Department of Anatomy and Neurobiology, University of Tennessee,
Memphis, Tennessee 38163
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INTRODUCTION |
The phenomenon of synchronous neuronal
oscillations has attracted much attention during the last decade (for
review, see Engel et al. 1992
, 1997
; Farmer
1998
; Funke and Worgotter 1997
; Gray 1994
; MacKay 1997
; Singer 1993
;
Singer and Gray 1995
; Steriade 1993
,
1997
). Although neuronal oscillations have been extensively studied in cortical areas, oscillatory activity in some subcortical structures, such as neostriatum, is less well understood. The neostriatum may play an important role in regulating oscillatory interactions between cortical areas, because neostriatal neurons receive inputs from numerous cortical areas, and, in turn, ultimately project to the cortex. Thus, these neurons have the capacity to influence cortical activity via basal ganglia-thalamic-cortical loops
(Alexander et al. 1986
).
In this study, we sought to find rhythmically firing neurons (RFNs) in
monkey neostriatum. Networks containing RFNs have been suggested as
playing a role in initiating and propagating cortical oscillations
(Llinas 1990
, 1991
). Previously, we demonstrated a
subpopulation of monkey somatosensory cortical RFNs by using a task in
which monkeys extended or flexed their wrists in response to
vibrotactile stimuli (Lebedev and Nelson 1995
). In that
study, we found that the activity of cortical RFNs is disrupted at the onset of vibrotactile go-cues and/or prior to movement onset. We
hypothesized that cortical RFNs may be tonically active inhibitory interneurons (but see a different interpretation in Ahissar and Vaadia 1990
). Likewise, if any neostriatal neurons exhibited
rhythmic firing during this behavior, these RFNs could be part of an
electrophysiologically and morphologically unique subpopulation, for
example, neostriatal interneurons.
Several schemes for classifying neostriatal neurons using temporal
patterns of discharges have been proposed (Aldridge and Gilman
1991
; Anderson 1977
; Connor 1970
;
Crutcher and DeLong 1984
; Hikosaka et al.
1989
; Kimura et al. 1984
; Wilson
1993
). Aldridge and Gilman (1991)
described a
subpopulation of neostriatal neurons with clock-like regular firing
patterns recorded in awake, quiescent monkeys. Moreover, exquisitely
regular firing patterns of neostriatal neurons were seen in lesion
experiments in which neostriatum was depleted of its cortical
(Aldridge et al. 1990
; Aldridge and Gilman 1991
) or dopaminergic nigral inputs (Raz et al.
1996
). Despite these studies, the activity patterns of
neostriatal RFNs during movement initiation remain poorly understood.
To elucidate the functional role of neostriatal RFNs, we analyzed the
changes in their activity during the initiation of wrist movements.
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METHODS |
The behavioral paradigm has been described in detail elsewhere
(Lebedev and Nelson 1995
; Lebedev et al.
1994
). Briefly, each of three adult rhesus monkeys
(Macaca mulatta; subjects A, B, and N)
sat in an acrylic monkey chair. Each animal's hand rested on a
manipulandum, which was attached to the axle of a torque motor (see
Fig. 3F). A torque of 0.07 Nm, which assisted wrist extension and opposed flexion, was applied to the plate. A display consisting of 31 light-emitting diodes (LED) indicating that wrist position was placed 30 cm in front of the monkey at eye level. Each
monkey was trained to hold a centered wrist position. After a delay of
0.5, 1.0, 1.5, or 2.0 s (chosen pseudorandomly), monkeys flexed or
extended their wrists in response to vibration (27, 57, or 127 Hz) of
their palms through the manipulandum. Single-unit activity was recorded
in neostriatum (Fig. 1) using
platinum-iridium microelectrodes (1-2 M
at 1.0 kHz) by
conventional means (Lebedev and Nelson 1995
).

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Fig. 1.
Locations of the 306 recorded neostriatal neurons demarcated by
autocorrelation histogram (ACH) classifications. The recording sites of
these neurons are illustrated on two sets of line drawings of the
histological reconstructions and on photomicrographs of coronal
sections through the basal ganglia from one of the animals. Numbers
beneath sections indicate the stereotaxic levels of the sections.
Recording sites, in general, were located in the dorsal portion of the
putamen, the dorsolateral part of the caudate nucleus, and the cellular
bridges between these structures.
