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The Journal of Neuroscience, June 15, 2001, 21(12):4498-4504
Network Synchrony in the Nucleus Accumbens In
Vivo
Yukiori
Goto and
Patricio
O'Donnell
Center for Neuropharmacology and Neuroscience, Albany Medical
College, Albany, New York 12208
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ABSTRACT |
Nucleus accumbens neurons show membrane potential fluctuations
between a very negative resting membrane potential and periodical plateau depolarizations. Because action potential firing occurs only
during the depolarized state, the control of transitions between states
is important for information processing within this region, with an
impact on accumbens-related behaviors. It has been proposed that
ensembles of active neurons in the nucleus accumbens could be based on
a population of cells depolarizing simultaneously into the UP state. In
this study, in vivo intracellular recordings from
accumbens neurons were performed simultaneously with local field
potential recordings to examine whether the nucleus accumbens can
exhibit synchronization of membrane potential states in a population of
neurons. These simultaneous recordings indicated that local field
potential shifts occurred synchronously with transitions to the UP
state. Furthermore, manipulations that evoked prolonged plateau
depolarizations also evoked field potentials of similar duration. Such
signals likely occurred because of simultaneous membrane
potential changes in a population of neurons. Together with our
previous studies, these results suggest that membrane potential states
in the nucleus accumbens can be synchronized by synaptic inputs from
the hippocampus.
Key words:
nucleus accumbens; membrane potential; dopamine; ensemble; in vivo intracellular recording; field
potentials
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INTRODUCTION |
The nucleus accumbens (NAcc), also
known as the ventral striatum, participates in high-order brain
functions, including reward, motivation, learning, and memory (Apicella
et al., 1991 ; Kelley, 1999). A dysfunction of this region may be
involved in conditions such as schizophrenia (O'Donnell and Grace,
1998) and drug addiction (Koob and Bloom, 1988). Medium-sized, densely
spinous neurons are the projection neurons within the NAcc and comprise
>90% of its population (Chang and Kitai, 1985). These neurons show
robust membrane potential shifts between a negative DOWN state and a depolarized UP state (O'Donnell and Grace, 1995). Similar membrane potential fluctuations have also been observed in dorsal striatal (Wilson, 1993) and cortical (Steriade et al., 1993a; Lampl et al.,
1999; Lewis and O'Donnell, 2000) neurons. It has been reported recently that such membrane potential shifts in complex cells of the
primary visual cortex can be affected by presentation of visual stimuli
(Anderson et al., 2000), suggesting that membrane fluctuations in these
neurons may be related to sensory processing. The physiological role of
those membrane potential oscillations in other regions, including the
NAcc, is not yet understood.
Unveiling information-processing mechanisms in the NAcc has been a goal
in many laboratories over recent years. Whereas traditional electrophysiological studies have focused on individual cell firing, increasing attention is being given to the activity of neural ensembles
(O'Donnell, 1999). Neuronal ensembles can be readily detected in
behaving animals with chronically implanted multiple extracellular
electrodes (Deadwyler et al., 1996; Riehle et al., 1997; Jog et al.,
1999). Recent studies have shown that UP-DOWN state transitions in
striatal medium spiny neurons are correlated with frontal cortical EEGs
(Mahon et al., 2001) and that transitions to the UP state in NAcc
neurons are correlated with population activity in the ventral
hippocampus (Goto and O'Donnell, 2001). These results suggest that
coherent synaptic inputs can produce synchronous membrane potential
fluctuations, defining active neural ensembles. In the NAcc, such
inputs are primarily originated in the hippocampus. Furthermore,
synchronous membrane potential oscillations have been reported between
pairs of dorsal striatal neurons (Stern et al., 1998), suggesting that
a large number of these neurons can go into the UP state
simultaneously. If a population of neurons depolarizes synchronously by
10-20 mV, a substantial field potential would be produced. In this
study, we took advantage of detecting population activity with local
field potentials and combined these recordings with simultaneous
intracellular recordings from NAcc neurons in vivo to
determine the presence of neural network activity arising from
ensembles of depolarized neurons.
