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Logo of nihpaAbout Author manuscriptsSubmit a manuscriptHHS Public Access; Author Manuscript; Accepted for publication in peer reviewed journal;
 
J Neurosci. Author manuscript; available in PMC 2009 August 25.
Published in final edited form as:
PMCID: PMC2668607
NIHMSID: NIHMS97313

Distinct subtypes of basolateral amygdala taste neurons reflect palatability and reward

Abstract

The amygdala processes multiple, dissociable properties of sensory stimuli. Given its central location within a dense network of reciprocally connected regions, it is reasonable to expect that basolateral amygdala (BLA) neurons should produce a rich repertoire of dynamical responses to taste stimuli. Here, we examined single BLA neuron taste responses in awake rats, and report the existence of two distinct subgroups of BLA taste neurons operating simultaneously during perceptual processing. One neuron type produced long, protracted responses with dynamics that were strikingly similar to those previously observed in gustatory cortex. These responses reflect co-operation between amygdala and cortex for the purposes of processing palatability. A second type of BLA taste neuron may be part of the system often described as being responsible for reward learning: these neurons produced very brief, short-latency responses to rewarding stimuli; when the rat participated in procuring the taste by pressing a lever in response to a tone, however, those phasic taste responses vanished, phasic responses to the tone appearing instead. Our data provide strong evidence that the neural handling of taste is actually a distributed set of processes, and that BLA is a nexus of these multiple processes. These results offer new insights into how amygdala imbues naturalistic sensory stimuli with value.

Keywords: hedonics, sensory processing, single-unit, multi-electrode, electrophysiology, temporal coding

Introduction

The amygdala processes multiple attributes of sensory experiences simultaneously, including stimulus identity, emotional content (referred to as taste palatability), and reward value, by virtue of its connections with sensory cortex, orbitofrontal cortex, and the dopamine system (Berridge, 1996; Cardinal et al., 2002; Pare et al., 2002; O'Doherty, 2003; Saddoris et al., 2005). The involvement of the basolateral amygdalar nucleus (BLA) in these processes is suggested by electrophysiological and lesion experiments: BLA neurons respond in relation to a stimulus’ sensory properties (Pare and Collins., 2000; Paton et al., 2006) as well as to its learned emotional and reward associations (Quirk et al., 1995; Schoenbaum et al., 1998, 1999; Repa et al., 2001)—related but distinguishable properties that have been likened to “liking” and “wanting” respectively (Berridge, 1996). BLA damage, meanwhile, is associated with abnormal processing of sensory cues in disorders such as autism and Kluver-Bucy syndrome (Prather et al., 2001; Bauman and Kemper, 2003), and with impairments in the learning of new hedonic and reward-related responses to stimuli (Maren et al., 1996; LeDoux, 2000; Balleine et al., 2003; Schoenbaum et al., 2003; Maren and Quirk, 2004; Blankenship et al., 2005; Corbit and Balleine, 2005; Wang et al., 2006).

Taste stimuli, which are imbued with palatability and reward values, as well as with “pure” sensory properties, are excellent probes of amygdalar function. Both the central nucleus of the amygdala (Karimnamazi and Travers, 1998; Li et al., 2002; Huang et al., 2003; Lundy and Norgren, 2004; Tokita et al., 2004; Li et al., 2005) and BLA (Norgren, 1976; Veening, 1978; Bernard et al., 1993) receive taste input via reciprocal connections with gustatory brainstem, and BLA also receives taste feedback from cortex (Yamamoto et al., 1984; Bielavska and Roldan, 1996). Amygdala neurons are known to fire in response to taste administration (Scott et al., 1993; Nishijo et al., 1998; Nishijo et al., 2000, Grossman et al., 2008), and BLA lesions perturb both taste-related behaviors (Touzani et al., 1997; Ganaraja and Jeganathan, 2000) and taste learning (Gilbert et al., 2003; Reilly and Bornovalova, 2005; Touzani and Sclafani, 2005; Wang et al., 2006; St Andre and Reilly, 2007). Given the complexity of connectivity, it is reasonable to hypothesize that BLA taste responses should be rich and dynamic, and that these dynamics should be related to those previously observed in gustatory cortex (GC), which processes first a taste's existence, then its identity, and then its palatability across the 2 sec preceding swallowing (Katz et al., 2001a; Fontanini and Katz, 2006; Grossman et al., 2008). As of now, however, only the most general properties of amygdalar taste responses have been examined.