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Neurons were classified as RFNs following analysis of their
activity exhibited during the period of the task during which the
monkey held a consistent wrist position and awaited a go-cue. Autocorrelation histograms (ACHs) (Mountcastle et al. 1969
,
1990
; Perkel et al. 1967
; Poggio and
Viernstein 1964
) were constructed for this epoch. ACHs describe
the likelihood of neuronal discharge occurrence after a given
discharge. For RFNs, ACHs contain peaks at multiples of the rhythmic
period (Ahissar and Vaadia 1990
; Karmon and
Bergman 1993
; Lebedev and Nelson 1995
;
Perkel et al. 1967
; Poggio and Viernstein
1964
) (e.g., Fig. 2A,
rightmost column). ACHs were constructed for spike trains using 1-ms
bins for epochs of 250 ms and then were normalized so that ACH values
represented correlation coefficients (Abeles 1982
;
Eggermont 1992
; Palm et al. 1988
). This
normalization is preferable since correlation values can be compared
across records of single neurons having different firing rates
(Palm et al. 1988
). ACHs were smoothed using a 10-point
Gaussian filter (a conventional procedure for noise reduction; see
Aldridge and Gilman 1991
; Karmon and Bergman 1993
; Raz et al. 1996
). The first two ACH peaks
and the valley between them were evaluated (Fig. 2A). The
first peak was designated as the histogram maximum for the interval 0 to 150 ms. The second peak was designated as the histogram maximum for
the interval from 1.5 to 2.5 times the time of the first peak (i.e.,
the expected second peak time ± one-half of the time of the
rhythmic interval). This method facilitated the selection of the most
probable (highest) second peak as opposed to smaller peaks, perhaps
resulting from noise, for the rhythmically firing neurons. The valley
was designated as the minimum histogram value between the first and
second peak.

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Fig. 2.
Selection of rhythmically firing neurons from a larger population of
neostriatal neurons. A: examples of the three basic
types of firing patterns observed. These were early peak
autocorrelation histogram (ACH), flat ACH, and rhythmically firing
neurons (RFN). Top: interspike interval (ISI)
histograms; middle: ACH; bottom: renewal
density histograms (RDHs). Peaks and valleys in the ACHs and RDHs are
shown by the circles. B: ACHs of the populations of the
three types of neurons represented as surface plots. The ACHs are
ranked according to the time of the first peak. The ACHs for the whole
set of RFNs (n = 32) are shown (rightmost column).
To facilitate comparison, the same number of ACHs was randomly picked
from the subpopulations of the Early Peak ACH and Flat ACH neurons. A
line through each of the panels in B indicates the
location of the autocorrelation histograms corresponding to the
examples immediately above.
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The selection of RFNs was done using K-means cluster analysis
(Everitt 1980
; Hartigan 1975
) implemented
in Systat, version 5.2. Standard scores were calculated for two
variables: the difference between the magnitudes of the first peak and
the valley and the period jitter. Using the peak-to-valley difference
rather than the first peak height facilitated the selection of RFNs as
opposed to bursty neurons, which we often observed. ACHs for bursty
neurons contain prominent initial peaks (Aldridge and Gilman
1991
; Wilson 1993
; Wilson and Groves
1981
). However, they lack a prominent second peak at twice the
interval of the first peak and a valley between the first and the
second peaks. Four K-means clusters were specified because there are
four possible combinations of high/low, peak-to-valley differences and
high/low jitter values. High peak-to-valley differences and low jitters
characterized the cluster containing the RFNs. The whole set of ACHs
for the RFNs thus selected is presented in Fig. 2B
(rightmost panel) as a surface plot. The units that fell in the other
three K-means clusters were grouped together as nonrhythmically firing
units. By visual inspection, nonrhythmically firing units were
subdivided into the early peak (Fig. 2, leftmost column) and flat ACH
types (Fig. 2, middle column). This selection is described in detail in
Results. Because the first peak, the second peak, and the valley of the
ACH occur at one, two, and one and a half rhythmic cycles, respectively, three estimates of the rhythmic period could be derived
from these data: 1) the first peak time, 2)
two-thirds of the valley time, and 3) one-half of the
second peak time. The average rhythmic period and its jitter were
calculated as the mean and the SD, respectively, of these three values.