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MATERIALS AND METHODS |
Animals. Simultaneous in vivo
intracellular and local field potential recordings were performed in 55 rats. Sprague Dawley male adult rats (230-450 gm) were obtained from
Taconic Farms (Germantown, NY). All experimental procedures were
performed according to the United States Public Health Service
Guide for the Care and Use of Laboratory Animals and
approved by the Albany Medical College Institutional Animal Care and
Use Committee. Rats were anesthetized with chloral hydrate (400 mg/kg,
i.p.) and placed on a stereotaxic apparatus (David Kopf
Instruments, Tujunga, CA). Temperature was monitored with a
rectal temperature probe
(Fine Science Tools, Foster City, CA)
and maintained between 36 and 38°C. Supplemental anesthesia (chloral
hydrate, 24-30 mg/hr) was continuously delivered during the recording
session via a cannula inserted intraperitoneally and a minipump
(Bioanalytical Systems, West Lafayette, IN). Bupivacaine (0.25%) was
applied subcutaneously before any skin incision was made. Burr holes
were drilled into the skull for electrode placement.
Recordings. Intracellular and extracellular electrodes were
made from 1 mm outer diameter Omegadot borosilicate glass tubing (World Precision Instruments, Sarasota, FL) pulled with a P-97 Flaming-Brown puller (Sutter Instrument Company, Novato, CA). Intracellular electrodes were filled with 2 M
potassium acetate and 2% neurobiotin and had a resistance of 31-103
M . Extracellular electrodes were filled with 2 M NaCl and 2% pontamine sky blue and had an
impedance of 3-8 M. Intracellular electrodes were lowered into the
NAcc [anteroposterior (AP): bregma, +1.4 to +2.1 mm; lateral,
1.0-2.0 mm; vertical,  5.8 to 8.4 mm], and extracellular electrodes were also lowered in the vicinity using a second electrode holder angled 30° from the vertical (AP: bregma, +1.5 mm; lateral, 1.5 mm; vertical, 7.0 mm). Both recording electrodes were advanced by
hydraulic manipulators (Trent Wells, Coulterville, CA; David Kopf
Instruments); at the same time, their activity was monitored on a
Philips PM3337 oscilloscope (Fluke, Everett, WA). Intracellular signals
were amplified using an IR-283 Neurodata amplifier (Cygnus Technology,
Delaware Water Gap, PA) and filtered at 0.3-3 kHz with an eight-pole
Bessel filter (FLA-01; Cygnus). Extracellular signals were amplified
10,000 times with an AC amplifier (Warner Instrument
Corporation, Hamden, CT) and passed through a Humbug noise eliminator.
Both intracellular and extracellular signals were digitized with an
interface board (DAP3215a; Microstar Labs, Bellevue, WA) at 10 kHz and
fed to a computer (Gateway PII 266; Gateway, North Sioux City, SD) for
off-line analyses. Once a stable impalement was obtained with the
intracellular electrode, simultaneous intracellular and extracellular
baseline recordings were performed. Only neurons showing at least 50
mV resting membrane potential and overshooting action potentials were
analyzed and included in the study. All data handling was performed
using custom-made software (Neuroscope).
Electrical stimulation. Concentric bipolar electrodes with
0.5 mm between tips (NE-100X; Rhodes Instruments) were used for electrical stimulation. The electrodes were placed in the ventral tegmental area (VTA) (AP: bregma, 5.8 mm; lateral, 0.5 mm; vertical, 8.4 mm). Current pulses were generated by stimulus isolation units
driven by a Master 8 Stimulator (AMPI, Jerusalem, Israel). Stimulation
protocols were controlled by the computer using Neuroscope. Electrical
stimulation was performed by delivering five 0.1-0.5 mA current pulses
(0.1 msec each) at 20 Hz every 10 sec, to mimic dopamine (DA) cell
burst firing.
Drug administration. The D1 antagonist
SCH23390 and D2 antagonist sulpiride were
purchased from Sigma (St. Louis, MO). SCH23390 was dissolved in
distilled water to a final concentration of 0.25 mg/ml. Sulpiride was
first dissolved in a drop of NaOH and then diluted in distilled water
to final concentration of 20 mg/ml. pH was adjusted just before
administration. All drugs were administered intraperitoneally.
