Multisensory Integration Underlies the Distinct Representation of Odor–Taste Mixtures in the Gustatory Cortex of Behaving Rats

Sanaya Stocke; Chad L Samuelsen

doi:10.1523/JNEUROSCI.0071-24.2024

. 2024 Mar 28;44(20):e0071242024. doi: 10.1523/JNEUROSCI.0071-24.2024

Multisensory Integration Underlies the Distinct Representation of Odor–Taste Mixtures in the Gustatory Cortex of Behaving Rats

Sanaya Stocke ¹, Chad L Samuelsen ^2,^✉

PMCID: PMC11097261 PMID: 38548337

Abstract

The perception of food relies on the integration of olfactory and gustatory signals originating from the mouth. This multisensory process generates robust associations between odors and tastes, significantly influencing the perceptual judgment of flavors. However, the specific neural substrates underlying this integrative process remain unclear. Previous electrophysiological studies identified the gustatory cortex as a site of convergent olfactory and gustatory signals, but whether neurons represent multimodal odor–taste mixtures as distinct from their unimodal odor and taste components is unknown. To investigate this, we recorded single-unit activity in the gustatory cortex of behaving female rats during the intraoral delivery of individual odors, individual tastes, and odor–taste mixtures. Our results demonstrate that chemoselective neurons in the gustatory cortex are broadly responsive to intraoral chemosensory stimuli, exhibiting time-varying multiphasic changes in activity. In a subset of these chemoselective neurons, odor–taste mixtures elicit nonlinear cross-modal responses that distinguish them from their olfactory and gustatory components. These findings provide novel insights into multimodal chemosensory processing by the gustatory cortex, highlighting the distinct representation of unimodal and multimodal intraoral chemosensory signals. Overall, our findings suggest that olfactory and gustatory signals interact nonlinearly in the gustatory cortex to enhance the identity coding of both unimodal and multimodal chemosensory stimuli.

Keywords: behavioral electrophysiology, flavor, gustatory cortex, multimodal, odor, taste

Significance Statement

Flavor perception relies on the concurrent processing of odors and tastes, but how these signals are integrated by the brain remains unclear. We recorded from neurons in the gustatory cortex of behaving rats during the delivery of individual odors, individual tastes, and odor–taste mixtures. We found that the responses of a subset of neurons distinguish odor–taste mixtures from their odor and taste components. Our findings provide evidence that the gustatory cortex participates in the multisensory integration of chemosensory signals underlying the perception of flavor.

Introduction

Eating is an inherently multisensory experience involving the detection and integration of sensory signals from the mouth (Sclafani, 2001; Verhagen and Engelen, 2006). While all the senses play a role, concurrent activation of the olfactory and gustatory systems is crucial for giving food its flavor (Small, 2012; Prescott, 2015). Experience with odor–taste mixtures creates enduring odor–taste associations that influence chemosensory-dependent behaviors (Fanselow and Birk, 1982; Schul et al., 1996; Sakai and Yamamoto, 2001; Gautam and Verhagen, 2010; Green et al., 2012). For example, human psychophysical studies show that experience with an odor–taste mixture improves the detectability of its individual odor and taste components (Dalton et al., 2000; Delwiche and Heffelfinger, 2005; White and Prescott, 2007; Veldhuizen et al., 2010b; Green et al., 2012; Shepard et al., 2015) and enhances the perceptual intensity and perceived hedonic value of tastes (Frank and Byram, 1988; Schifferstein and Verlegh, 1996; Amsellem and Ohla, 2016). Broadly, the integration of inputs from multiple sensory systems is thought to enhance sensitivity, improving the detection and discrimination of environmental stimuli (Ernst and Bülthoff, 2004; Stein et al., 2014). In the perception of flavor, integration of olfactory and gustatory signals not only aids in identifying what we eat but also provides the nuanced experience of flavor that guides our food choices.

The perception of flavor relies upon a network of brain regions to integrate and process multimodal chemosensory signals (Samuelsen and Vincis, 2021). Research ranging from human neuroimaging to rodent physiological studies has implicated the gustatory cortex as a pivotal hub for processing both gustatory and olfactory signals (De Araujo et al., 2003; Small et al., 2004, 2008; Veldhuizen et al., 2010a; Maier et al., 2015; Maier, 2017; Samuelsen and Fontanini, 2017). In this region, neurons are multimodal; they represent the identity and palatability-related features of tastes (Katz et al., 2001; Fontanini and Katz, 2006; Piette et al., 2012; Levitan et al., 2019; Mukherjee et al., 2019) and respond to visual (Ifuku et al., 2006; Vincis and Fontanini, 2016), auditory (Samuelsen et al., 2012, 2013; Gardner and Fontanini, 2014), somatosensory (Kadohisa et al., 2005; Bouaichi et al., 2023), and both orthonasal and retronasal olfactory stimuli (Maier, 2017; Samuelsen and Fontanini, 2017). Despite these advancements in understanding the coding properties of the gustatory cortex, there remains a notable gap in our comprehension: How do these neurons represent the qualities inherent to the consummatory experience, specifically the complex chemosensory properties of odor–taste mixtures?

In other multisensory areas, temporal and spatial overlapping signals from different sensory systems produce nonlinear cross-modal responses that are enhanced or suppressed relative to the greatest unimodal response (Barraclough et al., 2005; Sugihara et al., 2006; Kayser et al., 2010; Ohshiro et al., 2011, 2017; Iurilli et al., 2012; Meijer et al., 2017; Fredericksen and Samuelsen, 2022; Idris et al., 2023). These nonlinear multisensory interactions are thought to enhance the reliability of sensory representations to better estimate features of the external environment (Kayser et al., 2010; Meijer et al., 2017). Based on this framework, we hypothesized that the convergence of odor and taste signals onto individual neurons in the gustatory cortex would produce nonlinear interactions, leading to distinct odor–taste mixture responses and enhancing the representation of the identity of intraoral chemosensory signals.

To determine whether neurons in the gustatory cortex represent odor–taste mixtures as different from their components, we recorded single-unit activity in behaving rats during the intraoral delivery of three stimulus categories: water and individual odors, individual tastes, and odor–taste mixtures. Our results demonstrate that chemoselective neurons in the gustatory cortex respond broadly across intraoral chemosensory stimuli with time-varying multiphasic changes in activity. Significantly, a subset of these chemoselective neurons exhibit time-dependent nonlinear responses to odor–taste mixtures, representing the identity of mixtures and their individual components. These findings are consistent with the gustatory cortex being a key component of the network processing unimodal and multimodal chemosensory information important to ingestive behaviors.

Materials and Methods

Experimental subjects

All procedures were performed in accordance with the university, state, and federal regulations regarding research animals and were approved by the University of Louisville Institutional Animal Care and Use Committee. Female Long–Evans rats (∼250–350 g, Charles Rivers Laboratories) were single-housed and maintained on a 12 h light/dark cycle with ad libitum access to food and distilled water unless specified otherwise.

Electrode implantation surgery

Rats were anesthetized with an intraperitoneal injection of a ketamine/xylazine/acepromazine mixture (KXA; 100, 5.2, and 1 mg/kg). Surgical levels of anesthesia were maintained with supplemental doses (30% of the induction dose) when required. Once a surgical level of anesthesia was reached, the scalp was shaved, and the rat was placed into a stereotaxic frame. Ophthalmic ointment was placed on the eyes, and the scalp was swabbed with a povidone–iodine solution followed by a 70% ethanol solution. A midline incision was made, and the skull was cleaned with a 3% hydrogen peroxide solution. Cranial holes were drilled for the placement of seven anchoring screws (Micro Fasteners, SMPPS0002). Six of the seven rats (one unilateral implant into the left gustatory cortex) had bilateral craniotomies performed above the gustatory cortex (AP, 1.4 mm; ML, ±5 mm from the bregma) to implant microdrivable bundles of sixteen 25 µm formvar-coated nichrome wires (A-M Systems, #761500) 0.5 mm dorsal to each gustatory cortex (4 mm from dura). Ground wires were secured to multiple anchoring screws. Intraoral cannulas (IOCs) were bilaterally inserted to allow for the delivery of solutions directly into the oral cavity. All implants and a head-bolt (for head restraint) were cemented to the skull with dental acrylic. Rats were allowed a recovery period of 7–10 d before beginning water restriction and behavioral training. Electrode placements were histologically verified using standard procedures (Fig. 1A).

Figure 1. — Representative image, electrode locations, and single-unit recording. A, Left, Example histological section showing the final electrode position (black asterisk) in the gustatory cortex. Right, Schematic summary of the reconstructed electrode paths in seven rats. The brown dashed lines correspond to the dorsoventral range of each of the left drivable electrode bundles, and the green dashed lines represent the right electrode bundles. Note that one rat only had a unilateral electrode bundle in the left gustatory cortex. AID, dorsal agranular insular cortex; AIV, ventral agranular insular cortex; DI, dysgranular insular cortex; GI, granular insular cortex; PIR, piriform cortex; S1, somatosensory cortex. B, Left, Representative single-unit recording in the gustatory cortex showing the principal component (PC) analysis of waveform shapes of two individual neurons. Middle, Average single-unit spike waveforms of the two neurons (blue and orange) and the noise (gray). Right, The two neurons’ interspike interval (ISI) distributions for the isolated waveforms. Inset, Zoom-in from 0 to 10 ms. The red dashed line indicates 1 ms ISI. C, Outline of the intraoral stimulus delivery paradigm. Schematic of a rat head illustrating the delivery of intraoral stimuli. The red dashed line shows the retronasal route via the oropharynx. After surgery, rats were limited to 1 h of water daily in the home cage and trained to remain calm in a head-restrained position for liquid delivery via an intraoral cannula. Before the initial recording session, for 3 d, they had 1 h access to two bottles in their home cage: one with isoamyl acetate–sucrose and the other with benzaldehyde–citric acid. Each recording session started with a 20 ± 5 s intertrial interval, and then ∼25–30 µl of 1 of 12 stimuli was delivered intraorally, followed by a ∼40 µl water rinse after 5 s. Stimuli were presented in a pseudorandom order, with each of the twelve delivered before any repetition. All stimuli were delivered 10 times, totaling 120 trials.

Stimulus delivery and recording procedure

Following recovery from surgery, rats began a water restriction regime where they received access to a bottle containing distilled water for 1 h each day in their home cage. To reduce neophobia and limit possible variability across sessions related to novel odor–taste experience, rats were given 1 h access in their home cage for three consecutive days to two bottles containing two different odor–taste mixtures: a palatable mixture of 0.01% isoamyl acetate and 100 mM sucrose and an unpalatable mixture of 0.01% benzaldehyde and 200 mM citric acid. Next, rats were trained to wait calmly in a head-restrained position for the intraoral delivery of liquids through the IOCs. Studies have shown that neurons in the gustatory cortex respond to tactile stimulation of the mouth and tongue (Yamamoto et al., 1981; Wang and Ogawa, 2002). Therefore, intraoral delivery aimed to ensure that all stimuli would share similar somatosensory and attentional attributes and facilitated the detection of odorized stimuli via retronasal olfaction, an essential aspect of flavor perception (Verhagen and Engelen, 2006; Prescott, 2012). All stimuli were mixed with distilled water and delivered via manifolds of polyimide tubes placed in the IOCs. Stimuli included distilled water, tastes (100 mM sucrose, 100 mM NaCl, 200 mM citric acid, and 1 mM quinine), odors (0.01% isoamyl acetate, 0.01% benzaldehyde, and 0.01% methyl valerate), the previously experienced odor–taste mixtures (isoamyl acetate–sucrose and benzaldehyde–citric acid), and mismatched pairings of those mixtures (isoamyl acetate–citric acid and benzaldehyde–sucrose). These odors have been used in previous studies investigating orally consumed odors (Aimé et al., 2007; Julliard et al., 2007; Gautam and Verhagen, 2010, 2012; Tong et al., 2011; Rebello et al., 2015; Samuelsen and Fontanini, 2017; Bamji-Stocke et al., 2018; Fredericksen et al., 2019; McQueen et al., 2020; Fredericksen and Samuelsen, 2022). At these concentrations, isoamyl acetate and benzaldehyde lack a gustatory component (Aimé et al., 2007; Gautam and Verhagen, 2010; Samuelsen and Fontanini, 2017). A trial began with an intertrial interval of 20 ± 5 s followed by the pseudorandom delivery of ∼25–30 µl (pressure infused via computer-controlled solenoid valves; opening time, ∼25 ms) of water, a single taste, a single odor, or an odor–taste mixture. Each stimulus delivery was followed 5 s later by a ∼40 µl distilled water rinse. Although intraoral delivery directly infuses chemosensory stimuli into the oral cavity, rats can still reject stimuli by not swallowing and allowing fluids to leak from the mouth. If a rat failed to consume an intraoral stimulus, the session was immediately aborted. Only trials where rats consumed all 12 stimuli were included in the analysis. All recording sessions consisted of 120 trials (i.e., 12 stimuli multiplied by 10 trials). After each recording session, electrode bundles were lowered ∼160 µm to obtain a new ensemble of neurons.

Electrophysiological recordings

Signals were sampled at 40 kHz, digitized, and bandpass filtered using the Plexon OmniPlex D system (Plexon, RRID:SCR_014803). Single units were isolated offline using a combination of template algorithms, cluster cutting, and examination of interspike interval (ISI) plots using Offline Sorter (Plexon, Offline Sorter; RRID:SCR_000012). Single units were required to have interspike intervals longer than the biological constraints of a neuronal refractory period (Fig. 1B; >1 ms; 0% refractory period violations). Data analysis was performed using NeuroExplorer (NexTechnologies; RRID:SCR_001818) and custom-written scripts in MATLAB (MathWorks, RRID:SCR_001622).

Analysis of single units

For each neuron, single-trial activity and peristimulus time histograms (PSTHs) were aligned to the stimulus presentation through the IOCs. Responses to chemosensory stimuli were evaluated by analyzing changes in firing rates as in previous studies (Jezzini et al., 2013; Samuelsen et al., 2013; Gardner and Fontanini, 2014; Liu and Fontanini, 2015; Samuelsen and Fontanini, 2017; Levitan et al., 2019; Bouaichi and Vincis, 2020; Fredericksen and Samuelsen, 2022). Neurons were defined as “chemoselective” when two criteria were satisfied: (1) stimulus-evoked activity significantly differed from baseline for at least one stimulus, and (2) there was a significant difference in the activity evoked by the twelve intraoral stimuli (Fredericksen and Samuelsen, 2022). A one-sample Kolmogorov–Smirnov test found that the spiking activity of neurons in the gustatory cortex was not normally distributed. Therefore, a significant difference from baseline for each stimulus was established using a nonparametric Wilcoxon rank-sum comparison between a 2 s baseline window and each 200 ms bin following stimulus delivery (5 s poststimulus) with correction for family-wise error (two consecutive significant bins, p < 0.05; Gardner and Fontanini, 2014; Samuelsen and Fontanini, 2017; Bouaichi and Vincis, 2020; Fredericksen and Samuelsen, 2022).

Since a nonparametric statistic is not available to determine significant interactions between chemosensory-evoked activity and time, a two-way ANOVA (stimulus × time; Jezzini et al., 2013; Samuelsen et al., 2013; Gardner and Fontanini, 2014; Liu and Fontanini, 2015; Samuelsen and Fontanini, 2017; Levitan et al., 2019; Bouaichi and Vincis, 2020; Fredericksen and Samuelsen, 2022) was used to determine differences in the magnitude and time course of the chemosensory-evoked activity across the 12 stimuli (200 ms bins from 0 to 5 s after stimulus delivery) with a conservative α (p < 0.01). A neuron was considered to respond differently among the intraoral stimuli when the stimulus main effect or the interaction term (stimulus × time) had a value of p < 0.01. The distribution of responses and tuning of the chemoselective population were compared using a X² test (p < 0.05) with post hoc comparisons performed using Fisher's exact test with Dunn–Sidak correction for family-wise error (Shan and Gerstenberger, 2017).

