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REPORT
1Department of Psychology, St. Lawrence University, Canton and 2Cold Spring Harbor Laboratory, Cold Spring Harbor, New York; 3McGovern Institute for Brain Research, 4Department of Electrical Engineering and Computer Science, and 5Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, Massachusetts; and 6Department of Exercise Science and Center for Studies in Behavioral Neurobiology, Concordia University, Montreal, Quebec, Canada
Submitted 31 January 2007; accepted in final form 23 February 2007
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ABSTRACT |
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 TOP ABSTRACT INTRODUCTIONMETHODSRESULTSDISCUSSIONGRANTSACKNOWLEDGMENTSREFERENCES |
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INTRODUCTION |
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TOPABSTRACTINTRODUCTIONMETHODSRESULTSDISCUSSIONGRANTSACKNOWLEDGMENTSREFERENCES |
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; Hasselmo 2005; Vertes 2005) and have increasingly been observed in other cortical and subcortical regions (Hasselmo 2005). Such rhythmic activity is thought to have a major function in organizing the encoding and retrieval of sequential information in cortico-hippocampal circuits.
In sharp contrast, oscillatory spike activity is normally weak in the striatum and becomes strong only in dopamine-depleted states (Boraud et al. 2005; Courtemanche et al. 2003; Goldberg et al. 2004; Raz et al. 2001). Despite the lack of oscillatory spiking in most striatal neurons, prominent oscillations do occur in the up- and down-state transitions of striatal projection neurons, and these membrane transitions are correlated with those of cortical neurons in anesthetized preparations and can exhibit oscillatory behavior that synchronizes with LFPs (Goto and O'Donnell 2001; Stern et al. 1997). Consistent with these findings, oscillatory LFP activity has been observed in the caudoputamen and related basal ganglia structures in the rat (Berke et al. 2004; Boraud et al. 2005; Magill et al. 2005; Masimore et al. 2005). Moreover, in normal, non-parkinsonian monkeys, it was shown that prominent rhythmic LFP activity occurs in the striatum and is strongly modulated as the monkeys perform sensorimotor tasks to receive reward (Courtemanche et al. 2003).
Here, we asked whether such behavioral modulation of oscillatory LFP activity occurs in the striatum of non-parkinsonian rats and, if so, what the characteristics of the task-dependent modulations were. To do this, we recorded LFP and spike activity in the dorsal caudoputamen as the rats rested, explored their environment, or performed a goal-directed instructed behavior in a T-maze. We found that behaviorally modulated oscillations are prominent features of LFP activity in the striatum, including striatal theta rhythms. We suggest that such rhythmic activity is likely to influence information processing in basal ganglia–based neural circuits.
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METHODS |
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and Barnes et al. (2005), headstages were designed so that a bundle of four to six tetrodes penetrated the brain tissue in a circular configuration (OD 
600 µm) with inter-tetrode spacing of about 300–600 µm. Tetrodes were then lowered until unit and LFP signals were identified within the estimated depth (3.6–4.6 mm).
During recording, rats engaged in spontaneous behaviors and performed a procedural task in a T-maze under dim red light. In the T-maze task (Barnes et al. 2005), the start gate opened 200–400 ms after a click warning cue signaled the beginning of the trial. When the rat had traveled halfway to the choice point, a 1- or 8-kHz tone instructing the correct turn direction sounded and was left on until the end of the trial. The rats received chocolate sprinkles at the correct goal. Before each training session of about 40 trials, neural activity was recorded as rats freely behaved (e.g., locomotion, grooming, and quiet rest) in the same T-maze.
Neuronal and behavioral data were acquired with a Cheetah system (Neuralynx, Bozeman, MT). For unit recording, amplified (gain: 2,000–10,000) and band-pass filtered (600–6,000 Hz) signals above a preset voltage threshold were sampled at 32 kHz. Either a dedicated reference electrode or a tetrode channel without spike activity served as reference. For LFP recording, amplified (gain: 1,000) and filtered (1–475 Hz) signals were continuously digitized at 1 kHz. During training, the animal ground (one of the skull screws) or the external ground (ground of the amplifier used for neuronal recording) was used as reference. In control sessions given to test whether locally recorded LFPs were generated by a distant source, a tetrode channel about 300–600 µm away served as reference, instead of the animal or external ground. Movement-related behavioral events were marked with the aid of video tracker data (sampled at 60 Hz). During training on the T-maze task, photobeams (Med Associates, St. Albans, VT) detected the times of gate opening and goal reaching and triggered the instruction tone.