Neuronal activity patterns were further analyzed using interspike
interval (ISI) histograms and the patterns' characteristics: mean,
median, and the coefficient of variation (e.g., Aldridge et al.
1990
). To evaluate serial dependencies in neuronal spike
trains, renewal density histograms (RDHs) were calculated and compared
with ACHs. RDH is the ACH calculated after ISIs are randomly shuffled,
that is, after the serial structure of the spike sequence is eliminated (Aldridge and Gilman 1991
; Lebedev and Nelson
1995
; Mountcastle et al. 1969
, 1990
;
Perkel et al. 1967
) (see Fig. 2A). In the
current implementation, RDHs were calculated after both within-trial
and across-trial hold period ISIs were shuffled. Serial dependencies of
ISIs were visualized using joint ISI scatterplots, which displayed a
given ISI on the x-axis and the subsequent ISI on the
y-axis (Rodieck et al. 1962
; Siebler
et al. 1991
; Surmeier and Towe 1987a
,b
) (Fig. 6,
C and D). The relationship between immediately
adjacent ISIs was analyzed using coefficients of serial correlation
(Perkel et al. 1967
; Surmeier and Towe
1987a
,b
).
Changes in activity patterns of RFNs related to task events were
analyzed using conventional perievent time histograms (Fig. 3B) and several techniques
that helped us to visualize the temporal structure of spike trains.
Temporal patterns of activity were visualized using ISI scatterplots
(see Lebedev and Nelson 1995
) (Fig. 3A). In
addition, we used joint perievent time histogram (JPSTH) methods
previously developed to analyze dynamic correlations between pairs of
neurons (Aertsen et al. 1989
; Gerstein and Perkel 1969
, 1972
). A JPSTH is a two-dimensional plot in which the
x- and y-axes represent spike occurrences of the
first and second neurons in the pair, respectively. The time is
measured in reference to a behavioral task event. In our
implementation, both axes represented spike occurrences of the same
neuron (Fig. 3E). Thus, these plots represent dynamic
autocorrelations rather than cross-correlations. As such, a JPSTH can
be thought of as a stack of instantaneous ACHs. The center of each
instantaneous ACH is on the JPSTH major diagonal, and the off-center
bins are on the line crossing the center, perpendicular to the major
diagonal. JPSTHs were normalized using conventional algorithms, so that
their bins (binwidth was 5 ms) represent correlation coefficients
(Aertsen et al. 1989
). Cumulative sum methods were used
to detect the onset of neuronal activity changes (Lebedev and
Nelson 1995
).

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Fig. 3.
Activity of a rhythmically firing neuron during the execution of
vibratory-cued wrist extension trials. All displays are centered on
movement onset. A: interspike intervals (ISIs) plotted
as a function of time for all trials. B: histogram, the
raster, and cumulative plot (ascending line) of activity. Marks to the
left of the center line (movement onset) in the raster indicate
vibratory go-cue onsets. Marks to the right indicate 5° of wrist
movement, which was also the time of reward delivery. C:
traces indicating wrist position change for each trial.
D: average position change for trials illustrated above.
E: joint perievent time histogram. The epoch 400 to
100 ms relative to movement onset is depicted. Both the
x- and y-axes represent the activity of
the same neuron. F: schematics of the manipulandum. This
neuron responded to passive extensions of the hand at the wrist joint.
G: the neuron was located in the dorsal aspect of the
putamen.
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RESULTS |
A total of 306 neurons were recorded in the neostriatum. In Fig.
1, the locations of recorded neurons demarcated by ACH classifications are illustrated on sets of line drawings of the histological
reconstructions for two animals and on photomicrographs of coronal
sections through the basal ganglia for the third animal. Recording
sites, in general, were located in the dorsal portion of the putamen,
the dorsolateral part of the caudate nucleus, and the cellular bridges
between these structures. Of the 306 neurons, 32 (10%) were classified as RFNs. RFNs were observed in the caudate nucleus, bridge, and putamen
with equal frequency (Table 1). Nineteen
RFNs were tested for peripheral receptive fields (RFs) by manipulating
the animal's forelimb skin surfaces and single joints. Of these RFNs,
approximately equal proportions either had no clear RF (10/19, 53%) or
responded to the bending of a single joint (8/19, 42%), whereas only
one neuron had a cutaneous RF on the hand.