Area under the receiver operating characteristic normalization method

To control for potential confounds stemming from variations in baseline and evoked firing rates across neurons, we normalized stimulus-evoked activity to its baseline levels using the area under the receiver operating characteristic curve (auROC) method when comparing responses by groups of neurons (Cohen et al., 2012; Jezzini et al., 2013; Gardner and Fontanini, 2014; Liu and Fontanini, 2015; Vincis and Fontanini, 2016; Samuelsen and Fontanini, 2017; Bouaichi and Vincis, 2020; Fredericksen and Samuelsen, 2022). This method normalizes each neuron's stimulus-evoked activity to its baseline activity, using a scale of 0–1. In this scale, 0.5 represents the median equivalence of the baseline activity. The auROC values represent the probability that the spike counts in each 200 ms bin are significantly greater than the spike counts during the baseline period (−2 to 0 s). A score of 1 indicates that all values in the tested bin are greater than the baseline, whereas a score of 0 indicates that all values are lesser than the baseline. Therefore, a value >0.5 indicates an excited response, whereas a value <0.5 indicates a suppressed response. Population PSTHs were generated by averaging the auROC-normalized responses to a given stimulus for each neuron in the observed population. Comparisons of the auROC-normalized population activity were performed using the Friedman test (p < 0.05).

Response onset and latency

To investigate the temporal characteristics of chemosensory-evoked activity, we determined the time window in which activity significantly differed from baseline. This was achieved using a sliding window of 100 ms, advanced in 20 ms increments, until the firing rate reached a level 1.96 standard deviations (corresponding to a 95% confidence level) above or below the average baseline firing rate (measured 2 s before stimulus delivery; Bouaichi and Vincis, 2020; Fredericksen and Samuelsen, 2022). The response latency—the time from stimulus delivery to the first significant deviation from baseline—was captured at the trailing edge of the first significant 20 ms bin. The response duration, representing the total time that the firing rate was significantly different from baseline, was calculated as the aggregate number of 20 ms bins that were either significantly above or below the average baseline firing rate. Comparisons of response latency and response duration across stimulus categories were performed using the Kruskal–Wallis test, with post hoc corrections using Tukey’s HSD test (p < 0.05).

Population decoding analysis

Population decoding analyses were conducted using an open-source pattern classifier algorithm (for a detailed description of the Neural Decoding Toolbox, see Meyers, 2013). This approach uses the firing rate patterns across a neuronal population to predict which stimulus was delivered during a specific trial. The classifier's accuracy provides insights into how the gustatory cortex represents intraoral stimuli and processes chemosensory information over time. For each neuronal subpopulation, a matrix of firing rates was constructed using the spike times of each neuron from 2 s before to 5 s after stimulus delivery. This matrix was aligned to the stimulus onset, compiled into 50 ms bins, and normalized to the Z-scores. The firing rate matrices included various classifications: (1) all 12 stimuli, (2) the four tastes, (3) the three odors and water, (4) the four odor–taste mixtures, and (5) the eight stimuli either presented as an odor–taste mixture or as an individual odor or taste component.

The matrix activity was partitioned into ten sets, nine of which served as “training sets” for the classifier algorithm to “learn” the relationship between the population's neural activity patterns and the different stimuli. One “testing set” was used to assess the algorithm's ability to predict which stimulus was delivered based on the neural activity pattern used to train the classifier. A max correlation coefficient classifier was used to assess stimulus-related information represented by the population activity. The classifier calculates the correlation coefficient between a test trial and each training set's stimulus templates; the template with the highest correlation coefficient is chosen as the predicted stimulus. To determine the classification accuracy, this procedure was repeated 10 times, each time using different testing and training sets. Classification accuracy was defined as the fraction of trials in each bin where the classifier correctly predicted the stimulus. Comparisons of the classification accuracy between neuronal populations were performed using a permutation test (p < 0.05; Ojala and Garriga, 2010).

A confusion matrix serves as a visual representation of the decoding performance for each stimulus, where columns indicate the actual stimulus, while rows signify the predicted stimulus. White squares denote classification accuracy less than chance, whereas darker hues signify better performance. The red diagonal squares represent the proportion of trials where the classifier correctly matched the predicted stimulus to its actual counterpart (i.e., predicted stimulus). Excluding these red diagonal squares, the cyan dashed boxes indicate the proportion of trials where the classifier incorrectly assigned the predicted stimulus to a stimulus category that includes the true stimulus (i.e., predicted category). The squares outside of the cyan dashed box represent the proportion of trials where the predicted stimulus fell outside of the true stimulus’ category, signifying false predictions.

The average decoding performance by category shows the average proportion of trials within each stimulus category—namely, stimuli containing sucrose (top row), odors (middle row), and citric acid (bottom row). In this matrix, the left column represents the proportion of trials where the classifier's prediction matched the true stimulus, the middle column shows the proportion of trials where the prediction matched a stimulus within the same category, and the right column indicates the proportion of trials where the classifier's prediction was outside the category. Comparisons between the proportion of trials where the prediction matched the true stimulus, and those where the prediction fell within the same category were conducted using the Wilcoxon rank-sum test, with a significance level set at p < 0.05.

Mixture-component difference analysis

The MCD is calculated as the difference in a neuron's firing rate (−2 to 5 s; 200 ms bins) between its response to an odor–taste mixture (e.g., isoamyl acetate–sucrose) and its response to either the odor component alone (e.g., isoamyl acetate) or the taste component alone (e.g., sucrose; Fredericksen and Samuelsen, 2022). A positive MCD score indicates that the mixture-evoked activity was greater than the component-evoked activity, while a negative MCD score indicates that the component-evoked activity was greater than the mixture-evoked activity. This analysis examined eight mixture-stimulus combinations (four mixture-odor and four mixture-taste) for each of the 215 chemoselective neurons, totaling 1,720 mixture-component difference (MCD) responses.

Unlike the activity triggered by intraoral stimulus delivery, baseline activity is not directly related to specific events. This variability in baseline activity can artificially inflate individual baseline MCD scores. Therefore, we calculated the mean and standard deviation of the baseline MCD scores for the four mixture-taste differences and the four mixture-odor differences for each chemoselective neuron. A mixture-taste response was deemed significant if the evoked mixture-taste MCD score exceeded the mean of the four mixture-taste baseline MCD scores ± 6 times the standard deviation. Mixture-odor responses were determined in a similar manner, using the mean of the four mixture-odor baseline MCD scores ± 6 times the standard deviation. Given that multiple time bins could yield significant MCD scores, the average of all significant bins was used to determine whether each MCD response was due to a greater mixture (positive MCD score) or component response (negative MCD score). To calculate the average MCD time course, each MCD score was first normalized to baseline using z-score, and then the absolute difference in the MCD score was used to account for the differences between mixtures and components, irrespective of whether the mixture or component had the greater firing rate. Significant differences from baseline and differences between populations in the normalized average MCD time course were assessed using the Wilcoxon rank-sum test with correction for family-wise error (two consecutive significant bins, p < 0.05).

Cross-modal response additivity and response polarity

For each of the four odor–taste mixtures, we calculated the response additivity and response polarity for each time bin that had a significant MCD score (Ohshiro et al., 2011). Cross-modal response additivity illustrates whether the activity evoked by the mixture is greater than (i.e., superadditive) or lesser than (i.e., subadditive) the combined responses to its individual components. It was determined using the formula as follows: [Mix − (Taste + Odor)]/[Mix + (Taste + Odor)] × 100. For this measure, the sum of the activity evoked by the unimodal components is subtracted from the mixture-evoked firing rate and divided by the total activity. Positive scores represent superadditive responses, and negative values signify subadditive responses. Cross-modal response polarity shows whether the activity evoked by the mixture is greater than or lesser than the greatest unimodal component response. It was determined using the formula as follows: [Mix − max (Taste, Odor)]/[Mix + max (Taste, Odor)] × 100. For this measure, the firing rate of the best unimodal response is subtracted from the mixture-evoked activity and divided by the combined activity to the mixture and best unimodal stimulus. Positive scores indicate cross-modal enhancement, and negative scores indicate cross-modal suppression. Fisher's exact tests were performed to compare the overall distributions of superadditive and subadditive responses, as well as enhanced and suppressed responses. Comparisons across the four odor–taste mixtures regarding response additivity (i.e., the proportion of superadditive vs subadditive responses) and response polarity (i.e., the proportion of enhanced vs suppressed responses) were conducted using X² tests (p < 0.05).

Palatability index analysis

A palatability index (PI) was used to evaluate whether the activity of neurons in the gustatory cortex represents the palatability-related features of tastes (Fontanini et al., 2009; Piette et al., 2012; Jezzini et al., 2013; Liu and Fontanini, 2015; Samuelsen and Fontanini, 2017; Bouaichi and Vincis, 2020). This analysis quantified differences in activity between tastes with similar hedonic values (sucrose/NaCl, citric acid/quinine) and tastes with opposite hedonic values (sucrose/quinine, sucrose/citric acid, NaCl/quinine, NaCl/citric acid). To control for differences in firing rates across the population of chemoselective neurons, the auROC-normalized activity (−2 to 5 s, 200 ms bins) was used to estimate the differences between taste pairs. The PI score was defined as the difference in the absolute value of the log-likelihood ratio of the auROC-normalized firing rate for taste responses with opposite (<|LR|>_opposite) and similar (<|LR|>_same) hedonic values. The PI was defined as follows (<|LR|>_opposite − <|LR|>_same), where:

\begin{aligned} < | LR | >_{same} = 0 .5 X (| In \frac{sucrose}{NaCl} | + | In \frac{quinine}{citric acid} |) \\ < | LR | >_{opposite} = \\ 0 .25 X (| In \frac{sucrose}{quinine} | + | In \frac{sucrose}{citric acid} | + | In \frac{NaCl}{quinine} | + | In \frac{NaCl}{citric acid} |) \end{aligned}

A positive PI score indicates that a neuron responds similarly to tastes with similar palatability and differently to stimuli with opposite palatability. A chemoselective neuron was deemed palatability-related when the evoked PI score was positive and exceeded the mean + 6 times the standard deviation of the baseline (Bouaichi and Vincis, 2020; Fredericksen and Samuelsen, 2022). Significant changes from baseline and comparisons between populations in the average PI score time course were determined using a Wilcoxon rank-sum test with correction for family-wise error (two consecutive significant bins, p < 0.05).

Histology

After recordings were completed, rats were anesthetized with a ketamine/xylazine/acepromazine mixture (100, 5.2, and 1 mg/kg), and DC (7 µA for 7 s) was applied to mark the electrode locations (Fig. 1A). Rats were transcardially perfused with cold phosphate buffer solution followed by 4% paraformaldehyde (PFA). Brains were extracted, postfixed in 4% PFA, and incubated in 30% sucrose. Sections were cut 70 µm thick using a cryostat, mounted, and stained with cresyl violet.

Experimental design and statistical analysis

Consistent with prior head-fixed recording experiments (Jones et al., 2007; Fontanini et al., 2009; Samuelsen et al., 2012, 2013; Gardner and Fontanini, 2014; Vincis and Fontanini, 2016; Samuelsen and Fontanini, 2017; Fredericksen and Samuelsen, 2022), only adult female rats were used because the size and strength of adult male rats significantly increase the risk of catastrophic headcap failure. All chemosensory stimuli were delivered in a pseudorandom order by custom-written MATLAB (MathWorks) scripts. The experimenters had no control over the sequence of stimulus delivery. Statistical analyses were performed using GraphPad Prism (GraphPad Software) and MATLAB (MathWorks), including population decoding analyses conducted through the Neural Decoding Toolbox (Meyers, 2013). No statistical methods were used to predetermine sample sizes, but the numbers of recorded neurons and animals in this study are comparable to those reported in previous studies.

Results

Previous electrophysiological studies have shown that neurons in the gustatory cortex represent the identity of individual tastes and odors (Maier, 2017; Samuelsen and Fontanini, 2017), but it is unclear how odor–taste mixtures are processed by the gustatory cortex. To this aim, we recorded single-unit activity in the gustatory cortex of behaving rats during the intraoral delivery of distilled water, individual odors, individual tastes, and odor–taste mixtures. Figure 1 shows a representative example and a schematic illustration of the dorsoventral range of recording electrodes in the gustatory cortex of each animal. A total of 273 single neurons were recorded from seven rats across 30 sessions (4.3 ± 0.7 sessions per rat) with an average yield of 9.1 ± 0.9 neurons per session.

Neurons in the gustatory cortex are broadly responsive to intraoral chemosensory signals

As an initial step in evaluating how neurons in the gustatory cortex represent odor–taste mixtures, we identified the population of neurons that responded differently to various odors, tastes, and odor–taste mixtures, termed “chemoselective” neurons. Given that neurons in the gustatory cortex can respond to tactile stimulation of the mouth and tongue (Yamamoto et al., 1981; Wang and Ogawa, 2002), we required that chemoselective neurons exhibit a significant change in activity compared with baseline for at least one stimulus and a significant difference in the activity evoked by the 12 intraoral stimuli (see Materials and Methods for details). We imposed these stringent criteria to mitigate potential confounding effects related to somatosensation or attention, rather than chemosensory-related activity. Of the neurons recorded from the gustatory cortex, we found that 78.7% (215/273) met both criteria, and we focused our analyses on this chemoselective population.

To further understand the processing of unimodal and multimodal chemosensory signals by the gustatory cortex, we analyzed the responses of this chemoselective population to water, odors, tastes, and odor–taste mixtures (Fig. 2). Visual inspection of representative neurons and population activity indicated that responses varied among the chemosensory categories, including the four tastes (Fig. 2A), water and the three odors (Fig. 2B), and the four odor–taste mixtures (Fig. 2C). A comparison of the auROC-normalized population activity revealed an overall difference in the responses evoked by the 12 intraoral stimuli (Friedman's test, X²₁₁ = 352.31, p < 0.001). Therefore, we next sought to determine whether the population responses differed within the three chemosensory stimulus categories. The chemoselective population's responses differed across the four tastes (Fig. 2A, Friedman's test, X²₃ = 53.19, p < 0.001), the three odors and water (Fig. 2B, Friedman's test, X²₃ = 32.51, p < 0.001), and the four odor–taste mixtures (Fig. 2C, Friedman's test, X²₃ = 21.60, p < 0.001).

Figure 2. — Neurons in the gustatory cortex represent chemosensory signals originating in the mouth. A–C, Left, Two representative chemoselective neurons firing rate raster plots and peristimulus time histograms (PSTHs) in response to intraoral delivery (time = 0, vertical dashed lines) of (A) the four tastes [sucrose (S, blue), NaCl (Na, magenta), citric acid (CA, yellow), quinine (Q, green)], (B) water and the three odors [isoamyl acetate (IA, red), benzaldehyde (B, cyan), methyl valerate (MV, black), water (W, gray)], and (C) the four odor–taste mixtures [isoamyl acetate–sucrose (IA-S, purple), benzaldehyde–sucrose (B-S, peach), benzaldehyde–citric acid (B-CA, light blue), isoamyl acetate–citric acid (IA-CA, light green)]. Insets, Average action potential waveforms for each neuron. Right, The chemoselective population's (n = 215) normalized response (auROC, area under the receiver operating characteristic curve) to (A) tastes, (B) odors and water, and (C) odor–taste mixtures. The vertical dashed lines indicate the stimulus delivery (time = 0). The horizontal dashed lines indicate the baseline. ***D–F***, Distribution of the number of chemoselective neurons responding to (D) the four tastes, (E) water and the three odors, and (F) the four odor–taste mixtures. ***G–I***, Tuning profiles within each stimulus category show the proportion of chemoselective neurons that did not respond, responded to a single stimulus, or responded to multiple stimuli for (G) tastes, (H) odors, and (I) odor–taste mixtures. ***p < 0.001.

