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 Previous Article
The Journal of Neuroscience, July 1, 2000, 20(13):5179-5189
Changes in Functional Connectivity in Orbitofrontal Cortex and
Basolateral Amygdala during Learning and Reversal Training
Geoffrey
Schoenbaum1,
Andrea A.
Chiba2, and
Michela
Gallagher1
1 Department of Psychology, Johns Hopkins University,
Baltimore, Maryland 21218, and 2 Cognitive Science
Department, University of California at San Diego, La Jolla, CA 92093
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ABSTRACT |
Interconnections between orbitofrontal cortex (OFC) and basolateral
amygdala (ABL) are critical for encoding and using associative information about the motivational significance of stimuli. Previously, we reported that neurons in OFC and ABL fired selectively to cues during odor discrimination learning and reversal training. Here we
conducted an analysis of correlated firing in the cell pairs recorded
in the previous study. Correlated firing during the intertrial intervals was compared across task phases during different phases of
acquisition and reversal learning. Changes in correlated activity during initial learning and subsequent accurate performance on the
discrimination problems closely resembled the changes in odor selectivity in OFC and ABL reported earlier. Increased correlated firing was most pronounced in OFC during accurate go, no-go performance in the postcriterion phase of performance, whereas correlated firing in
ABL increased primarily during an earlier phase of learning. In
contrast, findings during subsequent reversal training diverged from
our earlier report in which odor selectivity diminished in OFC and
reversed in ABL. When the reinforcement contingencies of the odors were
reversed after the rat had learned the original associations,
correlated firing further increased significantly in OFC but remained
stable in ABL. This evidence that associative encoding increments with
reversal learning in OFC suggests that the original associations,
although not expressed as stimulus driven activity, may be
maintained within the network as new associations are acquired.
Key words:
orbitofrontal; basolateral amygdala; prefrontal; agranular insular; olfactory; cross-correlation; functional
connectivity; electrophysiology; learning and memory
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INTRODUCTION |
Electrophysiological studies of
neural activity in behaving animals have demonstrated that associative
learning alters functional connectivity in the mammalian brain (Segal
et al., 1972 ). Much of this research has used pavlovian conditioning
procedures to detect changes in the responses of individual cells to
explicit cues that predict biologically significant events (for review, see Davis, 1992; LeDoux, 1996; Thompson et al., 1998). In such investigations, stimulus-evoked neural activity is presumed to provide
an index of plasticity that reflects functional connections within
neural circuits.
Advances in recording methods now allow simultaneous recordings from
small ensembles of individual neurons within a brain region. The use of
such methods has shown not only changes in the phasic responses of
individual neurons to informative cues but has also revealed more tonic
changes in the correlated activity of neurons within networks (Wilson
and McNaughton, 1993; Quirk et al., 1995; Vaadia et al., 1995; Skaggs
and McNaughton, 1996; Hatsopoulos et al., 1998; Kubota et al., 1999).
For example, Quirk et al. (1995) examined correlated firing in lateral
amygdala during intervals in the home cage between different phases of
a fear-conditioning task. They reported that functional connectivity
changed as a result of training and suggested that these changes
reflected alterations in the network supporting conditioned neural
responses. Similarly, McNaughton and colleagues (Wilson and McNaughton,
1993; Skaggs and McNaugton, 1996) used correlated firing during sleep periods to gain insight into experience-dependent changes in networks of hippocampal neurons. Like studies of stimulus-evoked single unit
activity, these studies have used correlated activity as an index of
the connectional properties of neural networks.
In the current report, the activity of 1080 pairs of neurons in the
orbitofrontal cortex (OFC) and 359 pairs of neurons in the basolateral
amygdala (ABL) was examined over the course of learning. During
discrimination and reversal training, rats learned that presentations
of distinct odors predicted different outcomes, signaling either access
to an appetitive (sucrose) or an aversive (quinine) fluid. Previous
analysis of the odor-evoked activity in these neurons revealed that
29% of neurons in OFC (96 of 328 cells) and 26% of neurons in ABL (60 of 229 cells) developed responses during sampling of the odors that
correlated with the informative significance of the odor cues
(Schoenbaum et al., 1999). Neural responses to odor presentation
emerged rapidly at a very early phase of training in ABL and later in
OFC; the later emergence of discriminative neural correlates in OFC was
closely linked to the adoption of a behavioral strategy based on the
predictive information provided by the cues. In both OFC and ABL,
marked changes in the encoding properties of neurons also occurred when the significance of the odor cues was altered during reversal training.
Here we ask whether experience-dependent changes in network properties
in OFC and ABL are limited to changes in single unit activity in
response to explicit cues or whether alterations in the connectional
properties may also be evident in correlated firing outside of the
learning trials.
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MATERIALS AND METHODS |
Subjects. Eight adult male Long-Evans rats served as
subjects. The rats were housed individually, maintained on a 12 hr
light/dark cycle, and given ad libitum access to food. Water
access was restricted during the 24 hr proceeding behavioral testing to
motivate performance in the task. During testing periods, the rats
received fluid during the performance of the task, amounting to
~5-10 ml/session, and were given ad libitum access to
water in a holding cage after the session was finished. During this
time, food was also available.
Electrodes, surgery, and histology. Recordings of
extracellular activity were obtained using a driveable bundle of 10 25-µm-diameter NiCrFe microwires (modified from Kubie, 1984).
