Coupled Dynamics of Stimulus-Evoked Gustatory Cortical and Basolateral Amygdalar Activity

Simultaneous single-neuron ensemble recordings from BLA and GC

We isolated multiple single neurons simultaneously from both GC and BLA in six rats. Electrode bundle tip placements are shown in Figure 2; a coronal schematic of the regions (GC, Fig. 2Ai; BLA Fig. 2Bi), with the locations of all bundle tips marked, is displayed on the left half. The right half is a photomicrograph showing an example placement.

The rest of Figure 2 presents representative recordings from each region. Figure 2, Aii and Bii, shows overlain PSTHs of example GC and BLA neuron responses to each taste. The colors behind the PSTHs show the average periods of GC coding epochs described previously (Katz et al., 2001; Fontanini et al., 2009; Sadacca et al., 2012). Note that both GC and BLA responses rearrange themselves (i.e., the ordering of which tastes induce larger/smaller response magnitudes changes, y-axis) around the times of epochal boundaries. The nature of the coding performed by each neuron changes with these epochal rearrangements as well (Katz et al., 2001; Sadacca et al., 2012, 2016; Moran and Katz, 2014; Li et al., 2016). In GC, the early epoch has little taste specificity, but the middle epoch responses code at least a subset of tastes distinctly, and in the late epoch, responses become generally organized by palatability. (Note, for instance, in Figure 2Aii that the late response is ordered from most aversive to most palatable; the strongest response is to quinine, followed in order by citric acid, NaCl, and sucrose.) In BLA, meanwhile, palatability-related responses appear in the middle epoch, and (unlike the GC responses) primarily reflect strong responses to tastes of only one (in this case, negative) valence; both of these aspects of BLA taste responses replicate previous investigations (Fontanini et al., 2009).

These findings are statistically confirmed by analyses summarized below the PSTHs in Figure 2. The middle row (Discrim in the y-axis; Figure 2Aiii,Biii), which plots the significance (p < 0.05) of a moving window of ANOVAs for tastes, reveals that the responses displayed in Figure 2A–Bii become distinctly taste specific starting at ∼250 ms after taste delivery (i.e., at the start of the second epoch; the BLA neuron stops doing so at the end of this epoch). At the bottom (Fig. 2Aiv,Biv), Pearson's r between the known palatability of each taste and the response of the neuron to each taste (shaded region, p < 0.05) shows that the palatability relatedness of the GC responses becomes significant a little before 1000 ms after taste delivery [i.e., at the onset of the third (late) epoch], and that the palatability relatedness of the BLA responses is significant for the one-epoch entirety (middle epoch) of the taste-specific response. Again, these results confirm findings published in multiple previous studies (Katz et al., 2001; Grossman et al., 2008; Fontanini et al., 2009; Sadacca et al., 2012) and motivate our basic hypothesis that the systems-level mechanisms of taste coding will intrinsically involve epoch-specific processes.

A good deal of previous research has demonstrated that taste-response epochs translate into sequences of states (each lasting hundreds of milliseconds) in single-trial-evoked GC ensemble activity (Jones et al., 2007; Sadacca et al., 2016; Mukherjee et al., 2019), with each pair of states separated by sudden (50–200 ms) coherent firing-rate transitions (spontaneous GC activity also contains such transitions; Mazzucato et al., 2015; La Camera et al., 2019). We note here that we use the term “epoch” to describe periods of distinct activity seen in single-neuron PSTHs (Fig. 2), whereas “state” is a term borrowed from the modeling literature and is used here to describe simultaneous changes in the recorded neural population activity while accounting for temporal variation at the single-trial level. However, as state transitions correspond to epoch transitions across trials (on average; Fig. 9C,D, comparison of average state-transition times in our data and previously shown epochal-transition times), it is sometimes difficult to cleanly delineate single-trial from trial-averaged analyses. In these cases, we use the term that seems most appropriate.