Here, we tested the above hypothesis in an analysis of awake rats’ BLA taste responses. This analysis revealed two subgroups of BLA neurons which produce distinct taste responses. One taste neuron subtype did in fact reflect co-operation between amygdala and cortex for the purposes of processing palatability. The protracted dynamics of the taste responses in these neurons were strikingly similar to those produced by GC neurons (Katz et al., 2001a), differing only in the fact that that palatability-specific information vanished in BLA at the latency at which it has been shown to appear in cortex. We also observed a second, distinct subset of BLA taste neurons, however. These taste neurons showed three of the cardinal traits of neurons in the reward learning system (Schultz, 2001): they produced very brief, short-latency responses to rewarding stimuli; these responses all but disappeared when the stimuli were self-administered; and in self-administration trials, these neurons instead responded to a tone announcing that a lever press will cause taste delivery.

Our data provide strong evidence that the neural handling of taste is a distributed process, and support previous studies suggesting a possible transmission of hedonic information from BLA to cortex (Escobar et al., 1998; Ferreira et al., 2005; Saddoris et al., 2005; Grossman et al., 2008). They offer new insights into how amygdala may imbue the coding of naturalistic sensory stimuli with value.

Methods

Subjects

Methods conform to the Brandeis University Institutional Animal Care and Use Committee guidelines. Female Long Evans rats (N=7, 250−300g at time of surgery) served as subjects in this study. Animals were maintained on a 12h/12h light/dark schedule and were given ad lib access to chow and water, unless otherwise specified.

Surgery

Rats were anesthetized using an intraperitoneal (IP) injection of a ketamine/xylazine/acepromozine cocktail (100mg/kg; 5.2mg/kg; 1mg/kg, respectively), with supplemental IP injections administered as needed. Each anesthetized rat was placed in a standard stereotaxic device, where its scalp was excised, and holes were bored in its skull for the insertion of 0−80 ground screws and electrode bundles. Multi-electrode bundles (16 nichrome microwires attached to a microdrive, see Katz et al., 2001b) were inserted 0.5 mm above BLA (AP −3, ML ±5.1, DV −6.5 from dura). Once in place, the assemblies were cemented to the skull, along with two intra-oral cannulae (IOC, Fontanini and Katz, 2006), using dental acrylic. Rats were given seven days to recover from the surgery.

Stimulus delivery protocol—passive and self-administrations

After recovery from surgery, rats were habituated to a 23.5 hour water restriction paradigm in order to ensure adequate motivation to drink during the experimental procedures. Experimental sessions ensued only after rats had become adapted to restraint and administration of fluid through IOCs. In each adaptation session, the experimenter administered 40 μl of water to the passive rat (these were referred to as “passive administrations”) and also on occasion gave the rat the opportunity to press a lever located in front of the right forepaw for additional water deliveries (these were “self-administrations”). For the latter, a 7 kHz tone signaled water availability; the rat had 3 seconds following tone onset to lever press, at which time 40 μl of water was delivered and the tone was interrupted. Lever presses in the absence of a tone went unrewarded. Within 2−4 sessions, rats learned to press once per tone, and then to wait through the fore-period (and possible passive administration) for the next opportunity to self-administer.

Stimulus delivery sessions were similar to adaptation sessions, but 40-μl aliquots of 100 mM NaCl, 100 mM sucrose, 100 mM citric acid, and 1 mM quinine·HCl replaced water. Tastes were selected randomly without replacement, separately for passive and self-administrations. Within each session, passive and self-administrations were interleaved, with an average of 20 sec between any two taste deliveries. Approximately five seconds following any taste delivery, rats received a 40-μl aliquot of water as a rinse. Sessions lasted until rats became inattentive or satiated—typically 60−90 minutes (Fontanini and Katz, 2005; de Araujo et al., 2006; Fontanini and Katz, 2006), for a total of 14−20 trials of each taste per session. Occasionally sessions were truncated because of inattention; neural responses obtained during these sessions were included in the population analysis of taste response properties (see Fig. 2) but were excluded in more fine-grained analyses of individual response dynamics.

Figure 2
Two types of taste neurons can be observed in basolateral amygdala (BLA)

Electrophysiology

Recordings were amplified (1,000−2,000), filtered (300−800 Hz), and digitized. Single neurons of > 3:1 signal to-noise ratio were isolated by using a waveform template, augmented with offline cluster cutting software (Plexon, Dallas, TX).

Analysis of Taste Profiles

Several analytic methods were used to characterize BLA neuron responses. A neuron was initially deemed a taste neuron if its firing rate in response to at least one taste (averaged over the first 2.5 sec.) was different than to others. The significance of the difference was established by using the main effect for taste in a 2-way mixed-effect ANOVA (taste [sucrose, NaCl, citric acid, quinine] × time [successive 250-msec bins of firing rate]). This is a relatively conservative measurement: a neuron producing strong but similar responses to all tastes will not be deemed taste-responsive by this analysis.

The interaction term of this 2-way ANOVA allowed us to more closely examine the taste response profile for each neuron. A p-value of < 0.05 on the interaction term reveals that the time-course of response to at least one taste was significantly different from that to other tastes—that is, that there is taste-related information in the temporal codes. Given the relatively low firing rates produced by BLA neurons, inhibitory neurons were difficult to evaluate and were thus excluded from most analyses.