Electrodes were implanted before any training. Rats weighed 325-375 gm
at the time of surgery to implant the electrode bundle. Surgical
procedures were similar to those described previously (Schoenbaum and
Eichenbaum, 1995a). A single bundle was implanted in the left
hemisphere in orbitofrontal cortex of four rats (3.0 mm anterior to
bregma, 3.2 mm lateral, 4.0 mm ventral) and basolateral complex of
amygdala of four rats (3.0 mm posterior to bregma, 5.0 mm lateral, 7.5 mm ventral). The rats were allowed 2 weeks to recover, during which
each animal received cephalexin (40 mg · kg 1 · d1)
to guard against infection. Once recording began, the electrode bundle
was advanced in 40 µm increments to acquire activity from new cells
for the following day. Recording was stopped in a given rat when the
estimated position of the electrode bundle was consistent with passage
beyond the region of interest. The rats were then deeply anesthetized
with sodium pentobarbital in preparation for perfusion. Immediately
before perfusion, the final electrode position was marked by passage of
a 15 µA current through each microwire for ~10 sec to create a
small iron deposit. The rats were then perfused transcardially using
physiological saline, followed by 10% formalin, followed by 100 ml of
10% formalin-3% potassium ferrocyanide solution to visualize the
iron deposit. The brains were then removed from the skulls and stored
in a 10% formalin-20% sucrose-3% potassium ferrocyanide solution
for several days before sectioning. Brains were cut into 30 µm
sections surrounding the electrode tracks and stained with thionin, and
the electrode tracks were reconstructed to determine approximate
recording sites using the marks left by the iron at the tips of the electrodes.
Behavioral methods. Behavioral testing was performed in an
operant chamber using a go, no-go olfactory discrimination task. The
operant chamber was constructed of aluminum and measured ~45 cm in
height, depth, and width. An odor port and a fluid well were located on
the right wall of the chamber, and two panel lights were located above
the odor port. A photograph of the ports removed from the training
chamber is shown in Figure 1, along with
schematics depicting their use in the task. All events and data
collection were controlled and monitored by computer as described
previously (Schoenbaum et al., 1998, 1999).
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Figure 1.
Photograph and schematic drawings illustrating the
apparatus and behavior in the odor discrimination task.
A, Photograph of the Plexiglas insert removed from the
operant chamber showing the odor port and the fluid delivery well. The
opening of the odor port was ~2.5 cm in diameter (white
circle). Behind the opening was a hemicylinder into which odors
were delivered by a computer-controlled system of solenoids and flow
meters. Odors were isolated and precisely controlled to provide an
onset latency of <40 msec from activation of the solenoid valve
controlling delivery. Air flow from the training chamber and into the
odor sampling port was maintained at a rate of at least 0.5 l/min to
prevent any diffusion of odors from the port into the chamber. The
fluid well consisted of a conical depression (black
circle) in a 1-inch-deep (front-back) polycarbonate ledge. The
depression could easily hold a 0.05 ml bolus of fluid. Four concealed
lines entered a central opening in the bottom of the depression to
allow the delivery of the two fluid reinforcers, water to flush the
well, and attachment of a vacuum-assisted drain line. Fluid delivery
and the vacuum drain were controlled by activation of solenoid valves.
Infrared photodetectors mounted in the opening to the odor port and in
the blocks on either side of the fluid well signaled behavioral
responses. B, Schematic drawings illustrating the
sequence of behaviors in the go, no-go olfactory discrimination task.
On each trial, the rat had to sample an odor presented to an enclosed
hemicylinder behind an odor port (Odor Sampling).
Nose-poke into the odor port triggered odor delivery. Based on the
identity of that odor, the rat then had to decide whether to respond
(Go Response) at a nearby fluid well. A go response
resulted in delivery of a rewarding sucrose solution, after
presentation of a "positive" odor, or an aversive quinine solution,
after presentation of a "negative" odor. Novel odors were presented
each day, and the rats began each session by responding rapidly after sampling of each odor. Learning
was evident in changes in the rat's latency to respond at the fluid
well and also in the shift to an adaptive strategy of only responding
on positive trails and of withholding responses on negative trials
(No-Go). These two measures of learning emerged at
different rates (see Fig. 8). Figure adapted from Schoenbaum et al.
(1999).
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Before the start of the recording phase of the experiment, each rat
received several sessions of shaping to perform the basic behaviors
required. In these sessions, the rat was trained to poke its nose into
the odor port for progressively longer intervals to receive a water
reward at the fluid well located on the ledge below the port. Over the
course of ~300 trials, each rat was trained to nose-poke after light
onset, wait with its nose in the port through a brief interval for odor
delivery, and then hold for another 500 msec during odor presentation.
In addition, the response to the well was required to be progressively
more rapid for reward to be delivered. By the end of shaping, a
response was required within 3000 msec after withdrawal from the odor
sampling port. The rats typically made a response to the well in <500
msec. A single demonstrator odor was used during shaping, and no
nonreinforced or negative trials were given.
After shaping, each rat was trained on several discrimination problems
in which sucrose and quinine were presented. In these sessions, one
odor (positive odor) signaled the availability of a 10% sucrose
solution, whereas the other odor (negative odor) signaled the
availability of a 0.03 M quinine solution. Rats were trained on each problem until they met a criterion of 90% performance in a moving block of 20 trials. In this manner, the rats were introduced to the outcomes associated with the odors and became adept
at solving new odor discrimination problems, ensuring a reasonable
probability of successful learning during each subsequent recording session.
During recording, a new odor discrimination problem was presented in
each session. These discrimination problems involved either two or four
novel odors. In the four-odor task, two distinct odors were associated
with sucrose and two with quinine. The rats began each session by
responding after odor sampling on every trial. Typically, rats would
start to withhold responses after sampling the negative odor(s) within
the first 20-30 trials. In all sessions analyzed, the rats reached a
behavioral criterion defined as 90% accurate performance in a moving
block of 20 trials, usually within 60-100 trials. The precriterion
phase was then followed by a period of postcriterion training of ~100
trials, characterized by highly accurate performance (~85%).
Reversal training was conducted in the sessions involving two-odor
discrimination problems, and in these sessions, reversal followed a
period of postcriterion training.
During all phases of a session, trials were separated by a short
intertrial interval. Illumination of two panel lights above the odor
port signaled the occurrence of a trial. These lights remained on until
the completion of each trial, which occurred when the rat left the
fluid well after reinforcement was delivered or when a no-go response
was recorded 3 sec after the end of odor sampling. The panel lights
were then extinguished for the duration of the intertrial interval.
Intertrial intervals were 4 sec after a correct trial and 9 sec after
trials in which an error was made. Activity during the final 2 sec of
each intertrial interval was used in the current analysis of correlated
activity. Thus, this period of analysis does not correspond to the
delivery of any signaling event (e.g., house-light dimming, odors,
reinforcers) or any behavioral response.