These state transitions represent an integral part of neural activity, in that they both predict the onset of (Sadacca et al., 2016) and drive (Mukherjee et al., 2019) consumption-related behavior; that is, rather than being trivial reflections of mouth movements, the dynamics represent the processing of tastes that leads to discriminative mouth movements (Katz et al., 2001; Jones et al., 2007). The seeming ramping onset of the late epoch containing palatability-related firing (Figure 2) described above is an artifact of averaging across the large trial-to-trial variability (on the order of hundreds of milliseconds) in the latency of this sudden transition (Jones et al., 2007; Sadacca et al., 2016). Furthermore, because this phenomenon is not sparse, (∼50% of the neurons in an ensemble will change their firing rates at any particular transition), it can be reliably observed in subensembles as small as six simultaneously recorded neurons (Jones et al., 2007).

Once again, the current data replicate these findings (Fig. 3), showing that even a small ensemble of simultaneously recorded GC neurons (here, 10; again, almost two times the number of neurons needed to resolve this phenomenon; Jones et al., 2007) can be observed to undergo sudden, coherent firing-rate changes in the process of responding to tastes (Fig. 3A). The states and state transitions identified by our changepoint algorithm are overlain on the ensemble raster plot, and the six (of 10, 60%) neurons that significantly increased or decreased their firing rates at the time of the transition into the third (putative palatability related) state are indicated with green or red lines, respectively, under their spike trains. (Note the sudden increase in the rate of spiking in neurons 4, 6, and 7, and the simultaneous decrease in firing rate in neurons 1, 2, and 3.) The algorithm was able to progress to near-perfect confidence in the transition across an extremely short time interval in almost every trial (Fig. 3Bi), which is to say that these changes in neural activity are, on average, very sudden on a single-trial basis (Fig. 3Bii); because the latency of the transition could vary wildly from trial to trial (Fig. 3Bi), however, the onset of the post-transition state appears to be slow-ramping when averaged across trials synchronized to stimulus delivery (Fig. 3Biii).

Once calculated from the ensemble data, the time of transitions could then be used to directly illustrate the sharpness of the firing-rate changes in even single-neuron data. Figure 3C shows a representative GC neuron; note that the firing-rate change that seems like a slow ramp in data synchronized to stimulus delivery (Fig. 3C, top, unaligned rasters; bottom, associated peristimulus time histograms) is in fact precipitous when the trial-to-trial variability of the latency of that change is accounted for (Fig. 3Cii, aligned raster and associated peritransition time histogram). Keep in mind that this change is not caused by the stimulus being removed from the tongue; it occurs at least a full second before swallowing and both precedes and reliably predicts (Sadacca et al., 2016), as well as participates in, driving (Mukherjee et al., 2019) discriminative oral behaviors.

BLA activity contains trial-specific, sudden ensemble firing-rate transitions

Having reconfirmed that GC epochal dynamics reflect sequences of single-trial states and are en route to assessing the coordination between BLA and GC neural responses and testing our hypothesis that BLA state transitions are coupled to those in GC, it is necessary that we determine whether these sort of nonlinearities in firing rate, which have been extensively described in GC (Fig. 3; Jones et al., 2007; Sadacca et al., 2016), also truly characterize BLA taste responses. That is, we must first test whether evoked BLA activity, which involves epochal dynamics that are similarly timed to those in GC (Fig. 2, single-neuron examples of the similarity between BLA and GC dynamics; Fontanini et al., 2009), are well described as sudden ensemble state transitions in single trials. Only if the population dynamics of BLA taste-evoked responses also consist of sudden transitions between a small number of ensemble states (a hypothesis that has not yet been tested) is our most novel hypothesis tenable.

To this end, we evaluated the spiking activity of BLA ensembles using the same changepoint model designed to detect coherent ensemble transitions in GC data. To ensure robust sampling of BLA population activity, this set of analyses was performed on data from a separate group of animals with bilateral BLA electrode implants (two animals, nine recording sessions; however, all results reported in this analysis were qualitatively similar to those obtained using unilateral BLA populations taken from BLA–GC dual region recordings; see below). First, we fit a battery of models containing 2–10 states to the evoked responses from the BLA ensembles and compared the goodness of fit of the probabilistic models (quantified using the ELBO; see above, Materials and Methods) with that of identical analyses performed on control datasets made by shuffling either whole trials or single spikes between trials for the same neuron (both manipulations preserve the across-trial dynamics and have equivalent PSTHs to the actual data; Fig. 4A). Performing this set of analyses allowed us to test whether the real data were better fit by the sudden transition model than one would expect by chance. We predicted that the unmanipulated data would achieve a higher goodness of fit to the sudden state change model.