Next, we examined the proportions of chemoselective neurons responding to the various intraoral stimuli. There was an overall significant difference in the proportion of neurons responding to the various intraoral stimuli (X²₁₁ = 25.66, p = 0.007), with the majority of chemoselective neurons (75.8%, 163/215) responding to stimuli from all three categories. However, within-category analyses showed no difference in response distributions for the different tastes (Fig. 2D, X²₃ = 0.762, p = 0.857), odors and water (Fig. 2E, X²₃ = 4.02, p = 0.260), or odor–taste mixtures (Fig. 2F, X²₃ = 0.501, p = 0.919).

We then assessed the tuning profiles of chemoselective neurons within each stimulus category to determine the proportion of neurons that responded to only a single stimulus (i.e., sparsely tuned) or multiple stimuli (i.e., broadly tuned). Of the 215 chemoselective neurons, 89.8% (193/215) responded to at least one odor–taste mixture, 89.0% (191/215) to at least one taste (89.0%, 191/215), and 79.5% (171/215) to at least one odor or water (79.5%, 171/215). For the taste category (Fig. 2G), the proportion of chemoselective neurons that responded to multiple taste stimuli (X²₂ = 214.6, p < 0.0001, 71.6%, 154/215) was significantly greater than the proportion that responded to only a single taste (17.2%, 37/215; Fisher's exact test, p < 0.001) or did not respond to tastes (11.2%, 24/215; Fisher's exact test, p < 0.001). Within the odor and water category (Fig. 2H), the proportion of chemoselective neurons that responded to multiple odor stimuli (X²₂ = 122.5, p < 0.0001, 62.3%, 134/215) was significantly greater than the proportion that responded to only a single odor or water (17.2%, 37/215; Fisher's exact test, p < 0.001) or did not respond to tastes (20.5%, 44/215; Fisher's exact test, p < 0.001). Similarly, in the odor–taste mixture category (Fig. 2I), a significantly greater proportion of neurons responded to multiple odor–taste mixtures (X²₂ = 275.6, p < 0.001, 77.2%, 166/215), compared with those responding to just a single odor–taste mixture (12.6%, 27/215; Fisher's exact test, p < 0.001) or not responding to any mixture (10.2%, 22/215; Fisher's exact test, p < 0.001). When we evaluated responses across all four stimuli within each category, we found that the neurons were significantly more likely to respond to all four stimuli than to only one, only two, or only three stimuli (tastes: Χ²₃ = 40.78, p < 0.001; odors and water: Χ²₃ = 29.92, p < 0.001; mixtures: Χ²₃ = 76.07, p < 0.001). In pairwise comparisons using Fisher's exact test, this held true for all categories: tastes, odors and water, and mixtures (Table 1; p < 0.001 for all comparisons).

Table 1.

The proportion of neurons responding to only one, two, three, or all four stimuli

	Only 1	Only 2	Only 3	All 4
Odors and water	37/215 (17.2%)	29/215 (13.5%)	35/215 (16.3%)	70/215 (32.6%)***
Tastes	37/215 (17.2%)	32/215 (14.9%)	41/215 (19.1%)	81/215 (37.7%)***
Odor–taste mixtures	27/215 (12.6%)	35/215 (16.3%)	37/215 (17.2%)	94/215 (43.7%)***

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^***

p < 0.001.

Next, we examined the temporal characteristics evoked by the different categories of chemosensory stimuli by determining their response latencies (i.e., time from stimulus delivery to the first significant difference from baseline) and response durations (i.e., the total amount of time the response was significantly different from baseline). There was no significant difference between the stimulus categories in response latency (tastes: 256.5 ± 21.0 ms; odors and water: 189.6 ± 20.9 ms; odor–taste mixtures: 240.2 ± 18.0 ms; Kruskal–Wallis, H₍₂₎ = 1.61, p = 0.448), but there was a significant difference in the duration of activity among stimulus categories (Kruskal–Wallis, H₍₂₎ = 18.43, p < 0.001). Tukey's HSD test for multiple comparisons showed that the response duration evoked by odor stimuli (1,597.2 ± 58.6 ms) was significantly shorter than the response duration evoked by taste stimuli (1,859.3 ± 53.9 ms; p = 0.0218) or odor–taste mixtures (1,977.7 ± 55.9 ms; p < 0.001).

These results indicate that most neurons in the gustatory cortex respond to the intraoral delivery of chemosensory stimuli, are primarily broadly tuned within chemosensory categories, and respond differently to unimodal and multimodal chemosensory signals. These results indicate that the gustatory cortex engages in processing unimodal and multimodal chemosensory signals originating from the mouth.

The population activity of chemoselective neurons reliably represents gustatory signals

While individual neurons represent specific features of chemosensory signals, neural networks integrate and process this information to influence behavior. We hypothesized that the heterogeneity displayed by the population of chemoselective neurons enables accurate representation of both odor–taste mixtures and their individual components. We performed a population decoding analysis (see Materials and Methods for details) to examine the extent to which firing patterns of chemoselective neurons in the gustatory cortex accurately encode stimulus identity over time (Jezzini et al., 2013; Meyers, 2013; Liu and Fontanini, 2015; Bouaichi and Vincis, 2020). We began by examining how well the population activity of chemoselective neurons (n = 215) and nonchemoselective neurons (n = 53) represented the 12 intraoral stimuli during the 5 s following stimulus delivery. Figure 3A illustrates the decoding performance of the two populations over time. The classification accuracy of the chemoselective population exceeded the chance level (8.3%) and significantly differed from that of the nonchemoselective population, beginning 100 ms after stimulus delivery and continuing for the entire 5 s window (permutation test, p < 0.05). Whereas the decoding performance of the nonchemoselective neurons indicated that their population activity poorly represents information related to the intraoral stimuli. These results are consistent with the population of chemoselective neurons encoding the identity of unimodal and multimodal chemosensory signals originating from the mouth.

The confusion matrices in Figure 3B illustrate the average classification performances of the two populations for each stimulus over the 5 s period following intraoral delivery. Within these matrices, the white squares depict classification accuracy lower than chance (8.3%), while the darker shades indicate higher accuracy. The diagonal red squares (i.e., predicted stimulus) represent the proportion of trials for which the classifier correctly assigned the predicted stimulus (rows) to its true stimulus (columns). The cyan dashed boxes, excluding the red diagonal squares (i.e., predicted category), represent the proportion of trials in which the classifier incorrectly assigned the predicted stimulus to a category containing the true stimulus. The squares outside of the cyan dashed box represent the proportion of trials the predicted stimulus was outside of the stimulus category (i.e., false predictions).

Overall, the classifier correctly predicted the true stimulus (diagonal red squares, predicted stimulus); however, it often misclassified stimuli within specific categories (cyan dashed boxes, predicted category). For the chemoselective population, the proportion of trials where the predicted stimulus matched the true stimulus (red squares; 31.43% ± 6.0%) was significantly greater than the proportion of trials where the classifier erroneously assigned it to a stimulus category (cyan dashed boxes: 16.71% ± 2.2%, Wilcoxon rank-sum, Z = 2.06, p = 0.039). In contrast, the classification accuracy of the nonchemoselective population was similarly poor for both the predicted stimulus (9.91% ± 1.2%) and the predicted category (10.21% ± 0.6%, Wilcoxon rank-sum, Z = −0.131, p = 0.896).

While the population activity of the chemoselective neurons effectively represented the 12 intraoral stimuli, we observed that the classifier frequently misidentified the predicted stimulus, instead categorizing it within groups of chemosensory stimuli that were also presented as odor–taste mixtures (e.g., sucrose-containing stimuli: sucrose, isoamyl acetate–sucrose, and benzaldehyde–sucrose; odors: isoamyl acetate and benzaldehyde; citric acid–containing stimuli: citric acid, benzaldehyde–citric acid, and isoamyl acetate–citric acid). Given this, we next examined how well the population activity of chemoselective and nonchemoselective neurons decoded stimuli within the taste, odor, and odor–taste mixture chemosensory categories.

For taste stimuli (Fig. 3C), the decoding performance of the chemoselective population displayed an early onset, with a classification accuracy above chance that significantly outperformed the nonchemoselective neurons starting just 200 ms after stimulus delivery (permutation test, p < 0.05). This significant decoding performance continued throughout the remaining observation window, as shown by the confusion matrix in Figure 3D. This confusion matrix, illustrating the average performance over the 5 s window, underscores how effectively the neural activity of the chemoselective population represents taste stimuli. Conversely, the chemoselective population poorly represented odors and water (Fig. 3E). The decoding performance of the chemoselective neurons only briefly surpassed chance levels and the nonchemoselective population between 0.7–0.95 and 3.25–4.5 s. The corresponding confusion matrix (Fig. 3F) revealed that the classifier essentially performed at random when it came to three odor stimuli, with only water representation being above chance. When decoding odor–taste mixtures (Fig. 3G), the chemoselective population exhibited a rapid onset, with a classification accuracy above chance and significantly differing from the nonchemoselective neurons 50 ms poststimulus delivery (permutation test, p < 0.05). The significant difference between the two populations was briefly disrupted ∼1 s after stimulus delivery before returning to significance from 2 to 4.5 s. However, the confusion matrix for this 5 s window (Fig. 3H) indicated that the classifier frequently misattributed the stimulus to mixtures containing the same taste component, essentially categorizing odor–taste mixtures as containing either sucrose or citric acid. These findings indicate that the total population of chemoselective neurons in the gustatory cortex reliably encodes gustatory signals over time, including categorizing odor–taste mixtures based on their common taste component.

Most chemoselective neurons respond to mixtures differently from one of their components

Prior research has shown that a subset of neurons in the gustatory cortex respond to both individual odor and individual taste stimuli (Samuelsen and Fontanini, 2017). Here, visual inspection of raster plots and PSTHs indicated that chemoselective neurons responded differently to odor–taste mixtures compared with their individual odor or taste components (Fig. 4A). A core concept in multisensory integration research is discerning whether a multimodal response is simply an additive result of its individual components (i.e., linear) or if it represents a distinct interaction arising from the combined stimulus (i.e., nonlinear). To assess this, we performed a mixture-component difference (MCD) analysis (see Materials and Methods for more details) that quantified the difference in firing rate across time between the neuronal response to an odor–taste mixture and its individual odor or taste component. A response was considered significantly different when the evoked MCD score exceeded the mean of the baseline MCD scores ± 6 times the standard deviation. Figure 4B shows the representative neuron's MCD scores for the difference between the activity evoked by the four odor–taste mixtures and two tastes (left) and the four odor–taste mixtures and two odors (right). Each MCD score represents the difference in firing rate evoked by an odor–taste mixture and one of its components, a positive MCD score signifies that the activity evoked by the mixture is greater than that evoked by the individual component (Fig. 4B, left), while a negative MCD score indicates that the activity evoked by the individual component was greater than that of the mixture (Fig. 4B, right).

Figure 4. — Most chemoselective neurons respond to mixtures differently than at least one of their components. A, Raster plots and PSTHs from a representative neuron illustrating the differences in activity evoked by four mixtures (right) and their individual taste and odor components (left). The vertical dashed lines indicate the stimulus delivery (time = 0). Inset, The average action potential waveform. B, The MCD scores for the responses by the representative neuron. Left, The mixture-taste MCD score is the difference between the activity evoked by the mixture (e.g., isoamyl acetate–sucrose) and the taste component alone (e.g., sucrose). Right, The mixture-odor MCD score is the difference between the activity evoked by the mixture (e.g., isoamyl acetate–sucrose) and the odor component alone (e.g., isoamyl acetate). The vertical dashed lines indicate the stimulus delivery (time = 0). The horizontal gray dashed lines indicate the significance threshold (average baseline mix-taste or mix-odor MCD score ± 6 times the standard deviation). C, Time course of the normalized absolute mixture-component difference score for the 617 significant MCD responses (green line) and the 1,103 non-MCD responses (black line) from 2 s before to 5 s after intraoral delivery (200 ms bins). The significant MCD responses differ from baseline beginning in the first 200 ms bin (black bar) after stimulus delivery, and the non-MCD responses never differ from baseline. The shaded area represents the SEM. D, A significantly greater proportion of chemoselective neurons in the gustatory cortex responded to a mixture differently than at least one of its components (MCD vs non-MCD).

The MCD analysis revealed that 35.9% (617 out of 1,720) of the odor–taste mixture responses differed from at least one of their components (Table 2). Of these, there was a significantly larger proportion of mixture-odor MCD responses (351/617, 56.9%) than mixture-taste MCD responses (266/617, 43.1%; Fisher's exact test, p < 0.001). Next, we determined whether each MCD response was due to greater mixture-evoked activity or greater component-evoked activity. We found that overall, significantly more MCD responses had higher mixture-evoked activity (332/617, 53.8%) than component-evoked activity (285/617, 46.2%; Fisher's exact test, p = 0.009). Within the mixture-taste MCD responses, there was no difference between the proportion with greater mixture-evoked activity (125/266; 47.0%) and those with greater taste-evoked activity (141/266; 53.0%; Fisher's exact test, p = 0.193). However, a distinct difference emerged among the mixture-odor MCD responses, where 59.0% (207/351) showed greater mixture-evoked activity compared with 41.0% (144/351) with greater odor-evoked activity (Fisher's exact test, p = 0.001). Together, these results show that responses to odor–taste mixtures were more likely to differ from the response to its odor than its taste component.

Table 2.

The proportion of neurons with mixture responses that differed from one of their components

	Isoamyl acetate–sucrose	Benzaldehyde–sucrose	Benzaldehyde–citric acid	Isoamyl acetate–citric acid
Mix-sucrose	72/215, 33.5%	61/215, 28.4%	-	-
Mix-citric acid	-	-	68/215, 31.6%	65/215, 30.2%
Mix-isoamyl acetate	76/215, 35.3%	-	-	106/215, 49.3%
Mix-benzaldehyde	-	70/215, 32.6%	99/215, 46.0%	-

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To examine the time course of MCD responses, we normalized each response to its baseline using a z-score transformation and calculated the mean absolute difference in MCD (−2 to 5 s; 200 ms bins) for the significant MCD responses and the non-MCD responses (Fig. 4C). We utilized the absolute value of the MCD score to account for differences between the odor–taste mixtures and their components, regardless of which had a greater firing rate. Our analysis showed that significant MCD responses differed from the non-MCD responses in the initial 200 ms bin. These responses peaked in the 0.5–1.5 s window and remained significantly above baseline for the entire time frame (Wilcoxon rank-sum test, two consecutive significant bins, p < 0.05). In contrast, the average absolute value of the non-MCD responses did not differ from the baseline (p > 0.05). This analysis suggested that the difference between mixtures and their unimodal component may be primarily represented ∼0.5–1.5 s after the stimulus delivery.

However, the broader multisensory literature reveals nuanced complexities in the relationship between unimodal and multimodal responses. Numerous studies show that nonlinear responses to multimodal stimuli manifest as a combination of cross-modal enhancement and suppression (Barraclough et al., 2005; Sugihara et al., 2006; Kayser et al., 2010; Ohshiro et al., 2011, 2017; Iurilli et al., 2012; Meijer et al., 2017; Fredericksen and Samuelsen, 2022; Idris et al., 2023). For example, some neurons respond to multimodal stimuli with enhanced activity relative to the sum of their response to both unimodal stimuli (i.e., superadditive), while other neurons respond to multimodal stimuli with activity lesser than the sum of the unimodal stimuli (i.e., subadditive). This suppression is thought to occur due to the dominant unimodal response being attenuated by the simultaneous presentation of the second, less effective stimulus (Ohshiro et al., 2011, 2017). These nonlinear multisensory interactions are thought to enhance the reliability of sensory representations to better estimate features of the external environment (Kayser et al., 2010; Meijer et al., 2017).