Electrophysiological methods. At the start of each recording
session, each wire of the microelectrode bundle was screened for neural
activity. If no activity was evident, the bundle of wires was advanced
40 or 80 µm to acquire cells for the following day. If neural
activity (S/N ratio > 21/2:1) was present on any of the
wires, a recording session was conducted. Neural activity on each
microwire was passed through a high-impedance JFET head stage,
and then differential activity up to eight microwires was filtered at
300-3000 Hz, amplified 5000× using Grass Instruments (Quincy,
MA) P5 Series Pre-Amplifiers, and recorded on analog tape along
with computer-generated TTL pulses to mark behavioral events
using a Vetter Model 400 PCM Data Recorder (AR Vetter, Rebersburg, PA).
Later, neural signals were digitized at 25 kHz, and then individual
units were discriminated using a template matching algorithm (Cambridge
Electronic Design Cambridge, UK) in concert with examination of the
oscilloscope tracing. Typically one to three neurons could be
discriminated on an active electrode wire. After each session, the
electrode bundle was advanced 40 or 80 µm and allowed to stabilize at
least 24 hr before the next session. Data were analyzed from 55 sessions in the eight rats. Typically, these sessions were not from
consecutive days; thus, neurons were usually isolated from sites
separated by at least 80 µm and frequently by larger distances.
Analysis of correlated activity. Correlated activity in
simultaneously recorded neuron pairs was examined during the intertrial intervals throughout each session. We analyzed correlated activity within the different phases of training as described previously (Schoenbaum et al., 1999). Briefly, performance in each session was
separated into four phases: an early phase comprised of trials before
the sixth negative trial (approximating the first no-go), a late phase
including the remaining trials before the rat met the behavioral
criterion, a postcriterion phase including trials after the criterion
was met but before any reversal training, and a reversal training phase
including all trials after the reinforcement contingencies of the odors
had been reversed. Correlated activity was quantified as the
effectiveness (Levick et al., 1972) of the reference cell on the target
cell, considering activity within 10 msec of a spike in the reference
cell. Effectiveness refers to the probability of firing in the target
neuron after a spike in the reference neuron. This value
(E) was calculated, within a given region of interest
(ROI), as:
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where  is the number of spikes in the target neuron in the
ROI, m is the upper confidence limit of the expected number
of target neuron spikes within the ROI, and  is the number of spikes in the reference neuron that contributed to the cross-correlogram. Normalization by the reference neuron firing rate assumes a linear relationship between excitability and firing rate. Neurons exhibiting either very low or very high rates of activity may violate this assumption (Melssen and Epping, 1987). As reported previously, the
firing rates of neurons recorded in OFC and ABL during intertrial intervals were 3.73 and 1.08 spikes/sec, respectively. To avoid inclusion of cells with very low rates and to provide adequate events
for analysis, neurons in the correlational analysis were required to
fire at a rate of 1 Hz or greater. At the same time, firing rates for
the cells included in the analysis were far below the activity levels
reached during stimulus presentation (Schoenbaum et al., 1999).
ROIs included the overall interval from 0 to 10 msec, and
intervals from 0 to 2 msec, and 2 to 10 msec in subsequent analyses. The expected number of spikes within the ROI
(x) was calculated as:
where   is the firing rate of the
target neuron in the given training phase, is the number of spikes
in the reference neuron contributing to the cross-correlogram, and ROI
is the region of interest in seconds. We determined the confidence
limits by modeling the expected number of target spikes
(x) as an independent Poisson process (Abeles,
1982), so the upper confidence limit was the number of spikes
(m) for which:
Where P is the Poisson formula:
A meaningful interaction was defined as an effectiveness of at
least 0.01, indicating that the target cell fired in excess of chance
after at least 1 in 100 spikes in the reference cell.
Neuron pairs that exhibited effectiveness in excess of chance of at
least 0.01 from 0 to 10 msec during initial training were further
examined. Excess effectiveness was calculated in these neuron pairs
within time intervals from 0 to 2 msec and from 2 to 10 msec, and the
degree of correlated activity in each structure was compared between
the previously defined initial training phases (early and late
precriterion and postcriterion) by ANOVA for repeated measures,
followed by post hoc testing (Tukey's honestly significant difference, p < 0.05). ANOVA for repeated
measures was also performed to evaluate the change in correlated
activity during reversal training, comparing the effectiveness of
interactions during postcriterion trials and reversal trials in the
specific pairs recorded during sessions in which reversal occurred.
Finally, changes in correlated activity across these training phases
were compared with changes in single unit selectivity described
previously for these neurons (Schoenbaum et al., 1998, 1999) and with
changes in performance and response latency presented here. Performance
accuracy was calculated as the percent of correct trials within each
phase. Response times were represented as the difference in average
response times between negative and positive trials, measured from the
time the rat left the odor port until the rat entered the fluid well.
No-go trials were not considered in the calculation of response times.
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RESULTS |
Figure 2 illustrates the electrode
placement in a representative photomicrograph from a rat in which
recordings were made in each structure. The schematic drawings in that
figure depict the area within which recordings were obtained in each
region. Cells were recorded in the ventrolateral and lateral orbital
regions and the ventral agranular insular region in the OFC group and in the basolateral nucleus in three rats and the lateral nucleus in one
rat in the ABL group. A total of 328 neurons in OFC and 229 neurons in
ABL were recorded in rats as they learned novel two-odor (22 sessions)
and four-odor (33 sessions) discrimination problems, yielding a total
of 1080 pairs in OFC and 359 pairs in ABL that met the criterion for
correlational analysis. This population comprised 43% of the available
pairs in OFC and 23% of the available pairs in ABL. As documented in
the description of our results, the neurons in these pairs were
representative of the data obtained from all cells; proportions of
neurons with neural correlates in the task were highly similar to the
proportions in the total sample. It is also important to note that that
average firing rates during intertrial intervals in these cell pairs
was relatively stable, changing little across the phases of training. For example, average rates for OFC neurons during intertrial intervals in the three phases of initial training were 8.67, 8.27, and 7.87 spikes/sec; those rates for ABL neurons were 8.88, 8.04, and 7.85 spikes/sec. Although firing rats changes overall were minimal, calculation of efficacy in each phase only considered those events above the number expected by chance given the firing rate of the target
cell in the particular phase, and the resultant values were normalized
by the phase-specific firing rate of the reference neuron.
