAi vector (instead of the signal) is subjected to filtering and down-sampling to obtain cAi + 1 and cDi + 1. From pairs of cAi and cDi vectors, the waveforms from which they were obtained (and ultimately the signal) can be reconstructed by iterative up-sampling and filtering. If zeros are used instead of the cAi vectors during reconstruction, the resulting waveforms are equivalent to band-passed versions of the original signal, the frequency content of which is determined by the decomposition level. The waveforms retaining information of the 0.5–2 Hz components of the signals were chosen to study transitions between active and silent cortical states and Up and Down striatal states, because the main frequency of these oscillations is 1 Hz (Stern et al. 1997; Steriade, 2000; Kasanetz et al. 2002).
In order to assess the degree of coupling between MSN membrane potential (Vm) and ECoG, we first computed cross-correlograms for delays of 0.75 s with a resolution of 1 ms for both pre- and post-stimulus 1 s epochs. Cross-correlograms of successive trials were averaged to obtain a mean correlation coefficient for each MSN–ECoG pair. The significance of cross-correlations for individual neurons was determined by contrasts against surrogate data. An additional estimate of synchronization was provided by the stability (or, conversely, the dispersion) of the phase lag between signals (Fig. 1D). Instantaneous phases (
MSNi and ECoGi) were calculated as the phase angle of the Hilbert-transformed and normalized (–1 to 1) waveforms (Oppenheim et al. 1999), and the instantaneous phase differences (IPD; 
i
=
MSNi
–
ECoGi) were depicted in circular distributions (Fig. 1E). These distributions were characterized by a mean direction and a circular dispersion, which were calculated as follows (Fisher, 1993):
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| (1) |
sin(i) and C =
cos(i)
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| (2) |
(cos(2 (i
–
)), and R2
= (C2
+ S2)/N.
To seek for phase resetting of ongoing slow rhythms, stimulation trials were aligned at stimulus onset. Then, for each millisecond and waveform we grouped i (calculated with the Hilbert transform) across trials, obtaining samples of N intertrial instantaneous phases (IIP), where N is the number of trials (Fig. 1F). We considered IIP constancy as an index of stimulus phase locking. To estimate IIP variability, we computed the probability for IIP distributions to be uniform by means of the Rayleigh test (Fisher, 1993), and computed their circular dispersion (
) as mentioned above. In addition, LD latency stability was taken as an index of phase locking. The LD latency was established as the post-stimulus time at which the normalized (–1 to 1) wavelet transformed signals first crossed zero with a positive slope during the late response. Similarly, spontaneous transitions between Down and Up states were detected as zero crossing with a positive or negative slope (Down-to-Up and Up-to-Down, respectively) in the 0.5–2 Hz wavelet transformed Vm of recordings with dominant slow waves. In order to make analogous computations on cortical multi-channel multiunit recordings, MU signals were first rectified. Then, a single waveform was obtained by adding, for every sampling interval, all simultaneously recorded MU channels. Finally, the resulting waveform was smoothed by averaging along a 50 ms sliding window to obtain a single representation of multiunit activity (MUras). The 0.5–2 Hz components were extracted with wavelet and Hilbert procedures, as described above.
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Results |
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Electrical cortical stimulation at 0.5 mm from cortical recording sites evoked a short latency dPSP in MSN located in the matched striatal region followed by a slow Vm modulation consisting of a hyperpolarization lasting about 350 ms (LLH) and a robust depolarization (LD) (Fig. 3; Table 1). As previously reported (Tseng et al. 2001, 2004), the LLH–LD sequence (‘late response’) closely resembled spontaneous Down-to-Up state transitions. The MSN late response was associated with an evoked ECoG slow wave, with an initial negative (silent) part. In addition, after stimulation of a non-matched cortical region, many MSNs responded with a dPSP (usually of smaller amplitude) and a largely more variable LLH–LD sequence (even in the case when no dPSP was observed). Non-matched delayed responses were not systematically analysed and will not be further discussed here (see Supplementary Fig. 1 for more details and a brief discussion).
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1 s and were not significantly different between both conditions (Fig. 6). This suggests that cortical stimulation exerts a short-lived phase shift in cortical and striatal dynamics that can be explained by the slow oscillation inherent variability.