Histology. After completion of the experiments, recording
sites were marked by ejection of pontamine sky blue from extracellular electrodes and neurobiotin from intracellular electrodes by passing positive current (1.0 nA, 200 msec pulses at 2 Hz) for at least 5 min.
Animals were given a lethal dose of pentobarbital (100 mg/kg) and
transcardially perfused with ice-cold saline followed by 4%
paraformaldehyde. Brains were removed from the skull, cryoprotected in
30% sucrose, and sectioned using a freezing microtome. Serial 50-µm-thick sections were cut coronally. Neurobiotin-injected sections were further incubated in 0.4% Triton X-100 (Sigma) in PBS
for 1-2 hr, followed by 2 hr in Vectastain Elite avidin-biotin complex reagent (Vector Laboratories, Burlingame, CA).
After a series of rinses, sections were reacted with
3,3'-diaminobenzidine (DAB) and urea-hydrogen peroxide (Sigma Fast DAB
set). All sections were mounted on gelatin-coated slides, air-dried for
24 hr, cleared in xylene, coverslipped in Permount, and examined on an
Olympus Optical (Tokyo, Japan) CH30 microscope. The locations of
intracellularly and extracellularly recorded neurons were identified
according to the atlas of Paxinos and Watson (1998).
Data analysis: Gaussian fit for membrane potential
distribution. Membrane potential distribution was calculated by
determining membrane potential values every 0.1 msec in 20 sec epochs
of recording. Two-peak Gaussian fit analyses were performed using the
following equation:
where A1 and
A2 are total areas under the curve
from baseline,
X 1 and
X2 are the
peaks of the histograms, and W1 and W2 are two SDs (W1 = 2 1;
W2 = 22).
Cross-correlation. Cross-correlation analyses were performed
to explore the correlation between transitions to the UP state and
local field potential shifts. First, cross-correlograms were constructed using onset time points of UP membrane potential states and
negative shifts of local field potentials. Calculation and construction
of the histogram were based on the methods described by Perkel et al.
(1967). Bin width was 100 msec. A Poisson distribution was assumed for
the correlograms, and the correlation peaks were judged significant if
they were three SDs (p < 0.013) above baseline. The strength of each pair of correlations
(Sccr) was defined as:
where Nc is the number of
coincident events between membrane potential and local field potential
shifts occurring within 100 msec as measured by the peak
cross-correlogram, Nb is the chance
level of coincident events given by the shuffled random cross-correlogram, and Nmp and
Nlfp are the total number of events in
membrane potential and local field potential shifts, respectively.
In addition, spectral analyses were performed in both recordings using
a fast Fourier transform routine with Statistica software (Statistica, Tulsa, OK). Cross-spectral density was also calculated using Statistica. Coherence between similar-frequency peaks in the
individual spectra was calculated by standardizing the cross-amplitude values. Cross-spectral densities were squared and divided by the product of spectral densities of each recording. The result was interpreted as a squared correlation coefficient
(r2) for statistical purposes.
High coherence values (>0.75) were taken as indicators of oscillatory
activity at that particular frequency being synchronized.
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RESULTS |
Intracellular recordings from NAcc medium spiny neurons
In vivo intracellular recordings were performed from 62 neurons in the core (n = 55) and shell
(n = 7) regions of the NAcc. Fifty-four of 62 cells
(87%) showed UP and DOWN membrane potential states (Fig.
1A). These were
determined when a histogram of time spent at different membrane
potential values revealed a bimodal distribution that could be fitted
with high confidence to a dual Gaussian function (Fig.
1B). The DOWN and UP membrane potential states were
76.8 ± 9.1 mV (mean ± SD) and 64.2 ± 9.3 mV,
respectively. Onset time points of UP events were determined when the
membrane potential crossed the trough in the membrane potential
distribution histogram (e.g., 64 mV in Fig. 1B) for
at least 100 msec. The frequency of transitions to the UP state was
0.64 ± 0.30 Hz. Neurobiotin staining revealed that these were
medium spiny neurons (Fig. 1C).