This analysis revealed that models with four states had the highest ELBO, that is, that four-state models best describe BLA population activity for the 2000 ms of poststimulus activity we have used in this study (Fig. 4B, Table 2 for statistics used to test these effects). Note that only three of these states typically appeared in the first 1500 ms; that is, in the period leading up to and including the GC palatability-related state, and that while we are interested in the transition out of that palatability-related state, we are not yet prepared to interpret the state following (which is postdecision). This number of states accords well with results from HMM modeling of GC activity (Jones et al., 2006; Sadacca et al., 2016), supporting our suggestion that GC and BLA taste-response dynamics are similar in kind (and demonstrating that these results hold across very different analysis techniques). Furthermore, this fit was significantly higher (as was the relative advantage of the four-state model) than that achieved by even the trial-shuffled surrogate dataset (F_(2,20) = 26.9, p = 2.13 × 10⁻⁶; two-way repeated measures ANOVA for Shuffle type and number of States, n = 9 recordings across two animals), control data which left spike trains of each trial intact (Fig. 2), eliminating only the between-neuron coherence of single-trial dynamics.

We further evaluated the BLA ensemble data by comparing the magnitude of firing-rate changes across state transitions identified by this optimal four-state changepoint model (Sadacca et al., 2016, a similar analysis of GC responses), compared with those observed in surrogate datasets. We predicted that both the average magnitude of transition-aligned changes in firing rates and the quantity of transitions showing larger changes in firing would be higher for the actual data than for the surrogate datasets in which we disrupted the coherence of single-trial changes in population activity. Our results confirmed these predictions; compared with the surrogate datasets, neurons and ensembles in the actual, unmanipulated BLA data showed consistently larger changes in firing rate across transitions (Fig. 4C, Table 3, an accounting of the extensive statistical analyses used in these tests). Furthermore, the percentage of BLA single neurons and ensembles for which changes in firing rate across transitions were largest in the actual data were more than twice that for either shuffled dataset (Fig. 4D; Table 4).

These findings suggest that BLA ensemble taste responses are well described as consisting of sudden, coherent firing-rate transitions in single trials. Figure 5A shows a representative ensemble taste response from eight simultaneously recorded BLA neurons that clearly involves rapid state transitions, one of which occurs at around the average time of GC transitions into palatability coding (Fig. 2). At this transition, five of eight neurons underwent either an increase or decrease of firing rate simultaneously (for another, neuron 3, the increase was almost but not quite significant). As in GC (Fig. 3; Jones et al., 2007; Sadacca et al., 2016), these transitions were variable in latency across trials (Fig. 5Bi), such that although the average transition time was brief (Fig. 5Bii), averaging across trials synchronized to stimulus delivery made that transition time appear artifactually slow (Fig. 5Biii).

Finally, we again fed back the results of this ensemble analysis to directly illustrate the sharpness of single-neuron firing changes across transitions. Figure 5C shows, for one representative BLA neuron, that firing-rate changes that seem like slow ramps in data synchronized to stimulus delivery (Figure 5Ci, unaligned raster and associated peristimulus time histogram) are in fact precipitous when the trial-to-trial variability of the latency of that change is accounted for (Fig. 5Cii, aligned raster and associated peritransition time histogram). Although we have no current explanation for the apparent slight delay in the firing rate change of this particular BLA neuron (we suspect that the answer lies in compromises made by the algorithm in settling on a best guess of transition time), it is clear that BLA ensembles, like GC ensembles, respond to tastes with sequences of states separated by sudden state transitions.

