The gustatory cortex (GC) region of the insular cortex processes taste information in manners important for taste-guided behaviors, including food intake itself. In addition to oral gustatory stimuli, GC activity is also influenced by physiological states including hunger. The specific cell types and molecular mechanisms that provide the GC with such abilities are unclear. Glucagon-like peptide 1 (GLP-1) is produced by neurons in the brain, where it can act on GLP-1 receptor-expressing (GLP-1R+) neurons found in several brain regions. In these brain regions, GLP-1R agonism suppresses homeostatic food intake and dampens the hedonic value of food. Here, we report in mice of both sexes that cells within the GC express Glp1r mRNA and further, by ex vivo brain slice recordings, that GC GLP-1R+ neurons are depolarized by the selective GLP-1R agonist, exendin-4. Next we found that chemogenetic stimulation of GLP-1R+ neurons, and also pharmacological stimulation of GC-GLP-1Rs themselves, both reduced homeostatic food intake. When mice were chronically maintained on diets with specific fat contents and then later offered foods with new fat contents, we also found that GLP-1R agonism reduced food intake toward foods with differing fat contents, indicating that GC GLP-1R influences may depend on palatability of the food. Together, these results provide evidence for a specific cell population in the GC that may hold roles in both homeostatic and hedonic food intake.SIGNIFICANCE STATEMENT The present study demonstrates that a population of neurons in the GC region of the insular cortex expresses receptors for GLP-1Rs, these neurons are depolarized by agonism of GLP-1Rs, and GC GLP-1Rs can influence food intake on their activation, including in manners depending on food palatability. This work is significant by adding to our understanding of the brain systems that mediate ingestive behavior, which holds implications for metabolic diseases.
Pleasant odorants are represented in the posterior olfactory bulb (pOB) in mice. How does this hedonic information generate odor-motivated behaviors? Using optogenetics, we here report that stimulating the representation of pleasant odorants in a sensory structure, the pOB, can be rewarding, self-motivating and is accompanied by ventral tegmental area activation. To explore the underlying neural circuitry downstream of the OB, we use 3D high-resolution imaging and determine that the pOB preferentially projects to the olfactory tubercle, whose activity is increased by exposure to attractive odorants. We further show that attractive odorants act as reinforcers in dopamine-dependent place preference learning. Finally, we extend those findings to human, which exhibit place preference learning and an increase BOLD signal in the olfactory tubercle in response to attractive odorants. These data reveal that strong and persistent attractiveness induced by some odorants is due to a direct gateway from the pOB to the reward system.
Abstract Critical animal behaviors, especially among rodents, are guided by odors in remarkably well-coordinated manners. While many extramodal sensory cues compete for cognitive resources in these ecological contexts, that rodents can engage in such odor-guided behaviors suggests that they selectively attend to odors. We developed a behavioral paradigm to reveal that rats are indeed capable of selectively attending to odors in the presence of competing extramodal stimuli and found that this selective attention facilitates accurate odor-guided decisions. Further, we uncovered that attention to odors adaptively sharpens their representation among neurons in a brain region considered integral for odor-driven behaviors. Thus, selective attention contributes to olfaction by enhancing the coding of odors in a manner analogous to that observed among other sensory systems.
To gain insight into which parameters of neural activity are important in shaping the perception of odors, we combined a behavioral measure of odor perception with optical imaging of odor representations at the level of receptor neuron input to the rat olfactory bulb. Instead of the typical test of an animal's ability to discriminate two familiar odorants by exhibiting an operant response, we used a spontaneously expressed response to a novel odorant—exploratory sniffing—as a measure of odor perception. This assay allowed us to measure the speed with which rats perform spontaneous odor discriminations. With this paradigm, rats discriminated and began responding to a novel odorant in as little as 140 ms. This time is comparable to that measured in earlier studies using operant behavioral readouts after extensive training. In a subset of these trials, we simultaneously imaged receptor neuron input to the dorsal olfactory bulb with near-millisecond temporal resolution as the animal sampled and then responded to the novel odorant. The imaging data revealed that the bulk of the discrimination time can be attributed to the peripheral events underlying odorant detection: receptor input arrives at the olfactory bulb 100–150 ms after inhalation begins, leaving only 50–100 ms for central processing and response initiation. In most trials, odor discrimination had occurred even before the initial barrage of receptor neuron firing had ceased and before spatial maps of activity across glomeruli had fully developed. These results suggest a coding strategy in which the earliest-activated glomeruli play a major role in the initial perception of odor quality, and place constraints on coding and processing schemes based on simple changes in spike rate.