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Figure 2.
Electrode recording sites.
Photomicrographs of histological sections showing the reconstruction of
recording sites in representative subjects in OFC
(A) and ABL (B). In each
photomicrograph, a vertical line represents the
dorsoventral range along the electrode track from which neurons were
recorded in the case shown. To the right of each
photomicrograph is a drawing that shows the approximate area in which
recordings were obtained in each group. The OFC encompasses the orbital
regions and agranular insular cortex. Recordings were localized to
ventrolateral and lateral orbital regions (VLO/LO) and
ventral agranular insular cortex (AIv) in the four rats
in the OFC group. In the ABL group, recordings were localized to the
basolateral nucleus in three of the rats (pictured in photomicrograph
and as BLAn in drawing) and lateral nucleus in the
fourth rat (LAn). Figure adapted from Schoenbaum et al.
(1998, 1999), and drawings adapted from Swanson (1992).
PIR, piriform cortex; Ald, dorsal agranular
insular cortex; int capsule, internal capsule.
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Correlated firing activity was observed during initial training and
subsequent reversal training. Overall, the correlational analysis
revealed that a population comprised of 250 of 1080 pairs in OFC and 40 of 359 pairs in ABL had an excess effectiveness of at least 0.01 during
the phases of initial training. Correlated firing in these neuron pairs
was considered with reference to two temporal categories of responses
similar to categories identified previously in rat cochlear nucleus
(Gochin et al., 1989), cat visual cortex (Hata et al., 1991), and more
recently in rat lateral amygdala (Quirk et al., 1995). These reports
identified short latency responses, exhibiting a peak within 1 or 2 msec of time 0 on the cross-correlogram, and also longer latency
responses, typically with a peak further displaced from time 0. Analysis of pairs with significant interactions during initial training revealed similar peaks to be present in the current data. Figure 3A shows the distribution of
peak responses within 10 msec of time 0 for the cell pairs considered
in this analysis. Note the sharp peak in firing of target neurons that
occurs at a latency of 1-2 msec and a less pronounced peak occurring
3-5 msec after spikes in the reference cell. This pattern corresponds
to those in earlier reports and appears to reflect the presence of two types of pairs similar to the examples shown in Figure 3, B
and C. Some pairs exhibited a restricted peak of correlated
activity close to time 0 (Fig. 3B). Other pairs had peaks
that were more broadly distributed and displaced from time 0 (Fig.
3C). As described below, somewhat different patterns were
evident for short and longer latency responses in relation to phases of
the task for OFC and ABL pairs.
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Figure 3.
Correlated firing patterns in OFC and
ABL. A, Latency of correlated firing in the cell
pairs analyzed for OFC and ABL. Cross-correlograms with a bin size of 1 msec were constructed for cell pairs recorded simultaneously in either
OFC or ABL using activity during the intertrial interval of
postcriterion training. The latency of the response in each cell pair
was designated as the bin within 10 msec of time 0 with the highest
spike count on the cross-correlogram. The graph
(A) reveals a large peak for cell pairs centered
at 1-2 msec and an additional smaller peak at 3-5 msec after activity
in the reference neuron. B, Cross-correlogram
showing correlated firing in a neuron pair exhibiting a short latency
interaction; the effectiveness of the interaction above chance is shown
in the top right. The short latency interactions
appeared to reflect the presence of pairs with restricted peaks close
to time 0. C, Cross-correlogram showing correlated
firing in a neuron pair exhibiting a longer latency interaction.
The longer latency interactions were typically weaker and reflected
cell pairs with a more displaced and broader pattern of correlated
activity.
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Correlated firing in OFC increased during accurate performance and
after reversal
Figure 4 shows the effect of initial
training on correlated activity in OFC neuron pairs partitioned into
short and longer latency windows based on the two types of responses
illustrated in Figure 3. The strength of the interactions increased
significantly with training in both the short (Fig.
4A) and longer (Fig. 4B) latency
windows. These increases came primarily during the postcriterion phase
of training when reliable discrimination performance was established
(see Fig. 8) and when the individual neurons developed differential
firing during odor sampling (Schoenbaum et al., 1999). It is important
to note that average firing rates changed little in the pairs of
neurons represented in Figure 4. Average rates for these cell pairs,
shown in Figure 4C, were 8.54, 8.02, and 7.55 spikes/sec in
the early, late, and postcriterion phases, respectively. These small
changes stand in contrast to the large differences in correlated
activity observed.
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Figure 4.
Changes in correlated activity in OFC during
intertrial intervals in the early (white bars) and late
(black bars) phases of precriterion training,
during postcriterion performance (gray bars), and
after reversal (striped bars). A,
Correlated activity at short latency (within 2 msec of a spike in the
reference cell) increased significantly during initial training
(F(2,498) = 13.0; p < 0.001). Post hoc comparisons revealed no significant
difference between the early and late phases of precriterion training
(p = 0.183). However, correlated activity in
the postcriterion phase differed significantly from each of the
precriterion phases (p < 0.001 and
p = 0.003 for early and late, respectively). A
separate analysis of neuron pairs recorded during reversals revealed
that efficacy of correlated firing within the short latency interval
increased further after reversal
(F(1,140) = 14.5; p < 0.001). B, Correlated activity at longer
latency (2-10 msec after a spike in the reference cell) also increased
significantly during initial training
(F(2,498) = 90.1; p < 0.001). Efficacy in OFC differed between the early and late
precriterion phases (p = 0.0386), and a
significant increase in the postcriterion phase was also evident
compared with each of the precriterion phases
(p < 0.001 for both comparisons). Efficacy
of correlated firing within the longer latency interval increased
further after reversal (F(1,140) = 58.3; p < 0.001). C, Activity in
cell pairs with correlated activity in OFC. Average firing rate is
shown for both neurons within each pair that was included in the
analyses presented in A and B. Note the
difference in both pattern and magnitude between the minimal changes in
activity and the changes in correlated firing across the training
phases.