In Fig. 2B (rightmost column), ACHs for the whole population
of RFNs are presented as a surface plot. An initial refractory period,
at least two ACH peaks and a valley between them can be seen. Rhythmic
periods ranged from 17 to 78 ms (across-unit statistics: mean ± SD; 40.5 ± 13.0 ms; median, 40.6 ms). This corresponded to the
frequency range 12-58 Hz (mean ± SD; 27.5 ± 9.8 Hz;
median, 24.6 Hz). The rhythmic frequency, calculated as the inverse of the rhythmic period, was 1.5 ± 0.1 times higher than the mean firing rate (MFR) for the hold period. This difference between the
rhythmic frequency and the MFR indicated the presence of pauses (long
ISIs) in otherwise rhythmic spike trains. Because of the pauses, MFR,
derived from the spike count, was typically lower than the rhythmic
frequency, which was derived from the ACH periodicity. The ISI
distributions for RFNs were typically unimodal, with "tails" corresponding to pauses in activity (e.g., Fig. 2A).
However, in three cases, clearly bimodal ISI distributions were seen,
with the modes corresponding to the rhythmic period and twice its
value. ISI counts at these modes were approximately equal. The average rhythmic frequency for these three unique neurons was higher than that
for the remainder of the population (51.3 ± 7.7 versus 25.9 ± 9.5 Hz; P = 0.0001; one-factor ANOVA, Scheffé
post hoc test).
Nonrhythmically firing neurons constituted the majority of the sample
(274/306, 90%). Using the classification of Aldridge and Gilman
(1991)
, these were subdivided qualitatively into the early peak
ACH (84/306, 28%) and flat ACH (190/306, 62%) types (Table 1, Fig.
2). Early peak ACH neurons can be described as bursty neurons
(Wilson and Groves 1981
; Wilson 1993
).
For these neurons, ACHs contained initial elevations that stabilized
over ~150 ms, a time corresponding to the characteristic burst
duration. In agreement with the results of Aldridge and Gilman
(1991)
, the initial peaks were lower in RDHs than in ACHs.
Thus, these activity patterns essentially depended on the sequential
arrangement of ISIs into groups of short ISIs (bursts) and longer ISIs
(interburst intervals). None of the selected RFNs had these qualitative
features of bursty firing patterns. Some of the ACHs classified as flat had initial rising parts of peculiar shapes that often changed after
spike shuffling. Examples of such shapes previously have been reported
(e.g., see Fig. 2B in Raz et al. 1996
).
Because these shapes were difficult to classify, we did not subdivide the flat ACH type any further.
ISI distribution characteristics
mean and median ISI and the
coefficient of variation of ISIs
have been used to classify
neostriatal neurons (Aldridge and Gilman 1991
). The
neuronal groups selected in our study differed as to these
characteristics (Table 2). Most notably,
RFNs had the lowest coefficients of variation of ISIs, whereas the
early peak ACH neurons had the highest. In addition, for early peak ACH
neurons, mean and median ISIs were markedly different (mean ISI ~1.5
times the median ISI), whereas this difference was less for RFNs (mean
ISI ~1.05 times the median ISI). These results are consistent with
the regularity of the ISIs for RFNs and the greater variability of the
ISIs for the early peak ACH neurons and flat ACH neurons.
Neostriatal neurons often are classified using MFR as one of the
criteria, the measurements of MFR being made during the periods when an
animal is not engaged in task performance (Aosaki et al. 1994
,
1995
; Raz et al. 1996
). Because, in these
experiments, it was difficult to produce a state of "no motor
behavior" without disrupting the overall task performance, neuronal
activity outside of task performance was not recorded. Therefore,
direct comparison of the findings below to those of previous studies
cannot be done with absolute certainty. However, it is worth noting
that the types of neurons selected by us were significantly different
in their MFRs (Table 2). RFNs had the highest MFRs during the hold period of the task (~17 spikes/s), whereas the early peak ACH neurons
had the lowest (~11 spikes/s). MFRs of the majority of RFNs were >10
spikes/s (30/32, 94%), whereas the corresponding proportion for the
early peak ACH neurons (39/84, 46%) was significantly less
(P < 0.0001; chi-squared test). The flat ACH neurons
exhibited a wide distribution of MFRs spanning the MFR ranges of the
RFNs and the early peak ACH neurons and averaging ~15 spikes/s. MFRs were not statistically different as a function of the neuron's location (putamen, caudate nucleus, or bridge).