BLA and GC responses show strong trial-to-trial coherence

The above data motivate our hypothesis that taste processing involves BLA–GC coupling of the brief transitions into the state, which in GC, predicts and controls consumption behavior. But given that the vast majority of published studies assessing multiregion coordination have done so not in terms of momentary coupling—they have instead focused on multisecond responses with quantification performed via evaluation of the magnitude of correlation between spike counts in simultaneously recorded pairs of neurons (Averbeck et al., 2006a,b) or, alternatively, via evaluation of similarity in LFPs simultaneously recorded from the regions under investigation (Place et al., 2016)—we first assessed whether GC taste processing is coherent with that of BLA according to these standard metrics. To maximize our ability to compare our results to those of these earlier studies, we began our investigation of BLA–GC taste-response coupling by looking at these broader measures and then moved on (in the following sections) to testing the epochal specificity of this coherence, a result that would more directly motivate our ultimate test of the hypothesized synchronous BLA–GC transitions into the behaviorally relevant state.

We first considered the trial-wise strength of coordination between BLA and GC by investigating trial-to-trial spike-count correlations (using the 02–2000 ms poststimulus time period, which is the period of the most prominent taste-evoked response) for every simultaneously recorded BLA–GC pair of neurons, thereby testing the broad hypothesis that BLA and GC responses rise and fall in a correlated (or anticorrelated) manner from trial to trial. The two example GC–BLA neuron pairs shown in Figure 6A in fact demonstrate strong coherence on this time scale. The trial-to-trial firing rates of the pair on the left are (significantly) anticorrelated, a fact that can be particularly well appreciated in the scatter plot of BLA and GC firing rates (each dot reflects one trial) below the trial-to-trial chart of responses; the pair to the right is similarly (albeit in this case positively) correlated.

Across the entire sample, BLA–GC neuron pairs (averaged across all pairs in a single session) almost always showed higher spike-count correlations than did trial-shuffled data (Fig. 6; 15/18 comparisons had values significantly higher than those expected by chance, n = 18 sessions from 6 animals), confirming that the taste responses of simultaneously recorded BLA and GC single neurons are coupled in magnitude. Of course, either positive or negative correlations indicate connectivity at the single-neuron level as a neuron pair can have either an effective excitatory or inhibitory connection between them. Both connection types characterize projections connecting BLA and GC, and either can create coordinated dynamics between both regions (Haley et al., 2016; Fu et al., 2020).

The time courses of BLA and GC taste responses are coupled

The above analysis reveals a broad coordination in BLA–GC neural activity; that is, trial-to-trial differences in firing rates in BLA neurons, measured in stimulus-evoked responses separated by >20 s, predict trial-to-trial differences in firing rates in simultaneously recorded GC neurons. But this result falls short of revealing whether this amygdala-cortical coupling has anything specific to do with taste processing. It is possible that taste responses are simply riding on generally coupled excitability existing at long time scales, not unlike that detected in studies of resting-state network dynamics (Raichle, 2015; Seitzman et al., 2019). Given our proposal that a central feature of BLA–GC coherence is the specific coupling of state-to-state transitions, it is important to first test whether this interregional coherence is epoch specific.

We therefore moved next to examining coherence in the time courses of taste-evoked responses, using analyses of LFPs that have often been used for such purposes (Antzoulatos and Miller, 2016; Place et al., 2016; Saravani et al., 2019). Even casual scrutiny reveals what appear to be striking similarities in the (trial averaged) spectral power/amplitude of BLA and GC field potentials (Fig. 7A). Although the dominant frequencies differ between regions, in both regions the frequency spectra change repeatedly across the 1–1.5 s following stimulus delivery in a manner that is well aligned with the approximate average timing of epochal transitions of ensemble firing described previously (Fig. 2; Katz et al., 2001; Fontanini et al., 2009; Sadacca et al., 2012).