Article Figures and data Abstract eLife digest Introduction Results Discussion Materials and methods Data availability References Decision letter Author response Article and author information Metrics Abstract Sucrose's sweet intensity is one attribute contributing to the overconsumption of high-energy palatable foods. However, it is not known how sucrose intensity is encoded and used to make perceptual decisions by neurons in taste-sensitive cortices. We trained rats in a sucrose intensity discrimination task and found that sucrose evoked a widespread response in neurons recorded in posterior-Insula (pIC), anterior-Insula (aIC), and Orbitofrontal cortex (OFC). Remarkably, only a few Intensity-selective neurons conveyed the most information about sucrose's intensity, indicating that for sweetness the gustatory system uses a compact and distributed code. Sucrose intensity was encoded in both firing-rates and spike-timing. The pIC, aIC, and OFC neurons tracked movement direction, with OFC neurons yielding the most robust response. aIC and OFC neurons encoded the subject's choices, whereas all three regions tracked reward omission. Overall, these multimodal areas provide a neural representation of perceived sucrose intensity, and of task-related information underlying perceptual decision-making. https://doi.org/10.7554/eLife.41152.001 eLife digest Imagine you wake up in the morning, and you pour yourself and your loved one coffee. They like it with two sugars but you only with one. Our ability to distinguish different sweet intensities allows us to detect how much sugar is in the coffee. It also helps us to predict the amount of energy present in foods and if it is safe to ingest. We can experience the sweet quality because our tongue contains sweet taste receptor cells that are switched on by sugar. This activates neurons across our taste system in the brain. However, we do not completely understand how these areas represent the intensity of sugar. Previous studies have only 'passively' measured different sugar concentrations, either using anesthetized animals or behavioral tasks that do not involve decision-making other than licking. But to accurately evaluate how animals perceive the intensity, active decision-making is required, such us 'reporting' the perceived concentration of sugar. Fonseca et al. set out to answer this question by training rats in a new sweet intensity discrimination task, in which the rats had to move to the left or right to obtain water as a reward. This way, the animals could 'indicate' how sweet they perceived the sugar water to be. At the same time, recordings from the three brain areas involved in taste responses were taken (called the anterior and posterior insular cortices, and the orbitofrontal cortex) to measure how the sugar intensity is processed in the brain. The results showed that a small group of neurons within all three areas contained more information about the sugar intensity than other neurons, suggesting the taste system uses a compact and distributed code to represent its intensity. The information about sugar intensity was contained in both the number of nerve impulses and in the precise timing with which these neurons fired. Many drinks and high-energy foods often contain large quantities of sugar, and their overconsumption contributes to the worldwide problems of obesity and its associated diseases. Therefore, a better understanding of the neurons that code information about the intensity of sugar could be a starting point for other studies to pinpoint the connections and areas in the brain involved in our irremediable attraction for sugar. https://doi.org/10.7554/eLife.41152.002 Introduction Chemical stimulation of taste receptor cells elicits signals that are transduced into neural representations of multiple attributes, such as taste quality, intensity (the strength or concentration of a stimulus), and palatability (hedonic value). These attributes form a single percept (Accolla et al., 2007; Breslin, 2013; Lemon, 2015) that informs the animal whether it is safe to ingest the food (Tapper and Halpern, 1968). Sucrose is the prototypical highly palatable tastant for sweet taste quality, and it provides a sensory cue predicting the presence of immediate energy sources. Although palatability and intensity usually change together, Wang et al., 2018 found this is not always the case and suggested that they are two distinct representations. In rodents, palatability is measured by an increase in positive oromotor responses (e.g., licking) elicited by increasing sucrose concentrations (Spector and Smith, 1984). In contrast, the intensity attribute