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Figure 1.
Nucleus accumbens medium spiny neurons exhibit a
membrane potential with UP and DOWN states. A, A
representative trace of a neuron with alternating DOWN (72 mV;
bottom arrowhead) and UP (59 mV; top
arrowhead) membrane potential states. B,
Membrane potential distribution of the trace shown in A
reveals two peaks that correspond to the UP and DOWN states. This
histogram fits to a two-peak Gaussian distribution (black
line; r2 = 0.91).
C, Neurobiotin staining revealed morphology typical of
medium spiny neurons.
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The membrane properties of some neurons exhibiting a bistable membrane
potential were also investigated by intracellular current injection
(n = 27). The input resistance of these neurons was 46.2 ± 18.5 M, measured from the DOWN state. Most cells
(n = 20) showed an inward rectification with
depolarizing current injections, similar to what had been reported in
NAcc core, but not shell, medium spiny neurons (O'Donnell and Grace,
1993). The time constant was 5.4 ± 1.7 msec, measured as the time
required to reach 63% of the maximal hyperpolarization in response to
a negative current pulse. These values were within the range reported previously.
Simultaneous local field potentials and intracellular recordings in
the NAcc in vivo
Simultaneous intracellular and local field potential recordings
were acquired from 12 pairs. In all cases, local field potentials exhibited large periodical negative shifts (Fig.
2A) similar to what has
been reported previously (Leung and Yim, 1993). The frequency of these
events was 0.50 ± 0.19 Hz, which is similar to that of intracellular membrane potential state transitions.
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Figure 2.
Membrane potential
transitions are correlated with local field potentials in the NAcc.
A, Example of a local field potential recording showing
negative shifts (top trace, dashed line
indicates 0 V) simultaneously with UP state transitions in the NAcc
neurons (bottom trace, dashed line
indicates the DOWN state, 78 mV). High-frequency components including
spike firing were removed from both traces by low-pass filtering (<50
Hz). B, Cross-correlogram from the pair illustrated in
A (bin width = 100 msec) showing a peak at time 0 bin. The dashed line indicates 3 SDs above the mean.
C, A control cross-correlogram was made using shuffled
periods of intracellular and local field potential recordings.
D, Linear regression analysis between the distances
separating intracellular from extracellular recording electrodes and
strength of cross-correlation showing decreasing
Sccr with increasing distances. The
arrow indicates the case shown in A.
E, Multiunit recordings showing spike firing occurring
during negative shifts of local field potential. Both traces are the
same data sample; the top trace was high-pass filtered
(>50 Hz) to reveal spike firing, whereas the bottom
trace was low-pass filtered (<50 Hz) to show slow negative
local field potential shifts. F, Locations of
intracellular and local field potential recording sites. All
intracellular recordings were obtained from the core region except one
that was located in the shell. All local field potential recordings
were performed in the core region.
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Cross-correlograms were created to ascertain the correlation between
onset times of transitions to the UP state and local field potential
negative shifts (Fig. 2B). Statistically significant peaks (three SDs above baseline; p < 0.013; normal
distribution was assumed for the histograms) were detected at time 0 with a bin width of 100 msec in every pair, indicating that the
potential shifts of these two parameters occurred within <100 msec.
Cross-correlograms between shuffled time epochs did not result in such
peaks (Fig. 2C), ruling out artifactual components in the
histogram peaks.
The cross-correlation strength (Sccr)
was calculated using the equation indicated in Materials and Methods,
based on the ratio of the number of coincident events to the total
number of events. The average strength from 12 pairs was
0.29 ± 0.06, whereas a shuffled
Sccr was only 0.05 ± 0.02 (p < 1.0 × 10 11; paired t test;
n = 12). Linear regression analyses revealed a
significant negative correlation (r = 0.77;
p < 0.01) between Sccr and the distance between
intracellular and extracellular electrodes (Fig. 2D).
Nonetheless, even in the pair with the longest interelectrode distance
(i.e., 1.7 mm), the cross-correlation strength was still very high
(Sccr = 0.25). In some cases,
multiunit activity was simultaneously collected with the local field
potential (n = 3). In all cases, spikes were observed
only during the negative local field potential shifts (Fig.