The LFP data were analyzed with open-source Chronux algorithms (http://chronux.org), in-house software, the Matlab Signal Processing Toolkit (The MathWorks, Natick, MA), and other libraries (Courtemanche et al. 2003; Pesaran et al. 2002). The multitaper method was used to estimate frequency spectra (Pesaran et al. 2002). Spectrograms were constructed by plotting spectral power during a series of overlapping constant-width time windows.
Coherence between two simultaneously recorded signals was computed as C = S12/sqrt (S1 x S2), where S12 denotes the averaged cross-spectrum computed from the FFTs of the tapered waveforms for each taper and trial, and S1 and S2 denote the averaged power spectra of the two signals. Confidence limits (95%) were estimated for coherence magnitude by a jackknife procedure (which does not assume coherence to be normally distributed).
For the bipolar recording data shown in Fig. 2, the differences between LFP voltage and reference voltage were computed by a differential amplifier with 100-dB common-mode rejection. For the recording data illustrated in Fig. 3D (bottom), the value of the signal on a local reference electrode was subtracted from the value of the LFP off-line in Matlab. Because the recording amplifier gains were specified to ±1% precision, the common-mode rejection ratio in this configuration was 
34 dB.
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Pearson's linear correlation coefficients between the band-limited power and the speed and acceleration of locomotion were computed with Matlab's corr function for a 1-s window moving in 0.1-s steps. To calculate speed and acceleration, video tracker data were linearly interpolated and smoothed with a Hanning window (2,001 samples wide).
Single units sorted with OfflineSorter (Plexon, Dallas, TX) and accepted on the bases of spike waveform overlays and autocorrelograms (Barnes et al. 2005) were included in the calculations of spike-LFP coherence. For these coherence calculations, spike trains were represented as impulse trains at the same sampling rate as the LFPs by placing a 1 at the sample closest to each spike time and a 0 at all other samples. Coarse-grained coherograms were computed around each of six task events between the spike train (n = 53–385) and each of five filtered LFP bands (1–5, 7–11, 11–14, 14–22, and 25–50 Hz). The distribution of maximum coherence magnitudes over all time–frequency points and all LFP channels was compared with the distribution computed after shuffling the order of the trials for the spike data to detect significant non-shuffled coherence at P < 0.05.
Tetrode tracks and microlesions marking the final tetrode position were identified in sections of formalin-fixed tissue cut at 24 µm and stained for Nissl substance.
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RESULTS |
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Robust theta-band activity was evident in the LFPs recorded in the caudoputamen during periods of spontaneous movement through the T-maze (Fig. 1, A and B) as well as during locomotion during task performance in the maze (Fig. 1, A and C). During active running, the power in the oscillatory signal was greatest at the 7- to 14-Hz band, conventionally defined in the rat as theta activity (Jones and Wilson 2005; McNaughton et al. 2006; O'Keefe and Recce 1993). Oscillatory activity was also present in the delta range (<5 Hz), beta range (about 14–22 Hz), and gamma range (about 30–50 Hz) as well (Fig. 1, B and D). Theta activity was less prominent during grooming and during wakeful rest, but less-rhythmic, higher-frequency oscillations were still observable (Fig. 1B). Our analyses focused on the theta band and on activity during performance of the T-maze task (Fig. 1).
The power of these striatal LFP rhythms was strongly modulated as the rats performed the T-maze runs (Fig. 1C and Table 1). We examined recordings made during the session in which the rats reached asymptotic running times in the T-maze task (5.6 ± 1.9 to 4.0 ± 0.7 s). At this point, the rats had achieved 37.5 to 90% correct performance. Theta-band activity at 9 Hz increased during the maze runs, peaked after the rats heard the instruction tone and around the start of turning, and then fell after turning (Fig. 1C). Just before goal reaching, there was activity in many trials at a slightly higher band (about 11–14 Hz), considered theta in the rodent literature but in human studies identified as alpha (Fig. 1C). Beta-band activity appeared especially during the tone and turn periods. Low-frequency delta rhythms were recorded throughout, but they peaked around gate opening. High-frequency gamma activity occurred, often in brief bursts (data not shown; Masimore et al. 2005). These basic features of the LFP rhythms recorded during the maze runs were consistent in general form across animals (Table 1), although we did observe some variations in the LFP patterns on a trial-by-trial basis in each rat (data not shown).