Figure 3 illustrates some common characteristics of the firing patterns
of RFNs during the behavioral task performance. During the epoch in
which the animal actively held its hand steady and awaited the
vibratory go-cue, the ISIs of this neuron were distributed around a
central value of ~50 ms. This neuron's firing rate increased prior
to the onset of extension movements (Fig. 3B) and was not changed prior to flexion movements (not shown). As can be seen from the
ISI scatterplot (Fig. 3A), ISI distribution center gradually shifted in correspondence to the firing rate change. JPSTH analysis (Fig. 3E) indicated that the peak-valley-peak pattern in the
autocorrelogram persisted as the neuron's firing rate increased.
Rhythmic frequency at a given time can be determined by drawing a line
perpendicular to the major diagonal of the JPSTH (see Methods) and
estimating the distance between the peaks along this line. By this
methods of visual inspection of the JPSTH, it can be seen that the
rhythmic frequency of this neuron increased from ~20 to ~80 Hz. We
term this type of rhythmic firing change a regular transition because it resembles the firing pattern of cortical regular spiking neurons (McCormick et al. 1985
). These latter neurons exhibit
very regular, rhythmic discharges on depolarization with constant
intracellular currents and rhythmic frequency increases with larger
current. During regular transitions, a neuron's rhythmic frequency is
linearly related to its firing rate. For RFNs, we observed a linear
relationship between the MFR and the rhythmic frequency. These
characteristics, compared during the hold period of the task, showed a
highly significant correlation (Pearson correlation coefficient = 0.75; P < 0.0001). Elsewhere, a different type of
transition has been reported for premotor cortical neurons
(Lebedev and Wise 1998
). For these neurons, the rhythmic
frequency remained nearly constant during firing rate changes in
individual units and also across units. Moreover, transitions from
rhythmic to nonrhythmic firing patterns have been described for primary
somatosensory cortical RFNs (Lebedev and Nelson 1995
).
For 26 of 32 (93%) RFNs, the rhythmic patterns of activity appeared to
undergo regular transitions, as judged by visual inspection of ISI
scattergrams and JPSTHs. Eight examples of these ISI plots with gradual
shifts in ISI distribution centers are provided in Fig.
4, along with the locations at which the neurons showing these regular transitions were recorded. For one of the
illustrated neurons (neuron 8), note a transitions from a
bimodal to a unimodal ISI distribution along with an increase in
rhythmic frequency ~200 ms prior to movement onset. Transitions from
bimodal to unimodal ISI distributions also were observed for two other
neurons. Transitions to nonrhythmic activity patterns were seen for
only 2 of 32 (7%) neurons.

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Fig. 4.
A: interspike intervals (ISI) scattergrams for 8 of the
28 rhythmically firing neurons that showed smooth transitions in ISI
distributions as a function of time relative to onset of wrist
movement. The left column shows the records of four neurons that
decreased their firing rates before movement; the right column shows
four examples of records from neurons with firing rate increases.
B: line drawings of coronal sections at stereotaxic
levels indicated by the lower numbers in each, along with the recording
locations for the examples shown in A.
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Modulations of activity related to the onset of wrist movement and its
direction were common for RFNs (Fig. 5).
For the majority of RFNs (28/32, 88%), the earliest changes in
activity preceded movement onsets. When cases involving flexion and
extension movements were grouped together, activity changes occurred at
159 ± 83 ms prior to movement onset, that is, 180 ± 68 ms
after go-cue onset. The timing of premovement activity changes was not
significantly different as a function of ACH classification (Table 3)
nor was the sign of the premovement activity change relative to the
activity exhibited during the hold period (Table
4). These results for timing of activity
changes are in correspondence to the results of a previous analysis
done for a portion of the non-RFNs (Gardiner and Nelson
1992
). Thus, RFNs did not exhibit any unique properties in the
timing of their task-related activity. However, the degree of firing
rate modulation in RFNs was different from the other unit types. Firing
rate modulations during premovement activity were expressed as the
ratio of the MFR during 100 ms after activity onset to that during 100 ms before activity onset. This ratio was on average less for
RFNs than for the early peak and flat ACH neurons for cases of rate
increase (Table 3). No significant difference was found for cases of exhibiting MFR decrease.