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Correlated firing increased further in OFC cell pairs between the
postcriterion phase and reversal training when the pairs recorded
during reversals were analyzed. An increase was seen for both the short
(Fig. 4A) and the longer (Fig. 4B)
latency interactions. The increase observed in correlated activity here differs markedly from the disruptive effect that reversal had on
selectivity during odor sampling reported previously for these neurons
(Schoenbaum et al., 1999). Again, changes in correlated firing were
observed in neuron pairs that exhibited little or no change in overall
activity (Fig. 4C). The cells in these pairs fired at a rate
of 7.84 spikes/sec before reversal, and after reversal the average
firing rate was essentially unchanged at 7.87 spikes/sec.
Examples of interactions between neuron pairs in OFC are illustrated in
Figure 5. The neuron pairs in the
top panels of Figure 5, A and B, show
increases in short latency and longer latency interactions,
respectively, during postcriterion training that were not present in
earlier training phases. Similarly, the neuron pairs in the
bottom panels of Figure 5, A and B,
developed significant interactions that increased greatly after
reversal.
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Figure 5.
Cross-correlograms for neuron pairs in OFC showing
correlated activity within the intertrial intervals during the early
and late phases of precriterion training, during postcriterion
performance, and after reversal. A, Typical examples of
neuron pairs with short latency interactions (0-2 msec).
B, Typical examples of neuron pairs with longer latency
interactions (2-10 msec). Values are shown in spikes per 1 msec bin,
and the horizontal dashed lines on each correlogram
designate the upper confidence limit (p < 0.01) for the interactions (see Materials and Methods). Precriterion
training is divided into an early and late phase as described in
Materials and Methods, and data from reversal training are shown for
neuron pairs recorded during reversal sessions. Note that the
confidence limit varies somewhat between graphs in some cases,
reflecting small changes in firing rate. Although these changes were
minimal, they were incorporated into the calculations of
efficacy.
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Correlated firing in ABL increased rapidly during initial training
but showed no further increase after reversal
In contrast to findings in OFC, correlated activity in ABL
increased more rapidly during initial training, before criterion performance was achieved. Figure 6 shows
the effect of training on correlated activity in ABL neuron pairs
partitioned into short and longer latency windows. Although no
significant effect of training was evident in the short latency
interactions, the longer latency interactions increased significantly,
and post hoc analyses showed that this increase occurred
between the early and late phases of precriterion training when the
rats first began to respond differently to the odors (see Fig. 8). The
late phase was also the period when the individual neurons in ABL
developed differential firing during odor sampling (Schoenbaum et al.,
1999). Note again that firing rates remained relatively stable in the
neuron pairs represented in Figure 6. Average firing rates, shown in
Figure 6C, were 7.90, 7.44, and 7.00 spikes/sec during the
early, late, and postcriterion phases, respectively.
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Figure 6.
Changes in correlated activity in ABL during
intertrial intervals in the early (white bars) and late
(black bars) phases of precriterion training, during
postcriterion performance (gray bars), and after
reversal (striped bars). A, Correlated
activity at short latency (within 2 msec of a spike in the reference
cell) did not change significantly during training in ABL
(F(2,78) = 0.48; p = 0.62). A separate analysis of neuron pairs recorded during reversals
also revealed no change in correlated activity at this latency
(F(1,19) = 2.31; p = 0.14). B, Changes in correlated activity at longer
latency (2-10 msec after a spike in the reference cell) increased
significantly during initial training
(F(2,78) = 6.29; p = 0.0029). Post hoc comparisons revealed that efficacy
increased between the early and late precriterion phases
(p = 0.0147) but did not increase in the
postcriterion phase relative to the late precriterion phase
(p = 0.924). Efficacy of correlated firing
within the longer latency interval did not change further after
reversal (F(1,19) = 2.3;
p = 0.14). C, Activity in cell pairs
with correlated activity in ABL. Average firing rate is shown for both
neurons within each pair that was included in the analyses presented in
A and B. Note the difference in both
pattern and magnitude between the minimal changes in activity and the
changes in correlated firing across the training phases.
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Correlated firing in ABL also did not change significantly during
reversal training in either the short or the longer latency time
interval (Fig. 6). This pattern is in marked contrast to the results of
the analysis in OFC (Fig. 4) and also differs from changes in odor
selectivity reported to occur after reversal in ABL neurons (Schoenbaum
et al., 1999). Instead of increasing, as correlated activity did in
OFC, correlated firing remained primarily unchanged in these ABL
cell pairs after reversal. As in the previous comparisons, the average
rates of the neuron pairs in this analysis remained relatively stable
(Fig. 6C). The neurons in these pairs fired at 6.04 spikes/sec during the postcriterion phase and at 5.60 spikes/sec during
reversal training.
Examples of correlated firing in individual pairs of ABL neurons are
shown in Figure 7. These examples
illustrate the patterns depicted in Figure 6. For example, both neuron
pairs showing short latency interactions in Figure 7A
exhibited correlated firing across all initial training phases.
Moreover, the effectiveness of the interactions in the top
panel did not increase after reversal and, in fact, appeared to
decrease somewhat. In contrast, the neuron pair showing longer latency
interactions in Figure 7B developed significant interactions
during the late portion of precriterion training that were not present
during early training. The efficacy of this interaction was then
maintained primarily unchanged during postcriterion training and after
reversal.
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Figure 7.
Cross-correlograms for neuron pairs in
ABL showing correlated activity within the intertrial intervals during
the early and late phases of precriterion training, during
postcriterion performance, and after reversal. A,
Examples of neuron pairs with short latency interactions (0-2 msec).
B, Example of neuron pair with longer latency
interactions (2-10 msec). Values are shown in spikes per 1 msec bin,
and the horizontal dashed lines on each correlogram
designate the upper confidence limit (p < 0.01) for the interactions (see Materials and Methods). Precriterion
training is divided into an early and late phase, and data from
reversal training are shown for neuron pairs recorded during reversal
sessions. Note that the confidence limit varies somewhat between graphs
in some cases, reflecting small changes in firing rate. Although these
changes were minimal, they were incorporated into the calculations of
efficacy.