Further simple calculations extended our analysis of “taste specificity” in BLA neurons. First, for each 250-msec post-administration bin we calculated the linear difference between the responses (spikes/sec) to each pair of tastes; the across-pair average provided a basic estimate of how distinctly that particular neuron responded to each taste at that particular post-stimulus time. We then extended this analysis to provide a basic estimate of the palatability-specific information provided by the neurons, separating the average differences into those between pairs of tastes with similar palatabilities (sucrose/NaCl, acid/quinine) and those between pairs with dissimilar palatabilities (sucrose/quinine, NaCl/quinine, sucrose/acid, NaCl/acid). If the latter average was significantly higher than the former, then the neurons fired more similarly to tastes with similar palatabilities—that is, they fired in a palatability-specific manner.

Finally, we compared the response of each neuron in a particular 250-msec bin to pre-stimulus baseline activity, in order to determine which tastes caused significant modulations at that time. A third measure of taste-specificity, and an initial measure of the duration of particular responses, was the tendency of a neuron to respond to only a subset of the taste stimuli at that particular time.

Analysis of response latencies and durations

In order to determine the onset and duration of significant fluctuations in firing rate with greater temporal precision than afforded by 250 ms bins, a moving-window analysis was employed on across-trial response summations (Katz et al., 2001a). Firing rates were calculated for each movement of the window, and 99% confidence intervals were calculated on the basis of pre-stimulus firing. When firing was determined to fall outside the confidence interval for multiple (> 3) consecutive windows, the first such window was determined to be the time of response onset; the response duration was the amount of time between the onset and the end of the last significant window.

Moving window analyses can cause artifactual response smoothing; sudden changes in firing rate are ‘distributed’ across time by the window. To minimize this potential problem in our estimates of onset and duration, our analysis differed from a standard moving window analysis in 3 ways. First, rather than using a peri-stimulus time histogram, a cumulative firing plot (cumulative sum, or cusum) was used; this allowed us to include each action potential separately. Second, the size of the window was set to be a specific percentage (5%) of the spikes in the cusum, rather than a specific number of msec; thus the window would ‘shrink’ around sudden firing rate increases. Finally, since a cusum plot travels diagonally up and to the right (the axes of such a plot are time and spike number [from first to last]), our analysis was technically not of firing rates themselves, but of slopes in the cusum line.

Classification of BLA responses: A classification algorithm was used to determine whether neural responses in the middle epoch would process palatability (Jones et al., 2007). Briefly, population PSTHs (bin size 250 ms) were computed for all the neurons showing protracted responses (LD + long duration = 10) by iteratively averaging for each taste all the trials but one. Single trials were then classified and a confusion matrix was compiled on the basis of their Euclidean distance from the population PSTH. To determine the average performance in the middle epoch, the classification results for the bins between 250−1250 ms post-stimulus were averaged.

Analysis of BLA neuron subtypes

K-mean clustering was applied to response latency and duration data, to determine whether neurons clustered into subtypes. To further confirm the separation of these clusters, neuronal groups suggested via k-means clustering were subjected to additional t-tests comparing their onset and duration values. A series of additional statistical analyses were performed on the separated neuronal groups in order to examine differences and similarities in their taste response profiles. 2-way repeated-measure ANOVAs were performed on each group separately using time (firing rates during consecutive 250 ms bins) and taste (response to 4 tastes) as within- and between-subject factors, respectively. Post-hoc tests supported and further explained these analyses.

Histology

Following the experimental sessions, subjects were deeply anesthetized and perfused through the heart with saline followed by 10% formalin in saline. Seven seconds of dc current (7 μA) were passed through selected microwires in preparation for staining. Brains were removed and immersed in a sucrose formalin mixture, where they remained, refrigerated, until fixed. Sections (40 μm) cut through BLA on a freezing microtome were stained with cresyl violet for cell bodies. Only data from animals with electrodes placed within the confines of BLA were included in the analyses.

RESULTS

Two subtypes of taste-specific responses in BLA

Figure 1, a representative photomicrograph, reveals the electrode cannula track and location of one set of electrode tips situated in BLA (Paxinos and Watson, 1997); overlain on this image are the locations of the other bundle tips. A total of 96 BLA neurons were collected from chronically indwelling electrode bundles in 7 rats (22 sessions in all; 4.4±2.1 neurons/session). Of these, 75 were held across multiple (> 7) applications of the full array of 4 tastes, and of these, 21 (28% of the total sample) responded with taste-specific average firing rates (p < 0.05 for the main effect of taste in a 2-way [taste × time] ANOVA). The interaction terms of the same ANOVAs revealed a further 9 (12% of the total sample) BLA neurons that produced responses with different time-courses to different tastes. Overall, 25 neurons (33% of the total BLA sample) were taste-specific according to rate, time-course, or some combination of the two. This represents a much higher percentage than that previously reported for monkeys (Scott et al., 1993) and rats (Nishijo et al., 1998)—a fact attributable to our consideration of time-course, and to the delivery of multiple trials of each taste (which added statistical power to our analysis).