2E), supporting the idea that these events were a
reflection of UP membrane potential transitions.
Power spectral density analyses were performed for every pair of
recordings. In all cases, the highest density peaks were observed at
~1 Hz, corresponding to the frequencies of UP membrane transitions
and negative shifts of local field potential (Fig. 3A,B). The synchrony between
the pairs of recordings was then analyzed by coherence analyses from
power density spectra, which are the frequency domain of correlation
analysis. Cross-spectral density analysis revealed a significant peak
at the same frequency as the individual spectra (Fig. 3C).
Normalizing the cross-spectral density to individual signal densities
revealed a coherence close to 1.0 at the frequency showing the highest
peaks of power spectra, suggesting that correlation between every pair
was very high at that frequency
(r2 = 0.97 ± 0.12).
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Figure 3.
Spectral density coherency analyses reveal high
synchrony between intracellular recordings and local field potential
fluctuations. A, Spectral density of the extracellular
recording shown in Figure 2A. The
arrow indicates the peak that corresponds to the
frequency of local field potential shifts (0.76 Hz). B,
Spectral density of an intracellular recording obtained simultaneously.
A peak (arrow) is observed at the same frequency as in
A. C, Cross-spectral density revealed a
peak (arrow) at the same frequency as in
A and B. Zero is the value expected for
nonsynchronous components. The near 1 Hz peak has a negative value in
this case, resulting from the 180° off-phase of both waveforms. (The
intracellular signal depolarizes as the field potential turns more
negative.) All graphs plot microvolts per square second over
Hz.
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Fimbria-fornix transection abolished negative shifts in the NAcc
local field potential
We had reported previously that a fornix transection eliminates UP
membrane potential states in NAcc neurons (O'Donnell and Grace, 1995).
To further explore the possibility that negative local field potential
shifts reflect transitions to the UP state in NAcc neurons, local field
potentials were also recorded before and after the fornix was cut with
a glass microknife (n = 4). This procedure eliminated
periodical negative shifts in NAcc local field potentials (Fig.
4A). In one case, a
small regular oscillation with a range of ~5 Hz appeared after the
transection (data not shown), corresponding to theta frequency. A sham
operation (n = 5) did not affect local field potentials
(Fig. 4B,C).
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Figure 4.
A fornix transection eliminated local field
potentials in the NAcc. A, Traces of local field
potential before (top) and after (bottom)
fornix transection. B, Traces of local field potential
before (top) and after (bottom) a
transection that spared the fornix. C, Schematic
description of fornix and sham transections. In the sham operation, the
transection was stopped before the knife reached the fornix.
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Responses to ventral tegmental area stimulation
Activation of the VTA, the source of DA projections to the
NAcc and prefrontal cortex (PFC) (Thierry et al., 1973; Voorn et al., 1986), has been shown to evoke a prolonged transition to the UP
state in PFC pyramidal neurons (Lewis and O'Donnell, 2000). To explore
whether a similar response occurs in the NAcc and whether it could be
reflected as prolonged negative local field potential, simultaneous
intracellular and extracellular recordings were performed in the NAcc
while electrically stimulating the VTA. Trains of five 0.1 mA pulses at
20 Hz, mimicking DA neuron burst firing, evoked simultaneous membrane
and local field potential transitions (Fig.
5A,B). The duration of
responses (measured as the decay to half the amplitude of the maximal
response) was 499 ± 249 msec (268-1090 msec; n = 10) in the intracellular recordings and 453 ± 217 msec (237-955
msec; n = 10) in the local field potential.
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Figure 5.
Simultaneous intracellular and
extracellular responses to VTA stimulation recorded in the NAcc.
A, Typical example of simultaneously recorded traces.
Trains of five pulses delivered to the VTA appear as sets of vertical
lines. Top, Overlay of five local field potentials
showing negative shifts in response to VTA stimulation.
Bottom, Overlay of five intracellular recordings
simultaneously recorded with local field potentials shown in top
traces. B, Averaged signals from 15 responses to
VTA stimulation. Evoked field potential negative shifts
(top) were observed simultaneously with membrane
potential depolarizations (bottom).