cannot be directly measured by any licking response per se, as an animal must actively report the perceived concentration of sucrose, a process necessarily involving decision-making. Historically, the neural representation of sweet taste intensity has been characterized by firing rates (spike counts) that monotonically increase with sucrose concentration along the gustatory pathway from the periphery to primary (IC) and secondary (OFC) taste cortices (Rolls et al., 1990; Roussin et al., 2012; Scott et al., 1991; Thorpe et al., 1983; Villavicencio et al., 2018). However, those responses were obtained in either anesthetized animals (Barretto et al., 2015; Wu et al., 2015), during passive intraoral delivery of tastants (Maier and Katz, 2013; Scott et al., 1991), or in behavioral tasks where animals do not have to make any decision other than to lick (Rosen and Di Lorenzo, 2012; Stapleton et al., 2006; Villavicencio et al., 2018). Thus, the neural representation of the perceived intensity of sucrose that the animal actively reports has not presently been studied. Likewise, how this representation is transformed into perceptual decision-variables, such as choice, movement direction, and the presence or absence of reward remains to be elucidated. Here we trained rats in a sucrose intensity discrimination task and recorded electrophysiological responses in the posterior (pIC), anterior (aIC) insular cortices, and the orbitofrontal cortex (OFC), with the aim of elucidating how these cortices encode sucrose intensity and use this information to guide behavior. These three cortical areas are multimodal and chemosensitive and are involved in disgust (pIC), tastant identification (aIC), and subjective value and reward (OFC) (Frank et al., 2013; Gardner and Fontanini, 2014; Jezzini et al., 2013; Jones et al., 2006; Katz et al., 2001; Kusumoto-Yoshida et al., 2015; Maffei et al., 2012; Maier and Katz, 2013; Verhagen et al., 2004). In rodents, the pIC has been shown to be involved in taste, disgust, expectancy, and aversive motivated behaviors (Bermúdez-Rattoni, 2004; Chen et al., 2011; Fletcher et al., 2017; Gardner and Fontanini, 2014; Gutierrez et al., 2010; Kusumoto-Yoshida et al., 2015; Wang et al., 2018). In contrast, the aIC is involved in appetitive behaviors, and besides having neurons that respond selectively to sweet taste (Chen et al., 2011), it also has neurons encoding reward probability and reward omission (Jo and Jung, 2016). Even though both pIC and aIC have roles in taste and decision-making, their contribution to sucrose intensity guided behavior remains unexplored. It is well known that OFC is involved in reward and subjective value (Conen and Padoa-Schioppa, 2015; Jo and Jung, 2016; Kennerley and Wallis, 2009; Roesch et al., 2006), and it is a critical brain region for encoding decision-variables such as choice, movement direction, and reward omission (Feierstein et al., 2006; Hirokawa et al., 2017; MacDonald et al., 2009; Nogueira et al., 2017). However, it is not known whether OFC neurons encode decision-variables guided by sucrose intensity. Equally unknown is how these variables are encoded along the posterior-anterior axis of the Insula. To address these questions, we designed a novel sweet intensity discrimination task in which, to obtain a water reward, rats had to make a rightward or leftward movement based on the perceived intensity of sucrose (Cue), while single-unit recordings in the pIC (1348), or aIC (1169), or OFC (1010) were performed. We found that stimulation with sucrose evoked a widespread response in these three cortical regions, indicating a distributed detection of taste/somatosensory information. 82% of the evoked responses showed no selectivity to sucrose intensity, whereas 18% could be labeled as sucrose intensity-selective. These selective neurons conveyed the most information about sucrose's sweet intensity. Analyses of the sucrose-evoked responses revealed that, in addition to firing rates, the spike timing of neurons contains additional information about sucrose's intensity. Several differences and similarities were identified between the evoked pIC, aIC, and OFC responses. Overall, the three recorded areas similarly decoded sucrose concentration and equally tracked the outcome (reward delivery or omission). A major difference among them was that the OFC neurons carry information about behavioral choice and movement direction, earlier and with higher quality than neurons in the Insula. In summary, these data show that the perceived intensity of sucrose is fully reconstructed from the firing rate and spike timing of a small population of neurons in the