Building on these general insights from the literature, we turned our focus to the temporal dynamics of the MCD responses. As highlighted earlier, multiple time bins could yield significant MCD scores, with the relationship between the response to mixtures and their individual components varying over time. For example, Figure 5 illustrates the activity and MCD scores of a representative neuron in response to two odor–taste mixtures and their individual components. A subadditive response to a mixture of isoamyl acetate–sucrose is suppressed relative to the taste component (Fig. 5B, left, blue line) but is enhanced relative to its odor component (Fig. 5B, right, blue line). Furthermore, that same neuron's superadditive response to a mixture of isoamyl acetate–citric acid is initially enhanced relative to both its taste and odor component (Fig. 5B, red lines) but becomes suppressed relative to the odor component response over time (Fig. 5B right, red line).

To assess the variance in these nonlinear responses across odor–taste mixtures, we calculated “response additivity” and “response polarity” for every time bin with a significant MCD response (Ohshiro et al., 2011). “Response additivity” indicates whether the activity evoked by the mixture is greater than (i.e., superadditive) or lesser than (i.e., subadditive) the sum of the responses to its individual components. “Response polarity” shows whether the activity evoked by the mixture is greater than (i.e., enhanced) or lesser than (i.e., suppressed) the greatest unimodal component response. In our analysis of all time bins with significant MCD responses, most mixture responses were found to be subadditive (1,298 out of 1,683; 76.1%), being lesser than the total response evoked by the two components, compared with superadditive (385 out of 1,683; 22.9%, Fisher’s exact test p < 0.001). The response polarity analysis showed that a significantly greater proportion of mixture responses were suppressed (946 out of 1,683; 56.2%), as opposed to enhanced (737 out of 1,683; 43.8%, Fisher’s exact test p < 0.001), relative to the best component response. Overall, nonlinear responses were more likely to be subadditive and suppressed relative to their components.

Next, we explored if there were differences in response additivity and response polarity between the four odor–taste mixtures. Figure 5C illustrates the relationship between response additivity and response polarity for all significant MCD scores for each of the four odor–taste mixtures (Table 3). There was no difference between the four odor–taste mixtures in measures of response additivity (i.e., the proportion of superadditive vs subadditive responses, X²₃ = 6.75, p = 0.08) or response polarity (i.e., the proportion of enhanced vs suppressed responses, X²₃ = 6.186, p = 0.10). These findings highlight the nuances of multimodal processing by neurons in the gustatory cortex, unveiling consistency in the nonlinear responses evoked by mixtures.

Table 3.

MCD response additivity and response polarity for the four odor–taste mixtures

	Isoamyl acetate–sucrose	Benzaldehyde–sucrose	Benzaldehyde–citric acid	Isoamyl acetate–citric acid
Superadditive	72/303, 23.8%	72/277, 26.0%	139/569, 24.4%	102/534, 19.1%
Subadditive	231/303, 76.2%***	205/277, 74.0%***	430/569, 75.6%***	432/534, 80.9%***
Enhanced	121/303, 39.9%	138/277, 49.8%	250/569, 43.9%	228/534, 42.7%
Suppressed	182/303, 60.1%***	139/277, 50.2%	319/569, 56.1%***	306/534, 57.3%***

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^***

p < 0.001.

To assess the prevalence of nonlinear responses within the chemoselective population, we sought to identify the number of neurons responsible for the 617 significant MCD responses, noting that a single neuron could generate up to eight significant MCD responses when comparing four odor–taste mixtures to their respective odor and taste components. Our analysis revealed that 162 out of 215 chemoselective neurons (75.3%) accounted for these significant MCD responses (Fig. 4D). Among these, 86 neurons (53.1%, 86/162) showed responses to odor–taste mixtures different from both individual odor and taste responses. In contrast, 45 neurons (27.8%, 45/162) had mixture responses differing only from odor responses, and 31 (19.1%, 31/162) differed only from taste responses. To determine if the distribution of MCD responses differed across these groups, specifically among neurons with mixture responses distinct from both components as opposed to just one, we calculated the MCD responses per neuron. We observed a significant difference in the distribution of MCD responses across groups (Kruskal–Wallis, H₍₃₎ = 23.03, p < 0.001). Tukey's HSD revealed that neurons with mixture responses differing only from the taste component had fewer MCD responses (average 1.74 ± 0.17) compared with those differing from both odor (average 2.85 ± 0.11; p < 0.001) and taste components (average, 2.47 ± 0.12; p = 0.012) and those differing only from the odor component (average 2.36 ± 0.18; p = 0.092). Notably, half of the MCD-responsive neurons (50%, 81/162) responded differently to at least one specific mixture compared with both of its individual components. For example, 54.3% (44/81) of these neurons responded differently to the isoamyl acetate–sucrose mixture than to isoamyl acetate or sucrose alone. Similarly, 40.7% (33/81) did so for benzaldehyde–sucrose, 58.0% (47/81) for benzaldehyde–citric acid, and 56.8% (46/81) for isoamyl acetate–citric acid and their respective components. Together, these findings indicate that both mixture-odor and mixture-taste MCD responses are widely represented across the chemoselective neuronal population, with the majority of MCD neurons encoding differences between multiple mixtures and their components.

A subset of chemoselective neurons represents the palatability-related features of tastes

We next turned our attention to investigating another key aspect of sensory processing by the gustatory cortex, taste palatability. It is well established that the neural activity in the gustatory cortex represents both the chemical and palatability-related features of tastes (Katz et al., 2001; Fontanini and Katz, 2006; Piette et al., 2012; Jezzini et al., 2013; Levitan et al., 2019; Mukherjee et al., 2019). Previous studies have demonstrated that neurons in the gustatory cortex, which are responsive to both individual odors and tastes—particularly when never presented in combination—represent the palatability-related features of tastes (Samuelsen and Fontanini, 2017).

To determine if both MCD and non-MCD neurons represent the palatability-related features of tastes, we calculated a palatability index (PI; see Materials and Methods for details). This analysis quantified the differences in activity between tastes of similar palatability (sucrose/NaCl, citric acid/quinine) and tastes of opposite palatability (sucrose/quinine, sucrose/citric acid, NaCl/quinine, NaCl/citric acid). A chemoselective neuron was considered to represent taste palatability when it had a positive PI score (i.e., it responded similarly to tastes with similar hedonic value but differently to tastes with opposite hedonic value) and the evoked PI score exceeded the mean + 6 times the standard deviation of the baseline (Bouaichi and Vincis, 2020; Fredericksen and Samuelsen, 2022).

Our analysis revealed that 21.9% (47/215) of chemoselective neurons represented the palatability-related features of tastes (Fig. 6A,B). To explore the temporal evolution of palatability-related neural activity, we calculated the average PI score (−2 to 5 s; 200 ms bins) for the group of chemoselective neurons classified as palatability-related (n = 47) and those deemed nonpalatability-related (n = 168). Figure 6C shows that the mean PI score for palatability-related neurons started to significantly differ from the baseline at about 1.2 s, returned to baseline levels around 2.2 s, and then elevated significantly above baseline again starting from 3.2 s (Wilcoxon rank-sum test, two consecutive significant bins, p < 0.05). The mean PI score for the nonpalatability-related group never significantly differed from the baseline (p > 0.05). Notably, no significant differences were found between the MCD and non-MCD populations, either in terms of their mean PI scores (Wilcoxon rank-sum test, two consecutive significant bins, p > 0.05) or proportions (Fig. 6D; non-MCD: 17.0%, 9/53; MCD: 38/162, 23.5%; Fisher's exact test, p = 0.444). These findings indicate that the representation of the palatability-related features of tastes is distributed across the chemoselective population.

Figure 6. — Processing of taste palatability by chemoselective neurons in the gustatory cortex. A, Raster plots and auROC-normalized PSTHs from two neurons that represent the palatability-related features of tastes. The vertical dashed lines indicate the stimulus delivery (time = 0). The horizontal dashed lines indicate the baseline. Insets, Average action potential waveforms for each neuron. B, PI scores of the two representative neurons. The gray dashed lines indicate the significance threshold (baseline PI score + 6 times the standard deviation). C, Time course of the average palatability index (PI) score of the 47 palatability-related neurons (green line) and 168 nonpalatability-related neurons (black line) 2 s before to 5 s after intraoral delivery (200 ms bins). The response of the palatability-related population significantly differs from baseline from 1.2–1.6, 1.8–2.2, and 3.2–5 s (black bars) after stimulus delivery, while the average PI score of the nonpalatability population never differs from baseline. The vertical dashed line indicates the stimulus delivery (time = 0). The horizontal dashed line indicates the baseline. The shaded area represents the SEM. D, There was no difference between the non-MCD and MCD populations in either the proportion of neurons with significant PI scores (non-MCD: 17.0%, 9/53; MCD: 38/162, 23.5%; Fisher's exact test, p = 0.444) or mean PI score (Wilcoxon rank-sum, two consecutive significant bins, p > 0.05).

A subset of neurons encodes the identity of odor–taste mixtures and their components

The decoding analysis of the entire chemoselective population (Fig. 2A) showed that the most accurately represented stimuli were those that were never presented as a component of an odor–taste mixture (e.g., NaCl, water, Quinine). To investigate how MCD neurons represented odor–taste mixtures differently from their components, we focused our further analysis on the eight chemosensory stimuli that were presented as either a mixture or as one of its components.

Figure 7A illustrates the temporal evolution of the decoding performance for both MCD neurons (n = 162) and the non-MCD chemoselective population (n = 53). The classification accuracy of the MCD population exceeded the chance level beginning at 150 ms after intraoral delivery, while the non-MCD population did not exceed the chance level until 700 ms. Furthermore, the classification accuracy of the MCD population was significantly better than that of the non-MCD population from 0.2–1.85 to 2.9–3.4 s after stimulus delivery (permutation test, p < 0.05). The confusion matrices in Figure 7B show the average classification performance for each stimulus during the 5 s after intraoral delivery for both MCD neurons (left) and the non-MCD population (right). Much like the entire chemoselective population (Fig. 2B), the classifier often incorrectly assigned stimuli within specific categories (the cyan dashed boxes excluding the red squares). Figure 7C breaks down the decoding performance by stimulus category for both populations [i.e., stimuli containing sucrose (top row), odors (middle row), and stimuli containing citric acid (bottom row)]. The left column shows the average proportion of trials that the classifier's prediction matched the true stimulus (i.e., red diagonal), the middle column shows the average proportion of trials that the classifier's prediction matched a stimulus within the same category (i.e., cyan dashed box), and the right column shows the average proportion of trials that the classifier's prediction was outside of the category (i.e., black box). There were no significant differences in the proportion of trials where the classifier's prediction matched either the actual stimulus or fell within the same category for either the MCD neurons (predicted stimulus: 30.5 ± 2.6%; predicted category: 31.02 ± 1.5%; Wilcoxon rank-sum, Z = −0.239, p = 0.811) or non-MCD population (predicted stimulus: 21.2 ± 1.8%; predicted category: 24.4 ± 1.3%; Wilcoxon rank-sum, Z = −1.194, p = 0.232). This indicates that both populations of chemoselective neurons are effective in categorizing stimuli, but the decoder struggles to distinguish individual stimuli within those categories.

In a more detailed analysis of the MCD population, we examined the decoding properties of two subtypes of MCD neurons: those whose responses to odor–taste mixtures differed from both odor and taste responses (i.e., MCD-both, n = 86) and those whose odor–taste mixtures responses differed from either odors or tastes, but not both (i.e., MCD-either, n = 76). First, we compared the temporal dynamics of MCD responses between the two groups (Fig. 8A). MCD-both neurons significantly differed from baseline in the first bin, while MCD-either neurons did not significantly differ from baseline for 600 ms (Wilcoxon rank-sum, two consecutive significant bins, p < 0.05). Furthermore, the average MCD score of MCD-both neurons significantly differed from the MCD-either neurons between 0.6 and 1 s (Wilcoxon rank-sum, two consecutive significant bins, p < 0.05). This analysis suggests that the peak decoding performance exhibited in the previous population decoding analyses may be driven by the population of MCD neurons with both mixture-odor and mixture-taste differences.

Figure 8B shows the decoding performance over time of the MCD-both and MCD-either populations. The classification accuracy of both populations exceeded the chance level beginning 200 ms after intraoral delivery. However, MCD-both neurons exhibited higher classification accuracy at various time intervals poststimulus delivery (black bars, 0.3–1.25, 1.5–2.125, and 4.3–4.95 s; permutation test, p < 0.05) compared with MCD-either neurons, particularly within the first second after intraoral delivery.

Figure 8, C and D, shows the confusion matrices and average decoding performance by category for each quarter-second during the first 1 s after stimulus delivery for MCD-both neurons (Fig. 8C) and MCD-either neurons (Fig. 8D). During the first 250 ms, there were no significant differences in the proportion of trials where the classifier's prediction matched either the actual stimulus or fell within the same category for MCD neurons with both mixture-taste and mixture-odor differences (predicted stimulus: 15.68 ± 2.6%; predicted category: 16.84 ± 1.6%; Wilcoxon rank-sum, Z = −0.171, p = 0.865). By the second 250 ms bin, the proportion of trials the classifier's prediction matched the true stimulus was significantly greater than those matching the stimulus category for each of the remaining bins (0.25–0.5 s; predicted stimulus: 30.43 ± 2.9%; predicted category: 22.50 ± 1.9%; Wilcoxon rank-sum, Z = 2.082, p = 0.037. 0.5–0.75 s; predicted stimulus: 44.25 ± 2.4%; predicted category: 23.51 ± 2.0%; Wilcoxon rank-sum, Z = 3.788, p < 0.001. 0.75–1 s; predicted stimulus: 49.42 ± 6.2%; predicted category: 22.89 ± 3.3%; Wilcoxon rank-sum, Z = 3.037, p = 0.002). Conversely, at no time did the proportion of trials the classifier's prediction matched the true stimulus differ from the predictions matching the stimulus category for MCD-either neurons (0–0.25 s; predicted true: 15.55 ± 5.2%; predicted category: 12.66 ± 1.1%; Wilcoxon rank-sum, Z = 0.102, p = 0.918. 0.25–0.5 s; predicted true: 20.93 ± 3.7%; predicted category: 20.59 ± 1.3%; Wilcoxon rank-sum, Z = 0.068, p = 0.946. 0.5–0.75 s; predicted true: 24.95 ± 2.4%; predicted category: 27.80 ± 2.7%; Wilcoxon rank-sum, Z = −0.307, p = 0.759. 0.75–1 s; predicted true: 27.25 ± 4.1%; predicted category: 29.37 ± 3.1%; Wilcoxon rank-sum, Z = −0.271, p = 0.785). These findings indicate that the MCD neurons, where mixture responses differed from both components, represent the identity of odors, tastes, and their mixtures.

In summary, these results show that the overall chemoselective population of neurons in the gustatory cortex is broadly responsive to intraoral chemosensory stimuli and encodes both the identity and value of taste stimuli but poorly represents individual odor–taste mixtures and their components. However, a subset of multimodal neurons represents differences between odor–taste mixtures and their odor or taste components primarily during the first second after stimulus delivery. Overall, the above findings demonstrate that the gustatory cortex dynamically encodes unimodal and multimodal chemosensory signals originating from the mouth.

Discussion

Here, our findings expand on previous studies investigating multimodal chemosensory processing in the gustatory cortex by directly comparing unimodal and multimodal responses evoked by multiple odor–taste mixtures (Maier, 2017; Samuelsen and Fontanini, 2017). We found that ∼25% of chemoselective neurons (i.e., non-MCD) had responses to odor–taste mixtures that did not differ from their components, while most responded to odor–taste mixtures differently from at least one of their unimodal odor or taste components (i.e., MCD neurons). Importantly, there was a key difference within this MCD population. We found that a little less than half had responses to odor–taste mixtures that differed from either odors or tastes, but not both (i.e., MCD-either), while the rest had responses to odor–taste mixtures that differed from both odors and tastes (i.e., MCD-both). Interestingly, the vast majority (94.1%) of MCD-both neurons did so for at least one specific mixture (e.g., isoamyl acetate–sucrose) compared with both its individual odor (e.g., isoamyl acetate) and taste components (e.g., sucrose). Fundamentally, this subset of neurons represents odor–taste mixtures as distinct chemosensory stimuli, differentiating them from their unimodal components. These dynamic nonlinear multimodal responses are similar to those reported at the single-neuron level in other associative and primary sensory cortical areas (Avillac et al., 2007; Sieben et al., 2013; Diehl and Romanski, 2014; Ibrahim et al., 2016; Bieler et al., 2017; Atilgan et al., 2018; Chanauria et al., 2019; Coen et al., 2023). Our findings demonstrate that a subset of neurons in the gustatory cortex of behaving rats integrate olfactory and gustatory signals to distinctly represent odor–taste mixtures, indicating a possible substrate for generating odor–taste associations underlying the perception of flavor.