These observations were statistically evaluated using quantification of the intertrial phase coherence (Stitt et al., 2017; Engel et al., 2020; Kramer and Eden, 2020; Zareian et al., 2020) between BLA and GC taste responses. Briefly, phase information for BLA and GC LFPs (across the 4–30 Hz band) was extracted using the Short-time Fourier transform, after which phase differences between the two regions were calculated (Fig. 7B). These measurements were aggregated across all trials for each time bin separately, and the variance was evaluated across poststimulus time bins (Fig. 7C); the tighter the resulting phase-difference distribution, the stronger the coupling. The results of this analysis revealed that although interareal coherence (blue bars) was inevitably smaller than intra-areal coherence (tan bars), it was significantly larger than the (control) coherence between mismatched trials (Fig. 7D). (It is worth noting that this measurement likely underestimates the true cross-coherence, because any epoch-to-epoch differences will add to the variance and reduce the across-trial average. This result held for all the frequency bands that we assessed (theta, 4–7 Hz; mu, 8–12 Hz; beta, 13–25 Hz; F_(2,34) = 654.5, p = 7.21 × 10⁻²⁸ for comparison type using repeated measures ANOVA; for all pairwise comparisons, p = 1.61 × 10⁻⁷, all p values are at the lower bound for numerical error; pairwise Mann–Whitney U tests, n = 18 recordings across six animals). Qualitatively similar results were observed for simple cross-correlations between LFP power spectra for BLA and GC (data not shown). The trial-averaged time courses of GC and BLA activity, as measured in evoked LFP activity, are coupled above and beyond overall magnitudes in a manner that looks, at first blush, to be related to epochal processing (Katz et al., 2001; Fontanini et al., 2009; Sadacca et al., 2012).

BLA and GC cross-coherence is epoch specific

Given the above, the fact that the different GC epochs have been shown to reflect distinct coding processes, and the likelihood that BLA is more integrally involved in the coding of palatability than taste identity (Piette et al., 2012; Lin et al., 2021), we hypothesized that, beyond the overall time courses being coupled, the cross-coherence between GC and BLA (evaluated in LFP time courses) would itself be epoch specific.

Our test of this hypothesis is summarized in Figure 8, which reveals that phase coherence between BLA and GC is in fact modulated in an epoch-specific (and, as it turns out, frequency-specific) manner by stimulus delivery. Changes in the theta (4–7 Hz) band were centered largely on the initial (i.e., taste nonspecific) epoch of the responses. In most (11/18) sessions, theta activity changed significantly from baseline; overwhelmingly (in 9/11 cases), these early response changes involved a decrease in phase coherence. In the mu band (7–11 Hz), meanwhile, changes were centered on the later (palatability specific; Katz et al., 2001; Sadacca et al., 2012) response epoch, These changes, which were even more ubiquitous than theta changes, in every case involved a decrease in coherence (Fig. 8A,B, right; n = 18 recordings across six animals). A reduction in coherence following stimulus delivery was predictable given previous results showing that strong levels of inter-region coherence tend to be associated with states of low cognitive engagement such as sedation, epilepsy, and cognitive impairment (Supp et al., 2011; Martinet et al., 2017; Arbab et al., 2018), as well as the fact that BLA and GC encode tastes nonidentically; hence, their specific neural responses are likely to differ (see below, Discussion). Note, however, that phase coherence always stays higher than random levels (BLA and GC are never functionally disconnected) and that state-specific reductions in coherence can be recapitulated in simple, tightly interconnected model networks (see below).

To aggregate these results across the entire dataset, we calculated the fraction of recordings in which coherence diverged significantly from baseline at each time point across the evoked response (Fig. 8C). These results confirmed the representativeness of the examples above, showing that 4–7 Hz coherence tends to be modulated early in the response and that 7–12 Hz coherence modulation comes on later in the response. Although we have no specific explanation for why theta coherence highlights an earlier epoch and mu a different epoch, what is clear is that BLA–GC coupling is modulated by epoch, as predicted.

To further illustrate the epoch specificity of BLA–GC functional connectivity, we subjected deviations of coherence from baseline across a broad range of frequency ranges (4–7, 7–12, 12–30, 30–70, and 70–100 Hz) to dimensionality reduction. In all bands we observed decrements in coherence, albeit with distinct temporal profiles (data for higher bands not shown). When we projected these time series of coherence deviation onto three principal components (Fig. 8D), the epoch-specific nature of BLA–GC coupling came into focus. The timing of sudden turning points in the trajectory, which are highlighted in Figure 8D using black circles, roughly correspond to the center points of the canonical epochs. Collectively, these data strongly recapitulate the epoch-wise nature of taste processing that has been recognized in GC and BLA spiking data, demonstrating that BLA–GC functional connectivity is not static and that BLA activity is explicitly tied to previously described GC dynamics (Lin et al., 2021).