Figure 1
Electrode placement

We further examined the time-courses of BLA taste responses, calculating the onset times and durations of firing rate changes (compared to baseline firing) using a moving-window analysis. In the entire sample, 65 significant elevations from baseline were observed in 28 neurons (this number is slightly different than those described above because the moving window analysis compares individual responses to pre-stimulus baselines, whereas the 2-way ANOVA compares responses between taste, and because the moving window analysis is more conservative with regard to detecting inhibitory responses). The vast majority (85.5%) of these modulations had latencies of less than 250 msec (mean, 86.6±12.2 msec), but response durations varied widely, from < 100 msec to well over a second.

When average response latencies and durations were plotted against one another, it became clear that taste-responsive BLA neurons fell naturally into two categories, one of short-latency, long-duration responses, and another of even shorter-latency, short-duration responses (Figure 2A). Cluster analysis confirmed this separation (Figure 2B). For convenience, we refer to these sub-groupings as long- and short-duration (LD and SD) neurons, respectively. The two clusters did not differ in either anatomical localization or waveform shape (data not shown), but they differed significantly along both dimensions of Figure 2A—SD neuron responses were of shorter average duration than those of LD neurons (144±28 vs 1388±71 msec respectively, t(22) = 19.9, p < 0.001), and were also of shorter average latency than LD neurons (61±9 vs 130±25 msec respectively, t(22) = 3.29, p < 0.01).

To simplify further analysis of the neurons that were tested with all 4 tastes, responses were collapsed into 250-msec bins (results were similar using 50-, 100-, or 200-msec bins, however). Figure 2C shows the general time-courses of taste responses in SD (red trace) and LD (green trace) neurons, averaged across taste. During the first 250 msec bin, the two types of neurons responded strongly and similarly, but afterward the responses were quite different—SD responses remained elevated above baseline for only the first 250 msec (t-tests comparing bin 1 to other bins, all p < 0.001), while LD responses declined much more slowly. A 2-way repeated-measures ANOVA of these data revealed that SD and LD responses had significantly different time-courses (interaction F(54,1) = 7.17, p <.001). Post-hoc tests demonstrated that SD and LD firing rates were different from 0.25 to 1.75 sec after taste administration.

LD neurons convey palatability-related information in the middle of three response epochs

In addition to differing in time-course, SD and LD responses also differed in information content. An initial appreciation of this fact can be gained by looking at a simple measure of taste-specificity, the average difference between responses to pairs of tastes (Figure 2D); in this analysis, neurons that respond identically to any pair of tastes (i. e., fire the same number of spikes/sec to all tastes) show a difference of 0 for those two tastes in that time bin.

This analysis revealed the brief SD responses to be taste-specific (although it does not reveal precisely which pairs of tastes contribute to that taste-specificity; that issue will be taken up below), in that the differences between responses to the different stimuli were significantly larger than those observed during pre-stimulus periods (t (59) = 5.01, p < 0.001). In fact, SD neurons responded more taste-distinctively than LD neurons during the first 250 msec bin of the taste responses—during this same period, the LD responses were not taste-specific at all (p > 0.2), despite the fact that their absolute firing rates peaked during this bin (Figure 2C). LD neurons responded in a more taste-specific manner than SD neurons in each of the next 4 bins, however (all p < 0.05), across a period in which the overall response amplitudes steadily declined. LD responses remained significantly taste-specific (p < 0.01) for a relatively long time following taste administration. A 2-way ANOVA (time by neuron type) revealed the difference between the patterns of SD and LD taste-specificity to be significant (interaction F (9,1) = 7.73, p < 0.001).

We predicted that the protracted LD neuron responses would progress through a series of three processing epochs, each containing distinct types of information, in reflection of co-operative coding between BLA and GC. Our analysis supported this prediction. The time-structure of BLA taste responses could be observed by eye (Figure 3A; shading denotes significantly elevated firing): LD neurons responded first to all tastes (we called this period Epoch 1), and then to a subset of tastes (in this case, NaCl and sucrose; Epoch 2); following Epoch 2, LD neurons typically responded to only one taste at any particular time (here, the response was to sucrose at one point and NaCl at another point)—this was called Epoch 3. Population analysis (Figure 3B) bore out these trends: LD neurons responded to all 4 tastes during the first 250-msec post-stimulus bin; this generality of response faded quickly however, and vanished completely before 0.5−0.75 sec of post-stimulus time had elapsed. During the period of 0.25 to 1.0 sec post-delivery, LD neurons instead responded to 2−3 tastes (red line). After 1.0 sec post-delivery, they responded to only 1 taste (blue line). In both the number of response epochs and the timing of epoch transitions, LD responses matched well with GC responses (Katz et al., 2001a).