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Fig. 5.
Surface-plot representations of the perievent time histograms for the
whole population of rhythmically firing neurons (n = 32). A: flexion trials. Centering is on movement onset.
B: extension trials. Centering is on movement onset.
C: flexion trials. Centering is on issuance of the pulse
that activated the reward solenoid. D: extension trials.
Centering is on reward onset. The histograms were ranked by mean firing
rate during the hold period in A. For panels B,
C, and D, the same order is kept.
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Neostriatal neurons with high background firing rates often exhibit
responses to reward delivery (Aosaki et al. 1994
, 1995
; Kimura et al. 1984
). Aligning the activity records on
reward revealed that there was as much activity surrounding the reward
onset as there was around the movement onset (Fig. 5). However, no
consistent activity change aligned with the reward was found for the
population of RFNs. Moreover, the activity patterns commonly depended
on the direction of wrist movement. It should be noted that, at the time of reward delivery, the wrist movement continued (see position traces in Fig. 3, C and D). Thus, the
reward-related activity modulations, if present, could be superimposed
on the movement-related activity, making the former difficult to
discriminate. In only one instance was there any evidence that a neuron
changed firing rate immediately after the delivery of the fruit juice
reward. Rasters for this neuron showed one or two spikes in some trials that were aligned with reward delivery and occurred at a latency of
about 25 ms.
Serial dependencies in the spike trains of RFNs were analyzed to
examine the possibility that rhythmic activity patterns in these
neurons were evoked by an external source, for example, a rhythmic
drive from the cortex. In the case of an external rhythmic drive,
negative serial correlation of ISIs may occur (Lebedev and
Nelson 1995
, 1996
; Lebedev and Wise 1998
;
Surmeier and Towe 1987a
,b
). The joint ISI scattergrams
for RFNs typically contained a cluster of dots along the major
diagonal, indicative of positive serial correlation (Fig.
6C). For the total set of
RFNs, the mean of the coefficients of serial correlation was 0.164 (SD,
0.105). In addition, 30 of 32 (94%) of the coefficients of serial
correlation were positive. These positive serial correlations appeared
related to changes in ISI distributions across trials (Fig. 6,
A and B). Thus, the following analysis was
conducted. The mean of a trial's ISIs was subtracted from each of that
trial's ISIs. The resultant joint ISI plots did not exhibit features
of positive correlation (Fig. 6D). The serial correlation
coefficients calculated for these normalized data were, on the average,
negative (mean ± SD; 0.120 ± 0.085). We conclude, therefore
that for short epochs (exhibiting intratrial variations), ISIs were
negatively correlated. That is, a short ISI was likely to be followed
by a longer ISI in a given trial. For longer epochs (exhibiting
intertrial variations), ISIs could be described as positively
correlated in the sense that short ISIs were grouped with short ISIs
and long ISIs with long ISIs in their corresponding trials.

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Fig. 6.
Analyses of serial correlation of interspike intervals.
A: scattergram showing interspike intervals (ISIs)
during the hold period for each trial. Extension trials are numbered 1 through 40. Flexion trials are numbered 41 through 80. B: same data as in A after rearranging
the trial order according to the mean ISIs of the trials.
C: joint ISI plot. The x-axis represents
an ISI n (ms), and the y-axis
represents its immediately adjacent ISIn+1
(ms). D: joint ISI plot normalized by subtracting the
trial mean ISI from each of the trial's ISIs.
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DISCUSSION |
This study of the activity patterns of single neostriatal neurons
recorded in awake, behaving monkeys showed that slightly over 10% of
neurons exhibited sustained rhythmic firing when monkeys actively held
against a load awaiting a go-cue. This rhythmic firing was often
modulated ~160 ms prior to movement onset. This modulation occurs
approximately in the middle of reaction time period that averaged
337 ± 73 ms. The rhythmic frequency changes accompanied
task-related changes in the firing rate. This pattern of rhythmic
frequency transition was termed a regular transition because it
resembles firing frequency changes in cortical regular spiking neurons
injected with intracellular currents (McCormick et al.