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Oscillations in striatal local field potentials are generated locally and are coherent with spike activity in a subset of striatal neurons
As a control for the possibility that electrotonic spread of voltage signal from remote oscillators could account for the rhythmic activity recorded in the striatum, we recorded LFP activity in the caudoputamen using as a local reference an adjacent tetrode about 300–600 µm away. The spectral content of oscillatory activity under these recording conditions was similar to that recorded with the amplifier ground as reference (Fig. 2, A and B). Moreover, in a parallel study, we found that striatal theta is not consistently correlated with hippocampal theta during such maze behavior, showing that volume conduction from the hippocampus is not responsible for the striatal rhythms (DeCoteau et al. 2007).
To test whether there was spike rhythmicity related to the LFP rhythms in the striatum, we computed spike-LFP coherence in five different frequency bands (1–5, 7–11, 11–14, 14–22, and 25–50 Hz) and we compared the results for each band to data for the same sessions in which trials were shuffled. The percentages of putative projection neurons with significant (P < 0.05) spike-LFP coherence were low (6–17%). In the example shown in Fig. 2C, they were highest for the theta (7–11 Hz) band, and the highest proportions were found for the tone-turn period of the task (roughly 17%). Thus the spiking of some striatal projection neurons was coordinated with the striatal theta rhythms we observed in the LFP recordings, but, in agreement with other studies in a range of species, they were a minority (Berke et al. 2004; Boraud et al. 2005; Courtemanche et al. 2003; Goldberg et al. 2004).
Functionally distinct zones of the striatum exhibit coherent LFP oscillations during performance of the T-maze task
In two of the rats, we recorded oscillatory LFP activity simultaneously in the dorsomedial (associative) caudoputamen, which receives prefrontal and limbic corticostriatal inputs, and in the dorsolateral (sensorimotor) caudoputamen, which receives cortical inputs from sensorimotor cortex (Fig. 3A). During free-run sessions, theta-band activity was highly synchronous within and across these striatal regions, with coherence values of 
0.9 during the instructed maze runs (Fig. 3B) and cross-covariance close to 1 at zero lag (Supplemental Fig. S1A).1 During instructed run sessions, there was appreciably reduced variance in the theta-band coherence during the tone-turn period, as shown in the plots for tone on and turn start (Fig. 3C, red arrows). Coherence values were low at frequencies <7 Hz (Fig. 3D) and fell to <0.5 at frequencies >100 Hz (Supplemental Fig. 1B). In some sessions (Fig. 3D, top), peaks of coherence in the beta-band occurred near the beginning and the end of the runs. Prominent coherence between the medial and lateral theta rhythms was still visible after subtraction of local reference signals from the medial and lateral recordings and was heightened around the tone-turn period. Overall, the coherence levels were lower under these conditions (Fig. 3D, bottom).
The phase relationships between the LFP oscillations in the two striatal regions were remarkably stable across task time for any one frequency band (Fig. 3E). The phase angles measured at 9 Hz varied from –3 ± 4 to 9 ± 7° (95% confidence limits). Functionally distinct zones of the striatum thus exhibit coherent LFP oscillations across a broad range of frequencies during instructed goal-directed behaviors, with the most stable coherence being at theta-band frequencies and during the tone-turn period.
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DISCUSSION |
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; Buhusi and Meck 2005; Buzsáki 2005; Engel et al. 2001; Gray 1994; Laurent et al. 2001; Lisman 1999; Mehta et al. 2002). Our findings suggest that task-dependent modulation of oscillatory activity in the striatum could be an important factor influencing cortico-basal ganglia loop function during active behavior. Theta rhythmicity was most conspicuous during spontaneous and instructed running and was weak during grooming and during wakeful rest. These characteristics held whether the LFP recordings were in the medial (associative) caudoputamen or in the lateral (sensorimotor) striatum. We found that roughly 15% of the striatal neurons classified as projection neurons exhibited oscillatory spike activity that was coherent with the LFP oscillations at theta-range frequencies at statistically significant levels. This oscillatory spiking and the results of our bipolar recording experiments strongly support the view that the striatal theta was locally generated rather than being the result of electrotonic spread.