pIC, aIC, and OFC. Results Behavior Twenty-eight rats were trained in a one-drop sucrose intensity discrimination task. The trial structure is depicted in Figure 1A. Briefly, trained rats initiate a trial by first visiting the central port (Return). Licking at the central spout triggers the delivery of either 10 µL of 3 (Low) or 18 (High) wt% sucrose (referred to as Cue-D; Stimulus). If a rat chooses correctly (by moving to one of the two lateral ports; Response), three drops of water are delivered as a reward (Outcome). Error trials were unrewarded. Subjects achieved the learning criterion (≥80% correct) in about 25 sessions (Figure 1B), and the implantation of an electrode array in one of the three cortical areas did not impair task performance (paired t-test before vs. after surgery; t(27) = 0.95; p=0.35; Figure 1B). Once the animals learned the discrimination task, they were tested in a variant named generalization session (Figure 1C). In these sessions that consisted of 20% of the trials, rats were required to classify 0, 3, 4.75, 7.5, 11.75, or 18 wt% sucrose as either 'Low' or 'High' (referred as Cue-G). In these trials, no reward was delivered (Reward omission) to avoid imposing an arbitrary Low/High threshold that could bias the behavioral report of the perceived sweetness intensity. In Cue-G trials the percentage of 'High' responses increased with increasing sucrose concentration (Figure 1D), thus showing that the animals used sucrose intensity as a cue to solve the task (since its quality is unchanged (Pfaffmann et al., 1979)). Surgery did not impair perceptual judgments based on sucrose intensity (see Before vs. After surgery; Figure 1D). Figure 1 with 2 supplements see all Download asset Open asset Behavioral report of sucrose's sweet intensity in a one-drop discrimination task. (A) Structure of a single trial. The behavioral box was equipped with three spouts each connected to a pressure-controlled solenoid valve that delivered 10 µL drops (not shown, see Materials and methods). One spout was in the central (stimulus) port and the others in the left and right lateral (choice) ports. After the first trial, in the Return epoch, animals after obtaining one of the outcomes in the lateral ports, returned to the central port to begin a new trial. In the Stimulus epoch, after two or three dry licks, the cues (Cue-D -for discrimination) were delivered, and the animals had to make a High/Low decision as to which lateral port to go (Response epoch). If they choose correctly, a water reward was delivered in the Outcome epoch. Errors were unrewarded. Thus, in this task, the perceived intensity of sucrose (i.e., concentration) served as a discriminative cue (Cue-D, see the red tick and drop). (B) Performance (percent correct choices) across training days before (dashed line squares) and after (circles) electrode implantation. (C) Interleaved in sessions, all animals were tested in a variant of the above-described intensity discrimination task -named generalization sessions. These sessions were composed of 80% discrimination trials (3% Low/18 wt% High) that were rewarded as indicated in 'A,' and 20% generalization trials; that is, 0, 3, 4.75, 7.5, 11.75, and 18 wt% sucrose cues (named Cue-G). For generalization trials, rats were required to 'classify' these sucrose concentrations as either a 'Low' or 'High,' but these trials were unrewarded. (D) The percent responses to the 'High' port during discrimination (Cue-D) and generalization (Cue-G) trials increases as the sucrose concentration increase. Note that the psychometric function was nearly identical both before (dashed line squares) and after surgery (circles). (E) Latency to stop licking after cue delivery. On average, the higher the sucrose concentration, the longer the latency to stop licking. (F–G) Movement time for making a leftward or rightward movement in the Return and Response epochs. (H) Time spent licking, in the Outcome epoch, in Cue-D trials that received water as a reward was longer then in Cue-G trials where the water reward was omitted. * Statistically significant with an alpha level of 0.05. https://doi.org/10.7554/eLife.41152.003 Other behavioral measurements, related to palatability (Perez et al., 2013; Spector et al., 1998), revealed that the latency to stop licking after High Cue-D delivery (18% sucrose) was longer (0.74 ± 0.02 s) than for the Low Cue-D (3% sucrose; 0.58 ± 0.01 s; p < 0.0001). A similar trend was observed for generalization cues (i.e., Cue-G trials), in that rats exhibited a longer time to stop licking in trials where sucrose intensities were ≥to 4.75% relative to Low Cue-D (One-way ANOVA: F(7,1360) = 