1985
). The simplest model of such transition is the
integrate-and-fire model in which rhythmic frequency increases with
increases in input strength (Segundo et al. 1968
;
Softky and Koch 1993
). Also consistent with an
integrate-and-fire model is our observation that mean ISIs drift
somewhat from trial to trial. Mean ISI drifts can be explained
sufficiently by changes in the level of excitatory inputs to
integrate-and-fire neurons.
Certain lines of evidence suggest that RFNs may be interneurons, a
heterogeneous cell type (for review, see Kawaguchi et al. 1995
). It is commonly believed that the principal neurons of
neostriatum, the medium spiny neurons, are unlikely to exhibit regular
firing because of their electrophysiological properties and the
characteristics of their cortical inputs (for review, see Wilson
1993
). It has been demonstrated that, in urethane-anesthetized
rats, medium spiny neurons typically fire in bursts (Stern et
al. 1997
; Wilson 1993
; Wilson and Groves
1981
). Bursty firing patterns have been described for the
majority of neostriatal neurons, presumably medium spiny neurons,
recorded in awake monkeys (Aldridge and Gilman 1991
).
Our analysis, which could depict bursts shorter than 250 ms, indicated
such was the case for ~28% of the neuronal sample. Bursty neurons
had the highest degree of task-related modulation of firing rate. These
neurons are likely to be principal neurons. Neostriatal interneurons,
unlike principal neurons, may be better suited electrophysiologically
for generating rhythmic patterns of activity. Cholinergic interneurons,
for instance, have prominent afterhyperpolarizations (Bennett
and Wilson 1998
; Wilson et al. 1990
). Thus,
given a steady excitatory input, cholinergic interneurons tend to fire
rhythmically in an integrate-and-fire mode. In addition, the proportion
of rhythmically firing neurons that we saw is roughly consistent with
the proportion of neostriatal cells thought to be aspiny interneurons
(Graveland and DiFiglia 1985
; Kemp and Powell
1971
).
The regular transitions in RFN firing, which were associated with wrist
movements, correspond well to the properties of cholinergic interneurons described by Wilson et al. (1990)
(also see
Bennett and Wilson 1998
). These authors studied
cholinergic interneurons in vivo in urethane-anesthetized rats.
Although spontaneous rhythmic firing was not observed under these
conditions, it was predicted that cholinergic interneurons may fire
rhythmically if they are depolarized so that their firing is determined
by spike afterhyperpolarization. A rhythmic firing frequency of ~16
Hz was predicted. This frequency corresponds to the lower frequency
range of RFNs. The same authors evoked rhythmic firing in cholinergic
interneurons by injecting current pulses. They saw firing frequencies
of 15 to 100 Hz that were linearly dependent on the amount of injected
current, i.e., regular transitions. This frequency range corresponds
well to the frequencies that we saw for RFNs during execution of the
behavioral task in our study. Moreover, the negative serial
correlations of ISIs in individual trials may be related to the spike
frequency adaptation in cholinergic interneurons (Bennett and
Wilson 1998
; Wilson et al. 1990
). We conclude
therefore that there is reasonable evidence to suggest that RFNs and
cholinergic interneurons may be the same type of cell.
Could the rhythmic firing patterns in RFNs be evoked by an external
rhythmic drive, for example, by rhythmic cortical input? Elsewhere
(Lebedev and Nelson 1995
, 1996
) we have discussed the features of serial correlations of ISIs that could indicate external rhythmic drive. Recently these features have been demonstrated for
premotor cortex neurons (Lebedev and Wise 1998
). One of
these features, negative serial correlation within trials, was indeed found for RFNs, but only after the normalization procedure, which corrected for ISI drifts across trials. As discussed above, this negative serial correlation could be due spike frequency adaptation. Other features such as diagonal bands in the joint ISI scatterplot and
multimodal ISI distribution typically were not seen. In addition, cortical oscillations wax and wane (Murthy and Fetz
1992
; Sanes and Donoghue 1993
), whereas the
activity of RFNs was sustained during the hold period of the task and
was modulated in association with wrist movements. By contrast, in
premotor oscillatory neurons, rhythmic frequency often remains constant
during task-related firing rate modulations (Lebedev and Wise
1998
). Thus, there is not sufficient evidence to suggest an
external rhythmic drive to RFNs. However, it is possible that rhythmic
inputs, for example, cortical inputs, could modulate the activity of RFNs.