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|
Correspondence between learning, performance, and selective firing
to cues within the trial and correlated firing during intertrial
intervals
In our previous analysis of this data set (Schoenbaum et al.,
1999), we reported that a substantial proportion of neurons in OFC and
ABL developed differential activity during odor sampling that depended
on the significance of the cue presented on the trial, i.e., whether it
signaled sucrose or quinine. Among neurons recorded in OFC, 29%
exhibited significant odor selectivity that was present during
postcriterion training but not evident earlier in the training sessions
(Fig. 8A). In contrast,
many neurons in ABL that exhibited selectivity postcriterion (26% of
recorded cells) developed this property by the late phase of
precriterion training (Fig. 8B). As has been noted in
the presentation of the results, the development of odor-selective
firing activity and the changes in correlated firing during the
intertrial intervals followed very similar courses during initial
acquisition. The development of selectivity in OFC neurons coincided
with the acquisition of accurate go, no-go performance (Fig.
8C), whereas the development of selectivity in ABL neurons
coincided with the earlier emergence of a response latency difference
between trials with positive and negative outcomes (Fig.
8D). The same relationship is evident between the
emergence of correlated activity in the two regions and the measures of
learning.
View larger version (24K):
[in this window]
[in a new window]
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Figure 8.
Changes in neural activity during odor sampling
and behavioral measures of learning during initial training. Contrast
in activity of neurons in OFC (A) and in ABL
(B) during odor sampling in the early
(white bars) and late (black bars) phases
of precriterion training and during postcriterion performance
(gray bars). The activity contrast was calculated
as the difference in firing rate during sampling of positive and
negative odors divided by the sum of those rates, expressed with
reference to the preferred odor during postcriterion trials. Activity
between trials was used to calculate a baseline contrast of 0.04 for
OFC and  0.01 for ABL. ANOVA with post hoc testing
showed that selectivity in OFC differed significantly from baseline
only during postcriterion training, whereas selectivity in ABL differed
in the late and postcriterion phases (*p < 0.05).
C, Go, no-go performance in the early and late
precriterion and the postcriterion phases (chance, 50%).
D, Changes in differential response latency in the early
and late precriterion and the postcriterion phases. The latency
difference increased significantly
(F(2,156) = 6.69; p = 0.0016) from the early to the late phase of precriterion training (*
p < 0.05). Latency to respond on positive trials
remained near 350 msec across training; thus, the increase was
primarily attributable to increases in latency to respond on
negative trials. The increase in selectivity in OFC neurons
postcriterion (A) coincided with improved
accuracy in go, no-go performance (C). Changes in
selectivity in ABL neurons (B) coincided more
closely with changes in the differential response latency across the
training phases (D). A and
B were adapted from Schoenbaum et al. (1999).
|
|
We examined whether pairs of neurons that showed increased correlated
firing during the intertrial intervals might preferentially involve
those neurons that also encode the significance of the odor cues during
training trials. Neuron pairs with significant correlated activity
included 186 cells in OFC (Fig. 4) and 45 cells in ABL (Fig. 6). Of
these neurons, 30% (56 cells) in OFC and 37% (17 cells) in ABL had
significant odor selectivity in the discrimination task, proportions
that approximate the prevalence of odor-selective neurons in the total
sample of neurons recorded in each region (Schoenbaum et al., 1999).
Thus, correlated activity does not disproportionately involve those
neurons that exhibit changes in firing activity during odor sampling as
a result of training.
We also know that neurons in OFC and other prefrontal regions can
encode an expectancy for upcoming events (Schoenbaum and Eichenbaum,
1995a; Watanabe, 1996; Lipton et al., 1999; Tremblay and
Schultz, 1999). Cells in amygdala show similar correlates in some
circumstances (Quirk et al., 1997). In fact, an earlier account of
these data found that some neurons in each structure developed
differential activity related to the rats' expectation of
reinforcement within the trial (Schoenbaum et al., 1998). Approximately 22% of neurons in OFC and 36% of neurons in ABL fired differently during a brief delay after each response depending on which reinforcer had been signaled by the odor on the trial. The majority of
those neurons only fired differentially after the rats had learned to distinguish between positive and negative odors based on their associative significance; thus, the differential firing appeared to
reflect expectancy for reinforcement.
Although the rats had no basis for forming expectancies regarding the
upcoming trial during the intertrial intervals, changes in expectations
within the trial as a result of learning might have also accounted for
the changes in correlated firing observed in this report. We examined
whether pairs of neurons that showed increased correlated firing during
the intertrial intervals might preferentially involve those neurons
that developed differential firing during the trials based on the rats
expectation of reinforcement. Only 24% (45 cells) of the 186 neurons
analyzed in Figure 4 and 40% (18 cells) of the 45 neurons analyzed in
Figure 6 developed differential activity in our earlier study. Again,
these proportions are similar to the prevalence of such correlates in
the total sample from which recordings were made (Schoenbaum et al.,
1998), so correlated activity does not disproportionately involve
neurons that develop anticipatory firing as a result of training in the discrimination task.
| |
DISCUSSION |
Changes in correlated activity may reflect neural plasticity that
alters functional interactions between neurons (Perkel et al., 1967;
Abeles, 1982; Aertsen et al., 1989; Hata et al., 1991; Quirk et
al., 1995; Hatsopoulos et al., 1998). In the present report, correlated activity in OFC and ABL was examined in intertrial intervals across phases of a learning task. Previous analysis of these
neurons revealed the development of encoding in both OFC and ABL for
informative cues during training trials (Schoenbaum, 1998; Schoenbaum
et al., 1998, 1999). The current analysis revealed that correlated
firing within the intertrial intervals also changed over the course of
training in a partially nonoverlapping population of neurons. In
interpreting these findings, two separate questions must be addressed.
One question concerns whether the changes in correlated firing reflect
functional connectivity rather than other potentially confounding
factors. A second and more interesting question concerns what
information is represented by the changes in neural interactions observed.