The theta-band oscillations recorded in these different regions were largely coherent. Theta rhythmicity is thus a general characteristic of LFP activity in the striatum of rats actively exploring and moving in their environment. These results suggest that the theta-band rhythmicity in the striatum does not depend exclusively on region-specific functions of particular striatum-based circuits. Rather, the LFP rhythms appear to be a shared feature of the temporal structuring of field activity in the striatum (Courtemanche et al. 2003; Magill et al. 2006). The striatal LFP oscillations we observed, and their marked coherence across medial and lateral striatal recording sites, could reflect different states imposed on striatal output neuron membrane potentials by other sites such as neocortex, thalamus, and pallidum, or by local circuits in the striatum operating in conjunction with these (Aldridge and Gilman 1991; Boraud et al. 2005; Courtemanche et al. 2003; Lebedev and Nelson 1999). For example, the fast-firing parvalbumin (PV)-containing interneurons of the striatum are inhibited by the external pallidum, itself part of an oscillatory pallido-subthalamic network under partial control by the neocortex (Bevan et al. 2002), and these neurons powerfully inhibit striatal output neurons. Moreover, these PV neurons have been proposed to be part of an intrastriatal electronically coupled inhibitory network appropriate for organizing the temporal activity of striatal neurons and for selecting input combinations leading to their activation (Berretta et al. 1997; Tepper et al. 2004). Coherent LFP oscillations could serve as a dynamic filter in the striatum (Courtemanche et al. 2003), setting a threshold for spike discharge in striatal projection neurons receiving cortical, thalamic, and other inputs. This view also accords with the proposal that oscillatory activity in corticostriatal circuits is part of a neural timing mechanism for encoding short intervals (Buhusi and Meck 2005).
The lack of a consistent relation between either velocity or acceleration and the power of the theta-band activity suggests that the striatal LFP rhythms may not be strictly linked to sensorimotor parameters, but rather to other behavioral-state characteristics engaged during exploration and instructed running. The modulation of both power and cross-striatal coherence during the tone-turn period of the T-maze task is consistent with this conclusion. During this period, the animals were required to use the tone cues to choose which way to turn to reach the baited goal. The heightened power and coherence could, in this view, be related to behavioral decision and execution.
There is strong precedent for the presence of oscillatory activity in other nuclei of the basal ganglia, particularly in the pallidum and recurrent subthalamo-pallidal circuits (Bevan et al. 2002; Plenz and Kitai 1999; Ruskin et al. 1999; Terman et al. 2002; Wichmann et al. 2002). In these basal ganglia circuits, oscillatory activity is greatly augmented by dopamine-depleting lesions mimicking parkinsonian states and in Parkinson's disease itself (Boraud et al. 2005; Brown et al. 2001; Goldberg et al. 2004; Levy et al. 2002; Ni et al. 2000; Raz et al. 2001). The functions of such oscillatory activity in normal basal ganglia circuits are unknown. However, our findings, together with those in behaving monkeys (Courtemanche et al. 2003), provide strong evidence that they are systematically modulated by behavioral context in the striatum and are coordinated across functionally different striatal regions. This result accords with the possibility that they reflect a dynamic process integral to a range of cortico-basal ganglia circuits.
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ACKNOWLEDGMENTS |
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TOPABSTRACTINTRODUCTIONMETHODSRESULTSDISCUSSIONGRANTSACKNOWLEDGMENTSREFERENCES |
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FOOTNOTES |
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The costs of publication of this article were defrayed in part by the payment of page charges. The article must therefore be hereby marked "advertisement" in accordance with 18 U.S.C. Section 1734 solely to indicate this fact.
1 The online version of this article contains supplemental data.
Address for reprint requests and other correspondence: A. M. Graybiel, Massachusetts Institute of Technology, 46-6133, 43 Vassar Street, Cambridge, MA 02139 (E-mail: graybiel{at}mit.edu)
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