Figure 3
LD neurons produce time-varying taste responses with dynamics similar to those of GC

Because the amygdala is known to be a primary processor of taste palatability, we tested the hypothesis that LD neurons—specifically, the 2nd epoch, in which they responded to either 2 or 3 tastes— provide palatability-related information. The term “palatability” is widely regarded to refer to how likeable and pleasing a taste is (Breslin et al., 1992; Berridge, 2000); for this analysis, we made use of the fact that the rat finds sucrose and NaCl pleasing and finds quinine and citric acid aversive, a fact evident in the palatability-specific faces that a rat makes when these tastes are on its tongue (Figure 4A, see also Grill and Norgren, 1978; Breslin et al., 1992; Fontanini and Katz, 2006; Caras et al., 2008).

Figure 4
LD neurons carry palatability-related information in the middle portion of their temporal codes

Figure 4B shows which tastes LD neurons responded to in Epoch 2. Most notable is what was absent: not one single neuron responded to both sucrose and quinine, the two extremes of the palatability continuum. In fact, the bulk (60%) of responses in the period between 0.25 and 1.0 sec following taste delivery were restricted to palatability-specific pairs of tastes; the percentage was even higher (86%) when analysis was restricted to bins in which neurons responded to 2 tastes alone. More LD neurons responded to the aversive tastes than to the palatable tastes, a finding that accords well with previous work (Zald et al., 1998; Oya et al., 2002).

NaCl and sucrose behaved similarly in the vast majority (80%) of these cases—both caused responses in 40% of the bins, and both were ineffective in 40% (the exceptions were the NCQ and NQ bins). N behaved like C and Q, meanwhile, in only 30% and 20% of the bins, respectively. Analogously, aversive Q behaved more like aversive C (70% of bins) than it did like either N or S (20% and 0%, respectively).

Figure 4C reveals the general impact that this pattern of responses had on palatability-specificity in LD responses, showing average between-taste differences for tastes with similar palatabilities (sucrose/NaCl, quinine/acid) or distinct palatabilities during each epoch of the responses—the first 250 msec of the response (i. e., the period when most neurons responded to all 4 tastes), the period between 0.25 and 1.0 sec after taste delivery (when most neurons responded to either 2 or 3 tastes), and later time points. For both the 1st and 3rd epochs of the responses, the differences between palatability-specific pairs were similar to those between pairs with distinct palatabilities. During Epoch 2, however, there was significantly less difference between sucrose and NaCl (and between quinine and citric acid) than between other taste pairs (t(96) = 2.69, p < 0.01). While a low but significant (t (22) = 2.07, p < 0.05) level of palatability-specificity could be detected in Epoch 3 in an analysis that collapsed across bins (data not shown), the vast majority of palatability-related LD response information lives in Epoch 2.

These results suggest that it should be harder to tell LD responses induced by NaCl from those induced by sucrose than from those induced by quinine and citric acid. To test this prediction, we built “templates” of the Epoch 2 population responses to each taste, and then used those templates to classify the individual trials. The results of this analysis (Figure 4D) shows both how reliably well-defined (i. e., taste-specific) each neural response was, and also reveals which tastes were most often confused for each other. Each taste was correctly identified at approximately twice the rate that one would expect by chance (chance = 25%). Furthermore, for each taste the most common error made by the classifier algorithm was to misidentify the trial as coming from the other taste with the same palatability—32.8% of the trials were within-palatability confusions, while only 9.7% were opposite-palatability confusions. This pattern of confusion confirms that LD responses serve as good predictors of stimulus palatability.

SD neurons reflect rewarding taste properties during surprise deliveries

As already noted (Figure 2D), taste-related information is available in BLA within 60 msec of taste delivery, via the responses of SD neurons. This response latency is much smaller than that observed in GC, and in fact is unlike that of any taste responses of which we are aware (Katz et al., 2001a; Fontanini and Katz, 2006; but see Stapleton et al., 2006; Grossman et al., 2008). What these responses do resemble, in their latency and brevity, are reward responses. Neurons within the reward system, including both dopamine neurons in the midbrain and their amygdalar targets, respond to the presentation of rewarding stimuli with phasic bursts of action potentials that strongly resemble those produced by SD neurons in response to tastes (Mirenowicz and Schultz, 1996; Pratt and Mizumori, 1998; Schultz, 2001; Paton et al., 2006; Roesch et al., 2007; Tye and Janak, 2007).