Correlated firing as an index of functional connectivity
As others have noted (Melssen and Epping, 1987; Palm et al., 1988;
Aertsen et al., 1989; Fetz and Schupe, 1994; Brody; 1999), peaks in the
cross-correlogram for any pair of neurons may represent factors
independent of neural plasticity or functional interactions between
cells. Specifically, such sources include covariations in excitability
or changes in activity in cells that are concurrently driven by some
stimulus. These sources do not appear to account for the current
results. Average rates changed little across phases of the task, even
within the neural pairs that formed the basis of our findings (Figs. 4,
6). Such changes that occurred were also considered in the analysis by
incorporation of the phase-specific firing rates of the neurons in the
equations used to calculate "excess" efficacy (see Materials and
Methods for description of procedure). Stimulus-driven effects are also
unlikely to be a source for changes in correlated activity in our
analysis primarily because our analysis was confined to a segment of
the intertrial intervals when no controlled stimuli were presented.
Moreover, the narrow peaks observed in the cross-correlograms differ
greatly in time scale from the stimulus-driven responses reported
previously (Schoenbaum et al., 1999), conforming to a criterion for
excluding such effects (Brody, 1999). Excluding those sources, changes
in correlated firing during the intertrial intervals are likely to reflect alterations in functional connectivity.
Changes in functional connectivity as an index of learning
Although many different variables might be represented in the
neural interactions observed during the intertrial intervals in the
task, the changes in functional connectivity were closely related to
learning and changes in behavioral performance in the task. Correlated
activity in OFC changed most markedly between the early precriterion
phase, when the rats first encountered novel odors, and the
postcriterion phase, when those odors had become the basis for reliable
performance of an adaptive response strategy. In contrast, an increase
in correlated activity in ABL was confined to a late phase of initial
training before rats achieved criterion. These patterns bear a striking
resemblance to the patterns in both the single cell responses and
behavioral measures illustrated in Figure 8. Firing characteristics of
OFC neurons during odor sampling reflect odor significance as a
function of the go, no-go performance in the task, whereas
characteristics of ABL neurons during sampling appear to be independent
of this behavioral measure, reflecting instead an earlier index of
learning evident in the rats' response latencies after sampling odor
cues. It is particularly noteworthy that, although changes in both
correlated activity during the intertrial intervals and stimulus-driven
firing selectivity appear to map onto behavioral measures (Fig. 8),
there was not a disproportionate representation of the odor-selective
neurons in the population of cells involved in correlated activity.
This observation suggests that the analysis of correlated firing
reveals changes in functional connectivity that are not readily
apparent in traditional approaches to the study of learning-induced
synaptic change. A substantial proportion of neurons involved in
changes in correlated activity do not exhibit selective firing during stimulus presentations in the task but nonetheless may participate in
alterations in networks that are important for the learning that occurs.
Although learning processes appear to be a likely basis for the changes
reported here, other factors that might conceivably have influenced
functional connectivity during training deserve comment. Such factors
may include changes in motivational state or arousal. Although such
variables may well be represented in the correlated firing, they are
unlikely to provide an account for the changes that were observed
between phases of the task. Motivation remained at a high level across
the phases in which changes in correlated activity were observed as
indicated by latencies to respond after odor sampling. For example,
responding after sampling the odor that signaled the positive outcome
of a sucrose solution occurred equally rapidly in the precriterion and
postcriterion phases (370 ± 18.1 and 366 ± 16.2 msec,
respectively). Another index of motivational state across sessions is
provided by the relative ease of reversal learning when contingencies
were altered at the end of the postcriterion phase. In addition,
generalized changes in motivational state or arousal might be expected
to yield similar patterns of change in correlated firing in the two brain systems. This was clearly not the case. Rather, the changes differed between OFC and ABL and coincided more closely to the patterns
observed in the behavioral measures of learning and in the neural
correlates of task-relevant cues in each region.
Similar arguments may be made for contextual cues, such as the house
lights or features of the box interior. These cues remained constant
throughout each session and, therefore, are unlikely to account for
changes in correlated firing between phases, particularly because the
rats were well experienced in the training environment before recording
began. One exception to this rule was the length of the intertrial
intervals. These intervals varied between 4 and 9 sec depending on
whether the previous trial had been completed correctly. Because the
rats made fewer errors as each session progressed, the proportion of 9 sec intervals decreased across the phases of initial training. A
difference in correlated firing related to the duration of the
intertrial interval might have thereby led to a change in efficacy
across these phases, as we have reported. Two factors, however, argue
against this explanation for our findings. First, correlated activity
increased in OFC and remained stable in ABL after reversal, despite the
fact that the proportion of 9 sec intertrial intervals increased
substantially during the reversal phase, representing a proportion
similar to the early and late phases of precriterion training. Second,
there is no readily apparent reason to expect any effect of interval length to differ between OFC and ABL, yet correlated firing did change
differently in the two regions.
The changes in correlated activity in OFC and ABL can be viewed in the
context of other evidence that these areas are important in adaptive
behaviors based on associative learning. The integrity of orbitofrontal
cortex is particularly important for the use of motivational
information in decision-making (Harlow, 1868; Jones and Mishkin, 1972;
Bechara et al., 1997; DeCoteau et al., 1997; Gallagher et al., 1999), a
role supported by functional neuroimaging studies with humans (Rogers
et al., 1999) and recording studies with laboratory animals (Thorpe et
al., 1983; Schoenbaum et al., 1995; Rolls et al., 1996; Schoenbaum et
al., 1998, 1999; Lipton et al., 1999; Tremblay and Schultz, 1999). ABL,
too, is crucial to certain behaviors based on associative learning
(Kluver and Bucy, 1939; Jones and Mishkin, 1972; Tranel and Hyman,
1990; LeDoux, 1996; Hatfield et al., 1996; Killcross et al., 1997), and
cells there encode the associative significance of stimuli (Fuster and
Uyeda, 1971; Sanghera et al., 1979; Nishijo et al., 1988;
Muramoto et al., 1993; Quirk et al., 1995; Schoenbaum et al.,
1999). Interconnections between OFC and ABL (Krettek and Price, 1977;
Kolb, 1984; Price et al., 1987; McDonald, 1991) appear to be critical
for encoding and using associative information about the motivational
significance of stimuli (Baxter et al., 2000).