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Potential shifts evoked by VTA stimulation in the NAcc depend on
activation of both D1 and D2 receptors
Electrical VTA stimulation can activate both DA and non-DA
projections to the NAcc. To elucidate whether the responses observed were dependent on DA and the receptor subtypes involved, some VTA
stimulation experiments were repeated in the presence of selective D1 and D2 antagonists.
Administration of either the D1 antagonist SCH23390 (0.5 mg/kg, i.p.) or the D2 antagonist
sulpiride (40 mg/kg, i.p.) failed to affect the duration and amplitude
of both membrane potential and local field potential responses (Fig.
6, Table
1). However, combined administration of
D1 and D2 antagonists reduced the duration of VTA-evoked states (repeated-measures ANOVA; F(3,23) = 4.29; p < 0.05) (post hoc Tukey test; p < 0.05). Local field potential shifts were reduced when the
D1 and D2 antagonists were
combined, although this reduction was not statistically significant (F(3,35) = 2.82; p = 0.053) (Fig. 5, Table 1). The amplitude of extracellular responses was
reduced with combined administration of SCH23390 and sulpiride
(F(3,35) = 6.54; p < 0.005) (post hoc Tukey test; p < 0.005). Spontaneous membrane potential transitions and local field
potential shifts were not altered by either antagonist (data not
shown).
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Figure 6.
Dopamine antagonists altered responses
to VTA stimulation. A, Traces showing a prolonged
depolarization resembling the UP state in response to VTA stimulation
(vertical lines) before (top) and after
(bottom) applying the dopamine D1 antagonist
SCH23390 (0.5 mg/kg). Both traces are overlays of five responses.
B, Traces from another cell showing a similar response
in control conditions (top), with the D2
antagonist sulpiride (40 mg/kg) (middle), and with the
D1 and D2 antagonists combined
(bottom). C, Bar graphs showing the
duration and amplitude of VTA-evoked depolarization obtained in the
presence of these drugs as a proportion of the baseline responses. A
combined administration of D1 and D2
antagonists reduced the duration of VTA-evoked responses.
Numbers in parentheses indicate the
number of samples. D, Negative field potential shifts
evoked by VTA stimulation in control conditions (top)
and in the presence of SCH23390 (bottom). Traces are
overlays of five responses. E, Similar response before
(top) and after drug administration
(middle, with sulpiride; bottom, with a
combination of SCH23390 and sulpiride). Again, traces are overlays of
five responses. F, Bar graphs summarizing changes in the
duration and amplitude of VTA-evoked events with dopamine antagonist
treatment. As with intracellular recordings, a combined administration
of D1 and D2 antagonists reduced the duration
of local field potential responses.
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DISCUSSION |
Our data indicate that transitions to the UP membrane potential
state in NAcc neurons occur simultaneously with negative shifts in
local field potentials detected with extracellular electrodes located
up to 2 mm apart from the intracellular electrode. Furthermore, manipulations that altered membrane potential state transitions also
changed local field potentials. A fornix transection had been reported
to eliminate UP states in NAcc neurons recorded intracellularly
(O'Donnell and Grace, 1995). In this study, a similar lesion also
eliminated negative shifts of local field potentials in the NAcc. VTA
stimulation evoked prolonged UP states as well as prolonged negative
shifts in local field potentials. These results support the possibility
that periodic local field potential shifts in the NAcc are a reflection
of a population of neurons in the vicinity of the recording electrodes
entering into the UP state simultaneously. This suggests that the NAcc has a network in which neural ensembles are based on the membrane potential state of their neurons.
Synchronous membrane potential fluctuations in NAcc neurons observed as
local field potentials may be produced by population activities in the
ventral hippocampus (Goto and O'Donnell, 2001). Because the recordings
were performed using electrodes located up to 1.7 mm apart, it is
unlikely that the neuronal activity synchrony detected is elicited by a
single common input. Rather, the synchrony of UP-DOWN transitions in a
NAcc network may be reflection of synchronous activity in a distributed
assembly of hippocampal neurons.