One reasonable hypothesis as to the nature of SD neurons is therefore related to BLA's known involvement in reward—in determining what stimuli an animal will work to receive or avoid. Midbrain dopamine neurons typically respond more vigorously to appetitive stimuli than to punishing stimuli (Mirenowicz and Schultz, 1996; Pan et al., 2005; Roesch et al., 2007), and BLA contains both neurons that respond to positive rewards and those that respond to punishment (Schoenbaum et al., 1999; Paton et al., 2006; Belova et al., 2007); furthermore, a subset of neurons in both locations have been shown to respond to rewards of either valence (sometimes called “non-valenced” neurons, see Figure 2 in Belova et al., 2007). So it was in the majority of our SD neuron sample: 4 out of 10 SD neurons responded most strongly to sucrose, which is by far the most rewarding of our four tastes (NaCl is palatable but not particularly rewarding, see Berridge and Schulkin, 1989) and least to quinine (the uniquely punishing taste in our array, Figure 5A), or else most strongly to quinine and least strongly to sucrose (Figure 5B); these patterns occurred more than twice as often as would be expected by chance, assuming equal probability of each pattern (16.7%). Furthermore, 5 of the remaining SD neurons responded to both sucrose and quinine with similar bursts of moderate magnitude.

Figure 5
SD responses code reward value

We confirmed that SD neurons did not, as a group, provide information on taste palatability, analyzing response differences for similar and dissimilar taste pairs as was done for LD neurons. SD responses to taste pairs with similar palatability (e. g., sucrose and NaCl) were not significantly more similar than responses to taste pairs with divergent palatability (e. g., sucrose and quinine). The average firing rate difference for similar taste pairs was 7.4 ± 1.8 spikes/sec while the average firing rate difference for dissimilar taste pairs was 8.8 ± 1.6 spikes/sec; (t (58) = 2.00, p > 0.5).

Previous results from human and non-human primates suggest that dopamine reward responses, both amygdalar and midbrain, are strongest when the reward, positive or negative, is unanticipated (Schultz et al., 1997; Belova et al., 2007; Roesch et al., 2007; Kufahl et al., 2008), much like behavioral “alpha” responses to strong, unexpected stimuli (Gruart et al., 2000). When rewards are expected, reward responses shift from the rewarding stimulus itself to the stimulus triggering that expectation (Schultz, 2001). We therefore analyzed the trials in which the rats initiated delivery of a randomly selected taste by pressing a lever (these trials were pseudo-randomly interspersed among the experimenter-initiated deliveries). While our rats could not learn to predict which taste would arrive following each lever press, they clearly learned to press a lever to receive tastes, and thus to expect taste delivery (delivery that, perhaps due to water restriction, carries a basal reward value). We reasoned that any BLA reward/alpha responses would be uniquely sensitive to the difference between and passive delivery and self-administration, and therefore predicted: 1) that SD taste responses would largely vanish during self-administration, and 2) that these same neurons would show strong, sharp tone responses.

In fact, SD taste responses were almost completely blocked by this manipulation of taste delivery. A full 60% of the SD neurons identified in experimenter-initiated trials responded significantly differently in self-administration trials; in 66.7% of these neurons, this was true for every taste response. Furthermore, the entirety of the changes in SD taste coding wrought by self-administration consisted of response diminutions or outright response eliminations, such that the average magnitude of an SD response was significantly smaller (reduced by > 75%) in self-administration trials (Figure6A), according to 2-way ANOVA (time post stimulus × delivery condition; Fcondition (9,1) = 8.8, p < 0.01; Finteraction (9, 1) = 25.6, p < 0.001). Only insignificant elevations of SD neuron activity were observed in anticipation of taste delivery, but self-administration nearly abolished taste responses themselves.

Figure 6
Self-administration affects SD responses but not LD responses

Figure 6B shows that the opposite was true for LD neurons. While anticipation of self-administration caused significant elevations of pre-stimulus LD firing rates in the 250 msec immediately preceding stimulus delivery (t(30) = 2.38, p < 0.05), the LD taste responses themselves were unchanged by self-administration (Finteraction (9, 1) = 1.54, p > 0.1). Self-administration changed only 12% of the bins in which LD neurons responded to tastes, the majority (57%) of which were found in the first 250 msec of the responses.

This blocking of taste response was unlikely a direct cause of motor inhibition. Rats were well trained, typically pressing only once upon hearing the tone (average number of presses in the 2.5 sec following the first lever press = 0.069 ±0.055). It is unlikely that any residual movement (end of lever release) could have caused activity that blocked the taste responses, and the latency of the next lever press within the 2.5 seconds post-taste was 1.42±0.46 sec. Furthermore, not only did lever pressing have no effect on LD taste responses, it also failed to inhibit the activity preparatory to LD responses.