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The above results indicate that the main effect of cortical stimulation on local networks and the MSN Vm is a concerted phase perturbation of ongoing slow oscillations. However, cortical stimulation delivered during ECoG activation (n = 8) produced an ECoG slow wave and a LLH followed by a LD with an almost fixed latency in all recorded MSN (Fig. 7A). In these cases, there was a low pre-stimulus ECoG–MSN Vm cross-correlation at the dominant low frequency of the late response that increased significantly following stimulation (see Fig. 4A). Phase lag variability, as assessed by computing IPD circular dispersion, was very high before the stimulus and decreased significantly post-stimulus (Fig. 4B). Indeed, the degree of coupling between evoked cortical and striatal waveforms was as strong in the activated ECoG condition as in the slow wave background condition (Fig. 4). This result further indicates that cortical stimulation affects cortical and striatal dynamics with a brief slow wave response time-locked to the stimulus.
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Visual inspection of traces shows that, for every neuron and ECoG condition, most transitions to the LD were narrowly clustered around the mean LD latency (439 ± 33 ms and 340 ± 32 ms for the slow wave and activated states, respectively; P = 0.06, t test), but the coefficients of variation of the LD (0.41 ± 0.03 and 0.20 ± 0.05 for slow wave and activated state conditions, respectively, P = 0.003, t test) and MSN Vm post-stimulus IIP distribution circular dispersion (0.32 ± 0.16 and –0.50 ± 0.24 for slow wave and activated state conditions, respectively, P = 0.003, t test) differed significantly between background ECoG conditions. We sought the source of increased LD latency variability in the slow wave condition by determining whether it depended on the phase of ongoing slow wave at the time of stimulus arrival. To that end, we plotted the time of striatal transitions to LD (radial axis) as a function of the instantaneous phase of ongoing cortical slow wave at the time of stimulus arrival (angular axis). Trials depicting LD time advances or delays were concentrated within discrete regions of ECoG phase space (Fig. 7B). Within the 180–300 deg ECoG phase range, corresponding to the second half of Down states and onset of Up states in MSNs (see Fig. 4D and dark grey line in Fig. 7C), trials showing LD time advances prevailed. Conversely, stimuli arriving within the 0–90 deg ECoG phase range, corresponding to the second half of MSN Up states, had increased chances of producing delayed LDs. Remarkably, LD advances and delays occurred without changes in IPD between the ECoG and MSN Vm (Fig. 5C). The fact that stimulus arriving during silent or active cortical states induced advanced or delayed LDs, respectively, indicates that cortical activity at the time of stimulus arrival influences the precise timing of evoked cortical and striatal slow waves. Nonetheless, the core dynamics of evoked cortical and striatal slow waves were independent of ongoing activity.
Cortical stimulation turns off cortical ensembles and striatal Up states
Delivery of an electrical pulse to the cerebral cortex can produce a LLH in MSN coupled to a negative deflection in the ECoG, regardless of slow wave phase at the time of stimulus arrival or background activity state. This implies that cortical stimulation can turn off MSN Up states (see Figs 5A and 7A). As local stimulation can turn off persistent spiking of cortical ensembles in vitro (Shu et al. 2003), it seemed likely that cortical stimulation was simultaneously turning off persistent cortical activity in the vicinity of the stimulating electrode and striatal Up states in the corresponding striatal territory in our in vivo experimental conditions. As the low frequency components of cortical field potentials are probably more closely related to synchronous synaptic input to cortical ensembles than to local neuronal spiking, we recorded spiking activity of cortical ensembles in the vicinity of the concentric bipolar electrode that picked up the ECoG, together with the Vm of a MSN from a connected striatal region (n = 9), to further understand the cortical dynamics related to MSN Up state termination and late responses. A waveform obtained after rectifying, adding and smoothing activities of the 16 probe channels (MUras) was remarkably similar to the ECoG (see Fig. 2A) and clearly indicated transitions between silent and active cortical states. In our experimental conditions, cortical stimulation produced a pause in local neuronal firing followed by sustained spiking, which were simultaneous with the negative and positive portions of evoked ECoG waves and the LLH and LD of MSN, regardless of the background ECoG condition (n = 9; 4 s inter-stimulus interval; Fig. 8). This indicates that cortical stimulation turns off cortical ensemble spiking in vivo concomitantly with MSN Up states.
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Discussion |
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Cortical stimulation elicits slow events resembling Down-to-Up state transitions in MSN independently of background cortical and striatal activities
Stimulus-related neural responses can be interpreted as partial phase shifts of ongoing neural oscillations or as transient novel neural activation (Makeig et al. 2002; Fell et al. 2004; Shah et al. 2004). In the former case, an oscillation with a dominant frequency similar to that of the neural response should be observed without a significant power change. In the latter, a novel frequency composition or an increase in power of an already existent oscillation should be detected. Our results show that MSN late responses have similar slow envelopes, regardless of ongoing cortical and striatal activity states, consisting of a phase perturbation of ongoing rhythms when the network is in the slow wave state or novel neural activity when the network is in the activated state. We also showed that MSN LLH occurs at the time of cortical stimulation-induced pauses in cortical ensemble spiking and that both LLH duration and cortical pauses are linearly related. Thus, our results provide strong evidence for a role of ‘cortical disfacilitation’ in the genesis of LLH, as proposed by Wilson et al. (1983). As functionally related regions of the frontal cortex and thalamus are interconnected and send convergent inputs to functionally related striatal territories (reviewed by Smith et al. 2004), a contribution of antidromic invasion of thalamocortical axons in the generation of dPSP in MSNs and of orthodromic activation of the thalamus by cortical input in determining the dynamics of the MSN late response cannot be denied.