DA may exert different modulations of membrane potential states in NAcc
and PFC neurons. In PFC pyramidal neurons, the prolonged UP state
transition evoked by VTA stimulation was shortened by administration of
a D1 antagonist (Lewis and O'Donnell, 2000). In
that study, a similar UP state transition in PFC neurons was evoked by
intra-VTA NMDA administration, indicating that cell depolarization by
VTA stimulation does not involve antidromic activation of PFC fibers.
Here we have shown that the duration of VTA-evoked UP states and field
potential shifts in the NAcc was reduced by a combined administration
of D1 and D2 antagonists. In vitro studies have indicated that NAcc neurons depolarize
in the presence of combined D1 and
D2 receptor activation (O'Donnell and Grace,
1996). A synergistic D1-D2
enhancement of glutamatergic excitatory synaptic responses has also
been observed in the striatum in vivo (Hu and White, 1997).
These results suggest that whereas DA may modulate UP events in the PFC
via D1 receptors, in the NAcc, a similar effect
of DA requires coactivation of D1 and
D2 receptors.
The periodicity and synchrony of membrane potential state transitions
raise the question of whether these phenomena are derived from the use
of anesthetized animals. Our data indicate that membrane potential
transitions occur in the presence of chloral hydrate; however, a number
of studies have reported similar membrane potential oscillations in the
striatum with a variety of anesthetic agents (Wilson, 1993; Stern et
al., 1998; Mahon et al., 2001). On the other hand, some pieces of
evidence argue against these fluctuations being anesthesia-induced or
sleep-related. First, similar membrane potential oscillations had been
also observed in dorsal striatal neurons of locally anesthetized awake
rats (Wilson and Groves, 1981). Second, step-like membrane potential
shifts in visual cortical neurons can be modified by presentation of
visual stimuli (Douglas et al., 1991; Anderson et al., 2000),
indicating that they participate in encoding sensory information in
these neurons. In addition, although large local field potential shifts
typically appear during slow-wave sleep, they have also been observed
in animals in a quiet, yet awake, state in the NAcc (Leung and Yim,
1993) and hippocampus (Chrobak and Buzsáki, 1994). According to
our results, such field potential shifts are a reflection of UP-DOWN
membrane potential state transitions of neurons in these regions.
Although speculative, it is conceivable that relatively periodical (<1 Hz) UP and DOWN membrane potential fluctuations are a characteristic of
an idling system during slow-wave sleep or quiet awake state and that
they reflect a widely synchronized network activity (Destexhe et al.,
1999). However, whenever a situation arises that demands the attention
of the animal, an ensemble of neurons would go into the depolarized
state. This could be accomplished by activation of attention-related
pathways such as DA (Lewis and O'Donnell, 2000), noradrenergic, or
cholinergic (Steriade et al., 1993b) systems. Indeed, ensemble coding
has been observed in the hippocampus of awake animals, in neurons
encoding spatial location (place cells) (Eichenbaum et al., 1989;
Deadwyler and Hampson, 1995). Place cells have also been reported in
the NAcc (Lavoie and Mizumori, 1994). It is possible that ensemble
activity in the hippocampus defines the neural ensemble in the NAcc.
Unveiling the functional roles of such ensembles could be attempted by
measuring field potential activity in behaving animals, a measure that
according to our results would be indicative of synchronous membrane
potential state transitions in a neural ensemble.
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FOOTNOTES |
Received Feb. 9, 2001; revised March 28, 2001; accepted March 30, 2001.
This work was supported by United States Public Health Service
Grants MH57683 and MH60131. We thank Barbara L. Lewis for her excellent
technical assistance, M. Gustavo Murer and Luis Riquelme (Universidad
de Buenos Aires, Buenos Aires, Argentina) for help with the
cross-correlation analysis, and Brian Lowry (University of Pittsburgh,
Pittsburgh, PA) for developing and providing the software used for data
acquisition and analysis (Neuroscope).
Correspondence should be addressed to Patricio O'Donnell, Albany
Medical College (MC-136), Center for Neuropharmacology and Neuroscience, Albany, NY 12208. E-mail: odonnep{at}mail.amc.edu.
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