17.10; p < 0.0001; Dunnett post-hoc; Figure 1E). Furthermore, we analyzed the relationship between licking and task performance and found that rats lick more rhythmically and similarly for both cues in sessions where their performance was better. This is reflected by a positive correlation between Low and High licking PSTHs and task performance (r = 0.17, p < 0.003; see Figure 1—figure supplement 1). Thus, rats did not solve the task by licking differently for both cues. In the Return epoch, rightward movements (left to center port direction) were faster than leftward (right to center port) movements (Figure 1F). In contrast, during the Response epoch, leftward or rightward movements were not significantly different (Figure 1G), and therefore these movements were independent of the sucrose concentration. Interestingly, rats moved faster in the Response than in the Return period, perhaps a result of the water reward (compare Figure 1F vs. Figure 1G). Finally, in the Outcome epoch, rats rapidly detected when the reward was omitted (Cue-G trials and Cue-D error trials). That is, they spent more time licking when water was delivered than when it was omitted (Figure 1H; see Supplementary file 1 for statistics). In sum, by using only a 10 µL drop of sensory stimulation, rats can make accurate perceptual decisions based on the perceived concentration of sucrose. Electrophysiology A total of 1348, 1169, and 1010 single-units were recorded from pIC, aIC, and OFC, respectively (see Figure 3—figure supplement 1A). Of these neuronal responses 480, 403, and 337, respectively were recorded in generalization sessions and the rest in discrimination sessions (with only cue-D trials). Recordings were performed unilaterally in the left hemisphere. Schematics and location of the recording sites are seen in Figure 1—figure supplement 2. Modulation profiles of Cue-D discrimination trials The temporal activation pattern of the neural responses in pIC, aIC, and OFC was classified as a function of the evoked response (Cue-evoked or Non-evoked), modulation profile (Phasic, Tonic, or Coherent; see Table 1), and selectivity (either Non-selective or Intensity-selective; see Table 2). Most recorded neurons exhibited a statistically significant evoked response 90.6% (1221/1348), 97.4% (1139/1169), and 92.8% (937/1010) for the pIC, aIC, and OFC, respectively. The remaining neurons, named Non-evoked, were 9.4% (127/1348), 2.6% (30/1169), and 7.2% (73/1010), respectively. Cue-evoked responses were then further classified according to five characteristic modulation profiles: Phasic, Tonic-Inactive (Inact), Tonic-Active (Act), Lick-coherent Inactive (Coh-Inact), and Active (Coh-Act) (Table 1). Table 1 Cue-Evoked and Non-Evoked neurons. https://doi.org/10.7554/eLife.41152.006 Brain regionCue-Evoked responsesNon-EvokedPhasicInactiveActiveCoh-InacCoh-ActNon-ModCoh-NonEvopIC (n=1348)75 (5.6)217 (16.1)193 (14.3)414 (30.7)322 (23.9)53 (3.9)74 (5.5)aIC (n=1169)67 (5.7)202 (17.3)192 (16.4)317 (27.2)361 (30.9)*7 (0.6)*23 (2)*OFC (n=1010)27 (2.7)*#386 (38.2)*#265 (26.2)*#169 (16.7)*#90 (8.9)*#62 (6.1)*#11 (1.1)* Number of Cue-D responsive neurons (%). Data in bold indicate statistically different against pIC (*) or aIC (#), detected by a chi-squared test. Alpha level set at 0.05. Table 2 Cue-Evoked responses: Non-selective and Intensity-selective neurons. https://doi.org/10.7554/eLife.41152.007 Brain regionCue-Evoked responsesPhasicInactiveActiveCoh-InacCoh-ActTotalNon-SelInt-SelNon-SelInt-SelNon-SelInt-SelNon-SelInt-SelNon-SelInt-SelNon-SelInt-SelpIC (n=1348)55 (4.1)20 (1.5)198 (14.7)19 (1.4)171 (12.7)22 (1.6)325 (24)89 (6.6)272 (20.2)50 (3.7)1021 (75.7)200 (14.8)aIC (n=1169)55 (4.7)12 (1)183 (15.7)19 (1.6)155 (13.3)37 (3.2)*254 (21.6)63 (5.4)283 (24.2)78 (6.7)*930 (79.6)209 (17.9)OFC (n=1010)21 (2.1)*#6 (0.6)*336 (33.3)*#50 (5)*#204 (20.2)*#61 (6)#124 (12.3)*#45 (4.5)*65 (6.4)*#25 (2.5)#750 (74.2)187 (18.5)* Number of Non-selective and Intensity-selective neurons (%). Data in bold indicate statistically different against pIC(*) or aIC (#) detected by a chi-squared test. Alpha level set at 0.05. Given that rats could use a drop of sucrose to make accurate perceptual decisions based on its intensity (Figure 1), we explored the neural correlates of these decisions in the pIC, aIC, and OFC. Figure 2 depicts the raster plots and corresponding peri-stimulus time histograms (PSTHs) of representative examples of Intensity-selective Cue-D evoked responses recorded in each of the three cortical regions. Examples of Non-selective Cue-D responses are shown in Figure 2—figure supplement 1. Action potentials are depicted as black ticks and were