In a series of studies conducted by Kimura and colleagues
(Aosaki et al. 1994
, 1995
; Kimura et al.
1984
), it was suggested that tonically active neurons (TAN) in
the neostriatum are cholinergic interneurons. How do RFNs compare with
TANs? TANs are identified by their high firing rates (2-10
spikes/s),when compared with the rest of population (e.g.,
Aosaki et al. 1994
). They discharge tonically but
nonrhythmically. Firing rates of neostriatal neurons in the present
task (~15 spikes/s) were somewhat higher than those previously
reported. The higher firing rates in this task are probably related to
the requirement to actively maintain hand position against a load of
0.07 Nm. In the tasks implemented by others, monkeys often sat quietly
(Aldridge and Gilman 1991
) or sat quietly and awaited a
reward (Aosaki et al. 1994
). It is possible that, under
conditions of increased task-related excitatory input, TANs could begin
to fire rhythmically like RFNs (Bennett and Wilson 1998
;
Wilson et al. 1990
). We, however, did not observe nor
document clear differences in the extracellular action potential
duration of RFNs compared with non-RFNs, although longer action
potential duration is a part of the classical definition of TANs
(Crutcher and DeLong 1984
; Kimura et al.
1984
). Thus, the relationship between RFNs and TANs awaits
further study.
Does the rhythmic pattern of activity of RFNs have a functional role?
An intriguing possibility is that RFNs could regulate oscillations in
other groups of neostriatal neurons. Indeed, cholinergic interneurons
are strategically located at the borders of neostriatal compartments
(patch and matrix) and influence the activity of neurons in both
(Kawaguchi 1993
; Kubota and Kawaguchi
1993
). Given that cortical neuronal populations periodically
oscillate, the activity of neostriatal neurons could become entrained
to these oscillations. The activity of RFNs may be modulated during
these oscillatory episodes, especially if the evoked oscillation
frequency and RFN frequency are similar. In addition, RFNs could
interact with the evoked oscillations in medium spiny neurons, for
example, by a phase-locked loop mechanism (Ahissar and Vaadia
1990
). This hypothesis has yet to be tested. Raz et al.
(1996)
reported that groups of TANs may fire at a high level of
synchrony
a potentially powerful mechanism for regulating network
activity. Moreover, after dopamine depletion, TANs exhibit synchronous
oscillations at ~16 Hz (Raz et al. 1996
). Thus, the
oscillations in TANs (and possibly RFNs) appear to be under
dopaminergic control.
Other classes of neostriatal neurons may have contributed to the
present RFN sample. Bimodal distributions of ISIs observed for some of
the higher firing rate RFNs (also see Aosaki et al. 1995
) do not fit the integrate-and-fire pattern of activity,
which presumably is characteristic for cholinergic interneurons
(Kawaguchi 1993
; Kubota and Kawaguchi
1993
). These neurons may have been some other type of
neostriatal interneurons.
Recent reports suggest that TANs in the neostriatum develop responses
to relevant parts of behavioral tasks after some acquisition period and
maintain these associations even after long lapses in task performance
(Aosaki et al. 1994
, 1995
). Most notably, TANs are
thought to develop responses associated with sensory stimuli coupled to
rewards. In our experiments, we were not able to demonstrate
reward-related responses except for one of the RFNs. However,
premovement changes in activity were observed for the majority of RFNs.
Moreover, these premovement activity changes occurred at approximately
the same time as the premovement activity changes in other neostriatal
neurons in this task (Gardiner and Nelson 1992
). We
suggest that RFNs have a role in movement initiation. Whether or not
RFNs alter their firing patterns during behavioral conditioning
requires further study.
We thank C. J. Wilson for helpful comments regarding analyses
of neuronal firing patterns and J. M. Denton for technical assistance.
This research was supported by National Institute of Neurological
Disorders and Stroke (NINDS) Grant NS-26473. Some analyses were
developed while R. J. Nelson was supported by NINDS Grant NS-36860.
Address for reprint requests: R. J. Nelson, Dept. of Anatomy and
Neurobiology, University of Tennessee, 855 Monroe Ave., Memphis, TN
38163.
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.