Throughout the cortex, multisensory responses manifest as a combination of nonlinear cross-modal enhancement and suppression (Barraclough et al., 2005; Sugihara et al., 2006; Kayser et al., 2010; Ohshiro et al., 2011, 2017; Iurilli et al., 2012; Meijer et al., 2017). The dynamic changes in nonlinear responses are thought to enable the coding of primary unimodal stimuli, while allowing the flexibility to represent multimodal signals, ultimately enhancing the information represented by the cortex (Kayser et al., 2010; Ohshiro et al., 2011; Meijer et al., 2017). In the present study, we found that ∼56% of mixture responses were suppressed relative to their best unimodal response when considering significant MCD responses as a whole. However, individual neuron’s MCD responses often alternated between cross-modal enhancement and suppression for different components over time. This interplay of enhancement and suppression was consistent across the four odor–taste mixtures, suggesting that temporal variations in cross-modal modulation might be a fundamental principle for representing odor–taste mixtures in the gustatory cortex.

Building on this observation of cross-modal modulation, examining the temporal dynamics of unimodal and multimodal responses provided further insight into the multisensory processing by the gustatory cortex. It is well established that neurons in the gustatory cortex respond to tastes with time-varying modulations in their activity (Katz et al., 2001; Fontanini and Katz, 2006; Samuelsen and Fontanini, 2017; Levitan et al., 2019; Bouaichi and Vincis, 2020). Single-unit recordings in behaving rodents show that neurons initially represent the presence (during first ∼0.25 s), then the identity (∼0.25–1 s), followed by the palatability-related features of tastes (starting ∼1 s after onset). Furthermore, these dynamic changes in taste-evoked activity are known to reflect and precede consummatory behaviors (Kusumoto-Yoshida et al., 2015; Mukherjee et al., 2019; Vincis et al., 2020). Here, the decoding performance of MCD-both neurons revealed that the identity of odor–taste mixtures and their components is represented during the same temporal window as taste identity. However, both the MCD and non-MCD populations similarly represented the palatability-related features of tastes, in terms of proportions of neurons and temporal dynamics. Together, our results suggest that ensembles of neurons in the gustatory cortex rapidly and accurately encode unimodal and multimodal chemosensory identities, while faithfully representing the palatability-related features of tastes.

Given that stimulus concentration influences the detectability of odor–taste mixtures (Dalton et al., 2000; Delwiche and Heffelfinger, 2005), it is prudent to consider how changes in concentration may impact the representation of chemosensory signals in the gustatory cortex. While the effects of odor concentration are not well-defined in the gustatory cortex, electrophysiological research has demonstrated complex, concentration-dependent responses to taste stimuli. These findings reveal that as taste concentration increases, some neurons increase their activity, others decrease their activity, and yet others only respond at specific concentrations (Stapleton et al., 2006). This suggests that employing different concentrations of tastes may recruit distinct chemoselective ensembles, activating new ones from the non-MCD population, modulating responses in some MCD neurons, and failing to activate other previously responsive MCD neurons. This complexity underscores the need for further investigations to understand the nuanced role that stimulus concentration may play in multisensory processing by the gustatory cortex.

Such understanding is pivotal, as the intricacies of how the gustatory cortex integrates olfactory and gustatory signals may directly influence sensory experiences. Therefore, it is also important to consider the role that multisensory processing by the gustatory cortex may play in shaping odor–taste associations. It is well known that repeated experience with odor–taste mixtures generates associations between an odor and the quality and value of a taste (Fanselow and Birk, 1982; Holder, 1991; Stevenson et al., 1995; Prescott et al., 2004; Gautam and Verhagen, 2010; Green et al., 2012; McQueen et al., 2020). Additionally, research has shown that lesions of the gustatory cortex interfere with generating odor–taste associations (Schul et al., 1996; Sakai and Imada, 2003), while optogenetic perturbation of the gustatory cortex disrupts odor preferences that depend specifically on learned retronasal odor–taste associations (Blankenship et al., 2019). Although chemosensory signals converge in a variety of subcortical and cortical areas (Di Lorenzo and Garcia, 1985; Maier et al., 2012; Escanilla et al., 2015; Maier, 2017; Samuelsen and Fontanini, 2017; Fredericksen and Samuelsen, 2022), a coordinated interaction between chemosensory cortical areas—specifically, the gustatory cortex and piriform cortex—is likely integral to the multisensory representation of odor–taste mixtures.

Recent electrophysiological and behavioral studies have demonstrated that corticocortical interactions between these regions influence functional and behavioral responses to odors and tastes. Specifically, optogenetic or pharmacological perturbation of the gustatory cortex eliminates posterior piriform cortical responses to tastes, modulates responses to odors, and disrupts olfactory-dependent behaviors (Fortis-Santiago et al., 2010; Maier et al., 2015; Blankenship et al., 2019). Furthermore, a recent study by Idris et al. (2023) examined multisensory processing of odor–taste mixtures by neurons in the posterior piriform cortex. Similar to our findings here, they show that a subset of neurons in the posterior piriform cortex (∼20%) respond to odor–taste mixtures with nonlinear cross-modal interactions to produce distinct multimodal responses. Although these neurons encode taste identity, they found no evidence for the representation of the palatability-related features of tastes. The authors suggest that multisensory interactions in the gustatory cortex may underlie the hedonic judgments related to odor–taste associations (Idris et al., 2023). These findings suggest a pivotal role for the gustatory cortex in integrating multisensory signals to build odor–taste associations, though the underlying mechanisms remain unknown.

Insights from multisensory integration studies of the superior colliculus suggest intriguing possibilities (Stein et al., 2014). For example, neurons in the superior colliculus adapt to multisensory signals following repeated cross-modal experiences (Wallace and Stein, 2007; Yu et al., 2010). However, both components have to be colocalized within the same receptive field, as experience with the individual components alone or with two signals from the same modality is insufficient for neurons to integrate the disparate signals (Alvarado et al., 2007; Yu et al., 2010). Interestingly, these neurons initially respond only to overlapping multimodal signals but begin to generalize to other stimulus configurations over time. Furthermore, it has been shown that communication between the association cortex and the superior colliculus is essential for this experience-dependent multisensory process (Rowland et al., 2014; Yu et al., 2016), suggesting that network interactions may be fundamental for multisensory integration. This adaptability hints at how the gustatory cortex may represent odor–taste associations through repeated experience with specific odor–taste mixtures and network interactions with higher-order areas of the “flavor network,” such as the mediodorsal thalamus and orbitofrontal cortex.

A fundamental tenet of multisensory processing is that integration requires spatial and temporal congruence (Stein et al., 2014). By delivering all stimuli directly into the mouth, we ensured not only spatial and temporal congruence but also facilitated odor sampling via retronasal olfaction, an essential aspect of flavor perception (Verhagen and Engelen, 2006; Prescott, 2012). Nevertheless, flavor perception also depends upon odor–taste congruence: how well an odor and taste “fit together” based on prior experience (Amsellem and Ohla, 2016). For instance, experience with an odor–taste mixture influences people's perceptions related to the detectability, intensity, and hedonic value of its components (Schifferstein and Verlegh, 1996; Dalton et al., 2000; Delwiche and Heffelfinger, 2005; Veldhuizen et al., 2010b; Green et al., 2012; Seo et al., 2013; Amsellem and Ohla, 2016). Before the first recording session, rats were given 1 h access to odor–taste mixtures of isoamyl acetate–sucrose and benzaldehyde–citric acid for 3 d. This experience was meant to reduce the likelihood of rats rejecting odorized stimuli because of neophobia (Miller et al., 1986; Lin et al., 2009; Fredericksen et al., 2019; McQueen et al., 2020). Although it is unclear how much experience is required to establish long-term odor–taste associations, our findings may reflect this pre-experimental mixture experience. For example, incongruence between odor–taste pairs could be responsible for the coding differences across the four odor–taste mixtures. Alternatively, if we had provided rats with long-term experience with specific odor–taste mixtures, it may have improved the representation of unimodal and multimodal information due to established congruent odor–taste associations. Future studies are needed to elucidate the effects of odor–taste congruence on the representation of unimodal and multimodal chemosensory signals in the gustatory cortex.

In summary, our results provide novel evidence of the multimodal processing of the chemosensory components of flavor by neurons in the gustatory cortex. We show that a subpopulation of gustatory cortical neurons dynamically encodes unimodal and multimodal chemosensory signals from the mouth. These findings suggest that a broad representation is vital for encompassing a range of sensory experiences, where distinctions between unimodal and multimodal signals are pivotal for the nuanced differentiation and rapid assessment of complex chemosensory signals from the mouth. Future studies probing corticocortical interactions between the gustatory and piriform cortex in behaving animals are necessary to determine how multisensory processing of chemosensory signals influences ingestive behaviors.