aligned to Cue-D delivery (time = 0 s). Trials were sorted as a function of Low (3% -green) and High (18 wt% -red). The left column shows three different neurons that exhibited selective phasic responses to 3 wt% sucrose in the pIC, aIC, and OFC, respectively. Examples of the Tonic-Inactive (second column) revealed a selective inhibition for 3 wt% sucrose (Low-preferred). After Cue-D delivery, the Tonic-Active neurons exhibited a sustained increase in firing rate (third column; the upper and lower panels depict a Low-preferred neuron, whereas the middle panel a High-preferred response). The last two columns on the right-hand side display examples of neurons that fired synchronously with licking (named Lick-coherent) and, after Cue-D delivery, exhibited either a decrease (Coh-Inact) or an increase (Coh-Act) in their firing rate. Figure 2 with 2 supplements see all Download asset Open asset Representative Intensity-selective Cue-evoked responses in the rat pIC, aIC, and OFC. Representative raster plots and PSTHs (in spikes/s solid lines) of sucrose Intensity-selective neurons belonging to each of the five classes of evoked responses in the pIC (upper), aIC (middle), and OFC (lower) rows: Phasic, Tonic-Inactive, Tonic-Active, Coh-Inactive, and Coh-Active. Coh indicates they are coherent with licking. These exemplar neuronal responses discriminated between 3 and 18 wt% sucrose (Intensity-selective neurons). Most of the Cue-evoked responses were Non-selective to sucrose intensity, and individual examples are presented in Figure 2—figure supplement 1. Action potentials are depicted as black ticks around −0.3 to 0.6 s, from Cue-D delivery (time = 0 s). Only correct trials were included in these plots. The horizontal black line separates the sorted trials according to Cue-D delivery. The licks after 3 and 18 wt% sucrose are indicated by green- and red-shaded area, respectively. The times that animals were licking at the central spout before cue delivery are shown in the shaded gray areas. Also shown are the PSTHs for licking (Licks/s; at right axis) either for Low (green-dashed) or High sucrose (red-dashed line). The rectangle in cyan highlights the best-window where the responses to 3 and 18 wt% sucrose are statistically distinct as determined by a Wilcoxon rank-sum test. The gray horizontal line on top indicates the times where the lick rates were significantly different. https://doi.org/10.7554/eLife.41152.008 Intensity-selective neurons were recorded in all three cortical regions and for all five classes of evoked responses, although with different proportions (see Figure 3—figure supplement 1A and Table 2; see Supplementary file 2 for statistics). In general, pIC and aIC Intensity-selective neurons exhibited more similar responses between them than those found in the OFC (see Table 2). The only exception was that the aIC contained more Intensity-selective neurons with Tonic-Active and Coh-Act responses than the pIC. In contrast, the OFC had more Intensity-selective neurons exhibiting Tonic-Inactive and Active responses (Table 2). Overall, the percentage of Intensity-selective neurons were 14.8%, 17.9%, and 18.5%, in the pIC, aIC, and OFC, respectively (see Table 2; Total, Inten-Sel). These data show that Intensity-selective neurons are found along the posterior-anterior taste neuroaxis. To determine, in fine-grain detail, the differences in licking and its impact upon neuronal responses, in Figure 2, we also depicted the corresponding PSTHs of licking behavior and the times where the lick rate was significantly different between Low and High cues (see dashed lines). We found that 45.1% of all Intensity-selective neurons have a 'best-window' (interval with maximal discrimination between concentrations) with no differences in licking (see Figure 2 grey-line above the PSTHs). The remaining 54.9% of neurons have a lick rate difference inside the best-window, but most frequently they only covered a small fraction of the window (Figure 2—figure supplement 2). Specifically, the overlap of the lick rate differences covered 31.4% of the entire best-window (Figure 2—figure supplement 2). Thus, we conclude that is unlikely that most sucrose intensity representation can be attributed to differences in licking behavior. Figure 3A shows the color-coded population PSTH of the responses of all Intensity-selective neurons in each brain region, sorted as a function of the modulation profile and preferred concentration. What is clear in the figure is that diverse temporal patterns are evoked in response to the delivery of the Cue-D (time = 0 s). The evoked responses can be transient, sustained, or oscillatory, with