BLA and GC ensembles undergo coupled state transitions

The structured dynamics of BLA–GC phase coherence suggests not only that BLA and GC population activity is coordinated but that this coordination is modulated according to the unfolding taste response. It further suggests that the trial-to-trial variability in amygdala-cortical dynamics might be coupled, providing indirect evidence for our riskiest hypothesis, which is that the sudden, behaviorally relevant transitions between ensemble firing-rate states (transitions that are a reliable facet of GC ensemble taste activity and that occur at different latencies in different trials) might be a distributed network phenomenon, coupled across GC–BLA ensembles. Such nonlinear coupling would suggest that BLA and GC behave as a distributed, yet single (putatively attractor), network, processing tastes in a unified manner in real time.

The analyses described in the above sections fall short of truly testing this hypothesis. In fact, they are strictly limited in their interpretability in this regard, specifically because population spiking changes in GC are quite sudden, and the latencies of these transitions vary by hundreds of milliseconds from trial to trial (Jones et al., 2006; Sadacca et al., 2016). These two properties of ensemble transitions ensure that any ability to evaluate their coupling will be essentially lost in across-trial averaging and obscured in moving-window analyses. Only through direct, single-trial analyses of the transitions themselves can we hope to evaluate BLA–GC coupling of such phenomena.

Given the novelty of this direct examination of inter-regional correlation of transition times, we first performed pilot analyses in which we divided datasets of simultaneously recorded GC neurons into halves, separately applied changepoint inference to each half-population, and correlated the latency of each transition between the two fits. We observed statistically significant correlations for >80% of our datasets and for each transition (n = 11 recordings; data not shown), confirming that our correlation measure is a robust metric for determining transition coupling between ensembles. Note that even these correlations were not significant for all datasets and did not reach ρ = 1.0 for reasons discussed below; see above, Materials and Methods.

We then brought this analysis to bear on simultaneously recorded BLA and GC ensembles, independently estimating transition times in each and then testing whether the latency of the first GC transition was correlated with the latency of the first BLA transition, and so on. The results of this test, which are summarized in Figure 9, demonstrate that certain transition times in BLA and GC ensembles are indeed robustly coordinated. Figure 9A presents one representative set of spike trains, showing inferred changepoints for a pair of simultaneously recorded BLA and GC ensembles and clearly revealing the close apposition of these transitions. Figure 9B (top) shows a scatter plot of independently calculated latencies of the second transition (the behaviorally relevant transition into palatability-related firing in GC; Sadacca et al., 2016; Mukherjee et al., 2019) from each of the trials for a representative session. Figure 9B (bottom) shows the scatter plot that would be expected by chance (produced after randomly shuffling a set of changepoint latencies between trials for the same dataset). The significant covariance between the GC and BLA changepoints on the top is lost with trial shuffling, proving that this transition alignment between the two regions is a within-trial phenomenon.

We statistically evaluated the strength of this BLA–GC transition coupling across the entire dataset by calculating the fraction of transitions that exceeded the 90th percentile of correlation strength relative to their respective trial-shuffled correlations (strongly correlated transitions) and statistically comparing this number to the fraction of strong correlations expected by chance for a similar-size dataset. This analysis revealed that the fraction of strongly correlated BLA and GC transition times in our data was significantly higher than those expected by chance but only for the transitions into and out of the GC palatability-related state (Fig. 9C; percentiles = 99.56 and 99.64, p = 4.05 × 10⁻³ and 3.65 × 10⁻³ for transitions 2 and 3, respectively; Bonferroni-corrected α = 0.05/3 = 16.67 × 10⁻³; shuffle test with Bonferroni's correction, n = 18 recordings across six animals; see above, Materials and Methods).

Note that this evidence of significant coupling—the fact that the cloud of dots in Figure 9B (top) is elongated—is observed despite the relatively small ensembles of BLA neurons isolable in awake rats. It is quite likely that a good deal of the noise visible here reflects unavoidable noise in the estimation of transition time (see above, Materials and Methods). To test this suspicion, we calculated the relationship between the amount of uncertainty in the estimation of transition time of the model (i.e., the summed average variance of inferred transition distributions) and the strength of correlation between GC–BLA transitions. As suspected, we found a significantly negative relationship between the two variables (Fig. 9C, inset; slope = −2.3, r² = 0.352, p = 0.021, one-tailed Wald test for regression slope), a result consistent with the suggestion that uncertainty in the inference blurs the correlation between transitions. The strength of coordination between BLA and GC transitions is, thus, likely even stronger than that estimated here.