References

Aimé P, Duchamp-Viret P, Chaput MA, Savigner A, Mahfouz M, Julliard AK (2007) Fasting increases and satiation decreases olfactory detection for a neutral odor in rats. Behav Brain Res 179:258–264. 10.1016/j.bbr.2007.02.012 [DOI] [PubMed] [Google Scholar]
Alvarado JC, Stanford TR, Vaughan JW, Stein BE (2007) Cortex mediates multisensory but not unisensory integration in superior colliculus. J Neurosci 27:12775. 10.1523/JNEUROSCI.3524-07.2007 [DOI] [PMC free article] [PubMed] [Google Scholar]
Amsellem S, Ohla K (2016) Perceived odor-taste congruence influences intensity and pleasantness differently. Chem Senses 41:677–684. 10.1093/chemse/bjw078 [DOI] [PubMed] [Google Scholar]
Atilgan H, Town SM, Wood KC, Jones GP, Maddox RK, Lee AKC, Bizley JK (2018) Integration of visual information in auditory cortex promotes auditory scene analysis through multisensory binding. Neuron 97:640–655.e4. 10.1016/j.neuron.2017.12.034 [DOI] [PMC free article] [PubMed] [Google Scholar]
Avillac M, Ben HS, Duhamel JR (2007) Multisensory integration in the ventral intraparietal area of the macaque monkey. J Neurosci 27:1922–1932. 10.1523/JNEUROSCI.2646-06.2007 [DOI] [PMC free article] [PubMed] [Google Scholar]
Bamji-Stocke S, Biggs BT, Samuelsen CL (2018) Experience-dependent c-Fos expression in the primary chemosensory cortices of the rat. Brain Res 1701:189–195. 10.1016/j.brainres.2018.09.019 [DOI] [PMC free article] [PubMed] [Google Scholar]
Barraclough NE, Xiao D, Baker CI, Oram MW, Perrett DI (2005) Integration of visual and auditory information by superior temporal sulcus neurons responsive to the sight of actions. J Cogn Neurosci 17:377–391. 10.1162/0898929053279586 [DOI] [PubMed] [Google Scholar]
Bieler M, Sieben K, Schildt S, Röder B, Hanganu-Opatz IL (2017) Visual–tactile processing in primary somatosensory cortex emerges before cross-modal experience. Synapse 71:e21958. 10.1002/syn.21958 [DOI] [PubMed] [Google Scholar]
Blankenship ML, Grigorova M, Katz DB, Maier JX (2019) Retronasal odor perception requires taste cortex, but orthonasal does not. Curr Biol 29:62–69.e3. 10.1016/j.cub.2018.11.011 [DOI] [PMC free article] [PubMed] [Google Scholar]
Bouaichi CG, Odegaard KE, Neese C, Vincis R (2023) Oral thermal processing in the gustatory cortex of awake mice. Chem Senses 48:bjad042. 10.1093/chemse/bjad042 [DOI] [PMC free article] [PubMed] [Google Scholar]
Bouaichi CG, Vincis R (2020) Cortical processing of chemosensory and hedonic features of taste in active licking mice. J Neurophysiol 123:1995–2009. 10.1152/jn.00069.2020 [DOI] [PMC free article] [PubMed] [Google Scholar]
Chanauria N, Bharmauria V, Bachatene L, Cattan S, Rouat J, Molotchnikoff S (2019) Sound induces change in orientation preference of V1 neurons: audio-visual cross-influence. Neuroscience 404:48–61. 10.1016/j.neuroscience.2019.01.039 [DOI] [PubMed] [Google Scholar]
Coen P, Sit TPH, Wells MJ, Carandini M, Harris KD (2023) Mouse frontal cortex mediates additive multisensory decisions. Neuron 111:2432–2447.e13. 10.1016/j.neuron.2023.05.008 [DOI] [PMC free article] [PubMed] [Google Scholar]
Cohen JY, Haesler S, Vong L, Lowell BB, Uchida N (2012) Neuron-type-specific signals for reward and punishment in the ventral tegmental area. Nature 482:85–88. 10.1038/nature10754 [DOI] [PMC free article] [PubMed] [Google Scholar]
Dalton P, Doolittle N, Nagata H, Breslin PAS (2000) The merging of the senses: integration of subthreshold taste and smell. Nat Neurosci 3:431–432. 10.1038/74797 [DOI] [PubMed] [Google Scholar]
De Araujo IET, Rolls ET, Kringelbach ML, McGlone F, Phillips N (2003) Taste-olfactory convergence, and the representation of the pleasantness of flavour, in the human brain. Eur J Neurosci 18:2059–2068. 10.1046/j.1460-9568.2003.02915.x [DOI] [PubMed] [Google Scholar]
Delwiche JF, Heffelfinger AL (2005) Cross-modal additivity of taste and smell. J Sens Stud 20:512–525. 10.1111/j.1745-459X.2005.00047.x [DOI] [Google Scholar]
Diehl MM, Romanski LM (2014) Responses of prefrontal multisensory neurons to mismatching faces and vocalizations. J Neurosci 34:11233–11243. 10.1523/JNEUROSCI.5168-13.2014 [DOI] [PMC free article] [PubMed] [Google Scholar]
Di Lorenzo PM, Garcia J (1985) Olfactory responses in the gustatory area of the parabrachial pons. Brain Res Bull 15:673–676. 10.1016/0361-9230(85)90219-9 [DOI] [PubMed] [Google Scholar]
Ernst MO, Bülthoff HH (2004) Merging the senses into a robust percept. Trends Cogn Sci 8:162–169. 10.1016/j.tics.2004.02.002 [DOI] [PubMed] [Google Scholar]
Escanilla OD, Victor JD, Di Lorenzo PM (2015) Odor-taste convergence in the nucleus of the solitary tract of the awake freely licking rat. J Neurosci 35:6284–6297. 10.1523/JNEUROSCI.3526-14.2015 [DOI] [PMC free article] [PubMed] [Google Scholar]
Fanselow MS, Birk J (1982) Flavor-flavor associations induce hedonic shifts in taste preference. Anim Learn Behav 10:223–228. 10.3758/BF03212274 [DOI] [Google Scholar]
Fontanini A, Grossman SE, Figueroa JA, Katz DB (2009) Distinct subtypes of basolateral amygdala taste neurons reflect palatability and reward. J Neurosci 29:2486–2495. 10.1523/JNEUROSCI.3898-08.2009 [DOI] [PMC free article] [PubMed] [Google Scholar]
Fontanini A, Katz DB (2006) State-dependent modulation of time-varying gustatory responses. J Neurophysiol 96:3183–3193. 10.1152/jn.00804.2006 [DOI] [PubMed] [Google Scholar]
Fortis-Santiago Y, Rodwin BA, Neseliler S, Piette CE, Katz DB (2010) State dependence of olfactory perception as a function of taste cortical inactivation. Nat Neurosci 13:158–159. 10.1038/nn.2463 [DOI] [PMC free article] [PubMed] [Google Scholar]
Frank RA, Byram J (1988) Taste-smell interactions are tastant and odorant dependent. Chem Senses 13:445–455. 10.1093/chemse/13.3.445 [DOI] [Google Scholar]
Fredericksen KE, McQueen KA, Samuelsen CL (2019) Experience-dependent c-Fos expression in the mediodorsal thalamus varies with chemosensory modality. Chem Senses 44:41–49. 10.1093/chemse/bjy070 [DOI] [PMC free article] [PubMed] [Google Scholar]
Fredericksen KE, Samuelsen CL (2022) Neural representation of intraoral olfactory and gustatory signals by the mediodorsal thalamus in alert rats. J Neurosci 42:8136–8153. 10.1523/JNEUROSCI.0674-22.2022 [DOI] [PMC free article] [PubMed] [Google Scholar]
Gardner MPH, Fontanini A (2014) Encoding and tracking of outcome-specific expectancy in the gustatory cortex of alert rats. J Neurosci 34:13000–13017. 10.1523/JNEUROSCI.1820-14.2014 [DOI] [PMC free article] [PubMed] [Google Scholar]
Gautam SH, Verhagen JV (2010) Evidence that the sweetness of odors depends on experience in rats. Chem Senses 35:767–776. 10.1093/chemse/bjq075 [DOI] [PMC free article] [PubMed] [Google Scholar]
Gautam SH, Verhagen JV (2012) Direct behavioral evidence for retronasal olfaction in rats. PLoS One 7:e44781. 10.1371/journal.pone.0044781 [DOI] [PMC free article] [PubMed] [Google Scholar]
Green BG, Nachtigal D, Hammond S, Lim J (2012) Enhancement of retronasal odors by taste. Chem Senses 37:77–86. 10.1093/chemse/bjr068 [DOI] [PMC free article] [PubMed] [Google Scholar]
Holder MD (1991) Conditioned preferences for the taste and odor components of flavors: blocking but not overshadowing. Appetite 17:29–45. 10.1016/0195-6663(91)90082-4 [DOI] [PubMed] [Google Scholar]
Ibrahim LA, Mesik L, Ji XY, Fang Q, Li HF, Li YT, Zingg B, Zhang LI, Tao HW (2016) Cross-modality sharpening of visual cortical processing through layer-1-mediated inhibition and disinhibition. Neuron 89:1031–1045. 10.1016/j.neuron.2016.01.027 [DOI] [PMC free article] [PubMed] [Google Scholar]
Idris A, Christensen BA, Walker EM, Maier JX (2023) Multisensory integration of orally-sourced gustatory and olfactory inputs to the posterior piriform cortex in awake rats. J Physiol 601:151–169. 10.1113/JP283873 [DOI] [PMC free article] [PubMed] [Google Scholar]
Ifuku H, Nakamura T, Hirata S, Ogawa H (2006) Neuronal activities in the reward phase in primary and higher-order gustatory cortices of monkeys. Neurosci Res 55:54–64. 10.1016/j.neures.2006.02.001 [DOI] [PubMed] [Google Scholar]
Iurilli G, Ghezzi D, Olcese U, Lassi G, Nazzaro C, Tonini R, Tucci V, Benfenati F, Medini P (2012) Sound-driven synaptic inhibition in primary visual cortex. Neuron 73:814–828. 10.1016/j.neuron.2011.12.026 [DOI] [PMC free article] [PubMed] [Google Scholar]
Jezzini A, Mazzucato L, La Camera G, Fontanini A (2013) Processing of hedonic and chemosensory features of taste in medial prefrontal and insular networks. J Neurosci 33:18966–18978. 10.1523/JNEUROSCI.2974-13.2013 [DOI] [PMC free article] [PubMed] [Google Scholar]
Jones LM, Fontanini A, Sadacca BF, Miller P, Katz DB (2007) Natural stimuli evoke dynamic sequences of states in sensory cortical ensembles. Proc Natl Acad Sci U S A 104:18772–18777. 10.1073/pnas.0705546104 [DOI] [PMC free article] [PubMed] [Google Scholar]
Julliard AK, Chaput MA, Apelbaum A, Aimé P, Mahfouz M, Duchamp-Viret P (2007) Changes in rat olfactory detection performance induced by orexin and leptin mimicking fasting and satiation. Behav Brain Res 183:123–129. 10.1016/j.bbr.2007.05.033 [DOI] [PubMed] [Google Scholar]
Kadohisa M, Rolls ET, Verhagen JV (2005) Neuronal representations of stimuli in the mouth: the primate insular taste cortex, orbitofrontal cortex and amygdala. Chem Senses 30:401–419. 10.1093/chemse/bji036 [DOI] [PubMed] [Google Scholar]
Katz DB, Simon SA, Nicolelis MAL (2001) Dynamic and multimodal responses of gustatory cortical neurons in awake rats. J Neurosci 21:4478–4489. 10.1523/JNEUROSCI.21-12-04478.2001 [DOI] [PMC free article] [PubMed] [Google Scholar]
Kayser C, Logothetis NK, Panzeri S (2010) Visual enhancement of the information representation in auditory cortex. Curr Biol 20:19–24. 10.1016/j.cub.2009.10.068 [DOI] [PubMed] [Google Scholar]
Kusumoto-Yoshida I, Liu H, Chen BT, Fontanini A, Bonci A (2015) Central role for the insular cortex in mediating conditioned responses to anticipatory cues. Proc Natl Acad Sci U S A 112:1190–1195. 10.1073/pnas.1416573112 [DOI] [PMC free article] [PubMed] [Google Scholar]
Levitan D, Lin JY, Wachutka J, Mukherjee N, Nelson SB, Katz DB (2019) Single and population coding of taste in the gustatory cortex of awake mice. J Neurophysiol 122:1342–1356. 10.1152/jn.00357.2019 [DOI] [PMC free article] [PubMed] [Google Scholar]
Lin J-YY, Roman C, St Andre J, Reilly S, St. Andre J, Reilly S (2009) Taste, olfactory and trigeminal neophobia in rats with forebrain lesions. Brain Res 1251:195–203. 10.1016/j.brainres.2008.11.040 [DOI] [PMC free article] [PubMed] [Google Scholar]
Liu H, Fontanini A (2015) State dependency of chemosensory coding in the gustatory thalamus (VPMpc) of alert rats. J Neurosci 35:15479–15491. 10.1523/JNEUROSCI.0839-15.2015 [DOI] [PMC free article] [PubMed] [Google Scholar]
Maier JX (2017) Single-neuron responses to intraoral delivery of odor solutions in primary olfactory and gustatory cortex. J Neurophysiol 117:1293–1304. 10.1152/jn.00802.2016 [DOI] [PMC free article] [PubMed] [Google Scholar]
Maier JX, Blankenship ML, Li JX, Katz DB (2015) A multisensory network for olfactory processing. Curr Biol 25:2642–2650. 10.1016/j.cub.2015.08.060 [DOI] [PMC free article] [PubMed] [Google Scholar]
Maier JX, Wachowiak M, Katz DB (2012) Chemosensory convergence on primary olfactory cortex. J Neurosci 32:17037–17047. 10.1523/JNEUROSCI.3540-12.2012 [DOI] [PMC free article] [PubMed] [Google Scholar]
McQueen KA, Fredericksen KE, Samuelsen CL (2020) Experience informs consummatory choices for congruent and incongruent odor-taste mixtures in rats. Chem Senses 45:371–382. 10.1093/chemse/bjaa025 [DOI] [PMC free article] [PubMed] [Google Scholar]
Meijer GT, Montijn JS, Pennartz CMA, Lansink CS (2017) Audio-visual modulation in mouse V1 depends on cross-modal stimulus configuration and congruency. J Neurosci 37:468–417. 10.1523/JNEUROSCI.0468-17.2017 [DOI] [PMC free article] [PubMed] [Google Scholar]
Meyers EM (2013) The neural decoding toolbox. Front Neuroinform 7:8. 10.3389/fninf.2013.00008 [DOI] [PMC free article] [PubMed] [Google Scholar]
Miller JS, Nonneman AJ, Kelly KS, Neisewander JL, Isaac WL (1986) Disruption of neophobia, conditioned odor aversion, and conditioned taste aversion in rats with hippocampal lesions. Behav Neural Biol 45:240–253. 10.1016/S0163-1047(86)90816-2 [DOI] [PubMed] [Google Scholar]
Mukherjee N, Wachutka J, Katz DB (2019) Impact of precisely-timed inhibition of gustatory cortex on taste behavior depends on single-trial ensemble dynamics. Elife 8:e45968. 10.7554/eLife.45968 [DOI] [PMC free article] [PubMed] [Google Scholar]
Ohshiro T, Angelaki DE, Deangelis GC (2011) A normalization model of multisensory integration. Nat Neurosci 14:775–782. 10.1038/nn.2815 [DOI] [PMC free article] [PubMed] [Google Scholar]
Ohshiro T, Angelaki DE, DeAngelis GC (2017) A neural signature of divisive normalization at the level of multisensory integration in primate cortex. Neuron 95:399–411.e8. 10.1016/j.neuron.2017.06.043 [DOI] [PMC free article] [PubMed] [Google Scholar]
Ojala M, Garriga GC (2010) Permutation tests for studying classifier performance. J Mach Learn Res 11:1833–1863. 10.5555/1756006.1859913 [DOI] [Google Scholar]
Piette CE, Baez-Santiago MA, Reid EE, Katz DB, Moran A (2012) Inactivation of basolateral amygdala specifically eliminates palatability-related information in cortical sensory responses. J Neurosci 32:9981–9991. 10.1523/JNEUROSCI.0669-12.2012 [DOI] [PMC free article] [PubMed] [Google Scholar]
Prescott J (2012) Chemosensory learning and flavour: perception, preference and intake. Physiol Behav 107:553–559. 10.1016/j.physbeh.2012.04.008 [DOI] [PubMed] [Google Scholar]
Prescott J (2015) Multisensory processes in flavour perception and their influence on food choice. Curr Opin Food Sci 3:47–52. 10.1016/j.cofs.2015.02.007 [DOI] [Google Scholar]
Prescott J, Johnstone V, Francis J (2004) Odor-taste interactions: effects of attentional strategies during exposure. Chem Senses 29:331–340. 10.1093/chemse/bjh036 [DOI] [PubMed] [Google Scholar]
Rebello MR, Kandukuru P, Verhagen JV (2015) Direct behavioral and neurophysiological evidence for retronasal olfaction in mice. PLoS One 10:16–19. 10.1371/journal.pone.0117218 [DOI] [PMC free article] [PubMed] [Google Scholar]
Rowland BA, Jiang W, Stein BE (2014) Brief cortical deactivation early in life has long-lasting effects on multisensory behavior. J Neurosci 34:7198–7202. 10.1523/JNEUROSCI.3782-13.2014 [DOI] [PMC free article] [PubMed] [Google Scholar]
Sakai N, Imada S (2003) Bilateral lesions of the insular cortex or of the prefrontal cortex block the association between taste and odor in the rat. Neurobiol Learn Mem 80:24–31. 10.1016/S1074-7427(03)00021-2 [DOI] [PubMed] [Google Scholar]
Sakai N, Yamamoto T (2001) Effects of excitotoxic brain lesions on taste-mediated odor learning in the rat. Neurobiol Learn Mem 75:128–139. 10.1006/nlme.2000.3969 [DOI] [PubMed] [Google Scholar]
Samuelsen CL, Fontanini A (2017) Processing of intraoral olfactory and gustatory signals in the gustatory cortex of awake rats. J Neurosci 37:244–257. 10.1523/JNEUROSCI.1926-16.2016 [DOI] [PMC free article] [PubMed] [Google Scholar]
Samuelsen CL, Gardner MPH, Fontanini A (2012) Effects of cue-triggered expectation on cortical processing of taste. Neuron 74:410–422. 10.1016/j.neuron.2012.02.031 [DOI] [PMC free article] [PubMed] [Google Scholar]
Samuelsen CL, Gardner MPH, Fontanini A (2013) Thalamic contribution to cortical processing of taste and expectation. J Neurosci 33:1815–1827. 10.1523/JNEUROSCI.4026-12.2013 [DOI] [PMC free article] [PubMed] [Google Scholar]
Samuelsen CL, Vincis R (2021) Cortical Hub for flavor sensation in rodents. Front Syst Neurosci 15:130. 10.3389/fnsys.2021.772286 [DOI] [PMC free article] [PubMed] [Google Scholar]
Schifferstein HNJJ, Verlegh PWJJ (1996) The role of congruency and pleasantness in odor-induced taste enhancement. Acta Psychol 94:87–105. 10.1016/0001-6918(95)00040-2 [DOI] [PubMed] [Google Scholar]
Schul R, Slotnick BM, Dudai Y (1996) Flavor and the frontal cortex. Behav Neurosci 110:760–765. 10.1037/0735-7044.110.4.760 [DOI] [PubMed] [Google Scholar]
Sclafani A (2001) Psychobiology of food preferences. Int J Obes 25:S13–S16. 10.1038/sj.ijo.0801905 [DOI] [PubMed] [Google Scholar]
Seo HS, Iannilli E, Hummel C, Okazaki Y, Buschhüter D, Gerber J, Krammer GE, van Lengerich B, Hummel T (2013) A salty-congruent odor enhances saltiness: functional magnetic resonance imaging study. Hum Brain Mapp 34:62–76. 10.1002/hbm.21414 [DOI] [PMC free article] [PubMed] [Google Scholar]
Shan G, Gerstenberger S (2017) Fisher’s exact approach for post hoc analysis of a chi-squared test. PLoS One 12:e0188709. 10.1371/journal.pone.0188709 [DOI] [PMC free article] [PubMed] [Google Scholar]
Shepard TG, Veldhuizen MG, Marks LE (2015) Response times to gustatory-olfactory flavor mixtures: role of congruence. Chem Senses 40:565–575. 10.1093/chemse/bjv042 [DOI] [PMC free article] [PubMed] [Google Scholar]
Sieben K, Roder B, Hanganu-Opatz IL (2013) Oscillatory entrainment of primary somatosensory cortex encodes visual control of tactile processing. J Neurosci 33:5736–5749. 10.1523/JNEUROSCI.4432-12.2013 [DOI] [PMC free article] [PubMed] [Google Scholar]
Small DM (2012) Flavor is in the brain. Physiol Behav 107:540–552. 10.1016/j.physbeh.2012.04.011 [DOI] [PubMed] [Google Scholar]
Small DM, Veldhuizen MG, Felsted J, Mak YE, McGlone F (2008) Separable substrates for anticipatory and consummatory food chemosensation. Neuron 57:786–797. 10.1016/j.neuron.2008.01.021 [DOI] [PMC free article] [PubMed] [Google Scholar]
Small DM, Voss J, Mak YE, Simmons KB, Parrish T, Gitelman D (2004) Experience-dependent neural integration of taste and smell in the human brain. J Neurophysiol 92:1892–1903. 10.1152/jn.00050.2004 [DOI] [PubMed] [Google Scholar]
Stapleton JR, Lavine ML, Wolpert RL, Nicolelis MAL, Simon SA (2006) Rapid taste responses in the gustatory cortex during licking. J Neurosci 26:4126–4138. 10.1523/JNEUROSCI.0092-06.2006 [DOI] [PMC free article] [PubMed] [Google Scholar]
Stein BE, Stanford TR, Rowland BA (2014) Development of multisensory integration from the perspective of the individual neuron. Nat Rev Neurosci 15:520–535. 10.1038/nrn3742 [DOI] [PMC free article] [PubMed] [Google Scholar]
Stevenson RJ, Prescott J, Boakes RA (1995) The acquisition of taste properties by odors. Learn Motiv 26:433–455. 10.1016/S0023-9690(05)80006-2 [DOI] [Google Scholar]
Sugihara T, Diltz MD, Averbeck BB, Romanski LM (2006) Integration of auditory and visual communication information in the primate ventrolateral prefrontal cortex. J Neurosci 26:11138–11147. 10.1523/JNEUROSCI.3550-06.2006 [DOI] [PMC free article] [PubMed] [Google Scholar]
Tong J, et al. (2011) Ghrelin enhances olfactory sensitivity and exploratory sniffing in rodents and humans. J Neurosci 31:5841–5846. 10.1523/JNEUROSCI.5680-10.2011 [DOI] [PMC free article] [PubMed] [Google Scholar]
Veldhuizen MG, Nachtigal D, Teulings L, Gitelman DR, Small DM (2010a) The insular taste cortex contributes to odor quality coding. Front Hum Neurosci 4:58. 10.3389/fnhum.2010.00058 [DOI] [PMC free article] [PubMed] [Google Scholar]
Veldhuizen MG, Shepard TG, Wang MF, Marks LE (2010b) Coactivation of gustatory and olfactory signals in flavor perception. Chem Senses 35:121–133. 10.1093/chemse/bjp089 [DOI] [PMC free article] [PubMed] [Google Scholar]
Verhagen JV, Engelen L (2006) The neurocognitive bases of human multimodal food perception: sensory integration. Neurosci Biobehav Rev 30:613–650. 10.1016/j.neubiorev.2005.11.003 [DOI] [PubMed] [Google Scholar]
Vincis R, Chen K, Czarnecki L, Chen J, Fontanini A (2020) Dynamic representation of taste-related decisions in the gustatory insular cortex of mice. Curr Biol 30:1834–1844.e5. 10.1016/j.cub.2020.03.012 [DOI] [PMC free article] [PubMed] [Google Scholar]
Vincis R, Fontanini A (2016) Associative learning changes cross-modal representations in the gustatory cortex. Elife 5:e16420. 10.7554/eLife.16420 [DOI] [PMC free article] [PubMed] [Google Scholar]
Wallace MT, Stein BE (2007) Early experience determines how the senses will interact. J Neurophysiol 97:921–926. 10.1152/jn.00497.2006 [DOI] [PubMed] [Google Scholar]
Wang X, Ogawa H (2002) Columnar organization of mechanoreceptive neurons in the cortical taste area in the rat. Exp Brain Res 147:114–123. 10.1007/s00221-002-1228-0 [DOI] [PubMed] [Google Scholar]
White TL, Prescott J (2007) Chemosensory cross-modal stroop effects: congruent odors facilitate taste identification. Chem Senses 32:337–341. 10.1093/chemse/bjm001 [DOI] [PubMed] [Google Scholar]
Yamamoto T, Yuyama N, Kawamura Y (1981) Cortical neurons responding to tactile, thermal and taste stimulations of the rat’s tongue. Brain Res 221:202–206. 10.1016/0006-8993(81)91075-1 [DOI] [PubMed] [Google Scholar]
Yu L, Rowland BA, Stein BE (2010) Initiating the development of multisensory integration by manipulating sensory experience. J Neurosci 30:4904–4913. 10.1523/JNEUROSCI.5575-09.2010 [DOI] [PMC free article] [PubMed] [Google Scholar]
Yu L, Xu J, Rowland BA, Stein BE (2016) Multisensory plasticity in superior colliculus neurons is mediated by association cortex. Cereb Cortex 26:1130. 10.1093/cercor/bhu295 [DOI] [PMC free article] [PubMed] [Google Scholar]