either increasing or decreasing firing rates. Figure 3 with 3 supplements see all Download asset Open asset A subpopulation of Intensity-selective neurons decodes sucrose concentrations (3 vs. 18 wt%) better than other neuron classes. (A) The color-coded PSTHs of the five Cue-evoked responses in pIC (left panel), aIC (middle panel), and OFC (right panel) sorted by modulation profile and Intensity-selectivity (Low/High). Response types were first sorted from top to bottom as follows: Phasic (orange vertical line at the left edge), Inactive (dark blue), Active (red), Lick-coherent Inactive (cyan), and Lick-coherent Active (magenta). The white horizontal dashed lines separate each modulation profile as a function of Low and High selectivity (see green and red vertical lines at the left edge). Each row represents the firing rate normalized in Z-score of a neuronal response aligned to 3 (Low, left panel) and 18 wt% (High, right panel) sucrose delivery (time = 0, black vertical line). (B) Percent decoding accuracy of sucrose intensity achieved by the neurons recorded in pIC, aIC, and OFC. Each colored bar represents a different group of neurons: Non-evoked (gray), All (black), Non-selective (blue), and Intensity-selective (red). A black dashed line indicates the 50% chance level, and the upper dashed line the behavioral performance. * Indicates significant differences against the Non-evoked population, while # indicates significant differences against All group. Only correct Cue-D trials were included for analysis. The white horizontal line in each bar indicates the percent decoding achieved by each population when spike timing information was removed (i.e., shuffled spikes but maintaining same firing rates). The gray horizontal lines depict the contribution of noise correlations for population decoding. https://doi.org/10.7554/eLife.41152.011 In the Stimulus epoch, the population responses revealed that the pIC and aIC were more excited, whereas the OFC was inhibited (Figure 3—figure supplement 1B), suggesting an opposite interaction between the Insula and OFC during licking behavior. In agreement with the idea that in a default brain-network state, these two brain regions function out of phase (Gutierrez-Barragan et al., 2018). In line with previous studies (de Araujo et al., 2006; Gutierrez et al., 2010), we found among these taste cortices that the pIC (60.3%) and aIC (59.5%) had a higher proportion of (either increasing or decreasing) lick-induced oscillatory responses than the OFC (27.6%). Likewise, we found that the coherence values of the OFC (0.24 ± 0.005) were significantly lower relative to pIC (0.26 ± 0.003) and aIC (0.26 ± 0.003) (F(2, 1672)=3.77; p = 0.02) (Figure 3—figure supplement 3A). Therefore, the pIC and aIC had not only a higher proportion of Lick-coherent neurons than OFC, but also IC neurons were better entrained with rhythmic licking. More importantly, we also uncovered, for the first time, that the level of coherence was significantly higher in the Stimulus-epoch in comparison with the pre-Stimulus and the Outcome epochs (all p's < 0.0001), suggesting that lick-spike coherence reflects more than oromotor responses, perhaps it prepares taste cortices to receive sensory inputs. For the neuronal populations of each brain region, a linear decoder was used to estimate the accuracy for discriminating Low and High sucrose trials (Meyers, 2013; see Materials and methods). As seen in Figure 3B, the contribution of the Non-evoked responses (grey bars) was found to be at chance level (50%), indicating that they contained little, if any, information about sucrose intensity. In contrast, all Cue-D evoked neuronal responses (All- black bars) significantly decoded sucrose concentrations above chance level (Figure 3B). Importantly, we found that the small population of Intensity-selective neurons (red bars) contained more information than the larger Non-selective population (blue bars). Interestingly, the Non-selective group also decoded sucrose intensity significantly above chance level. One possibility is that they have subtle differences in firing rates that are not consistent enough across trials to produce a significant effect at single neuronal level. However, at the population level there is sufficient information about sucrose intensity. Alternatively and despite their similar firing rates (spike counts) evoked by Low and High Cue-D, these neurons could use spike timing to discriminate sucrose concentrations (Gutierrez and Simon, 2013). To test this hypothesis, the spikes of all Non-selective neurons were shuffled without changing their average firing rates. When the spike timing