Self-administration trials were preceded by a tone that announced taste availability. SD neurons specifically responded to that tone in self-administration trials with short-latency phasic increases in firing (Figure 6C), just as reward neurons in the dopamine system have been shown to do (Schultz, 2001). The population average and representative example (inset) show these to be classic examples of auditory responses—phasic, and sharply time-locked to tone onset; as each consisted of one or a few spikes/trial, and rats’ reaction times to the tone varied widely (avg ± s.d. = 684 ± 258msec) , these tone responses had no noticeable impact on PSTHs keyed to taste delivery (i. e., Figure 6A). LD neurons, meanwhile, responded only slightly and gradually to the tone, despite showing strong anticipatory firing leading up to taste delivery (Figure 6B). SD tone responses were significantly stronger than LD tone responses (t(966) = 1.96, p < 0.001).

In summary, SD neuron taste responses were strongly affected by stimulus self-administration, and LD neuron taste responses were not (Finteraction (9, 1) = 7.19, p < 0.001). Given the similarity between these neurons’ responses and those noted in work on amygdalar reward coding (Schoenbaum et al., 1999; Belova et al., 2007; Kufahl et al., 2008)—their brevity, their short latencies, their patterns of taste and tone responses, and their sensitivity to expectation—we argue that SD neurons are likely involved in the coding of stimulus reward value (while recognizing that they may also be involved in the coding of palatability or motor variables, see Discussion).

Discussion

The 2 subtypes of BLA taste neurons described here were dissociated, without prior prejudice, on the basis of response duration—one (LD neurons) produces taste responses which last longer than 1 sec on average; the other (SD neurons), meanwhile, responds to each taste phasically (durations ~200 msec).

The differences between LD and SD neurons go far beyond duration, however. Taste specificity is lacking in the first 250 msec of LD responses, for instance, whereas SD responses are taste-specific within ~60 msec of stimulus onset. Taste-specificity in LD neurons, when it does emerge, is related to response duration, whereas SD responses are taste-specific in magnitude. LD responses code palatability, while SD responses appear to code reward intensity (e. g., LD neurons code sucrose and NaCl similarly, SD neurons code them distinctly). SD taste responses vanish when the taste is self- as opposed to experimenter-administered, whereas LD taste responses are largely unaffected by self-administration. Finally, SD neurons respond to a tone that signals taste availability, whereas LD neurons do not. These results suggest that LD neurons are a part of the basic taste network that includes gustatory cortex; SD neurons, meanwhile, are more likely linked to the dopamine reward system.

BLA involvement in hedonic taste processing

Tastes of similar palatability cause similar responses in amygdalar neurons (Scott et al., 1993; Yasoshima et al., 1995; Nishijo et al., 1998; Nishijo et al., 2000). Our data demonstrate that this palatability coding is embedded within response dynamics. The initial LD responses are strong in terms of spikes/second, but carry no chemosensory information. Palatability coding, first apparent after 250 msec of processing time, is a function of duration—each neuron's responses to tastes of a particular palatability last longer than responses to tastes of the opposing palatability—and mostly fades within 1 sec.

LD response dynamics are highly reminiscent of those observed in GC (Katz et al., 2001a), and confirm our expectation that BLA and GC work together for the purposes of processing tastes. In both regions, tastes elicit responses that change in information content at ~200−250 msec, and again at ~1.0 sec (Figure 7). These two transitions, which occur quite suddenly in single trials (Jones et al., 2007), reflect critical moments dividing the first 2 sec of the taste response into functional epochs (Fontanini and Katz, 2006; Grossman et al., 2008). The early GC neuron responses resemble early LD responses much more than they do SD responses, in that they contain no taste-specific information of any sort (Katz et al., 2001).

Figure 7
Comparison of results from cortical and LD neurons suggests amygdala-cortical coupling

LD and GC taste responses are not identical, however. For instance, the somatosensory information that drives some of the early- and late-epoch activity in GC (Katz et al., 2001a) is absent from LD neurons (data not shown). More obviously (Figure 7), palatability-related information appears in GC only as it vanishes in BLA. These data are in good accord with our recent work showing that taste palatability learning changes early aspects of BLA responses, and later aspects of GC responses (Grossman et al., 2008), and is consistent with a large literature suggesting that BLA's primary job is to evaluate stimulus hedonics (Murray et al., 1993; Scott et al., 1993; Nishijo et al., 2000). The late onset of palatability-related information in GC may reflect the completion of processing in BLA; regardless, the similarity of LD and GC response dynamics suggests cooperativity during processing of a unified taste experience. Such cooperativity has been suggested to facilitate GC's involvement in learning taste-illness associations (Escobar et al., 1998; Escobar and Bermudez-Rattoni, 2000; Miranda et al., 2002; Ferreira et al., 2005; Grossman et al., 2008).