[B1] Aimé P, Duchamp-Viret P, Chaput MA, Savigner A, Mahfouz M, Julliard AK (2007) Fasting increases and satiation decreases olfactory detection for a neutral odor in rats. Behav Brain Res 179:258–264. 10.1016/j.bbr.2007.02.012 [DOI] [PubMed] [Google Scholar]

[B2] Alvarado JC, Stanford TR, Vaughan JW, Stein BE (2007) Cortex mediates multisensory but not unisensory integration in superior colliculus. J Neurosci 27:12775. 10.1523/JNEUROSCI.3524-07.2007 [DOI] [PMC free article] [PubMed] [Google Scholar]

[B3] Amsellem S, Ohla K (2016) Perceived odor-taste congruence influences intensity and pleasantness differently. Chem Senses 41:677–684. 10.1093/chemse/bjw078 [DOI] [PubMed] [Google Scholar]

[B4] Atilgan H, Town SM, Wood KC, Jones GP, Maddox RK, Lee AKC, Bizley JK (2018) Integration of visual information in auditory cortex promotes auditory scene analysis through multisensory binding. Neuron 97:640–655.e4. 10.1016/j.neuron.2017.12.034 [DOI] [PMC free article] [PubMed] [Google Scholar]

[B5] Avillac M, Ben HS, Duhamel JR (2007) Multisensory integration in the ventral intraparietal area of the macaque monkey. J Neurosci 27:1922–1932. 10.1523/JNEUROSCI.2646-06.2007 [DOI] [PMC free article] [PubMed] [Google Scholar]

[B6] Bamji-Stocke S, Biggs BT, Samuelsen CL (2018) Experience-dependent c-Fos expression in the primary chemosensory cortices of the rat. Brain Res 1701:189–195. 10.1016/j.brainres.2018.09.019 [DOI] [PMC free article] [PubMed] [Google Scholar]

[B7] Barraclough NE, Xiao D, Baker CI, Oram MW, Perrett DI (2005) Integration of visual and auditory information by superior temporal sulcus neurons responsive to the sight of actions. J Cogn Neurosci 17:377–391. 10.1162/0898929053279586 [DOI] [PubMed] [Google Scholar]

[B8] Bieler M, Sieben K, Schildt S, Röder B, Hanganu-Opatz IL (2017) Visual–tactile processing in primary somatosensory cortex emerges before cross-modal experience. Synapse 71:e21958. 10.1002/syn.21958 [DOI] [PubMed] [Google Scholar]

[B9] Blankenship ML, Grigorova M, Katz DB, Maier JX (2019) Retronasal odor perception requires taste cortex, but orthonasal does not. Curr Biol 29:62–69.e3. 10.1016/j.cub.2018.11.011 [DOI] [PMC free article] [PubMed] [Google Scholar]

[B10] Bouaichi CG, Odegaard KE, Neese C, Vincis R (2023) Oral thermal processing in the gustatory cortex of awake mice. Chem Senses 48:bjad042. 10.1093/chemse/bjad042 [DOI] [PMC free article] [PubMed] [Google Scholar]

[B11] Bouaichi CG, Vincis R (2020) Cortical processing of chemosensory and hedonic features of taste in active licking mice. J Neurophysiol 123:1995–2009. 10.1152/jn.00069.2020 [DOI] [PMC free article] [PubMed] [Google Scholar]

[B12] Chanauria N, Bharmauria V, Bachatene L, Cattan S, Rouat J, Molotchnikoff S (2019) Sound induces change in orientation preference of V1 neurons: audio-visual cross-influence. Neuroscience 404:48–61. 10.1016/j.neuroscience.2019.01.039 [DOI] [PubMed] [Google Scholar]

[B13] Coen P, Sit TPH, Wells MJ, Carandini M, Harris KD (2023) Mouse frontal cortex mediates additive multisensory decisions. Neuron 111:2432–2447.e13. 10.1016/j.neuron.2023.05.008 [DOI] [PMC free article] [PubMed] [Google Scholar]

[B14] Cohen JY, Haesler S, Vong L, Lowell BB, Uchida N (2012) Neuron-type-specific signals for reward and punishment in the ventral tegmental area. Nature 482:85–88. 10.1038/nature10754 [DOI] [PMC free article] [PubMed] [Google Scholar]

[B15] Dalton P, Doolittle N, Nagata H, Breslin PAS (2000) The merging of the senses: integration of subthreshold taste and smell. Nat Neurosci 3:431–432. 10.1038/74797 [DOI] [PubMed] [Google Scholar]

[B16] De Araujo IET, Rolls ET, Kringelbach ML, McGlone F, Phillips N (2003) Taste-olfactory convergence, and the representation of the pleasantness of flavour, in the human brain. Eur J Neurosci 18:2059–2068. 10.1046/j.1460-9568.2003.02915.x [DOI] [PubMed] [Google Scholar]

[B17] Delwiche JF, Heffelfinger AL (2005) Cross-modal additivity of taste and smell. J Sens Stud 20:512–525. 10.1111/j.1745-459X.2005.00047.x [DOI] [Google Scholar]

[B18] Diehl MM, Romanski LM (2014) Responses of prefrontal multisensory neurons to mismatching faces and vocalizations. J Neurosci 34:11233–11243. 10.1523/JNEUROSCI.5168-13.2014 [DOI] [PMC free article] [PubMed] [Google Scholar]

[B19] Di Lorenzo PM, Garcia J (1985) Olfactory responses in the gustatory area of the parabrachial pons. Brain Res Bull 15:673–676. 10.1016/0361-9230(85)90219-9 [DOI] [PubMed] [Google Scholar]

[B20] Ernst MO, Bülthoff HH (2004) Merging the senses into a robust percept. Trends Cogn Sci 8:162–169. 10.1016/j.tics.2004.02.002 [DOI] [PubMed] [Google Scholar]

[B21] Escanilla OD, Victor JD, Di Lorenzo PM (2015) Odor-taste convergence in the nucleus of the solitary tract of the awake freely licking rat. J Neurosci 35:6284–6297. 10.1523/JNEUROSCI.3526-14.2015 [DOI] [PMC free article] [PubMed] [Google Scholar]

[B22] Fanselow MS, Birk J (1982) Flavor-flavor associations induce hedonic shifts in taste preference. Anim Learn Behav 10:223–228. 10.3758/BF03212274 [DOI] [Google Scholar]

[B23] Fontanini A, Grossman SE, Figueroa JA, Katz DB (2009) Distinct subtypes of basolateral amygdala taste neurons reflect palatability and reward. J Neurosci 29:2486–2495. 10.1523/JNEUROSCI.3898-08.2009 [DOI] [PMC free article] [PubMed] [Google Scholar]

[B24] Fontanini A, Katz DB (2006) State-dependent modulation of time-varying gustatory responses. J Neurophysiol 96:3183–3193. 10.1152/jn.00804.2006 [DOI] [PubMed] [Google Scholar]

[B25] Fortis-Santiago Y, Rodwin BA, Neseliler S, Piette CE, Katz DB (2010) State dependence of olfactory perception as a function of taste cortical inactivation. Nat Neurosci 13:158–159. 10.1038/nn.2463 [DOI] [PMC free article] [PubMed] [Google Scholar]

[B26] Frank RA, Byram J (1988) Taste-smell interactions are tastant and odorant dependent. Chem Senses 13:445–455. 10.1093/chemse/13.3.445 [DOI] [Google Scholar]

[B27] Fredericksen KE, McQueen KA, Samuelsen CL (2019) Experience-dependent c-Fos expression in the mediodorsal thalamus varies with chemosensory modality. Chem Senses 44:41–49. 10.1093/chemse/bjy070 [DOI] [PMC free article] [PubMed] [Google Scholar]

[B28] Fredericksen KE, Samuelsen CL (2022) Neural representation of intraoral olfactory and gustatory signals by the mediodorsal thalamus in alert rats. J Neurosci 42:8136–8153. 10.1523/JNEUROSCI.0674-22.2022 [DOI] [PMC free article] [PubMed] [Google Scholar]

[B29] Gardner MPH, Fontanini A (2014) Encoding and tracking of outcome-specific expectancy in the gustatory cortex of alert rats. J Neurosci 34:13000–13017. 10.1523/JNEUROSCI.1820-14.2014 [DOI] [PMC free article] [PubMed] [Google Scholar]

[B30] Gautam SH, Verhagen JV (2010) Evidence that the sweetness of odors depends on experience in rats. Chem Senses 35:767–776. 10.1093/chemse/bjq075 [DOI] [PMC free article] [PubMed] [Google Scholar]

[B31] Gautam SH, Verhagen JV (2012) Direct behavioral evidence for retronasal olfaction in rats. PLoS One 7:e44781. 10.1371/journal.pone.0044781 [DOI] [PMC free article] [PubMed] [Google Scholar]

[B32] Green BG, Nachtigal D, Hammond S, Lim J (2012) Enhancement of retronasal odors by taste. Chem Senses 37:77–86. 10.1093/chemse/bjr068 [DOI] [PMC free article] [PubMed] [Google Scholar]

[B33] Holder MD (1991) Conditioned preferences for the taste and odor components of flavors: blocking but not overshadowing. Appetite 17:29–45. 10.1016/0195-6663(91)90082-4 [DOI] [PubMed] [Google Scholar]

[B34] Ibrahim LA, Mesik L, Ji XY, Fang Q, Li HF, Li YT, Zingg B, Zhang LI, Tao HW (2016) Cross-modality sharpening of visual cortical processing through layer-1-mediated inhibition and disinhibition. Neuron 89:1031–1045. 10.1016/j.neuron.2016.01.027 [DOI] [PMC free article] [PubMed] [Google Scholar]

[B35] Idris A, Christensen BA, Walker EM, Maier JX (2023) Multisensory integration of orally-sourced gustatory and olfactory inputs to the posterior piriform cortex in awake rats. J Physiol 601:151–169. 10.1113/JP283873 [DOI] [PMC free article] [PubMed] [Google Scholar]

[B36] Ifuku H, Nakamura T, Hirata S, Ogawa H (2006) Neuronal activities in the reward phase in primary and higher-order gustatory cortices of monkeys. Neurosci Res 55:54–64. 10.1016/j.neures.2006.02.001 [DOI] [PubMed] [Google Scholar]

[B37] Iurilli G, Ghezzi D, Olcese U, Lassi G, Nazzaro C, Tonini R, Tucci V, Benfenati F, Medini P (2012) Sound-driven synaptic inhibition in primary visual cortex. Neuron 73:814–828. 10.1016/j.neuron.2011.12.026 [DOI] [PMC free article] [PubMed] [Google Scholar]

[B38] Jezzini A, Mazzucato L, La Camera G, Fontanini A (2013) Processing of hedonic and chemosensory features of taste in medial prefrontal and insular networks. J Neurosci 33:18966–18978. 10.1523/JNEUROSCI.2974-13.2013 [DOI] [PMC free article] [PubMed] [Google Scholar]

[B39] Jones LM, Fontanini A, Sadacca BF, Miller P, Katz DB (2007) Natural stimuli evoke dynamic sequences of states in sensory cortical ensembles. Proc Natl Acad Sci U S A 104:18772–18777. 10.1073/pnas.0705546104 [DOI] [PMC free article] [PubMed] [Google Scholar]

[B40] Julliard AK, Chaput MA, Apelbaum A, Aimé P, Mahfouz M, Duchamp-Viret P (2007) Changes in rat olfactory detection performance induced by orexin and leptin mimicking fasting and satiation. Behav Brain Res 183:123–129. 10.1016/j.bbr.2007.05.033 [DOI] [PubMed] [Google Scholar]

[B41] Kadohisa M, Rolls ET, Verhagen JV (2005) Neuronal representations of stimuli in the mouth: the primate insular taste cortex, orbitofrontal cortex and amygdala. Chem Senses 30:401–419. 10.1093/chemse/bji036 [DOI] [PubMed] [Google Scholar]

[B42] Katz DB, Simon SA, Nicolelis MAL (2001) Dynamic and multimodal responses of gustatory cortical neurons in awake rats. J Neurosci 21:4478–4489. 10.1523/JNEUROSCI.21-12-04478.2001 [DOI] [PMC free article] [PubMed] [Google Scholar]