information was eliminated from these neuronal responses, their ability to decode among the sucrose's intensities dropped to chance level (Figure 3B; see the horizontal white lines across the blue bars). Thus, the additional information in the Non-selective population was likely conveyed by precise spike timing patterns of activity. The decoding algorithm also revealed that the Intensity-selective neurons in the three cortical regions decoded sucrose intensity better than Non-selective neurons (Figure 3B; red bars). It is unlikely that these results were due to the differences in the population size since Intensity-selective neurons were always fewer in number than the Non-selective and All Cue-evoked neurons. Thus, the Intensity-selective population (i.e., less than 18% of neurons) contained more information about sucrose intensity than the entire population. These data suggest the existence of a neuronal representation of sucrose concentration across these three gustatory cortical regions. That is, each taste cortex seems to contain a copy of this information. Finally, we note that by removing the spike timing information contained in the Intensity-selective neurons, their percent decoding dropped to nearly chance level, indicating that the neural representation of sucrose intensity is also conveyed in the spike timing of neurons. It has been reported that spike counts in a pair of simultaneously recorded neurons, elicited by a stimulus, can covary across the session
Sensory information acquires meaning to adaptively guide behaviors. Despite odors mediating a number of vital behaviors, the components of the olfactory system responsible for assigning meaning to odors remain unclear. The olfactory tubercle (OT), a ventral striatum structure that receives monosynaptic input from the olfactory bulb, is uniquely positioned to transform odor information into behaviorally relevant neural codes. No information is available, however, on the coding of odors among OT neurons in behaving animals. In recordings from mice engaged in an odor discrimination task, we report that the firing rate of OT neurons robustly and flexibly encodes the valence of conditioned odors over identity, with rewarded odors evoking greater firing rates. This coding of rewarded odors occurs before behavioral decisions and represents subsequent behavioral responses. We predict that the OT is an essential region whereby odor valence is encoded in the mammalian brain to guide goal-directed behaviors.
Summary Sensory cortices process stimuli in manners essential for perception. The piriform ‘primary’ olfactory cortex (PCX) extends dense association fibers into the ventral striatum’s olfactory tubercle (OT), yet the function of this cortico-striatal pathway is unknown. We optically stimulated channelrhodopsin-transduced PCX glutamatergic neurons or their association fibers while recording OT neural activity in mice performing an olfactory task. Activation of PCX neurons or their association fibers within the OT controlled the firing of some OT neurons and bidirectionally modulated odor coding dependent upon the neuron’s intrinsic odor responsivity. Further, patch clamp recordings and retroviral tracing from D1 and D2 dopamine receptor-expressing OT medium spiny neurons revealed this input can be monosynaptic and that both cell types receive most of their input from a specific spatial zone localized within the ventro-caudal PCX. These results demonstrate that the PCX functionally accesses the direct and indirect pathways of the basal ganglia within the OT.
Abstract Self-grooming is a stereotyped behavior displayed by nearly all animals. Among other established functions, self-grooming is implicated in social communication in some animals. However, whether self-grooming specifically influences behaviors of nearby individuals has not been directly tested, partly due to the technical challenge of inducing self-grooming in a reliable and temporally controllable manner. We recently found that optogenetic activation of dopamine D3 receptor expressing neurons in the ventral striatal islands of Calleja robustly induces orofacial grooming in mice. Using this optogenetic manipulation, here we demonstrate that observer mice exhibit social preference for mice that groom more regardless of biological sex. Moreover, grooming-induced social attraction depends on volatile chemosensory cues broadcasted from grooming mice. Collectively, our study establishes self-grooming as a means of promoting social attraction among mice via volatile cues, suggesting an additional benefit for animals to allocate a significant amount of time to this behavior.