Cortical stimulation turns off cortical ensembles
Neuronal ensembles in the isolated cerebral cortex can display spontaneous episodes of persistent spiking activity, which are sustained by recurrent excitatory and inhibitory connections and promoted by dopamine (Sanchez-Vives & McCormick, 2000; Timofeev et al. 2000; Cossart et al. 2003; Tseng & O'Donnell, 2005). Local electrical stimulation may activate or suppress persistent cortical activity, depending on stimulus intensity and timing relative to the initiation of persistent firing episodes, in cortical slices (Shu et al. 2003). In this preparation, high intensity stimuli arriving late during active network states could preferentially activate inhibitory interneurons, increasing the probability of silencing the network. In our in vivo experiments cortical stimulation did not elicit persistent cortical activity, but led to cortical inactivation. This is in good agreement with reports showing that cortical stimulation may trigger active states in cortical slabs but not in the intact brain (Timofeev et al. 2000). Although extracellular recordings do not allow unequivocal identification of recorded cell types, systemic GABAA receptor antagonists reduce MSN LLH more effectively than via intrastriatal administration (Calabresi et al. 1990). This suggests that cortical inhibitory circuits can contribute to turning off cortical and striatal persistent activity in vivo.
There is a debate as to whether MSN can exhibit bi-stable activity (Nicola et al. 2000). When striatal MSN are recorded in vitro or in isolation, spontaneous persistent activity cannot be observed. Up state transitions do not occur in the dorsal striatum of decorticated animals (Wilson, 1993) or in the nucleus accumbens after fornix transection (O'Donnell & Grace, 1995), and cannot be induced by depolarizing current pulses or single-pulse cortical stimulation in slices (O'Donnell & Grace, 1994, 1996). Furthermore, blocking intrinsic inward currents that could sustain MSN Up states does not eliminate spontaneous Up states in vivo (Wilson & Kawaguchi, 1996). These results suggest that MSN Up states are initiated and sustained by excitatory (mainly cortical) inputs. However, corticostriatal neuron firing takes place mainly during the first 200 ms of cortical slow waves and striatal Up states often last longer (Mahon et al. 2001). Together with this synchronized corticostriatal barrage, local GABA inputs may depolarize MSN from the Down state and contribute to initiating plateau depolarizations (Czubayko & Plenz, 2002; Blackwell et al. 2003; Bracci & Panzeri, 2006). It has been suggested that once Up states are initiated, their duration can be influenced by intrinsic voltage-dependent currents (Surmeier & Kitai, 1993, 1991; Galarraga et al. 1994). Brief depolarizing pulses can induce plateau depolarizations in slices in the presence of D1/D5 dopamine agonists, through a mechanism involving voltage-dependent calcium channels (Hernandez-Lopez et al. 1997). More recently, Vergara et al. (2003) reported that brief cortical stimulation can evoke slow oscillations resembling Up states in MSN. These studies suggest that striatal network interactions and intrinsic cell mechanisms can extend the time course of striatal Up states beyond that of cortical ensemble firing. Our experiments showed that cortical ensemble activity suppression (spontaneous or stimulus-induced) is invariably associated with the termination of MSN Up states in anaesthetized animals. The effect of cortical stimulation was not dependent on background brain activity or on the time of stimulus arrival relative to ongoing slow oscillation phase (i.e. MSN Up state duration prior to stimulation). A recent computational model using intrinsic conductance values obtained from real recordings could not produce MSN Up states unless persistent cortical inputs and an appropriate NMDA/AMPA ratio was added (Wolf et al. 2005). Furthermore, in vivo recordings from MSN while delivering intracellular current injection failed to affect Up–Down transitions (O'Donnell & Grace, 1995; Wilson & Kawaguchi, 1996; Charpier et al. 1999), suggesting that voltage-dependent conductances play a minor role in vivo. Of course, in these and other similar experiments, space clamp limitations preclude ruling out a contribution of distal dendritic currents to MSN Up states. However, our present results unequivocally establish that MSN Up states do not persist in the absence of cortical ensemble firing in vivo.