[B43] Kayser C, Logothetis NK, Panzeri S (2010) Visual enhancement of the information representation in auditory cortex. Curr Biol 20:19–24. 10.1016/j.cub.2009.10.068 [DOI] [PubMed] [Google Scholar]

[B44] Kusumoto-Yoshida I, Liu H, Chen BT, Fontanini A, Bonci A (2015) Central role for the insular cortex in mediating conditioned responses to anticipatory cues. Proc Natl Acad Sci U S A 112:1190–1195. 10.1073/pnas.1416573112 [DOI] [PMC free article] [PubMed] [Google Scholar]

[B45] Levitan D, Lin JY, Wachutka J, Mukherjee N, Nelson SB, Katz DB (2019) Single and population coding of taste in the gustatory cortex of awake mice. J Neurophysiol 122:1342–1356. 10.1152/jn.00357.2019 [DOI] [PMC free article] [PubMed] [Google Scholar]

[B46] Lin J-YY, Roman C, St Andre J, Reilly S, St. Andre J, Reilly S (2009) Taste, olfactory and trigeminal neophobia in rats with forebrain lesions. Brain Res 1251:195–203. 10.1016/j.brainres.2008.11.040 [DOI] [PMC free article] [PubMed] [Google Scholar]

[B47] Liu H, Fontanini A (2015) State dependency of chemosensory coding in the gustatory thalamus (VPMpc) of alert rats. J Neurosci 35:15479–15491. 10.1523/JNEUROSCI.0839-15.2015 [DOI] [PMC free article] [PubMed] [Google Scholar]

[B48] Maier JX (2017) Single-neuron responses to intraoral delivery of odor solutions in primary olfactory and gustatory cortex. J Neurophysiol 117:1293–1304. 10.1152/jn.00802.2016 [DOI] [PMC free article] [PubMed] [Google Scholar]

[B49] Maier JX, Blankenship ML, Li JX, Katz DB (2015) A multisensory network for olfactory processing. Curr Biol 25:2642–2650. 10.1016/j.cub.2015.08.060 [DOI] [PMC free article] [PubMed] [Google Scholar]

[B50] Maier JX, Wachowiak M, Katz DB (2012) Chemosensory convergence on primary olfactory cortex. J Neurosci 32:17037–17047. 10.1523/JNEUROSCI.3540-12.2012 [DOI] [PMC free article] [PubMed] [Google Scholar]

[B51] McQueen KA, Fredericksen KE, Samuelsen CL (2020) Experience informs consummatory choices for congruent and incongruent odor-taste mixtures in rats. Chem Senses 45:371–382. 10.1093/chemse/bjaa025 [DOI] [PMC free article] [PubMed] [Google Scholar]

[B52] Meijer GT, Montijn JS, Pennartz CMA, Lansink CS (2017) Audio-visual modulation in mouse V1 depends on cross-modal stimulus configuration and congruency. J Neurosci 37:468–417. 10.1523/JNEUROSCI.0468-17.2017 [DOI] [PMC free article] [PubMed] [Google Scholar]

[B53] Meyers EM (2013) The neural decoding toolbox. Front Neuroinform 7:8. 10.3389/fninf.2013.00008 [DOI] [PMC free article] [PubMed] [Google Scholar]

[B54] Miller JS, Nonneman AJ, Kelly KS, Neisewander JL, Isaac WL (1986) Disruption of neophobia, conditioned odor aversion, and conditioned taste aversion in rats with hippocampal lesions. Behav Neural Biol 45:240–253. 10.1016/S0163-1047(86)90816-2 [DOI] [PubMed] [Google Scholar]

[B55] Mukherjee N, Wachutka J, Katz DB (2019) Impact of precisely-timed inhibition of gustatory cortex on taste behavior depends on single-trial ensemble dynamics. Elife 8:e45968. 10.7554/eLife.45968 [DOI] [PMC free article] [PubMed] [Google Scholar]

[B56] Ohshiro T, Angelaki DE, Deangelis GC (2011) A normalization model of multisensory integration. Nat Neurosci 14:775–782. 10.1038/nn.2815 [DOI] [PMC free article] [PubMed] [Google Scholar]

[B57] Ohshiro T, Angelaki DE, DeAngelis GC (2017) A neural signature of divisive normalization at the level of multisensory integration in primate cortex. Neuron 95:399–411.e8. 10.1016/j.neuron.2017.06.043 [DOI] [PMC free article] [PubMed] [Google Scholar]

[B58] Ojala M, Garriga GC (2010) Permutation tests for studying classifier performance. J Mach Learn Res 11:1833–1863. 10.5555/1756006.1859913 [DOI] [Google Scholar]

[B59] Piette CE, Baez-Santiago MA, Reid EE, Katz DB, Moran A (2012) Inactivation of basolateral amygdala specifically eliminates palatability-related information in cortical sensory responses. J Neurosci 32:9981–9991. 10.1523/JNEUROSCI.0669-12.2012 [DOI] [PMC free article] [PubMed] [Google Scholar]

[B60] Prescott J (2012) Chemosensory learning and flavour: perception, preference and intake. Physiol Behav 107:553–559. 10.1016/j.physbeh.2012.04.008 [DOI] [PubMed] [Google Scholar]

[B61] Prescott J (2015) Multisensory processes in flavour perception and their influence on food choice. Curr Opin Food Sci 3:47–52. 10.1016/j.cofs.2015.02.007 [DOI] [Google Scholar]

[B62] Prescott J, Johnstone V, Francis J (2004) Odor-taste interactions: effects of attentional strategies during exposure. Chem Senses 29:331–340. 10.1093/chemse/bjh036 [DOI] [PubMed] [Google Scholar]

[B63] Rebello MR, Kandukuru P, Verhagen JV (2015) Direct behavioral and neurophysiological evidence for retronasal olfaction in mice. PLoS One 10:16–19. 10.1371/journal.pone.0117218 [DOI] [PMC free article] [PubMed] [Google Scholar]

[B64] Rowland BA, Jiang W, Stein BE (2014) Brief cortical deactivation early in life has long-lasting effects on multisensory behavior. J Neurosci 34:7198–7202. 10.1523/JNEUROSCI.3782-13.2014 [DOI] [PMC free article] [PubMed] [Google Scholar]

[B65] Sakai N, Imada S (2003) Bilateral lesions of the insular cortex or of the prefrontal cortex block the association between taste and odor in the rat. Neurobiol Learn Mem 80:24–31. 10.1016/S1074-7427(03)00021-2 [DOI] [PubMed] [Google Scholar]

[B66] Sakai N, Yamamoto T (2001) Effects of excitotoxic brain lesions on taste-mediated odor learning in the rat. Neurobiol Learn Mem 75:128–139. 10.1006/nlme.2000.3969 [DOI] [PubMed] [Google Scholar]

[B67] Samuelsen CL, Fontanini A (2017) Processing of intraoral olfactory and gustatory signals in the gustatory cortex of awake rats. J Neurosci 37:244–257. 10.1523/JNEUROSCI.1926-16.2016 [DOI] [PMC free article] [PubMed] [Google Scholar]

[B68] Samuelsen CL, Gardner MPH, Fontanini A (2012) Effects of cue-triggered expectation on cortical processing of taste. Neuron 74:410–422. 10.1016/j.neuron.2012.02.031 [DOI] [PMC free article] [PubMed] [Google Scholar]

[B69] Samuelsen CL, Gardner MPH, Fontanini A (2013) Thalamic contribution to cortical processing of taste and expectation. J Neurosci 33:1815–1827. 10.1523/JNEUROSCI.4026-12.2013 [DOI] [PMC free article] [PubMed] [Google Scholar]

[B70] Samuelsen CL, Vincis R (2021) Cortical Hub for flavor sensation in rodents. Front Syst Neurosci 15:130. 10.3389/fnsys.2021.772286 [DOI] [PMC free article] [PubMed] [Google Scholar]

[B71] Schifferstein HNJJ, Verlegh PWJJ (1996) The role of congruency and pleasantness in odor-induced taste enhancement. Acta Psychol 94:87–105. 10.1016/0001-6918(95)00040-2 [DOI] [PubMed] [Google Scholar]

[B72] Schul R, Slotnick BM, Dudai Y (1996) Flavor and the frontal cortex. Behav Neurosci 110:760–765. 10.1037/0735-7044.110.4.760 [DOI] [PubMed] [Google Scholar]

[B73] Sclafani A (2001) Psychobiology of food preferences. Int J Obes 25:S13–S16. 10.1038/sj.ijo.0801905 [DOI] [PubMed] [Google Scholar]

[B74] Seo HS, Iannilli E, Hummel C, Okazaki Y, Buschhüter D, Gerber J, Krammer GE, van Lengerich B, Hummel T (2013) A salty-congruent odor enhances saltiness: functional magnetic resonance imaging study. Hum Brain Mapp 34:62–76. 10.1002/hbm.21414 [DOI] [PMC free article] [PubMed] [Google Scholar]

[B75] Shan G, Gerstenberger S (2017) Fisher’s exact approach for post hoc analysis of a chi-squared test. PLoS One 12:e0188709. 10.1371/journal.pone.0188709 [DOI] [PMC free article] [PubMed] [Google Scholar]

[B76] Shepard TG, Veldhuizen MG, Marks LE (2015) Response times to gustatory-olfactory flavor mixtures: role of congruence. Chem Senses 40:565–575. 10.1093/chemse/bjv042 [DOI] [PMC free article] [PubMed] [Google Scholar]

[B77] Sieben K, Roder B, Hanganu-Opatz IL (2013) Oscillatory entrainment of primary somatosensory cortex encodes visual control of tactile processing. J Neurosci 33:5736–5749. 10.1523/JNEUROSCI.4432-12.2013 [DOI] [PMC free article] [PubMed] [Google Scholar]

[B78] Small DM (2012) Flavor is in the brain. Physiol Behav 107:540–552. 10.1016/j.physbeh.2012.04.011 [DOI] [PubMed] [Google Scholar]

[B79] Small DM, Veldhuizen MG, Felsted J, Mak YE, McGlone F (2008) Separable substrates for anticipatory and consummatory food chemosensation. Neuron 57:786–797. 10.1016/j.neuron.2008.01.021 [DOI] [PMC free article] [PubMed] [Google Scholar]

[B80] Small DM, Voss J, Mak YE, Simmons KB, Parrish T, Gitelman D (2004) Experience-dependent neural integration of taste and smell in the human brain. J Neurophysiol 92:1892–1903. 10.1152/jn.00050.2004 [DOI] [PubMed] [Google Scholar]

[B81] Stapleton JR, Lavine ML, Wolpert RL, Nicolelis MAL, Simon SA (2006) Rapid taste responses in the gustatory cortex during licking. J Neurosci 26:4126–4138. 10.1523/JNEUROSCI.0092-06.2006 [DOI] [PMC free article] [PubMed] [Google Scholar]

[B82] Stein BE, Stanford TR, Rowland BA (2014) Development of multisensory integration from the perspective of the individual neuron. Nat Rev Neurosci 15:520–535. 10.1038/nrn3742 [DOI] [PMC free article] [PubMed] [Google Scholar]

[B83] Stevenson RJ, Prescott J, Boakes RA (1995) The acquisition of taste properties by odors. Learn Motiv 26:433–455. 10.1016/S0023-9690(05)80006-2 [DOI] [Google Scholar]

[B84] Sugihara T, Diltz MD, Averbeck BB, Romanski LM (2006) Integration of auditory and visual communication information in the primate ventrolateral prefrontal cortex. J Neurosci 26:11138–11147. 10.1523/JNEUROSCI.3550-06.2006 [DOI] [PMC free article] [PubMed] [Google Scholar]

[B85] Tong J, et al. (2011) Ghrelin enhances olfactory sensitivity and exploratory sniffing in rodents and humans. J Neurosci 31:5841–5846. 10.1523/JNEUROSCI.5680-10.2011 [DOI] [PMC free article] [PubMed] [Google Scholar]

[B86] Veldhuizen MG, Nachtigal D, Teulings L, Gitelman DR, Small DM (2010a) The insular taste cortex contributes to odor quality coding. Front Hum Neurosci 4:58. 10.3389/fnhum.2010.00058 [DOI] [PMC free article] [PubMed] [Google Scholar]

[B87] Veldhuizen MG, Shepard TG, Wang MF, Marks LE (2010b) Coactivation of gustatory and olfactory signals in flavor perception. Chem Senses 35:121–133. 10.1093/chemse/bjp089 [DOI] [PMC free article] [PubMed] [Google Scholar]

[B88] Verhagen JV, Engelen L (2006) The neurocognitive bases of human multimodal food perception: sensory integration. Neurosci Biobehav Rev 30:613–650. 10.1016/j.neubiorev.2005.11.003 [DOI] [PubMed] [Google Scholar]

[B89] Vincis R, Chen K, Czarnecki L, Chen J, Fontanini A (2020) Dynamic representation of taste-related decisions in the gustatory insular cortex of mice. Curr Biol 30:1834–1844.e5. 10.1016/j.cub.2020.03.012 [DOI] [PMC free article] [PubMed] [Google Scholar]

[B90] Vincis R, Fontanini A (2016) Associative learning changes cross-modal representations in the gustatory cortex. Elife 5:e16420. 10.7554/eLife.16420 [DOI] [PMC free article] [PubMed] [Google Scholar]

[B91] Wallace MT, Stein BE (2007) Early experience determines how the senses will interact. J Neurophysiol 97:921–926. 10.1152/jn.00497.2006 [DOI] [PubMed] [Google Scholar]

[B92] Wang X, Ogawa H (2002) Columnar organization of mechanoreceptive neurons in the cortical taste area in the rat. Exp Brain Res 147:114–123. 10.1007/s00221-002-1228-0 [DOI] [PubMed] [Google Scholar]

[B93] White TL, Prescott J (2007) Chemosensory cross-modal stroop effects: congruent odors facilitate taste identification. Chem Senses 32:337–341. 10.1093/chemse/bjm001 [DOI] [PubMed] [Google Scholar]

[B94] Yamamoto T, Yuyama N, Kawamura Y (1981) Cortical neurons responding to tactile, thermal and taste stimulations of the rat’s tongue. Brain Res 221:202–206. 10.1016/0006-8993(81)91075-1 [DOI] [PubMed] [Google Scholar]

[B95] Yu L, Rowland BA, Stein BE (2010) Initiating the development of multisensory integration by manipulating sensory experience. J Neurosci 30:4904–4913. 10.1523/JNEUROSCI.5575-09.2010 [DOI] [PMC free article] [PubMed] [Google Scholar]

[B96] Yu L, Xu J, Rowland BA, Stein BE (2016) Multisensory plasticity in superior colliculus neurons is mediated by association cortex. Cereb Cortex 26:1130. 10.1093/cercor/bhu295 [DOI] [PMC free article] [PubMed] [Google Scholar]

PERMALINK

Multisensory Integration Underlies the Distinct Representation of Odor–Taste Mixtures in the Gustatory Cortex of Behaving Rats

Sanaya Stocke

Chad L Samuelsen

Abstract

Significance Statement

Introduction

Materials and Methods

Experimental subjects

Electrode implantation surgery

Figure 1.

Stimulus delivery and recording procedure

Electrophysiological recordings

Analysis of single units

Area under the receiver operating characteristic normalization method

Response onset and latency

Population decoding analysis

Mixture-component difference analysis

Cross-modal response additivity and response polarity

Palatability index analysis

Histology

Experimental design and statistical analysis

Results

Neurons in the gustatory cortex are broadly responsive to intraoral chemosensory signals

Figure 2.

Table 1.

The population activity of chemoselective neurons reliably represents gustatory signals

Figure 3.

Most chemoselective neurons respond to mixtures differently from one of their components

Figure 4.

Table 2.

Figure 5.

Table 3.

A subset of chemoselective neurons represents the palatability-related features of tastes

Figure 6.

A subset of neurons encodes the identity of odor–taste mixtures and their components

Figure 7.

Figure 8.

Discussion

References

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