Note, however, that only transitions in and out of the state that have been previously shown to be related to the reaching of consumption decisions (Sadacca et al., 2016), namely, transitions that signaled the onset and offset of the palatability state in GC (Fig. 9D), were coupled across the BLA–GC network. Transitions from the GC Detection state into GC Identity state were not. This result dovetails nicely both with classic thinking about the specific role of BLA in emotional processing (Yamamoto, 2008; Baxter and Croxson, 2012) and with recent work from the lab that shows that optogenetic perturbation of the BLA→GC projection primarily affects GC processing during the palatability epoch in GC (Lin et al., 2021). Whereas BLA activity during the late epoch (Fig. 9D) has hitherto not been explored, the strong BLA–GC coupling during that time period recommends further investigation into processing performed by BLA during that time period. Together, these results suggest that the role of BLA–GC communication likely changes between epochs and even may only be important during the GC palatability epoch.

Modeling coupled state transitions with state-specific coherence

Together, the above results fill in a picture of BLA and GC as processing tastes as a single distributed unit, with significant (above chance) cross-coherence punctuated by coupled state transitions across which the coherence drops in certain frequency ranges (a change indicative of intense processing). From a dynamical systems perspective, this phenomenology makes sense. Instantaneous coherence will often be state specific within a system in which state transitions are synchronized across brain regions; for just one example, brain regions collectively transition between different states of sleep; (Stitt et al., 2017). We would argue, in fact, that it is common for distributed networks of interconnected neurons to perform coupled transitions between states, some of which demonstrate decreases in LFP phase coherence.

We tested this contention by constructing a simple network model. Briefly, the network was composed of firing-rate units that can be taken to roughly represent interconnected subpopulations of BLA and GC neurons (Fig. 10A; right, connection strengths between subpopulations). When challenged with a moderate amount of input delivered to both regions (simulating either noise or taste stimulation, which initially arrives at BLA and GC via distinct paths; Gal-Ben-Ari and Rosenblum, 2012), the network enters a mode in which it switches between a more coherent state characterized by strong oscillations and a less coherent state where clear oscillations are disrupted (a switch that necessarily changes functional connectivity). Figure 10B presents the summed firing rates of simulated BLA and GC units across three trials in which the network can be seen to collectively transition between the states, one of which is clearly endowed with higher cross-coherence (State 1) than the other (State 2).

Figure 10C provides a specific analysis of these appearances, revealing that coherence indeed decreases following the transition into the less oscillatory state (State 2). In neither state, however, does this cross-coherence decline to baseline levels; the populations never fully disconnect (Fig; 10C,D; two-way ANOVA with State and Dataset, Actual vs Shuffle, as factors; State, F₍₁₎ = 184, p = 0.001; Dataset, F₍₃₎ = 184, p = 0.001; State * Dataset, F₍₃₎ = 233, p = 0.001; Tukey's HSD post hoc tests, Actual State 1 vs State 2, t = 42.7, p = 0.001; Shuffle State 1 vs State 2, t = 1.74, p = 0.302). And although this network is admittedly simple (a fact that no doubt explains the simple, randomly timed back-and-forth between two states), it is not the only way that such a model can be constructed. Other versions can allow each region to intrinsically oscillate at different resonant frequencies, for instance, where such frequencies would inevitably be expressed to differing degrees in different states. The important point is that this variant of the model would, like many such variants (and the real networks recorded for this study), progress through state transitions in a unified manner and that phase coherence would be lower following certain transitions. Although our investigation of the model is limited to recapitulating the state-specific changes in coherence we see in our data, this recapitulation further highlights the aptness of such attractor models for explaining activity in GC and BLA and therefore being good candidates for theoretical investigation to generate predictions about the system to be tested in future experimental work (see below, Discussion).

Ultimately, these simulations demonstrate that a multiregion network can undergo collective, coupled state transitions while having variable functional connectivity during each state. We suggest that the amygdala-cortical system is just such a network.