The clear evidence of BLA palatability coding observed here seems, at first blush, to be at odds with some human imaging studies, in which the amygdala seems to respond on the basis of stimulus intensity rather than hedonics (O'Doherty et al., 2001; Small et al., 2003, but see Zald et al., 1998). Our data offer a simple explanation for this discrepancy. While LD neurons differentiated pleasant from aversive tastes, there was no observed spatial organization of responses—i. e., sucrose-responsive neurons and quinine-responsive neurons were found in close proximity to each other (data not shown). It is likely that the voxels of an fMRI analysis, each of which covers a relatively large spatial region, will reflect the activity of similar numbers of “palatable-best” and “aversive-best” neurons; thus, responses to strong palatable and unpalatable tastes will cancel each other out in fMRI comparisons.

BLA involvement in processing taste rewards

When the rat receives tastes passively, SD neurons produce a brief, short-latency burst of action potentials. Responses to different tastes are highly similar in time-course but differ markedly in magnitude, such that the amygdala becomes privy to information concerning taste quality well before chemosensory information is available in GC (Katz et al., 2001a).

We propose that SD neurons are likely a part of the reward system, for several reasons: First, just as BLA reward neurons have been shown to code the reward value of stimuli delivered to passive primates (e. g., Belova et al., 2007), SD neurons code the reward value of tastes—most SD neurons respond most strongly to the most rewarding and least strongly to the most punishing taste (or vice versa), or else respond to both; this latter subtype, observed elsewhere, has been referred to as “non-valenced” neurons (Belova et al., 2007). Second, just as BLA reward responses are inhibited by self-administration—unexpected cocaine administration induces stronger BOLD activation than expected cocaine administration (Kufahl et al., 2008), while single-neuron responses to rewarding juice and aversive somatosensory stimuli are inhibited by anticipation (Belova et al., 2007) and shrink with growing predictability (Schoenbaum et al., 1999; Roesch et al., 2007)—SD responses vanish when the rat lever-presses to receive tastes. Emotional responses, meanwhile, are unaffected or even enhanced by anticipation (Bermpohl et al., 2006). Third, just as reward neurons have been shown to respond to stimuli that predict the availability of rewarding stimuli (e. g., Mirenowicz and Schultz, 1996), SD neurons respond to a tone announcing taste availability.

Of course, the comparability of this to other datasets is limited. Our rats were unable to predict whether any particular lever press would result in delivery of a rewarding (vs aversive) taste. Thus, the “anticipation” effect observed here is different from that shown in earlier studies; we cannot reject the possibility that the striking similarity between SD and classic reward responses would break down if our rats were able to predict which taste was coming. Certainly we cannot conclusively rule out the possibility that SD tone responses represent motor preparation. We can, however, say that the inhibition of taste responses themselves is unrelated to surprise (passive deliveries are surprising, and caused robust responses) and motor activity (the lever press well before taste delivery).

The inhibition of SD responses with self-administration may shed new light on the enigmatic finding that BLA lesions/inactivations render rats unable to acquire CTA when tastes are unexpected, while having less effect on their ability to learn CTAs to expected tastes (Schafe et al., 1998; Wang et al., 2006; see also Wieskopf, Rubin, Grossman, Yoshida, Figueroa, Kratchmann, and Katz, manuscript submitted). It is possible that SD neuron responses are crucial for amygdalar involvement in CTA, and that stimulus anticipation has a significant impact on the circuitry brought to bear on the processing of tastes. Changes in the states of network models affects how information filters through those networks (Jaeger and Bower, 1994; Chance et al., 2002; Santamaria et al., 2002). Such changes, in the taste system, could effectively re-route or “shunt” activity that normally reaches BLA from the dopamine system.

Given the latency of this activity in BLA, it is possible that the normal source of SD responses is the nucleus accumbens, which receives direct input from gustatory brainstem (Hajnal and Norgren, 2005). The same mechanism that keeps SD responses from reaching BLA in the actively sensing rat may in fact be the source of early chemosensory information that appears in the gustatory cortex of rats only when they are obtaining tastes via a lick-spout (Stapleton et al., 2006; Katz et al., 2001a).

Conclusions

BLA is involved in multiple aspects of taste processing (Berridge, 1996). Here, we show that these inter-related but distinguishable processes are subtended, at least in part, by separable neuron groups. Furthermore, we reveal evidence of co-operation between BLA and other parts of the distributed system involved in taste—in particular, gustatory cortex—and offer clues to how these processes occur through time as taste information flows through the CNS.

Ultimately, of course, taste stimuli are perceived as wholistic objects for the purposes of feeding (Gibson, 1966). Future work will examine how cortex and amygdala couple together for that purpose, and where in the system—obvious candidates being orbitofrontal cortex (Saddoris et al., 2005) and central amygdala (Ahn and Phillips, 2002)—the types of information that are separated in BLA converge.

Funding

This work was supported by National Institute on Deafness and Other Communication Disorders Grants DC-008885 (A.F.), DC-006666 and DC-007703 (D.B.K.), and DC- 008720 (S.E.G.). A.F. was also supported by the Sloan-Swartz Center for Theoretical Neuroscience at Brandeis.

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