By recording from each of these regions in the same behavioral task,
the analyses presented here and in two earlier studies (Schoenbaum et
al., 1998, 1999) help to define the different contributions of these
structures in learning and the organization of behavior. The current
results, like our earlier findings, indicate that associative encoding
occurs first in ABL and emerges subsequently in OFC when accurate
performance is established. The close correspondence between
experience-dependent plasticity in OFC and the development of accurate
responding in the go, no-go task supports the role of this structure in
discrimination performance in a number species and paradigms (Jones and
Mishkin, 1972; Eichenbaum et al., 1983; Rolls et al., 1994; Diaz et
al., 1996; DeCoteau et al., 1997). This prefrontal structure, like
others, represents information when it is relevant to the behavior at
hand (Schoenbaum and Eichenbaum, 1995b; Miller et al., 1996;
Rainer et al., 1998; Tremblay and Schultz, 1999). In contrast, encoding
in ABL appears to be more generally related to the significance of the
cues and is not tightly coupled to the go, no-go decision in the
discrimination task. These findings are consistent with behavioral
studies that show initial learning in many discrimination paradigms to
be unaffected by amygdala lesions (Jones and Mishkin, 1972;
Slotnick, 1985; Eichenbaum et al., 1986). Nevertheless, changes in
response latencies that parallel experience-dependent plasticity in ABL
suggest that some aspects of behavior in the task may depend on these
networks. Thus, these structures may cooperate in guiding behavior
within a domain based on motivational and incentive information
(Schoenbaum et al., 1999) in much the same way that other prefrontal
networks have been proposed to function in cooperation with specialized systems in other informational domains (Goldman-Rakic, 1987; Wilson et
al., 1993; Miller et al., 1996).
Changes in functional connectivity persist more strongly in OFC
networks after reversal
Notable differences were evident in comparing the analysis of
correlated activity during the reversal phase with the characteristics of individual neurons responsive to odor cues (Schoenbaum et al., 1999). The majority of neurons in OFC that developed encoding during
odor sampling over the course of initial learning stopped firing
selectively to the odor cues when contingencies were altered during
reversal training. At the same time, a population of previously nonselective OFC neurons began to fire selectively to the odor cues
after reversal. This pattern is consistent with the notion that
encoding in OFC is strongly influenced by conjunctions between specific
stimuli and their associated motivational significance. In contrast,
the majority of neurons with odor-selective encoding in ABL reversed
their firing selectivity when the reinforcement contingencies of the
odors were reversed. Such neurons appear to encode motivational
significance independent of the specific stimulus properties of the
odor cues. Within that framework, the further increase in correlated
activity in OFC, but not in ABL, during reversal training is of
interest. Indeed, if encoding of contingencies in ABL involved many of
the same connections engaged in the original associative encoding, no
further increase in correlated activity would be expected. Encoding of
new conjunctions between events in OFC after reversal of contingencies,
on the other hand, might produce further increases in correlated
activity, particularly if new connections were recruited, as was the
case for the individual neurons during odor sampling in OFC (Schoenbaum
et al., 1999). An overall increase in functional connectivity in OFC
during reversal training suggests that encoding of the original
conjunctions, although no longer expressed in response to the odors,
nonetheless must be maintained within the network during this time period.
Persistence of encoding in OFC, indicated by the cumulative increase in
correlated activity after reversal, is particularly noteworthy given
past reports concerning the effects of lesions in OFC during reversal
training (Jones and Mishkin, 1972; Eichenbaum et al., 1983; Rolls et
al., 1994; Diaz et al., 1996; Meunier et al., 1997). Although
OFC-lesioned subjects are able to learn the original discrimination,
they are unable to shift their behavior to reflect new information
regarding the cues. OFC lesions in particular appear to promote
perseverative errors in which the subject is unable to withhold
prepotent responses based on the original associations (Jones and
Mishkin, 1972; Eichenbaum et al., 1983). These behavioral findings
suggest that the role of OFC in discrimination tasks is not limited to
encoding the associations between cues and reinforcers but rather is
critical for guiding the selection of the appropriate behavioral
responses in the context of changing task contingencies and context,
provided by both internal and external signals. Neurophysiological
findings support this hypothesis (Schoenbaum and Eichenbaum, 1995b;
Critchley and Rolls, 1996; Schoenbaum et al., 1999, Tremblay and
Schultz, 1999). Persistent encoding of the original associations in
networks in OFC after reversal would facilitate the comparison of new
information and old, permitting the processing and output to be
adaptively biased by current context. In the absence of this
contribution from OFC, the original encoding is more difficult to alter
and exerts a stronger control over behavior. In other words, behavior
becomes more rigid and less amenable to control by changing
contingencies and more subtle contextual features of the environment.
It is also interesting to note that the increased correlated activity appears not to persist across sessions. The decline between sessions may reflect the limited value of these representations in a task in
which rats were accustomed to encountering new odor problems each day.
In summary, our observation of altered functional connectivity over the
course of learning lends support to the emerging concept that analysis
of interactions between simultaneously recorded neurons can reveal
changes that occur as a function of experience and episodes of learning
(Wilson and McNaughton, 1993; Quirk et al., 1995; Skaggs and
McNaughton, 1996; Kubota et al., 1999). Use of these methods in concert
with a traditional analysis of the firing characteristics of the
individual neurons to cues in the task may provide additional
information about the performance of neural networks.
| |
FOOTNOTES |
Received March 16, 2000; revised April 20, 2000; accepted April 24, 2000.
This work was supported by National Institutes of Health Grants RO1
MH53667 and KO5-MH01149 to M.G. and K08-AG00882 to G.S.
Correspondence should be addressed to Dr. Geoffrey Schoenbaum,
Department of Psychology, Johns Hopkins University, 3400 North Charles
Street, Room 25, Ames Hall, Baltimore, MD 21218. E-mail: schoenbg{at}jhu.edu.
| |
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INTRODUCTION