Another element that needs to be considered is the potential role of striatal fast spiking interneurons (FSI) in ending stimulation-induced MSN Up states. Striatal FSI respond to cortical stimulation more strongly and at lower intensities than MSN do (Mallet et al. 2005) and induce fast GABAA receptor-mediated IPSPs when MSN are in the Up state (Plenz & Kitai, 1998; Koos & Tepper, 1999; Blackwell et al. 2003). Therefore, FSI-mediated intra-striatal inhibition and passive membrane properties could counterbalance any effect of voltage-dependent membrane currents, forcing MSN to the Down state when persistent cortical firing falls below a critical threshold.
MSN membrane potential fluctuation remains phase-locked to cortical ensemble spiking after stimulus-induced perturbations of cortical dynamics
Persistent activity in cortical ensembles occurs spontaneously across different brain activity states and can be phase-modulated by afferent inputs. Its slowest frequency components may bind distributed neuronal networks and set the timing for high frequency oscillations to be transmitted through neuronal ensembles. Therefore, concerted fast phase modulations of low frequency oscillations may be essential for communication among brain regions (Steriade, 2000; Varela et al. 2001; Engel et al. 2001; Buzsaki & Draguhn, 2004). In this context, we explored whether phase perturbations of cortical activity impact on dorsal striatal MSN physiology, showing that cortical stimulation produces almost simultaneous phase perturbations of ongoing slow wave activity in cortical networks and in MSN. A short delay between the activity of cortical ensembles and MSN Vm is conserved throughout the LD as well as during spontaneous Up–Down alternation. Furthermore, during cortical activation, cortical stimulation induces a slow MSN Vm modulation tightly phase-locked to the activity evoked in cortical ensembles. The sole remarkable difference among MSN late responses evoked during slow wave or activated states was the higher LD latency variability in the slow wave condition. Such variability was present in cortical responses to local stimulation and was related to the phase of ongoing slow waves at the time of stimulus arrival. Our results indicate that MSN LD dynamics can be entirely explained by effects of local electrical stimulation on cortical activity. After being turned off by the stimulus, cortical ensembles may resume persistent activity impelling MSN in connected striatal territories into the Up state.
Functional implications
Definitive evidence of the occurrence of MSN Up–Down state transitions in behavioural contexts is still lacking. Theoretical speculations about MSN subthreshold activity in behavioural contexts perceive Up states as time gates during which MSN can fire action potentials in response to specific cortical inputs. Indeed, previous studies revealed that synchronous activation of hippocampal afferents induce transitions to the Up state in nucleus accumbens MSN, which fire action potentials in response to prefrontal cortical stimulation only during these hippocampal-driven plateau depolarizations (O'Donnell & Grace, 1995). Dorsal striatal MSN are impelled to the Up state by neocortical neurons and presumably fire action potentials in response to specific input embedded within a broad neocortical signal (Stern et al. 1998). We and others have reported that spontaneous transitions between Up and Down states in MSN are driven by cortical slow waves (Charpier et al. 1999; Goto & O'Donnell, 2001; Mahon et al. 2001; Tseng et al. 2001; Kasanetz et al. 2002; Goldberg et al. 2003) and that cortical activated states are associated with non-rhythmic MSN depolarizations (Mahon et al. 2001; Kasanetz et al. 2002). The present results extend these findings by demonstrating a precise alignment between MSN Up states and episodes of persistent cortical activity within connected cortical and dorsal striatum territories. It is important to note that, even though we provided direct functional evidence of connections between the recorded cortical and striatal territories, most of the recorded cortical units were probably not corticostriatal neurons. This establishes a difference from previous studies focused on corticostriatal neuron activity which revealed that single corticostriatal neurons display Up states and fire at specific times during cortical slow waves (Stern et al. 1997; Charpier et al. 1999; Mahon et al. 2001). Therefore, our findings truly imply that MSN Up states are representations of persistent firing in cortical ensembles that would be involved in local computations and influence extra-striatal targets as well. Besides, our findings indicate that corticostriatal neuron activity alone or in conjunction with indirect connections between the cortex and striatum (for example, via functionally related thalamic nuclei) transmit a reliable depiction of cortical ensemble activity to the striatum, as postulated in theoretical models (Houk & Wise, 1995; Graybiel, 1998; Redgrave et al. 1999; Bar-Gad et al. 2003). The precise alignment between episodes of persistent cortical firing and striatal Up states would let MSN detect specific discharge patterns embedded within the more general cortical input that dictates Up state transitions and duration. This could allow MSN to detect coincident cortical events for estimating time (Matell & Meck, 2004), for example, or encoding singular events during the execution of learned behavioural sequences (Fujii & Graybiel, 2005).
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i, instantaneous phase; 
